bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mentoring task-based language teaching as an innovation in an Indonesian secondary school
Authors: Ariatna  , Ellis Rod 
Published: November 2024

Abstract
This article seeks to address a lack of research documenting how mentoring can help teachers to introduce TBLT in an Asian context. We report a whole semester project in an Indonesian secondary school where a mentor (an experienced teacher-educator) worked with a teacher, who was used to a structural approach, to introduce task-based language teaching (TBLT) into his classroom. We describe how the mentoring was set up and carried out to pre-empt problems that evaluation studies have shown can arise with TBLT. To evaluate to what extent the mentoring was successful, we conducted a case study employing qualitative and quantitative data collection methods, examining the extent to which the mentoring enabled the teacher to implement TBLT and whether the TBLT resulted in greater learning than in a traditional, structural-oriented classroom. The main findings were that the teacher was successful in transitioning to TBLT as evidenced in his ability to design his own tasks and by the kinds of questions he asked, the strategic use of the learners’ L1 and focus-on-form during the performance of the tasks (i.e., the kinds of interactional features fundamental to TBLT). Another finding was that the learners in the TBLT class also demonstrated greater language development than a comparable structural-oriented class. We utilize the evidence collected from the teacher’s implementation of TBLT to discuss the mentoring that took place and to identify ways of improving it.
The historical development of asymmetries
Authors: Koenig Ekkehard 
Published: November 2024

Abstract
The structuring and encoding of space through language, the shape of the linguistic inventories found in
individual languages, as well as the symmetries and asymmetries manifested by such systems have received a great deal of attention
during the last four decades. The vast majority of the relevant studies adopted a purely synchronic perspective and diachronic
studies of the relevant phenomena are rare. The aim of this paper is to contribute to this discussion by analyzing the historical
development of directional demonstratives in Germanic languages, the structure of the relevant systems at different stages, the
reduction and loss of oppositions and the resultant creation of asymmetries. The results of the descriptions will be examined and
evaluated from a comparative and typological perspective.
Effects of experience and directionality on cognitive load in dialogue interpreting
Authors: Adler Aleksandra 
Published: November 2024

Abstract
The aim of the study was to investigate the effects of interpreter experience and interpreting direction on cognitive load of dialogue interpreters. The study was conducted on two groups of interpreters (experienced and inexperienced) during a simulated interpreter-mediated encounter and cognitive load was operationalized with disfluency durations, and disfluency counts. The data suggest that both experience and directionality modulate cognitive load in dialogue interpreters. All participants manifested an increase in cognitive load while interpreting into their weaker language (L2), which suggest that directionality had an effect on cognitive load of dialogue interpreters. The results also suggest that inexperienced interpreters are likely to exhibit higher cognitive load compared to experienced interpreters. The study underscores the significance of experience and directionality in understanding the cognitive demands faced by dialogue interpreters, contributing valuable insights to research on interpreting and cognitive load.
Consistent theories for the DESI dark energy fit
Authors: Notari Alessio , Redi Michele , Tesi Andrea 
Published: November 2024

Abstract
We search for physically consistent realizations of evolving dark energy suggested by the
cosmological fit of DESI, Planck and Supernovae data. First we note that any lagrangian
description of the standard Chevallier-Polarski-Linder (CPL) parametrization for the dark energy
equation of state w, allows for the addition of a cosmological constant. We perform the
cosmological fit finding new regions of parameter space that however continue to favour dark
energy with w < -1 at early times, that is challenging to realize in consistent theories. Next,
in the spirit of effective field theories, we consider the effect of higher order terms in the
Taylor expansion of the equation of state of dark energy around the present epoch. We find that
non-linear corrections of the equation of state are weakly constrained, thus opening the way to
scenarios that differ from CPL at early times, possibly with w > -1 at all times. We present
indeed scenarios where evolving dark energy can be realized through quintessence models. We
introduce in particular the ramp model where dark energy coincides with CPL at late times and
approximates to a cosmological constant at early times. The latter model provides a much better
fit than ΛCDM, and only slightly worse than w
0
w

a
CDM, but with the notable advantage of
being described by a simple and theoretically consistent lagrangian of a canonical quintessence model.
Multiparty semi-quantum key agreement protocol based on multiparticle entangled states
Authors: Hong Wan-li , Bai Chen-ming , Zhang Su-juan , Liu Lu 
Published: November 2024

Abstract
In this paper, we study the properties of a set of quantum states. Based on these properties, we propose a multiparty semi-quantum key agreement (MSQKA) protocol. For the sake of clarity, we provide a three-party example to illustrate the protocol. Through rigorous security analysis, it is demonstrated that the proposed protocol can resist both inside attacks and outside attacks, which include Trojan horse attacks, measure-resend attacks, entangle-measure attacks and collective attacks. Besides, the proposed protocol achieves fairness among participants. Finally, we compare our protocol with other SQKA protocols. Compared to previous three-party and MSQKA protocols, our proposed protocol involves more classical parties, needs no external assistance and achieves relatively high key generation efficiency.
Tilting the odds at the lottery: the interplay of overparameterisation and curricula in neural networks*
Authors: Sarao Mannelli Stefano , Ivashynka Yaraslau , Saxe Andrew , Saglietti Luca 
Published: November 2024

Abstract
A wide range of empirical and theoretical works have shown that overparameterisation can amplify the performance of neural networks. According to the lottery ticket hypothesis, overparameterised networks have an increased chance of containing a sub-network that is well-initialised to solve the task at hand. A more parsimonious approach, inspired by animal learning, consists in guiding the learner towards solving the task by curating the order of the examples, i.e.  providing a curriculum. However, this learning strategy seems to be hardly beneficial in deep learning applications. In this work, we undertake an analytical study that connects curriculum learning and overparameterisation. In particular, we investigate their interplay in the online learning setting for a 2-layer network in the XOR-like Gaussian Mixture problem. Our results show that a high degree of overparameterisation—while simplifying the problem—can limit the benefit from curricula, providing a theoretical account of the ineffectiveness of curricula in deep learning.
An Inertial-relaxed Conjugate Gradient Algorithm Based on the Mathematical Model of Rural Pipeline Network in Image-restoration
Authors: Yan Gang , Yuan Gonglin , Tang Jiahuan 
Published: November 2024

Abstract
As time progresses, the complexity of nonlinear systems continues to escalate, presenting significant challenges in problem-solving, particularly with increasing dimensions. To address this challenge, based on solving nonlinear equation systems, we propose an inertial-relaxed modified NLS (N Liu-Storey) CG(conjugate gradient) method tailored for image restoration. By employing inertial acceleration technique and a modified secant equation, this method presents a novel approach to image restoration. The search direction of algorithm integrates sufficient descent and trust region feature, incorporating gradient, modified convex combination, and function value, thereby enhancing adaptability and global convergence. Numerical experiments substantiate the efficacy of this algorithm in image restoration tasks. Through the equilibrium of local and global search dynamics via inertial acceleration technique and modified secant equations, this method expedites convergence and enhances solution accuracy. Its versatility enables robust performance across various image restoration scenarios, underscoring its potential as a valuable tool in solving nonlinear equation systems and optimization.
Gap Junctions in Different Neuronal Groups Exert Different Effects on Sharp Wave-ripples of a Neuronal Network in CA1
Authors: Yang Xinyuan , Sun Xiaojuan 
Published: November 2024

Abstract
Gap junctions are indispensable for achieving brain functions. The direct coupling between neurons connected by gap junctions may contribute to synchronization of neuronal firing and emergence of sharp wave-ripples(SWR), which affect brain functions such as memory consolidation. However, considering the heterogeneity of excitatory and inhibitory neurons, it is not quite clear whether gap junctions have the same effect on the emergence of SWR in network activity in different neuron types. In order to explore the above problems, we constructed a neuronal network located in CA1 region of hippocampus, which contains excitatory pyramidal cells, PV+BCs and axo-axonic cells. Taking into account diverse connections between neurons and properties of neurons, we investigated effects of gap junctions on SWR in different kinds of neuronal populations in the constructed network with chemical synaptic connections by neurodynamical modeling. Numerical results show that gap junctions within pyramidal neurons and PV+BCs promote the emergence of SWR, whereas gap junctions within axo-axonic cells suppress it. At the same time, it is revealed that gap junctions in axo-axonic cells play a dominant role in modulating SWR. We hope that these findings provide some inspiration for studies on neuronal heterogeneity and the enhancement of synchronicity of oscillations by gap junctions.
Direct Meshless Method for Stable Seepage Flow Calculation
Authors: Li Jiale , Zhang Zhongliang , Mei Yinjie , Hu Yutong 
Published: November 2024

Abstract
Predicting stable groundwater seepage using known data is an important task in groundwater dynamics. This is crucial for ensuring the rational utilization of groundwater resources and effectively controlling groundwater pollution. However, traditional finite difference methods and analytical solutions have significant limitations in dealing with complex groundwater flow problems, such as poor applicability and low computational efficiency under irregular boundary conditions. In recent years, although methods based on radial basis function (RBF) have improved in accuracy, they still face the problem of insufficient flexibility in high-dimensional space. This article proposes a new method for predicting stable groundwater seepage, using the multiple quadratic curve (MQ) numerical method to address this challenge. We first use RBF interpolation to decompose the groundwater flow field, and then solve the corresponding linear equation system to obtain a numerical solution for stable groundwater seepage. This method abandons the traditional construction of grids and elements, provides greater flexibility in high-dimensional space, and adopts a node based approximation method to ensure computational accuracy. The experimental results show that this method performs well in simulating groundwater flow and changes, with relative errors as low as a specific value. Compared with higher precision radial basis function methods, this new method improves the accuracy by two orders of magnitude, providing a new solution for accurate prediction of groundwater flow.
Research on Interception Model of Surface-to-air Missile Anti-cruise Missile Weapon System
Authors: Ding Kun , Cui Xuan , Wang Zhimei , Qiu Zepeng , Hu Yubao , Zhang Haiyan 
Published: November 2024

Abstract
In air defense operations, resisting cruise missile attacks is a major challenge. Firstly, based on judging the direction and route of the cruise missile weapon system, the paper establishes the radar detection zone algorithm, the killing zone of surface-to-air missile weapon system and the interception model of surface-to-air missile weapon system needed in the process of physical interception. Secondly, according to whether the effective interception condition is satisfied, an algorithm is established to judge the surface-to-air missile weapon system on several fixed positions. Lastly, it gives the maximum interception expectation of the incoming route to achieve effective interception. We set up scenarios through examples to reasonably divide the Red Army’s combat area, analyze quantitatively the effectiveness of blue ground-to-air missiles intercepting red cruise missiles under different terrain conditions, after comprehensively judging the entry situation of detection and killing zones and calculating whether there is an interception arc, the interception efficiency can be calculated. This model incorporates terrain constraints, making the results more reasonable than other models. It provides a scientific basis for command assisted decision-making.
Research on position Model of Surface-to-air Missile Defense against Cruise Missile Based on Rule GA
Authors: Zhang Haiyan , Cui Xuan , Suo Yan , Hou Shuyu , Wang Junjie , Liu Zhoucheng 
Published: November 2024

Abstract
The deployment of weapon systems prior to the engagement of anti-cruise missiles constitutes the primary research challenge in surface-to-air missile operations. Initially, it is essential to analyze operational requirements and scientifically delineate the guided radar detection area, kill zone, and interception zone based on physical realities. A model should be established to ascertain the fundamental conditions for site deployment, taking into account factors such as position height, shielding angle, and geographic coordinates. In relation to low-altitude approaches by adversarial forces, key considerations include the depth and breadth of firepower from the weapon system in the attack direction, firepower density in that direction, and lethality probability. An Operational Effectiveness model should be developed based on coverage area of firepower, effective interceptions per unit time, and lethality probabilities of weapon systems to evaluate each location’s effectiveness. Finally, leveraging land selection principles and external information coordination mechanisms will facilitate establishing a genetic algorithm-based strategy for deploying surface-to-air missile systems. The validity of this algorithm has been demonstrated through practical examples.
Aerodynamic Parameter Identification Based on Multi Group Adaptive Search Optimization Algorithm
Authors: Wang Yu , Wang Xiaoming 
Published: November 2024

Abstract
Due to the strong coupling and nonlinearity of spinning-stabilized correction projectile’s motion and dynamic model, its aerodynamic parameter identification can be transformed into a high-dimensional multi extremum optimization problem. To avoid the excessive computational complexity and susceptibility to local optima, this paper proposes a multiple swarm adaptive search optimization method. This method automatically divides the samples into several subpopulations through an adaptive density peak clustering method, and then searches and updates in each subpopulation. To balance convergence speed and population diversity, the update operator for each generation combines the random search and optimal convergence step size, whose weights can be adaptively adjusted based on the update rate and dispersion characteristics of the subpopulations. Applying this method to the aerodynamic parameter identification, and comparing with improved particle swarm optimization and genetic algorithm, simulation results show that a better identification result can be obtained at the same iterations.
A Low-complexity Decision Variables Classification Brainstorm Optimization Algorithm
Authors: Li Qiyue , Qiu Lipeng , Zhou Aoran , Wu Yali , Wang Ziren 
Published: November 2024

Abstract
In the complex optimization scenarios of real life, we often need to not only weigh the conflicts between multi-objectives but also face the challenges brought by large-scale decision variables. When the scale of decision variables increases sharply, the search space will expand exponentially, which makes it difficult for the algorithm to traverse the whole search space under limited resources, so that the number of local optimal solutions increases sharply. The so-called ‘dimension disaster’ appears. To effectively deal with this problem, this paper proposes a large-scale multi-objective brainstorm optimization algorithm (LMaOBSO) based on low complexity decision variable classification method and improved penalty boundary intersection strategy. The algorithm uses a low-complexity tree sorting variable classification method to divide the decision variables into converges and diversity variables quickly. At the same time, the convergent variables are optimized by the improved penalty boundary crossover strategy to solve the problems existing in large-scale optimization problems.
Bayesian Method for Determining the Three Parameters of Weibull Distribution
Authors: Xu Jiajin 
Published: November 2024

Abstract
In recent decades, it has been found that the three parameter Weibull distribution plays an increasingly important role in the fields of structural fatigue and reliability. However, due to the complexity of the methods for estimating these three parameters, the application of the three parameter Weibull distribution has been greatly affected. The author has successively proposed the Zhentong Gao method and the generalized Zhentong Gao method in recent years, which have better solved this problem. Later, by using the characteristics of the Monte Carlo method and the Metropolis-Hastings algorithm to upgrade the Zhentong Gao method and the generalized Zhentong Gao method, it is further found that they can be used to improve the Bayesian method. This paper mainly discusses how to improve the Bayesian method so that the improved Bayesian method is more convenient than the commonly used Gibbs method, and can also estimate the three parameters of the Weibull distribution, and at the same time give their confidence intervals.
Vessel Routing Robust Optimization Model with Multi-Time-Window for Floating Marine Debris Collection
Authors: Yu Zhaoyang , Duan Gang 
Published: November 2024

Abstract
Marine debris is increasingly damaging the environment and impacting the fishing economy, making it essential to dispatch vessels for debris collection. This paper studies how to plan the routes for marine debris collection vessel. By introducing the concept of multiple time windows, allowing the vessel to select any time window to arrive at the floating location, the model considers the uncertainty of debris weight and uses robust optimization to establish a vessel routing robust model optimization with multi-time-window. This method does not increase the complexity of the problem, the operation is simple, and the control parameters are few. In addition, the proposed solution is robust and can meet the constraints with a high probability. Finally, the correctness of the model is verified using the Lingo solver.
Exploration of Factors Affecting Resonance in Photovoltaic Cluster Grid Integration System
Authors: Gao Jing , Zhao Yuan , Lu Yi , Gao Yanfeng , Zhang Xu 
Published: November 2024

Abstract
A significant number of distributed photovoltaics being connected to the grid leads to a more complex harmonic content in the photovoltaic cluster grid integration system. When a specific harmonic frequency approaches the system resonance frequency of the system, a resonance phenomenon occurs. This paper conducts an analysis of the complex resonance coupling relationship. Subsequently, it separately examines the impact of grid impedance, filter capacitor parameters, and the number of parallel inverters on the resonant frequency. It is verified through simulation that the aforementioned factors will bring about a change in the resonant frequency.
Local Discontinuous Galerkin Methods for the Two-component μ-Camassa-Holm Equations
Authors: Zhao Jianli , Hu Qingjie , Xie Xinxin , Zhang Chao 
Published: November 2024

Abstract
In this paper, we have developed two local discontinuous Galerkin (LDG) methods for the two-component μ-Camassa-Holm equations: a conservative scheme and a dissipative scheme. Exploiting the bi-Hamiltonian structure of the two-component μ-Camassa-Holm system, we introduce two significant Hamiltonian invariants and demonstrate that both schemes preserve discrete versions of these invariants. Additionally, we provide and prove a priori error estimates for both LDG schemes. Numerical experiments are conducted to validate the accuracy and effectiveness of the proposed methods.
Response and Reliability of Composite Post Insulators Under Random Earthquake
Authors: Wang Haibo , Cheng Yongfeng , Lu Zhicheng , Huan Ronghua , Lü Qiangfeng , Liu Zhen-lin 
Published: November 2024

Abstract
This article examines the stochastic response and seismic reliability of composite post-insulators under random seismic excitation. A random seismic motion model, tailored for power facility seismic research in China and compliant with the “Seismic Design Specification for Power Facilities” (GB50260-2013), is developed. This model accounts for both amplitude and frequency non-stationary. A nonlinear dynamic model for composite post insulators is then established, incorporating the nonlinearity at the multi-section insulator and flange connections. Numerical methods are employed for random seismic response analysis to address the non-stationary nature of seismic motion. The proposed model’s validity is confirmed by comparing it with the vibration table test results. Finally, the seismic reliability of composite post insulators, with root stress as the control criterion, is assessed using the probability density evolution method.
Research on Optimal Path Planning for Automatic Parking Systems
Authors: Yang Jian , Deng Hua 
Published: November 2024

Abstract
The issue of autonomous parking for self-driving vehicles is a critical problem in autonomous driving. With the rapid development of the automotive industry and the increasing number of vehicles, the issues of traffic congestion and parking space shortage have become more prominent, further exacerbating the difficulty of parking. Therefore, there is a growing demand for automatic parking functions. This paper focuses on the problem of self-driving vehicles automatically parking in complex conditions without colliding with other self-driving vehicles and obstacles. The main content includes establishing a vehicle parking kinematic model and solving the minimum turning radius based on the Ackermann vehicle model data. When the self-driving vehicle travels in a straight line, a route from the initial position to the parking space is designed, and an automatic parking model is established. The mathematical model is used to solve the self-driving vehicle’s starting position and end position, as well as the trajectory of the self-driving vehicle, and MATLAB is used to simulate and visualize the parking trajectory. The aim is to solve the problem of parking difficulties caused by factors such as insufficient parking spaces and congested parking scenes. To ensure the safe completion of automatic parking, the path planning algorithm in this paper can plan the optimal parking path.