bluuebunny/crossref_metadata_2025 huggingface dataset:
The Average Grain Size and Grain Aspect Ratio in Metal Laser Powder Bed Fusion: Modeling and Experiment
Authors: Letenneur Morgan , Kreitcberg Alena , Brailovski Vladimir
Published: March 2020

The additive manufacturing (AM) process induces high uncertainty in the mechanical properties of 3D-printed parts, which represents one of the main barriers for a wider AM processes adoption. To address this problem, a new time-efficient microstructure prediction algorithm was proposed in this study for the laser powder bed fusion (LPBF) process. Based on a combination of the melt pool modeling and the design of experiment approaches, this algorithm was used to predict the microstructure (grain size/aspect ratio) of materials processed by an EOS M280 LPBF system, including Iron and IN625 alloys. This approach was successfully validated using experimental and literature data, thus demonstrating its potential efficiency for the optimization of different LPBF powders and systems.
Establishment and Verification of a Constitutive Model of Ice Material Considering the Effect of Temperature
Authors: Yu Tongqiang , Liu Kun , Wang Jiaxia , Wang Zili
Published: March 2020

The increase in global warming has secured the arctic region as a research hotspot, and the existence of ice floes and massive icebergs poses a great challenge to the navigational safety of polar ships. For the finite simulation of ship–ice collisions, a reasonable description of the ice constitutive model is the most important factor for the accuracy of ice load prediction and structural deformation assessment. Due to the complex physical properties of natural sea ice materials, there are still many difficulties in achieving a widely accepted ice material model. In this paper, a constitutive model of ice material considering the influence of temperature is established and embedded into finite element software LS-DYNA, and the material property parameters are validated and analyzed. Then, the drop test in a published paper is recapitulated by the numerical simulation with the proposed method, and the results are compared. Good agreement is attained between the numerical simulation and published results. The influences of temperature and drop height are discussed, and the results show that both of them have an important effect on structural deformation. The research results can be used for ice load prediction and polar ship structure design.
Dielectric Characterization of Non-Conductive Fabrics for Temperature Sensing through Resonating Antenna Structures
Authors: Ibanez-Labiano Isidoro , Alomainy Akram
Published: March 2020

Seamless integration of electronics within clothing is key for further development of efficient and convenient wearable technologies. Therefore, the characterization of textile and fabric materials under environmental changes and other parametric variations is an important requirement. To our knowledge, this paper presents for the first time the evaluation of dielectric characterization over temperature for non-conductive textiles using resonating structures. The paper describes the effects of temperature variations on the dielectric properties of non-conductive fabrics and how this can be derived from the performance effects of a simple microstrip patch antenna. Organic cotton was chosen as the main substrate for this research due to its broad presence in daily clothing. A dedicated measurement setup is developed to allow reliable and repeatable measurements, isolating the textile samples from external factors. This work shows an approximately linear relation between temperature and textile’s dielectric constant, giving to fabric-based antennas temperature sensing properties with capability up to 1 degree Celsius at millimeter-wave frequencies.
Lubrication and Wear Characteristics of Mechanical Face Seals under Random Vibration Loading
Authors: He Wentao , Wang Shaoping , Zhang Chao , Wang Xi , Liu Di
Published: March 2020

The service life of mechanical face seals is related to the lubrication and wear characteristics. The stable analytical methods are commonly used, but they cannot address effects of random vibration loading, which, according to experimental studies, are important factors for lubrication and wear of mechanical face seals used in air and space vehicles. Hence, a dynamic model for mechanical face seals is proposed, with a focus on the effects of random vibration loading. The mechanical face seal in the axial direction is described as a mass-spring-damping system. Spectrum analysis specified for random vibration is then performed numerically to obtain the response power spectral density (PSD) of the mechanical face seal and calculate the root mean square (RMS) values under random vibration conditions. A lumped parameter model is then developed to examine how dynamic parameters such as stiffness and damping affect the lubrication regimes of mechanical face seals. Based on the dynamic model and Archard wear equation, a numerical wear simulation method is proposed. The results elucidated that the increase of input acceleration PSDs, the decrease of axial damping, and the increase of axial stiffness lead to the probability of the mechanical face seal operating under full film lubrication regime increase and finally the decrease of wear. This research provides a guideline for improving the adaptability of mechanical face seals under random vibration environments.
Preparation of MWCNT Microbeads for the Application of Bed Materials in a Fluidized Bed Heat Exchanger
Authors: Lee Min Ji , Kim Sung Won
Published: March 2020

Fluidized beds have been utilized for various chemical and physical applications including heat transfer such as the gas–solid heat exchanger. It is advantageous to use carbon nanotubes (CNTs) with high thermal conductivity as bed materials for heat transfer enhancement in a direct gas–solid contacting heat exchanger. However, the poor fluidization of CNTs is the biggest challenge due to the strong cohesive force between the particles. A control over the macroscopic shapes of CNT powders is required for their application. A preparation method of CNT microbeads has been proposed to be suitable for fluidized bed applications. The method is characterized by using m-cresol known as processing solvents for fabrication of the CNT microbeads. Multiwalled CNT powders were directly mixed with m-cresol to yield a thick paste-like material. The paste droplets were rolled into round particles with in pure water with and without surfactant. The obtained particles were dried in a vacuum oven. The obtained microbeads have diameters ranging 300–2200 μm and apparent particle density of 350–400 kg/m3, which corresponds to Geldart group B in the fluidization classification. The micrograph of the CNT microbeads exhibited stacked nanotubes array on the surface, indicating obvious densification of the raw CNT powders. The microbeads prepared in water containing surfactant have better shape factor such as circularity and solidity. The thermal conductivity of the microbeads is about 1.18 W/mK in a bulk state, which is much higher than raw CNT powder (0.032 W/mK). The flowability and fluidization characteristics of the multiwalled CNT (MWCNT) microbeads showed a possibility as promising bed material suitable for the fluidized bed heat exchanger.
Interfacial Contact Behavior between CNTs and AgNW with Molecular Dynamics Simulation
Authors: Cui Jianlei , Mei Huanhuan , Zhang Jianwei , Fan Zhengjie , Yang Jun , Wang Wenjun , Tohmyoh Hironori , Mei Xuesong
Published: March 2020

The behavior at an interface between carbon nanotubes (CNTs) and silver nanowire (AgNW) could hardly be observed experimentally on an atomic scale, and the interaction is difficult to accurately calculate due to nanometer size effects. In this work, the contact behavior is studied with the molecular dynamics (MD) simulation, which indicates that the CNTs and AgNW can move towards each other to form aligned structures with their interfaces in full contact. In these different composite systems, nanotubes may either keep their form of an inherent cylindrical structure or completely collapse into the nanoribbons that can tightly scroll on the AgNW periphery while wrapping it in a core-shell structure. Thus, the atomic configuration evolution that is affected by the van der Waals (vdW) interaction is closely analyzed to assist the understanding of interfacial contact behavior.
The Super-Diffusive Singular Perturbation Problem
Authors: Alvarez Edgardo , Lizama Carlos
Published: March 2020

In this paper we study a class of singularly perturbed defined abstract Cauchy problems. We investigate the singular perturbation problem

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A Parametric Kind of the Degenerate Fubini Numbers and Polynomials
Authors: Sharma Sunil Kumar , Khan Waseem A. , Ryoo Cheon Seoung
Published: March 2020

In this article, we introduce the parametric kinds of degenerate type Fubini polynomials and numbers. We derive recurrence relations, identities and summation formulas of these polynomials with the aid of generating functions and trigonometric functions. Further, we show that the parametric kind of the degenerate type Fubini polynomials are represented in terms of the Stirling numbers.
Antioxidant, Hypolipidemic and Hepatic Protective Activities of Polysaccharides from Phascolosoma esculenta
Authors: Wu Yaqing , Jiang Hongying , Lin Jyuan-Siou , Liu Jia , Wu Chang-Jer , Xu Ruian
Published: March 2020

The aims of this study were to investigate the antioxidant, hypolipidemic and hepatic protective effects of Phascolosoma esculenta polysaccharides (PEP). PEP was prepared from Phascolosoma esculenta by enzyme hydrolysis and its characterization was analyzed. The antioxidant activities of PEP were evaluated by the assays of scavenging 1,1-Diphenyl-2-picrylhydrazyl (DPPH), superoxide anion, hydroxyl radicals and chelating ferrous ion in vitro. It showed that PEP could scavenge radicals effectively and had favorable antioxidant activities. In the meantime, the hypolipidemic effect of PEP was investigated in vivo by using mice model fed with high-fat diet with or without PEP treatment. Compared with the hyperlipidemic mice without treatment, the serum levels of total cholesterol (TC) (30.1–35.7%, p < 0.01), triglyceride (TG) (24.5–50.8%, p < 0.01 or p < 0.05), low-density lipoprotein cholesterol (LDL-C) (49.6–56.8%, p < 0.01) and liver levels of TC (21.0–28.4%, p < 0.01), TG (23.8–37.0%, p < 0.01) decreased significantly, whereas serum high-density lipoprotein cholesterol (HDL-C) (47.7–59.9%, p < 0.01 or p < 0.05) increased significantly after treatment with different dosage of PEP (0.2, 0.4 and 0.8 g per kg body weight, respectively). In addition, superoxide dismutase (SOD) (10.2–22.2% and 18.8–26.9%, p < 0.05), glutathione peroxidase (GSH-Px) (11.9–15.4% and 26.6–30.4%, p < 0.05) activities in serum and liver enhanced markedly while aspartate aminotransferase (AST) (18.7–29.6% and 42.4–58.0%, p < 0.05), alanine transaminase (ALT) (42.7–46.0% and 31.2–42.2%, p < 0.05) activities, as well as the levels of malondialdehyde (MDA) (15.9–24.4% and 15.0–16.8%, p < 0.01 or p < 0.05) in serum and liver reduced markedly. Moreover, the histopathological observation of livers indicated that PEP could attenuate liver cell injury. The animal experimental results demonstrated that PEP exerted hypolipidemic and hepatoprotective roles in hyperlipidemic mice. In summary, our results above suggest that PEP might be a potential natural antioxidant and utilized as a therapeutic candidate for hyperlipidemia.
Types and Distribution of Bioactive Polyunsaturated Aldehydes in a Gradient from Mesotrophic to Oligotrophic Waters in the Alborán Sea (Western Mediterranean)
Authors: Bartual Ana , Hernanz-Torrijos María , Sala Iria , Ortega María J. , González-García Cristina , Bolado-Penagos Marina , López-Urrutia Angel , Romero-Martínez Leonardo , Lubián Luís M. , Bruno Miguel , Echevarría Fidel , García Carlos M.
Published: March 2020

Polyunsaturated aldehydes (PUAs) are bioactive molecules suggested as chemical defenses and infochemicals. In marine coastal habitats, diatoms reach high PUA production levels during bloom episodes. Two fractions of PUA can usually be analyzed: pPUA obtained via artificial breakage of collected phytoplankton cells and dissolved PUA already released to the environment (dPUA). In nature, resource supply arises as a main environmental controlling factor of PUA production. In this work, we monitored the vertical distribution and daily variation of pPUA associated with large-size phytoplankton and dPUA, at three sites located in the Alborán Sea from mesotrophic to oligotrophic waters. The results corroborate the presence of large-size PUA producers in oligotrophic and mesotrophic waters with a significant (58%–85%) diatom biomass. In addition to diatoms, significant correlations between pPUA production and dinoflagellate and silicoflagellate abundance were observed. 2E,4E/Z-Heptadienal was the most abundant aldehyde at the three sites with higher values (17.1 fg·cell−1) at the most oligotrophic site. 2E,4E/Z-Decadienal was the least abundant aldehyde, decreasing toward the oligotrophic site. For the first time, we describe the daily fluctuation of pPUA attributable to cellular physiological state and not exclusively to taxonomical composition. Our results demonstrate the persistence of threshold levels of dPUA deep in the water column, as well as the different chromatographic profiles of dPUA compared with pPUA. We propose different isomerization processes that alter the chemical structure of the released PUAs with unknown effects on their stability, biological function, and potential bioactivity.
A Prospective Intervention Trial on Tailored Radiofrequency Ablation of Uterine Myomas
Authors: Fasciani Alessandro , Turtulici Giovanni , Siri Giacomo , Ferrero Simone , Sirito Rodolfo
Published: March 2020

Background and Objective: Investigating the use of radiofrequency myolysis (RFM) for the treatment of fibroids through less invasive access by combining transvaginal ultrasound, hysteroscopy and laparoscopy. Materials and Methods: Fifty-four premenopausal women with 106 symptomatic uterine myomas. Patients underwent RFM in three ways: Vaginal Ultrasound-guided RFM (VU-RFM), Laparoscopic RFM (L-RFM) and Hysteroscopic-RFM (H-RFM). The mean patient age was 43 years; 52 symptomatic uterine myomas were subserosal, 44 intramural and 10 submucosal. The outcomes evaluated at 1 and 12 months after RFM were myoma size (volume-diameter), “Uterine Fibroid Symptom and Quality of Life (UFS-QOL)” questionnaire and a 10-point Visual Analogue Scale (VAS). The therapy was completed with a single ablation in all patients, no complication was registered. The average number of fibroids treated per intervention was two with the use of different accesses: 64/106 VU-RFMs (60.4%), 32/106 L-RFMs (30.2%) and 10/106 H-RFMs (9.4%). Results: Volume and diameter of fibroids were significantly reduced by, respectively, 51.3% and 20.1% in the first 30 days post-intervention (p < 0.001) up to a maximum of 73.5% and 37.1% after the second follow-up visit at 12 months (p < 0.001). A similar trend was shown in terms of disability with a progressive and significant reduction of symptoms (menorrhagia, dysmenorrhea, dyspareunia and pollakiuria) demonstrated by percentage variation of UFS-QOL Symptom Severity and VAS scores to −74.3% and −45.3% as well as −84.9% and −74.3%, respectively, at 1 and 12 months after RFM (p < 0.001). An overall improvement in the quality of life was also demonstrated by a significant increase in the UFS-QOL total score of +38.2% in the first 30 days post-intervention up to +44.9% after the second follow-up visit at 12 months (p < 0.001). The overall average surgery time of the RFM for each patient was 48 minutes, and the time to treat each fibroid by Vaginal Ultrasound-guided RFM (23 min) was found to be significantly less than those of laparoscopy or hysteroscopy (respectively 35 and 34 min) (p < 0.05). An electromagnetic virtual needle tracking system (VNTS) was successfully tested during the RFM procedures, and real-time contrast-enhanced ultrasound (CEUS) has proven to be effective in determining the duration of myolysis through the identification of eventual residual areas of enhancement within the fibroids. Conclusion: Radiofrequency can be considered a minimally invasive and safe procedure for the treatment of uterine myomas through the customization and possible combination of transvaginal, laparoscopic or hysteroscopic accesses. The standardization of the ablation technique with pre-intervention biopsy and new technologies such as VNTS and CEUS spares healthy uterine tissue and may change the future management of symptomatic uterine fibroids.
Implementation and Validation of the Roche Light Cycler 480 96-Well Plate Platform as a Real-Time PCR Assay for the Quantitative Detection of Cytomegalovirus (CMV) in Clinical Specimens Using the Luminex MultiCode ASRs System
Authors: Li Shengwen Calvin , Sparks Kara J. , Sender Leonard S.
Published: March 2020

Allogenic stem-cell therapies benefit patients in the treatment of multiple diseases; however, the side effects of stem-cell therapies (SCT) derived from the concomitant use of immune suppression agents often include triggering infection diseases. Thus, analysis is required to improve the detection of pathogen infections in SCT. We develop a polymerase chain reaction (PCR)-based methodology for the qualitative real-time DNA detection of cytomegalovirus (CMV), with reference to herpes simplex virus types 1 (HSVI), Epstein–Barr virus (EBV), and varicella-zoster virus (VZV) in blood, urine, solid tissues, and cerebrospinal fluid. This real-time PCR of 96-well plate format provides a rapid framework as required by the Food and Drug Administration (FDA) for clinical settings, including the processing of specimens, reagent handling, special safety precautions, quality control criteria and analytical accuracy, precisely reportable range (analyst measurement range), reference range, limit of detection (LOD), analytical specificity established by interference study, and analyte stability. Specifically, we determined the reportable range (analyst measurement range) with the following criteria: CMV copies ≥200 copies/mL; report copy/mL value; CMV copies ≤199 copies/mL; report detected but below quantitative range; CMV copies = 0 with report <200 copies/mL. That is, with reference range, copy numbers (CN) per milliliter (mL) of the LOD were determined by standard curves that correlated Ct value and calibrated standard DNA panels. The three repeats determined that the measuring range was 1E2~1E6 copies/mL. The standard curves show the slopes were within the range −2.99 to −3.65 with R2 ≥ 0.98. High copy (HC) controls were within 0.17–0.18 log differences of DNA copy numbers; (2) low copy (LC) controls were within 0.17–0.18 log differences; (3) LOD was within 0.14–0.15 log differences. As such, we set up a fast, simple, inexpensive, sensitive, and reliable molecular approach for the qualitative detection of CMV pathogens. Conclusion: This real-time PCR of the 96-well plate format provides a rapid framework as required by the FDA for clinical settings.
Important Considerations for Sample Collection in Metabolomics Studies with a Special Focus on Applications to Liver Functions
Authors: Smith Lorraine , Villaret-Cazadamont Joran , Claus Sandrine P. , Canlet Cécile , Guillou Hervé , Cabaton Nicolas J. , Ellero-Simatos Sandrine
Published: March 2020

Metabolomics has found numerous applications in the study of liver metabolism in health and disease. Metabolomics studies can be conducted in a variety of biological matrices ranging from easily accessible biofluids such as urine, blood or feces, to organs, tissues or even cells. Sample collection and storage are critical steps for which standard operating procedures must be followed. Inappropriate sample collection or storage can indeed result in high variability, interferences with instrumentation or degradation of metabolites. In this review, we will first highlight important general factors that should be considered when planning sample collection in the study design of metabolomic studies, such as nutritional status and circadian rhythm. Then, we will discuss in more detail the specific procedures that have been described for optimal pre-analytical handling of the most commonly used matrices (urine, blood, feces, tissues and cells).
Molecular Dissection of the Campylobacter jejuni CadF and FlpA Virulence Proteins in Binding to Host Cell Fibronectin
Authors: Talukdar Prabhat K. , Negretti Nicholas M. , Turner Kyrah L. , Konkel Michael E.
Published: March 2020

Campylobacter jejuni, a zoonotic pathogen that frequently colonizes poultry, possesses two Microbial Surface Components Recognizing Adhesive Matrix Molecule(s) (MSCRAMMs) termed CadF and FlpA that bind to the glycoprotein fibronectin (FN). Previous to this study, it was not known whether the CadF and FlpA proteins were functionally redundant or if both were required to potentiate host cell binding and signaling processes. We addressed these questions by generating a complete repertoire of cadF and flpA mutants and complemented isolates, and performing multiple phenotypic assays. Both CadF and FlpA were found to be necessary for the maximal binding of C. jejuni to FN and to host cells. In addition, both CadF and FlpA are required for the delivery of the C. jejuni Cia effector proteins into the cytosol of host target cells, which in turn activates the MAPK signaling pathway (Erk 1/2) that is required for the C. jejuni invasion of host cells. These data demonstrate the non-redundant and bi-functional nature of these two C. jejuni FN-binding proteins. Taken together, the C. jejuni CadF and FlpA adhesins facilitate the binding of C. jejuni to the host cells, permit delivery of effector proteins into the cytosol of a host target cell, and aid in the rewiring of host cell signaling pathways to alter host cell behavior.
Vitamin D Deficient Older Adults Are More Prone to Have Metabolic Syndrome, but Not to a Greater Number of Metabolic Syndrome Parameters
Authors: Pott-Junior Henrique , Nascimento Carla Manuela Crispim , Costa-Guarisco Letícia Pimenta , Gomes Grace Angelica de Oliveira , Gramani-Say Karina , Orlandi Fabiana de Souza , Gratão Aline Cristina Martins , Orlandi Ariene Angelini dos Santos , Pavarini Sofia Cristina Iost , Vasilceac Fernando Augusto , Zazzetta Marisa Silvana , Cominetti Marcia Regina
Published: March 2020

This study investigated the relationship between metabolic parameters and low serum 25-hydroxyvitamin D (25(OH)D) levels in older adults (n = 265). They were assessed for anthropometrics and metabolic measurements, including 25(OH)D, insulin, glucose, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG) and other inflammatory markers. Vitamin D deficiency was defined as a 25(OH)D level below 50 nmol/L. Comparisons between groups were performed using Wilcoxon–Mann–Whitney or Pearson’s Chi-squared test. A multivariate adjusted Poisson regression was used to model the number of metabolic parameters as a function of a set of explanatory variables. Subjects with 25(OH)D deficiency were predominantly females and presented higher body weight, body mass index, waist circumference, triglycerides and Tumor Necrosis Factor-α (TNF-α), and higher insulin resistance. Metabolic syndrome was also more prevalent among 25(OH)D-deficient subjects. In those without metabolic syndrome, 25(OH)D deficiency was related only to obesity and higher insulin resistance. Female sex, hypertension, higher waist circumference and higher levels of hemoglobin A1C (%), HDL-C, and TG were significantly associated with an increased number of metabolic syndrome parameters after adjusting for covariates, but 25(OH)D was not. The fact that serum 25(OH)D concentration was inversely associated with metabolic syndrome and insulin resistance not only reaffirms the relevance to consider serum 25(OH)D concentration as an influencing factor for insulin resistance, but also the need to actively screen for hypovitaminosis D in all patients with this condition.
The Cost of Potentially Inappropriate Medications in Nursing Homes in West Occitanie
Authors: Caucat Marie , Zacarin Alice , Rousseau Vanessa , Montastruc Jean-Louis , Bagheri Haleh
Published: March 2020

Introduction: As of 2019, people older than 65 years represent 20% of the French population. Despite several guidelines suggesting to avoid potentially inappropriate medication (PIM) use in elderly, the prevalence of their prescription remains high (25%). Furthermore, PIM could lead to preventable adverse drug reactions (ADRs). The main objective of this study was to determine the direct cost of PIM in older persons living in residential care homes for the elderly (nursing homes). A secondary objective was to assess the potential impact of PIM deprescribing on drug-related health care costs. Methods: We undertook a multicenter, retrospective study in 19 care homes for the elderly including 1240 residents. The analysis of prescriptions was carried out according to the European EU(7) PIM list. The cost of each drug was estimated according to the French Medication Insurance database. Furthermore, patient’s comorbidities were studied using Charlson’s comorbidity index. In order to estimate the economic impact of PIM, we used the list of alternative appropriate drugs suggested by EU(7) PIM list and French National Health Authority. An incremental cost per patient was calculated by the difference in costs between PIMs and alternative drugs. Results: A total of 7768 lines of drug prescriptions were analyzed. The mean age was 87.6 ± 7.6 years. About 70% (n = 872) of residents received more than five drugs. We identified 959 residents (77.3%) with at least one PIM. The mean cost of PIM was 0.58 euros versus 0.48 euros for alternatives. PIM substitution by alternatives led to save 12 centimes/resident/day. The mean cost of prescription with PIM was 2.8 euros per resident per day (28% of the overall cost of prescription). According to these results, more than 25 million euros can be overall saved for aged persons living in nursing homes for the older people in France per year. Conclusion: The prevalence of PIMs among the elderly in nursing homes is high and leads to a significant cost. Deprescribing of these medications could decrease both drug misuse and cost of drug prescription. Further research is needed to estimate the overall cost of PIM exposure outcomes, taking into account the ADRs leading to hospitalization.
The Leaders, the Laggers, and the “Vulnerables”
Authors: Arakelian Veni , Qamhieh Hashem Shatha
Published: March 2020

We examine the lead-lag effect between the large and the small capitalization financial institutions by constructing two global weekly rebalanced indices. We focus on the 10% of stocks that “survived” all the rebalancings by remaining constituents of the indices. We sort them according to their systemic importance using the marginal expected shortfall (MES), which measures the individual institutions’ vulnerability over the market, the network based MES, which captures the vulnerability of the risks generated by institutions’ interrelations, and the Bayesian network based MES, which takes into account different network structures among institutions’ interrelations. We also check if the lead-lag effect holds in terms of systemic risk implying systemic risk transmission from the large to the small capitalization, concluding a mixed behavior compared to the index returns. Additionally, we find that all the systemic risk indicators increase their magnitude during the financial crisis.
A Key Management Scheme Based on Pairing-Free Identity Based Digital Signature Algorithm for Heterogeneous Wireless Sensor Networks
Authors: Yuan Erdong , Wang Liejun , Cheng Shuli , Ao Naixiang , Guo Qingrui
Published: March 2020

The secure transmission of data within a network has received great attention. As the core of the security management mechanism, the key management scheme design needs further research. In view of the safety and energy consumption problems in recent papers, we propose a key management scheme based on the pairing-free identity based digital signature (PF-IBS) algorithm for heterogeneous wireless sensor networks (HWSNs). Our scheme uses the PF-IBS algorithm to complete message authentication, which is safer and more energy efficient than some recent schemes. Moreover, we use the base station (BS) as the processing center for the huge data in the network, thereby saving network energy consumption and improving the network life cycle. Finally, we indirectly prevent the attacker from capturing relay nodes that upload data between clusters in the network (some cluster head nodes cannot communicate directly). Through performance evaluation, the scheme we proposed reasonably sacrifices part of the storage space in exchange for entire network security while saving energy consumption.
Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders
Authors: Kucharski Dariusz , Kleczek Pawel , Jaworek-Korjakowska Joanna , Dyduch Grzegorz , Gorgon Marek
Published: March 2020

In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the detection of nests of nevus cells in histopathological images of skin specimens is an important step in dermatopathology. The diagnostic criteria based on the degree of uniformity and symmetry of border irregularities are particularly vital in dermatopathology, in order to distinguish between benign and malignant skin lesions. However, to the best of our knowledge, it is the first described method to segment the nests region. The novelty of our approach is not only the area of research, but, furthermore, we address a problem with a small ground-truth dataset. We propose an effective computer-vision based deep learning tool that can perform the nests segmentation based on an autoencoder architecture with two learning steps. Experimental results verified the effectiveness of the proposed approach and its ability to segment nests areas with Dice similarity coefficient 0.81, sensitivity 0.76, and specificity 0.94, which is a state-of-the-art result.
Analysis of Quasi-Zenith Satellite System Signal Acquisition and Multiplexing Characteristics in China Area
Authors: Ye Hongjun , Jing Xiaojun , Liu Liang , Wang Maolei , Hao Shuo , Lang Xingkang , Yu Baoguo
Published: March 2020

On the basis of realizing regional navigation, the Quasi-Zenith Satellite System (QZSS) has advanced navigation function, which leads to the broadcasting of more signals in a single frequency of QZSS signals. Current signal transmission technology cannot solve this problem, so it is necessary to design a signal multiplexing method. The current QZSS satellite interface document does not disclose the multiplexing modulation method of the signal transmission, which has a certain impact on the acquisition of high-precision observation data and further data processing. The iGMAS (International GNSS Monitoring & Assessment System) Monitoring and Evaluation Center of the 54th Research Institute of China Electronics Technology Group Corporation has used the low-distortion data acquisition and processing platform and refined signal software receiving processing algorithm of the iGMAS to complete the signal acquisition and analysis of QZSS satellites. Analysis of the multiplexing and modulation method and signal characteristics for the QZSS has been carried out, which can provide a reference for the design and data processing of high-precision receivers.
An End-to-End Trainable Multi-Column CNN for Scene Recognition in Extremely Changing Environment
Authors: Li Zhenyu , Zhou Aiguo , Shen Yong
Published: March 2020

Scene recognition is an essential part in the vision-based robot navigation domain. The successful application of deep learning technology has triggered more extensive preliminary studies on scene recognition, which all use extracted features from networks that are trained for recognition tasks. In the paper, we interpret scene recognition as a region-based image retrieval problem and present a novel approach for scene recognition with an end-to-end trainable Multi-column convolutional neural network (MCNN) architecture. The proposed MCNN utilizes filters with receptive fields of different sizes to have Multi-level and Multi-layer image perception, and consists of three components: front-end, middle-end and back-end. The first seven layers VGG16 are taken as front-end for two-dimensional feature extraction, Inception-A is taken as the middle-end for deeper learning feature representation, and Large-Margin Softmax Loss (L-Softmax) is taken as the back-end for enhancing intra-class compactness and inter-class-separability. Extensive experiments have been conducted to evaluate the performance according to compare our proposed network to existing state-of-the-art methods. Experimental results on three popular datasets demonstrate the robustness and accuracy of our approach. To the best of our knowledge, the presented approach has not been applied for the scene recognition in literature.
Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach
Authors: Conforti Ilaria , Mileti Ilaria , Del Prete Zaccaria , Palermo Eduardo
Published: March 2020

Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.
Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network
Authors: Lv Xiaoming , Duan Fajie , Jiang Jia-jia , Fu Xiao , Gan Lin
Published: March 2020

Metallic surface defect detection is an essential and necessary process to control the qualities of industrial products. However, due to the limited data scale and defect categories, existing defect datasets are generally unavailable for the deployment of the detection model. To address this problem, we contribute a new dataset called GC10-DET for large-scale metallic surface defect detection. The GC10-DET dataset has great challenges on defect categories, image number, and data scale. Besides, traditional detection approaches are poor in both efficiency and accuracy for the complex real-world environment. Thus, we also propose a novel end-to-end defect detection network (EDDN) based on the Single Shot MultiBox Detector. The EDDN model can deal with defects with different scales. Furthermore, a hard negative mining method is designed to alleviate the problem of data imbalance, while some data augmentation methods are adopted to enrich the training data for the expensive data collection problem. Finally, the extensive experiments on two datasets demonstrate that the proposed method is robust and can meet accuracy requirements for metallic defect detection.
Reusable, Non-Invasive, and Ultrafast Radio Frequency Biosensor Based on Optimized Integrated Passive Device Fabrication Process for Quantitative Detection of Glucose Levels
Authors: Li Yang , Yao Zhao , Yue Wenjing , Zhang Chunwei , Gao Song , Wang Cong
Published: March 2020

The increase in the number of people suffering diabetes has been the driving force behind the development of glucose sensors to overcome the current testing shortcomings. In this work, a reusable, non-invasive and ultrafast radio frequency biosensor based on optimized integrated passive device fabrication process for quantitative detection of glucose level was developed. With the aid of the novel biosensor design with hammer-shaped capacitors for carrying out detection, both the resonance frequency and magnitude of reflection coefficient can be applied to map the different glucose levels. Meanwhile, the corresponding fabrication process was developed, providing an approach for achieving quantitative detection and a structure without metal-insulator-metal type capacitor that realizes low cost and high reliability. To enhance the sensitivity of biosensor, a 3-min dry etching treatment based on chlorine/argon-based plasma was implemented for realizing hydrophilicity of capacitor surface to ensure that the biosensor can be touched rapidly with glucose. Based on above implementation, a non-invasive biosensor having an ultrafast response time of superior to 0.85 s, ultralow LOD of 8.01 mg/dL and excellent reusability verified through five sets of measurements are realized. The proposed approaches are not limited the development of a stable and accurate platform for the detection of glucose levels but also presents a scheme toward the detection of glucose levels in human serum.
Ultrasensitive Magnetic Field Sensors for Biomedical Applications
Authors: Murzin Dmitry , Mapps Desmond J. , Levada Kateryna , Belyaev Victor , Omelyanchik Alexander , Panina Larissa , Rodionova Valeria
Published: March 2020

The development of magnetic field sensors for biomedical applications primarily focuses on equivalent magnetic noise reduction or overall design improvement in order to make them smaller and cheaper while keeping the required values of a limit of detection. One of the cutting-edge topics today is the use of magnetic field sensors for applications such as magnetocardiography, magnetotomography, magnetomyography, magnetoneurography, or their application in point-of-care devices. This introductory review focuses on modern magnetic field sensors suitable for biomedicine applications from a physical point of view and provides an overview of recent studies in this field. Types of magnetic field sensors include direct current superconducting quantum interference devices, search coil, fluxgate, magnetoelectric, giant magneto-impedance, anisotropic/giant/tunneling magnetoresistance, optically pumped, cavity optomechanical, Hall effect, magnetoelastic, spin wave interferometry, and those based on the behavior of nitrogen-vacancy centers in the atomic lattice of diamond.
A Novel Genetically Encoded Single Use Sensory Cellular Test System Measures Bicarbonate Concentration Changes in Living Cells
Authors: Bernhard Kevin , Stahl Cordula , Martens Regina , Frey Manfred
Published: March 2020

Bicarbonate plays a central role in human physiology from cellular respiration to pH homeostasis. However, so far, the measurement of bicarbonate concentration changes in living cells has only been possible by measuring intracellular pH changes. In this article, we report the development of a genetically encoded pH-independent fluorescence-based single-use sensory cellular test system for monitoring intracellular bicarbonate concentration changes in living cells. We describe the usefulness of the developed biosensor in characterizing the bicarbonate transport activities of anionophores—small molecules capable of facilitating the membrane permeation of this anion. We also demonstrate the ability of the bicarbonate sensory cellular test system to measure intracellular bicarbonate concentration changes in response to activation and specific inhibition of wild-type human CFTR protein when co-expressed with the bicarbonate sensing and reporting units in living cells. A valuable benefit of the bicarbonate sensory cellular test system could be the screening of novel anionophore library compounds for bicarbonate transport activity with efficiencies close to the natural anion channel CFTR, which is not functional in the respiratory epithelia of cystic fibrosis patients.
Fabrication of Si Micropore and Graphene Nanohole Structures by Focused Ion Beam
Authors: Md Ibrahim Nik Noor Nabilah , Hashim Abdul Manaf
Published: March 2020

A biosensor formed by a combination of silicon (Si) micropore and graphene nanohole technology is expected to act as a promising device structure to interrogate single molecule biopolymers, such as deoxyribonucleic acid (DNA). This paper reports a novel technique of using a focused ion beam (FIB) as a tool for direct fabrication of both conical-shaped micropore in Si3N4/Si and a nanohole in graphene to act as a fluidic channel and sensing membrane, respectively. The thinning of thick Si substrate down to 50 µm has been performed prior to a multi-step milling of the conical-shaped micropore with final pore size of 3 µm. A transfer of graphene onto the fabricated conical-shaped micropore with little or no defect was successfully achieved using a newly developed all-dry transfer method. A circular shape graphene nanohole with diameter of about 30 nm was successfully obtained at beam exposure time of 0.1 s. This study opens a breakthrough in fabricating an integrated graphene nanohole and conical-shaped Si micropore structure for biosensor applications.
Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network
Authors: Zhu Li , Huang Lianghao , Fan Linyu , Huang Jinsong , Huang Faming , Chen Jiawu , Zhang Zihe , Wang Yuhao
Published: March 2020

Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.
Using an Active Sensor to Develop New Critical Nitrogen Dilution Curve for Winter Wheat
Authors: Jiang Jie , Wang Cuicun , Wang Yu , Cao Qiang , Tian Yongchao , Zhu Yan , Cao Weixing , Liu Xiaojun
Published: March 2020

Critical nitrogen (N) dilution curves (CNDCs) have been developed to describe the dilution dynamic of N and to diagnose N status in plants. In this study, to develop a convenient alternative CNDC determination method, four field experiments involving different N rates (0–360 kg N ha-1) and six wheat varieties were performed at different eco-sites from 2014 to 2019. The normalised difference red-edge (NDRE) index extracted from the RapidSCAN CS-45 (Holland Scientific Inc., Lincoln, NE, USA) sensor was used as a driving factor instead of plant dry matter (PDM) to establish a new alternative winter wheat CNDC. The newly developed CNDC was described by the equation Nc = 0.90NDRE−0.88, when NDRE values were ≤ 0.19 and constant Nc = 3.81%, which was independent of the NDRE values. Compared to PDM-derived CNDC (R2 = 0.73) developed with the same dataset, a comparable precision was obtained using NDRE-derived CNDC (R2 = 0.76) and both CNDCs could accurately discriminate wheat N status. Moreover, the NDRE could be inexpensively and rapidly measured using the active sensor. The relationship between NDRE-derived CNDC and grain yield was also analysed to facilitate in-season N management, and the R2 value reached 0.79 and 0.87 at jointing and booting stages, respectively. The NDRE-based CNDC can be used to effectively diagnose wheat N status and as an alternative approach for non-destructive determination of crop N levels.
A Multi-User, Single-Authentication Protocol for Smart Grid Architectures
Authors: Alfakeeh Ahmed S. , Khan Sarmadullah , Al-Bayatti Ali Hilal
Published: March 2020

In a smart grid system, the utility server collects data from various smart grid devices. These data play an important role in the energy distribution and balancing between the energy providers and energy consumers. However, these data are prone to tampering attacks by an attacker, while traversing from the smart grid devices to the utility servers, which may result in energy disruption or imbalance. Thus, an authentication is mandatory to efficiently authenticate the devices and the utility servers and avoid tampering attacks. To this end, a group authentication algorithm is proposed for preserving demand–response security in a smart grid. The proposed mechanism also provides a fine-grained access control feature where the utility server can only access a limited number of smart grid devices. The initial authentication between the utility server and smart grid device in a group involves a single public key operation, while the subsequent authentications with the same device or other devices in the same group do not need a public key operation. This reduces the overall computation and communication overheads and takes less time to successfully establish a secret session key, which is used to exchange sensitive information over an unsecured wireless channel. The resilience of the proposed algorithm is tested against various attacks using formal and informal security analysis.