bluuebunny/crossref_metadata_2025 huggingface dataset:
Response of Apple Flesh to Compression under the Quasi-Static and Impact Loading Conditions
Authors: Stropek Zbigniew , Gołacki Krzysztof
Published: November 2022

Compression tests of cylindrical samples were carried out using two ‘Rubin’ and ‘Red Jonaprince’ apple cultivars with flesh firmness differing in a statistically significant way. The tests were conducted under both the quasi-static and impact loading conditions, which required the use of two test stands. For this purpose, an impact measuring stand was designed and built. The tests proved that the firmness of the apple flesh influenced the mechanical response under both the quasi-static and impact loading conditions. The elasticity modulus had much higher values under the impact than quasi-static loading conditions. This indicates that the stiffness of the apple tissue was larger during the impact than at the low-speed compression. Different failure mechanisms of cylindrical apple flesh samples were found depending on the loading conditions. Under the quasi-static loading conditions, the apples of both cultivars were damaged at the same strain value. However, during the impact, apples were apt to damage at a constant stress value regardless of the firmness of the tested cultivar. The toughness of the apple tissue depended on firmness and was larger under the quasi-static loading conditions for the apples with larger firmness. However, under the impact loading conditions, the toughness was greater for the apples with smaller firmness.
Morphological Changes of Calcium Carbonate and Mechanical Properties of Samples during Microbially Induced Carbonate Precipitation (MICP)
Authors: Gu Zhaorui , Chen Qing , Wang Lishuang , Niu Shuang , Zheng Junjie , Yang Min , Yan Yunjun
Published: November 2022

Recently, microbially induced carbonate precipitation (MICP) has shown potent potential in the field of civil engineering. The calcium carbonate crystals produced by bacteria during the MICP process play a central role in sticking the soil. However, the morphological changes of calcium carbonate crystals in this process and the mechanical performance of soil in the corresponding stages have not been clearly explored. In this paper, the alterations in the morphology of calcium carbonate crystals were continuously observed via scanning electron microscopy during the MICP process in one week, and the mechanical changes of the samples were monitored every day, so as to reveal the relationship between the morphology of calcium carbonate crystals and the mechanical performance of the samples. The results show that the calcium carbonate crystals undergo a gradual change from ellipsoid to rhombic at the 72nd hour. The mechanical properties of both were greatly improved, among which the compressive strength was increased by 2.78 times compared with the previous time point, and the flexure strength was increased by 2.57 times; this time point was also the time when calcite appears. In addition, we found direct evidence on the first day that bacteria act as the nucleation site of calcium carbonate formation. The above findings have certain guiding significance for the in-depth understanding of the internal microscopic changes of MICP and the influence of calcium carbonate morphology on sample mechanics.
Performance Analysis of Anode-Supported Solid Oxide Fuel Cells: A Machine Learning Approach
Authors: Golbabaei Mohammad Hossein , Saeidi Varnoosfaderani Mohammadreza , Zare Arsalan , Salari Hirad , Hemmati Farshid , Abdoli Hamid , Hamawandi Bejan
Published: November 2022

Prior to the long-term utilization of solid oxide fuel cell (SOFC), one of the most remarkable electrochemical energy conversion devices, a variety of difficult experimental validation procedures is required, so it would be time-consuming and steep to predict the applicability of these devices in the future. For numerous years, extensive efforts have been made to develop mathematical models to predict the effects of various characteristics of solid oxide fuel cells (SOFCs) components on their performance (e.g., voltage). Taking advantage of the machine learning (ML) method, however, some issues caused by assumptions and calculation costs in mathematical modeling could be alleviated. This paper presents a machine learning approach to predict the anode-supported SOFCs performance as one of the most promising types of SOFCs based on architectural and operational variables. Accordingly, a dataset was collected from a study about the effects of cell parameters on the output voltage of a Ni-YSZ anode-supported cell. Convolutional machine learning models and multilayer perceptron neural networks were implemented to predict the current-voltage dependency. The resulting neural network model could properly predict, with more than 0.998 R2 score, a mean squared error of 9.6 × 10−5, and mean absolute error of 6 × 10−3 (V). Conventional models such as the Gaussian process as one of the most powerful models exhibits a prediction accuracy of 0.996 R2 score, 10−4 mean squared, and 6 × 10−3 (V) absolute error. The results showed that the built neural network could predict the effect of cell parameters on current-voltage dependency more accurately than previous mathematical and artificial neural network models. It is noteworthy that this procedure used in this study is general and can be easily applied to other materials datasets.
Adhesive Joint Degradation Due to Hardener-to-Epoxy Ratio Inaccuracy under Varying Curing and Thermal Operating Conditions
Authors: Szabelski Jakub , Karpiński Robert , Jonak Józef , Frigione Mariaenrica
Published: November 2022

This paper presents the results of an experimental study of adhesive joint strength with consideration of the inaccuracy of the hardener dosage, in the context of evaluating the degradation of joints when used either at ambient or elevated temperatures. The butt joint strength characteristics were assessed for two types of adhesives—rigid and flexible—and two curing scenarios—with and without heat curing. An excess hardener was shown to be significantly more unfavourable than its deficiency, which can ultimately be considered as a recommendation for forming epoxy adhesive joint assemblies. In order to fully understand the relationship between the analysed mechanical properties of the material and the influence of component ratio excesses and heating, a process of fitting basic mathematical models to the obtained experimental data was also performed.
The Tribological Behaviors in Zr-Based Bulk Metallic Glass with High Heterogeneous Microstructure
Authors: Ma Yubai , Li Mei , Zu Fangqiu
Published: November 2022

Microstructural inhomogeneity of bulk metallic glasses (BMGs) plays a significant role in their mechanical properties. However, there is hardly ant research concerning the influence of heterogeneous microstructures on tribological behaviors. Hence, in this research, the tribological behaviors of different microstructural-heterogeneity BMGs sliding in-air were systematically investigated, and the corresponding wear mechanisms were disclosed via analyzing the chemical composition and morphology of the wear track. Higher microstructural-heterogeneity BMGs can possess a better wear resistance both under dry sliding and a 3.5% NaCl solution. The results suggest that microstructural heterogeneity enhancement is a valid strategy to improve the tribological performance of BMGs.
The Application of Chitosan for Protection of Cultural Heritage Objects of the 15–16th Centuries in the State Tretyakov Gallery
Authors: Zhgun Alexander , Avdanina Darya , Shagdarova Balzhima , Nuraeva Gulgina , Shumikhin Kirill , Zhuikova Yuliya , Il’ina Alla , Troyan Egor , Shitov Michail , Varlamov Valery
Published: November 2022

Microorganisms are one of the main factors in the deterioration of cultural heritage, in particular art paintings. The antiseptics currently used in painting have significant limitations due to insufficient effectiveness or increased toxicity and interaction with art materials. In this regard, the actual challenge is the search for novel materials that effectively work against microorganisms in the composition with painting materials and do not change their properties. Chitosan has pronounced antimicrobial properties but was not used previously as an antiseptic for paintings. In our study we developed a number of mock layers based on sturgeon glue, supplemented which chitosan (molecular weight 25 kDa or 45 kDa), standard antiseptics for paintings (positive controls) or without additives (negative control). According to Fourier transform infrared spectroscopy and atomic force microscopy, the addition of chitosan did not significantly affect the optical and surface properties of this material. The ability of chitosan to effectively protect paintings was shown after inoculation on the created mock-up layers of 10 fungi-destructors of tempera painting, previously isolated from cultural heritage of the of the 15–16th centuries in the State Tretyakov Gallery, on the created mock layers. Our study demonstrated the principled opportunity of using chitosan in the composition of painting materials to prevent biodeterioration for the first time.
Assessment of the Life Cycle of a Wind and Photovoltaic Power Plant in the Context of Sustainable Development of Energy Systems
Authors: Piotrowska Katarzyna , Piasecka Izabela , Kłos Zbigniew , Marczuk Andrzej , Kasner Robert
Published: November 2022

The conversion of kinetic energy from wind and solar radiation into electricity during the operation of wind and photovoltaic power plants causes practically no emissions of chemical compounds that are harmful to the environment. However, the production of their materials and components, as well as their post-use management after the end of their operation, is highly consumptive of energy and materials. For this reason, this article aims to assess the life cycle of a wind and photovoltaic power plant in the context of the sustainable development of energy systems. The objects of the research were two actual technical facilities—a 2 MW wind power plant and a 2 MW photovoltaic power plant, both located in Poland. The analysis of their life cycle was carried out on the basis of the LCA (life-cycle assessment) method, using the ReCiPe 2016 calculation procedure. The impact of the examined renewable energy systems was assessed under 22 impact categories and 3 areas of influence (i.e., human health, ecosystems, and resources), and an analysis was conducted for the results obtained as part of three compartments (i.e., air, water, and soil). The life cycle of the wind power plant was distinguished by a higher total potential negative environmental impact compared to the life cycle of the photovoltaic power plant. The highest levels of potential harmful impacts on the environment in both life cycles were recorded for areas of influence associated with negative impacts on human health. Emissions to the atmosphere accounted for over 90% of all emissions in the lifetimes of both the wind and the photovoltaic power plants. On the basis of the obtained results, guidelines were proposed for pro-ecological changes in the life cycle of materials and elements of the considered technical facilities for renewable energy sources, aimed at better implementation of the main assumptions of contemporary sustainable development (especially in the field of environmental protection).
Phosphate-Trapping Liposomes for Long-Term Management of Hyperphosphatemia
Authors: Tzror-Azankot Chen , Anaki Adi , Sadan Tamar , Motiei Menachem , Popovtzer Rachela
Published: November 2022

Hyperphosphatemia is a typical complication of end-stage renal disease, characterized by elevated and life-threatening serum phosphate levels. Hemodialysis does not enable sufficient clearance of phosphate, due to slow cell-to-plasma kinetics of phosphate ions; moreover, dietary restrictions and conventional treatment with oral phosphate binders have low success rates, together with adverse effects. Here, we developed a new concept of phosphate-trapping liposomes, to improve and prolong the control over serum phosphate levels. We designed liposomes modified with polyethylene glycol and encapsulated with the phosphate binder ferric citrate (FC liposomes). These liposomes were found to trap phosphate ions in their inner core, and thereby lower free phosphate ion concentrations in solution and in serum. The FC liposomes showed higher phosphate binding ability as phosphate concentrations increased. Moreover, these liposomes showed a time-dependent increase in uptake of phosphate, up to 25 h in serum. Thus, our findings demonstrate effective long-term phosphate trapping by FC liposomes, indicating their potential to reduce serum phosphate toxicity and improve current management of hyperphosphatemia.
The Synthesis and Characterisation of the High-Hardness Magnetic Material Mn2N0.86
Authors: Zhang Shoufeng , Zhou Chao , Wang Xin , Bao Kuo , Zhao Xingbin , Zhu Jinming , Tao Qiang , Ge Yufei , Yu Zekun , Zhu Pinwen , Zhao Wei , Cheng Jia’en , Ma Teng , Ma Shuailing , Cui Tian
Published: November 2022

High-quality P6322 Mn2N0.86 samples were synthesised using a high-pressure metathesis reaction, and the properties of the material were investigated. The measurements revealed that the Vickers hardness was 7.47 GPa, which is less than that predicted by commonly used theoretical models. At low air pressure, Mn2N0.86 and MnO coexist at 500 to 600 °C, and by excluding air, we succeeded in producing Mn4N by heating Mn2N0.86 in nitrogen atmosphere; we carefully studied this process with thermogravimetry and differential scanning calorimetry (TG-DSC). This gives a hint that to control temperature, air pressure and gas concentration might be an effective way to prepare fine Mn-N-O catalysis. Magnetic measurements indicated that ferromagnetism and antiferromagnetism coexist within Mn2N0.86 at room temperature and that these magnetic properties are induced by nitrogen vacancies. Ab intio simulation was used to probe the nature of the magnetism in greater detail. The research contributes to the available data and the understanding of Mn2N0.86 and suggests ways to control the formation of materials based on Mn2N0.86.
Parameter Study of Interfacial Capacities for FRP–Steel Bonded Joints Based on 3D FE Modeling
Authors: Liu Jie , Yuan Yu , Wang Libin , Liu Zhongxiang , Yang Jun
Published: November 2022

This paper investigated the stress distribution of an adhesive layer for GFRP–steel bonded joints under 22.48 kN tensile loading using a three-dimensional numerical simulation. Firstly, a stress analysis of three paths was conducted, and after comparison, path II (through the middle layer of the bonding layer) was adopted as the analyzing path. Furthermore, a systemically parametric study of the effects of the FRP stiffness (i.e., elastic modulus and thickness), bonding length, adhesive thickness, and adhesive modulus was conducted. For the joints with different FRP elastic moduli, the minimum value of normal peeling stress was calculated as −3.80 MPa by the FRP for 10 GPa, showing a significantly severe stress concentration of FRP for 10 GPa. An analysis of the von Mises stresses proved that the increase in FRP stiffness could reduce the stress concentration of the adhesive layer effectively. The study of the effect of bonding lengths indicated that a more uniform peeling stress distribution could result from the longest bonding size; the largest peeling stress of 6.54 MPa was calculated for a bonding length of 30 mm. Further parameter analysis showed that the stress concentration of the adhesive layer could be influenced by the FRP thickness, bonding thickness, and elastic modulus of the adhesive layer.
A Trajectory Tracking Approach for Aerial Manipulators Using Nonsingular Global Fast Terminal Sliding Mode and an RBF Neural Network
Authors: Shen Lirui , Mao Pengjun , Fang Qian , Wang Jun
Published: November 2022

An unmanned aerial manipulator (UAM) is a novel flying robot consisting of an unmanned aerial vehicle (UAV) and a multi-degree-of-freedom (DoF) robotic arm. It can actively interact with the environment to conduct dangerous or inaccessible tasks for humans. Owing to the underactuated characteristics of UAVs and the coupling generated by the rigid connection with the manipulator, robustness and a high-precision controller are critical for UAMs. In this paper, we propose a nonsingular global fast terminal sliding mode (NGFTSM) controller for UAMs to track the expected trajectory under the influence of disturbances based on a reasonably simplified UAM system dynamics model. To achieve active anti-disturbance and high tracking accuracy in a UAM system, we incorporate an RBF neural network into the controller to estimate lumped disturbances, including internal coupling and external disturbances. The controller and neural network are derived according to Lyapunov theory to ensure the system’s stability. In addition, we propose a set of illustrative metrics to evaluate the performance of the designed controller and compare it with other controllers by simulations. The results show that the proposed controller can effectively enhance the robustness and accuracy of a UAM system with satisfactory convergence. The experimental results also verify the effectiveness of the proposed controller.
Multi-Disciplinary Analysis of Working Fluids on Thermal Performance of the High-Power Diesel Engine System
Authors: Lee Geesoo
Published: November 2022

Multi-disciplinary analysis was performed to analyze and investigate the thermal performance during transient operation of a 2 L diesel engine system with two different cooling systems. The multi-disciplinary model consisted of the engine thermal management system (ETMS) comprising a zero-dimensional engine model that can simulate the engine performance, and a one-dimensional flow model for cooling and lubrication systems with a controller. By deploying this approach, we were able to model different physical domains, including mechanical for the engine and the dynamometer and thermodynamic for the heat exchangers. Therefore, the thermal performance of the ETMS could be numerically predicted and analyzed. To develop the ETMS model, the physical properties, the heat transfer model, and the pressure drop were modeled. The base fluid, a 50/50 mixture of water and ethylene glycol (EG), and an Al2O3 nanofluid with a 1.5% volume ratio were modeled based on the thermodynamic properties such as density, dynamic viscosity, thermal conductivity, and specific heat. Nanofluid, with its higher thermal conductivity and higher heat transfer coefficient, absorbed more heat from the combustion chamber through the water-jacket in the engine block. Therefore, the oil temperature for the nanofluid was effectively 2.5 °C less than for the base fluid following the step-load condition. Simulation results showed the better effect of nanofluid on thermal performance. The total flow rate of nanofluid decreased by 2.2 L/min, although the flow rate through the radiator with nanofluid increased by 0.81 L/min to obtain greater heat dissipation. Eventually, the piston and the liner temperatures with the nanofluid were drastically reduced by 7.55 and 8 °C, respectively, compared to those of the base fluid. Finally, when nanofluids was applied in automotive cooling systems, the temperature of the piston decreased by 7.3 °C due to the reduced overall thermal resistance from combustion chambers to outside air. The effect of working fluid on the diesel engine system could be predicted through the multi-disciplinary model.
A Coupling Model of High-Speed Train-Axle Box Bearing and the Vibration Characteristics of Bearing with Defects under Wheel Rail Excitation
Authors: Ma Qiaoying , Liu Yongqiang , Yang Shaopu , Liao Yingying , Wang Baosen
Published: November 2022

A three-dimensional vehicle-axle box bearing coupling model is established. The model can calculate the contact force in three directions and obtain the dynamic response of axle box bearing under the real vehicle running environment. The load distribution on the double row tapered roller bearing and the vehicle is analyzed, and the co-simulation is conducted by comprehensively considering the force transmission between vehicle and bearing. Taking into account the great impact of defects on the bearing, three different types of bearing defects are added into the model, respectively. The simulation results verify the effectiveness of the model. The model is also verified by using the rolling and vibrating test rig of single wheelset. It is concluded that the simulation results show good agreement with experimental results. The influence of track irregularity on the system motion state is studied by using axis trajectory and vibration RMS (Root Mean Square value). The results show that the influence of track irregularity and wheel flat scar on axle box bearing cannot be ignored. The RMS of acceleration will change greatly due to the existence of defects. Wheel flat scar will greatly interfere with the extraction of bearing defect, but it can be selected at high speed and low frequency to monitor the existence of wheel flat scar, and select low speed and high frequency to monitor the existence of bearing defect. The research results are helpful to the detection of wheel flat scar and axle box bearing defect.
Machine Learning Feedback Control Approach Based on Symbolic Regression for Robotic Systems
Authors: Diveev Askhat , Shmalko Elizaveta
Published: November 2022

A control system of an autonomous robot produces a control signal based on feedback. This type of control implies the control of an object according to its state that is mathematically the control synthesis problem. Today there are no universal analytical methods for solving the general synthesis problem, and it is solved by certain particular approaches depending on the type of control object. In this paper, we propose a universal numerical approach to solving the problem of optimal control with feedback using machine learning methods based on symbolic regression. The approach is universal and can be applied to various objects. However, the use of machine learning methods imposes two aspects. First, when using them, it is necessary to reduce the requirements for optimality. In machine learning, optimization algorithms are used, but strictly optimal solutions are not sought. Secondly, in machine learning, analytical proofs of the received properties of solutions are not required. In machine methods, a set of tests is carried out and it is shown that this is sufficient to achieve the required properties. Thus, in this article, we initially introduce the fundamentals of machine learning control, introduce the basic concepts, properties and machine criteria for application of this technique. Then, with regard to the introduced notations, the feedback optimal control problem is considered and reformulated in order to add to the problem statement that such a property adjusts both the requirements of stability and optimality. Next, a description of the proposed approach is presented, theoretical formulations are given, and its efficiency is demonstrated on the computational examples in mobile robot control tasks.
cpd: An R Package for Complex Pearson Distributions
Authors: Olmo-Jiménez María José , Vílchez-López Silverio , Rodríguez-Avi José
Published: November 2022

The complex Pearson (CP) distributions are a family of probability models for count data generated by the Gaussian hypergeometric function with complex arguments. The complex triparametric Pearson (CTP) distribution and its biparametric versions, the complex biparametric Pearson (CBP) and the extended biparametric Waring (EBW) distributions, belong to this family. They all have explicit expressions of the probability mass function (pmf), probability generating function and moments, so they are easy to handle from a computational point of view. Moreover, the CTP and EBW distributions can model over- and underdispersed count data, whereas the CBP can only handle overdispersed data, but unlike other well-known overdispersed distributions, the overdispersion is not due to an excess of zeros but other low values of the variable. Finally, the EBW distribution allows the variance to be split into three uniquely identifiable components: randomness, liability and proneness. These properties make the CP distributions of interest in the modeling of a great variety of data. For this reason, and for trying to spread their use, we have implemented an R package called cpd that contains the pmf, distribution function, quantile function and random generation for these distributions. In addition, the package contains fitting functions according to the maximum likelihood. This package is available from the Comprehensive R Archive Network (CRAN). In this work, we describe all the functions included in the cpd package, and we illustrate their usage with several examples. Moreover, the release of a plugin in order to use the package from the interface R Commander tries to contribute to the spreading of these models among non-advanced users.
Semi-Analytical Solution for Thermo-Piezoelectric Bending of FG Porous Plates Reinforced with Graphene Platelets
Authors: Alsebai Fatemah , Al Mukahal Fatemah H. H. , Sobhy Mohammed
Published: November 2022

This research is devoted to investigating the thermo-piezoelectric bending of functionally graded (FG) porous piezoelectric plates reinforced with graphene platelets (GPLs). A refined four-variable shear deformation plate theory is utilized considering the transverse shear strain to describe the displacement components. The porous nanocomposite plate is composed of polymer piezoelectric material containing internal pores and reinforced with FG GPLs. In accordance with modified distribution laws, the porosity and GPLs volume fraction are presumed to vary continuously through the plate thickness. Four GPLs and porosity distribution types are presented. By applying the Halpin–Tsai model, the elastic properties of the nanocomposite plate are calculated. The governing equations are derived based on the present theory and the principle of virtual work. The deduced partial differential equations are converted to ordinary equations by employing Levy-type solution. These equations are numerically solved based on the differential quadrature method (DQM). In order to determine the minimum grid points sufficient to gain a converging solution, a convergence study is introduced. Moreover, the accuracy of the present formulations are examined by comparing the obtained results with those published in the literature. Additional parametric analyses are introduced to investigate the influences of the GPLs weight fraction, distribution types, side-to-thickness ratio, external electric voltage and temperature on the thermal bending of FG GPLs porous nanocomposite piezoelectric plates.
Machine Learning Combined with Restriction Enzyme Mining Assists in the Design of Multi-Point Mutagenic Primers
Authors: Cheng Yu-Huei , Chuang Li-Yeh , Yang Cheng-Hong
Published: November 2022

The polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) experiment has the characteristics of low-cost, rapidity, simplicity, convenience, high sensitivity and high specificity; thus, many small and medium laboratories use it to perform all kinds of single nucleotide polymorphisms (SNPs) genotyping works, and as a molecular biotechnology for disease-related analysis. However, many single nucleotide polymorphisms lack available restriction enzymes to distinguish the specific genotypes on a target SNP, and that causes the PCR-RFLP assay which is unavailable to be called mismatch PCR-RFLP. In order to completely solve the problem of mismatch PCR-RFLP, we have created a teaching–learning-based optimization (TLBO) multi-point mutagenic primer design algorithm which, combined with REHUNT, provides a complete and specific restriction enzyme mining solution. The proposed method not only introduces several search strategies suitable for multi-point mutagenesis primers, but also enhances the reliability of mutagenic primer design. In addition, this study is also designed for more complex SNP structures (with multiple dNTPs and insertion and deletion) to provide specific solutions for SNP diversity. We tested against fifteen mismatch PCR-RFLP SNPs in the human SLC6A4 gene on the NCBI dbSNP database as experimental templates. The experimental results indicate that the proposed method is helpful for providing the multi-point mutagenic primers that meet the constrain conditions of PCR experiments.
BreaST-Net: Multi-Class Classification of Breast Cancer from Histopathological Images Using Ensemble of Swin Transformers
Authors: Tummala Sudhakar , Kim Jungeun , Kadry Seifedine
Published: November 2022

Breast cancer (BC) is one of the deadly forms of cancer, causing mortality worldwide in the female population. The standard imaging procedures for screening BC involve mammography and ultrasonography. However, these imaging procedures cannot differentiate subtypes of benign and malignant cancers. Here, histopathology images could provide better sensitivity toward benign and malignant cancer subtypes. Recently, vision transformers have been gaining attention in medical imaging due to their success in various computer vision tasks. Swin transformer (SwinT) is a variant of vision transformer that works on the concept of non-overlapping shifted windows and is a proven method for various vision detection tasks. Thus, in this study, we investigated the ability of an ensemble of SwinTs in the two-class classification of benign vs. malignant and eight-class classification of four benign and four malignant subtypes, using an openly available BreaKHis dataset containing 7909 histopathology images acquired at different zoom factors of 40×, 100×, 200×, and 400×. The ensemble of SwinTs (including tiny, small, base, and large) demonstrated an average test accuracy of 96.0% for the eight-class and 99.6% for the two-class classification, outperforming all the previous works. Thus, an ensemble of SwinTs could identify BC subtypes using histopathological images and may lead to pathologist relief.
Exponential Stability of Impulsive Neutral Stochastic Functional Differential Equations
Authors: Li Yunfeng , Cheng Pei , Wu Zheng
Published: November 2022

This paper focuses on the problem of the pth moment and almost sure exponential stability of impulsive neutral stochastic functional differential equations (INSFDEs). Based on the Lyapunov function and average dwell time (ADT), two sufficient criteria for the exponential stability of INSFDEs are derived, which manifest that the result obtained in this paper is more convenient to be used than those Razumikhin conditions in former literature. Finally, two numerical examples and simulations are given to verify the validity of our result.
An Aggregation Metric Based on Partitioning and Consensus for Asymmetric Distributions in Likert Scale Responses
Authors: Moreno-Garcia Juan , Yáñez-Araque Benito , Hernández-Perlines  Felipe , Rodriguez-Benitez Luis
Published: November 2022

A questionnaire is a basic tool for collecting information in survey research. Often, these questions are measured using a Likert scale. With multiple items on the same broad object, these codes could be summed or averaged to give an indication of each respondent’s overall positive or negative orientation towards that object. This is the basis for Likert scales. Aggregation methods have been widely used in different research areas. Most of them are mathematical methods, such as the arithmetic mean, the weighted arithmetic mean, or the OWA (Ordered Weighted Averaging) operator. The usual presentation of Likert scale derived data are Mean. This paper presents a new approach to compute an aggregate value that represents Likert scale responses as a histogram adequate to treat better than Mean with asymmetric distributions. This method generates a set of partitions using an approach based on successive division. After every division, each partition is evaluated using a consensus measure and the one with the best value is then selected. Once the process of division has finished, the aggregate value is computed using the resulting partitions. Promising results have been obtained. Experiments show that our method is appropriate for distributions with large asymmetry and is not far from the behavior of the arithmetic mean for symmetric distributions. Overall, the article sheds light on the need to consider other presentations of Likert scale derived data beyond Mean more suitable for asymmetric distributions.
Applied Geospatial Bayesian Modeling in the Big Data Era: Challenges and Solutions
Authors: Byers Jason S. , Gill Jeff
Published: November 2022

Two important trends in applied statistics are an increased usage of geospatial models and an increased usage of big data. Naturally, there has been overlap as analysts utilize the techniques associated with each. With geospatial methods such as kriging, the computation required becomes intensive quickly, even with datasets that would not be considered huge in other contexts. In this work we describe a solution to the computational problem of estimating Bayesian kriging models with big data, Bootstrap Random Spatial Sampling (BRSS), and first provide an analytical argument that BRSS produces consistent estimates from the Bayesian spatial model. Second, with a medium-sized dataset on fracking in West Virginia, we show that bootstrap sample effects from a full-information Bayesian model are reduced with more bootstrap samples and more observations per sample as in standard bootstrapping. Third, we offer a realistic illustration of the method by analyzing campaign donors in California with a large geocoded dataset. With this solution, scholars will not be constrained in their ability to apply theoretically relevant geospatial Bayesian models when the size of the data produces computational intractability.
Anti-Inflammatory Effects of Compounds from Echinoderms
Authors: Ghelani Hardik , Khursheed Md , Adrian Thomas Edward , Jan Reem Kais
Published: November 2022

Chronic inflammation can extensively burden a healthcare system. Several synthetic anti-inflammatory drugs are currently available in clinical practice, but each has its own side effect profile. The planet is gifted with vast and diverse oceans, which provide a treasure of bioactive compounds, the chemical structures of which may provide valuable pharmaceutical agents. Marine organisms contain a variety of bioactive compounds, some of which have anti-inflammatory activity and have received considerable attention from the scientific community for the development of anti-inflammatory drugs. This review describes such bioactive compounds, as well as crude extracts (published during 2010–2022) from echinoderms: namely, sea cucumbers, sea urchins, and starfish. Moreover, we also include their chemical structures, evaluation models, and anti-inflammatory activities, including the molecular mechanism(s) of these compounds. This paper also highlights the potential applications of those marine-derived compounds in the pharmaceutical industry to develop leads for the clinical pipeline. In conclusion, this review can serve as a well-documented reference for the research progress on the development of potential anti-inflammatory drugs from echinoderms against various chronic inflammatory conditions.
Emergence of High Antimicrobial Resistance among Critically Ill Patients with Hospital-Acquired Infections in a Tertiary Care Hospital
Authors: E. Abou Warda Ahmed , Molham Fatma , Salem Heba F. , Mostafa-Hedeab Gomaa , ALruwaili Bashayer F. , Moharram Ayman N. , Sebak Mohamed , Sarhan Rania M.
Published: November 2022

Background and Objectives: Inappropriate antibiotic usage in hospitalized patients contributes to microbial resistance. Our study aimed to examine the incidence of clinical bacterial isolates and their antibiotic resistance burden among critically ill patients in different hospital units. Materials and Methods: A single-centered cross-sectional study was conducted in a 120-bed tertiary care hospital that included 221 critically ill patients with hospital-acquired infections. Bacterial cultures and sensitivity reports were obtained and followed by a formal analysis of the antibiogram results to explore recovered isolates’ prevalence and antibiotic susceptibility patterns. Results: Gram-negative bacteria were the most predominant pathogens among recovered isolates from the various hospital units (71%). Klebsiella sp. was the most prevalent microbe, followed by Acinetobacter sp., with an incidence level of 28% and 16.2%, respectively. Among the Gram-positive organisms, the coagulase-negative Staphylococci were the most predominant organism (11.3%), while (6.3%) methicillin-resistant Staphylococcus aureus (MRSA) isolates were recovered from different hospital units. Antibiotic sensitivity testing showed that polymyxin B was the most effective antibiotic against Gram-negative bacteria, whereas vancomycin and linezolid were the most active antibiotics against Gram-positive pathogens. Moreover, 7% of the Gram-negative bacteria isolated from different units showed positive production of extended-spectrum beta-lactamase (ESBL). Conclusions: The current study describes the high antibiotic resistance patterns in various hospital units that need extra legislation to prevent healthcare providers from misprescription and overuse of antibiotics.
Exploring Membrane Binding Targets of Disordered Human Tau Aggregates on Lipid Rafts Using Multiscale Molecular Dynamics Simulations
Authors: Cheng Kwan H. , Graf Angela , Lewis Amber , Pham Thuong , Acharya Aakriti
Published: November 2022

The self-aggregation of tau, a microtubule-binding protein, has been linked to the onset of Alzheimer’s Disease. Recent studies indicate that the disordered tau aggregates, or oligomers, are more toxic than the ordered fibrils found in the intracellular neurofibrillary tangles of tau. At present, details of tau oligomer interactions with lipid rafts, a model of neuronal membranes, are not known. Using molecular dynamics simulations, the lipid-binding events, membrane-damage, and protein folding of tau oligomers on various lipid raft surfaces were investigated. Tau oligomers preferred to bind to the boundary domains (Lod) created by the coexisting liquid-ordered (Lo) and liquid-disordered (Ld) domains in the lipid rafts. Additionally, stronger binding of tau oligomers to the ganglioside (GM1) and phosphatidylserine (PS) domains, and subsequent protein-induced lipid chain order disruption and beta-sheet formation were detected. Our results suggest that GM1 and PS domains, located exclusively in the outer and inner leaflets, respectively, of the neuronal membranes, are specific membrane domain targets, whereas the Lod domains are non-specific targets, of tau oligomers binding to neurons. The molecular details of these specific and non-specific tau bindings to lipid rafts may provide new insights into understanding membrane-associated tauopathies leading to Alzheimer’s Disease.
Percolation Effects in Mixed Matrix Membranes with Embedded Carbon Nanotubes
Authors: Eremin Yury , Grekhov Alexey , Belogorlov Anton
Published: November 2022

Polymeric membranes with embedded nanoparticles, e.g., nanotubes, show a significant increase in permeability of the target component while maintaining selectivity. However, the question of the reasons for this behavior of the composite membrane has not been unequivocally answered to date. In the present work, based on experimental data on the permeability of polymer membranes based on Poly(vinyl trimethylsilane) (PVTMS) with embedded CNTs, an approach to explain the abnormal behavior of such composite membranes is proposed. The presented model considered the mass transfer of gases and liquids through polymeric membranes with embedded CNTs as a parallel transport of gases through the polymeric matrix and a “percolation” cluster—bound regions around the embedded CNTs. The proposed algorithm for modeling parameters of a percolation cluster of embedded tubular particles takes into account an agglomeration and makes it possible to describe the threshold increase and subsequent decrease permeability with increasing concentration of embedded particles. The numerical simulation of such structures showed: an increase in the particle length leads to a decrease in the percolation concentration in a matrix of finite size, the power of the percolation cluster decreases significantly, but the combination of these effects leads to a decrease in the influence of the introduced particles on the properties of the matrix in the vicinity of the percolation threshold; an increase in the concentration of embedded particles leads to an increase in the probability of the formation of agglomerates and the characteristic size of the elements that make up the percolation cluster, the influence of individual particles decreases and the characteristics of the percolation transition determine the ratio of the sizes of agglomerates and matrix; and an increase in the lateral linear dimensions of the matrix leads to a nonlinear decrease in the proportion of the matrix, which is affected by the introduced particles, and the transport characteristics of such MMMs deteriorate.
The Formation and Disintegration of Rafts from Different Aluminas and Fines
Authors: Gylver Sindre Engzelius , Bekkevoll Simen , Rørvik Stein , Einarsrud Kristian Etienne
Published: November 2022

Raft formation is a frequently encountered problem during alumina feeding in the Hall-Héroult-process and will delay alumina from being dissolved into the melt. The mechanisms for the formation and disintegration of rafts are however not thoroughly understood yet. The current study investigates the dissolution behavior and raft structure from three different types of secondary alumina in a lab cell, with a particular attention to effect of fines, and involves both sampling of rafts and video recordings of the feeding. The mass loss rate was calculated to vary between −1.57 and −0.42 g min−1 for regular bulk alumina, and −1.15 and −0.06 g min−1 for fines. Rafts created from bulk alumina were flat with a distinct bulge or crater placed in the center of it, while rafts created from fines had a pellet-shaped structure and traces of undissolved alumina in the middle. The observed structure is due to the initial spreading of powder, confirmed by video recordings.
Superplastic Behavior of Overaged 2024 Aluminum Alloy after Friction Stir Processing
Authors: Orozco-Caballero Alberto , Álvarez-Leal Marta , Carreño Fernando , Ruano Oscar A.
Published: November 2022

A commercial 2024 aluminum alloy was heat treated at 280 °C for 48 h and then slow cooled in a furnace to obtain minimum hardness. This material was then friction stir processed (FSP) using three sets of processing conditions. To study the effect of the processing on the microstructure and the high temperature mechanical properties, the materials were tested in tension at an initial strain rate of 10−2 s−1 and temperature range 200 to 450 °C. Processing severity was selected as the main factor for obtaining fine grain sizes right after FSP. The grain size was enormously reduced from about 50 µm to 1 µm. This grain reduction gave rise to very high elongations to failure of about 400%. Strain–rate-change tests showed a stress exponent close to 2 at intermediate strain rates, which was related to grain boundary sliding as the controlling deformation mechanism and to superplasticity, which is strongly grain-size dependent. A possible controlling deformation mechanism by solute-drag creep, as proposed by other authors, was disregarded since tests conducted at 450 °C, where the microstructure of the FSP materials coarsens rapidly, gave a low elongation to failure and high resistance, which showed the importance of the grain size dependence of the operative deformation mechanism at 250–400 °C, which was only compatible with grain boundary sliding.
Effect of Ta Doping on the Microstructure and Thermoelectric Properties of Bi2O2Se
Authors: Jiang Jia-Ling , Dong Song-Tao , Fu Zhuang , Yu Miao-Cheng , Zhao Lijun , Wang Lei
Published: November 2022

In this study, Bi2−xTaxO2Se (x = 0, 0.02, 0.04, 0.06, and 0.08) ceramics were prepared using a synthesis method combining high-energy ball milling and cold pressing. Furthermore, the effects of tantalum (Ta) doping on the microstructure and thermoelectric properties of Bi2O2Se were systematically investigated. The results indicate that Ta doping effectively improves the carrier concentration and mobility, thus increasing the electrical conductivity from 8.75 S cm−1 to 39.03 S cm−1 at 323 K. Consequently, the power factor is improved, reaching a maximum value of 124 μW m−1 K−2 for the Bi1.92Ta0.08O2Se sample at 773 K. Moreover, the thermal conductivity of Bi1.96Ta0.04O2Se is reduced to 0.50 Wm−1 K−1. Finally, the maximum dimensionless figure of merit (ZT) value of the Bi1.94Ta0.06O2Se sample reached 0.18, which was 64% higher than that of Bi2O2Se (0.11). These results indicate that Ta doping and high-energy ball milling can optimize the electrical and thermal properties and thus improve the thermoelectric properties of ceramics.
State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning
Authors: Subedi Upadesh , Poudel Sachin , Gyanwali Khem , Amorim Coutinho Yuri , Matula Grzegorz , Kunwar Anil
Published: November 2022

Though the martensitic transformation has been a commonly investigated topic in the field of experimental and computational materials science, the understanding of this mechanism in a variety of alloys is yet far from complete. In this era of Industry 4.0, there have been ongoing trends on employing machine learning (ML) techniques for the study of the martensitic alloys, and such data-driven approaches are expected to unravel a great amount of information about the process-structure-property behaviour relationship in this class of materials. However, with the availability of a large variety of datasets and with an option to use different ML models, a bulk amount of information has already been generated with regard to martensitic alloys. The discovery and design of shape memory alloys can be accelerated if the multi-principal element functional alloys and martensitic transformation phenomenon are studied extensively using machine learning techniques. Thus, it is necessary to highlight the major categories or aspects of these alloys that have been predicted with ML. The present work performs a state-of-the-art review on the machine learning models developed for the quantification of aspects such as martensitic start temperature (Ms), materials properties, microstructure, mechanisms etc., on the alloys.