bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Gas Channeling and Construction of a Gel-Enhanced Foam Plugging System for Oxygen-Reduced Air Flooding in the Changqing Oilfield
Authors: Wang Tengfei , Wang Liangliang , Qin Haoliang , Zhao Cong , Bai Zongxian , Meng Xingbang
Published: June 2022

The accurate identification of gas channeling channels during foam-assisted oxygen-reduced air flooding (FAORAF) and the analysis of the main controlling factors are essential to propose reasonable and effective countermeasures to enhance oil recovery (EOR). However, there are few comprehensive studies on identifying gas channeling channels, the influencing factors, and the corresponding plugging EOR systems in FAORAF. The channeling channels of the injection and production wells of the Changqing Oilfield, China, under varying development schemes are identified utilizing fuzzy membership function theory in this work to obtain their primary distribution. The characteristics and influence factors of gas channeling channels are analyzed by numerical simulation using CMG. The recovery performance of each foam blocking system is evaluated by twin-tube sand pack models. As well, based on the features of reservoir fractures, a new gel-enhanced foam plugging system is developed. The results show that channeling channels chiefly develop along NE 60–70° and that foam could reduce gas channeling. Natural and artificial fractures are the principal factors causing gas channeling, followed by the injection method and gas injection rate. Under the premise of the injection and migration efficiency, the optimal gel system is a 0.1% HPAM + 0.1% organic chromium crosslinking agent. The addition of gel increases the viscosity of the liquid phase and strengthens the mechanical strength of the foam liquid film. At a permeability ratio of 12, the recovery factors of the binary plugging systems composed of microspheres, PEG, and gel combined with foam are 40.89%, 45.85%, and 53.33%, respectively. The movable gel foam system has a short breaking time (only 18 days) and a recovery factor of about 40% at a permeability ratio of 20. To be suitable for oil reservoirs with microfractures, an improved ternary gel foam system—0.1% HPAM + 0.1% chromium crosslinking agent + 0.05–0.1% nano-SiO2—is developed. Compared with the binary gel foam system, the recovery rate of the new nano-SiO2 gel foam system after 15 days of ageing using the core splitting test is 25.24% during the FAORAF process, increasing by 12.38%.
Morphological, Biochemical, and Physiological Response of Butterhead Lettuce to Photo-Thermal Environments
Authors: Iqbal Zafar , Munir Muhammad , Sattar Muhammad Naeem
Published: June 2022

Plants actively respond to changes in photoperiods, light intensities, and temperatures; an interplay of these three factors can result in increased biomass, growth, and improved plant morphology. This study aimed to investigate the effects of different photoperiods, light intensities, and day/night temperatures on the growth and quality of glasshouse-grown lettuce, as well as to determine the optimal photo-thermal conditions. Two separate experiments were conducted; in the first experiment, the effects of photoperiods (11, 14, and 17 h d−1) and light intensities (150, 200, 250, and 300 μmol m−2 s−1 PPFD) were investigated. In the second experiment, the effects of day/night temperature combinations (15/20, 20/20, 25/20, and 30/20 °C) were assessed. The highest growth, biochemical, and physiological attributes of lettuce were obtained either at 14 or 17 h d−1 + 200 and 250 μmol m−2 s−1 PPFD. Lettuce plants grown at a 20/20 °C day/night temperature also showed promising results compared to other temperature combinations. Based on a principal component analysis and factor analysis of mixed data analyses, most of the lettuce attributes showed a negative correlation with an 11 h d−1 photoperiod and a positive correlation with 14 and 17 h d−1 photoperiods. The most positive responsive attributes to higher light intensity were leaf fresh weight:root fresh weight ratio, root dry, and water use efficiency. An increase in the day/night temperature significantly affected the N, P, K, root fresh weight:leaf fresh weight ratio, E, Pn, and root dry weight. Our findings suggest that the growth, biochemical, and physiological attributes of lettuce significantly increased to a certain level of light intensity (200 and 250 μmol m−2 s−1 PPFD), photoperiod (14 and 17 h d−1), and day/night temperature (20/20 °C), referred to as an effective combination.
Effective Natural Killer Cell Degranulation Is an Essential Key in COVID-19 Evolution
Authors: Garcinuño Sara , Gil-Etayo Francisco Javier , Mancebo Esther , López-Nevado Marta , Lalueza Antonio , Díaz-Simón Raquel , Pleguezuelo Daniel Enrique , Serrano Manuel , Cabrera-Marante Oscar , Allende Luis M. , Paz-Artal Estela , Serrano Antonio
Published: June 2022

NK degranulation plays an important role in the cytotoxic activity of innate immunity in the clearance of intracellular infections and is an important factor in the outcome of the disease. This work has studied NK degranulation and innate immunological profiles and functionalities in COVID-19 patients and its association with the severity of the disease. A prospective observational study with 99 COVID-19 patients was conducted. Patients were grouped according to hospital requirements and severity. Innate immune cell subpopulations and functionalities were analyzed. The profile and functionality of innate immune cells differ between healthy controls and severe patients; CD56dim NK cells increased and MAIT cells and NK degranulation rates decreased in the COVID-19 subjects. Higher degranulation rates were observed in the non-severe patients and in the healthy controls compared to the severe patients. Benign forms of the disease had a higher granzymeA/granzymeB ratio than complex forms. In a multivariate analysis, the degranulation capacity resulted in a protective factor against severe forms of the disease (OR: 0.86), whereas the permanent expression of NKG2D in NKT cells was an independent risk factor (OR: 3.81; AUC: 0.84). In conclusion, a prompt and efficient degranulation functionality in the early stages of infection could be used as a tool to identify patients who will have a better evolution.
Transcatheter Mitral Valve Repair or Replacement: Competitive or Complementary?
Authors: Xiling Zhang , Puehler Thomas , Sondergaard Lars , Frank Derk , Seoudy Hatim , Mohammad Baland , Müller Oliver J. , Sellers Stephanie , Meier David , Sathananthan Janarthanan , Lutter Georg
Published: June 2022

Over the last two decades, transcatheter devices have been developed to repair or replace diseased mitral valves (MV). Transcatheter mitral valve repair (TMVr) devices have been proven to be efficient and safe, but many anatomical structures are not compatible with these technologies. The most significant advantage of transcatheter mitral valve replacement (TMVR) over transcatheter repair is the greater and more reliable reduction in mitral regurgitation. However, there are also potential disadvantages. This review introduces the newest TMVr and TMVR devices and presents clinical trial data to identify current challenges and directions for future research.
How Does SARS-CoV-2 Affect Our Eyes—What Have We Learnt So Far about the Ophthalmic Manifestations of COVID-19?
Authors: Baj Jacek , Forma Alicja , Teresińska Barbara , Tyczyńska Magdalena , Zembala Julita , Januszewski Jacek , Flieger Jolanta , Buszewicz Grzegorz , Teresiński Grzegorz
Published: June 2022

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection has become a worldwide threat resulting in a pandemic in 2020. SARS-CoV-2 infection manifests itself as coronavirus disease 2019 (COVID-19) that is evidenced in a vast number of either specific or nonspecific symptoms. Except for typical (but nonspecific) symptoms such as fever, dry cough, or muscle weakness, the infected patients might also present atypical symptoms including neurological, dermatological, or ophthalmic manifestations. This paper summarizes the current state of knowledge regarding the onset, progression, and types of ophthalmic symptoms induced by SARS-CoV-2 infection recognized amongst the infected patients.
Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud
Authors: Niu Hui , Ito Takahiro , Desclaux Damien , Ayusawa Ko , Yoshiyasu Yusuke , Sagawa Ryusuke , Yoshida Eiichi
Published: June 2022

Estimation of muscle activity is very important as it can be a cue to assess a person’s movements and intentions. If muscle activity states can be obtained through non-contact measurement, through visual measurement systems, for example, muscle activity will provide data support and help for various study fields. In the present paper, we propose a method to predict human muscle activity from skin surface strain. This requires us to obtain a 3D reconstruction model with a high relative accuracy. The problem is that reconstruction errors due to noise on raw data generated in a visual measurement system are inevitable. In particular, the independent noise between each frame on the time series makes it difficult to accurately track the motion. In order to obtain more precise information about the human skin surface, we propose a method that introduces a temporal constraint in the non-rigid registration process. We can achieve more accurate tracking of shape and motion by constraining the point cloud motion over the time series. Using surface strain as input, we build a multilayer perceptron artificial neural network for inferring muscle activity. In the present paper, we investigate simple lower limb movements to train the network. As a result, we successfully achieve the estimation of muscle activity via surface strain.
The Morphotactics of the Cypriot Greek Augment
Authors: Pavlou Natalia
Published: June 2022

This paper adopts a morphological approach to the reduplication of the past-tense augment in Cypriot Greek and explores the morphotactic constraints that apply. Phonological reduplication phenomena have been addressed in morphology by developing a framework that can account for both doubling and metathesis. This phenomenon has been a focus of discussion, but less is known about the application of this mechanism to tense prefixes, known as augments. Doubling of the augment appears in verbal complexes depending on the position of its components, what I will argue are cases that support the post-syntactic morphological doubling and metathesis analysis in Distributed Morphology. The data from this non-standard variety provide a novel analysis of augments and contribute to a better understanding of their distribution by redefining this phenomenon as morphological and supporting a unified framework for the formalism designed to account for similar post-syntactic morphological phenomena.
A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete
Authors: de-Prado-Gil Jesús , Palencia Covadonga , Jagadesh P. , Martínez-García Rebeca
Published: June 2022

Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor (ETR), to predict the splitting tensile strength of 28-day-old self-compacting concrete (SCC) made from recycled aggregates (RA), using data obtained from the literature. A database of 381 samples from literature published in scientific journals was used to develop the models. The samples were randomly divided into three sets: training, validation, and test, with each having 267 (70%), 57 (15%), and 57 (15%) samples, respectively. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) metrics were used to evaluate the models. For the training data set, the results showed that all four models could predict the splitting tensile strength of SCC made with RA because the R2 values for each model had significance higher than 0.75. XG Boost was the model with the best performance, showing the highest R2 value of R2 = 0.8423, as well as the lowest values of RMSE (=0.0581) and MAE (=0.0443), when compared with the GB, CB, and ETR models. Therefore, XG Boost was considered the best model for predicting the splitting tensile strength of 28-day-old SCC made with RA. Sensitivity analysis revealed that the variable contributing the most to the split tensile strength of this material after 28 days was cement.
Elevated Temperature Baseplate Effect on Microstructure, Mechanical Properties, and Thermal Stress Evaluation by Numerical Simulation for Austenite Stainless Steel 316L Fabricated by Directed Energy Deposition
Authors: Kiran Abhilash , Li Ying , Koukolíková Martina , Brázda Michal , Hodek Josef , Urbánek Miroslav , Džugan Ján , Raghavan Srinivasan , Odehnal Josef
Published: June 2022

In the present study, the effect of material deposition at the elevated temperature baseplate on the microstructure and mechanical properties was investigated and correlated to the unique thermal history by using numerical simulation. Numerical results agreed well with the experimental results of microstructure and mechanical properties. Numerical results revealed a significant decrease in temperature gradient and a 40% decrease in thermal stress due to material deposition on the elevated temperature baseplate. The reduced thermal stress and temperature gradient resulted in coarser grain features, which in turn led to a decrease in hardness and tensile strength, especially for the bottom region near the baseplate. Meanwhile, no significant effect could be found for ductility. In addition, an elevated temperature baseplate promoted less heterogeneity in hardness and tensile properties along the building direction. The current work demonstrates a collective and direct understanding of the baseplate preheating effect on thermal stress, microstructure and mechanical properties and their correlations, which is believed beneficial for the better utilization of baseplate preheating positive effects.
Gallic Acid-Functionalized, TiO2-Based Nanomaterial—Preparation, Physicochemical and Biological Properties
Authors: Bakun Pawel , Czarczynska-Goslinska Beata , Mlynarczyk Dariusz T. , Musielak Marika , Mylkie Kinga , Dlugaszewska Jolanta , Koczorowski Tomasz , Suchorska Wiktoria M. , Ziegler-Borowska Marta , Goslinski Tomasz , Krakowiak Rafal
Published: June 2022

Wound healing and skin tissue regeneration remain the most critical challenges faced by medical professionals. Titanium(IV) oxide-based materials were proposed as components of pharmaceutical formulations for the treatment of difficult-to-heal wounds and unsightly scarring. A gallic acid-functionalized TiO2 nanomaterial (TiO2-GA) was obtained using the self-assembly technique and characterized using the following methods: scanning electron microscopy (SEM), transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), X-ray powder diffraction (XRPD), infrared spectroscopy (IR), Raman spectroscopy and thermogravimetry (TG). Additionally, physicochemical and biological tests (DPPH assay, Microtox® acute toxicity test, MTT assay) were performed to assess antioxidant properties as well as to determine the cytotoxicity of the novel material against eukaryotic (MRC-5 pd19 fibroblasts) and prokaryotic (Staphylococcus aureus, Escherichia coli, Candida albicans, Aliivibrio fischeri) cells. To determine the photocytotoxicity of the material, specific tests were carried out with and without exposure to visible light lamps (425 nm). Following the results, the TiO2-GA material could be considered an additive to dressings and rinsing suspensions for the treatment of difficult-to-heal wounds that are at risk of bacterial infections.
A Bivariate Post-Warranty Maintenance Model for the Product under a 2D Warranty
Authors: Shang Lijun , Shang Guojun , Qiu Qingan
Published: June 2022

In this study, by integrating preventive maintenance (PM) into a two-dimensional warranty region, a two-dimensional warranty with customized PM (2D warranty with customized PM) is proposed from the manufacturer’s perspective to reduce the warranty cost. The warranty cost of a 2D warranty with customized PM is derived. The manufacturer’s tradeoff between PM cost and minimal repair cost saving is obtained by choosing the proper reliability threshold and the number of customized PMs, and the advantage of a 2D warranty with customized PM is illustrated. Second, by integrating PM into the post-warranty period, a bivariate post-warranty maintenance (BPWM) policy is proposed from the consumer’s perspective to ensure the reliability of the product through the 2D warranty with customized PM. The expected cost rate model of BPWM is derived. Optimal BPWM is obtained in the numerical experiments. It is shown that a 2D warranty with customized PM is beneficial for both the manufacturer and the consumer, since both the manufacturer’s warranty cost and the consumer’s total cost are reduced.
Some New Quantum Hermite–Hadamard Inequalities for Co-Ordinated Convex Functions
Authors: Wannalookkhee Fongchan , Nonlaopon Kamsing , Ntouyas Sotiris K. , Sarikaya Mehmet Zeki , Budak Hüseyin , Ali Muhammad Aamir
Published: June 2022

In this paper, we establish some new versions of Hermite–Hadamard type inequalities for co-ordinated convex functions via q1,q2-integrals. Since the inequalities are newly proved, we therefore consider some examples of co-ordinated convex functions and show their validity for particular choices of q1,q2∈(0,1). We hope that the readers show their interest in these results.
Supervisory Event-Triggered Control of Uncertain Process Networks: Balancing Stability and Performance
Authors: Xue Da , El-Farra Nael H.
Published: June 2022

This work presents a methodological framework for the design of a resource-aware supervisory control system for process networks with model uncertainty and communication resource constraints. The developed framework aims to balance the objective of closed-loop stabilization of the overall network with that of meeting the local performance requirements of the component subsystems while keeping the rate of data transfer between the local control systems to a minimum. First, a quasi-decentralized networked control structure, with a set of local model-based controllers communicating with one another over a shared communication medium at discrete times, is designed. A Lyapunov stability analysis of the closed-loop system is then carried out, and the results are used to derive appropriate bounds on the local model state estimation errors as well as the dissipation rates of the local control Lyapunov functions. These bounds are used as stability and performance thresholds to trigger communication between the local control systems and a higher-level supervisor that coordinates the transfer of state measurements between the distributed control systems. A breach of the local stability and performance thresholds generates alarm signals which are transmitted to the supervisor to determine which subsystems should communicate with one another. The supervisor employs a composite Lyapunov function to assess the impact of the local threshold breaches on the stability of the overall closed-loop system. The supervisory communication logic takes account of the evolution of the local and composite Lyapunov functions in order to balance the stability and local performance requirements. Finally, the developed framework is demonstrated using a representative chemical process network and compared with other unsupervised event-based control approaches. It is shown that the supervisory event-based control approach leads to a more judicious utilization of network resources that helps improve closed-loop process performance in the presence of unexpected disturbances and input rate constraints.
Knowledge Trajectories on Public Crisis Management Research from Massive Literature Text Using Topic-Clustered Evolution Extraction
Authors: Wu Feng , Xu Wanqiang , Lin Chaoran , Zhang Yanwei
Published: June 2022

Current research has ignored the hiddenness and the stochasticity of the evolution of public crisis management research, making the knowledge trajectories still unclear. This paper introduces a combined approach, LDA-HMM, to mine the hidden topics, present the evolutionary trajectories of the topics, and predict the future trends in the coming years to fill the research gaps. We reviewed 8543 articles in WOS from 1997 to 2021, extracted 39 hidden topics from the text using the LDA; 33 remained by manual labeling. The development of the topics over the years verifies that the topics are co-evolving with the public crisis events. The confusion and transition features indicate that most topics are confused or transferred to the others. The transition network and the direction of the topics show that six main transfer paths exist, and in the evolution process, the topics have become more focused. By training the HMM, we predict the trends in the next five years; the results show that the heat of the topic that focuses on traditional crisis issues will decrease while the focus on non-traditional issues will increase. We take the average error to test this model’s prediction effect by comparing it with the other approaches, concluding that it is better than the others. This study has practical implications for preventing crisis events, optimizing related policies, and grasping key research areas in the future.
Analyzing the Collatz Conjecture Using the Mathematical Complete Induction Method
Authors: Orús-Lacort Mercedes , Jouis Christophe
Published: June 2022

In this paper, we demonstrate the Collatz conjecture using the mathematical complete induction method. We show that this conjecture is satisfied for the first values of natural numbers, and in analyzing the sequence generated by odd numbers, we can deduce a formula for the general term of the Collatz sequence for any odd natural number n after several iterations. This formula is used in one case that we analyze using the mathematical complete induction method in the process of demonstrating the conjecture.
Prospective Teachers’ Use of Conceptual Advances of Learning Trajectories to Develop Their Teaching Competence in the Context of Pattern Generalization
Authors: Zapatera Llinares Alberto
Published: June 2022

(1) Background: This research shows how the identification of conceptual advances (CA) that determine the transition between the stages of a progression model in a learning trajectory will help prospective primary teachers (PPT) to develop the teaching competence “professional noticing of students’ mathematical thinking”. Conceptual advances are key moments in the construction of mathematical structures and involve a change in the way students understand mathematical relationships. (2) Methods: A teaching module has been designed in which students of the Teaching Degree will analyze the responses of primary education students to tasks of pattern generalization from the identification and use of conceptual advances. (3) Results: The results of the teaching module show that professional noticing can be developed in suitable teaching environments. (4) Conclusions: The recognition of conceptual advances helps to interpret students’ thinking and learning trajectories which are effective tools to structure and develop professional noticing.
Analysis of a Class of Stochastic Animal Behavior Models under Specific Choice Preferences
Authors: Turab Ali , Mlaiki Nabil , Fatima Nahid , Mitrović Zoran D. , Ali Wajahat
Published: June 2022

The behavior of animals can be studied in two ways: experimentally, in labs or in the field, or theoretically, via modeling. Extensive research on animal behavior in probabilistic learning circumstances has produced findings that are consistent with so-called occurrence studies. However, such cases can be classified into four different events, depending on the location of the food and the side chosen by the animals. This article sought to overcome these limitations by offering a generic stochastic model under these conditions that can be utilized to analyze the broad range of models that have been reported in the literature. We explored the existence, uniqueness, and stability results of the proposed model using well-known fixed-point methods. Additionally, we present some examples to highlight the significance of our results.
DWNN: Deep Wavelet Neural Network for Solving Partial Differential Equations
Authors: Li Ying , Xu Longxiang , Ying Shihui
Published: June 2022

In this paper, we propose a deep wavelet neural network (DWNN) model to approximate the natural phenomena that are described by some classical PDEs. Concretely, we introduce wavelets to deep architecture to obtain a fine feature description and extraction. That is, we constructs a wavelet expansion layer based on a family of vanishing momentum wavelets. Second, the Gaussian error function is considered as the activation function owing to its fast convergence rate and zero-centered output. Third, we design the cost function by considering the residual of governing equation, the initial/boundary conditions and an adjustable residual term of observations. The last term is added to deal with the shock wave problems and interface problems, which is conducive to rectify the model. Finally, a variety of numerical experiments are carried out to demonstrate the effectiveness of the proposed approach. The numerical results validate that our proposed method is more accurate than the state-of-the-art approach.
Are Citizens Credentialist or Post-Nationalists? A Fuzzy-Eco Apostle Model Applied to National Identity
Authors: Indelicato Alessandro , Martín Juan Carlos
Published: June 2022

The debate on national identity has been receiving more attention from academics, social planners, and the public in recent years. This study aims to analyse citizens’ different national identity typologies using data from the International Social Survey Program (ISSP) in two waves: 2003 and 2013. The study is based on 10 countries for which the dimensions of the national identity construct are first examined through multi-group confirmatory factor analysis (MGCFA). Then, a fuzzy hybrid approach is used to obtain two synthetic indicators for each dimension of national identity, namely ethnic and civic orientations. Then, a fuzzy clustering analysis is used to extend the Classical Apostle Model, obtaining 16 different national identity categories that expand the four Apostle categories denominated as credentialists, post-nationalists, ethnic-oriented and civic-oriented. The study ends by applying the Binary Probit Model to analyse whether some socioeconomic characteristics can partly explain the four pure national identity categories. Our results show that people seem to be increasingly credentialists. Furthermore, the civic–ethnic dichotomy of France–Germany is no longer confirmed, as Germans appear to be included in the civic-oriented national identity group.
Approximate Solution of Nonlinear Time-Fractional PDEs by Laplace Residual Power Series Method
Authors: Aljarrah Hussam , Alaroud Mohammad , Ishak Anuar , Darus Maslina
Published: June 2022

Most physical phenomena are formulated in the form of non-linear fractional partial differential equations to better understand the complexity of these phenomena. This article introduces a recent attractive analytic-numeric approach to investigate the approximate solutions for nonlinear time fractional partial differential equations by means of coupling the Laplace transform operator and the fractional Taylor’s formula. The validity and the applicability of the used method are illustrated via solving nonlinear time-fractional Kolmogorov and Rosenau–Hyman models with appropriate initial data. The approximate series solutions for both models are produced in a rapid convergence McLaurin series based upon the limit of the concept with fewer computations and more accuracy. Graphs in two and three dimensions are drawn to detect the effect of time-Caputo fractional derivatives on the behavior of the obtained results to the aforementioned models. Comparative results point out a more accurate approximation of the proposed method compared with existing methods such as the variational iteration method and the homotopy perturbation method. The obtained outcomes revealed that the proposed approach is a simple, applicable, and convenient scheme for solving and understanding a variety of non-linear physical models.
An Application of Ordered Weighted Averaging Operators to Customer Classification in Hotels
Authors: Pons-Vives Pere Josep , Morro-Ribot Mateu , Mulet-Forteza Carles , Valero Oscar
Published: June 2022

An algorithm widely used in hotel companies for demand analysis is the so-called K-means. The aforementioned algorithm is based on the use of the Euclidean distance as a dissimilarity measure and this fact can cause a main handicap. Concretely, the Euclidean distance provides a global difference measure between the values of the descriptive variables that can blur the relative differences in each component separately and, hence, the cluster algorithm can assign a custom to an incorrect cluster. In order to avoid this drawback, this paper proposes an application of the use of Ordered Weighted Averaging (OWA) operators and an OWA-based K-means for clustering customers staying at a real five-star hotel, located in a mature sun-and-beach area, according to their propensity to spend. It must be pointed out that OWA-based distance calculates relative distances and it is sensitive to the differences in each component separately. All experiments show that the use of the OWA operator improves the performance of the classical K-means up to 21.6% and reduces the number of convergence iterations up to 48.46%. Such an improvement has been tested through a ground truth, designed by the marketing department of the firm, which states the cluster to which each tourist belongs. Moreover, the customer classification is achieved regardless of the season in which the customer stays at the hotel. All these facts confirm that the OWA-based K-means could be used as an appropriate tool for classifying tourists in purely exploratory and predictive stages. Furthermore, the new methodology can be implemented without requiring radical changes in the implementation of the classical methodology and in data processing which is crucial so that it can be incorporated into the control panel of a real hotel without additional implementation costs.
Implementation of ANN Controller Based UPQC Integrated with Microgrid
Authors: Mahar Hina , Munir Hafiz Mudasir , Soomro Jahangir Badar , Akhtar Faheem , Hussain Rashid , Elnaggar Mohamed F. , Kamel Salah , Guerrero Josep M.
Published: June 2022

This study discusses how to increase power quality by integrating a unified power quality conditioner (UPQC) with a grid-connected microgrid for clean and efficient power generation. An Artificial Neural Network (ANN) controller for a voltage source converter-based UPQC is proposed to minimize the system’s cost and complexity by eliminating mathematical operations such as a-b-c to d-q-0 translation and the need for costly controllers such as DSPs and FPGAs. In this study, nonlinear unbalanced loads and harmonic supply voltage are used to assess the performance of PV-battery-UPQC using an ANN-based controller. Problems with voltage, such as sag and swell, are also considered. This work uses an ANN control system trained with the Levenberg-Marquardt backpropagation technique to provide effective reference signals and maintain the required dc-link capacitor voltage. In MATLAB/Simulink software, simulations of PV-battery-UPQC employing SRF-based control and ANN-control approaches are performed. The findings revealed that the proposed approach performed better, as presented in this paper. Furthermore, the influence of synchronous reference frame (SRF) and ANN controller-based UPQC on supply currents and the dc-link capacitor voltage response is studied. To demonstrate the superiority of the suggested controller, a comparison of percent THD in load voltage and supply current utilizing SRF-based control and ANN control methods is shown.
An Enhanced Slime Mould Optimizer That Uses Chaotic Behavior and an Elitist Group for Solving Engineering Problems
Authors: Sarhan Shahenda , Shaheen Abdullah Mohamed , El-Sehiemy Ragab A. , Gafar Mona
Published: June 2022

This article suggests a novel enhanced slime mould optimizer (ESMO) that incorporates a chaotic strategy and an elitist group for handling various mathematical optimization benchmark functions and engineering problems. In the newly suggested solver, a chaotic strategy was integrated into the movement updating rule of the basic SMO, whereas the exploitation mechanism was enhanced via searching around an elitist group instead of only the global best dependence. To handle the mathematical optimization problems, 13 benchmark functions were utilized. To handle the engineering optimization problems, the optimal power flow (OPF) was handled first, where three studied cases were considered. The suggested scheme was scrutinized on a typical IEEE test grid, and the simulation results were compared with the results given in the former publications and found to be competitive in terms of the quality of the solution. The suggested ESMO outperformed the basic SMO in terms of the convergence rate, standard deviation, and solution merit. Furthermore, a test was executed to authenticate the statistical efficacy of the suggested ESMO-inspired scheme. The suggested ESMO provided a robust and straightforward solution for the OPF problem under diverse goal functions. Furthermore, the combined heat and electrical power dispatch problem was handled by considering a large-scale test case of 84 diverse units. Similar findings were drawn, where the suggested ESMO showed high superiority compared with the basic SMO and other recent techniques in minimizing the total production costs of heat and electrical energies.
Membrane Computing after 25 Years
Authors: Aman Bogdan , Ciobanu Gabriel
Published: June 2022

Natural sciences are influencing the area of information sciences, and the meaning of computation has been modified [...]
Research on the Period-Doubling Bifurcation of Fractional-Order DCM Buck–Boost Converter Based on Predictor-Corrector Algorithm
Authors: Xie Lingling , Shi Jiahao , Yao Junyi , Wan Di
Published: June 2022

DC–DC converters are widely used. They are a typical class of strongly nonlinear time-varying systems that show rich nonlinear phenomena under certain working conditions. Therefore, an in-depth study of their nonlinear phenomena, characteristics, and generation mechanism is of great practical significance for gaining a deep understanding of this kind of switching converter, revealing the essence of these nonlinear phenomena and then optimizing the design of this kind of converter. Based on the fact that most of the inductance and capacitance are fractional-order, the nonlinear dynamic characteristics of the fractional-order (FO) DCM buck–boost converter are researched in this paper. The main research work and achievements of this paper include: (1) using the predictor–corrector method of fractional calculus, which is not limited by fractional order and can directly calculate the accurate values of the inductance current and capacitor voltage of the fractional converter; the predictor–corrector model of the FO converter is established. (2) The bifurcation diagrams are obtained based on this model, and the period-doubling bifurcation and chaotic behavior of the FO buck–boost converter are analyzed. (3) The phase diagrams are obtained and verified to the point that period-doubling bifurcation occurs; then, some conclusions are drawn. The results show that under certain operating and parameters conditions, the FO buck–boost converter will appear as a bifurcation and chaotic nonlinear phenomenon. Under the condition of the same circuit parameters, the stability parameter domains of the integer-order buck–boost converter and the FO buck–boost converter are different. Compared with the integer-order converter, the parameter stability region of the FO buck–boost converter is bigger. The FO buck–boost converter is more accurate at describing the nonlinear dynamic characteristics. Furthermore, the predictor–corrector method can also be applied to other FO power converters and provides theoretical guidance for converter parameter optimization and controller design.
Optimal Weighted Multiple-Testing Procedure for Clinical Trials
Authors: Hammouri Hanan , Alquran Marwan , Abdel Muhsen Ruwa , Altahat Jaser
Published: June 2022

This paper describes a new method for testing randomized clinical trials with binary outcomes, which combines the O’Brien and Fleming (1979) multiple-testing procedure with optimal allocations and unequal weighted samples simultaneously. The O’Brien and Fleming method of group sequential testing is a simple and effective method with the same Type I error and power as a fixed one-stage chi-square test, with the option to terminate early if one treatment is clearly superior to another. This study modified the O’Brien and Fleming procedure, resulting in a more flexible new procedure, where the optimal allocation assists in allocating more subjects to the winning treatment without compromising the integrity of the study, while unequal weighting allows for different samples to be chosen for different stages of a trial. The new optimal weighted multiple-testing procedure (OWMP), based on simulation studies, is relatively robust to the added features because it showed a high preference for decreasing the Type I error and maintaining the power. In addition, the procedure was illustrated using simulated and real-life examples. The outcomes of the current study suggest that the new procedure is as effective as the original. However, it is more flexible.
Interval Uncertainty Quantification for the Dynamics of Multibody Systems Combing Bivariate Chebyshev Polynomials with Local Mean Decomposition
Authors: Jiang Xin , Bai Zhengfeng
Published: June 2022

Interval quantification for multibody systems can provide an accurate dynamic prediction and a robust reliability design. In order to achieve a robust numerical model, multiple interval uncertain parameters should be considered in the uncertainty propagation of multibody systems. The response bounds obtained by the bivariate Chebyshev method (BCM) present an intensive deterioration with the increase of time history in the interval dynamic analysis. To circumvent this problem, a novel method that combines the bivariate Chebyshev polynomial and local mean decomposition (BC-LMD) is proposed in this paper. First, the multicomponent response of the system was decomposed into the sum of several mono-component responses and a residual response, and the corresponding amplitude and phase of the mono-component were obtained. Then, the bivariate function decomposition was performed on the multi-dimensional amplitude, phase, and residual to transform a high-dimensional problem into several one-dimensional and two-dimensional problems. Subsequently, a low order Chebyshev polynomial can be used to construct surrogate models for the multi-dimensional amplitude, phase, and residual responses. Then, the entire coupling surrogate model of the system can be established, and the response bounds of the system can be enveloped. Illustrative examples of a slider-crank mechanism and a double pendulum are presented to demonstrate the effectiveness of the proposed method. The numerical results indicate that, compared to the BCM, BC-LMD can present a tight envelope in the long time-dependent dynamic analysis under multiple interval parameters.
Towards the Sign Function Best Approximation for Secure Outsourced Computations and Control
Authors: Babenko Mikhail , Tchernykh Andrei , Pulido-Gaytan Bernardo , Avetisyan Arutyun , Nesmachnow Sergio , Wang Xinheng , Granelli Fabrizio
Published: June 2022

Homomorphic encryption with the ability to compute over encrypted data without access to the secret key provides benefits for the secure and powerful computation, storage, and communication of resources in the cloud. One of its important applications is fast-growing robot control systems for building lightweight, low-cost, smarter robots with intelligent brains consisting of data centers, knowledge bases, task planners, deep learning, information processing, environment models, communication support, synchronous map construction and positioning, etc. It enables robots to be endowed with secure, powerful capabilities while reducing sizes and costs. Processing encrypted information using homomorphic ciphers uses the sign function polynomial approximation, which is a widely studied research field with many practical results. State-of-the-art works are mainly focused on finding the polynomial of best approximation of the sign function (PBAS) with the improved errors on the union of the intervals [−1,−ϵ]∪[ϵ,1]. However, even though the existence of the single PBAS with the minimum deviation is well known, its construction method on the complete interval [−1,1] is still an open problem. In this paper, we provide the PBAS construction method on the interval [−1,1], using as a norm the area between the sign function and the polynomial and showing that for a polynomial degree n≥1, there is (1) unique PBAS of the odd sign function, (2) no PBAS of the general form sign function if n is odd, and (3) an uncountable set of PBAS, if n is even.
Insight in Thermally Radiative Cilia-Driven Flow of Electrically Conducting Non-Newtonian Jeffrey Fluid under the Influence of Induced Magnetic Field
Authors: Ishtiaq Fehid , Ellahi Rahmat , Bhatti Muhammad Mubashir , Alamri Sultan Z.
Published: June 2022

This paper investigates the mobility of cilia in a non-uniform tapered channel in the presence of an induced magnetic field and heat transfer. Thermal radiation effects are included in the heat transfer analysis. The Jeffrey model is a simpler linear model that uses time derivatives rather than convected derivatives as the Oldroyd-B model does; it depicts rheology other than Newtonian. The Jeffrey fluid model is used to investigate the rheology of a fluid with cilia motion. The proposed model examines the behavior of physiological fluids passing through non-uniform channels, which is responsible for symmetrical wave propagation and is commonly perceived between the contraction and expansion of concentric muscles. To formulate the mathematical modeling, the lubrication approach is used for momentum, energy, and magnetic field equations. The formulated linear but coupled differential equations have been solved analytically. Graphs for velocity profile, magnetic force function, induced magnetic field, current density, pressure rise, and heat profile are presented to describe the physical mechanisms of significant parameters. It is found that the eccentricity parameter of the cilia equations opposes the velocity and the magnetic force functions. The thermal radiation decreases the temperature profile while it increases for Prandtl and Eckert numbers. A promising impact of the magnetic Reynolds number and electric field on the current density profile is also observed.
Safety Margin Prediction Algorithms Based on Linear Regression Analysis Estimates
Authors: Tsitsiashvili Gurami , Losev Alexandr
Published: June 2022

In this paper, we consider the problem of approximating the safety margin of a single instance of a technical system based on inaccurate observations at specified time points. The solution to this problem is based on the selection of a certain set of reference points in time, in a small neighbourhood of which a sufficiently large number of inaccurate measurements are carried out. Analogously with the failure rate, it is assumed that the rate of decrease in the safety margin over time is represented by a polynomial of the fourth degree, and the safety margin itself is a polynomial of the fifth degree. A system of linear algebraic equations is constructed that determine the coefficients of this polynomial through its values and the values of its derivative at reference points in time. These values themselves are estimated by the method of linear regression analysis based on numerous observations in a small neighbourhood of reference points in time. A detailed computational experiment is carried out to verify the accuracy of the approximation of a fifth-degree polynomial and alternative ways of estimating it are constructed in the vicinity of points where the quality of approximation is insufficient.
Containment Control for Discrete-Time Multi-Agent Systems with Nonconvex Control Input and Position Constraints
Authors: Gao Ning , Yang Yikang
Published: June 2022

With increasing attention on containment control problems in several areas, we investigate this specific problem which can be more practical. Systems with nonconvex input and position constraints are common but can be strongly nonlinear. A distribute algorithm using a projection operator is proposed to ensure that the control input of every follower remains in a nonconvex set and that all followers stay in the closed set given by leaders. In analysis, a model transformation is proposed, and then we introduce a method utilizing two similar triangles to prove the acceptability of the algorithm. The findings of the research could be pragmatic in robotics, astronautics, and so on. At last, numerical simulations are provided to show the contrast and results.
Electricity Spot Price Forecast by Modelling Supply and Demand Curve
Authors: Pinhão Miguel , Fonseca Miguel , Covas Ricardo
Published: June 2022

Electricity price forecasting has been a booming field over the years, with many methods and techniques being applied with different degrees of success. It is of great interest to the industry sector, becoming a must-have tool for risk management. Most methods forecast the electricity price itself; this paper gives a new perspective to the field by trying to forecast the dynamics behind the electricity price: the supply and demand curves originating from the auction. Given the complexity of the data involved which include many block bids/offers per hour, we propose a technique for market curve modeling and forecasting that incorporates multiple seasonal effects and known market variables, such as wind generation or load. It is shown that this model outperforms the benchmarked ones and increases the performance of ensemble models, highlighting the importance of the use of market bids in electricity price forecasting.
Optimal Shadow Allocations of Secret Sharing Schemes Arisen from the Dynamic Coloring of Extended Neighborhood Coronas
Authors: Falcón Raúl M. , Mohanapriya Nagaraj , Aparna Venkitachalam
Published: June 2022

Every t-dynamic proper n-coloring of a graph G describes a shadow allocation of any (n,t+1)-threshold secret sharing scheme based on G, so that, after just one round of communication, each participant can either reconstruct the secret, or obtain a different shadow from each one of his/her neighbors. Thus, for just one round of communication, this scheme is fair if and only if the threshold is either less than or equal to the minimum degree of G, or greater than or equal to its maximum degree. Despite that the dynamic coloring problem has widely been dealt with in the literature, a comprehensive study concerning this implementation in cryptography is still required. This paper delves into this topic by focusing on the use of extended neighborhood coronas for modeling communication networks whose average path lengths are small even after an asymptotic growth of their center and/or outer graphs. Particularly, the dynamic coloring problem is solved for any extended neighborhood corona with center path or star, for which we establish optimal shadow allocations of any (fair) threshold secret sharing scheme based on them. Some bounds are also established for the dynamic chromatic number of any extended neighborhood corona.