bluuebunny/crossref_metadata_2025 huggingface dataset:
Operational Modelling of Umbrella Cloud Growth in a Lagrangian Volcanic Ash Transport and Dispersion Model
Authors: Webster Helen N. , Devenish Benjamin J. , Mastin Larry G. , Thomson David J. , Van Eaton Alexa R.
Published: February 2020

Large explosive eruptions can result in the formation of an umbrella cloud which rapidly expands, spreading ash out radially from the volcano. The lateral spread by the intrusive gravity current dominates the transport of the ash cloud. Hence, to accurately forecast the transport of ash from large eruptions, lateral spread of umbrella clouds needs to be represented within volcanic ash transport and dispersion models. Here, we describe an umbrella cloud parameterisation which has been implemented into an operational Lagrangian model and consider how it may be used during an eruption when information concerning the eruption is limited and model runtime is key. We examine different relations for the volume flow rate into the umbrella, and the rate of spreading within the cloud. The scheme is validated against historic eruptions of differing scales (Pinatubo 1991, Kelud 2014, Calbuco 2015 and Eyjafjallajökull 2010) by comparing model predictions with satellite observations. Reasonable predictions of umbrella cloud spread are achieved using an estimated volume flow rate from the empirical equation by Bursik et al. and the observed eruption height. We show how model predictions can be refined during an ongoing eruption as further information and observations become available.
Basic Biology of Hypoxic Responses Mediated by the Transcription Factor HIFs and Its Implication for Medicine
Authors: Hirota Kiichi
Published: February 2020

Oxygen (O2) is essential for human life. Molecular oxygen is vital for the production of adenosine triphosphate (ATP) in human cells. O2 deficiency leads to a reduction in the energy levels that are required to maintain biological functions. O2 acts as the final acceptor of electrons during oxidative phosphorylation, a series of ATP synthesis reactions that occur in conjunction with the electron transport system in mitochondria. Persistent O2 deficiency may cause death due to malfunctioning biological processes. The above account summarizes the classic view of oxygen. However, this classic view has been reviewed over the last two decades. Although O2 is essential for life, higher organisms such as mammals are unable to biosynthesize molecular O2 in the body. Because the multiple organs of higher organisms are constantly exposed to the risk of “O2 deficiency,” living organisms have evolved elaborate strategies to respond to hypoxia. In this review, I will describe the system that governs oxygen homeostasis in the living body from the point-of-view of the transcription factor hypoxia-inducible factor (HIF).
Label-Free Biosensors for Laboratory-Based Diagnostics of Infections: Current Achievements and New Trends
Authors: Andryukov Boris G. , Besednova Natalya N. , Romashko Roman V. , Zaporozhets Tatyana S. , Efimov Timofey A.
Published: February 2020

Infections pose a serious global public health problem and are a major cause of premature mortality worldwide. One of the most challenging objectives faced by modern medicine is timely and accurate laboratory-based diagnostics of infectious diseases. Being a key factor of timely initiation and success of treatment, it may potentially provide reduction in incidence of a disease, as well as prevent outbreak and spread of dangerous epidemics. The traditional methods of laboratory-based diagnostics of infectious diseases are quite time- and labor-consuming, require expensive equipment and qualified personnel, which restricts their use in case of limited resources. Over the past six decades, diagnostic technologies based on lateral flow immunoassay (LFIA) have been and remain true alternatives to modern laboratory analyzers and have been successfully used to quickly detect molecular ligands in biosubstrates to diagnose many infectious diseases and septic conditions. These devices are considered as simplified formats of modern biosensors. Recent advances in the development of label-free biosensor technologies have made them promising diagnostic tools that combine rapid pathogen indication, simplicity, user-friendliness, operational efficiency, accuracy, and cost effectiveness, with a trend towards creation of portable platforms. These qualities exceed the generally accepted standards of microbiological and immunological diagnostics and open up a broad range of applications of these analytical systems in clinical practice immediately at the site of medical care (point-of-care concept, POC). A great variety of modern nanoarchitectonics of biosensors are based on the use of a broad range of analytical and constructive strategies and identification of various regulatory and functional molecular markers associated with infectious bacterial pathogens. Resolution of the existing biosensing issues will provide rapid development of diagnostic biotechnologies.
Assessment of the Portable C-320 Electronic Nose for Discrimination of Nine Insectivorous Bat Species: Implications for Monitoring White-Nose Syndrome
Authors: Doty Anna C. , Wilson A. Dan , Forse Lisa B. , Risch Thomas S.
Published: February 2020

The development of new C-320 electronic-nose (e-nose) methods for pre-symptomatic detection of White-Nose Syndrome (WNS) in bats has required efficacy studies of instrument capabilities to discriminate between major sources of volatile organic compounds (VOCs) derived from clinical samples. In this phase-2 study, we further tested this e-nose for capabilities to distinguish between bat species based on differences in whole-body VOC emissions. Live healthy individuals of nine bat species were temporarily captured outside of caves in Arkansas and Louisiana. VOC emissions from bats were collected using newly developed portable air collection and sampling-chamber devices in tandem. Sensor-array output responses to bat VOC emissions were compared to those of 22 pure VOC analytical standards from five chemical classes. Distinct smellprint signatures were produced from e-nose analyses of VOC metabolites derived from individual bat species. Smellprint patterns were analyzed using 2-dimensional and 3-dimensional Principal Component Analysis (PCA) to produce aroma map plots showing effective discrimination between bat species with high statistical significance. These results demonstrate potential instrument efficacy for distinguishing between species-specific, bat-derived VOC metabolite emissions as major components of clinical samples collected from bats in caves for disease detection prior to symptom development. This study provided additional information required to fully test the efficacy of a portable e-nose instrument for diagnostic applications in subsequent phase-3 testing of noninvasive, early WNS disease detection in intra-cave hibernating bats.
Saccular Aneurysm Models Featuring Growth and Rupture: A Systematic Review
Authors: Marbacher Serge , Wanderer Stefan , Strange Fabio , Grüter Basil E. , Fandino Javier
Published: February 2020

Background. Most available large animal extracranial aneurysm models feature healthy non-degenerated aneurysm pouches with stable long-term follow-ups and extensive healing reactions after endovascular treatment. This review focuses on a small subgroup of extracranial aneurysm models that demonstrated growth and potential rupture during follow-up. Methods. The literature was searched in Medline/Pubmed to identify extracranial in vivo saccular aneurysm models featuring growth and rupture, using a predefined search strategy in accordance with the PRISMA guidelines. From eligible studies we extracted the following details: technique and location of aneurysm creation, aneurysm pouch characteristics, time for model creation, growth and rupture rate, time course, patency rate, histological findings, and associated morbidity and mortality. Results. A total of 20 articles were found to describe growth and/or rupture of an experimentally created extracranial saccular aneurysm during follow-up. Most frequent growth was reported in rats (n = 6), followed by rabbits (n = 4), dogs (n = 4), swine (n = 5), and sheep (n = 1). Except for two studies reporting growth and rupture within the abdominal cavity (abdominal aortic artery; n = 2) all other aneurysms were located at the neck of the animal. The largest growth rate, with an up to 10-fold size increase, was found in a rat abdominal aortic sidewall aneurysm model. Conclusions. Extracranial saccular aneurysm models with growth and rupture are rare. Degradation of the created aneurysmal outpouch seems to be a prerequisite to allow growth, which may ultimately lead to rupture. Since it has been shown that the aneurysm wall is important for healing after endovascular therapy, it is likely that models featuring growth and rupture will gain in interest for preclinical testing of novel endovascular therapies.
Computer Aided Assembly of Buildings
Authors: Marcinkowski Roman , Banach Maciej
Published: February 2020

This article presents an interactive method of computer-aided assembly planning. It is estimated that such planning will be more and more desirable due to the increasing use of prefabrication in construction. Prefabrication meets the trends of sustainable development and digitization as it enables the application of intelligent control systems at the stage of highly specialized production, assembly and facility maintenance. The presented planning method is based on the Monte Carlo simulations and logical algorithms for assembly work planning. It was determined on the basis of the literature studied and our own observations. The paper introduces a detailed model of assembly works planning and is an example of using a computer application developed on the basis of the described model. The example confirms the correctness of the algorithm and indicates its usefulness in the scope of analyzing many decision variants. Further research on labor productivity rates for assembly works, implementation of digital databases of assembly machines and prefabricated elements, as well as integration of the proposed application with the BIM environment should make it easier to commercialize the developed application.
Novel Insights into the Existence of the Putative UDP-Glucuronate 5-Epimerase Specificity
Authors: Gevaert Ophelia , Van Overtveldt Stevie , Da Costa Matthieu , Beerens Koen , Desmet Tom
Published: February 2020

C5-epimerases are promising tools for the production of rare l-hexoses from their more common d-counterparts. On that account, UDP-glucuronate 5-epimerase (UGA5E) attracts attention as this enzyme could prove to be useful for the synthesis of UDP-l-iduronate. Interestingly, l-iduronate is known as a precursor for the production of heparin, an effective anticoagulant. To date, the UGA5E specificity has only been detected in rabbit skin extract, and the respective enzyme has not been characterized in detail or even identified at the molecular level. Accordingly, the current work aimed to shed more light on the properties of UGA5E. Therefore, the pool of putative UGA5Es present in the UniProt database was scrutinized and their sequences were clustered in a phylogenetic tree. However, the examination of two of these enzymes revealed that they actually epimerize UDP-glucuronate at the 4- rather than 5-position. Furthermore, in silico analysis indicated that this should be the case for all sequences that are currently annotated as UGA5E and, hence, that such activity has not yet been discovered in nature. The detected l-iduronate synthesis in rabbit skin extract can probably be assigned to the enzyme chondroitin-glucuronate C5-epimerase, which catalyzes the conversion of d-glucuronate to l-iduronate on a polysaccharide level.
Titanium Dioxide Coatings Doubly-Doped with Ca and Ag Ions as Corrosion Resistant, Biocompatible, and Bioactive Materials for Medical Applications
Authors: Burnat Barbara , Olejarz Patrycja , Batory Damian , Cichomski Michal , Kaminska Marta , Bociaga Dorota
Published: February 2020

The aim of this study was to develop a multifunctional biomedical coating that is highly corrosion resistant, biocompatible, and reveals the bioactive properties. For that purpose, titanium dioxide coatings doubly-doped with Ca and Ag ions were deposited by dip-coating onto M30NW biomedical steel. The influence of different ratios of Ca and Ag dopants on morphology, surface structure, corrosion resistance, bioactivity, wettability, and biological properties of TiO2-based sol-gel coatings was studied and discussed. Comprehensive measurements were performed including atomic force microscopy (AFM), scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray reflectivity (XRR), corrosion tests, immersion test, contact angle, as well as biological evaluation. The obtained results confirmed that anatase-based coatings containing Ca and Ag ions, independently of their molar ratio in the coating, are anticorrosive, hydrophilic, and bioactive. The results of the biological evaluation indicated that investigated coatings are biocompatible and do not reduce the proliferation ability of the osteoblasts cells.
Optimization of Performance Parameters and Mechanism of Bionic Texture on Friction Surface
Authors: Hou Qimin , Yang Xuefeng , Cheng Jian , Wang Shouren , Duan Derong , Xiao Jupeng , Li Wanyang
Published: February 2020

In this paper, a variety of micro-textures made by imitating the biological body surface are mentioned, and four common biomimetic texture types—convex hull, pit, groove and corrugation—are summarized by referring to a large number of literatures. These texture types that are widely used are those of the grooves and the pits of non-smooth surface because their viscosity drag reduction effects are relatively optimal for wear-resistance; in view of these two types of textures (with others including pit diameter, groove width, depth and area of share, and morphology spacing), we use data analysis and comparisons to find optimal parameter values in order to find the optimal effect of drag reduction and anti-sticking wear-resistance. Several texture processing methods are briefly introduced through case analysis and an illustration of the viscosity drag reduction mechanism of wear-resistance, and general fluid dynamic pressure is deduced from a theory formula in order to facilitate future research work on the basis of the optimal parameters to further improve the friction, wear lubrication, and hydrophobic properties, thus improving the bionic texture surface efficiency of saving energy and reducing consumption in industrial applications.
The Effect of the Methyl and Ethyl Group of the Acrylate Precursor in Hybrid Silane Coatings Used for Corrosion Protection of Aluminium Alloy 7075-T6
Authors: Hamulić Damir , Rodič Peter , Poberžnik Matic , Jereb Marjan , Kovač Janez , Milošev Ingrid
Published: February 2020

This study investigated polysiloxane hybrid sol-gel coatings synthesized from tetraethyl orthosilicate (TEOS), 3-(trimethoxysilyl)propyl methacrylate (MAPTMS) and two different precursors, i.e., methyl- or ethyl- methacrylate (MMA or EMA), as corrosion protection of aluminium alloy 7075-T6. The hypothesis was that the additional alkyl group might affect the chemical properties and, consequently, the corrosion properties. Synthesis of the sols proceeded in two steps, each involving either MMA or EMA in the same molar ratio. The resulting sols, siloxane-(poly(methyl methacrylate-co-MAPTMS)) or siloxane-(poly(ethyl methacrylate-co-MAPTMS)), were applied on aluminium alloy followed by characterization in terms of chemical structure and composition, topography, wettability, adhesion and corrosion resistance in 0.1 M sodium chloride solution. The chemical properties of sols, monoliths and coatings were investigated using Fourier transform infrared spectrometry, solid state nuclear magnetic resonance spectrometry, X-ray photoelectron spectroscopy and time-of-flight secondary ion mass spectrometry. Coatings were similar in terms of surface topography, while the wettability of the coating with EMA showed 6° greater water contact angle compared to the coating with MMA. Both coatings were shown, by electrochemical impedance spectroscopy in 0.1 M NaCl solution, to act as barriers to protect the underlying substrate in which coating with EMA exhibits better protection properties after 2 months of immersion. Adhesion tests confirmed the highest grade of adhesion to the substrate for both coatings. Testing in a salt-spray chamber demonstrated excellent corrosion protection, where coatings remaining intact after more than 600 h of exposure.
Modeling Bimodal Social Networks Subject to the Recommendation with the Cold Start User-Item Model
Authors: Kłopotek Robert
Published: February 2020

This paper describes the modeling of social networks subject to a recommendation. The Cold Start User-Item Model (CSUIM) of a bipartite graph is considered, which simulates bipartite graph growth based on several parameters. An algorithm is proposed to compute parameters of this model with desired properties. The primary desired property is that the generated graph has similar graph metrics. The next is a change in our graph growth process due to recommendations. The meaning of CSUI model parameters in the recommendation process is described. We make several simulations generating networks from the CSUI model to verify theoretical properties. Also, proposed methods are tested on real-life networks. We prove that the CSUIM model of bipartite graphs is very flexible and can be applied to many different problems. We also show that the parameters of this model can be easily obtained from an unknown bipartite graph.
Simulation and Optimization Design of SiC-Based PN Betavoltaic Microbattery Using Tritium Source
Authors: Lin Zhang
Published: February 2020

In this paper, the Monte Carlo method and numerical model are used to build the electrical model of a SiC-based betavoltaic microbattery using a 3H source, and the influences of structural parameters and the surface recombination effect on the output characteristics of the SiC PN battery are simulated. According to Monte Carlo calculations based on the energy spectrum of the 3H source, the ionization energy deposition approaches the exponential distribution along the depth direction, and most of the 22rs are concentrated near the material surface. The ionization energy deposition data is converted into non-equilibrium carrier information for the numerical simulation of the battery’s output characteristics. The simulation results show that the conversion efficiency of the battery rises first, and then decreases with the increase of the doping concentration of the N region. This is because the N region-doping affects the depletion region width and the built-in electrical potential at the same time. After considering the surface recombination effect, the conversion efficiency decreased significantly. Thinning the thickness of or moderately reducing the doping concentration of the P region will weaken the surface recombination effect.
Double-Track Waveguides inside Calcium Fluoride Crystals
Authors: Gebremichael Wendwesen , Canioni Lionel , Petit Yannick , Manek-Hönninger Inka
Published: February 2020

Calcium Fluoride (CaF2) was selected owing to its cubic symmetry and excellent luminescence properties as a crystal of interest, and ultrafast laser inscription of in-bulk double-track waveguides was realized. The guiding properties of these waveguides in relation to the writing energy of the femtosecond pulse are presented. The modified double-track waveguides have been studied by systematic developments of beam propagation experiments and numerical simulations. Furthermore, an adapted model and concepts were engaged for the quantitative and qualitative characterization of the waveguides, particularly for the transmission loss measurements and the three-dimensional refractive index mappings of the modified zones. Additionally, polarization-dependent guiding was investigated.
Numerical Study of Three-Dimensional Melt Flows during the TSSG Process of SiC Crystal for the Influence of Input Parameters of RF-Coils and an External Rotating Magnetic Field
Authors: Wang Lei , Takehara Yuto , Sekimoto Atsushi , Okano Yasunori , Ujihara Toru , Dost Sadik
Published: February 2020

Three-dimensional numerical simulations were conducted for the Top-Seeded Solution Growth (TSSG) process of silicon carbide (SiC) crystals. We investigated the influence of coils frequency and peak current, and an applied rotating magnetic field (RMF) on the melt flow developing in this system. Numerical simulation results show that the Marangoni flow in the melt becomes stronger at higher coils frequencies due to the decreasing coils-induced electromagnetic field strength. Results also show that the use of external RMF may improve supersaturation uniformity along the seed if it is properly adjusted with respect to the coils-induced electromagnetic field strength. Furthermore, it is predicted that the application of RMF and seed rotation in the same direction may enhance supersaturation below the seed.
Biological vs. Crystallographic Protein Interfaces: An Overview of Computational Approaches for Their Classification
Authors: Elez Katarina , Bonvin Alexandre M. J. J. , Vangone Anna
Published: February 2020

Complexes between proteins are at the basis of almost every process in cells. Their study, from a structural perspective, has a pivotal role in understanding biological functions and, importantly, in drug development. X-ray crystallography represents the broadest source for the experimental structural characterization of protein-protein complexes. Correctly identifying the biologically relevant interface from the crystallographic ones is, however, not trivial and can be prone to errors. Over the past two decades, computational methodologies have been developed to study the differences of those interfaces and automatically classify them as biological or crystallographic. Overall, protein-protein interfaces show differences in terms of composition, energetics and evolutionary conservation between biological and crystallographic ones. Based on those observations, a number of computational methods have been developed for this classification problem, which can be grouped into three main categories: Energy-, empirical knowledge- and machine learning-based approaches. In this review, we give a comprehensive overview of the training datasets and methods so far implemented, providing useful links and a brief description of each method.
Diagnostic Performance of 18F-FDG PET or PET/CT for Detection of Post-Transplant Lymphoproliferative Disorder: A Systematic Review and a Bivariate Meta-Analysis
Authors: Ballova Veronika , Muoio Barbara , Albano Domenico , Bertagna Francesco , Canziani Luca , Ghielmini Michele , Ceriani Luca , Treglia Giorgio
Published: February 2020

Background: Some studies evaluated the diagnostic performance of fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography or positron emission tomography/computed tomography (PET or PET/CT) for the detection of post-transplant lymphoproliferative disorder (PTLD). As there is no clear consensus about the diagnostic accuracy of these imaging methods, we performed a meta-analysis on this topic. Methods: A comprehensive computer literature search of PubMed, Embase, and Cochrane library databases through December 2019 was performed. Pooled sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR−), and diagnostic odds ratio (DOR) of 18F-FDG PET or PET/CT for detection of PTLD were calculated. Results: Five studies reporting data on the diagnostic performance of 18F-FDG PET or PET/CT in 336 transplant recipients were included in the systematic review and bivariate meta-analysis. Pooled sensitivity and specificity for detection of PTLD were 89.7% (95% confidence interval (95%CI): 84.6–93.2%) and 90.9% (95%CI: 85.9–94.3%), respectively. Pooled LR+, LR−, and DOR were 8.9 (95%CI: 5.7–14), 0.13 (95%CI: 0.08–0.2), and 70.4 (95%CI: 35.4–140), respectively. A significant heterogeneity among studies was not detected. Conclusions: Despite limited literature data, 18F-FDG PET or PET/CT demonstrated good diagnostic performance for the detection of PTLD, but large prospective studies are needed to strengthen these findings.
Finite-Time Thermodynamic Model for Evaluating Heat Engines in Ocean Thermal Energy Conversion
Authors: Yasunaga Takeshi , Ikegami Yasuyuki
Published: February 2020

Ocean thermal energy conversion (OTEC) converts the thermal energy stored in the ocean temperature difference between warm surface seawater and cold deep seawater into electricity. The necessary temperature difference to drive OTEC heat engines is only 15–25 K, which will theoretically be of low thermal efficiency. Research has been conducted to propose unique systems that can increase the thermal efficiency. This thermal efficiency is generally applied for the system performance metric, and researchers have focused on using the higher available temperature difference of heat engines to improve this efficiency without considering the finite flow rate and sensible heat of seawater. In this study, our model shows a new concept of thermodynamics for OTEC. The first step is to define the transferable thermal energy in the OTEC as the equilibrium state and the dead state instead of the atmospheric condition. Second, the model shows the available maximum work, the new concept of exergy, by minimizing the entropy generation while considering external heat loss. The maximum thermal energy and exergy allow the normalization of the first and second laws of thermal efficiencies. These evaluation methods can be applied to optimized OTEC systems and their effectiveness is confirmed.
Sustainable Enterprise Capital Management
Authors: Klimek Dariusz
Published: February 2020

The paper presents the idea of a new method of sustainable enterprise capital management. The idea is based on a principle that states that the faster an enterprise achieves and maintains a balance between its capitals, the more effective the management is. The concept forms a part of the search for an alternative to net profit, which is a basic but outdated measure in modern times. After the introduction, the author outlines the basic principles and foundations of the new method. After introducing the principles and assumptions of the new idea, the author, in his paper, describes the studies that aimed at a practical checking of the possibilities of measuring the effectiveness of the company’s economic condition and the effects of management in accordance with the new principles. They were carried out in one of the food industry companies, whose shares are listed on the Warsaw Stock Exchange. Two new economic indicators were used in the study. The general conclusion of the study is as follows: new principles of sustainable capital management can be applied in practice to measure the effectiveness of the enterprise and the work of the management board, but there are still many conditions and problems listed in the paper that should be the subject of further work. The issue that still needs a lot of research within the whole idea is the problem of valuing the current and optimal level of corporate capital. The conclusions outline the strengths and weaknesses of the new method.
A Survey of Indonesian Science Teachers’ Experience and Perceptions toward Socio-Scientific Issues-Based Science Education
Authors: Nida Safwatun , Rahayu Sri , Eilks Ingo
Published: February 2020

This survey explored Indonesian science teachers’ experience and perceptions toward science teaching that is based on socio-scientific issues (SSIs). The participants were asked whether or not they already used corresponding practices in their own teaching and whether they experienced any challenges in implementing SSI-based pedagogies. Further focal points were the teachers’ views on student competencies that can be fostered through SSI-based education, the connection of SSI-based pedagogies with students’ character formation, potential topics for implementing SSIs in science education, and the teachers’ interest in such implementation. Data were collected with the help of a questionnaire that was administered to 99 science teachers. This was then followed up by interviews with 20 intentionally selected teachers taken from the overall sample. The study revealed that teachers’ familiarity with SSI-based pedagogies varies greatly. Regardless of their familiarity with the term, some of the teachers had already implemented corresponding practices at varying levels of intensity. Although almost all of the participants saw potential in SSI-based pedagogies for increasing student competency development and character formation, most of the respondents did not implement SSI-based teaching very often in their lessons. They mentioned several challenges that hindered them in implementing SSI in their teaching practices. Reasons included the lack of necessary students’ competencies, a lack of teacher expertise, the content in the official curriculum, inadequate facilities, and a lack of time for lesson preparation and implementation. When asked for ideas in implementing SSI-based education, teachers basically suggested topics related to the environment or technology as suitable for SSI-based education. In spite of the many challenges, most of the teachers were still interested in implementing SSIs in their classes.
Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions
Authors: Maria Malvoni , Yassine Chaibi
Published: February 2020

It is important to investigate the long-term performances of an accurate modeling of photovoltaic (PV) systems, especially in the prediction of output power, with single and double diode models as the configurations mainly applied for this purpose. However, the use of one configuration to model PV panel limits the accuracy of its predicted performances. This paper proposes a new hybrid approach based on classification algorithms in the machine learning framework that combines both single and double models in accordance with the climatic condition in order to predict the output PV power with higher accuracy. Classification trees, k-nearest neighbor, discriminant analysis, Naïve Bayes, support vector machines (SVMs), and classification ensembles algorithms are investigated to estimate the PV power under different conditions of the Mediterranean climate. The examined classification algorithms demonstrate that the double diode model seems more relevant for low and medium levels of solar irradiance and temperature. Accuracy between 86% and 87.5% demonstrates the high potential of the classification techniques in the PV power predicting. The normalized mean absolute error up to 1.5% ensures errors less than those obtained from both single-diode and double-diode equivalent-circuit models with a reduction up to 0.15%. The proposed hybrid approach using machine learning (ML) algorithms could be a key solution for photovoltaic and industrial software to predict more accurate performances.
A Performance Review of Collision-Free Path Planning Algorithms
Authors: Shin Hyunwoo , Chae Junjae
Published: February 2020

Path planning for mobile agents is one of the areas that has drawn the attention of researchers’, as evidenced in the large number of papers related to the collision-free path planning (CFPP) algorithm. The purpose of this paper is to review the findings of those CFPP papers and the methodologies used to generate possible solutions for CFPP for mobile agents. This survey shows that the previous CFPP papers can be divided based on four characteristics. The performance of each method primarily used to solve CFPP in previous research is evaluated and compared. Several methods are implemented and tested in same computing environment to compare the performance of generating solution in specified spatial environment with different obstacles or size. The strengths and weakness of each methodology for CFPP are shown through this survey. Ideally, this paper will provide reference for new future research.
Energy-Efficient Method for Wireless Sensor Networks Low-Power Radio Operation in Internet of Things
Authors: Amirinasab Nasab Mehdi , Shamshirband Shahaboddin , Chronopoulos Anthony , Mosavi Amir , Nabipour Narjes
Published: February 2020

The radio operation in wireless sensor networks (WSN) in Internet of Things (IoT) applications is the most common source for power consumption. Consequently, recognizing and controlling the factors affecting radio operation can be valuable for managing the node power consumption. Among essential factors affecting radio operation, the time spent for checking the radio is of utmost importance for monitoring power consumption. It can lead to false WakeUp or idle listening in radio duty cycles and ContikiMAC. ContikiMAC is a low-power radio duty-cycle protocol in Contiki OS used in WakeUp mode, as a clear channel assessment (CCA) for checking radio status periodically. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Furthermore, we propose a lightweight CCA (LW-CCA) as an extension to ContikiMAC to reduce the Radio Duty-Cycles in false WakeUps and idle listening though using dynamic received signal strength indicator (RSSI) status check time. The simulation results in the Cooja simulator show that LW-CCA reduces about 8% energy consumption in nodes while maintaining up to 99% of the packet delivery rate (PDR).
Robust Detection of Bearing Early Fault Based on Deep Transfer Learning
Authors: Mao Wentao , Zhang Di , Tian Siyu , Tang Jiamei
Published: February 2020

In recent years, machine learning techniques have been proven to be a promising tool for early fault detection of rolling bearings. In many actual applications, however, bearing whole-life data are not easy to be historically accumulated, while insufficient data may result in training a detection model that is not good enough. If utilizing the available data under different working conditions to facilitate model training, the data distribution of different bearings are usually quite different, which does not meet the precondition of i n d e p e n d e n t a n d i d e n t i c a l d i s t r i b u t i o n ( i . i . d ) and tends to cause performance reduction. In addition, disturbed by the unstable noise under complex conditions, most of the current detection methods are inclined to raise false alarms, so that the reliability of detection results needs to be improved. To solve these problems, a robust detection method for bearings early fault is proposed based on deep transfer learning. The method includes offline stage and online stage. In the offline stage, by introducing a deep auto-encoder network with domain adaptation, the distribution inconsistency of normal state data among different bearings can be weakened, then the common feature representation of the normal state is obtained. With the extracted common features, a new state assessment method based on the robust deep auto-encoder network is proposed to evaluate the boundary between normal state and early fault state in the low-rank feature space. By training a support vector machine classifier, the detection model is established. In the online stage, along with the data batch arriving sequentially, the features of target bearing are extracted using the common representation learnt in the offline stage, and online detection is conducted by feeding them into the SVM model. Experimental results on IEEE PHM Challenge 2012 bearing dataset and XJTU-SY dataset show that the proposed approach outperforms several state-of-the-art detection methods in terms of detection accuracy and false alarm rate.
Modeling the Effects of Global Change on Ecosystem Processes in a Tropical Rainforest
Authors: Russell Ann E. , Parton William J.
Published: February 2020

Research Highlights: Ongoing land-use change and climate change in wet tropical forests can potentially drive shifts in tree species composition, representing a change in individual species within a functional group, tropical evergreen trees. The impacts on the global carbon cycle are potentially large, but unclear. We explored the differential effects of species within this functional group, in comparison with the effects of climate change, using the Century model as a research tool. Simulating effects of individual tree species on biome-level biogeochemical cycles constituted a novel application for Century. Background and Objectives: A unique, long-term, replicated field experiment containing five evergreen tree species in monodominant stands under similar environmental conditions in a Costa Rican wet forest provided data for model evaluation. Our objectives were to gain insights about this forest’s biogeochemical cycles and effects of tree species within this functional group, in comparison with climate change. Materials and Methods: We calibrated Century, using long-term meteorological, soil, and plant data from the field-based experiment. In modeling experiments, we evaluated effects on forest biogeochemistry of eight plant traits that were both observed and modeled. Climate-change simulation experiments represented two climate-change aspects observed in this region. Results: Model calibration revealed that unmodeled soil processes would be required to sustain observed P budgets. In species-traits experiments, three separate plant traits (leaf death rate, leaf C:N, and allocation to fine roots) resulted in modeled biomass C stock changes of >50%, compared with a maximum 21% change in the climate-change experiments. Conclusions: Modeled ecosystem properties and processes in Century were sensitive to changes in plant traits and nutrient limitations to productivity. Realistic model output was attainable for some species, but unusual plant traits thwarted predictions for one species. Including more plant traits and soil processes could increase realism, but less-complex models provide an accessible means for exploring plant-soil-atmosphere interactions.
Unsteady Multi-Element Time Series Analysis and Prediction Based on Spatial-Temporal Attention and Error Forecast Fusion
Authors: Wang Xiaofan , Xu Lingyu
Published: February 2020

Harmful algal blooms (HABs) often cause great harm to fishery production and the safety of human lives. Therefore, the detection and prediction of HABs has become an important issue. Machine learning has been increasingly used to predict HABs at home and abroad. However, few of them can capture the sudden change of Chl-a in advance and handle the long-term dependencies appropriately. In order to address these challenges, the Long Short-Term Memory (LSTM) based spatial-temporal attentions model for Chlorophyll-a (Chl-a) concentration prediction is proposed, a model which can capture the correlation between various factors and Chl-a adaptively and catch dynamic temporal information from previous time intervals for making predictions. The model can also capture the stage of Chl-a when values soar as red tide breaks out in advance. Due to the instability of the current Chl-a concentration prediction model, the model is also applied to make a prediction about the forecast reliability, to have a basic understanding of the range and fluctuation of model errors and provide a reference to describe the range of marine disasters. The data used in the experiment is retrieved from Fujian Marine Forecasts Station from 2009 to 2011 and is combined into 8-dimension data. Results show that the proposed approach performs better than other Chl-a prediction algorithms (such as Attention LSTM and Seq2seq and back propagation). The result of error prediction also reveals that the error forecast method possesses established advantages for red tides prevention and control.
Bacterial Communities of Novaya Zemlya Archipelago Ice and Permafrost
Authors: Belov Andrey A. , Cheptsov Vladimir S. , Manucharova Natalia A. , Ezhelev Zakhar S.
Published: February 2020

The study of bacterial communities associated with extreme ecosystems is one of the most important tasks in modern microbial ecology. Despite a large number of studies being performed, the ecosystems that have not been sufficiently explored from the microbiological point of view still exist. Such research is needed for improving the understanding of the limits and mechanisms of bacterial survival under extreme conditions, and for revealing previously undescribed species and their role in global biospheric processes and their functional specifics. The results of the complex microbiological characteristics of permafrost and ice—collected on the Severniy Island in the northern part of the Novaya Zemlya archipelago—which have not previously been described from microbiological point of view, are presented in this article. The analysis included both culture-independent and culture-dependent methods, in particular, the spectra of metabolic activity range analysis in vitro under different temperature, pH and salinity conditions. High values for the total number of prokaryotes in situ (1.9 × 108–3.5 × 108 cells/g), a significant part of which was able to return to a metabolically active state after thawing, and moderate numbers of culturable bacteria (3.3 × 106–7.8 × 107 CFU/g) were revealed. Representatives of Proteobacteria, Actinobacteria, and Bacteroidetes were dominant in situ; Actinobacteria, Firmicutes, Proteobacteria, and Bacteroidetes were the most abundant phyla in vitro. Physiological assays revealed the mesophilic and neutrophilic optima of temperature and pH of culturing conditions, respectively, and wide temperature and pH ranges of culturable communities’ reproduction activity. Isolated strains were characterized by moderate halotolerant properties and antibiotic resistance, including multiple antibiotic resistance. It was found that almost all cultured bacterial diversity revealed (not just a few resistant species) had extremotolerant properties regarding a number of stress factors. This indicates the high adaptive potential of the studied microbial communities and their high sustainability and capability to retain functional activity under changing environmental (including climatic) conditions in wide ranges.
Distinguishing between Deep-Water Sediment Facies: Turbidites, Contourites and Hemipelagites
Authors: Stow Dorrik , Smillie Zeinab
Published: February 2020

The distinction between turbidites, contourites and hemipelagites in modern and ancient deep-water systems has long been a matter of controversy. This is partly because the processes themselves show a degree of overlap as part of a continuum, so that the deposit characteristics also overlap. In addition, the three facies types commonly occur within interbedded sequences of continental margin deposits. The nature of these end-member processes and their physical parameters are becoming much better known and are summarised here briefly. Good progress has also been made over the past decade in recognising differences between end-member facies in terms of their sedimentary structures, facies sequences, ichnofacies, sediment textures, composition and microfabric. These characteristics are summarised here in terms of standard facies models and the variations from these models that are typically encountered in natural systems. Nevertheless, it must be acknowledged that clear distinction is not always possible on the basis of sedimentary characteristics alone, and that uncertainties should be highlighted in any interpretation. A three-scale approach to distinction for all deep-water facies types should be attempted wherever possible, including large-scale (oceanographic and tectonic setting), regional-scale (architecture and association) and small-scale (sediment facies) observations.
Factors Determining Home versus Rehabilitation Discharge Following Primary Total Joint Arthroplasty for Patients Who Live Alone
Authors: Fang Christopher , Lim Sara J. , Tybor David J. , Martin Joseph , Pevear Mary E. , Smith Eric L.
Published: February 2020

Patients who are discharged home following primary hip and knee arthroplasty have lower associated costs and better outcomes than patients who are discharged to skilled nursing facilities (SNFs). However, patients who live alone are more likely to be discharged to an SNF. We studied the factors that determine the discharge destination for patients who live alone after total joint arthroplasty (TJA) at an urban tertiary care academic hospital between April 2016 and April 2017. We identified 127 patients who lived alone: 79 (62.2%) were sent home, and 48 (37.8%) were sent to an SNF after surgery. Patients who went home versus to an SNF differed in age, employment status, exercise/active status, patient expectation of discharge to an SNF, ASA score, and the length of stay. After controlling for expectations of discharge to an SNF (OR: 28.98), patients who were younger (OR: 0.03) and employed (OR: 6.91) were more likely to be discharged home. In conclusion, the expectation of discharge location was the strongest predictor of discharge to an SNF even after controlling for age and employment. Future research should include a multi-hospital approach to strengthen the validity of our findings and investigate additional factors that impact discharge destination.
The Correlation between Complete Blood Count Parameters and Appendix Diameter for the Diagnosis of Acute Appendicitis
Authors: Daldal Emin , Dagmura Hasan
Published: February 2020

Acute appendicitis is one of the most common causes of acute abdominal diseases seen between the ages of 10 and 19, mostly seen in males. The lifetime risk of developing acute appendicitis is 8.6% for males and 6.7% for females. We aimed to investigate the efficacy of the complete blood count parameters, C-reactive protein, and Lymphocyte-C-reactive Protein Ratio laboratory tests in the diagnosis of acute appendicitis, as well as their relationship with appendix diameter. We retrospectively examined all patients who underwent appendectomy between 1 January 2012 and 30 June 2019 in the General Surgery Clinic of Gaziosmanpasa University Faculty of Medicine. Laboratory tests, imaging findings, age, and gender were recorded. Lymphoid hyperplasia is considered as normal appendix—in other words, as negative appendicitis. The distribution of Lymphoid hyperplasia and appendicitis rates were statistically different in the groups formed according to appendix diameter (≤6 and >6 mm) (p < 0.001). We found a significant correlation between appendix diameter and WBC (White blood count), Lymphocyte, Neutrophil, RDW(Red blood cell distribution width), NLR(Neutrophil to lymphocyte ratio), and PLT/L (Platelet to lymphocyte ratio), MPV (Mean platelet volume) and RDW were significantly different in patients with an appendix diameter of ≤6 mm (p = 0.007, p = 0.006, respectively). WBC, Neutrophil, PDW, and NLR values were significantly different between appendicitis and hyperplasia groups in patients with an appendix diameter of >6 mm. The sensitivity of the NLR score (cutoff = 2.6057) in the diagnosis of appendicitis was 86.1% and selectivity was 50% in these patients. Complete blood count parameters evaluation with the clinical findings revealed that NLR is an important parameter that may help the diagnosis of acute appendicitis with an appendix diameter of >6 mm. In patients whose pathological results indicated acute appendicitis but who had a diameter of ≤6 mm, we found an elevated MPV and low RDW values.