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This paper addresses the limitations of current graph neural network-based recommendation systems, which often neglect the integration of side information and the modeling of complex high-order interactions among nodes. The research motivation stems from the need to enhance recommendation performance by effectively utilizing all available data. We propose a novel method called MSHCN, which leverages hypergraph neural networks to integrate side information and model complex interactions, thereby improving user and item representations.

The MSHCN method employs a hypergraph structure to incorporate various types of side information, including social relationships among users and item attributes, which are essential for enriching user and item representations. The k-means clustering algorithm is utilized to create item-associated hypergraphs, while sentiment analysis on user reviews refines the modeling of user interests. Additionally, hypergraphs are constructed for user-user and item-item interactions based on interaction similarity. MSHCN also incorporates contrastive learning as an auxiliary task to enhance the representation learning process.

Extensive experiments demonstrate that MSHCN significantly outperforms existing recommendation models, particularly in its ability to capture and utilize side information and high-order interactions. This results in superior user and item representations and improved recommendation performance.

The novelty of MSHCN lies in its use of a hypergraph structure to integrate diverse side information and model intricate high-order interactions. The incorporation of contrastive learning as an auxiliary task sets it apart from other hypergraph-based models, providing a significant enhancement in recommendation accuracy.

Evaluating the multiphysics performance of an electric motor can be a computationally intensive process, especially where several complex subsystems of the motor are coupled together. For example, evaluating acoustic noise requires the coupling of the electromagnetic, structural and acoustic models of the electric motor. Where skewed poles are considered in the design, the problem becomes a purely three-dimensional (3D) multiphysics problem, which could increase the computational burden astronomically. This study, therefore, aims to introduce surrogate models in the design process to reduce the computational cost associated with solving such 3D-coupled multiphysics problems.

The procedure involves using the finite element (FE) method to generate a database of several skewed rotor pole surface-mounted permanent magnet synchronous motors and their corresponding electromagnetic, structural and acoustic performances. Then, a surrogate model is fitted to the data to generate mapping functions that could be used in place of the time-consuming FE simulations.

It was established that the surrogate models showed promising results in predicting the multiphysics performance of skewed pole surface-mounted permanent magnet motors. As such, such models could be used to handle the skewing aspects, which has always been a major design challenge due to the scarcity of simulation tools with stepwise skewing capability.

The main contribution involves the use of surrogate models to replace FE simulations during the design cycle of skewed pole surface-mounted permanent magnet motors without compromising the integrity of the electromagnetic, structural, and acoustic results of the motor.

Multiphysical models are often useful for the design of electrical devices such as electrical machines. In this way, the modeling of thermal, magnetic and electrical phenomena by using an equivalent circuit approach is often used in sizing problems. The coupling of such models with other models is difficult to take into account, partly because it adds complexity to the process. The paper proposes an automatic modelling of thermal and magnetic aspects from an equivalent circuit approach, with its computation of gradients, using selectivity on the variables. Then, it discusses the coupling of various physical models, for the sizing by optimization algorithms. Sensibility analyses are discussed and the multiphysical approach is applied on a permanent magnet synchronous machine.

The paper allows one to describe thermal and magnetic models by equivalent circuits. Magnetic aspects are represented by reluctance networks and thermal aspects by thermal equivalent circuits. From circuit modelling and analytical equations, models are generated, coupled and translated into computational codes (Java, C), including the computation of their jacobians. To do so, model generators are used: CADES, Reluctool, Thermotool. The paper illustrates the modelling and automatic programming aspects with Thermotool. The generated codes are directly available for optimization algorithms. Then, the formulation of the coupling with other models is studied in the case of a multiphysical sizing by optimization of the Toyota PRIUS electrical motor.

A main specificity of the approach is the ability to easily deal with the selectivity of the inputs and outputs of the generated model according to the problem specifications, thus reducing drastically the size of the jacobian matrix and the computational complexity. Another specificity is the coupling of the models using analytical equations, possibly implicit equations.

At the present time, the multiphysical modeling is considered only for static phenomena. However, this limit is not important for numerous sizing applications.

The analytical approach with the selectivity gives fast models, well-adapted for optimization. The use of model generators allows robust programming of the models and their jacobians. The automatic calculation of the gradients allows the use of determinist algorithms, such as SQP, well adapted to deal with numerous constraints.

This study explores hedging strategies that use the KTB futures to hedge the price risk of the KTB spot portfolio. The study establishes the price sensitivity, risk-minimization, bivariate GARCH (1,1) models as hedging models, and analyzes their hedging performances. The sample period covers from September 29, 1999 to September 18, 2001. Time-matched prices at 11:00 (11:30) of the KTB futures and spot were used in the analysis. The most important findings may be summarized as follows. First, while the average hedge ration of the price sensitivity model is close to one, both the risk-minimization and GARCH model exhibit hedge ratios that are substantially lower than one. Hedge ratios tend to be greater for daily data than for weekly data. Second, for the daily in-sample data, hedging effectiveness is the highest for the GARCH model with time-varying hedge ratios, but the risk-minimization model with constant hedge ratios is not far behind the GARCH model in its hedging performance. In the case of out-of-sample hedging effectiveness, the GARCH model is the best for the KTB spot portfolio, and the risk-minimization model is the best for the corporate bond portfolio. Third, for daily data, the in-sample hedge shows a better performance than the out-of-sample hedge, except for the risk-minimization hedge against the corporate bond portfolio. Fourth, for the weekly in-sample hedges, the price sensitivity model is the worst and the risk-minimization model is the best in hedging the KTB spot portfolio. While the GARCH model is the best against the KTB +corporate bond portfolio, the risk-minimization model is generally as good as the GARCH model. The risk-minimization model performs the best for the weekly out-of-sample data, and the out-of-sample hedges are better than the in-sample hedges. Fifth, while the hedging performance of the risk-minimization model with daily moving window seems somewhat superior to the traditional risk-minimization model when the trading volume increased one year after the inception of the KTB futures, on the average the traditional model is better than the moving-window model. For weekly data, the traditional model exhibits a better performance. Overall, in the Korean bond markets, investors are encouraged to use the simple risk-minimization model to hedge the price risk of the KTB spot and corporate bond portfolios.

Recently, domestic market participants have a growing interest in synthetic Collateralized Debt Obligation (CDO) as a security to reduce credit risk and create new profit. Therefore, the valuation method and hedging strategy for synthetic CDO become an important issue. However, there is no won-denominated credit default swap transactions, which are essential for activating synthetic CDO transaction‘ In addition, there is no transparent market information for the default probability, asset correlation, and recovery rate, which are critical variables determining the price of synthetic CDO.

This study first investigates the method of estimating the default probability, asset correlation coefficient, and recovery rate. Next, using five synthetiC CDO pricing models‘ widely used OFGC (One-Factor Non-Gaussian Copula) model. OFNGC (One-Factor Non-Gaussian Copula) model such as OFDTC (One-Factor Double T-distribution Copula) model of Hull and White (2004) or NIGC (Normal Inverse Gaussian Copula) model of Kalemanova et al.(2005), SC<Stochastic Correlation) model of Burtschell et al.(2005), and FL (Forward Loss) model of Bennani (2005), I Investigate and compare three points: 1) appropriateness for portfolio loss distribution, 2) explanation for standardized tranche spread, 3) sensitivity for delta-neutral hedging strategy. To compare pricing models, parameter estimation for each model is preceded by using the term structure of iTraxx Europe index spread and the tranch spreads with different maturities and exercise prices Remarkable results of this study are as follows. First, the probability for loss interval determining mezzanine tranche spread is lower in all models except SC model than OFGC model. This result shows that all mαdels except SC model in some degree solve the implied correlation smile phenomenon, where the correlation coefficient of mezzanine tranche must be lower than other tranches when OFGC model is used. Second, in explaining standardized tranche spread, NIGC model is the best among various models with respect to relative error. When OFGC model is compared with OFDTC model, OFOTC model is better than OFGC model in explaining 5-year tranche spreads. But for 7-year or 10-year tranches, OFDTC model is better with respect to absolute error while OFGC model is better with respect to relative error. Third, the sensitivity sign of senior tranctle spread with respect to asset correlation is sometime negative in NIG model while it is positive in other models. This result implies that a long position may be taken by the issuers of synthet.ic COO as a correlation delta-neutral hedging strategy when OFGC model is used, while a short position may be taken when NIGC model is used.

This paper presents an introduction to the modelling of virtual garment design process in 3D… Our global project of virtual clothing design, along with the conception of a virtual adaptive mannequin, is devoted to creating and modelling garments in 3D. Starting from ideas of mass customization, e-commerce and the need of numerical innovations in the garment industry, this article presents a model of virtual garment and methodology enabling virtual clothing to be conceived directly on an adaptive mannequin morphotype in 3D. A short description of the overall garment model under constraints is presented. To explain the overall methodology, the basic pattern of trousers is given. The global model of garment creation in 3D is composed of three parts - a human body model, an ease model and a garment model. The most essential part is the ease model, which is necessary for the proposed process of garment modelling. After describing each garment modelling element influencing this process, a detailed presentation of the ease model in relation to the garment model is proposed. The combination of the previously mentioned models may be considered as 2 interconnected sub-models. The first sub-model is linked with the front pattern position on the body and the second with the back pattern position on the trousers with appropriate ease values. In order to execute the identification procedure of the correct ease values and consequently their right positions on the human body, an algorithm of identification is proposed. The two sub-models are strongly connected as in the feedback effect caused by the interactions of the trouser front and back patterns. The aforementioned connection phenomenon appears during modelling and it depends on the structure of the proposed ease model. The relatively significant number of parameters requires the use of the identification technique. Finally, the superposition of virtual and real patterns was done in order to visualise the results.

This paper estimates and forecasts yield curve of korea bond market using a three factor term structure model based on the Nelson-Siegel model. The Nelson-Siegel model is in-terpreted as a model of level, slope and curvature and has the flexibility required to match the changing shape of the yield curve. To estimate this model, we use the two-step estima-tion procedure as in Diebold and Li. Estimation results show our model is Quite flexible and gives a very good fit to data.

To see the forecasting ability of our model, we compare the RMSEs (root mean square error) of our model to random walk (RW) model and principal component model for out-of sample period as well as in-sample period. we find that our model has better forecasting performances over principal component model but shows slight edge over RW model especially for long run forecasting period. Considering that it is difficult for any model to show better forecasting ability over the RW model in out-of-sample period, results suggest that our model is useful for practitioners to forecast yields curve dynamics.

The primary objective of this research is to introduce a pioneering takaful model that provides both provision and protection to the aging population by combining the concept of cash waqf with takaful. This model is designed to align with Shariah principles, ensuring sustainability and enduring impact.

This research adopts a qualitative methodology, where a focus group discussion was conducted with six stakeholders. The participants consisted of takaful operators, legal experts and other industry players. The participants were presented with the proposed cash waqf takaful model and their feedback was recorded. Legal issues related to linking waqf with takaful were also identified and discussed.

The study highlights the need for innovative financial solutions to support Malaysia's aging population. It proposes a cash waqf takaful model, leveraging crowd funding for sustainability. Legal hurdles and recommendations for overcoming them are discussed, along with suggestions for future research on quantitative validation and regulatory frameworks. Ultimately, the study emphasizes the holistic approach of the proposed model in addressing the well-being of Malaysia's senior citizens.

The proposed takaful model presents opportunities for takaful operators to integrate Islamic social finance into their operations, enabling easier access to takaful for the elderly community. By eliminating financial barriers, it can transform the takaful landscape, ensuring inclusivity and financial security for aging populations. Moreover, policymakers see it as a blueprint for sustainable financial solutions and social welfare enhancement globally.

The study introduces a novel cash waqf takaful model to support Malaysia's aging population, leveraging crowdfunding for sustainability. It addresses legal challenges unique to Malaysia and proposes collaboration with State Islamic Religious Authorities. Furthermore, it emphasizes the need for further research to validate the model's effectiveness and explores its potential global policy implications.

The purpose of this paper is to propose a new combined finite-discrete element method (FDEM) to analyze the mechanical properties, failure behavior and slope stability of soil rock mixtures (SRM), in which the rocks within the SRM model have shape randomness, size randomness and spatial distribution randomness.

Based on the modeling method of heterogeneous rocks, the SRM numerical model can be built and by adjusting the boundary between soil and rock, an SRM numerical model with any rock content can be obtained. The reliability and robustness of the new modeling method can be verified by uniaxial compression simulation. In addition, this paper investigates the effects of rock topology, rock content, slope height and slope inclination on the stability of SRM slopes.

Investigations of the influences of rock content, slope height and slope inclination of SRM slopes showed that the slope height had little effect on the failure mode. The influences of rock content and slope inclination on the slope failure mode were significant. With increasing rock content and slope dip angle, SRM slopes gradually transitioned from a single shear failure mode to a multi-shear fracture failure mode, and shear fractures showed irregular and bifurcated characteristics in which the cut-off values of rock content and slope inclination were 20% and 80°, respectively.

This paper proposed a new modeling method for SRMs based on FDEM, with rocks having random shapes, sizes and spatial distributions.

The purpose of this paper is to explore networked business models on a nascent market for a sustainable innovation.

The study takes a qualitative approach through a comparative case study of three solar photovoltaic (PV) parks in Sweden. Data was collected from 14 interviews with multiple supply chain and network actors as well as secondary data. Industrial marketing and purchasing is applied for theoretical framing.

The study demonstrates transactional, relational, environmental and social drivers for participating in the network. The study reveals the duplicity of the nascent market, which encourages supply chain actors to develop their individual business models to take a larger market share or become future competitors to current collaborators. On the nascent market with few developed regulations, the network enables actors to influence regulations on local and regional levels.

The study is limited to the nascent solar PV industry in Sweden, which is characterized by institutional turbulence, market uncertainties and few established supply networks.

Practitioners need to consider multifarious drivers for participating in networked business models, where the economic driver may be the least motivating.

This study provides several multiactor business models and classifies them into specific applications and general applications. The study provides unique insight into the complexity of interactions among supply chain actors in networked business models on a nascent market for sustainable innovation. Due to the scarcity of available partners on the nascent market, actors need to look beyond their on-going relationships and their network horizon, or actors’ roles evolve to include activities that was not part of their individual business models.