bluuebunny/crossref_metadata_2025 huggingface dataset:
Complex Regulatory Role of the TRPA1 Receptor in Acute and Chronic Airway Inflammation Mouse Models
Authors: Hajna Zsófia , Csekő Kata , Kemény Ágnes , Kereskai László , Kiss Tamás , Perkecz Anikó , Szitter István , Kocsis Béla , Pintér Erika , Helyes Zsuzsanna
Published: June 2020

The Transient Receptor Potential Ankyrin 1 (TRPA1) cation channel expressed on capsaicin-sensitive afferents, immune and endothelial cells is activated by inflammatory mediators and exogenous irritants, e.g., endotoxins, nicotine, crotonaldehyde and acrolein. We investigated its involvement in acute and chronic pulmonary inflammation using Trpa1 gene-deleted (Trpa1−/−) mice. Acute pneumonitis was evoked by intranasal Escherichia coli endotoxin (lipopolysaccharide: LPS) administration, chronic bronchitis by daily cigarette smoke exposure (CSE) for 4 months. Frequency, peak inspiratory/expiratory flows, minute ventilation determined by unrestrained whole-body plethysmography were significantly greater, while tidal volume, inspiratory/expiratory/relaxation times were smaller in Trpa1−/− mice. LPS-induced bronchial hyperreactivity, myeloperoxidase activity, frequency-decrease were significantly greater in Trpa1−/− mice. CSE significantly decreased tidal volume, minute ventilation, peak inspiratory/expiratory flows in wildtypes, but not in Trpa1−/− mice. CSE remarkably increased the mean linear intercept (histopathology), as an emphysema indicator after 2 months in wildtypes, but only after 4 months in Trpa1−/− mice. Semiquantitative histopathological scores were not different between strains in either models. TRPA1 has a complex role in basal airway function regulation and inflammatory mechanisms. It protects against LPS-induced acute pneumonitis and hyperresponsiveness, but is required for CSE-evoked emphysema and respiratory deterioration. Further research is needed to determine TRPA1 as a potential pharmacological target in the lung.
A Probabilistic Model for Optimal Bridge Inspection Interval
Authors: Ilbeigi Mohammad , Pawar Bhushan
Published: June 2020

The US Department of Transportation and Federal Highway Administration require routine inspections to monitor bridge deterioration. Typically, bridge inspections are conducted every 24 months. This timeframe was determined solely based on engineering judgment. The objective of this study is to develop probabilistic models to forecast bridge deterioration and statistically determine the optimal inspection intervals. A two-dimensional Markov process model that considers the current condition of a bridge, and number of years that the bridge has been in that condition, is created to predict future bridge conditions based on historical data. Using the forecasting model, a statistical process is developed to determine the optimal inspection intervals. The proposed methodology in this study is implemented, utilizing a dataset consisting of information about deterioration conditions of more than 17,500 bridges in the state of New York from 1992 to 2018. The outcomes of the statistical analysis indicate that the typical 24-month inspection interval is considerably pessimistic, and not necessary for all bridges currently in condition 5 or higher. However, the 24-month interval is too optimistic and risky for bridges currently in condition 4 or lower. The outcomes of this study help bridge owners and transportation agencies assign maintenance resources efficiently, and invest the millions of dollars currently allocated for unnecessary inspections in much-needed infrastructure development projects.
A Study of the Interlaminar Fracture Toughness of Unidirectional Flax/Epoxy Composites
Authors: Saadati Yousef , Chatelain Jean-Francois , Lebrun Gilbert , Beauchamp Yves , Bocher Philippe , Vanderesse Nicolas
Published: June 2020

Having environmental and economic advantages, flax fibers have been recognized as a potential replacement for glass fibers as reinforcement in epoxy composites for various applications. Its widening applications require employing failure criteria and analysis methods for engineering design, analysis, and optimization of this material. Among different failure modes, delamination is known as one of the earliest ones in laminated composites and needs to be studied in detail. However, the delamination characteristics of unidirectional (UD) flax/epoxy composites in pure Mode I has rarely been addressed, while Mode II and Mixed-mode I/II have never been addressed before. This work studies and evaluates the interlaminar fracture toughness and delamination behavior of UD flax/epoxy composite under Mode I, Mode II, and Mixed-mode I/II loading. The composites were tested following corresponding ASTM standards and fulfilled all the requirements. The interlaminar fracture toughness of the composite were determined and validated based on the specific characteristics of natural fibers. Considering the variation in the composite structure configuration and its effects, the results of interlaminar fracture toughness fit in the range of those reported for similar composites in the literature and provide a basis for the material properties of this composite.
Join Products K2,3 + Cn
Authors: Staš Michal
Published: June 2020

The crossing number cr ( G ) of a graph G is the minimum number of edge crossings over all drawings of G in the plane. The main goal of the paper is to state the crossing number of the join product K 2 , 3 + C n for the complete bipartite graph K 2 , 3 , where C n is the cycle on n vertices. In the proofs, the idea of a minimum number of crossings between two distinct configurations in the various forms of arithmetic means will be extended. Finally, adding one more edge to the graph K 2 , 3 , we also offer the crossing number of the join product of one other graph with the cycle C n .
Evaluation of VLBI Observations with Sensitivity and Robustness Analyses
Authors: Küreç Nehbit Pakize , Heinkelmann Robert , Schuh Harald , Glaser Susanne , Lunz Susanne , Mammadaliyev Nicat , Balidakis Kyriakos , Konak Haluk , Tanır Kayıkçı Emine
Published: June 2020

Very Long Baseline Interferometry (VLBI) plays an indispensable role in the realization of global terrestrial and celestial reference frames and in the determination of the full set of the Earth Orientation Parameters (EOP). The main goal of this research is to assess the quality of the VLBI observations based on the sensitivity and robustness criteria. Sensitivity is defined as the minimum displacement value that can be detected in coordinate unknowns. Robustness describes the deformation strength induced by the maximum undetectable errors with the internal reliability analysis. The location of a VLBI station and the total weights of the observations at the station are most important for the sensitivity analysis. Furthermore, the total observation number of a radio source and the quality of the observations are important for the sensitivity levels of the radio sources. According to the robustness analysis of station coordinates, the worst robustness values are caused by atmospheric delay effects with high temporal and spatial variability. During CONT14, it is determined that FORTLEZA, WESTFORD, and TSUKUB32 have robustness values changing between 0.8 and 1.3 mm, which are significantly worse in comparison to the other stations. The radio sources 0506-612, NRAO150, and 3C345 have worse sensitivity levels compared to other radio sources. It can be concluded that the sensitivity and robustness analysis are reliable measures to obtain high accuracy VLBI solutions.
Cytotoxic Activity of Christia vespertilionis Root and Leaf Extracts and Fractions against Breast Cancer Cell Lines
Authors: Lee Joanna Jinling , Saiful Yazan Latifah , Kassim Nur Kartinee , Che Abdullah Che Azurahanim , Esa Nurulaidah , Lim Pei Cee , Tan Dai Chuan
Published: June 2020

Christia vespertilionis, commonly known as ‘Daun Rerama’, has recently garnered attention from numerous sources in Malaysia as an alternative treatment. Its herbal decoction was believed to show anti-inflammatory and anti-cancer effects. The present study investigated the cytotoxicity of the extract of root and leaf of C. vespertilionis. The plant parts were successively extracted using the solvent maceration method. The most active extract was further fractionated to afford F1–F8. The cytotoxic effects were determined using MTT assay against human breast carcinoma cell lines (MCF-7 and MDA-MB-231). The total phenolic content (TPC) of the extracts were determined. The antioxidant properties of the extract were also studied using DPPH and β-carotene bleaching assays. The ethyl acetate root extract demonstrated selective cytotoxicity especially against MDA-MB-231 with the highest TPC and antioxidant properties compared to others (p < 0.05). The TPC and antioxidant results suggest the contribution of phenolic compounds toward its antioxidant strength leading to significant cytotoxicity. F3 showed potent cytotoxic effects while F4 showed better antioxidative strength compared to others (p < 0.05). Qualitative phytochemical screening of the most active fraction, F3, suggested the presence of flavonoids, coumarins and quinones to be responsible toward the cytotoxicity. The study showed the root extracts of C. vespertilionis to possess notable anti-breast cancer effects.
Subacute Oral Administration of Clinacanthus nutans Ethanolic Leaf Extract Induced Liver and Kidney Toxicities in ICR Mice
Authors: Aliyu Abdullahi , Shaari Mohd Rosly , Ahmad Sayuti Nurul Syahirah , Reduan Mohd Farhan Hanif , Sithambaram Shanmugavelu , Noordin Mustapha Mohamed , Shaari Khozirah , Hamzah Hazilawati
Published: June 2020

This study investigated the leaves of Clinacanthus nutans for its bioactive compounds and acute and subacute toxicity effects of C. nutans ethanolic leaf extract (CELE) on blood, liver and kidneys of ICR mice. A total of 10 8-week-old female mice were divided into groups A (control) and B (2000 mg/kg) for the acute toxicity study. A single dose of 2000 mg/kg was administered to group B through oral gavage and mice were monitored for 14 days. In the subacute toxicity study, mice were divided into five groups: A (control), B (125 mg/kg), C (250 mg/kg), D (500 mg/kg) and E (1000 mg/kg). The extract was administered daily for 28 days via oral gavage. The mice were sacrificed, and samples were collected for analyses. Myricetin, orientin, isoorientin, vitexin, isovitexin, isookanin, apigenin and ferulic acid were identified in the extract. Twenty-eight days of continuous oral administration revealed significant increases (p < 0.05) in creatinine, ALT and moderate hepatic and renal necrosis in groups D and E. The study concluded that the lethal dose (LD50) of CELE in mice is greater than 2000 mg/kg and that repeated oral administrations of CELE for 28 days induced hepatic and renal toxicities at 1000 mg/kg in female ICR mice.
Hyaluronic Acid and Controlled Release: A Review
Authors: Bayer Ilker S.
Published: June 2020

Hyaluronic acid (HA) also known as hyaluronan, is a natural polysaccharide—an anionic, non-sulfated glycosaminoglycan—commonly found in our bodies. It occurs in the highest concentrations in the eyes and joints. Today HA is used during certain eye surgeries and in the treatment of dry eye disease. It is a remarkable natural lubricant that can be injected into the knee for patients with knee osteoarthritis. HA has also excellent gelling properties due to its capability to bind water very quickly. As such, it is one the most attractive controlled drug release matrices and as such, it is frequently used in various biomedical applications. Due to its reactivity, HA can be cross-linked or conjugated with assorted bio-macromolecules and it can effectively encapsulate several different types of drugs, even at nanoscale. Moreover, the physiological significance of the interactions between HA and its main membrane receptor, CD44 (a cell-surface glycoprotein that modulates cell–cell interactions, cell adhesion and migration), in pathological processes, e.g., cancer, is well recognized and this has resulted in an extensive amount of studies on cancer drug delivery and tumor targeting. HA acts as a therapeutic but also as a tunable matrix for drug release. Thus, this review focuses on controlled or sustained drug release systems assembled from HA and its derivatives. More specifically, recent advances in controlled release of proteins, antiseptics, antibiotics and cancer targeting drugs from HA and its derivatives were reviewed. It was shown that controlled release from HA has many benefits such as optimum drug concentration maintenance, enhanced therapeutic effects, improved efficiency of treatment with less drug, very low or insignificant toxicity and prolonged in vivo release rates.
Research Progress of Small Molecule VEGFR/c-Met Inhibitors as Anticancer Agents (2016–Present)
Authors: Zhang Qian , Zheng Pengwu , Zhu Wufu
Published: June 2020

Vascular endothelial growth factor receptor 2 (VEGFR-2) binds to VEGFR-A, VEGFR-C and VEGFR-D and participates in the formation of tumor blood vessels, mediates the proliferation of endothelial cells, enhances microvascular permeability, and blocks apoptosis. Blocking or downregulating the signal transduction of VEGFR is the main way to discover new drugs for many human angiogenesis-dependent malignancies. Mesenchymal epithelial transfer factor tyrosine kinase (c-Met) is a high affinity receptor for hepatocyte growth factor (HGF). Abnormal c-Met signaling plays an important role in the formation, invasion and metastasis of human tumors. Therefore, the HGF/c-Met signaling pathway has become a significant target for cancer treatment. Related studies have shown that the conduction of the VEGFR and c-Met signaling pathways has a synergistic effect in inducing angiogenesis and inhibiting tumor growth. In recent years, multi-target small molecule inhibitors have become a research hotspot, among which the research of VEGFR and c-Met dual-target small molecule inhibitors has become more and more extensive. In this review, we comprehensively summarize the chemical structures and biological characteristics of novel VEGFR/c-Met dual-target small-molecule inhibitors in the past five years.
Preparation, Characterization, and Radiolabeling of [68Ga]Ga-NODAGA-Pamidronic Acid: A Potential PET Bone Imaging Agent
Authors: Ashhar Zarif , Yusof Nor Azah , Ahmad Saad Fathinul Fikri , Mohd Nor Siti Mariam , Mohammad Faruq , Bahrin Wan Kamal Wan Hamirul , Hassan Muhammad Hishar , Ahmad Hassali Hazlina , Al-Lohedan Hamad A.
Published: June 2020

Early diagnosis of bone metastases is crucial to prevent skeletal-related events, and for that, the non-invasive techniques to diagnose bone metastases that make use of image-guided radiopharmaceuticals are being employed as an alternative to traditional biopsies. Hence, in the present work, we tested the efficacy of a gallium-68 (68Ga)-based compound as a radiopharmaceutical agent towards the bone imaging in positron emitting tomography (PET). For that, we prepared, thoroughly characterized, and radiolabeled [68Ga]Ga-NODAGA-pamidronic acid radiopharmaceutical, a 68Ga precursor for PET bone cancer imaging applications. The preparation of NODAGA-pamidronic acid was performed via the N-Hydroxysuccinimide (NHS) ester strategy and was characterized using liquid chromatography-mass spectrometry (LC-MS) and tandem mass spectrometry (MSn). The unreacted NODAGA chelator was separated using the ion-suppression reverse phase-high performance liquid chromatography (RP-HPLC) method, and the freeze-dried NODAGA-pamidronic acid was radiolabeled with 68Ga. The radiolabeling condition was found to be most optimum at a pH ranging from 4 to 4.5 and a temperature of above 60 °C. From previous work, we found that the pamidronic acid itself has a good bone binding affinity. Moreover, from the analysis of the results, the ionic structure of radiolabeled [68Ga]Ga-NODAGA-pamidronic acid has the ability to improve the blood clearance and may exert good renal excretion, enhance the bone-to-background ratio, and consequently the final image quality. This was reflected by both the in vitro bone binding assay and in vivo animal biodistribution presented in this research.
Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People
Authors: Buisseret Fabien , Catinus Louis , Grenard Rémi , Jojczyk Laurent , Fievez Dylan , Barvaux Vincent , Dierick Frédéric
Published: June 2020

Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past 30 years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and nonfallers. The aim of this study is twofold. First, to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and six-minute walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at a six-month interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and nonfallers. We show that the TUG test results coupled to gait variability indicators, measured during a six-minute walking test, improve (from 68% to 76%) the accuracy of risk of fall’s prediction at six months. In addition, we show that an artificial intelligence algorithm trained on the sensor raw data of 57 participants reveals an accuracy of 75% on the remaining 16 participants.
Muscle Synergies in Parkinson’s Disease
Authors: Mileti Ilaria , Zampogna Alessandro , Santuz Alessandro , Asci Francesco , Del Prete Zaccaria , Arampatzis Adamantios , Palermo Eduardo , Suppa Antonio
Published: June 2020

Over the last two decades, experimental studies in humans and other vertebrates have increasingly used muscle synergy analysis as a computational tool to examine the physiological basis of motor control. The theoretical background of muscle synergies is based on the potential ability of the motor system to coordinate muscles groups as a single unit, thus reducing high-dimensional data to low-dimensional elements. Muscle synergy analysis may represent a new framework to examine the pathophysiological basis of specific motor symptoms in Parkinson’s disease (PD), including balance and gait disorders that are often unresponsive to treatment. The precise mechanisms contributing to these motor symptoms in PD remain largely unknown. A better understanding of the pathophysiology of balance and gait disorders in PD is necessary to develop new therapeutic strategies. This narrative review discusses muscle synergies in the evaluation of motor symptoms in PD. We first discuss the theoretical background and computational methods for muscle synergy extraction from physiological data. We then critically examine studies assessing muscle synergies in PD during different motor tasks including balance, gait and upper limb movements. Finally, we speculate about the prospects and challenges of muscle synergy analysis in order to promote future research protocols in PD.
A Self-Organizing Fuzzy Logic Classifier for Benchmarking Robot-Aided Blasting of Ship Hulls
Authors: Muthugala M. A. Viraj J. , Le Anh Vu , Cruz Eduardo Sanchez , Rajesh Elara Mohan , Veerajagadheswar Prabakaran , Kumar Madhu
Published: June 2020

Regular dry dock maintenance work on ship hulls is essential for maintaining the efficiency and sustainability of the shipping industry. Hydro blasting is one of the major processes of dry dock maintenance work, where human labor is extensively used. The conventional methods of maintenance work suffer from many shortcomings, and hence robotized solutions have been developed. This paper proposes a novel robotic system that can synthesize a benchmarking map for a previously blasted ship hull. A Self-Organizing Fuzzy logic (SOF) classifier has been developed to benchmark the blasting quality of a ship hull similar to blasting quality categorization done by human experts. Hornbill, a multipurpose inspection and maintenance robot intended for hydro blasting, benchmarking, and painting, has been developed by integrating the proposed SOF classifier. Moreover, an integrated system solution has been developed to improve dry dock maintenance of ship hulls. The proposed SOF classifier can achieve a mean accuracy of 0.9942 with an execution time of 8.42 µs. Realtime experimenting with the proposed robotic system has been conducted on a ship hull. This experiment confirms the ability of the proposed robotic system in synthesizing a benchmarking map that reveals the benchmarking quality of different areas of a previously blasted ship hull. This sort of a benchmarking map would be useful for ensuring the blasting quality as well as performing efficient spot wise reblasting before the painting. Therefore, the proposed robotic system could be utilized for improving the efficiency and quality of hydro blasting work on the ship hull maintenance industry.
A Multimodal Analysis Combining Behavioral Experiments and Survey-Based Methods to Assess the Cognitive Effect of Video Game Playing: Good or Evil?
Authors: Jeong Ji Hyeok , Park Hyun-Jung , Yeo Sang-Hoon , Kim Hyungmin
Published: June 2020

This study aims to bridge the gap between the discrepant views of existing studies in different modalities on the cognitive effect of video game play. To this end, we conducted a set of tests with different modalities within each participant: (1) Self-Reports Analyses (SRA) consisting of five popular self-report surveys, and (2) a standard Behavioral Experiment (BE) using pro- and antisaccade paradigms, and analyzed how their results vary between Video Game Player (VGP) and Non-Video Game Player (NVGP) participant groups. Our result showed that (1) VGP scored significantly lower in Behavioral Inhibition System (BIS) than NVGP (p = 0.023), and (2) VGP showed significantly higher antisaccade error rate than NVGP (p = 0.005), suggesting that results of both SRA and BE support the existing view that video game play has a maleficent impact on the cognition by increasing impulsivity. However, the following correlation analysis on the results across individual participants found no significant correlation between SRA and BE, indicating a complex nature of the cognitive effect of video game play.
A PointNet-Based Solution for 3D Hand Gesture Recognition
Authors: Mirsu Radu , Simion Georgiana , Caleanu Catalin Daniel , Pop-Calimanu Ioana Monica
Published: June 2020

Gesture recognition is an intensively researched area for several reasons. One of the most important reasons is because of this technology’s numerous application in various domains (e.g., robotics, games, medicine, automotive, etc.) Additionally, the introduction of three-dimensional (3D) image acquisition techniques (e.g., stereovision, projected-light, time-of-flight, etc.) overcomes the limitations of traditional two-dimensional (2D) approaches. Combined with the larger availability of 3D sensors (e.g., Microsoft Kinect, Intel RealSense, photonic mixer device (PMD), CamCube, etc.), recent interest in this domain has sparked. Moreover, in many computer vision tasks, the traditional statistic top approaches were outperformed by deep neural network-based solutions. In view of these considerations, we proposed a deep neural network solution by employing PointNet architecture for the problem of hand gesture recognition using depth data produced by a time of flight (ToF) sensor. We created a custom hand gesture dataset, then proposed a multistage hand segmentation by designing filtering, clustering, and finding the hand in the volume of interest and hand-forearm segmentation. For comparison purpose, two equivalent datasets were tested: a 3D point cloud dataset and a 2D image dataset, both obtained from the same stream. Besides the advantages of the 3D technology, the accuracy of the 3D method using PointNet is proven to outperform the 2D method in all circumstances, even the 2D method that employs a deep neural network.
An Automatic Unmixing Approach to Detect Tissue Chromophores from Multispectral Photoacoustic Imaging
Authors: Grasso Valeria , Holthof Joost , Jose Jithin
Published: June 2020

Multispectral photoacoustic imaging has been widely explored as an emerging tool to visualize and quantify tissue chromophores noninvasively. This modality can capture the spectral absorption signature of prominent tissue chromophores, such as oxygenated, deoxygenated hemoglobin, and other biomarkers in the tissue by using spectral unmixing methods. Currently, most of the reported image processing algorithms use standard unmixing procedures, which include user interaction in the form of providing the expected spectral signatures. For translational research with patients, these types of supervised spectral unmixing can be challenging, as the spectral signature of the tissues can differ with respect to the disease condition. Imaging exogenous contrast agents and accessing their biodistribution can also be problematic, as some of the contrast agents are susceptible to change in spectral properties after the tissue interaction. In this work, we investigated the feasibility of an unsupervised spectral unmixing algorithm to detect and extract the tissue chromophores without any a-priori knowledge and user interaction. The algorithm has been optimized for multispectral photoacoustic imaging in the spectral range of 680–900 nm. The performance of the algorithm has been tested on simulated data, tissue-mimicking phantom, and also on the detection of exogenous contrast agents after the intravenous injection in mice. Our finding shows that the proposed automatic, unsupervised spectral unmixing method has great potential to extract and quantify the tissue chromophores, and this can be used in any wavelength range of the multispectral photoacoustic images.
Repetitive Detection of Aromatic Hydrocarbon Contaminants with Bioluminescent Bioreporters Attached on Tapered Optical Fiber Elements
Authors: Zajíc Jakub , Ripp Steven , Trögl Josef , Kuncová Gabriela , Pospíšilová Marie
Published: June 2020

In this study, we show the repetitive detection of toluene on a tapered optical fiber element (OFE) with an attached layer of Pseudomonas putida TVA8 bioluminescent bioreporters. The bioluminescent cell layer was attached on polished quartz modified with (3-aminopropyl)triethoxysilane (APTES). The repeatability of the preparation of the optical probe and its use was demonstrated with five differently shaped OFEs. The intensity of measured bioluminescence was minimally influenced by the OFE shape, possessing transmittances between 1.41% and 5.00%. OFE probes layered with P. putida TVA8 were used to monitor liquid toluene over a two-week period. It was demonstrated that OFE probes layered with positively induced P. putida TVA8 bioreporters were reliable detectors of toluene. A toluene concentration of 26.5 mg/L was detected after <30 min after immersion of the probe in the toluene solution. Additional experiments also immobilized constitutively bioluminescent cells of E. coli 652T7, on OFEs with polyethyleneimine (PEI). These OFEs were repetitively induced with Lauria-Bertani (LB) nutrient medium. Bioluminescence appeared 15 minutes after immersion of the OFE in LB. A change in pH from 7 to 6 resulted in a decrease in bioluminescence that was not restored following additional nutrient inductions at pH 7. The E. coli 652T7 OFE probe was therefore sensitive to negative influences but could not be repetitively used.
Estimation of Heart Rate and Respiratory Rate from PPG Signal Using Complementary Ensemble Empirical Mode Decomposition with both Independent Component Analysis and Non-Negative Matrix Factorization
Authors: Lei Ruisheng , Ling Bingo Wing-Kuen , Feng Peihua , Chen Jinrong
Published: June 2020

This paper proposes a framework combining the complementary ensemble empirical mode decomposition with both the independent component analysis and the non-negative matrix factorization for estimating both the heart rate and the respiratory rate from the photoplethysmography (PPG) signal. After performing the complementary ensemble empirical mode decomposition on the PPG signal, a finite number of intrinsic mode functions are obtained. Then, these intrinsic mode functions are divided into two groups to perform the further analysis via both the independent component analysis and the non-negative matrix factorization. The surrogate cardiac signal related to the heart activity and another surrogate respiratory signal related to the respiratory activity are reconstructed to estimate the heart rate and the respiratory rate, respectively. Finally, different records of signals acquired from the Medical Information Mart for Intensive Care database downloaded from the Physionet Automated Teller Machine (ATM) data bank are employed for demonstrating the outperformance of our proposed method. The results show that our proposed method outperforms both the digital filtering approach and the conventional empirical mode decomposition based methods in terms of reconstructing both the surrogate cardiac signal and the respiratory signal from the PPG signal as well as both achieving the higher accuracy and the higher reliability for estimating both the heart rate and the respiratory rate.
Psychophysiological Models to Identify and Monitor Elderly with a Cardiovascular Condition: The Added Value of Psychosocial Parameters to Routinely Applied Physiological Assessments
Authors: Kallen Victor , Marck Jan Willem , Stam Jacqueline , Issa Amine , Johnson Bruce , van Meeteren Nico
Published: June 2020

The steadily growing elderly population calls for efficient, reliable and preferably ambulant health supervision. Since cardiovascular risk factors interact with psychosocial strain (e.g., depression), we investigated the potential contribution of psychosocial factors in discriminating generally healthy elderly from those with a cardiovascular condition, on and above routinely applied physiological assessments. Fifteen elderly (aged 60 to 88) with a cardiovascular diagnosis were compared to fifteen age and gender matched healthy peers. Six sequential standardized lab assessments were conducted (one every two weeks), including an autonomic test battery, a 6-min step test and questionnaires covering perceived psychological state and experiences over the previous two weeks. Specific combinations of physiological and psychological factors (most prominently symptoms of depression) effectively predicted (clinical) cardiovascular markers. Additionally, a highly significant prognostic model was found, including depressive symptoms, recently experienced negative events and social isolation. It appeared slightly superior in identifying elderly with or without a cardiovascular condition compared to a model that only included physiological parameters. Adding psychosocial parameters to cardiovascular assessments in elderly may consequently provide protocols that are significantly more efficient, relatively comfortable and technologically feasible in ambulant settings, without necessarily compromising prognostic accuracy.
A Low-Cost, High-Precision Vehicle Navigation System for Deep Urban Multipath Environment Using TDCP Measurements
Authors: Kim Jungbeom , Park Minhuck , Bae Yonghwan , Kim O-Jong , Kim Donguk , Kim Bugyeom , Kee Changdon
Published: June 2020

In this study, we developed a low-cost, high-precision vehicle navigation system for deep urban multipath environments using time-differenced carrier phase (TDCP) measurements. Although many studies are being conducted to navigate autonomous vehicles using the global positioning system (GPS), it is difficult to obtain accurate navigation solutions due to multipath errors in urban environments. Low-cost GPS receivers that determine the solution based on pseudorange measurements are vulnerable to multipath errors. We used carrier phase measurements that are more robust for multipath errors. Without correction information from reference stations, the limited information of a low-cost, single-frequency receiver makes it difficult to quickly and accurately determine integer ambiguity of carrier phase measurements. We used TDCP measurements to eliminate the need to determine integer ambiguity that is time-invariant and we combined TDCP-based GPS with an inertial navigation system to overcome deep urban multipath environments. Furthermore, we considered a cycle slip algorithm for its accuracy and a multi-constellation navigation system for its availability. The results of dynamic field tests in a deep urban area indicated that it could achieve horizontal accuracy of at the submeter level.
Estimating Human Wrist Stiffness during a Tooling Task
Authors: Phan Gia-Hoang , Hansen Clint , Tommasino Paolo , Budhota Aamani , Mohan Dhanya Menoth , Hussain Asif , Burdet Etienne , Campolo Domenico
Published: June 2020

In this work, we propose a practical approach to estimate human joint stiffness during tooling tasks for the purpose of programming a robot by demonstration. More specifically, we estimate the stiffness along the wrist radial-ulnar deviation while a human operator performs flexion-extension movements during a polishing task. The joint stiffness information allows to transfer skills from expert human operators to industrial robots. A typical hand-held, abrasive tool used by humans during finishing tasks was instrumented at the handle (through which both robots and humans are attached to the tool) to assess the 3D force/torque interactions between operator and tool during finishing task, as well as the 3D kinematics of the tool itself. Building upon stochastic methods for human arm impedance estimation, the novelty of our approach is that we rely on the natural variability taking place during the multi-passes task itself to estimate (neuro-)mechanical impedance during motion. Our apparatus (hand-held, finishing tool instrumented with motion capture and multi-axis force/torque sensors) and algorithms (for filtering and impedance estimation) were first tested on an impedance-controlled industrial robot carrying out the finishing task of interest, where the impedance could be pre-programmed. We were able to accurately estimate impedance in this case. The same apparatus and algorithms were then applied to the same task performed by a human operators. The stiffness values of the human operator, at different force level, correlated positively with the muscular activity, measured during the same task.
Monitoring the Water Stress of an Indoor Living Wall System Using the “Triangle Method”
Authors: Yuan Xu , Laakso Kati , Davis Chad Daniel , Guzmán Q. J. Antonio , Meng Qinglin , Sanchez-Azofeifa Arturo
Published: June 2020

Living walls are important vertical greening systems with modular prevegetated structures. Studies have suggested that living walls have many social benefits as an ecological engineering technique with notable potential for reconciliation ecology. Despite these benefits, there are currently no mature workflows or technologies for monitoring the health status and water stress of living wall systems. To partially fill the current knowledge gap related to water stress, we acquired thermal, multispectral, and hyperspectral remote sensing data from an indoor living wall in the Cloud Forest of the Gardens by the Bay, Singapore. The surface temperature (Ts) and a normalized difference vegetation index (NDVI) were obtained from these data to construct a Ts-NDVI space for applying the “triangle method”. A simple and effective algorithm was proposed to determine the dry and wet edges, the key components of the said method. The pixels associated with the dry and wet edges were then selected and highlighted to directly display the areas under water-stress conditions. Our results suggest that the proposed algorithm can provide a reasonable overview of the water-stress information of the living wall; therefore, our method can be simple and effective to monitor the health status of a living wall. Furthermore, our work confirms that the triangle method can be transferred from the outdoors to an indoor environment.
The Effect of a Flow Field on Chemical Detection Performance of Quadrotor Drone
Authors: Do Sangwon , Lee Myeongjae , Kim Jong-Seon
Published: June 2020

The determination of a suitable sensor location on quadrotor drones is a very important issue for chemical reconnaissance platforms because the magnitude and direction of air velocity is different for each location. In this study, we investigated a customized chemical reconnaissance system consisting of a quadrotor drone and a chip-sized chemical sensor for detecting dimethyl-methylphosphonate (DMMP; a Sarin simulant) and investigated the chemical detection properties with respect to the sensor position through indoor experiments and particle image velocimetry (PIV) analysis of the system. The PIV results revealed an area free of vortex–vortex interaction between the drone rotors, where there was distinctly stable and uniform chemical detection of DMMP. The proposed chemical reconnaissance system was found to be realistic for practical application.
Robust Weighted l1,2 Norm Filtering in Passive Radar Systems
Authors: Satar Baris , Soysal Gokhan , Jiang Xue , Efe Murat , Kirubarajan Thiagalingam
Published: June 2020

Conventional methods such as matched filtering, fractional lower order statistics cross ambiguity function, and recent methods such as compressed sensing and track-before-detect are used for target detection by passive radars. Target detection using these algorithms usually assumes that the background noise is Gaussian. However, non-Gaussian impulsive noise is inherent in real world radar problems. In this paper, a new optimization based algorithm that uses weighted

l
1

and

l
2

norms is proposed as an alternative to the existing algorithms whose performance degrades in the presence of impulsive noise. To determine the weights of these norms, the parameter that quantifies the impulsiveness level of the noise is estimated. In the proposed algorithm, the aim is to increase the target detection performance of a universal mobile telecommunication system (UMTS) based passive radars by facilitating higher resolution with better suppression of the sidelobes in both range and Doppler. The results obtained from both simulated data with

α

stable distribution, and real data recorded by a UMTS based passive radar platform are presented to demonstrate the superiority of the proposed algorithm. The results show that the proposed algorithm provides more robust and accurate detection performance for noise models with different impulsiveness levels compared to the conventional methods.
WCTECGdb: A 12-Lead Electrocardiography Dataset Recorded Simultaneously with Raw Exploring Electrodes’ Potential Directly Referred to the Right Leg
Authors: Moeinzadeh Hossein , Assad Joseph , Bifulco Paolo , Cesarelli Mario , O’Loughlin Aiden , Tapson Jonathan C. , Shugman Ibrahim M. , Thiagalingam Aravinda , Gargiulo Gaetano D.
Published: June 2020

With this paper we communicated the existence of a surface electrocardiography (ECG) recordings dataset, named WCTECGdb, that aside from the standard 12-lead signals includes the raw electrode biopotential for each of the nine exploring electrodes refereed directly to the right leg. This dataset, comprises of 540 ten second segments recorded from 92 patients at Campbelltown Hospital, NSW Australia, and is now available for download from the Physionet platform. The data included in the dataset confirm that the Wilson’s Central Terminal (WCT) has a relatively large amplitude (up to 247% of lead II) with standard ECG characteristics such as a p-wave and a t-wave, and is highly variable during the cardiac cycle. As further examples of application for our data, we assess: (1) the presence of a conductive pathway between the legs and the heart concluding that in some cases is electrically significant and (2) the initial assumption about the limbs potential stating the dominance of the left arm concluding that this is not always the case and that might requires case to case assessment.
Reconstruction of 12-Lead Electrocardiogram from a Three-Lead Patch-Type Device Using a LSTM Network
Authors: Sohn Jangjay , Yang Seungman , Lee Joonnyong , Ku Yunseo , Kim Hee Chan
Published: June 2020

Reconstructing a standard 12-lead electrocardiogram (ECG) from signals received from electrodes packed into a patch-type device is a challenging task in the field of medical instrumentation. All attempts to obtain a clinically valid 12-lead ECG using a patch-type device were not satisfactory. In this study, we designed the hardware for a three-lead patch-type ECG device and employed a long short-term memory (LSTM) network that can overcome the limitations of the linear regression algorithm used for ECG reconstruction. The LSTM network can overcome the issue of reduced horizontal components of the vector in the electric signal obtained from the patch-type device attached to the anterior chest. The reconstructed 12-lead ECG that uses the LSTM network was tested against a standard 12-lead ECG in 30 healthy subjects and ECGs of 30 patients with pathologic findings. The average correlation coefficient of the LSTM network was found to be 0.95. The ability of the reconstructed ECG to detect pathologic abnormalities was identical to that of the standard ECG. In conclusion, the reconstruction of a standard 12-lead ECG using a three-lead patch-type device is feasible, and such an ECG is an equivalent alternative to a standard 12-lead ECG.
Combined Use of Sentinel-1 SAR and Landsat Sensors Products for Residual Soil Moisture Retrieval over Agricultural Fields in the Upper Blue Nile Basin, Ethiopia
Authors: Ayehu Getachew , Tadesse Tsegaye , Gessesse Berhan , Yigrem Yibeltal , M. Melesse Assefa
Published: June 2020

The objective of this paper is to investigate the potential of sentinel-1 SAR sensor products and the contribution of soil roughness parameters to estimate volumetric residual soil moisture (RSM) in the Upper Blue Nile (UBN) basin, Ethiopia. The backscatter contribution of crop residue water content was estimated using Landsat sensor product and the water cloud model (WCM). The surface roughness parameters were estimated from the Oh and Baghdadi models. A feed-forward artificial neural network (ANN) method was tested for its potential to translate SAR backscattering and surface roughness input variables to RSM values. The model was trained for three inversion configurations: (i) SAR backscattering from vertical transmit and vertical receive (SAR VV) polarization only; (ii) using SAR VV and the standard deviation of surface heights ( h r m s ), and (iii) SAR VV, h r m s , and optimal surface correlation length ( l e f f ). Field-measured volumetric RSM data were used to train and validate the method. The results showed that the ANN soil moisture estimation model performed reasonably well for the estimation of RSM using the single input variable of SAR VV data only. The ANN prediction accuracy was slightly improved when SAR VV and the surface roughness parameters ( h r m s and l e f f ) were incorporated into the prediction model. Consequently, the ANN’s prediction accuracy with root mean square error (RMSE) = 0.035 cm3/cm3, mean absolute error (MAE) = 0.026 cm3/cm3, and r = 0.73 was achieved using the third inversion configuration. The result implies the potential of Sentinel-1 SAR data to accurately retrieve RSM content over an agricultural site covered by stubbles. The soil roughness parameters are also potentially an important variable to soil moisture estimation using SAR data although their contribution to the accuracy of RSM prediction is slight in this study. In addition, the result highlights the importance of combining Sentinel-1 SAR and Landsat images based on an ANN approach for improving RSM content estimations over crop residue areas.
A Two-Layer, Energy-Efficient Approach for Joint Power Control and Uplink–Downlink Channel Allocation in D2D Communication
Authors: Zhou Li , Wu Yucheng , Yu Haifei
Published: June 2020

Energy efficiency (EE) is a critical performance indicator for the device-to-device (D2D) communication underlaying cellular networks due to limited battery capacity and serious interference between user equipment. In this study, we proposed a power control and channel allocation scheme for the EE maximization of the D2D pairs, while jointly reusing uplink–downlink resources and guaranteeing the cellular users’ (CUs) quality of service (QoS). The formulated problem was a mixed-integer nonlinear programming (MINLP) problem, which is generally an unsolved non-deterministic polynomial-time hardness (NP-hard) problem within polynomial time. To make it tractable to solve, the original problem was divided into two sub-problems: power control and channel allocation. A power control algorithm based on the Lambert W function was proposed to maximize the EE of the individual D2D pair. Assigning either an uplink or downlink resource to reuse, the EE of each D2D pair was calculated using the power control results. A channel allocation scheme based on the Kuhn–Munkres algorithm utilized the EE weights to optimize the overall EE of the D2D pairs. The simulation results verified the theoretical analysis and proved that the proposed algorithm could remarkably improve the EE of D2D pairs while guaranteeing the QoS of the CUs.
Reducing Variability and Removing Natural Light from Nighttime Satellite Imagery: A Case Study Using the VIIRS DNB
Authors: Coesfeld Jacqueline , Kuester Theres , Kuechly Helga U. , Kyba Christopher C. M.
Published: June 2020

Temporal variation of natural light sources such as airglow limits the ability of night light sensors to detect changes in small sources of artificial light (such as villages). This study presents a method for correcting for this effect globally, using the satellite radiance detected from regions without artificial light emissions. We developed a routine to define an approximate grid of locations worldwide that do not have regular light emission. We apply this method with a 5 degree equally spaced global grid (total of 2016 individual locations), using data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB). This code could easily be adapted for other future global sensors. The correction reduces the standard deviation of data in the Earth Observation Group monthly DNB composites by almost a factor of two. The code and datasets presented here are available under an open license by GFZ Data Services, and are implemented in the Radiance Light Trends web application.
The Power of Big Data and Data Analytics for AMI Data: A Case Study
Authors: Guerrero-Prado Jenniffer Sidney , Alfonso-Morales Wilfredo , Caicedo-Bravo Eduardo , Zayas-Pérez Benjamín , Espinosa-Reza Alfredo
Published: June 2020

In recent years, there has been a transformation in the value chain of different industrial sectors, like the electricity networks, with the appearance of smart grids. Currently, the underlying knowledge in raw data coming from numerous devices can mark a significant competitive advantage for utilities. It is the case of the Advanced Metering Infrastructure (AMI). Such technology gets user consumption characteristics at levels of detail that were previously not possible. In this context, the terms big data and data analytics become relevant, which are tools that allow using large volumes of information and the generation of valuable knowledge from raw data that can support data-driven decisions for operating on the grid. This paper presents the results of the big data implementation and data analytics techniques in a case study with smart metering data from the city of London. Implemented big data and data analytic techniques to show how to understand user consumption patterns on a broader horizon, the relationships with seasonal variables identify behaviors related to specific events and atypical consumptions. This knowledge helps support decision making about improving demand response programs and, in general, the planning and operation of the Smart Grid.