Shared-nothing geo-distributed SQL databases, such as CockroachDB, are increasingly vital for enterprise applications requiring data resilience and locality. However, we encountered significant performance degradation at the customer side, especially when their deployments span multiple data centers over a Wide Area Network (WAN). Our investigation identifies the bottleneck in the performance of the Distributed Hash Join (Dist-HJ) algorithm, which is contingent upon a crucial balance between communication overhead and computational load. This balance is severely disrupted when processing skewed data from real-world customer workloads, leading to the observed performance decline. To tackle this challenge, we introduce Bala-Join, an adaptive solution to balance the computation and network load in Dist-HJ execution. Our approach consists of the Balanced Partition and Partial Replication (BPPR) algorithm and a distributed online skewed join key detector. The former achieves balanced redistribution of skewed data through a multicast mechanism to improve computational performance and reduce network overhead. The latter provides real-time skewed join key information tailored to BPPR. Furthermore, an Active-Signaling and Asynchronous-Pulling (ASAP) mechanism is incorporated to enable efficient, real-time synchronization between the detector and the redistribution process with minimal overhead. Empirical study shows that Bala-Join outperforms the popular Dist-HJ solutions, increasing throughput by 25%-61%.

Surface-mount device (SMD) diodes offer compact size, light weight, and enhanced reliability over traditional diodes but demand higher precision and more complex manufacturing processes. While research often centers on environmental and material factors affecting electronic packaging reliability, the impact of manufacturing processes on device failure is less explored. This study simulates the pin bending process of an SMD diode, analyzing stress conditions on internal structures for two mold design schemes. The distinct geometric features of molds in the two schemes result in different mold motion patterns and contact locations during operation. Notably, comparative analysis reveals that Design Scheme 1 sustains 2.66-fold greater stress concentration in the heat sink structure while demonstrating 2.16-times elevated chip stress levels relative to Design Scheme 2. Through simulation analysis, in conjunction with real-world manufacturing conditions, the primary factors leading to device failure are identified. Under the influence of mold tolerances and processing errors, the chip stress in the SMD diode increases from 23.603 to 188.11 MPa, a value approximately eight times the original stress level. It is emphasized that dimensional tolerances in the production process significantly impact device reliability, and the design of molds must account for these variations. This research offers valuable theoretical insights into ensuring the reliability of pin bending for various SMD devices. Moreover, the simulation outcomes for the SMD diode contribute to a deeper understanding of the internal stresses encountered in other plastic devices during their pin bending stages.

With the rapid growth of data and the increasing awareness of privacy protection, data privacy issues have become particularly important in the field of machine learning. Federated learning, as a distributed learning method, achieves collaborative training of models while preserving data privacy by keeping the data stationary and allowing the model to move. However, during the federated learning process, there is still a risk of privacy leakage when aggregating the intermediate parameters of models trained by different data providers. Researchers have found that adding noise to the intermediate parameters of the model using differential privacy can effectively prevent privacy inference on the data contributors. Nevertheless, there exists an inherent trade-off between the accuracy and privacy in federated learning models under differential privacy. Strengthening privacy protection often leads to a decrease in model performance. This trade-off becomes more pronounced in complex deep learning models that require multiple iterations to converge. To address the issues of data privacy, data silos, and the trade-off between data privacy leakage and model availability in deep learning within federated learning, this paper proposes a relaxed differential privacy federated learning approach. It reduces the impact of noise on the final results by selectively perturbing gradients when data providers return intermediate model parameters. Experiments demonstrate that this approach achieves a high level of accuracy while preserving data privacy. Additionally, it exhibits superior performance in terms of computational efficiency, striking a well-balanced compromise between accuracy and privacy.

Plant leaves can be used to effectively detect plant diseases. However, the number of images of unhealthy leaves collected from various plants is usually unbalanced. It is difficult to detect diseases using such an unbalanced dataset. We used DoubleGAN (a double generative adversarial network) to generate images of unhealthy plant leaves to balance such datasets. We proposed using DoubleGAN to generate high-resolution images of unhealthy leaves using fewer samples. DoubleGAN is divided into two stages. In stage 1, we used healthy leaves and unhealthy leaves as inputs. First, the healthy leaf images were used as inputs for the WGAN (Wasserstein generative adversarial network) to obtain the pretrained model. Then, unhealthy leaves were used for the pretrained model to generate 64*64 pixel images of unhealthy leaves. In stage 2, a superresolution generative adversarial network (SRGAN) was used to obtain corresponding 256*256 pixel images to expand the unbalanced dataset. Finally, compared with images generated by DCGAN (Deep convolution generative adversarial network). The dataset expanded with DoubleGAN, the generated images are clearer than DCGAN, and the accuracy of plant species and disease recognition reached 99.80 and 99.53 percent, respectively. The recognition results are better than those from the original dataset.

Soil moisture (SM) plays a decisive function during the grassland degradation and restoration process. In this paper, SM in desert steppe is measured with various methods at different spatial scale, including point-, field- and regional-scale, and the SM results from FDR, CRS and remote sensing (RS) are verified mutually. The results show that CRS is adaptable to measure desert steppe, of which with FDR R2is 0.83 and RMSE is 0.0162kg/kg; substituting CRS SM validated for traditional point measurement to verify RS retrieval, a much stronger correlation is achieved with R2of 0.97 much bigger than that of FDR and RS, whose R2is only 0.80, proving quantifiably that CRS is obviously a new effective means for verifying SM of RS retrieval.