Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Approach to Air Passenger Index Prediction: Based on Mutual Information Principle and Support Vector Regression Blended Model

Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. And then, based on mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by air passenger throughput data of the Shanghai Pudong International Airport (PVG), China. The experimental results prove MI-SVR model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on MI. The main findings are considered instrumental to the airport operation and air traffic optimization.
Application of an Improved RBF Neural Network on Aircraft Winglet Optimization Design

A self-adaptive radial basis function(RBF)neural network is proposed in order to improve the prediction accuracy of the original RBF.A self-adaptive vector with the same dimension as the sample vector is introduced into the traditional RBF network.In contrast to other RBF neural network models,the current approach achieves the self-adaptive construction of the network by altering the form of the basis function directly,which reduces the number of variables to be optimized.This adaptive approach substantially changes the impact of the center and width of the RBF neural network on its prediction as well as the influence of each variable of the independent vector on the dependent vector.Thus the introduced vector enables the adaptability of the RBF neural network with respect to variant problems.Moreover,the accuracy and the universality of the prediction model are also improved due to the optimization of the self-adaptive vector.The proposed self-adaptive RBF neural network is applied to a winglet optimization design of a wing-body-winglet configuration.The optimization objective is to minimize the cruise drag with wing-root bending moment restriction.The optimization result confirms the effectiveness and the capability for engineering application of the self-adaptive RBF neural network.
EL ACOMPAÑAMIENTO PEDAGÓGICO: UNA REVISION SISTEMATICA

El presente artículo es un análisis desarrollado sobre el acompañamiento pedagógico como estrategia de formar a un docente. Se analizan diversas perspectivas de la forma como de acompañamiento en nuestro continente y otros. Basada en la metodología de Okoli et al. (2010) como soporte técnico de lo realizado, para lo cual se realizó una revisión bibliográfica sistemática con el objeto de documentarse sobre el problema, se efectuó la búsqueda en bases de datos y repositores de revistas científicas. del 2014 al 2021. La información procesada ha contribuido, de acuerdo con Cruz, Siles y Bustos (2021), de manera conceptual, antecedentes y diversas estrategias del acompañamiento pedagógico. Los resultados indicaron que el acompañamiento pedagógico es una estrategia importante que fortalece de manera efectiva el desempeño docente, asimismo, en la práctica pedagógica, se busca crear un aula en un ambiente de armonía y de comunicación afectiva entre el acompañante y acompañado para obtener mejores aprendizajes. Palabras clave: acompañamiento pedagógico, practica pedagógico y formación docente.
Talking about the Birth Family since the Beginning: The Communicative Openness in the New Adoptive Family

Communicative openness within the adoptive family changes over time and helps the child explore his/her history. We aimed to evaluate whether adoptive families communicate about specific adoption-related themes from the beginning of their lives as a family. We created an instrument to track the communication process during the first year of adoption, involving a sample of 537 internationally adopted children (313 males, 224 females, mean age of adoption: 4.9 years) at two time points: six (T1) and twelve (T2) months after adoption. Our results suggest that in the first year of placement, children express memories about the past but tend to not speak about their birth families. We discovered a significant difference (Wald test = 4.889; p = 0.027) in communication about the biological family between the two points. The presence of adoptive parents who speak about it impacts the child’s questions about the past (exp (B) = 2.452, p = 0.006) and whether the child speaks about his/her biological family (exp (B) = 2.373; p = 0.017). Then, in the first year of adoption, the presence of an adoptive parent who communicates openly helps the child to ask questions and share his/her thoughts.
Magnetohydrodynamic mixed convection in a non‐Newtonian third‐grade fluid flowing through vertical parallel plates: A semianalytical study of flow and heat transfer

Buoyancy assisted and buoyancy opposed mixed convection of a third‐grade fluid, which flows through vertically oriented parallel plates, subjected to uniform and constant wall heat fluxes, under the effect of an externally applied magnetic field, are investigated. The coupled, nonlinear conservation equations of momentum and energy are solved employing the collocation method (CM) and velocity and temperature distributions are solved semianalytically. The results produced by the CM and the results of exact solution are compared for the buoyancy assisted and buoyancy opposed flow of a Newtonian fluid through the vertically oriented parallel plates arrangement without the effect of the externally applied magnetic field. An excellent agreement is exhibited by demonstrating the efficacy of the CM. The effects of the third‐grade fluid parameter, Hartmann number, and mixed convection parameter on the dimensionless velocity, temperature, and Nusselt number are studied. The results imply that in the case of buoyancy assisted flow, an increment in the non‐Newtonian third‐grade fluid parameter causes a decrease in the fluid velocity near the plate walls, which finally causes an increase in the velocity in the central core of the plates. In buoyancy opposed flow, the effect of the same parameter is to oppose the flow reversal near the walls and with higher values of this parameter, it can totally prevent the flow reversal near the walls. The results of the present study can be useful in the fields of flow and heat transfer of various grades of polymers, paints, and food processing.
Implementation of an accurate method for the analysis and simulation of electrical R‐L circuits

In this study, we propose to introduce and apply an accurate numerical procedure to solve the mathematical model of fractional order, which describes the electrical circuits composed of resistors and inductors (RL) driven by a voltage of the current source. Our research is based on the spectral collocation method, which utilizes the advantageous characteristics of the third‐order Chebyshev polynomials. Some convergence analysis and error theorems are presented. The proposed research concludes by comparing the approximate solutions obtained for the considered model to the exact solution obtained in the classical case. The derived conclusions are further subjected to illustrative graphical and numerical examinations.
Lumped Element Model of THOR‐NT and PMHS Abdomen under Seatbelt and Impactor Loading

Abdominal injuries represent a small proportion of crash injuries but it increases considerably with regard to serious to severe injuries. It is important to assess abdomen injury risk using a biofidelic crash test dummy, especially under seatbelt loading. A lumped element model has been used to simulate seatbelt and impactor loading cases on Post Mortem Human Subjects (PMHS) subject and on the THOR dummy. The model consists of two blocks of one spring and one damper with a mass between the blocks and a mass at the front of the model. The front block has the spring and damper in series, whereas they are in parallel for the rear block, to reproduce the abdomen and back force respectively. The mechanical parameters of the model have been optimised to fit the force‐penetration response of PMHS and of the dummy. The results show that the PMHS response is more damper‐dependent, whereas the dummy response is more spring‐dependent. Moreover, unlike the PMHS, the THOR dummy model needs a non‐linear spring in order to fit the impactor test data. The conclusions are that the dummy abdomen should be modified to make it more viscously deformable and to include pelvis design modifications in order to prevent a too stiff response.
Green and Resilient Mixed Supply Chain Network Design to Reduce Environmental Impacts and Deal With Disruptions

Disruption risks may halt or adversely affect supply chain operations and can lead to deviations in its objectives. One of the most important objectives of the supply chain which can be adversely affected by disruptions, is environmental objective. Therefore, considering supply chain resilience and environmental aspects simultaneously is of great importance. In this paper, the problem of designing a green and resilient mixed open and closed-loop supply chain network under operational and disruption risks has been studied. A bi-objective mixed integer linear programming model is proposed to formulate the problem. Some resilience strategies are applied to deal with disruption risks and enhance supply chain resilience. In order to overcome the complexity of the problem and solve the problems with medium and large sizes, a new meta-heuristic algorithm called multi-objective hybrid Ant-colony optimization and teaching and learning based optimization (ACO-TLBO) has been proposed and compared with two hybrid metaheuristics and the augmented ε-constraint method through various test problems. The outcomes showed that the ACO-TLBO algorithm is very efficient in obtaining high-quality Pareto solutions and is the best method among the proposed methods. Also, in order to show the applicability of the problem and validate the model and solution methods, a real case study in the tire industry has been presented and analyzed. The results of analyses demonstrate the high effectiveness of resilience strategies and the necessity of joint consideration of resilience and greenness in the supply chain design.
Water Ecosystems Tool (WET) 0.1.0 – a new generation of flexible aquatic ecosystem model

Abstract. We present the Water Ecosystems Tool (WET) – a new generation of an open source, highly customizable aquatic ecosystem model. WET is a completely modularized aquatic ecosystem model, developed in the syntax of the Framework for Aquatic Biogeochemical Models (FABM), which enables coupling to multiple physical models ranging from zero to three dimensions, and is based on the FABM-PCLake model. The WET model has been extensively modularized, empowering users with flexibility of food web configurations, and also incorporates model features from other state-of-the-art models, with new options for nitrogen fixation and vertical migration. With the new structure, features and flexible customization options, WET is suitable in a wide range of aquatic ecosystem applications. We demonstrate these new features and their impacts on model behavior for a temperate lake for which a model calibration of the FABM-PCLake model was previously published, and discuss the benefits of the new model.
Compensation of Unbalanced Low-Voltage Grids Using a Photovoltaic Generation System with a Dual Four-Leg, Two-Level Inverter

In this paper, a grid-connected photovoltaic (PV) generation system is proposed with the purpose of providing support to low-voltage grids, namely through the elimination or attenuation of the grid imbalances. This compensation must consider the load types, which can be either linear or non-linear, and whether the reactive power and current harmonics generated by the non-linear loads need to be compensated in addition to the unbalanced active power. This must be well considered, since the compensation of all aspects requires oversized PV inverters. Thus, the different unbalanced compensation schemes are addressed. Several schemes for the generation of the inverter current references taking into consideration the compensation and load type are presented. For this PV generation system, a dual four-leg, two-level inverter is proposed. It provides full unbalanced compensation owing to the fourth leg of the inverter and also extends the AC voltage, which is important when this compensation is required. To control this inverter, a control scheme for the inverter that considers several compensation factors is proposed. A vector voltage modulator associated with the controller is another aspect that is addressed in the paper. This modulator considers the balance between the DC voltages of the inverters. Several compensation schemes are verified through computational tests. The results validate the effectiveness of the proposed PV generation system.
Microbiological Characteristics of Some Stations of Moscow Subway

Simple Summary Public transport facilities, including subway systems, provide the most suitable conditions for the transfer of microorganisms between people and the environment, contributing to the pathogenic potential of the urban habitat. Investigation of microbiome diversity and description of its characteristic properties, e.g., antibiotic-resistance profiles, leads to understanding of these interactions. In this study, we aimed to conduct an extended analysis of the bioaerosol and surface microbiome of the Moscow subway, using 16S rRNA gene sample sequencing and classical microbiology methods. The microbiomes of two subway stations (Novokosino and Cherkizovskaya) were reconstructed which differ in terms of passenger traffic and duration of exploitation. It was shown that most bacterial genera were ubiquitous; however, the unique genera were presented in aerosol samples. The relatively older Cherkizovskaya station possessed greater diversity in antibiotic resistance among the identified microorganisms compared to Novokosino station. We also provided a comparative analysis of these results with the previously published data, which allowed us to identify the distribution of microorganisms associated with the human microbiome and the environment regardless of the seasonal fluctuations. The obtained results provide valuable information on the diversity of bacterial communities in the Moscow subway, one of the most socially important facilities in metropolitan areas. Abstract The subway is one of the most actively used means of transport in the traffic infrastructure of large metropolitan areas. More than seven million passengers use the Moscow subway every day, which promotes the exchange of microorganisms between people and the surrounding subway environment. In this research, a study of the bacterial communities of two Moscow subway stations was conducted and the common subway microbiome was determined. However, there were differences in microbiological and antibiotic-resistance profiles, depending on the station. The station’s operational period since opening correlated with the taxonomic diversity and resistance of the identified bacteria. Moreover, differences between aerosol and surface bacterial communities were found at the two subway stations, indicating the importance of diversified sampling during the microbiome profiling of public areas. In this study, we also compared our data with previously published results obtained for the Moscow subway. Despite sample collection at different stations and seasons, we showed the presence of 15 common genera forming the core microbiome of the Moscow subway, which represents human commensal species, as well as widespread microorganisms from the surrounding environment.
Identification of Vibration Modes and Wave Propagation of Operational Rails by Multipoint Hammering and Reciprocity Theorem

Vertical bending vibration modes and rail wave propagation, including the damping characteristics, are the factors that cause rail corrugation. However, the ability to identify actual railways has been limited because of the huge number of sensors required for field tests. In this study, a novel and field-applicable method for identifying rail vibration modes and wave propagation characteristics is developed by multipoint hammering and the reciprocity theorem instead of multipoint measuring. Additionally, the proposed method is applied to an actual rail with a direct fastening track system on a bridge that has corrugation with a wavelength of approximately 0.04 m. As a result, the wavelength (wavenumber)-, group velocity-, and distance damping (attenuation) frequency relationship of the wave propagation is clarified in addition to the rail frequencies and mode shapes up to approximately 1500 Hz, including the pinned-pinned mode. Finally, the identified wavelength-frequency relationships and the measured rail irregularity can empirically demonstrate that the generated corrugation on the rail is produced by wave interference on the two axles in the bogie.
An innovative electrically conductive biopolymer based on poly(β‐cyclodextrin) towards recognition of ascorbic acid in real sample: Utilization of biocompatible advanced materials in biomedical analysis

In this study, a sensitive platform was designed for the electrocatalytical oxidation and recognition of ascorbic acid (AA) based on poly(β‐cyclodextrin) modified glassy carbon electrode (p(β‐CD‐GCE). Electropolymerization of β‐CD on the surface of GCE was performed on the potential range of −1 to 1.5 V. So, a novel biopolymer was prepared on the surface of GCE towards sensitive recognition of AA in human plasma samples. The developed platform has good sensitivity and accuracy for electrooxidation and detection of AA with lower limit of quantification (LLOQ) of 1 nM and linear range of 1 nM to 100 mM. Moreover, the designed electrochemical sensor was employed for the analysis of AA on human plasma samples with high sensitivity. Based on advantages of p(β‐CD) prepared by electropolymerization procedure (green, fast, homogeny, and efficient eletrocatalytical behaviour), this conductive biopolymer showed excellent analytical behaviour towards electrooxidation of AA. It is expected that the prepared polymeric interface is able to use in the analysis of biological species in clinical samples.
Numerical Experiment and Blade Profile Optimization of a Plane Cascade in a High-load Helium Compressor

Based on a speed triangle design conception for elementary stages with a large deflection angle,a numerical calculation was performed of a total of 324 flow fields of a plane cascade by using a CFD software,including three air inlet mach numbers,six air inlet angles,three pressure diffusion factors,three cascade solidities and two blade profile maximal thickness ratios.To enhance the performance of the new type cascade,an artificial neural network was employed to constitute an approximate function based on the database sample space and the genetic algorithm was used to look for new designs and predict their aerodynamic performances.Furthermore,the mid-arc line and thickness distribution were optimized and the total pressure loss coefficient has decreased by 14.48% after the optimization.
Heuristic Strategy of Service Function Chain Deployment Based on N-Base Continuous Digital Coding in Network Function Virtualization Environment

As a novel network architecture, network function virtualization (NFV) greatly improves the flexibility and scalability of service provision. Deploying the service function chain (SFC) in an NFV environment needs to coordinate the instantiation of the virtual network function descriptor, network function embedding, and traffic steering, which also improves the complexity of the problem. Although heuristic algorithms are widely used to optimize this problem, due to the lack of complete SFC deployment solution space and digital coding scheme, the time complexity of the algorithm is difficult to meet the requirements. Therefore, this paper studies the digital coding scheme of the heuristic SFC deployment to improve time efficiency without reducing performance. Firstly, we model the SFC deployment as a 0–1 integer linear programming, considering the above factors, and then design a continuous digital solution space construction scheme based on N-Base coding (NBACO-SS) to optimize the above problems. NBACO-SS uses integer size and carry to map the complex SFC deployment to simple digital coding, evaluates the continuity of solution space through the Manhattan distance, and optimizes the continuity through the ant colony algorithm. Based on NBACO-SS, we reconstruct two heuristic algorithms to solve the SFC deployment problem. Experimental results demonstrate that NBACO-SS can improve the time efficiency by 20% without reducing the total network service traffic.
Attenuated and normalized item-item product network for sequential recommendation

Sequential recommendation has become a research trending that exploits user’s recent behaviors for recommendation. The user-item interactions contain a sequential dependency that we need to capture to better recommend. Item-item Product (IIP), which models item co-occurrence, has shown good potential by characterizing the pairwise item relationships. Generally, recent behaviors have a greater impact on the current than long-term historical behaviors. And the decaying rate of influence around infrequent behaviors is fast. However, IIP ignores such a phenomenon when considering item-item relevance and leads to suboptimal performance. In this paper, we propose an attenuated IIP mechanism which is position-aware and decays the influence of historical items at an exponential rate. Besides, In order to make up for scenarios where the influence is not in a monotonous decline trend, we add another normalized IIP mechanism to complement the attenuated IIP mechanism. It also strengthen the model’s ability in discriminating favorite items under the sparse data condition by enlarging the gap of matching degree between items. Experiments conducted on five real-world datasets demonstrate that our proposed model achieves better performance than a set of state-of-the-art sequential recommendation models.