Flexible wearable devices and solar flexible units often use thermally sensitive organic materials as substrates, which are prone to thermal damage during the bonding process in 3D packaging, leading to chip deformation or failure. Multicomponent solders, with well-designed multicomponent metallic elements, exhibit unique low-melting-point characteristics. The application of low-temperature multicomponent solders in electronic packaging can significantly reduce bonding temperatures and minimize thermal damage to chips. This paper reviews the wettability and preparation methods of low-temperature multicomponent solders, and concludes the effect of different metallic elements on the solders. Additionally, this paper discusses the research on interfacial reactions, mechanical properties of low-temperature multicomponent solder joints, providing valuable insights for future research and development in this field.

Semantic segmentation of remote sensing images is extensively used in crop cover and type analysis, and environmental monitoring. In the semantic segmentation of remote sensing images, owning to the specificity of remote sensing images, not only the local context is required, but also the global context information makes an important role in it. Inspired by the powerful global modelling capability of Swin Transformer, we propose the LSENet network, which follows the encoder-decoder architecture of the UNet network. In encoding phase, we propose spatial enhancement module (SEM), which helps Swin Transformer further enhance feature extraction by encoding spatial information. In decoding stage, we propose local enhancement module (LEM), which is embedded in the Swin Transformer to improve the Swin Transformer to assist the network to obtain more local semantic information so as to classify pixels more accurately, especially in the edge region, the adding of LEM enables to obtain smoother edges. The experimental results on the Vaihingen and Potsdam datasets demonstrate the effectiveness of our proposed method. Specifically, the mIoU metric is 78.58% on the Potsdam dataset, 72.59% on the Vaihingen dataset and 64.49% on the OpenEarthMap dataset.

Due to the variation of temperature during service, the leads in Quad Flat Packages (QFP) and Plastic Leaded Chip Carrier (PLCC) undergo high thermal stress which can lead to large deformation and failure. Thus, the prediction of thermal stress and strain distributions is a pre-requisite for reliability analysis of such components. Along this line, the present work analyzed the performances for gull wing and J-lead with different geometries. The thermal stress and strain distributions of both leads were determined by using Finite Element Method (FEM). For each type of the lead, a parametric study was conducted in order to evaluate the effect of lead geometry on stress and strain distributions. Based on the simulation results, optimum lead design was suggested. In addition, simulation work of package under thermal cyclic loading has been done, accumulated creep strain was found in critical location of solder in package and life time was predicted.

In this paper, we focus on the resource optimization issue in Wireless Body Area Networks (WBANs) section of a novel Biomedical Cyber Physical System (BM-CPS) in which the Digital Twin (DT) technique is adopted concurrently. We propose a scenario where multiple physiological sensor nodes continuously monitor the electrophysiology signals from patients and connect to a Cyber Managing Center (Cyb-MC) in a wireless way to transmit the physiological indices to the paramedic reliably. Specifically, to optimize the energy efficiency of physiological sensors while ensuring the reliability of emergency-critical electrophysiology signals transmissions and modeling the uncertain stochastic environments, the optimization issue is transformed into a Markov Decision Process (MDP) in terms of joint transmission mode, relay assignment, transmission power and time slot scheduling consideration. Subsequently, we propose a Random Graph-enabled Deep Deterministic Policy Gradient (RG-DDPG) scheme to tackle the challenge while releasing computing complexity. In addition, as an innovative paradigm for reshaping cyberspace applications, the DT of WBAN is created to capture the time-varying resource status, where virtualized and intelligent resource management can be performed uniformly. Finally, extensive simulation studies verified the advancement of the proposed scheme and demonstrated that the DT can mirror the topology, predict the behavior, and administrate the resources of the WBANs.

Remote sensing images, which obtain surface information from aerial or satellite platforms, are of great significance in fields such as environmental monitoring, urban planning, agricultural management, and disaster response. However, due to the complex and diverse types of ground coverage and significant differences in spectral characteristics in remote sensing images, achieving high-quality semantic segmentation still faces many challenges, such as blurred target boundaries and difficulty in recognizing small-scale objects. To address these issues, this study proposes a novel deep learning model, MKF-NET. The fusion of KAN convolution and Vision Transformer (ViT), combined with the multi-scale feature extraction and dense connection mechanism, significantly improves the semantic segmentation performance of remote sensing images. Experiments were conducted on the LoveDA dataset to systematically evaluate the segmentation performance of MKF-NET and several existing traditional deep learning models (U-net, Unet++, Deeplabv3+, Transunet, and U-KAN). Experimental results show that MKF-NET performs best in many indicators: it achieved a pixel precision of 78.53%, a pixel accuracy of 79.19%, an average class accuracy of 76.50%, and an average intersection-over-union ratio of 64.31%; it provides efficient technical support for remote sensing image analysis.