Abstract ：

Cable -net structural design generally includes the initial state, load state, and zero -stress state, and adjusting design variables through manual intervention is difficult to correlate these different states. Thus, correlating the structural configurations among these states and finding the corresponding design variables is a challenge. By clarifying the relationship between the three states, a complete automated design process with three states connected by two new parameters is presented. Two typical cable nets are designed and validated, and the corresponding indicators are evaluated and recommended. This method can be applied to almost all flexible cable nets with constrained boundaries and provides an efficient way for designers to obtain reasonable results in the preliminary design stage. The proposed method expands the entire design path from initial form -finding to the load state and the zero -stress state, providing an important reference for the standardized paradigm and automated approach to flexible structural design.

Keyword ：

Parametric design Form finding Stiffness design Cable -net structure Automated structural design

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Abstract ：

Quantum network coding can effectively alleviate bottlenecks in quantum networks thus improving the transmission efficiency and the throughput of the quantum network. Although various quantum network coding protocols have been constructed, most of them focus mainly on addressing the communication congestion issue of simultaneously transmitting single-qubit states via the bottleneck channel in quantum networks. In this paper, a protocol for the quantum twopair unicast transmission of multi-particle states using quantum time -bin multiplexing is proposed. Parallel entanglement is established in a heralded way between two-dimensional quantum memories (QMs) of intermediate nodes by transmitting a high -dimensional encoded single photon via the bottleneck channel. Then the quantum unicast transmission of multi-particle states can be achieved by using quantum teleportation and the resultant entangled pairs. Additionally, a security enhanced protocol is presented when dishonest intermediate nodes or outside eavesdroppers are taken into account. No alternative measurements are needed to check security. This makes the present protocol cost-effective and efficient. Compared with existing methods, no preshared entangled states are required. This provides a new perspective for the development of quantum networks.

Keyword ：

Quantum teleportation Quantum time -bin multiplexing Quantum entanglement Quantum network coding

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Abstract ：

Incorporating phase change materials (PCM) into multi-glazed windows (MW) has been recognized as an effective way to regulate the indoor thermal environment and reduce the energy consumption of buildings. However, the design and calculation of PCM-filled multi-glazed windows (MW + PCM) remain challenging due to the lack of general transient simulation methods and integrated tools. To address this issue, the present study developed a dynamic coupled thermal and optical model for MW + PCM based on the enthalpy-temperature and interface energy balance methods. Then, the model was implemented in C++ and integrated into the TRNSYS software as a new module (Type 2602). In parallel, a test platform comprising two test chambers was established to evaluate the effect of PCM on the thermal and optical properties of the window and to validate the newly proposed module. It was found that compared to the traditional triple-glazed window (TW), the PCM-filled triple-glazed window (TW + PCM) effectively reduces the irradiance through the window by 34.17 % and the maximum temperature of the inner surface by approximately 5.56 degrees C. While the increase in external surface radiation flux density from 600 to 1200 W/m2 has a diminishing effect on the heat flux variation rate during the heat dissipation process for TW + PCM, it effectively shortens the latent heat absorption time by 0.50 h and increases the temperature difference between the inner and outer surfaces by 3.02 degrees C. The new module demonstrates satisfactory accuracy through experimental validation and simulation comparisons, with the maximum heat flux coefficient of variance of the root mean square error (CV(RMSE)) less than 6.35 % and 11.25 % under TW and TW + PCM configurations.

Keyword ：

Multi-glazed windows Thermal performance Latent heat storage Phase change material Optical performance

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Abstract ：

Multi-class segmentation of eye images plays a pivotal role in assessing patients with myasthenia gravis, and the measurement results rely heavily on the segmentation accuracy. However, there is still a problem with inaccurate boundary segmentation. Compared to heuristic-based network structure optimization, exploring effective loss function is an intuitive, simple, and interpretable way to address this issue. In this paper, we experimentally verify the effectiveness of boundary loss for multi-class segmentation of eye images and investigate its hybrid law with other segmentation losses. The application of the study significantly enhances the accuracy of myasthenia gravis scoring and holds promise for assisting in the evaluation of various other eye diseases. © 2024 IEEE.

Keyword ：

Diagnosis Image segmentation

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With the rapid advancement of urban modernization and the sustainable development of economy, the problems of driving difficulty and parking difficulty are increasingly prominent in large, medium and small cities. In order to alleviate the contradiction between urban space and vehicle growth rate and improve the utilization efficiency of parking resources, based on the roadside parking data of Nanshan District in Shenzhen City, this paper combines the time series data visualization method and geospatial data visualization method in multi-dimensional data visualization, and adopts the mode of view collaborative interaction to design the intelligent roadside parking collaborative interactive visualization system. The system integrates multisource data, displays roadside parking data information in an all-round way, supports exploratory analysis of roadside parking data characteristics and laws, and helps to understand parking demand and parking distribution at different times and regions. It provides decision support for optimizing parking resource allocation and roadside parking management. © 2024 IEEE.

Keyword ：

Decision support systems Data visualization Urban growth Smart city Visualization Roadsides

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Abstract ：

To control the seismic response of the core-frame (CF) structure and attenuate the stress level of the core tube, a novel core-damper-frame (CDF) system is proposed by decoupling the perimeter frame and the central core with flexible damper connectors. In this way, the large inertia force of the perimeter frame cannot be fully transmitted to the core, and the stress of the core is mitigated. In the meantime, the damper connectors would absorb a considerable amount of seismic energy, protecting the perimeter frame as the relative displacement between the two subsystems (perimeter frame and core tube) occurs. By incorporating the soil-structure-interaction (SSI) and the filtered Gaussian white-noise excitation model, the "excitation-soil-core-damper-frame" augmented system is established. Combing the augmented system and the genetic algorithm, a stochastic optimization procedure of the CDF considering the SSI effect is proposed. The optimization aims to minimize a series of performance objectives including displacement, acceleration, shear force, and overturning moment of the CDF system. The conflicting relationships between the multiple objectives are discussed; the influence of the SSI effect and the excitation parameters are evaluated. From an illustrative 40-story CDF structural example, the system shows great superiority in controlling the higher mode vibrations, and could significantly reduce the peak core acceleration (by 54.44 %), frame acceleration (by 56.48 %), core drift (by 28.83 %), frame drift (by 39.28 %), core shear (by 83.67 %), and frame moment (by 22.41 %), as compared to its conventional CF counterpart. In special, over 50 % of the input seismic energy is dissipated by the damper connectors in the CDF system.

Keyword ：

Core-damper-frame system Multi-objective optimization Soil-structure interaction Pareto front Energy dissipation Tall buildings

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Abstract ：

Objectives: In pose estimation, semi-supervised learning is a crucial approach to overcome the lack of information problem of labeled data. However, for semi-supervised learning, the insufficient number of labeled samples also severely affects its functionality. The fewer labeled the data, the less stable the prediction. Deep ensemble is a good way to improve model accuracy and stability. However, the training time of model ensemble is long and the resource consumption is high, so it cannot be applied in many practical scenarios. Therefore, the methods we propose the Decomposed Channel based Multi-Stream Ensemble (DCMSE) network, which can extend a single model to a stream-ensemble structure and generate the ensemble prediction to solve the large variance of prediction from the lack of labeled data, and improve the performance. The Channel Deconstruction and Ensembling (CDE) module makes the network benefits from both diversity and commonality by implementing ensemble without increasing the size of parameters. The output features are split into two parts, common-channels and private-channels. In feature sampling, on the one hand, common channels can provide commonality between streams. On the other hand, private channels can provide diversity for each stream and avoid homogenization of the predictions for each stream. Both diversity and commonality allow the network to not only gain in the ensemble of streams, but also improve the prediction accuracy of each stream itself. Results: Moreover, we propose mean-stream consistency constraints and cross-stack consistency constraints to obtain gains from unlabeled data. The Mean-Stream (MS) consistency constraint uses multi-stream ensemble prediction to additionally supervise each stream. Based on the characteristics of the Stacked Hourglass model, the Cross-Stage consistency constraint (CS) uses the forecasting results of later stages to supervise the forecasting of previous stages from the perspective of stages. Conclusion: Our approach achieves better results than SOTAs on the FLIC and Openfield-Pranav and our Sniffing data-set. Specifically, on the MSE, our method achieves at least 0.88, 0.13, and 0.08 improvements over the SOTA method on the FLIC, Openfield-Pranav, and our Sniffing datasets, respectively.

Keyword ：

Multi -Stream Ensemble Openfield-Pranav CDE module DCMSE network UDA-AP

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The goal of mixed-modality clustering, which differs from typical multi-modality/view clustering, is to divide samples derived from various modalities into several clusters. This task has to solve two critical semantic gap problems: i) how to generate the missing modalities without the pairwise-modality data; and ii) how to align the representations of heterogeneous modalities. To tackle the above problems, this paper proposes a novel mixed-modality clustering model, which integrates the missing-modality generation and the heterogeneous modality alignment into a unified framework. During the missing-modality generation process, a bidirectional mapping is established between different modalities, enabling generation of preliminary representations for the missing-modality using information from another modality. Then the intra-modality bipartite graphs are constructed to help generate better missing-modality representations by weighted aggregating existing intra-modality neighbors. In this way, a pairwise-modality representation for each sample can be obtained. In the process of heterogeneous modality alignment, each modality is modelled as a graph to capture the global structure among intra-modality samples and is aligned against the heterogeneous modality representations through the adaptive heterogeneous graph matching module. Experimental results on three public datasets show the effectiveness of the proposed model compared to multiple state-of-the-art multi-modality/view clustering methods.

Keyword ：

multi-view clustering Web sites adaptive graph structure learning Data models Bipartite graph Semantics Correlation heterogeneous graph matching Task analysis Feature extraction Mixed-modality clustering

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Currently, three-dimensional packaging (3D packaging) is used as the most feasible technological way to break Moore's law. In 3D packaging, three different sizes of solder joints must work together to achieve highly integrated vertical interconnects. This architecture needs to be realized by multiple reflow in industrial production. Therefore, it is necessary to choose three solders with different melting points. In order to further extend the reflow temperature range and increase the flexibility of 3D integration, the development of low melting point solders is required.SnBi solder joints often suffer from brittle fracture due to the inherent brittleness of Bi, and SnIn solder joints is limited in its low tensile strength. Therefore, a series of SnBiIn alloys with different ratios were prepared. During the composition design, orthogonal experimental method was used to determine composition ratios. The effects of different composition on the micro-morphology as well as the mechanical properties of SnBiIn alloys were investigated. Samples were prepared using a vacuum induction furnace and the microstructure of the samples was analyzed. The mechanical properties of the alloys with different compositions were measured by tensile test. The results of this study provide a research basis for the design of low melting point solder.

Keyword ：

Low melting point Sn-Bi-In alloy mechanical properties microstructure Composition design

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Abstract ：

Mineralized collagen scaffold is one of the best choices for bone defects treatment, but weak mechanical strength is the main factor restricting its development. Recent studies demonstrated that despite being a fundamental form of mechanical stimulation in human activities, the impact of cyclic compressive stress on collagen mineralization remains unclear, with even less known about the dynamic mechanical mechanism. This study employed cyclic compressive stress to investigate its effect on collagen mineralization. The findings revealed that cyclic compressive strain promotes collagen mineralization by facilitating increased mineral penetration into the collagen and altering mineral morphology on the collagen surface. As the mineral volume fraction of mineralized collagen rises, its elastic modulus also increases. Additionally, the finite element simulation results proved that cyclic compressive stress can impact mineral distribution by affecting their transport and deposition, consequently influencing the stress distribution and regulating mechanical properties of mineralized collagen. Alterations in mechanical properties provide feedback on internal stress distribution, subsequently impacting mineral mineralization. This study achieves a closed-loop study on the mechanical regulated collagen mineralization, offers insight into the mechanism of collagen mineralization, paving the way for further exploration of biomineralization mechanisms and potentially inspiring novel approaches for the fabrication of mineralized collagen scaffolds.

Keyword ：

Mechanical property Mechano-regulatory Biomineralization Multi -morphology Stress distribution Multiscale modelling

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