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Josephson Junctions are important components in superconducting qubits. It introduces anharmonicity to the energy level spacings of the qubit which allow us to identify two unique quantum energy states for computing. It is difficult to fabricate multiple junctions within the same desired parameter range. Characterisation of the junctions is, therefore, a necessary step after fabrication. In particular, the critical current of the junctions is determined by measuring their normal state resistance. This is done via two-point or four-point resistance measurement at a manual probe station which is a time-consuming process, especially for wafer-scale fabrication. This bottleneck can be circumvented by automation with object detection. The base of the automated probe station is a 3D printer modified with multiple Arduino Uno microcontrollers and motorized linear stages. The automation process is achieved via auto-alignment of the probes and an automatic measurement procedure. As a result, the fully automated process will take about 27–29 s to measure the resistance of one junction which saves 28%–51% of the time compared to the manual probe station and can be unsupervised. Due to the reuse of a commercial 3D printer, the cost of this system is 800 SGD which is much less than comparable commercial solutions. [ABSTRACT FROM AUTHOR]

The incidence rate of melanoma varies across regions, with Europe, the United States, and Australia having 10–25, 20–30, and 50–60 cases per 1 00 000 people. In China, patients with melanoma exhibit different clinical manifestations, pathogenesis, and outcomes. Current treatments include surgery, adjuvant therapy, and immune checkpoint inhibitors. Nonetheless, complications may arise during treatment. Melanoma development is heavily reliant on cell adhesion molecules (CAMs), and studying these molecules could provide new research directions for metastasis and progression. CAMs include the integrin, immunoglobulin, selectin, and cadherin families, and they affect multiple processes, such as maintenance, morphogenesis, and migration of adherens junction. In this study, a cell adhesion‐related risk prognostic signature was constructed using bioinformatics methods, and survival analysis was performed. Plakophilin 1 (PKP1) was observed to be crucial to the immune microenvironment and has significant effects on melanoma cell proliferation, migration, invasion, and the cell cycle. This signature demonstrates high reliability and has potential for clinical applications. [ABSTRACT FROM AUTHOR]

In reality, wind power data are often accompanied by data losses, which can affect the accurate prediction of wind power and subsequently impact the real-time scheduling of the power system. Existing methods for recovering missing data primarily consider the environmental conditions of individual wind farms, thereby overlooking the spatiotemporal correlations between neighboring wind farms, which significantly compromise their recovery effectiveness. In this paper, a joint missing data recovery model based on power data from adjacent wind farms is proposed. At first, a spatial–temporal module (STM) is designed using a combination of graph convolution network and recurrent neural networks to learn spatiotemporal dependencies and similarities. Subsequently, to provide a solid computational foundation for the STM, a Euclidean-directed graph based on Granger causality is constructed to reflect the hidden spatiotemporal information in the data. Finally, comprehensive tests on data recovery for both missing completely at random and short-term continuous missing are conducted on a real-world dataset. The results demonstrate that the proposed model exhibits a significant advantage in missing data recovery compared to baseline models. [ABSTRACT FROM AUTHOR]

This study bridges finance and physics by applying thermodynamic concepts to model the limit order book (LOB) with high-frequency trading data on the Bitcoin spot. We derive the measures of Market Temperature and Market Entropy from the kinetic and potential energies in the LOB to provide a deeper understanding of order activities and market participant behavior. Market Temperature emerges as a robust indicator of market liquidity, correlating with liquidity measures such as Active Quote Volume, bid–ask spread and match volume. Market Entropy, on the other hand, quantifies the degree of disorder or randomness in the LOB, providing insights into the instantaneous volatility of price in the high-frequency trading market. Our empirical findings not only broaden the theoretical framework of econophysics but also enhance comprehensive understanding of the market microstructure and order book dynamics. [ABSTRACT FROM AUTHOR]

Data privacy and security concerns have attracted significant attention, leading to the frequent occurrence of data silos in deep learning. To address this issue, federated learning (FL) has emerged. However, simple federated learning frameworks still face two security risks during the training process. Firstly, sharing local gradients instead of private datasets among users does not completely eliminate the possibility of data leakage. Secondly, malicious servers could obtain inaccurate aggregation parameters by forging or simplifying the aggregation process, ultimately leading to model training failures. To address these issues and achieve high-performance training models, we have designed a verifiable federated learning scheme called CVFL, where users exist in a serial manner to resist inference attacks and further protect the privacy of user dataset information through serial encryption. We ensure the secure aggregation of models through a verification protocol based on Lagrange interpolation functions. The serial transmission of local gradients effectively reduces the communication burden on cloud servers, and our verification protocol avoids the computational overhead caused by a large number of encryption and decryption operations without sacrificing model accuracy. Experimental results on the MNIST dataset demonstrate that, after 10 epochs of training with 100 users, our solution achieves a model accuracy of 90.63% for MLP architecture under IID data distribution and 87.47% under non-IID data distribution. For CNN architecture, our solution achieves a model accuracy of 96.72% under IID data distribution and 93.53% under non-IID data distribution. Experimental evaluations corroborate the practical performance of the presented scheme with high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) technology uses the layer-by-layer stacking method to print parts, which simplifies the process of complex parts. The requirements for batch printing in AM are continuously growing now. In order to improve the economic and time efficiency of AM, the printing layout needs to be optimized. However, considering the diversity of part construction directions and accuracy requirements, as well as the limitations of time and materials, the printing layout still lacks comprehensive optimization models and methods, and the existing placement algorithms have not effectively utilized the holes inside parts and gaps between parts. In this paper, a comprehensive weighted general optimization model for 2D nesting is proposed to maximize time and economic benefits in AM. Moreover, a contour similarity matching method based on chain code for part placement is proposed to solve the problems about utilizing holes and gaps for the compact layout, and the approximate optimal solution is obtained by integrating annealing evolution algorithm. Experiments are conducted to verify the effectiveness of the proposed algorithm for regular geometry and real-world part layout. [ABSTRACT FROM AUTHOR]

Although many multiplexed arrays of a superconducting nanowire single-photon detector (SNSPD) have been reported, it is still a major challenge to develop pixel-level readout arrays with high efficiency, parallel detection, and fast processing for real-time imaging. Here, we report a SNSPD array with inductor-shaping pulses for approaching the pixel-level readout. Optimized inductors are introduced to shape the output pulses of each pixel, and the response pulses of all pixels are synthesized in a series-connected structure. Then, the on/off states of all pixels can be encoded to the widths, amplitudes, and areas of the output pulses by the single-channel readout. This proposal is verified by a 4-pixel SNSPD array and a 16-pixel SNSPD array. It shows that the array not only inherits the features of the single-pixel SNSPD, such as photosensitive area, filling factor, quantum efficiency, and dark count rate, but also implements parallel operation of all pixels, which is always confused in traditional multiplexed SNSPD arrays. At the same time, the single-channel readout simplifies the system, and the serial digital signal converted from the shaped pulse enabled an easy and fast readout process, paving the way for high performance and real-time imaging. [ABSTRACT FROM AUTHOR]

Styrene – glycidyl acrylate copolymer Joncryl ADR4368 (ADR) was employed as a compatibilizer in the immiscible blends of poly(lactic acid) (PLA) and poly(ε-caprolactone) (PCL). The morphology, crystallization behavior and mechanical properties were investigated. The compatibility between PLA and PCL was poor, thus leading to poor tensile properties for PLA/PCL blends. The addition of ADR reduced the size of PCL domains due to the formation of long chain branched copolymers in the interface, indicating the improved compatibility of PLA and PCL. PCL enhanced the cold crystallization ability of PLA, while ADR delayed the cold crystallization ability of PLA. Compared with PLA/PCL blends, PLA/PCL/ADR obtained a more uniform microcrystalline dispersion during cooling. Therefore, when the PCL content was 30 wt%, the elongation at break and impact strength of PLA/PCL/ADR were effectively improved at 1 wt% ADR. [ABSTRACT FROM AUTHOR]