bluuebunny/crossref_metadata_2025 huggingface dataset:
The Characteristics of Swelling Pressure for Superabsorbent Polymer and Soil Mixtures
Authors: Misiewicz Jakub , Głogowski Arkadiusz , Lejcuś Krzysztof , Marczak Daria
Published: November 2020

Superabsorbent polymers (SAPs) are used in agriculture and environmental engineering to increase soil water retention. Under such conditions, the swelling pressure of the SAP in soil affects water absorption by SAP, and soil structure. The paper presents the results of swelling pressure of three cross-linked copolymers of acrylamide and potassium acrylate mixed at the ratios of 0.3%, 0.5% and 1.0% with coarse sand and loamy sand. The highest values of swelling pressure were obtained for the 1% proportion, for coarse sand (79.53 kPa) and loamy sand (78.23 kPa). The time required to reach 90% of swelling pressure for each type of SAP differs. Samples of coarse sand mixed with SAP K2 in all concentrations reached 90% of total swelling pressure in 100 min, while the loamy sand mixtures needed only about 60 min. The results were the basis for developing a model for swelling pressure of the superabsorbent and soil mixtures, which is a fully stochastic model. The conducted research demonstrated that the course of pressure increase depends on the available pore capacity and the grain size distribution of SAPs. The obtained results and the proposed model may be applied everywhere where mixtures of SAPs and soils are used to improve plant vegetation conditions.
AlgoLabel: A Large Dataset for Multi-Label Classification of Algorithmic Challenges
Authors: Iacob Radu Cristian Alexandru , Monea Vlad Cristian , Rădulescu Dan , Ceapă Andrei-Florin , Rebedea Traian , Trăușan-Matu Ștefan
Published: November 2020

While semantic parsing has been an important problem in natural language processing for decades, recent years have seen a wide interest in automatic generation of code from text. We propose an alternative problem to code generation: labelling the algorithmic solution for programming challenges. While this may seem an easier task, we highlight that current deep learning techniques are still far from offering a reliable solution. The contributions of the paper are twofold. First, we propose a large multi-modal dataset of text and code pairs consisting of algorithmic challenges and their solutions, called AlgoLabel. Second, we show that vanilla deep learning solutions need to be greatly improved to solve this task and we propose a dual text-code neural model for detecting the algorithmic solution type for a programming challenge. While the proposed text-code model increases the performance of using the text or code alone, the improvement is rather small highlighting that we require better methods to combine text and code features.
Inference for the Two Parameter Reduced Kies Distribution under Progressive Type-II Censoring
Authors: Shrahili Mansour , Alotaibi Naif , Kumar Devendra , Alyami Salem A.
Published: November 2020

In this paper, we obtained several recurrence relations for the single and product moments under progressively Type-II right censored order statistics and then use these results to compute the means and variances of two parameter reduced Kies distribution. Besides, these moments are then utilized to derived best linear unbiased estimators of the scale and location parameters of two parameter reduced Kies distribution. The parameters of the two parameter reduced Kies distribution are estimated under progressive type-II censoring scheme. The model parameters are estimated using the maximum likelihood estimation method. Further, we explore the asymptotic confidence intervals for the model parameters. Monte Carlo simulations are performed to compare between the proposed estimation methods under progressive type-II censoring scheme. Based on our study, we can conclude that maximum likelihood estimators is decreasing with respect to an increase of the schemes and comparing the three censoring schemes, it is clear that the mean sum of squares, confidence interval lengths are smaller for scheme 1 than schemes 2 and 3.
Pharmacokinetics of Marine-Derived Drugs
Authors: Shikov Alexander N. , Flisyuk Elena V. , Obluchinskaya Ekaterina D. , Pozharitskaya Olga N.
Published: November 2020

Marine organisms represent an excellent source of innovative compounds that have the potential for the development of new drugs. The pharmacokinetics of marine drugs has attracted increasing interest in recent decades due to its effective and potential contribution to the selection of rational dosage recommendations and the optimal use of the therapeutic arsenal. In general, pharmacokinetics studies how drugs change after administration via the processes of absorption, distribution, metabolism, and excretion (ADME). This review provides a summary of the pharmacokinetics studies of marine-derived active compounds, with a particular focus on their ADME. The pharmacokinetics of compounds derived from algae, crustaceans, sea cucumber, fungus, sea urchins, sponges, mollusks, tunicate, and bryozoan is discussed, and the pharmacokinetics data in human experiments are analyzed. In-depth characterization using pharmacokinetics is useful for obtaining information for understanding the molecular basis of pharmacological activity, for correct doses and treatment schemes selection, and for more effective drug application. Thus, an increase in pharmacokinetic research on marine-derived compounds is expected in the near future.
Diagnostic Value of Unenhanced CT Attenuation and CT Histogram Analysis in Differential Diagnosis of Adrenal Tumors
Authors: Szász Paulína , Kučera Petr , Čtvrtlík Filip , Langová Kateřina , Hartmann Igor , Tüdös Zbyněk
Published: November 2020

Background and Objectives: Our aim was to verify the optimal cut-off value for unenhanced CT attenuation and the percentage of negative voxels in the volume CT histogram analysis of adrenal masses. Materials and Methods: We retrospectively analyzed the CT data of patients who underwent an adrenalectomy in the period 2002–2019. In total, 413 adrenalectomies were performed. Out of these, 233 histologically verified masses (123 adenomas, 58 pheochromocytomas, 18 carcinomas, and 34 metastases) fulfilled the inclusion criteria and were selected for analysis. The mean unenhanced attenuation in Hounsfield units (HU) and the percentage of voxels with attenuation less than 0 HU (negative voxels) were measured in each mass. Results: The mean unenhanced attenuation with a cut-off value of 10 HU reached a sensitivity of 59.4% and a specificity of 99.1% for benign adenomas. The mean unenhanced attenuation with a cut-off value of 15 HU reached a sensitivity of 69.1% and a specificity of 98.2%. For the histogram analysis, a cut-off value of 10% of negative pixels reached a sensitivity of 82.9% and a specificity of 98.2%, whereas a cut-off value of 5% of negative pixels reached a sensitivity of 87.8% and a specificity of 75.5%. The percentage of negative voxels reached a slightly better area under the curve (0.919) than unenhanced attenuation (0.908). Conclusion: Mean unenhanced attenuation with a cut-off value of 10 HU represents a simple tool, and the most specific one, to distinguish adrenal adenomas from non-adenomas. CT histogram analysis with cut-off values of 10% of negative voxels improves sensitivity without any loss of specificity.
Structural, Fatigue Behavior, and Mechanical Properties of Zirconium Tungstate-Reinforced Casted A356 Aluminum Alloy
Authors: Raza Muhammad , Alrobei Hussein , Malik Rizwan Ahmed , Hussain Azhar , Alzaid Meshal , Saleem Mohsin , Imran Mian
Published: November 2020

The aim of this study is to investigate the structure–property relationship of the zirconium tungstate-reinforced casted A356 aluminum alloy. The reinforcement ceramic used was zirconium tungstate of the negative thermal coefficient type, which assists in the weldment of crack growth and enhances the fatigue life. The specimens used in this study were casted by stir casting method and prepared according to Compact Tension standard E-399, and microstructural, fatigue behavior, and mechanical properties were investigated systematically. Microstructural analysis showed reduction in porosity by the addition of ZrW2O8 particles. Fatigue results depict the increase in the fatigue life of aluminum reinforced ceramic as compared to the casted base aluminum alloy. Brinell hardness of ZrW2O8 reinforced alloy samples increased 7% as compared to the base aluminum alloy hardness value. Tensile strength also significantly improved from 176 MPa for the base A356 alloy to 198 MPa for the ZrW2O8 reinforced composite. Furthermore, addition of ZrW2O8 ceramic powder increased the fatigue life more than 50% of the base alloy. These results suggest that the ZrW2O8 reinforced A356 composites may be potential candidates for aerospace industry, military, transportation and in structural sites.
Metallurgical Model of Diffusible Hydrogen and Non-Metallic Slag Inclusions in Underwater Wet Welding of High-Strength Steel
Authors: Parshin Sergey G. , Levchenko Alexey M. , Maystro Alexey S.
Published: November 2020

High susceptibility to cold cracking induced by diffusible hydrogen and hydrogen embrittlement are major obstacles to greater utilization of underwater wet welding for high-strength steels. The aim of the research was to develop gas–slag systems for flux-cored wires that have high metallurgical activity in removal of hydrogen and hydroxyl groups. Thermodynamic modeling and experimental research confirmed that a decrease in the concentration of diffusible hydrogen can be achieved by reducing the partial pressure of hydrogen and water vapor in the vapor–gas bubble and by increasing the hydroxyl capacity of the slag system in metallurgical reactions leading to hydrogen fluoride formation and ionic dissolution of hydroxyl groups in the basic fluorine-containing slag of a TiO2–CaF2–Na3AlF6 system.
First Report of Filamentous Phages Isolated from Tunisian Orchards to Control Erwinia amylovora
Authors: Akremi Ismahen , Holtappels Dominique , Brabra Wided , Jlidi Mouna , Hadj Ibrahim Adel , Ben Ali Manel , Fortuna Kiandro , Ahmed Mohammed , Meerbeek Bart Van , Rhouma Ali , Lavigne Rob , Ben Ali Mamdouh , Wagemans Jeroen
Published: November 2020

Newly discovered Erwinia amylovora phages PEar1, PEar2, PEar4 and PEar6 were isolated from three different orchards in North Tunisia to study their potential as biocontrol agents. Illumina sequencing revealed that the PEar viruses carry a single-strand DNA genome between 6608 and 6801 nucleotides and belong to the Inoviridae, making them the first described filamentous phages of E. amylovora. Interestingly, phage-infected cells show a decreased swimming and swarming motility and a cocktail of the four phages can significantly reduce infection of E. amylovora in a pear bioassay, potentially making them suitable candidates for phage biocontrol.
3D Fabric Analysis in Fault Rock Using Synchrotron μ-CT: A Statistical Approach to SPO (Shape Preferred Orientation) for Estimation of Fault Motion
Authors: Sim Ho , Song Yungoo , Hong Seongsik , Choi Sung-Ja
Published: November 2020

This study provides information about fault motion by statistically presenting shape and orientation information for tens of thousands of grains. The recently developed shape preferred orientation (SPO) measurement method using synchrotron micro-computed tomography was used. In addition, various factors that were not considered in previous SPO analysis were analyzed in-depth. The study area included the Yangsan and Ulsan fault zones, which are the largest fault zones in the southeastern part of the Korean Peninsula. Samples were collected from five outcrops in two regions. According to the field observation results, the samples in the area were largely divided into fault gouge and cataclasite, and as a result of SPO analysis, we succeeded in restoring the three-dimensional fault motion direction for each outcrop and identified the fault type. In addition, the analysis results of the fault gouge and cataclasite samples collected from the thin fault zone were interpreted using the focal mechanism solution. As a result, the statistical SPO analysis approach supplements the shortcomings of previous research methods on two-dimensional planes and can quantitatively infer the three-dimensional fault motion for various fault rock samples in the same sequence, thus, presenting useful evidence for structural analysis.
Effect of Interaction between 17β-Estradiol, 2-Methoxyestradiol and 16α-Hydroxyestrone with Chromium (VI) on Ovary Cancer Line SKOV-3: Preliminary Study
Authors: Sawicka Ewa , Saczko Jolanta , Roik Joanna , Kulbacka Julita , Piwowar Agnieszka
Published: November 2020

Ovarian cancer is the leading cause of death from gynecologic malignancies. Some estrogens, as well as xenoestrogens, such as chromium (VI) (Cr(VI)), are indicated as important pathogenic agents. The objective of this study was to evaluate the role of estradiol and some its metabolites upon exposure to the metalloestrogen Cr(VI) in an in vitro model. The changes in cell viability of malignant ovarian cancer cells (SKOV-3 resistant to cisplatin) exposed to 17β-estradiol (E2) and its two metabolites, 2-methoxyestradiol (2-MeOE2) and 16α-hydroxyestrone (16α-OHE1), upon exposure to potassium chromate (VI) and its interactions were examined. The single and mixed models of action, during short and long times of incubation with estrogens, were applied. The different effects (synergism and antagonism) of estrogens on cell viability in the presence of Cr(VI) was observed. E2 and 16α-OHE1 caused a synergistic effect after exposure to Cr(VI). 2-MeOE2 showed an antagonistic effect on Cr(VI). The examined estrogens could be ranked according to the most protective effect or least toxicity in the order: 2-MeOE2 > E2 > 16α-OHE1. Early pre-incubation (24 h or 7 days) of cells with estrogens caused mostly an antagonistic effect—protective against the toxic action of Cr(VI). The beneficial action of estrogens on the toxic effect of Cr(VI), in the context of the risk of ovarian cancer, seems to be important and further studies are needed.
Proteome Analysis of Human Natural Killer Cell Derived Extracellular Vesicles for Identification of Anticancer Effectors
Authors: Choi Jung-Won , Lim Soyeon , Kang Jung Hwa , Hwang Sung Hwan , Hwang Ki-Chul , Kim Sang Woo , Lee Seahyoung
Published: November 2020

Cancer immunotherapy is a clinically validated therapeutic modality for cancer and has been rapidly advancing in recent years. Adoptive transfer of immune cells such as T cells and natural killer (NK) cells has emerged as a viable method of controlling the immune system against cancer. Recent evidence indicates that even immune-cell-released vesicles such as NK-cell-derived exosomes also exert anticancer effect. Nevertheless, the underlying mechanisms remain elusive. In the present study, the anticancer potential of isolated extracellular vesicles (EVs) from expanded and activated NK-cell-enriched lymphocytes (NKLs) prepared by house-developed protocol was evaluated both in vitro and in vivo. Moreover, isolated EVs were characterized by using two-dimensional electrophoresis (2-DE)-based proteome and network analysis, and functional study using identified factors was performed. Our data indicated that the EVs from expanded and active NKLs had anticancer properties, and a number of molecules, such as Fas ligand, TRAIL, NKG2D, β-actin, and fibrinogen, were identified as effector candidates based on the proteome analysis and functional study. The results of the present study suggest the possibility of NK-cell-derived EVs as a viable immunotherapeutic strategy for cancer.
In Vitro Anti-Inflammatory Activity in Arthritic Synoviocytes of A. brachypoda Root Extracts and Its Unusual Dimeric Flavonoids
Authors: Salgado Carlota , Morin Hugo , Coriolano de Aquino Nayara , Neff Laurence , Quintino da Rocha Cláudia , Vilegas Wagner , Marcourt Laurence , Wolfender Jean-Luc , Jordan Olivier , Ferreira Queiroz Emerson , Allémann Eric
Published: November 2020

Arrabidaea brachypoda is a plant commonly used for the treatment of kidney stones, arthritis and pain in traditional Brazilian medicine. Different in vitro and in vivo activities, ranging from antinociceptive to anti-Trypanosoma cruzi, have been reported for the dichloromethane root extract of Arrabidaea brachypoda (DCMAB) and isolated compounds. This work aimed to assess the in vitro anti-inflammatory activity in arthritic synoviocytes of the DCMAB, the hydroethanolic extract (HEAB) and three dimeric flavonoids isolated from the DCMAB. These compounds, brachydin A (1), B (2) and C (3), were isolated both by medium pressure liquid and high-speed counter current chromatography. Their quantification was performed by mass spectrometry on both DCMAB and HEAB. IL-1β activated human fibroblast-like synoviocytes were incubated with both extracts and isolated compounds to determine the levels of pro-inflammatory cytokine IL-6 by enzyme-linked immunosorbent assay (ELISA). DCMAB inhibited 30% of IL-6 release at 25 µg/mL, when compared with controls while HEAB was inactive. IC50 values determined for 2 and 3 were 3-fold higher than 1. The DCMAB activity seems to be linked to higher proportions of compounds 2 and 3 in this extract. These observations could thus explain the traditional use of A. brachypoda roots in the treatment of osteoarthritis.
Potential of Nanonutraceuticals in Increasing Immunity
Authors: Jampilek Josef , Kralova Katarina
Published: November 2020

Nutraceuticals are defined as foods or their extracts that have a demonstrably positive effect on human health. According to the decision of the European Food Safety Authority, this positive effect, the so-called health claim, must be clearly demonstrated best by performed tests. Nutraceuticals include dietary supplements and functional foods. These special foods thus affect human health and can positively affect the immune system and strengthen it even in these turbulent times, when the human population is exposed to the COVID-19 pandemic. Many of these special foods are supplemented with nanoparticles of active substances or processed into nanoformulations. The benefits of nanoparticles in this case include enhanced bioavailability, controlled release, and increased stability. Lipid-based delivery systems and the encapsulation of nutraceuticals are mainly used for the enrichment of food products with these health-promoting compounds. This contribution summarizes the current state of the research and development of effective nanonutraceuticals influencing the body’s immune responses, such as vitamins (C, D, E, B12, folic acid), minerals (Zn, Fe, Se), antioxidants (carotenoids, coenzyme Q10, polyphenols, curcumin), omega-3 fatty acids, and probiotics.
Systems Biology in Neuroscience: the Paramount Importance of Data Sharing and Citation
Authors: Gallart-Palau Xavier
Published: November 2020

The emergence and maturation, in the last decade, of high powerful technologies in the fields of neurogenomics, neurometabolomics, and neuroproteomics has opened exciting novel possibilities of research [...]
Numerical Investigation on a Hyperlens with a Large Radius Inner-Surface for Super-Resolution Imaging
Authors: Yang Jiming , Lv Jiangtao , Gu Qiongchan , Ying Yu , Jiang Xiaoxiao , Si Guangyuan
Published: November 2020

Hyperlensing devices have drawn great attention in recent years due to their ability to amplify the subwavelength image of objects with more detail and information. In this work, a hyperlens with a radian inner surface is designed and demonstrated. The proposed hyperlens is capable of imaging different types of sub-wavelength objects efficiently. Plasmonic resonant cavity is also employed in order to achieve a super-resolution imaging effect. Different objects are investigated to test the performance of the proposed hyperlens. As expected, our hyperlens shows better tolerance than the conventional hyperlensing designs and can achieve imaging resolution down to 60 nm for different types of objects.
Phylogeny, Taxonomy, and Biogeography of Pterocarya (Juglandaceae)
Authors: Song Yi-Gang , Li Ying , Meng Hong-Hu , Fragnière Yann , Ge Bin-Jie , Sakio Hitoshi , Yousefzadeh Hamed , Bétrisey Sébastien , Kozlowski Gregor
Published: November 2020

Relict species play an important role in understanding the biogeography of intercontinental disjunctions. Pterocarya (a relict genus) is the valuable model taxon for studying the biogeography of East Asian versus southern European/West Asian disjunct patterns. This disjunction has not been as well studied as others (e.g., between Eastern Asia and North America). Several phylogenetic studies on Pterocarya have been conducted, but none have provided a satisfactory phylogenetic resolution. Here, we report the first well-resolved phylogeny of Pterocarya using restriction site-associated DNA sequencing data based on the sampling of all taxa across the entire distribution area of the genus. Taxonomic treatments were also clarified by combining morphological traits. Furthermore, fossil-calibrated phylogeny was used to explore the biogeography of Pterocarya. Our results support the existence of two sections in Pterocarya, which is in accordance with morphological taxonomy. Section Platyptera comprises three species: P. rhoifolia, P. macroptera, and P. delavayi. Section Pterocarya also comprises three species: P. fraxinifolia, P. hupehensis, and P. stenoptera. The divergence between the two sections took place during the early Miocene (20.5 Ma). The formation of the Gobi Desert and climate cooling of northern Siberia in the Middle Miocene (15.7 Ma) might have caused the split of the continuous distribution of this genus and the formation of the East Asian versus southern European/West Asian disjunct pattern. Lastly, the divergence between P. hupehensis and P. stenoptera as well as between P. rhoifolia and P. macroptera/P. delavayi (10.0 Ma) supports the late Miocene diversification hypothesis in East Asia.
ER-Localized PIN Carriers: Regulators of Intracellular Auxin Homeostasis
Authors: Abdollahi Sisi Nayyer , Růžička Kamil
Published: November 2020

The proper distribution of the hormone auxin is essential for plant development. It is channeled by auxin efflux carriers of the PIN family, typically asymmetrically located on the plasma membrane (PM). Several studies demonstrated that some PIN transporters are also located at the endoplasmic reticulum (ER). From the PM-PINs, they differ in a shorter internal hydrophilic loop, which carries the most important structural features required for their subcellular localization, but their biological role is otherwise relatively poorly known. We discuss how ER-PINs take part in maintaining intracellular auxin homeostasis, possibly by modulating the internal levels of IAA; it seems that the exact identity of the metabolites downstream of ER-PINs is not entirely clear as well. We further review the current knowledge about their predicted structure, evolution and localization. Finally, we also summarize their role in plant development.
“Change Direction”: Influencing the National Church through the Vatican during the Pinochet Dictatorship in Chile
Authors: Ruderer Stephan
Published: November 2020

The relations between the Chilean Church and the dictatorship of Augusto Pinochet (1973–1990) are often characterized as conflictive. After a short period of accommodation and legitimation, the Chilean episcopate started to confront the dictatorship in the name of the poor and persecuted, but never breaking entirely with the regime. This led to a complicated relationship between the Church and the dictatorship, which tried to legitimize authoritarian rule by reference to Christian values and the defense of “Christian civilization”. Much historiography has examined this relation from the point of view of the Church. When examined from the point of view of the State important nuances appear. Documents from the Chilean Ministry of Foreign Relations and correspondence with the Chilean ambassador to the Vatican, shed new light on efforts by the Chilean state to shape relations with the Church and to change the position of bishops who were critical of the regime. These data help understand better the dynamics of conflict between Church and State in Chile during the dictatorship.
Driver Characteristics Oriented Autonomous Longitudinal Driving System in Car-Following Situation
Authors: Kim Haksu , Min Kyunghan , Sunwoo Myoungho
Published: November 2020

Advanced driver assistance system such as adaptive cruise control, traffic jam assistance, and collision warning has been developed to reduce the driving burden and increase driving comfort in the car-following situation. These systems provide automated longitudinal driving to ensure safety and driving performance to satisfy unspecified individuals. However, drivers can feel a sense of heterogeneity when autonomous longitudinal control is performed by a general speed planning algorithm. In order to solve heterogeneity, a speed planning algorithm that reflects individual driving behavior is required to guarantee harmony with the intention of the driver. In this paper, we proposed a personalized longitudinal driving system in a car-following situation, which mimics personal driving behavior. The system is structured by a multi-layer framework composed of a speed planner and driver parameter manager. The speed planner generates an optimal speed profile by parametric cost function and constraints that imply driver characteristics. Furthermore, driver parameters are determined by the driver parameter manager according to individual driving behavior based on real driving data. The proposed algorithm was validated through driving simulation. The results show that the proposed algorithm mimics the driving style of an actual driver while maintaining safety against collisions with the preceding vehicle.
Classification of Aggressive Movements Using Smartwatches
Authors: Tchuente Franck , Baddour Natalie , Lemaire Edward D.
Published: November 2020

Recognizing aggressive movements is a challenging task in human activity recognition. Wearable smartwatch technology with machine learning may be a viable approach for human aggressive behavior classification. This research identified a viable classification model and feature selector (CM-FS) combination for separating aggressive from non-aggressive movements using smartwatch data and determined if only one smartwatch is sufficient for this task. A ranking method was used to select relevant CM-FS models across accuracy, sensitivity, specificity, precision, F-score, and Matthews correlation coefficient (MCC). The Waikato environment for knowledge analysis (WEKA) was used to run 6 machine learning classifiers (random forest, k-nearest neighbors (kNN), multilayer perceptron neural network (MP), support vector machine, naïve Bayes, decision tree) coupled with three feature selectors (ReliefF, InfoGain, Correlation). Microsoft Band 2 accelerometer and gyroscope data were collected during an activity circuit that included aggressive (punching, shoving, slapping, shaking) and non-aggressive (clapping hands, waving, handshaking, opening/closing a door, typing on a keyboard) tasks. A combination of kNN and ReliefF was the best CM-FS model for separating aggressive actions from non-aggressive actions, with 99.6% accuracy, 98.4% sensitivity, 99.8% specificity, 98.9% precision, 0.987 F-score, and 0.984 MCC. kNN and random forest classifiers, combined with any of the feature selectors, generated the top models. Models with naïve Bayes or support vector machines had poor performance for sensitivity, F-score, and MCC. Wearing the smartwatch on the dominant wrist produced the best single-watch results. The kNN and ReliefF combination demonstrated that this smartwatch-based approach is a viable solution for identifying aggressive behavior. This wrist-based wearable sensor approach could be used by care providers in settings where people suffer from dementia or mental health disorders, where random aggressive behaviors often occur.
Functional Evaluation of a Force Sensor-Controlled Upper-Limb Power-Assisted Exoskeleton with High Backdrivability
Authors: Liu Chang , Liang Hongbo , Ueda Naoya , Li Peirang , Fujimoto Yasutaka , Zhu Chi
Published: November 2020

A power-assisted exoskeleton should be capable of reducing the burden on the wearer’s body or rendering his or her work improved and efficient. More specifically, the exoskeleton should be easy to wear, be simple to use, and provide power assistance without hindering the wearer’s movement. Therefore, it is necessary to evaluate the backdrivability, range of motion, and power-assist capability of such an exoskeleton. This evaluation identifies the pros and cons of the exoskeleton, and it serves as the basis for its subsequent development. In this study, a lightweight upper-limb power-assisted exoskeleton with high backdrivability was developed. Moreover, a motion capture system was adopted to measure and analyze the workspace of the wearer’s upper limb after the exoskeleton was worn. The results were used to evaluate the exoskeleton’s ability to support the wearer’s movement. Furthermore, a small and compact three-axis force sensor was used for power assistance, and the effect of the power assistance was evaluated by means of measuring the wearer’s surface electromyography, force, and joint angle signals. Overall, the study showed that the exoskeleton could achieve power assistance and did not affect the wearer’s movements.
Analysis of Copernicus’ ERA5 Climate Reanalysis Data as a Replacement for Weather Station Temperature Measurements in Machine Learning Models for Olive Phenology Phase Prediction
Authors: Oses Noelia , Azpiroz Izar , Marchi Susanna , Guidotti Diego , Quartulli Marco , G. Olaizola Igor
Published: November 2020

Knowledge of phenological events and their variability can help to determine final yield, plan management approach, tackle climate change, and model crop development. THe timing of phenological stages and phases is known to be highly correlated with temperature which is therefore an essential component for building phenological models. Satellite data and, particularly, Copernicus’ ERA5 climate reanalysis data are easily available. Weather stations, on the other hand, provide scattered temperature data, with fragmentary spatial coverage and accessibility, as such being scarcely efficacious as unique source of information for the implementation of predictive models. However, as ERA5 reanalysis data are not real temperature measurements but reanalysis products, it is necessary to verify whether these data can be used as a replacement for weather station temperature measurements. The aims of this study were: (i) to assess the validity of ERA5 data as a substitute for weather station temperature measurements, (ii) to test different machine learning models for the prediction of phenological phases while using different sets of features, and (iii) to optimize the base temperature of olive tree phenological model. The predictive capability of machine learning models and the performance of different feature subsets were assessed when comparing the recorded temperature data, ERA5 data, and a simple growing degree day phenological model as benchmark. Data on olive tree phenology observation, which were collected in Tuscany for three years, provided the phenological phases to be used as target variables. The results show that ERA5 climate reanalysis data can be used for modelling phenological phases and that these models provide better predictions in comparison with the models trained with weather station temperature measurements.
Cooperative UAV–UGV Autonomous Power Pylon Inspection: An Investigation of Cooperative Outdoor Vehicle Positioning Architecture
Authors: Cantieri Alvaro , Ferraz Matheus , Szekir Guido , Antônio Teixeira Marco , Lima José , Schneider Oliveira André , Aurélio Wehrmeister Marco
Published: November 2020

Realizing autonomous inspection, such as that of power distribution lines, through unmanned aerial vehicle (UAV) systems is a key research domain in robotics. In particular, the use of autonomous and semi-autonomous vehicles to execute the tasks of an inspection process can enhance the efficacy and safety of the operation; however, many technical problems, such as those pertaining to the precise positioning and path following of the vehicles, robust obstacle detection, and intelligent control, must be addressed. In this study, an innovative architecture involving an unmanned aircraft vehicle (UAV) and an unmanned ground vehicle (UGV) was examined for detailed inspections of power lines. In the proposed strategy, each vehicle provides its position information to the other, which ensures a safe inspection process. The results of real-world experiments indicate a satisfactory performance, thereby demonstrating the feasibility of the proposed approach.
Attention-Deficit/Hyperactivity Disorder (ADHD): Integrating the MOXO-dCPT with an Eye Tracker Enhances Diagnostic Precision
Authors: Elbaum Tomer , Braw Yoram , Lev Astar , Rassovsky Yuri
Published: November 2020

Clinical decision-making may be enhanced when combining psychophysiological sensors with computerized neuropsychological tests. The current study explored the utility of integrating an eye tracker with a commercially available continuous performance test (CPT), the MOXO-dCPT. As part of the study, the performance of adult attention-deficit/hyperactivity disorder (ADHD) patients and healthy controls (n = 43, n = 42, respectively) was compared in the integrated system. More specifically, the MOXO-dCPT has four stages, which differ in their combinations of ecological visual and auditory dynamic distractors. By exploring the participants’ performance in each of the stages, we were able to show that: (a) ADHD patients spend significantly more time gazing at irrelevant areas of interest (AOIs) compared to healthy controls; (b) visual distractors are particularly effective in impacting ADHD patients’ eye movements, suggesting their enhanced utility in diagnostic procedures; (c) combining gaze direction data and conventional CPT indices enhances group prediction, compared to the sole use of conventional indices. Overall, the findings indicate the utility of eye tracker-integrated CPTs and their enhanced diagnostic precision. They also suggest that the use of attention-grabbing visual distractors may be a promising path for the evolution of existing CPTs by shortening their duration and enhancing diagnostic precision.
A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes
Authors: Martínez-Gramage Javier , Albiach Juan Pardo , Moltó Iván Nacher , Amer-Cuenca Juan José , Huesa Moreno Vanessa , Segura-Ortí Eva
Published: November 2020

Background: The running segment of a triathlon produces 70% of the lower limb injuries. Previous research has shown a clear association between kinematic patterns and specific injuries during running. Methods: After completing a seven-month gait retraining program, a questionnaire was used to assess 19 triathletes for the incidence of injuries. They were also biomechanically analyzed at the beginning and end of the program while running at a speed of 90% of their maximum aerobic speed (MAS) using surface sensor dynamic electromyography and kinematic analysis. We used classification tree (random forest) techniques from the field of artificial intelligence to identify linear and non-linear relationships between different biomechanical patterns and injuries to identify which styles best prevent injuries. Results: Fewer injuries occurred after completing the program, with athletes showing less pelvic fall and greater activation in gluteus medius during the first phase of the float phase, with increased trunk extension, knee flexion, and decreased ankle dorsiflexion during the initial contact with the ground. Conclusions: The triathletes who had suffered the most injuries ran with increased pelvic drop and less activation in gluteus medius during the first phase of the float phase. Contralateral pelvic drop seems to be an important variable in the incidence of injuries in young triathletes.
Improving the Accuracy of Low-Cost Sensor Measurements for Freezer Automation
Authors: Koritsoglou Kyriakos , Christou Vasileios , Ntritsos Georgios , Tsoumanis Georgios , Tsipouras Markos G. , Giannakeas Nikolaos , Tzallas Alexandros T.
Published: November 2020

In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor’s accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method’s outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area—resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM).
Hybrid Routing, Modulation, Spectrum and Core Allocation Based on Mapping Scheme
Authors: Rodrigues Edson , Cerqueira Eduardo , Rosário Denis , Oliveira Helder
Published: November 2020

With the persistently growing popularity of internet traffic, telecom operators are forced to provide high-capacity, cost-efficient, and performance-adaptive connectivity solutions to fulfill the requirements and increase their returns. However, optical networks that make up the core of the Internet gradually reached physical transmission limits. In an attempt to provide new solutions emerged, the Space-Division Multiplexing Elastic Optical Network emerged as one of the best ways to deal with the network depletion. However, it is necessary to establish lightpaths using routing, modulation, spectrum, and core allocation (RMSCA) algorithms to establish connections in these networks. This article proposes a crosstalk-aware RMSCA algorithm that uses a multi-path and mapping scheme for improving resource allocation. The results show that the proposed algorithm decreases the blocking ratio by up to four orders of magnitude compared with other RMSCA algorithms in the literature.
GeSi Nanocrystals Photo-Sensors for Optical Detection of Slippery Road Conditions Combining Two Classification Algorithms
Authors: Palade Catalin , Stavarache Ionel , Stoica Toma , Ciurea Magdalena Lidia
Published: November 2020

One of the key elements in assessing traffic safety on the roads is the detection of asphalt conditions. In this paper, we propose an optical sensor based on GeSi nanocrystals embedded in SiO2 matrix that discriminates between different slippery road conditions (wet and icy asphalt and asphalt covered with dirty ice) in respect to dry asphalt. The sensor is fabricated by magnetron sputtering deposition followed by rapid thermal annealing. The photodetector has spectral sensitivity in the 360–1350 nm range and the signal-noise ratio is 102–103. The working principle of sensor setup for detection of road conditions is based on the photoresponse (photocurrent) of the sensor under illumination with the light reflected from the asphalt having different reflection coefficients for dry, wet, icy and dirty ice coatings. For this, the asphalt is illuminated sequentially with 980 and 1064 nm laser diodes. A database of these photocurrents is obtained for the different road conditions. We show that the use of both k-nearest neighbor and artificial neural networks classification algorithms enables a more accurate recognition of the class corresponding to a specific road state than in the case of using only one algorithm. This is achieved by comparing the new output sensor data with previously classified data for each algorithm and then by performing an intersection of the algorithms’ results.
Bacterial Respiration Used as a Proxy to Evaluate the Bacterial Load in Cooling Towers
Authors: Toman Stepan , Kiilerich Bruno , Marshall Ian P.G. , Koren Klaus
Published: November 2020

Evaporative cooling towers to dissipate excess process heat are essential installations in a variety of industries. The constantly moist environment enables substantial microbial growth, causing both operative challenges (e.g., biocorrosion) as well as health risks due to the potential aerosolization of pathogens. Currently, bacterial levels are monitored using rather slow and infrequent sampling and cultivation approaches. In this study, we describe the use of metabolic activity, namely oxygen respiration, as an alternative measure of bacterial load within cooling tower waters. This method is based on optical oxygen sensors that enable an accurate measurement of oxygen consumption within a closed volume. We show that oxygen consumption correlates with currently used cultivation-based methods (R2 = 0.9648). The limit of detection (LOD) for respiration-based bacterial quantification was found to be equal to 1.16 × 104 colony forming units (CFU)/mL. Contrary to the cultivation method, this approach enables faster assessment of the bacterial load with a measurement time of just 30 min compared to 48 h needed for cultivation-based measurements. Furthermore, this approach has the potential to be integrated and automated. Therefore, this method could contribute to more robust and reliable monitoring of bacterial contamination within cooling towers and subsequently increase operational stability and reduce health risks.
Analysis on the Possibility of Eliminating Interference from Paraseismic Vibration Signals Induced by the Detonation of Explosive Materials
Authors: Pyra Józef , Kłaczyński Maciej , Burdzik Rafał
Published: November 2020

This article presents the results of studies on the impact of acoustic waves on geophones and microphones used to measure airblasts carried out in a reverberation chamber. During the tests, a number of test signals were generated, of which two are presented in this article: frequency-modulated sine (sine sweep) waves in the 30–300 Hz range, and the result of detonating 3 g of pyrotechnic material inside the chamber. Then, based on the short-time Fourier transform, the spectral subtraction method was used to remove unwanted disruption interfering with the recorded signal. Using MATLAB software, a program was written that was calibrated and adapted to the specifics of the measuring equipment based on the collected test results. As a result, it was possible to clean the signals of interference and obtain a vibration signal propagated by the substrate. The results are based on signals registered in the laboratory and made in field conditions during the detonation of explosive materials.
Ultrafast Resolution-Enhanced Digital Optical Frequency Comb-Based BOTDA with Pump Pulse Array Coding
Authors: Wu Yichang , Yang Chengkun , Pan Jingshun , Sui Qi , Wang Dawei
Published: November 2020

In this letter, a resolution enhancement and signal-to-noise ratio (SNR) improvement scheme for digital optical frequency comb (DOFC)-based Brillouin optical time-domain analysis (BOTDA) ultrafast distributed sensing employing a pump pulse array is proposed. Based on the properties of the time-invariant linear system and the cyclic revolution theorem, experimental results indicate that its spatial resolution reaches 10.24 m while the frequency uncertainty is below 2 MHz over a 9.5 km fiber. Moreover, the response time is only 209.6 μs and the temperature measurement error is less than 0.52 °C.
Wearable Sensors Incorporating Compensatory Reserve Measurement for Advancing Physiological Monitoring in Critically Injured Trauma Patients
Authors: Convertino Victor A. , Schauer Steven G. , Weitzel Erik K. , Cardin Sylvain , Stackle Mark E. , Talley Michael J. , Sawka Michael N. , Inan Omer T.
Published: November 2020

Vital signs historically served as the primary method to triage patients and resources for trauma and emergency care, but have failed to provide clinically-meaningful predictive information about patient clinical status. In this review, a framework is presented that focuses on potential wearable sensor technologies that can harness necessary electronic physiological signal integration with a current state-of-the-art predictive machine-learning algorithm that provides early clinical assessment of hypovolemia status to impact patient outcome. The ability to study the physiology of hemorrhage using a human model of progressive central hypovolemia led to the development of a novel machine-learning algorithm known as the compensatory reserve measurement (CRM). Greater sensitivity, specificity, and diagnostic accuracy to detect hemorrhage and onset of decompensated shock has been demonstrated by the CRM when compared to all standard vital signs and hemodynamic variables. The development of CRM revealed that continuous measurements of changes in arterial waveform features represented the most integrated signal of physiological compensation for conditions of reduced systemic oxygen delivery. In this review, detailed analysis of sensor technologies that include photoplethysmography, tonometry, ultrasound-based blood pressure, and cardiogenic vibration are identified as potential candidates for harnessing arterial waveform analog features required for real-time calculation of CRM. The integration of wearable sensors with the CRM algorithm provides a potentially powerful medical monitoring advancement to save civilian and military lives in emergency medical settings.
IRT and GPR Techniques for Moisture Detection and Characterisation in Buildings
Authors: Garrido Iván , Solla Mercedes , Lagüela Susana , Fernández Norberto
Published: November 2020

The integrity, comfort, and energy demand of a building can be negatively affected by the presence of moisture in its walls. Therefore, it is essential to identify and characterise this building pathology with the most appropriate technologies to perform the required prevention and maintenance tasks. This paper proposes the joint application of InfraRed Thermography (IRT) and Ground-Penetrating Radar (GPR) for the detection and classification of moisture in interior walls of a building according to its severity level. The IRT method is based on the study of the temperature distribution of the thermal images acquired without an application of artificial thermal excitation for the detection of superficial moisture (less than 15 mm deep in plaster with passive IRT). Additionally, in order to characterise the level of moisture severity, the Evaporative Thermal Index (ETI) was obtained for each of the moisture areas. As for GPR, with measuring capacity from 10 mm up to 30 cm depth with a 2300 MHz antenna, several algorithms were developed based on the amplitude and spectrum of the received signals for the detection and classification of moisture through the inner layers of the wall. In this work, the complementarity of both methods has proven to be an effective approach to investigate both superficial and internal moisture and their severity. Specifically, IRT allowed estimating superficial water movement, whereas GPR allowed detecting points of internal water accumulation. Thus, through the combination of both techniques, it was possible to provide an interpretation of the water displacement from the exterior surface to the interior surface of the wall, and to give a relative depth of water inside the wall. Therefore, it was concluded that more information and greater reliability can be gained by using complementary IRT-GPR, showing the benefits of combining both techniques in the building sector.