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To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method integrates textual and numerical information using TCN-BiGRU–Attention.

The Word2Vec model is initially employed to process the gathered textual data concerning battery-grade lithium carbonate. Subsequently, a dual-channel text-numerical extraction model, integrating TCN and BiGRU, is constructed to extract textual and numerical features separately. Following this, the attention mechanism is applied to extract fusion features from the textual and numerical data. Finally, the market price prediction results for battery-grade lithium carbonate are calculated and outputted using the fully connected layer.

Experiments in this study are carried out using datasets consisting of news and investor commentary. The findings reveal that the MFTBGAM model exhibits superior performance compared to alternative models, showing its efficacy in precisely forecasting the future market price of battery-grade lithium carbonate.

The dataset analyzed in this study spans from 2020 to 2023, and thus, the forecast results are specifically relevant to this timeframe. Altering the sample data would necessitate repetition of the experimental process, resulting in different outcomes. Furthermore, recognizing that raw data might include noise and irrelevant information, future endeavors will explore efficient data preprocessing techniques to mitigate such issues, thereby enhancing the model’s predictive capabilities in long-term forecasting tasks.

The price prediction model serves as a valuable tool for investors in the battery-grade lithium carbonate industry, facilitating informed investment decisions. By using the results of price prediction, investors can discern opportune moments for investment. Moreover, this study utilizes two distinct types of text information – news and investor comments – as independent sources of textual data input. This approach provides investors with a more precise and comprehensive understanding of market dynamics.

We propose a novel price prediction method based on TCN-BiGRU Attention for “text-numerical” information fusion. We separately use two types of textual information, news and investor comments, for prediction to enhance the model's effectiveness and generalization ability. Additionally, we utilize news datasets including both titles and content to improve the accuracy of battery-grade lithium carbonate market price predictions.

From the buying club’s perspective, the transfer of a player can be interpreted as an investment from which the club expects uncertain future benefits. This paper aims to develop a decision-oriented approach for the valuation of football players that could theoretically help clubs determine the subjective value of investing in a player to assess its potential economic advantage.

We build on a semi-investment-theoretical risk-value model and elaborate an approach that can be applied in imperfect markets under uncertainty. Furthermore, we illustrate the valuation process with a numerical example based on fictitious data. Due to this explicitly intended decision support, our approach differs fundamentally from a large part of the literature, which is empirically based and attempts to explain observable figures through various influencing factors.

We propose a semi-investment-theoretical valuation approach that is based on a two-step model, namely, a first valuation at the club level and a final calculation to determine the decision value for an individual player. In contrast to the previous literature, we do not rely on an econometric framework that attempts to explain observable past variables but rather present a general, forward-looking decision model that can support managers in their investment decisions.

This approach is the first to show managers how to make an economically rational investment decision by determining the maximum payable price. Nevertheless, there is no normative requirement for the decision-maker. The club will obviously have to supplement the calculus with nonfinancial objectives. Overall, our paper can constitute a first step toward decision-oriented player valuation and for theoretical comparison with practical investment decisions in football clubs, which obviously take into account other specific sports team decisions.

This study aims to establish an evaluation method for cross-regional major infrastructure project (CRMIP) supportability. The focus is to identify evaluation indicators from a complexity perspective and develop an evaluation model using qualitative and quantitative methods. Case studies are carried out to verify the reliability of the evaluation model, thereby providing theoretical and practical guidance for CRMIP operations and maintenance (O&M).

Guided by the idea of complexity management, the evaluation indicators of CRMIP supportability are determined through literature analysis, actual O&M experience and expert interviews. A combination of qualitative and quantitative methods, consisting of sequential relationship analysis, entropy weighting, game theory and cloud model, is developed to determine the indicator weights. Finally, the evaluation model is used to evaluate the supportability of the Hong Kong–Zhuhai–Macao Bridge (HZMB), which tests the rationality of the model and reveals its supportability level.

The results demonstrate that CRMIPs' supportability is influenced by 6 guideline-level and 18 indicator-level indicators, and the priority of the influencing factors includes “organization,” “technology,” “system,” “human resources,” “material system,” and “funding.” As for specific indicators, “organizational objectives,” “organizational structure and synergy mechanism,” and “technical systems and procedures” are critical to CRMIPs' O&M supportability. The results also indicate that the supportability level of the HZMB falls between good and excellent.

Under the guidance of complexity management thinking, this study proposes a supportability evaluation framework based on the combined weights of game theory and the cloud model. This study provides a valuable reference and scientific judgment for the health and safety of CRMIPs' O&M.

The lubricating fluid stored in the porous matrix will spontaneously exude to supplement the lubricating film in the damaged area, thus ensuring the long-term self-lubricating function of the porous surface. To reveal the repair mechanism of oil film, it is necessary to understand the flow characteristics of oil in micropores. The purpose of this study guides the design of micropore structure to realize the rapid exudation of oil to the porous surface and the rapid repair of the lubricating film.

In this paper, cylindrical orifice, convergent orifice and divergent orifice were studied. The numerical model of lubricating oil exudation in micropores was established. The distribution characteristics of oil pressure, velocity and three-phase contact line in the process of oil exudation were investigated. The effects of different orifice shapes and orifice structure parameters on the pinning and spreading characteristics of oil droplet were analyzed. Then the internal mechanisms of oil droplet formation and spread on the orifice surface were summarized.

The results show that during the process of oil exudation, the three-phase contact line of the oil drop is pinned once at the edge of the cylindrical and convergent orifice. Compared with the three orifice structures, the inlet pressure of the oil drop is low, and the oil velocity at the pinning point is stable in the divergent orifice. Resulting in favorable oil exudation. It is easier for oil droplet to depin by appropriately reducing the wall wetting angle, increasing the aperture or controlling the wall inclination angle. Ensure the self-healing and long-lasting lubrication film of porous oil-bearing surfaces.

The effect of pore structure on the flow behavior of lubricating fluid has always been concerned. But the mechanism by which different orifice shape affect the pinning behavior of oil droplets is not yet clear, which is crucial for understanding the self-healing mechanism of oil films on porous surfaces. It is meaningful to analyze the mechanism of oil exudation and spreading on the porous surface of oil in the special orifice, to optimize the design of the orifice structure.

Orifice shape has influence on internal flow field parameters. There is no report on the influence of orifice shape on the film formation process of oil seepage and diffusion from pores. The effects of different orifice shapes and orifice structure parameters on the characteristics of oil droplet pinning and diffusion were studied.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0118/

This research proposes a novel risk assessment model to elucidate the risk propagation process of industrial safety accidents triggered by natural disasters (Natech), identifies key factors influencing urban carrying capacity and mitigates uncertainties and subjectivity due to data scarcity in Natech risk assessment.

Utilizing disaster chain theory and Bayesian network (BN), we describe the cascading effects of Natechs, identifying critical nodes of urban system failure. Then we propose an urban carrying capacity assessment method using the coefficient of variation and cloud BN, constructing an indicator system for infrastructure, population and environmental carrying capacity. The model determines interval values of assessment indicators and weights missing data nodes using the coefficient of variation and the cloud model. A case study using data from the Pearl River Delta region validates the model.

(1) Urban development in the Pearl River Delta relies heavily on population carrying capacity. (2) The region’s social development model struggles to cope with rapid industrial growth. (3) There is a significant disparity in carrying capacity among cities, with some trends contrary to urban development. (4) The Cloud BN outperforms the classical Takagi-Sugeno (T-S) gate fuzzy method in describing real-world fuzzy and random situations.

The present research proposes a novel framework for evaluating the urban carrying capacity of industrial areas in the face of Natechs. By developing a BN risk assessment model that integrates cloud models, the research addresses the issue of scarce objective data and reduces the subjectivity inherent in previous studies that heavily relied on expert opinions. The results demonstrate that the proposed method outperforms the classical fuzzy BNs.

Parallelogram linkages are used to increase the stiffness of manipulators and allow precise control of end-effectors. They help maintain the orientation of connected links when the manipulator changes its position. They are implemented in many palletizing robots connected with binary, ternary and quaternary links through both active and passive joints. This limits the motion of some joints and hence results in relative and negative joint angles when assigning coordinate axes. This study aims to provide a simplified accurate model for manipulators built with parllelogram linkages to ease the kinematics calculations.

This study introduces a simplified model, replacing each parallelogram linkage with a single (binary) link with an active and a passive joint at the ends. This replacement facilitates countering motion while preserving subsequent link orientations. Validation of kinematics is performed on palletizing manipulators from five different OEMs. The validation of Dobot Magician and ABB IRB1410 was carried out in real time and in their control software. Other robots from ABB, Yaskawa, Kuka and Fanuc were validated using control environments and simulators.

The proposed model enables the straightforward derivation of forward kinematics and transforms hybrid robots into equivalent serial-link robots. The model demonstrates high accuracy streamlining the derivation of kinematics.

The proposed model facilitates the use of classical methods like the Denavit–Hartenberg procedure with ease. It not only simplifies kinematics derivation but it also helps in robot control and motion planning within the workspace. The approach can also be implemented to simplify the parallelogram linkages of robots with higher degrees of freedom such as the IRB1410.

The purpose of this study is to present a novel extended hybrid analytical method (HAM) that leverages a two-dimensional (2-D) coupling between the semi-analytical Maxwell–Fourier analysis and the finite element method (FEM) in Cartesian coordinates.

The proposed model is applied to flat permanent-magnet linear electrical machines with rotor-dual. The magnetic field solution across the entire machine is established by coupling an exact analytical model (AM), designed for regions with relative magnetic permeability equal to unity, with a FEM in ferromagnetic regions. The coupling between AM and FEM occurs bidirectionally (x, y) along the edges separating teeth regions and their adjacent regions through applied boundary conditions.

The developed HAM yields accurate results concerning the magnetic flux density distribution, cogging force and induced voltage under various operating conditions, including magnetic or geometric parameters. A comparison with hybrid finite-difference and hybrid reluctance network methods demonstrates very satisfactory agreement with 2-D FEM.

The original contribution of this paper lies in establishing a direct coupling between the semi-analytical Maxwell–Fourier analysis and the FEM, particularly at the interface between adjacent regions with differing magnetic parameters.

Heterogeneous and micro-structured materials have been the object of multiscale and homogenization techniques aimed at recognizing the elastic properties of the equivalent continuum. The proposed investigation deals with the mechanical characterization of the heterogeneous material structured metamaterials through analyzing the ultimate strength using the limit analysis of the Representative Volume Element (RVE). To get the desired material strength, a novel finite element formulation based on the derivation of self-equilibrated solutions through the finite elements devoted to calculating the lower bound theorem has been implemented together with the limit analysis in Melàn’s formulation.

The finite element formulation is based on discrete mapping of Volterra dislocations in the structure using isoparametric representation. Using standard finite element techniques, the linear operator V, which relates the self-equilibrated internal solicitation to displacement-like nodal parameters, has been built through finite element discretization of displacement and strain.

The proposed work presented an elastic homogenization of the mechanical properties of an elementary cell with a geometry known in the literature, the isotropic truss. The matrix of elastic constants was calculated by subjecting the RVE to numerical load tests, simulated with a commercial FEM calculation code. This step showed the dependence of the isotropy properties, verified with Zener theory, on the density of the RVE. The isotropy condition of the material is only achieved for certain section ratios between body-centered cubic (BCC) and face-centered cubic (FCC), neglecting flexural effects at the nodes. The density that satisfies Zener’s conditions represents the isotropic geomatics of the isotropic truss.

For the isotropic case, the VFEM procedure was used to evaluate the isotropy of the limit domain and was compared with the Mises–Schleicher limit domain. The evaluation of residual ductility and dissipation energy allowed a measurement parameter for the limit anisotropy to be defined. The novelty of the proposal consisted in the formulation of both the linearized and the nonlinear limit locus of the material; hence, it furnished the starting point for further limit analysis of the structures whose elementary volume has been described through the proposed approach.

Finite element (FE) analysis can be used for both design and verification of components. In the case of 3D-printed materials, a proper characterization of properties, accounting for anisotropy and raster angles, can help develop efficient material models. This study aims to use compression tests to characterize short carbon-reinforced PA12 made by fused filament fabrication (FFF) and to model its behaviour by the FE method.

In this work, the authors focus on compression tests, using post-processed specimens to overcome external defects introduced by the FFF process. The material’s elastoplastic mechanical behaviour is modelled by an elastic stiffness matrix, Hill’s anisotropic yield criterion and Voce’s isotropic hardening law, considering the stacking sequence of raster angles. A FE analysis is conducted to reproduce the material’s compressive behaviour through the LS-DYNA software.

The proposed model can capture stress values at different deformation levels and peculiar aspects of deformed shapes until the onset of damage mechanisms. Deformation and damage mechanisms are strictly correlated to orientation and raster angle.

The paper aims to contribute to the understanding of 3D-printed material’s behaviour through compression tests on bulk 3D-printed material. The methodology proposed, enriched with an anisotropic damage criterion, could be effectively used for design and verification purposes in the field of 3D-printed components through FE analysis.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.