Xuzhou Construction Machinery Group (China)

ABSTRACT Stretchable hybrid electronics offer a compelling pathway for integrating rigid microelectronic components within soft, deformable platforms, crucial for the development of next‐generation wearable and epidermal devices. However, the pronounced mechanical mismatch at the interface between stiff components and elastomeric substrates often leads to interfacial failure, including delamination and cracking, under dynamic strain conditions. In this study, we present a mechanically optimized and electrically resilient stretchable hybrid electronic system by co‐designing substrate mechanics and conductive architectures. A photolithographically defined gradient crosslinking technique is utilized to fabricate a modulus‐graded polydimethylsiloxane (PDMS) substrate, exhibiting a spatially tunable elastic modulus ranging from 0.12 to 1.4 MPa. This gradient‐modulus configuration enables effective redistribution of localized strain, thereby alleviating stress concentration and enhancing mechanical durability at heterogeneous interfaces. Concurrently, a silver‐PDMS composite conductor is developed, achieving high conductivity (1 Ω/sq under 50% strain), excellent stretchability, and environmental robustness. Direct‐ink‐writing using an inkjet platform facilitates precise patterning of conductive traces, allowing seamless integration of multifunctional sensor arrays capable of real‐time monitoring of wrist kinematics and complex finger movements. This work underscores the synergistic integration of gradient mechanical engineering and scalable microfabrication strategies, paving the way for robust, high‐fidelity wearable electronics and electronic skin systems.

The naval air defense planning (NADP) problem entails the defense of a naval fleet against aerial threats. This complex and dynamic problem requires real-time decision-making and adaptation to evolving warfare environment. While our previous work addressed the static NADP problem by proposing a mathematical model and heuristic solutions for sensor allocation, engagement scheduling, and ship routing, this study extends to the dynamic NADP problem. Unlike the static version, which assumes complete knowledge of future threats, the dynamic NADP problem requires continuous updates and real-time adjustments to decisions as new threats emerge and situational parameters change. We present modifications in the mathematical formulation, which is based on a mixed-integer nonlinear programming (MINLP) model, alongside a comprehensive simulation structure. We employ heuristic solution approaches that utilize a combination of a genetic algorithm, construction of an engagement graph to solve the shortest path problem, and dynamic programming (DP) techniques. Computational experiments are conducted to evaluate the effectiveness of these methods in addressing the dynamic NADP problem. The study also explores machine learning models for threat prioritization, offering innovative solutions to the challenges posed by dynamic naval air defense scenarios.

The hanging net shielding system, employing a suspended cage-type enclosed structure to restrict the high-voltage transmission wire, has seen increasingly widespread application in transmission line crossing construction. However, the lack of a comprehensive dynamic analysis methodology has limited the standardization of its design and usage. In this investigation, a systematical dynamic modeling and analysis procedure of the hanging net shielding system is proposed based on the absolute nodal coordinate formulation (ANCF). The carrier cable, slings and transmission wire are discretized by the ANCF cable element. The spatial flexible beam–beam contact model and the assumption of a single contact area are adopted to perform the contact searching between the transmission wire and the horizontal pulley. The system dynamics analysis equation is assembled and solved by generalized alpha method. A full-scale model is simulated for the transmission wire impact condition and the variation history of the tension in carrier cable and the sling cable are given. The peak value of the tension in carrier cable could be 110 kN, while the largest tension in sling cable is 9 kN. Results could help to ensure construction safety, shorten the design cycle of the protection system and reduce the development cost at the same time.

Neural networks commonly employ the McCulloch-Pitts neuron model, which is a linear model followed by a point-wise non-linear activation. Various researchers have already advanced inherently non-linear neuron models, such as quadratic neurons, generalized operational neurons, generative neurons, and super neurons, which offer stronger non-linearity compared to point-wise activation functions. In this paper, we introduce a novel and better non-linear neuron model called Padé neurons ( $\mathrm {\textit {Paon}}$ s), inspired by Padé approximants. $\mathrm {\textit {Paon}}$ s offer several advantages, such as diversity of non-linearity, since each $\mathrm {\textit {Paon}}$ learns a different non-linear function of its inputs, and layer efficiency, since $\mathrm {\textit {Paon}}$ s provide stronger non-linearity in much fewer layers compared to piecewise linear approximation. Furthermore, $\mathrm {\textit {Paon}}$ s include all previously proposed neuron models as special cases, thus any neuron model in any network can be replaced by $\mathrm {\textit {Paon}}$ s. We note that there has been a proposal to employ the Padé approximation as a generalized point-wise activation function, which is fundamentally different from our model. To validate the efficacy of $\mathrm {\textit {Paon}}$ s, in our experiments, we replace classic neurons in some well-known neural image super-resolution, compression, and classification models based on the ResNet architecture with $\mathrm {\textit {Paon}}$ s. Our comprehensive experimental results and analyses demonstrate that neural models built by $\mathrm {\textit {Paon}}$ s provide better or equal performance than their classic counterparts with a smaller number of layers. The PyTorch implementation code for $\mathrm {\textit {Paon}}$ is open-sourced at https://github.com/onur-keles/Paon.

This article investigates the presence and impact of stray currents in rotor windings of large salient-pole generators, where field measurements from several large hydrogenerators have shown that unexpected stray currents can significantly affect machine sensor operations and readings, leading therefore to incorrect diagnostics and even failures. This study aims to contribute to a better understanding of the circulation of stray currents in rotor pole windings during machine operation. It highlights some interesting information contained in the stray current that could offer critical insights into machine operations. An experimental setup in a real environment, with in-service conditions, was reproduced to test a 310 MVA, 13.8 kV, 128.6 rpm – 60 Hz (56 poles) hydrogenerator poles with a nominal field current of 2475 A. The tested poles in a real environment with a 2000T press and static excitation of 3000 A DC have been used to reproduce and follow the path of these stray currents under insulation failures. Correlating the obtained results with more field measurements collected in hydrogenerators of 166 and 285 MVA shows that the impact of overvoltages generated by the excitation system, due to thyristor turn-off recovery currents, may have caused a malfunction of some air-gap sensors. It will be shown that pole winding inter-turn insulation failure could be more sensitively detected using AC and even HF parasitic voltage. Moreover, the comprehension of the stray current circulation in a pole winding under real inter-turn short circuit (ITSC) condition could be improved by understanding the remaining current that still contributes to the magnetomotive force as demonstrated by experimental results.