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As forestry contributes to the reduction of greenhouse gases by CO₂ fixation, in recent years, use of wood in buildings has attracted all over the world more attention. However, construction of large wood structures is almost inexistent within urban areas in Japan. This is due to the Japanese law on fire protection of wood buildings in cities, which is considered very strict with severe requirements. This paper aims to present a research work relative to the development of one-hour fire-resistant wood structural elements for buildings in cities. The developed elements are composed of three layers made of laminated timber.

These wood structural elements, made of glued laminated timber with self-charring-stop, have sufficient fire resistance during and after a fire and comply with the strict Japanese standard for wood structural elements, which stipulates that such elements have to withstand the whole dead-load of concerned buildings after fire. To comply with such requirements, new elements of glued laminated timber with self-charring-stop layer were developed, and their performance was confirmed. Several fire-resistant tests conducted on columns, beams, column-beam joints, connections between beams and walls and beams with holes were carried out.

All tests proved that the elements have sufficient fire resistance. No damage was found out at the load-bearing part of the elements after testing. As the developed elements have two layers protecting the load-bearing part, the temperature in the load-bearing part could be retained below 260°C (carbonization temperature) and provide the elements with a sufficient fire resistance for 1 h.

These wood structural elements have already been applied in six projects, where large-size wooden buildings were constructed in urban areas in Japan.

The proposed structural elements use a novel technique. Every wooden element is composed of three layers made of glued laminated timber. The elements have a typical performance of self-charring-stop after fire without need for water of firefighters. More technologies related to these elements, including column-beam joints and beams with holes and effect of crack, were also developed to design and construct safe wooden buildings.

This paper describes a boundary element analysis of magnetic shieldings for electron microscopes. Since the thickness of the shielding layer is considerably small compared with its overall size, numerical analysis of electromagnetic fields inside the layer leads to an ill‐conditioned matrix. This problem can be overcome by analytical evaluation of the interior electromagnetic field, which yields the impedance boundary condition (IBC) valid for static and eddy current fields, which expresses the relationship between the electromagnetic fields on both surfaces of the layer. In this paper the magnetic fields around a shielding layer are analyzed by the boundary element method under the IBC on the shielding layer. Two‐dimensional and axisymmetric magnetic fields are analyzed to evaluate the shielding efficiency of shielding immersed in an ac magnetic field. It is shown that magnetic disturbances can be reduced to less than one‐hundredth inside a shielding consisting of double shielding layers.

This paper aims to present a deep learning–based surrogate model for fast multi-material topology optimization of an interior permanent magnet (IPM) motor. The multi-material topology optimization based on genetic algorithm needs large computational burden because of execution of finite element (FE) analysis for many times. To overcome this difficulty, a convolutional neural network (CNN) is adopted to predict the motor performance from the cross-sectional motor image and reduce the number of FE analysis.

To predict the average torque of an IPM motor, CNN is used as a surrogate model. From the input cross-sectional motor image, CNN infers dq-inductance and magnet flux to compute the average torque. It is shown that the average torque for any current phase angle can be predicted by this approach, which allows the maximization of the average torque by changing the current phase angle. The individuals in the multi-material topology optimization are evaluated by the trained CNN, and the limited individuals with higher potentials are evaluated by finite element method.

It is shown that the proposed method doubles the computing speed of the multi-material topology optimization without loss of search ability. In addition, the optimized motor obtained by the proposed method followed by simplification for manufacturing is shown to have higher average torque than a reference model.

This paper proposes a novel method based on deep learning for fast multi-material topology optimization considering the current phase angle.

To analyze effectively magnetic shielding effects by shields with fine structure.

Simplification of the fine structure makes it possible to analyze them efficiently. The authors have introduced a homogenization method to estimate effective permeability of magnetic composite structure for the static field. The homogenization method is applied to the analysis of magnetic shields composed of steel plates and steel rods against DC power lines to test its feasibility.

The properties of the magnetic shielding are analyzed by using the homogenization method. The errors of the magnetic fields increase in case of very few layers.

The simplification of the magnetic shields with fine structure by using the homogenization method makes it possible to analyze efficiently magnetic shielding effects, although the accuracy becomes worse in case of very few layers.

Three-dimensional (3D) mesh generation for shape optimizations needs long computational time. This makes it difficult to perform 3D shape optimizations. The purpose of this paper is to present a new meshing method with light computational cost for 3D shape optimizations.

This paper presents a new meshing method on the basis of nonconforming voxel finite element method. The 3D mesh generation is performed with light computational cost keeping the computational accuracy.

It is shown that the computational cost for 3D mesh generation can be reduced without deteriorating numerical accuracy in the FE analysis. It is reported the performance of the present method.

The validity of the nonconforming voxel elements is tested to apply it to the optimization of 3D optimizations.

This paper aims to investigate the approach combine the deep learning (DL) and finite element method for the magneto-thermal coupled problem.

To achieve the DL of electrical device with the hypothesis of a small dataset, with ground truth data obtained from the FEM analysis, U-net, a highly efficient convolutional neural network (CNN) is used to extract hidden features and trained in a supervised manner to predict the magneto-thermal coupled analysis results for different topologies. Using part of the FEM results as training samples, the DL model obtained from effective off-line training can be used to predict the distribution of the magnetic field and temperature field of other cases.

The possibility and feasibility of the proposed approach are investigated by discussing the influence of various network parameters, in particular, the four most important factors are training sample size, learning rate, batch size and optimization algorithm respectively. It is shown that DL based on U-net can be used as an efficiency tool in multi-physics analysis and achieve good performance with only small datasets.

It is shown that DL based on U-net can be used as an efficiency tool in multi-physics analysis and achieve good performance with only small datasets.

Generation Z will be the leading consumer group in the future. Using convenience stores, the study provides an in-depth analysis on Gen Z’s current experience and future expectations from retail stores. The study further highlights the differences between Gen Z’s perception of convenience stores across three different regions – the USA, South Korea and Japan.

This study conducted a series of in-depth, semi-structured interviews with 36 Gen Z participants from the USA (12), South Korea (11) and Japan (13). All interviews were first coded based on a preselected list of themes and were further coded with new themes that emerged from exploratory coding.

Each regional cohort varied in terms of how they experienced and what they expected from convenience stores. US participants showed negative or utilitarian attitudes toward convenience stores, whereas South Korean participants had a positive, personal attachment to them. In comparison, Japanese participants had a relatively neutral attitude. However, all three groups showed a common preference for smart technology and health concerns surrounding convenience store foods.

Convenience store chains should consider the cultural nuances when designing future services. The chains should further strive to remove the health concerns about the foods provided at the stores and design smart technologies that enhance user experience.

The present study broadens the knowledge in this budding consumer segment where current research is limited. It further sheds light on the variance among Gen Zers across different cultural contexts.

This study aims to investigate the possible use of a deep neural network (DNN) as an inverse solver.

Different models based on DNNs are designed and proposed for the resolution of inverse electromagnetic problems either as fast solvers for the direct problem or as straightforward inverse problem solvers, with reference to the TEAM 25 benchmark problem for the sake of exemplification.

Using DNNs as straightforward inverse problem solvers has relevant advantages in terms of promptness but requires a careful treatment of the underlying problem ill-posedness.

This work is one of the first attempts to exploit DNNs for inverse problem resolution in low-frequency electromagnetism. Results on the TEAM 25 test problem show the potential effectiveness of the approach but also highlight the need for a careful choice of the training data set.

The tensor complex permeability of a multi-turn coil with elliptic cross-section is analytically expressed. In field analysis, a multi-turn coil can be modeled by the uniform material that has the present tensor complex permeability. It is shown that the frequency characteristic of the present tensor complex permeability is in good agreement with that evaluated by finite element method applied to a unit cell of the multi-turn coil region.

The authors introduce a new method to evaluate the complex permeability of a multi-turn rectangular coil. To obtain the complex permeability of a rectangular coil in a closed form, it is approximated as an elliptic coil. Because the rectangular coil has different complex permeabilities in the vertical and horizontal directions, the complex permeability have to be defined in a tensor form. It suffices to discretize the coil region into rather coarse finite elements without considering the skin depth in contrast to the conventional finite element method.

The proposed method is shown to give the impedance of multi-turn coils which is in good agreement with results obtained by the conventional finite element (FE) analysis. By extending the proposed approach, the authors can easily perform 3D FE analysis without difficulty in discretization of the coil region with fairly fine finite elements. Moreover, they found that the approximation of rectangular coils as the elliptic coils is valid for analysis of quasi-static fields using this homogenization method.

The novelty of this study is in the approximation of the rectangular coils with elliptic coils, and the complex permeability for them is formulated here in a closed form. The proposed formula includes that for the round coils. Using the present method, the authors analyze the rectangular coils without fine discretization.

The calculation of magnetic shielding with ferromagnetic material by an effective reluctivity method and a time step method based on the finite element calculation is investigated. The calculation results of both methods are compared with measurement results and with each other in order to check their reliability and accuracy. It turns out that both methods give similar results for the field inside the shielding material, whereas in the surrounding air the effective reluctivity method gives more accurate results than the present time step method.