bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of the Shear Cap Size and Stiffness on the Distribution of Shear Forces in Flat Slabs
Authors: Grabski Maciej , Ambroziak Andrzej
Published: December 2021

The scope of this paper is to investigate analytically and numerically the influence of shear cap size and stiffness on the distribution of shear forces in flat slabs in a slab–column-connections-reinforced concrete structure. The effect of support (shear cap) stiffness on the calculation of the length of the shear control perimeter according to the available methods is presented. Based on the analysis, the authors indicate in what range of support stiffness the corner concentrations become important in the calculation of the punching resistance. For shear caps with high flexibility (α1 ≤ 0.5), the concentration of internal forces in the corners does not occur. The authors compare the numerical results obtained from the calculation methods and indicate the correlations, which can be useful guidance for structural designers. In the case of large shear caps, the simplified MC2010 method gives a significantly lower value of the effective control perimeter length compared to more accurate methods. This paper is intended to provide scientists, civil engineers, and designers with guidelines on which factors influence punching shear load capacity of the slab–column connections with shear caps.
Setting Behavior and Phase Evolution on Heat Treatment of Metakaolin-Based Geopolymers Containing Calcium Hydroxide
Authors: Kim Byoungkwan , Lee Sujeong , Chon Chul-Min , Cho Shinhu
Published: December 2021

The setting behavior of geopolymers is affected by the type of source materials, alkali activators, mix formulations, and curing conditions. Calcium hydroxide is known to be an effective additive to shorten the setting period of geopolymers. However, there is still room for improvement in the understanding of the effect of calcium hydroxide on the setting and phase evolution of geopolymers. In this study, the setting behavior and phase evolution of geopolymer containing calcium hydroxide were investigated by XRD analysis. The setting time of the geopolymer was inconsistently shortened as the amount of calcium hydroxide increased. A low calcium hydroxide dose of up to 2% of the total mix weight could contribute to the enhancement of compressive strength of geopolymers besides a fast-setting effect. The C-S-H gel is rapidly precipitated at the early stage of reaction in geopolymers containing high calcium hydroxide with some of the calcium hydroxide remaining intact. The ex-situ high-temperature XRD analysis and Rietveld refinement results revealed that geopolymer and C-S-H gel transformed into Si-rich nepheline and wollastonite, respectively. The wollastonite was also observed in heat-treated geopolymers with a low calcium hydroxide dose. It is believed that C-S-H gel can be precipitated along with geopolymers regardless of how much calcium hydroxide is added.
Using the Effect of Compression Stress in Fatigue Analysis of the Roller Bearing for Bimodal Stress Histories
Authors: Romanowicz Paweł J. , Smolarski Dariusz , Kozień Marek S.
Published: December 2021

A new approach based on the direct spectral method for fatigue analysis of elements subjected to bimodal stress histories, including high compression effects, is proposed. A correction factor, taking into account the influence of the mean compressive stresses, is used in the proposed method. Equivalent amplitude is estimated, based on criteria proposed by Smith, Watson, and Tooper, and by Bergmann and Seeger. The method is presented with example of a thrust roller bearing. Two cases in which the rollers were subjected to constant force 206 N (where constant amplitude stresses occurred in the rollers) and cyclic force (where bimodal stresses with variable amplitudes occurred in the rollers) are studied. It is observed that multiaxial fatigue criteria (Crossland, Papadopoulos) do not include the influence of bimodal stresses and should not be used for such loading conditions. The proposed method includes both kinds of stress waveforms in the fatigue analysis and can be applied for the accurate identification of stress components and the determination of fatigue life. The damage rate calculated by the proposed approach for rollers subjected to a cyclic force (equivalent load equal to 151 N) was 0.86, which is in good agreement with the recommendations provided in the literature. The obtained accuracy of the proposed method is above 95%.
Spreadability of Metal Powders for Laser-Powder Bed Fusion via Simple Image Processing Steps
Authors: Vakifahmetoglu Cekdar , Hasdemir Beyza , Biasetto Lisa
Published: December 2021

This paper investigates the spreadability of the spherical CoCrWMo powder for laser- powder bed fusion (PBF-LB) by using image processing algorithms coded in MATLAB. Besides, it also aims to examine the spreadability dependence with the other characteristics such as powder size distribution, apparent density, angle of repose. Powder blends in four different particle size distributions are prepared, characterized, and spreadability tests are performed with the PBF-LB. The results demonstrate that an increase in fine particle ratio by volume (below 10 µm) enhances the agglomeration and decreases the flowability, causing poor spreadability. These irregularities on the spread layers are quantified with simple illumination invariant analysis. A clear relation between powder spreadability and 3D printed structures properties in terms of residual porosity could not be defined since structural defects in 3D printed parts also depends on other processing parameters such as spatter formation or powder size over layer height ratio.
SQL and NoSQL Databases in the Context of Industry 4.0
Authors: de Oliveira Vitor Furlan , Pessoa Marcosiris Amorim de Oliveira , Junqueira Fabrício , Miyagi Paulo Eigi
Published: December 2021

The data-oriented paradigm has proven to be fundamental for the technological transformation process that characterizes Industry 4.0 (I4.0) so that big data and analytics is considered a technological pillar of this process. The goal of I4.0 is the implementation of the so-called Smart Factory, characterized by Intelligent Manufacturing Systems (IMS) that overcome traditional manufacturing systems in terms of efficiency, flexibility, level of integration, digitalization, and intelligence. The literature reports a series of system architecture proposals for IMS, which are primarily data driven. Many of these proposals treat data storage solutions as mere entities that support the architecture’s functionalities. However, choosing which logical data model to use can significantly affect the performance of the IMS. This work identifies the advantages and disadvantages of relational (SQL) and non-relational (NoSQL) data models for I4.0, considering the nature of the data in this process. The characterization of data in the context of I4.0 is based on the five dimensions of big data and a standardized format for representing information of assets in the virtual world, the Asset Administration Shell. This work allows identifying appropriate transactional properties and logical data models according to the volume, variety, velocity, veracity, and value of the data. In this way, it is possible to describe the suitability of relational and NoSQL databases for different scenarios within I4.0.
Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization
Authors: Liu Jiaqi , Fang Hongbin , Xu Jian
Published: December 2021

Robotic exoskeletons have great potential in the medical rehabilitation and augmentation of human performance in a variety of tasks. Proposing effective and adaptive control strategies is one of the most challenging issues for exoskeleton systems to work interactively with the user in dynamic environments and variable tasks. This research, therefore, aims to advance the state of the art of the exoskeleton adaptive control by integrating the excellent search capability of metaheuristic algorithms with the PID feedback mechanism. Specifically, this paper proposes an online adaptive PID controller for a multi-joint lower extremity exoskeleton system by making use of the particle swarm optimization (PSO) algorithm. Significant improvements, including a ‘leaving and re-searching mechanism’, are introduced into the PSO algorithm for better and faster update of the solution and to prevent premature convergence. In this research, a 9-DOF lower extremity exoskeleton with seven controllable joints is adopted as a test-bench, whose first-principle dynamic model is developed, which includes as many uncertain factors as possible for generality, including human–exoskeleton interactions, environmental forces, and joint unilateral constraint forces. Based upon this, to illustrate the effectiveness of the proposed controller, the human–exoskeleton coupled system is simulated in four characteristic scenarios, in which the following factors are considered: exoskeleton parameter perturbations, human effects, walking terrain switches, and walking speed variations. The results indicate that the proposed controller is superior to the standard PSO algorithm and the conventional PID controller in achieving rapid convergence, suppressing the undesired chattering of PID gains, adaptively adjusting PID coefficients when internal or external disturbances are encountered, and improving tracking accuracy in both position and velocity. We also demonstrate that the proposed controller could be used to switch the working mode of the exoskeleton for either performance or an energy-saving consideration. Overall, aiming at a multi-joint lower extremity exoskeleton system, this research proposes a PSO-based online adaptive PID controller that can be easily implemented in applications. Through rich and practical case studies, the excellent anti-interference capability and environment/task adaptivity of the controller are exemplified.
Nested Optimization of Oil-Circulating Hydro-Pneumatic Energy Storage System for Hybrid Mining Trucks
Authors: Yi Tong , Jin Chun , Gao Lulu , Hong Jichao , Liu Yanbo
Published: December 2021

In order to recover and utilize the potential energy of mining trucks efficiently, this paper proposes a nested optimization method of a novel energy storage system. By analyzing the multi-objective optimization problem of the oil-circulating hydro-pneumatic energy storage system, a nested optimization method based on the advanced adaptive Metamodel-based global optimization algorithm is carried out. Research shows that this method only requires a short time to solve the complex nonlinear hybrid optimization problem and achieves better results. The optimized energy storage system has higher system efficiency, energy density, and volume utilization rate, thus obtaining a smaller system volume and weight. Verified by the bench experiment of its powertrain, the hydro-pneumatic hybrid mining truck with the optimized energy storage system significantly reduces its fuel consumption and CO2 emission. Thus, it lays the foundation for the practical application of hydro-pneumatic hybrid mining trucks.
A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption Prediction
Authors: Fujii Tiago Yukio , Hayashi Victor Takashi , Arakaki Reginaldo , Ruggiero Wilson Vicente , Bulla Romeo , Hayashi Fabio Hirotsugu , Khalil Khalil Ahmad
Published: December 2021

Using extensive databases and known algorithms to predict short-term energy consumption comprises most computational solutions based on artificial intelligence today. State-of-the-art approaches validate their prediction models in offline environments that disregard automation, quality monitoring, and retraining challenges present in online scenarios. The existing demand response initiatives lack personalization, thus not engaging consumers. Obtaining specific and valuable recommendations is difficult for most digital platforms due to their solution pattern: extensive database, specialized algorithms, and using profiles with similar aspects. The challenges and present personalization tactics have been researched by adopting a digital twin model. This study creates a different approach by adding structural topology to build a new category of recommendation platform using the digital twin model with real-time data collected by IoT sensors to improve machine learning methods. A residential study case with 31 IoT smart meter and smart plug devices with 19-month data (measurements performed each second) validated Digital Twin MLOps architecture for personalized demand response suggestions based on online short-term energy consumption prediction.
In-Process Chatter Detection Using Signal Analysis in Frequency and Time-Frequency Domain
Authors: Perrelli Michele , Cosco Francesco , Gagliardi Francesco , Mundo Domenico
Published: December 2021

All machining processes involve vibrations generated by structural sources such as a machine’s moving parts or by the interaction between cutting tools and work-pieces. Relative vibrations between the work-pieces and the cutting tool are the most relevant from the point of view of the regenerative chatter phenomenon. In fact, these vibrations can lead to a chip yregeneration effect, which results in unwanted consequences, rapidly degenerating towards a very poor quality of surface finishing or, in case of severe chatter conditions, to machine-tool or work-piece damage. In the past decades, two different approaches for chatter avoidance were proposed by the scientific community, and they are commonly referred to as Out-of-Process (OuP) and in-Process (iP) solutions. The OuP solutions are off-line approaches, which allow to properly set the working parameters before machining starts. Ip solutions are on-line techniques, which allow to dynamically change the working parameters during machining by using single or multiple sensors. By monitoring the machining process, iP algorithms try to keep the machining process in stable working conditions while keeping high productivity levels. This study dealt with a novel iP chatter-detection strategy based on the Power Spectral Density (PSD) analysis and on the Wavelet Packet Decomposition (WPD) of different sensor signals. The preliminary results demonstrate the stability and feasibility of proposed indicators for chatter detection in industrial application.
The Study of the New Classes of m-Fold Symmetric bi-Univalent Functions
Authors: Breaz Daniel , Cotîrlă Luminiţa-Ioana
Published: December 2021

In this paper, we introduce three new subclasses of m-fold symmetric holomorphic functions in the open unit disk U, where the functions f and f−1 are m-fold symmetric holomorphic functions in the open unit disk. We denote these classes of functions by FSΣ,mp,q,s(d), FSΣ,mp,q,s(e) and FSΣ,mp,q,s,h,r. As the Fekete-Szegö problem for different classes of functions is a topic of great interest, we study the Fekete-Szegö functional and we obtain estimates on coefficients for the new function classes.
A Krasnoselskii–Ishikawa Iterative Algorithm for Monotone Reich Contractions in Partially Ordered Banach Spaces with an Application
Authors: Hussain Nawab , Alsulami Saud M. , Alamri Hind
Published: December 2021

Iterative algorithms have been utilized for the computation of approximate solutions of stationary and evolutionary problems associated with differential equations. The aim of this article is to introduce concepts of monotone Reich and Chatterjea nonexpansive mappings on partially ordered Banach spaces. We describe sufficient conditions for the existence of an approximate fixed-point sequence (AFPS) and prove certain fixed-point results using the Krasnoselskii–Ishikawa iterative algorithm. Moreover, we present some interesting examples to highlight the superiority of our results. Lastly, we provide both weak and strong convergence results for such mappings and consider an application of our results to prove the existence of a solution to an initial value problem.
Impact of Trapezoidal Demand and Deteriorating Preventing Technology in an Inventory Model in Interval Uncertainty under Backlogging Situation
Authors: Mondal Rajan , Shaikh Ali Akbar , Bhunia Asoke Kumar , Hezam Ibrahim M. , Chakrabortty Ripon K.
Published: December 2021

The demand for a product is one of the important components of inventory management. In most cases, it is not constant; it may vary from time to time depending upon several factors which cannot be ignored. For any seasonal product, it is observed that at the beginning of the season, demand escalates over time, then it is stable and after that, it decreases. This type of demand is known as the trapezoidal type. Also, due to the uncertainty of customers’ behavior, inventory parameters are not always fixed. Combining these two concepts together, an inventory model is formulated for decaying items in an interval environment. Preservative technology is incorporated to preserve the product from deterioration. The corresponding mathematical formulation is derived in such a way that the profit of the inventory system is maximized. Consequently, the corresponding optimization problem is converted into an interval optimization problem. To solve the same, different variants of quantum-behaved particle swarm optimization (QPSO) techniques are employed to determine the duration of stock-in time and preservation technology cost. To illustrate and also to validate the model, three numerical examples are considered and solved. Then the computational results are compared. Thereafter, to study the impact of different parameters of the proposed model on the best found (optimal or very close to optimal) solution, sensitivity analysis are performed graphically.
A Methodological Approximation of the Measurement of Instrumental Social Exclusion from the Capability Approach: The Case of Vulnerable Areas in the City of Murcia, Spain
Authors: Sánchez-Martí Jose Antonio , Angoa Pérez María Isabel
Published: December 2021

The indirect effects of social exclusion in different fields of the lives of individuals have been scarcely studied. The literature that addresses social exclusion from the capability approach is sparse, and a methodology for measuring the instrumental effects of social exclusion on capability deprivation has not yet been implemented. Therefore, the main objective of this research is to propose a methodological framework to quantify these effects, based on two techniques: Structural equation modelling and principal component analysis. Likewise, this study presents a practical application in the vulnerable areas of the city of Murcia, Spain. In order to obtain the data, fieldwork was carried out using 464 questionnaires. The theorised model was statistically contrasted, confirming that it is not false. The study revealed that instrumental social exclusion had a moderate impact on the deprivations experienced by the study population.
Damage Detection and Isolation from Limited Experimental Data Using Simple Simulations and Knowledge Transfer
Authors: Khan Asif , Kim Jun-Sik , Kim Heung Soo
Published: December 2021

A simulation model can provide insight into the characteristic behaviors of different health states of an actual system; however, such a simulation cannot account for all complexities in the system. This work proposes a transfer learning strategy that employs simple computer simulations for fault diagnosis in an actual system. A simple shaft-disk system was used to generate a substantial set of source data for three health states of a rotor system, and that data was used to train, validate, and test a customized deep neural network. The deep learning model, pretrained on simulation data, was used as a domain and class invariant generalized feature extractor, and the extracted features were processed with traditional machine learning algorithms. The experimental data sets of an RK4 rotor kit and a machinery fault simulator (MFS) were employed to assess the effectiveness of the proposed approach. The proposed method was also validated by comparing its performance with the pre-existing deep learning models of GoogleNet, VGG16, ResNet18, AlexNet, and SqueezeNet in terms of feature extraction, generalizability, computational cost, and size and parameters of the networks.
f(R,T)-Gravity Model with Perfect Fluid Admitting Einstein Solitons
Authors: Siddiqi Mohd Danish , Chaubey Sudhakar K. , Khan Mohammad Nazrul Islam
Published: December 2021

f(R,T)-gravity is a generalization of Einstein’s field equations (EFEs) and f(R)-gravity. In this research article, we demonstrate the virtues of the f(R,T)-gravity model with Einstein solitons (ES) and gradient Einstein solitons (GES). We acquire the equation of state of f(R,T)-gravity, provided the matter of f(R,T)-gravity is perfect fluid. In this series, we give a clue to determine pressure and density in radiation and phantom barrier era, respectively. It is proved that if a f(R,T)-gravity filled with perfect fluid admits an Einstein soliton (g,ρ,λ) and the Einstein soliton vector field ρ of (g,ρ,λ) is Killing, then the scalar curvature is constant and the Ricci tensor is proportional to the metric tensor. We also establish the Liouville’s equation in the f(R,T)-gravity model. Next, we prove that if a f(R,T)-gravity filled with perfect fluid admits a gradient Einstein soliton, then the potential function of gradient Einstein soliton satisfies Poisson equation. We also establish some physical properties of the f(R,T)-gravity model together with gradient Einstein soliton.
Multi-UAV Coverage Path Planning Based on Hexagonal Grid Decomposition in Maritime Search and Rescue
Authors: Cho Sung-Won , Park Jin-Hyoung , Park Hyun-Ji , Kim Seongmin
Published: December 2021

In the event of a maritime accident, surveying the maximum area efficiently in the least amount of time is crucial for rescuing survivors. Increasingly, unmanned aerial vehicles (UAVs) are being used in search and rescue operations. This study proposes a method to generate a search path that covers all generated nodes in the shortest amount of time with multiple heterogeneous UAVs. The proposed model, which is a mixed-integer linear programming (MILP) model based on a hexagonal grid-based decomposition method, was verified through a simulation analysis based on the performance of an actual UAV. This study presents both the optimization technique’s calculation time as a function of the search area size and the various UAV routes derived as the search area grows. The results of this study can have wide-ranging applications for emergency search and rescue operations.
En-AR-PRNS: Entropy-Based Reliability for Configurable and Scalable Distributed Storage Systems
Authors: Tchernykh Andrei , Babenko Mikhail , Avetisyan Arutyun , Drozdov Alexander Yu.
Published: December 2021

Storage-as-a-service offers cost savings, convenience, mobility, scalability, redundant locations with a backup solution, on-demand with just-in-time capacity, syncing and updating, etc. While this type of cloud service has opened many opportunities, there are important considerations. When one uses a cloud provider, their data are no longer on their controllable local storage. Thus, there are the risks of compromised confidentiality and integrity, lack of availability, and technical failures that are difficult to predict in advance. The contribution of this paper can be summarized as follows: (1) We propose a novel mechanism, En-AR-PRNS, for improving reliability in the configurable, scalable, reliable, and secure distribution of data storage that can be incorporated along with storage-as-a-service applications. (2) We introduce a new error correction method based on the entropy (En) paradigm to correct hardware and software malfunctions, integrity violation, malicious intrusions, unexpected and unauthorized data modifications, etc., applying a polynomial residue number system (PRNS). (3) We use the concept of an approximation of the rank (AR) of a polynomial to reduce the computational complexity of the decoding. En-AR-PRNS combines a secret sharing scheme and error correction codes with an improved multiple failure detection/recovery mechanism. (4) We provide a theoretical analysis supporting the dynamic storage configuration to deal with varied user preferences and storage properties to ensure high-quality solutions in a non-stationary environment. (5) We discuss approaches to efficiently exploit parallel processing for security and reliability optimization. (6) We demonstrate that the reliability of En-AR-PRNS is up to 6.2 times higher than that of the classic PRNS.
Oscillation of Solutions to Third-Order Nonlinear Neutral Dynamic Equations on Time Scales
Authors: Qiu Yang-Cong , Chiu Kuo-Shou , Grace Said R. , Liu Qingmin , Jadlovská Irena
Published: December 2021

In this paper, we are concerned with the oscillation of solutions to a class of third-order nonlinear neutral dynamic equations on time scales. New oscillation criteria are presented by employing the Riccati transformation and integral averaging technique. Two examples are shown to illustrate the conclusions.
Vibrational Resonance and Electrical Activity Behavior of a Fractional-Order FitzHugh–Nagumo Neuron System
Authors: Mao Jia-Wei , Hu Dong-Liang
Published: December 2021

Making use of the numerical simulation method, the phenomenon of vibrational resonance and electrical activity behavior of a fractional-order FitzHugh–Nagumo neuron system excited by two-frequency periodic signals are investigated. Based on the definition and properties of the Caputo fractional derivative, the fractional L1 algorithm is applied to numerically simulate the phenomenon of vibrational resonance in the neuron system. Compared with the integer-order neuron model, the fractional-order neuron model can relax the requirement for the amplitude of the high-frequency signal and induce the phenomenon of vibrational resonance by selecting the appropriate fractional exponent. By introducing the time-delay feedback, it can be found that the vibrational resonance will occur with periods in the fractional-order neuron system, i.e., the amplitude of the low-frequency response periodically changes with the time-delay feedback. The weak low-frequency signal in the system can be significantly enhanced by selecting the appropriate time-delay parameter and the fractional exponent. In addition, the original integer-order model is extended to the fractional-order model, and the neuron system will exhibit rich dynamical behaviors, which provide a broader understanding of the neuron system.
Evaluating Auction Mechanisms for the Preservation of Cost-Aware Digital Objects under Constrained Digital Preservation Budgets
Authors: El-Fakdi Andres , de la Rosa Josep Lluis
Published: December 2021

Digital preservation is a field of research focused on designing strategies for maintaining digital objects accessible for general use in the coming years. Out of the many approaches to digital preservation, the present research article is a continuation work of a previously published article containing a proposal for a novel object-centered paradigm to address the digital preservation problem where digital objects share part of the responsibility for self-preservation. In the new framework, the behavior of digital objects is modeled to find the best preservation strategy. The results presented in the current article add a new economic constraint to the object behavior. Now, differently from the previous paper, migrations, copies and updates are not free to use, but subject to budget limitations to ensure the economic sustainability of the whole preservation system, forcing the now-called cost-aware digital objects for efficient management of available budget. The presented approach compares two auction-based mechanisms, a multi-unit auction and a combinatorial auction, with a simple direct purchase strategy as possible efficient behaviors for budget management. TiM, a simulated environment for running distributed digital ecosystems, is used to perform the experiments. The simulated results map the relation between the studied purchase models with the sustained quality level of digital objects, as a measure of its accessibility, together with its budget management capabilities. About the results, the best performance corresponds to the combinatorial auction model. The results are a good approach to deal with the digital preservation problem from a sustainable point of view and open the door to future implementations with other purchase strategies.
Solute Transport Control at Channel Junctions Using Adjoint Sensitivity
Authors: Gordillo Geovanny , Morales-Hernández Mario , García-Navarro Pilar
Published: December 2021

Water quality control and the control of contaminant spill in water in particular are becoming a primary need today. Gradient descent sensitivity methods based on the adjoint formulation have proved to be encouraging techniques in this context for river and channel flows. Taking into account that most channels and rivers include junctions with other branches, the objective of this study is to explore the adjoint technique on a channel network to reconstruct the upstream boundary condition of the convection-reaction equation. For this purpose, the one-dimensional shallow water equations and the transport equation for a reactive solute are considered. The control is formulated through the gradient-descent technique supplied with a first-order iterative process. Both the physical and the adjoint equations are supplied with suitable internal boundary conditions at the junction and are numerically solved using a finite volume upwind scheme. The results reveal that the adjoint technique is capable of reconstructing the inlet solute concentration boundary condition in an acceptable number of iterations for both steady state and transient configurations using a downstream measurement location. It was also observed that the reconstruction of the boundary condition tends to be less effective the further away the measurement station is from the target.
Lattice Structure of Some Closed Classes for Three-Valued Logic and Its Applications
Authors: Kalimulina Elmira Yu.
Published: December 2021

This paper provides a brief overview of modern applications of nonbinary logic models, where the design of heterogeneous computing systems with small computing units based on three-valued logic produces a mathematically better and more effective solution compared to binary models. For application, it is necessary to implement circuits composed of chipsets, the operation of which is based on three-valued logic. To be able to implement such schemes, a fundamentally important theoretical problem must be solved: the problem of completeness of classes of functions of three-valued logic. From a practical point of view, the completeness of the class of such functions ensures that circuits with the desired operations can be produced from an arbitrary (finite) set of chipsets. In this paper, the closure operator on the set of functions of three-valued logic that strengthens the usual substitution operator is considered. It is shown that it is possible to recover the sublattice of closed classes in the general case of closure of functions with respect to the classical superposition operator. The problem of the lattice of closed classes for the class of functions T2 preserving two is considered. The closure operators R1 for the functions that differ only by dummy variables are considered equivalent. This operator is withiin the scope of interest of this paper. A lattice is constructed for closed subclasses in T2={f|f(2,…,2)=2}, a class of functions preserving two.
Space and Genotype-Dependent Virus Distribution during Infection Progression
Authors: Bessonov Nicholas , Bocharov Gennady , Volpert Vitaly
Published: December 2021

The paper is devoted to a nonlocal reaction-diffusion equation describing the development of viral infection in tissue, taking into account virus distribution in the space of genotypes, the antiviral immune response, and natural genotype-dependent virus death. It is shown that infection propagates as a reaction-diffusion wave. In some particular cases, the 2D problem can be reduced to a 1D problem by separation of variables, allowing for proof of wave existence and stability. In general, this reduction provides an approximation of the 2D problem by a 1D problem. The analysis of the reduced problem allows us to determine how viral load and virulence depend on genotype distribution, the strength of the immune response, and the level of immunity.
Exact Maximum Clique Algorithm for Different Graph Types Using Machine Learning
Authors: Reba Kristjan , Guid Matej , Rozman Kati , Janežič Dušanka , Konc Janez
Published: December 2021

Finding a maximum clique is important in research areas such as computational chemistry, social network analysis, and bioinformatics. It is possible to compare the maximum clique size between protein graphs to determine their similarity and function. In this paper, improvements based on machine learning (ML) are added to a dynamic algorithm for finding the maximum clique in a protein graph, Maximum Clique Dynamic (MaxCliqueDyn; short: MCQD). This algorithm was published in 2007 and has been widely used in bioinformatics since then. It uses an empirically determined parameter, Tlimit, that determines the algorithm’s flow. We have extended the MCQD algorithm with an initial phase of a machine learning-based prediction of the Tlimit parameter that is best suited for each input graph. Such adaptability to graph types based on state-of-the-art machine learning is a novel approach that has not been used in most graph-theoretic algorithms. We show empirically that the resulting new algorithm MCQD-ML improves search speed on certain types of graphs, in particular molecular docking graphs used in drug design where they determine energetically favorable conformations of small molecules in a protein binding site. In such cases, the speed-up is twofold.
Cognitive Load Balancing Approach for 6G MEC Serving IoT Mashups
Authors: Attanasio Barbara , Mazayev Andriy , du Plessis Shani , Correia Noélia
Published: December 2021

The sixth generation (6G) of communication networks represents more of a revolution than an evolution of the previous generations, providing new directions and innovative approaches to face the network challenges of the future. A crucial aspect is to make the best use of available resources for the support of an entirely new generation of services. From this viewpoint, the Web of Things (WoT), which enables Things to become Web Things to chain, use and re-use in IoT mashups, allows interoperability among IoT platforms. At the same time, Multi-access Edge Computing (MEC) brings computing and data storage to the edge of the network, which creates the so-called distributed and collective edge intelligence. Such intelligence is created in order to deal with the huge amount of data to be collected, analyzed and processed, from real word contexts, such as smart cities, which are evolving into dynamic and networked systems of people and things. To better exploit this architecture, it is crucial to break monolithic applications into modular microservices, which can be executed independently. Here, we propose an approach based on complex network theory and two weighted and interdependent multiplex networks to address the Microservices-compliant Load Balancing (McLB) problem in MEC infrastructure. Our findings show that the multiplex network representation represents an extra dimension of analysis, allowing to capture the complexity in WoT mashup organization and its impact on the organizational aspect of MEC servers. The impact of this extracted knowledge on the cognitive organization of MEC is quantified, through the use of heuristics that are engineered to guarantee load balancing and, consequently, QoS.
Theonellamides J and K and 5-cis-Apoa-theopalauamide, Bicyclic Glycopeptides of the Red Sea Sponge Theonella swinhoei
Authors: Hasin Ohad , Shoham Shani , Kashman Yoel , Ilan Micha , Carmeli Shmuel
Published: December 2021

Theonella swinhoei is a fairly common inhabitant of reefs throughout the Indian and Pacific Oceans. Metabolomic analyses of samples of T. swinhoei collected in different depths in the Gulf of Aqaba revealed two chemotypes differing in the profiles of the theonellamides they produce, some of which seem to be unknown. Driven by this finding, we examined a sample of T. swinhoei collected more than 40 years ago in the southern part of the Gulf of Aqaba. Large-scale extract of this sample yielded four theonellamides, the known theopalauamide (4), as the major component, and three new metabolites, theonellamide J (1), 5-cis-Apoa-theopalauamide (2), and theonellamide K (3), as the minor components. The planar structure of these complex cyclic glycopeptides was elucidated by combination of 1D and 2D NMR techniques and HRESIMS. The absolute configuration of the amino acids was established by Marfey’s and advanced Marfey’s methods, and the absolute configuration of its galactose unit using “Tanaka’s method” for monosaccharides. The biological activity of the pure compounds was tested for antibacterial activity and for cytotoxicity to HTC-116 cell line. The compounds presented significant cytotoxicity against the HTC-116 cell line, illuminating the importance of the Apoa subunit for the activity.
Effects of Acupuncture on Cardiac Remodeling in Patients with Persistent Atrial Fibrillation: Results of a Randomized, Placebo-Controlled, Patient- and Assessor-Blinded Pilot Trial and Its Implications for Future Research
Authors: Lee Jung Myung , Lee Seung Min Kathy , Leem Jungtae , Kim Jin-Bae , Park Jimin , Park Jun Hyeong , Lee Suji , Kim Hyung Oh , Chung Hyemoon , Woo Jong Shin , Kim Woo-Shik , Lee Sanghoon , Kim Weon
Published: December 2021

Background and Objectives: In this study, we attempted to determine the effects of acupuncture on cardiac remodeling and atrial fibrillation (AF) recurrence rates in patients with AF after electrical cardioversion (EC). Materials and Methods: We randomly assigned 44 patients with persistent AF to an acupuncture group or a sham acupuncture group. An electroacupuncture treatment session was administered once weekly for 12 weeks at four acupuncture points (left PC5, PC6, ST36, and ST37). Results: Among the 44 recruited participants, 16 (treatment group) and 15 (control group) completed the trial. The three-month AF recurrence rate (primary outcome) was not significantly different between the two groups. Following the completion of treatment, patients who had been treated with acupuncture had a significant reduction in left atrial volume index (42.2 ± 13.9 to 36.1 ± 9.7 mL/m2; p = 0.028), whereas no change in atrial size was observed in the sham acupuncture group. No serious adverse events were observed. The AF recurrence rate and cardiac function did not differ significantly between the two groups. At three months, the acupuncture treatment group showed more favorable atrial structural remodeling compared to the sham acupuncture group. Conclusion: In future research on acupuncture in AF management, it is recommended that the inclusion criteria be amended to include only symptomatic AF, that an appropriate control group is designed, and that the acupuncture treatment frequency is increased to several times per week.
Awake Implementation of Extracorporeal Life Support in Refractory Cardiogenic Shock
Authors: Riebandt Julia , Haberl Thomas , Distelmaier Klaus , Bernardi Martin H. , Schaefer Anne-Kristin , Laufer Guenther , Zimpfer Daniel , Wiedemann Dominik
Published: December 2021

Background and objectives: Extracorporeal life support (ECLS) is a widely accepted and effective strategy for use in patients presenting with refractory cardiogenic shock. Implantation in awake and non-intubated patients allows for optimized evaluation of further therapy options while avoiding potential side effects associated with the need for sedation and intubation. The aim of the study was the assessment of safety and feasibility of awake ECLS implementation and of outcomes in patients treated with this concept. Materials and Methods: We retrospectively reviewed the concept of awake ECLS implantation in 16 consecutive patients (mean age 58 ± 8 years; male: 88%; ischemic cardiomyopathy: 50%) from 02/2017 to 01/2021. Study endpoints were survival to weaning or bridging to durable support or organ replacement and development of end-organ function and hemodynamic parameters on ECLS. Results: Fourteen patients (88%) were able to be successfully transitioned to definite therapy options. ECLS support stabilized end-organ function, led to a decrease in mean lactate levels (5.3 ± 3.7 mmol/L at baseline to 1.9 ± 1.3 mmol/L 12 h after ECLS start; p = 0.01) and improved hemodynamics (median central venous pressure 20 ± 5 mmHg vs. 10 ± 2 mmHg, p = 0.001) over a median duration of two days (1–8 days IQR). Two patients (13%) died on ECLS support due to multi-organ dysfunction syndrome. Survival to discharge of initially successfully bridged or weaned patients was 64%. Conclusions: Awake ECLS implantation is feasible and safe with the key advantage of omitting or delaying general anesthesia and intubation, with their associated risks in cardiogenic-shock patients, facilitating further decision making.
Designing 3D Membrane Modules for Gas Separation Based on Hollow Fibers from Poly(4-methyl-1-pentene)
Authors: Markova Svetlana Yu. , Dukhov Anton V. , Pelzer Martin , Shalygin Maxim G. , Vad Thomas , Gries Thomas , Teplyakov Vladimir V.
Published: December 2021

Designing hollow fiber (HF) membrane modules occupies one of the key positions in the development of efficient membrane processes for various purposes. In developing HF membrane modules, it is very important to have a uniform HF distribution and flow mixing in the shell side to significantly improve mass transfer and efficiency. This work suggests the application of different textile 3D HF structures (braided hoses and woven tape fabrics). The 3D structures consist of melt-spun, dense HFs based on poly(4-methyl-1-pentene) (PMP). Since the textile processing of HFs can damage the wall of the fiber or close the fiber bore, the membrane properties of the obtained structures are tested with a CO2/CH4 mixture in the temperature range of 0 to 40 °C. It is shown that HFs within the textile structure keep the same transport and separation characteristics compared to initial HFs. The mechanical properties of the PMP-based HFs allow their use in typical textile processes for the production of various membrane structures, even at a larger scale. PMP-based membranes can find application in separation processes, where other polymeric membranes are not stable. For example, they can be used for the separation of hydrocarbons or gas mixtures with volatile organic compounds.
Improving Fatigue Limit and Rendering Defects Harmless through Laser Peening in Additive-Manufactured Maraging Steel
Authors: Tsuchiya Shiori , Takahashi Koji
Published: December 2021

Additive-manufactured metals have a low fatigue limit due to the defects formed during the manufacturing process. Surface defects, in particular, considerably degrade the fatigue limit. In order to expand the application range of additive-manufactured metals, it is necessary to improve the fatigue limit and render the surface defects harmless. This study aims to investigate the effect of laser peening (LP) on the fatigue strength of additive-manufactured maraging steel with crack-like surface defects. Semicircular surface slits with depths of 0.2 and 0.6 mm are introduced on the specimen surface, and plane bending-fatigue tests are performed. On LP application, compressive residual stress is introduced from the specimen surface to a depth of 0.7 mm and the fatigue limit increases by 114%. In a specimen with a 0.2 mm deep slit, LP results in a high-fatigue-limit equivalent to that of a smooth specimen. Therefore, a semicircular slit with a depth of 0.2 mm can be rendered harmless by LP in terms of the fatigue limit. The defect size of a 0.2 mm deep semicircular slit is greater than that of the largest defect induced by additive manufacturing (AM). Thus, the LP process can contribute to improving the reliability of additive-manufactured metals. Compressive residual stress is the dominant factor in improving fatigue strength and rendering surface defects harmless. Moreover, the trend of the defect size that can be rendered harmless, estimated based on fracture mechanics, is consistent with the experimental results.
Machine Learning Based Predictions of Fatigue Crack Growth Rate of Additively Manufactured Ti6Al4V
Authors: Konda Nithin , Verma Raviraj , Jayaganthan Rengaswamy
Published: December 2021

The present work focusses on machine learning assisted predictions of the fatigue crack growth rate (FCGR) of Ti6Al4V (Ti64) processed through laser powder bed fusion (L-PBF) and post processing. Various machine learning techniques have provided a flexible approach for explaining the complex mathematical interrelationship among processing-structure-property of the materials. In the present work, four machine learning (ML) algorithms, such as K- Nearest Neighbor (KNN), Decision Trees (DT), Random Forests (RF), and Extreme Gradient Boosting (XGB) algorithms are implemented to analyze the Fatigue Crack growth rate (FCGR) of Ti64 alloy. After tuning the hyper parameters for these algorithms, the trained models were found to estimate the unseen data as equally well as the trained data. The four tested ML models are compared with each other over the training as well as testing phase, based on their mean squared error and R2 scores. Extreme Gradient Boosting has performed better for the FCGR predictions providing least mean squared errors and higher R2 scores compared to other models.
Friction Stir Processing of Additively Manufactured Ti-6Al-4V Alloy: Structure Modification and Mechanical Properties
Authors: Kalashnikov Kirill , Chumaevskii Andrey , Kalashnikova Tatiana , Cheremnov Andrey , Moskvichev Evgeny , Amirov Alihan , Krasnoveikin Vladimir , Kolubaev Evgeny
Published: December 2021

This work explores the possibility of using friction stir processing to harden the Ti-6Al-4V titanium alloy material produced by wire-feed electron beam additive manufacturing. For this purpose, thin-walled workpieces of titanium alloy with a height of 30 cm were printed and, after preparation, processed with an FSW-tool made of heat-resistant nickel-based superalloy ZhS6U according to four modes. Studies have shown that the material structure and properties are sensitive to changes in the tool loading force. In contrast, the additive material’s processing direction, relative to the columnar grain growth direction, has no effect. It is shown that increasing the axial load leads to forming a 𝛽-transformed structure and deteriorates the material strength. At the same time, compared to the additive material, the ultimate tensile strength increase during friction stir processing can achieve 34–69%.