bluuebunny/crossref_metadata_2025 huggingface dataset:
FeSiCr Alloy Powder to Carbonyl Iron Powder Mixing Ratio Effects on the Magnetic Properties of the Iron-Based Alloy Powder Cores Prepared Using Screen Printing
Authors: Hsiang Hsing-I. , Chuang Kai-H. , Lee Wen-H.
Published: February 2021

A screen printing process was used to substitute dry molding to solve the uneven compaction problem in the coil center column during molding in this study. FeSiCr alloy powders (FSC) with a large particle size were mixed with fine spherical carbonyl iron powder to increase the compaction density. FSC to carbonyl iron powder (CIP) mixing ratio effects on magnetic paste rheological behaviors and magnetic properties of the molding coil prepared using screen printing were investigated. A magnetic paste with the lowest viscosity can be obtained using 3C7F (30% CIP + 70% FSC) due to the small-sized CIP adsorbed onto the FSC surface. This process reduces the interlocked network formation resulting from the CIP. The toroidal core with 3C7F exhibited the highest relative density and highest inductance. The coils with pure CIP and higher CIP content exhibited the better DC superposition characteristic. The toroidal core loss increased rapidly as the FSC content was increased. A proper trade-off between the inductance, DC-bias superposition characteristic, and magnetic core loss can be reached by choosing a suitable powder mixing ratio.
Temperature-Dependent Photoluminescence of ZnO Thin Films Grown on Off-Axis SiC Substrates by APMOCVD
Authors: Shtepliuk Ivan , Khranovskyy Volodymyr , Ievtushenko Arsenii , Yakimova Rositsa
Published: February 2021

The growth of high-quality ZnO layers with optical properties congruent to those of bulk ZnO is still a great challenge. Here, for the first time, we systematically study the morphology and optical properties of ZnO layers grown on SiC substrates with off-cut angles ranging from 0° to 8° by using the atmospheric pressure meta–organic chemical vapor deposition (APMOCVD) technique. Morphology analysis revealed that the formation of the ZnO films on vicinal surfaces with small off-axis angles (1.4°–3.5°) follows the mixed growth mode: from one side, ZnO nucleation still occurs on wide (0001) terraces, but from another side, step-flow growth becomes more apparent with the off-cut angle increasing. We show for the first time that the off-cut angle of 8° provides conditions for step-flow growth of ZnO, resulting in highly improved growth morphology, respectively structural quality. Temperature-dependent photoluminescence (PL) measurements showed a strong dependence of the excitonic emission on the off-cut angle. The dependences of peak parameters for bound exciton and free exciton emissions on temperature were analyzed. The present results provide a correlation between the structural and optical properties of ZnO on vicinal surfaces and can be utilized for controllable ZnO heteroepitaxy on SiC toward device-quality ZnO epitaxial layers with potential applications in nano-optoelectronics.
Microstructures and Mechanical Properties of Hybrid, Additively Manufactured Ti6Al4V after Thermomechanical Processing
Authors: Hemes Susanne , Meiners Frank , Sizova Irina , Hama-Saleh Rebar , Röhrens Daniel , Weisheit Andreas , Häfner Constantin , Bambach Markus
Published: February 2021

In the present study, we propose a hybrid manufacturing route to produce high-quality Ti6Al4V parts, combining additive powder laser directed energy deposition (L-DED) for manufacturing of preforms, with subsequent hot forging as a thermomechanical processing (TMP) step. After L-DED, the material was hot formed at two different temperatures (930 °C and 1070 °C) and subsequently heat-treated for stress relief annealing. Tensile tests were performed on small sub-samples, taking into account different sample orientations with respect to the L-DED build direction and resulting in very good tensile strengths and ductility properties, similar or superior to the forged material. The resulting microstructure consists of very fine grained, partially globularized alpha grains, with a mean diameter ~0.8–2.3 µm, within a beta phase matrix, constituting between 2 and 9% of the sample. After forging in the sub-beta transus temperature range, the typical L-DED microstructure was no longer discernible and the anisotropy in tensile properties, common in additive manufacturing (AM), was significantly reduced. However, forging in the super-beta transus temperature range resulted in remaining anisotropies in the mechanical properties as well as an inferior tensile strength and ductility of the material. It was shown, that by combining L-DED with thermomechanical processing in the sub-beta transus temperature range of Ti6Al4V, a suitable microstructure and desirable mechanical properties for many applications can be obtained, with the advantage of reducing the material waste.
Theoretical Study on Structural Stability and Elastic Properties of Fe25Cr25Ni25TixAl(25-x) Multi-Principal Element Alloys
Authors: Liu Li , Paudel Ramesh , Liu Yong , Zhu Jing-Chuan
Published: February 2021

Material genetic engineering studies the relationship between the composition, microstructure, and properties of materials. By adjusting the atomic composition, structure, or configuration of the material and combining different processes, new materials with target properties obtained. In this paper, the design, and properties of the ordered phases in Fe25Cr25Ni25TixAl(25-x) (subscript represents the atomic percentage) multi-principal element alloys are studied. By adjusting the percentages of Ti and Al atoms, the effect of the atomic percentage content on ordered phases’ structural stability in multi-principal element alloys are studied. Thermodynamic analysis predicted the composition phase and percentage of the alloy. Formation heat, binding energy, and elastic constants confirmed the structural stability and provide a theoretical basis for designing alloys with target properties. The results showed that the disordered BCC A2 phase and the ordered BCC B2 phase are the ductile phases, while the Laves phase is brittle. The research method in this paper is used to design multi-principal element alloys or other various complex materials that meet the target performance.
The Mechanical Investigation of Filament-Wound CFRP Structures Subjected to Different Cooling Rates in Terms of Compressive Loading and Residual Stresses—An Experimental Approach
Authors: Błażejewski Wojciech , Barcikowski Michał , Lubecki Marek , Stabla Paweł , Bury Paweł , Stosiak Michał , Lesiuk Grzegorz
Published: February 2021

Although cooling at ambient temperature is widely used and is said to be safe and convenient, faster cooling may have an influence not only on the time of the manufacturing process but also on the mechanical response, especially the residual stress. The study aimed to investigate the influence of the cooling rate after curing on the mechanical response of filament-wound thick-walled carbon fiber reinforced polymer (CFRP) rings. Three cooling rates were taking into consideration: cooling with the oven, at room temperature, and in the water at 20 °C. The splitting method was used to examine the residual strains. In the radial compression test, the mechanical response was investigated between the rings with different cooling regimes. The FEM analysis of the compression test in elastic range was also performed. Both the splitting method and the radial compression test showed no significant difference in the mechanical response of the CFRP rings. The presented results showed that the fast-cooling rate slightly decreases the mechanical performance of the filament-wound rings.
The Functional Equation max{χ(xy),χ(xy-1)}=χ(x)χ(y) on Groups and Related Results
Authors: Sarfraz Muhammad , Liu Qi , Li Yongjin
Published: February 2021

This research paper focuses on the investigation of the solutions χ:G→R of the maximum functional equation max{χ(xy),χ(xy−1)}=χ(x)χ(y), for every x,y∈G, where G is any group. We determine that if a group G is divisible by two and three, then every non-zero solution is necessarily strictly positive; by the work of Toborg, we can then conclude that the solutions are exactly the e|α| for an additive function α:G→R. Moreover, our investigation yields reliable solutions to a functional equation on any group G, instead of being divisible by two and three. We also prove the existence of normal subgroups Zχ and Nχ of any group G that satisfy some properties, and any solution can be interpreted as a function on the abelian factor group G/Nχ.
Educational Technologies and Their Application to Music Education: An Action-Research Study in an Ecuadorian University
Authors: Bolívar-Chávez Oscar-Elías , Paredes-Labra Joaquín , Palma-García Yury-Vladimir , Mendieta-Torres Yessenia-Anabel
Published: February 2021

Information and Communication Technologies (ICT) are tools that are part of the process of teaching and learning music. These didactic/pedagogical resources are widely used by teachers. They strengthen, motivate, and increase the student’s interest in learning. This study is an action-research (AR). It involves 12 teachers and 68 students in the subject of music education in an Ecuadorian university. A Holistic and Technological Model of Music Education (HTMME) was generated. The performance of the plan was evaluated by means of an original questionnaire and qualitative work. The AR procedure involved an analysis of data at the end of each implementation cycle. The appreciation of the new model was very positive. With the methodology implemented, new teaching experiences and relevant learning for students were achieved. Learning music with ICT induces creative-musical processes in students.
Evaluating Industry 4.0 Technology Application in SMEs: Using a Hybrid MCDM Approach
Authors: Chang Shih-Chia , Chang Hsu-Hwa , Lu Ming-Tsang
Published: February 2021

Evaluating Industry 4.0 technology application in small and medium-sized enterprises (SMEs) is an issue that requires a multi-criteria strategy comprising quantitative and qualitative elements. The purpose of this study is to integrate performance estimation of Industry 4.0 technology application using the technology–organization–environment (TOE) framework. Relating TOE to Industry 4.0 technology application evaluation is more multifaceted than other methods and it requires comprehensive analysis. In this study, we applied a multiple-criteria decision-making (MCDM) approach to develop a model which integrates MCDM to perform an assessment that prioritizes the influence weights of Industry 4.0 technology application to SMEs’ factors. Firstly, we carried out a review of the literature and the TOE framework was selected to generate nine elements, along with three aspects used to measure Industry 4.0 technology application in SMEs. Secondly, the approach of the decision-making trial and evaluation laboratory (DEMATEL) was set up using an influence network relations digraph (INRD). The DEMATEL-based analytic network process (DANP) was used to indicate the influence weights linking the above aspects and elements. Lastly, the modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique applied influence weights to assess the aspects/elements in the gaps identified and to investigate how to reduce the gaps so as to estimate the application of Industry 4.0 technology by SMEs. The results show that the technology aspect is the most influential factor.
The Quintuple Helix of Innovation Model and the SDGs: Latin-American Countries’ Case and Its Forgotten Effects
Authors: Barcellos-Paula Luciano , De la Vega Iván , Gil-Lafuente Anna María
Published: February 2021

The sustainable development of countries is associated with a set of actions that must be implemented in the long term. In this process, society must be a valid partner in the decisions that are made. Studies show the interrelationship between the Sustainable Development Goals (SDGs), which increases uncertainty and makes decision-making more difficult. On the other hand, the Quintuple Helix of Innovation Model (QHIM) provides an analytical framework to explain the systems’ interactions. The motivation of the study lies in knowing the relationships between the variables that affect SDGs. The manuscript aims to broaden the discussion on sustainable development and propose two models to support decision making. The first one suggests 20 indicators linked to the QHIM with the SDGs in Latin American countries. The second identifies the forgotten effects through the application of a Fuzzy Logic algorithm. The main contribution is to know these effects and to support decision-making. The research carried out can be classified as applied, with the explanatory objective and the combined approach (quantitative-qualitative), modeling and simulation, and case study methods. The QHIM results indicate that Chile leads the ranking, followed by Brazil, Mexico, Peru, and Colombia. Also, it reveals the importance of correctly identifying cause-effects by seeking harmony between systems. A limitation would be the number of variables used. The study indicates promising lines of research.
Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach
Authors: Barber Xavier , Conesa David , López-Quílez  Antonio , Martínez-Minaya  Joaquín , Paradinas Iosu , Pennino Maria Grazia
Published: February 2021

In this work, we discuss the use of a methodological approach for modelling spatial relationships among species by means of a Bayesian spatial coregionalized model. Inference and prediction is performed using the integrated nested Laplace approximation methodology to reduce the computational burden. We illustrate the performance of the coregionalized model in species interaction scenarios using both simulated and real data. The simulation demonstrates the better predictive performance of the coregionalized model with respect to the univariate models. The case study focus on the spatial distribution of a prey species, the European anchovy (Engraulis encrasicolus), and one of its predator species, the European hake (Merluccius merluccius), in the Mediterranean sea. The results indicate that European hake and anchovy are positively associated, resulting in improved model predictions using the coregionalized model.
Analysis of a Batch Arrival, Batch Service Queuing-Inventory System with Processing of Inventory While on Vacation
Authors: Krishnamoorthy Achyutha , Joshua Anu Nuthan , Kozyrev Dmitry
Published: February 2021

A single-server queuing-inventory system in which arrivals are governed by a batch Markovian arrival process and successive arrival batch sizes form a finite first-order Markov chain is considered in this paper. Service is provided in batches according to a batch Markovian service process, with consecutive service batch sizes forming a finite first-order Markov chain. A service starts for the next batch on completion of the current service, provided that inventory is available at that epoch; otherwise, there will be a delay in starting the next service. When the service of a batch is completed, the inventory decreases by 1 unit, irrespective of batch size. A control policy in which the server goes on vacation when a service process is frozen until a quorum can initiate the next batch service is proposed to ensure idle-time utilization. During the vacation, the server produces inventory (items) for future services until it hits a specified level L or until the number of customers in the system reaches a maximum service batch size N, with whichever occurring first. In the former case, a server stays idle once the processed inventory level reaches L until the number of customers reaches (or even exceeds because of batch arrival) a maximum service batch size N. The time required for processing one unit of inventory follows a phase-type distribution. In this paper, the steady-state probability vector of this infinite system is computed. The distributions of inventory processing time in a vacation cycle, idle time in a vacation cycle, and vacation cycle length are found. The effect of correlation in successive inter-arrival times and service times on performance measures for such a queuing system is illustrated with a numerical example. An optimization problem is considered. The proposed system is then compared with a queuing-inventory system without the Markov-dependent assumption on successive arrivals as well as service batch sizes using numerical examples.
Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization
Authors: Li Gui , Wang Gai-Ge , Wang Shan
Published: February 2021

Due to the complexity of many-objective optimization problems, the existing many-objective optimization algorithms cannot solve all the problems well, especially those with complex Pareto front. In order to solve the shortcomings of existing algorithms, this paper proposes a coevolutionary algorithm based on dynamic learning strategy. Evolution is realized mainly through the use of Pareto criterion and non-Pareto criterion, respectively, for two populations, and information exchange between two populations is used to better explore the whole objective space. The dynamic learning strategy acts on the non-Pareto evolutionary to improve the convergence and diversity. Besides, a dynamic convergence factor is proposed, which can be changed according to the evolutionary state of the two populations. Through these effective heuristic strategies, the proposed algorithm can maintain the convergence and diversity of the final solution set. The proposed algorithm is compared with five state-of-the-art algorithms and two weight-sum based algorithms on a many-objective test suite, and the results are measured by inverted generational distance and hypervolume performance indicators. The experimental results show that, compared with the other five state-of-the-art algorithms, the proposed algorithm achieved the optimal performance in 47 of the 90 cases evaluated by the two indicators. When the proposed algorithm is compared with the weight-sum based algorithms, 83 out of 90 examples achieve the optimal performance.
Optimal Operation of the Voltage-Doubler Boost Converter through an Evolutionary Algorithm
Authors: Ibarra-Nuño Cesar , Rodríguez Alma , Alejo-Reyes Avelina , Cuevas Erik , Ramirez Juan M. , Rosas-Caro Julio C. , Robles-Campos Héctor R.
Published: February 2021

This manuscript presents the numerical optimization (through a mathematical model and an evolutionary algorithm) of the voltage-doubler boost converter, also called the series-capacitor boost converter. The circuit is driven by two transistors, each of them activated according to a switching signal. In the former operation, switching signals have an algebraic dependence from each other. This article proposes a new method to operate the converter. The proposed process reduces the input current ripple without changing any converter model parameter, only the driving signals. In the proposed operation, switching signals of transistors are independent of each other, providing an extra degree of freedom, but on the other hand, this produces an infinite number of possible combinations of duty cycles (the main parameter of switching signals) to achieve the desired voltage gain. In other words, this leads to a problem with infinite possible solutions. The proposed method utilizes an evolutionary algorithm to determine the switching functions and, at the same time, to minimize the input current ripple of the converter. A comparison made between the former and the proposed operation shows that the proposed process achieves a lower input current ripple while achieving the desired voltage gain.
Differential Evolution Optimal Parameters Tuning with Artificial Neural Network
Authors: Centeno-Telleria Manu , Zulueta Ekaitz , Fernandez-Gamiz Unai , Teso-Fz-Betoño Daniel , Teso-Fz-Betoño Adrián
Published: February 2021

Differential evolution (DE) is a simple and efficient population-based stochastic algorithm for solving global numerical optimization problems. DE largely depends on algorithm parameter values and search strategy. Knowledge on how to tune the best values of these parameters is scarce. This paper aims to present a consistent methodology for tuning optimal parameters. At the heart of the methodology is the use of an artificial neural network (ANN) that learns to draw links between the algorithm performance and parameter values. To do so, first, a data-set is generated and normalized, then the ANN approach is performed, and finally, the best parameter values are extracted. The proposed method is evaluated on a set of 24 test problems from the Black-Box Optimization Benchmarking (BBOB) benchmark. Experimental results show that three distinct cases may arise with the application of this method. For each case, specifications about the procedure to follow are given. Finally, a comparison with four tuning rules is performed in order to verify and validate the proposed method’s performance. This study provides a thorough insight into optimal parameter tuning, which may be of great use for users.
Method for Obtaining Coefficients of Powers of Bivariate Generating Functions
Authors: Kruchinin Dmitry , Kruchinin Vladimir , Shablya Yuriy
Published: February 2021

In this paper, we study methods for obtaining explicit formulas for the coefficients of generating functions. To solve this problem, we consider the methods that are based on using the powers of generating functions. We propose to generalize the concept of compositae to the case of generating functions in two variables and define basic operations on such compositae: composition, addition, multiplication, reciprocation and compositional inversion. These operations allow obtaining explicit formulas for compositae and coefficients of bivariate generating functions. In addition, we present several examples of applying the obtained results for getting explicit formulas for the coefficients of bivariate generating functions. The introduced mathematical apparatus can be used for solving different problems that are related to the theory of generating functions.
The Influence of an Integration Time Step on Dynamic Calculation of a Vehicle-Track-Bridge under High-Speed Railway
Authors: Ye Junjie , Sun Hao
Published: February 2021

In order to study the influence of an integration time step on dynamic calculation of a vehicle-track-bridge under high-speed railway, a vehicle-track-bridge (VTB) coupled model is established. The influence of the integration time step on calculation accuracy and calculation stability under different speeds or different track regularity states is studied. The influence of the track irregularity on the integration time step is further analyzed by using the spectral characteristic of sensitive wavelength. According to the results, the disparity among the effect of the integration time step on the calculation accuracy of the VTB coupled model at different speeds is very small. Higher speed requires a smaller integration time step to keep the calculation results stable. The effect of the integration time step on the calculation stability of the maximum vertical acceleration of each component at different speeds is somewhat different, and the mechanism of the effect of the integration time step on the calculation stability of the vehicle-track-bridge coupled system is that corresponding displacement at the integration time step is different. The calculation deviation of the maximum vertical acceleration of the car body, wheel-sets and bridge under the track short wave irregularity state are greatly increased compared with that without track irregularity. The maximum vertical acceleration of wheel-sets, rails, track slabs and the bridge under the track short wave irregularity state all show a significant declining trend. The larger the vibration frequency is, the smaller the range of integration time step is for dynamic calculation.
On the Multistage Differential Transformation Method for Analyzing Damping Duffing Oscillator and Its Applications to Plasma Physics
Authors: Aljahdaly Noufe , El-Tantawy S.
Published: February 2021

The multistage differential transformation method (MSDTM) is used to find an approximate solution to the forced damping Duffing equation (FDDE). In this paper, we prove that the MSDTM can predict the solution in the long domain as compared to differential transformation method (DTM) and more accurately than the modified differential transformation method (MDTM). In addition, the maximum residual errors for DTM and its modification methods (MSDTM and MDTM) are estimated. As a real application to the obtained solution, we investigate the oscillations in a complex unmagnetized plasma. To do that, the fluid govern equations of plasma species is reduced to the modified Korteweg–de Vries–Burgers (mKdVB) equation. After that, by using a suitable transformation, the mKdVB equation is transformed into the forced damping Duffing equation.
Reliable Learning with PDE-Based CNNs and DenseNets for Detecting COVID-19, Pneumonia, and Tuberculosis from Chest X-Ray Images
Authors: Marginean Anca Nicoleta , Muntean Delia Doris , Muntean George Adrian , Priscu Adelina , Groza Adrian , Slavescu Radu Razvan , Timbus Calin Lucian , Munteanu Gabriel Zeno , Morosanu Cezar Octavian , Cosnarovici Maria Margareta , Pintea Camelia-M.
Published: February 2021

It has recently been shown that the interpretation by partial differential equations (PDEs) of a class of convolutional neural networks (CNNs) supports definition of architectures such as parabolic and hyperbolic networks. These networks have provable properties regarding the stability against the perturbations of the input features. Aiming for robustness, we tackle the problem of detecting changes in chest X-ray images that may be suggestive of COVID-19 with parabolic and hyperbolic CNNs and with domain-specific transfer learning. To this end, we compile public data on patients diagnosed with COVID-19, pneumonia, and tuberculosis, along with normal chest X-ray images. The negative impact of the small number of COVID-19 images is reduced by applying transfer learning in several ways. For the parabolic and hyperbolic networks, we pretrain the networks on normal and pneumonia images and further use the obtained weights as the initializers for the networks to discriminate between COVID-19, pneumonia, tuberculosis, and normal aspects. For DenseNets, we apply transfer learning twice. First, the ImageNet pretrained weights are used to train on the CheXpert dataset, which includes 14 common radiological observations (e.g., lung opacity, cardiomegaly, fracture, support devices). Then, the weights are used to initialize the network which detects COVID-19 and the three other classes. The resulting networks are compared in terms of how well they adapt to the small number of COVID-19 images. According to our quantitative and qualitative analysis, the resulting networks are more reliable compared to those obtained by direct training on the targeted dataset.
Dynamics of a Two Prey and One Predator System with Indirect Effect
Authors: Colucci Renato , Diz-Pita Érika , Otero-Espinar M. Victoria
Published: February 2021

We study a population model with two preys and one predator, considering a Holling type II functional response for the interaction between first prey and predator and taking into account indirect effect of predation. We perform the stability analysis of equilibria and study the possibility of Hopf bifurcation. We also include a detailed discussion on the problem of persistence. Several numerical simulations are provided in order to illustrate the theoretical results of the paper.
Vitamin D and Glomerulonephritis
Authors: Gembillo Guido , Siligato Rossella , Amatruda Michela , Conti Giovanni , Santoro Domenico
Published: February 2021

Vitamin D presents a plethora of different functions that go beyond its role in skeletal homeostasis. It is an efficient endocrine regulator of the Renin–Angiotensin–Aldosterone System (RAAS) and erythropoiesis, exerts immunomodulatory effects, reduces the cardiovascular events and all-cause mortality. In Chronic Kidney Disease (CKD) patients, Vitamin D function is impaired; the renal hydrolyzation of its inactive form by the action of 1α-hydroxylase declines at the same pace of reduced nephron mass. Moreover, Vitamin D major carrier, the D-binding protein (DBP), is less represented due to Nephrotic Syndrome (NS), proteinuria, and the alteration of the cubilin–megalin–amnionless receptor complex in the renal proximal tubule. In Glomerulonephritis (GN), Vitamin D supplementation demonstrated to significantly reduce proteinuria and to slow kidney disease progression. It also has potent antiproliferative and immunomodulating functions, contributing to the inhibitions of kidney inflammation. Vitamin D preserves the structural integrity of the slit diaphragm guaranteeing protective effects on podocytes. Activated Vitamin D has been demonstrated to potentiate the antiproteinuric effect of RAAS inhibitors in IgA nephropathy and Lupus Nephritis, enforcing its role in the treatment of glomerulonephritis: calcitriol treatment, through Vitamin D receptor (VDR) action, can regulate the heparanase promoter activity and modulate the urokinase receptor (uPAR), guaranteeing podocyte preservation. It also controls the podocyte distribution by modulating mRNA synthesis and protein expression of nephrin and podocin. Maxalcalcitol is another promising alternative: it has about 1/600 affinity to vitamin D binding protein (DBP), compared to Calcitriol, overcoming the risk of hypercalcemia, hyperphosphatemia and calcifications, and it circulates principally in unbound form with easier availability for target tissues. Doxercalciferol, as well as paricalcitol, showed a lower incidence of hypercalcemia and hypercalciuria than Calcitriol. Paricalcitol demonstrated a significant role in suppressing RAAS genes expression: it significantly decreases angiotensinogen, renin, renin receptors, and vascular endothelial growth factor (VEGF) mRNA levels, thus reducing proteinuria and renal damage. The purpose of this article is to establish the Vitamin D role on immunomodulation, inflammatory and autoimmune processes in GN.
Recent Impact of Microfluidics on Skin Models for Perspiration Simulation
Authors: Rabost-Garcia Genís , Farré-Lladós Josep , Casals-Terré Jasmina
Published: February 2021

Skin models offer an in vitro alternative to human trials without their high costs, variability, and ethical issues. Perspiration models, in particular, have gained relevance lately due to the rise of sweat analysis and wearable technology. The predominant approach to replicate the key features of perspiration (sweat gland dimensions, sweat rates, and skin surface characteristics) is to use laser-machined membranes. Although they work effectively, they present some limitations at the time of replicating sweat gland dimensions. Alternative strategies in terms of fabrication and materials have also showed similar challenges. Additional research is necessary to implement a standardized, simple, and accurate model representing sweating for wearable sensors testing.
Effect of Transition Metal Elements on High-Temperature Properties of Al–Si–Cu–Mg Alloys
Authors: Gao Chao , Zhang Lingkun , Zhang Bingrong
Published: February 2021

In the present work, we studied the effects of transition metal elements on microstructure evolution and high-temperature mechanical properties via the preparation of new modified alloys with micro-additions of Cr, Ti, V, Zr, Mo, and Mn to address the poor high-temperature performance of Al–Si–Cu–Mg alloys for automotive engines. The results show that the addition of transition metal elements formed a variety of new intermetallic phases that were stable at high temperatures, such as (AlSi)3(TiVZr), (AlSi)3Ti, (AlSi)3(CrVTi), Al74Si6Mn4Cr2Fe, Al85Si5Mn2Mo2CrFe, Al0.78Fe4.8Mn0.27Mo4.15Si2, (AlSi)2(CrVTi)Mo, and Al13(MoCrVTi)4Si4, and these phases evidently improved the ultimate high-temperature tensile strength and yield strength. The ultimate tensile strength and yield strength of the modified alloy increased by 17.49% and 31.65% when the test temperature increased to 240 °C, respectively, and by 71.28% and 74.73% when the test temperature increased to 300 °C, respectively. The fundamental reason for this change is that the intermetallic phase hinders the expansion of cracks, which can exist stably at high temperatures. When a crack extends to the intermetallic phases, it will break along with the intermetallic phases or propagate along the morphological edge of the intermetallic phases.
Investigation of the Dynamic Recovery and Recrystallization of Near-β Titanium Alloy Ti-55511 during Two-Pass Hot Compression
Authors: Wang Hande , Ge Jinyang , Zhang Xiaoyong , Chen Chao , Zhou Kechao
Published: February 2021

The two-pass thermal compression behavior of near-β Ti-55511 alloy was investigated. The first-pass restoration mechanisms changed from dynamic recrystallization (DRX) to dynamic recovery (DRV) as the first-pass deformation temperature increased from 700 °C to 850 °C. The occurrence of recrystallization reduced the dislocation density, resulting in a slower grain growth rate in the subsequent process. Because of the static recrystallization (SRX) and β grain growth, the β grain size increased and the morphology became less uniform during the subsequent β holding process, which also changed the restoration mechanism during second-pass compression. The level of continuous dynamic recrystallization (CDRX) and discontinuous dynamic recrystallization (DDRX) become weaker during second-pass deformation. The changes in the restoration mechanism and the microstructures slightly increased the peak stress during the second-pass deformation.
The Impact of Microbiota on the Pathogenesis of Amyotrophic Lateral Sclerosis and the Possible Benefits of Polyphenols. An Overview
Authors: Casani-Cubel Julia , Benlloch María , Sanchis-Sanchis Claudia Emmanuela , Marin Raquel , Lajara-Romance Jose María , de la Rubia Orti Jose Enrique
Published: February 2021

The relationship between gut microbiota and neurodegenerative diseases is becoming clearer. Among said diseases amyotrophic lateral sclerosis (ALS) stands out due to its severity and, as with other chronic pathologies that cause neurodegeneration, gut microbiota could play a fundamental role in its pathogenesis. Therefore, polyphenols could be a therapeutic alternative due to their anti-inflammatory action and probiotic effect. Thus, the objective of our narrative review was to identify those bacteria that could have connection with the mentioned disease (ALS) and to analyze the benefits produced by administering polyphenols. Therefore, an extensive search was carried out selecting the most relevant articles published between 2005 and 2020 on the PubMed and EBSCO database on research carried out on cell, animal and human models of the disease. Thereby, after selecting, analyzing and debating the main articles on this topic, the bacteria related to the pathogenesis of ALS have been identified, among which we can positively highlight the presence mainly of Akkermansia muciniphila, but also Lactobacillus spp., Bifidobacterium spp. or Butyrivibrio fibrisolvens. Nevertheless, the presence of Escherichia coli or Ruminococcus torques stand out negatively for the disease. In addition, most of these bacteria are associated with molecular changes also linked to the pathogenesis of ALS. However, once the main polyphenols related to improvements in any of these three ALS models were assessed, many of them show positive results that could improve the prognosis of the disease. Nonetheless, epigallocatechin gallate (EGCG), curcumin and resveratrol are the polyphenols considered to show the most promising results as a therapeutic alternative for ALS through changes in microbiota.
Host-Microbiome Interactions Mediated by Phenolic Metabolites in Chronically Critically Ill Patients
Authors: Chernevskaya Ekaterina , Klimenko Natalia , Pautova Alisa , Buyakova Irina , Tyakht Alexander , Beloborodova Natalia
Published: February 2021

The community structure and metabolic potential of gut microbiome is not well investigated, especially in chronically critically ill patients with prolonged dependence on support systems after severe brain disorders. Microbial phenolic metabolites can target the brain function by the direct and indirect modulation of inflammation. The aim of this study was to investigate the features of the gut microbiota and profile of certain metabolites in the progression and reversibility of neurological disorders in chronically critically ill patients. Fecal samples were collected in dynamics from such patients (n = 44) and analyzed using 16S rRNA sequencing. Serum microbial and mitochondrial metabolites were measured by GC-MS and compared with the biomarkers and clinical neurological scores. The identified associations between specific bacterial taxa in fecal samples, neurological status and serum levels of metabolites suggest that impacts on specific members of the gut microbiota and their metabolism might be a promising tool for regulating brain function in future.
A Self-Powered and Highly Accurate Vibration Sensor Based on Bouncing-Ball Triboelectric Nanogenerator for Intelligent Ship Machinery Monitoring
Authors: Du Taili , Zuo Xusheng , Dong Fangyang , Li Shunqi , Mtui Anaeli Elibariki , Zou Yongjiu , Zhang Peng , Zhao Junhao , Zhang Yuewen , Sun Peiting , Xu Minyi
Published: February 2021

With the development of intelligent ship, types of advanced sensors are in great demand for monitoring the work conditions of ship machinery. In the present work, a self-powered and highly accurate vibration sensor based on bouncing-ball triboelectric nanogenerator (BB-TENG) is proposed and investigated. The BB-TENG sensor consists of two copper electrode layers and one 3D-printed frame filled with polytetrafluoroethylene (PTFE) balls. When the sensor is installed on a vibration exciter, the PTFE balls will continuously bounce between the two electrodes, generating a periodically fluctuating electrical signals whose frequency can be easily measured through fast Fourier transform. Experiments have demonstrated that the BB-TENG sensor has a high signal-to-noise ratio of 34.5 dB with mean error less than 0.05% at the vibration frequency of 10 Hz to 50 Hz which covers the most vibration range of the machinery on ship. In addition, the BB-TENG can power 30 LEDs and a temperature sensor by converting vibration energy into electricity. Therefore, the BB-TENG sensor can be utilized as a self-powered and highly accurate vibration sensor for condition monitoring of intelligent ship machinery.
Phylogenetic Characterization of Crimean-Congo Hemorrhagic Fever Virus Detected in African Blue Ticks Feeding on Cattle in a Ugandan Abattoir
Authors: Wampande Eddie M. , Waiswa Peter , Allen David J. , Hewson Roger , Frost Simon D. W. , Stubbs Samuel C. B.
Published: February 2021

Crimean-Congo hemorrhagic fever virus (CCHFV) is the most geographically widespread of the tick-borne viruses. However, African strains of CCHFV are poorly represented in sequence databases. In addition, almost all sequence data collected to date have been obtained from cases of human disease, while information regarding the circulation of the virus in tick and animal reservoirs is severely lacking. Here, we characterize the complete coding region of a novel CCHFV strain, detected in African blue ticks (Rhipicephalus (Boophilus) decoloratus) feeding on cattle in an abattoir in Kampala, Uganda. These cattle originated from a farm in Mbarara, a major cattle-trading hub for much of Uganda. Phylogenetic analysis indicates that the newly sequenced strain belongs to the African genotype II clade, which predominantly contains the sequences of strains isolated from West Africa in the 1950s, and South Africa in the 1980s. Whilst the viral S (nucleoprotein) and L (RNA polymerase) genome segments shared >90% nucleotide similarity with previously reported genotype II strains, the glycoprotein-coding M segment shared only 80% nucleotide similarity with the next most closely related strains, which were derived from ticks in Western India and Northern China. This genome segment also displayed a large number of non-synonymous mutations previously unreported in the genotype II strains. Characterization of this novel strain adds to our limited understanding of the natural diversity of CCHFV circulating in both ticks and in Africa. Such data can be used to inform the design of vaccines and diagnostics, as well as studies exploring the epidemiology and evolution of the virus for the establishment of future CCHFV control strategies.
DNA Polymerase B1 Binding Protein 1 Is Important for DNA Repair by Holoenzyme PolB1 in the Extremely Thermophilic Crenarchaeon Sulfolobus acidocaldarius
Authors: Miyabayashi Hiroka , Sakai Hiroyuki D. , Kurosawa Norio
Published: February 2021

DNA polymerase B1 (PolB1) is a member of the B-family DNA polymerase family and is a replicative DNA polymerase in Crenarchaea. PolB1 is responsible for the DNA replication of both the leading and lagging strands in the thermophilic crenarchaeon Sulfolobus acidocaldarius. Recently, two subunits, PolB1-binding protein (PBP)1 and PBP2, were identified in Saccharolobus solfataricus. Previous in vitro studies suggested that PBP1 and PBP2 influence the core activity of apoenzyme PolB1 (apo-PolB1). PBP1 contains a C-terminal acidic tail and modulates the strand-displacement synthesis activity of PolB1 during the synthesis of Okazaki fragments. PBP2 modestly enhances the DNA polymerase activity of apo-PolB1. These subunits are present in Sulfolobales, Acidilobales, and Desulfurococcales, which belong to Crenarchaea. However, it has not been determined whether these subunits are essential for the activity of apo-PolB1. In this study, we constructed a pbp1 deletion strain in S. acidocaldarius and characterized its phenotypes. However, a pbp2 deletion strain was not obtained, indicating that PBP2 is essential for replication by holoenzyme PolB1. A pbp1 deletion strain was sensitive to various types of DNA damage and exhibited an increased mutation rate, suggesting that PBP1 contribute to the repair or tolerance of DNA damage by holoenzyme PolB1. The results of our study suggest that PBP1 is important for DNA repair by holoenzyme PolB1 in S. acidocaldarius.
Anti-Biofilm Activity of Cannabidiol against Candida albicans
Authors: Feldman Mark , Sionov Ronit Vogt , Mechoulam Raphael , Steinberg Doron
Published: February 2021

Candida albicans is a common fungal pathogen in humans. Biofilm formation is an important virulence factor of C. albicans infections. We investigated the ability of the plant-derived cannabidiol (CBD) to inhibit the formation and removal of fungal biofilms. Further, we evaluated its mode of action. Our findings demonstrate that CBD exerts pronounced time-dependent inhibitory effects on biofilm formation as well as disruption of mature biofilm at a concentration range below minimal inhibitory and fungicidal concentrations. CBD acts at several levels. It modifies the architecture of fungal biofilm by reducing its thickness and exopolysaccharide (EPS) production accompanied by downregulation of genes involved in EPS synthesis. It alters the fungal morphology that correlated with upregulation of yeast-associated genes and downregulation of hyphae-specific genes. Importantly, it represses the expression of C. albicans virulence-associated genes. In addition, CBD increases ROS production, reduces the intracellular ATP levels, induces mitochondrial membrane hyperpolarization, modifies the cell wall, and increases the plasma membrane permeability. In conclusion, we propose that CBD exerts its activity towards C. albicans biofilm through a multi-target mode of action, which differs from common antimycotic agents, and thus can be explored for further development as an alternative treatment against fungal infections.
The History of Colistin Resistance Mechanisms in Bacteria: Progress and Challenges
Authors: Hamel Mouna , Rolain Jean-Marc , Baron Sophie Alexandra
Published: February 2021

Since 2015, the discovery of colistin resistance genes has been limited to the characterization of new mobile colistin resistance (mcr) gene variants. However, given the complexity of the mechanisms involved, there are many colistin-resistant bacterial strains whose mechanism remains unknown and whose exploitation requires complementary technologies. In this review, through the history of colistin, we underline the methods used over the last decades, both old and recent, to facilitate the discovery of the main colistin resistance mechanisms and how new technological approaches may help to improve the rapid and efficient exploration of new target genes. To accomplish this, a systematic search was carried out via PubMed and Google Scholar on published data concerning polymyxin resistance from 1950 to 2020 using terms most related to colistin. This review first explores the history of the discovery of the mechanisms of action and resistance to colistin, based on the technologies deployed. Then we focus on the most advanced technologies used, such as MALDI-TOF-MS, high throughput sequencing or the genetic toolbox. Finally, we outline promising new approaches, such as omics tools and CRISPR-Cas9, as well as the challenges they face. Much has been achieved since the discovery of polymyxins, through several innovative technologies. Nevertheless, colistin resistance mechanisms remains very complex.
Competitive Exclusion of Intra-Genus Salmonella in Neonatal Broilers
Authors: Pineda Megan , Kogut Michael , Genovese Kenneth , Farnell Yuhua Z. , Zhao Dan , Wang Xi , Milby Allison , Farnell Morgan
Published: February 2021

Salmonellosis is a zoonotic infection caused by Salmonella enterica serotypes contracted from contaminated products. We hypothesized that competitive exclusion between Salmonella serotypes in neonatal broilers would reduce colonization and affect the host immune response. Day of hatch broilers were randomly allocated to one of six treatment groups: (1) control, which received saline, (2) Salmonella Kentucky (SK) only on day 1 (D1), (3) Salmonella Typhimurium (ST) or Salmonella Enteritidis (SE) only on D1, (4) SK on D1 then ST or SE on day 2 (D2), (5) ST or SE on D1 then SK on D2, and (6) SK and ST or SE concurrently on D1. Salmonella gut colonization and incidence were measured from cecal contents. Livers and spleens were combined and macerated to determine systemic translocation. Relative mRNA levels of interleukin-1β (IL-1β), IL-6, IL-10, IL-18, and gamma interferon (IFN-γ) were measured in cecal tonsils and liver to investigate local and systemic immune responses. When a serotype was administered first, it was able to significantly reduce colonization of the following serotype. Significant changes were found in mRNA expression of cytokines. These results suggest competitive exclusion by Salmonella enterica serotypes affect local and systemic immune responses.