The motion of magnetic microrobots relies on an external magnetic field. Current electromagnetic propulsion systems are often complex in structural design, challenging in control strategies, and associated with high energy consumption. In this study, we introduce a mechano-electromagnetic hybrid actuation system that employs only a pair of Helmholtz coils to generate an oscillating uniform magnetic field, combined with a rotating platform to modify the field’s direction, thereby enabling the control of microrobot speed and direction. The properties of this hybrid actuation system were modeled and validated to analyze the magnetic field distribution within the workspace. The design and actuation of two microrobots are described in detail. Furthermore, a visual feedback closed-loop control strategy for the system was developed. A series of navigation control experiments were conducted, demonstrating that the proposed hybrid actuation system achieves effective microrobot actuation and precise control while reducing manufacturing costs and simplifying control strategies. The proposed novel mechano-electromagnetic hybrid structure contributes to the field of electromagnetically driven systems and magnetically controlled microrobots.

Deep and rapid peak shaving requirements of thermal power unit are becoming more significant in the low-carbon power system consisting of high-proportion renewable energy sources. In order to meet these requirements, this article proposes an improved Mamba-2-based intelligent modeling method to obtain the precise deep peak shaving process model of thermal power-molten salt heat storage coupled system. Based on the state space duality, the Mamba-2 shows great prediction accuracy and speed in dealing with the multiple long-time sequences. To efficiently model the wide-range and rapidly changing deep peak shaving sequences, the context learning capacity, computational efficiency and convergence speed of Mamba-2 must be improved. First, the sparse attention mechanism that can selectively process some of the tokens is established to promote the modeling precision of Mamba-2. Moreover, the novel least-squares interpolation (Lion) optimizer is successfully utilized to accelerate the training process in Mamba-2 for the first time. Then, both the inputs and outputs sequences inside Mamba-2 are normalized to facilitate the modeling accuracy improvement of thermal power-molten salt heat storage coupled system. The improved Mamba-2 with stable state space model, prominent modeling precision and speed effectively provides guarantee for the deep peak shaving of unit. Finally, the superior model accuracy of the proposed intelligent modeling method is verified based on the on-site deep peak shaving sequences of 660 MW thermal power unit and 40 MW molten salt heat storage system.

Escape analysis plays a crucial role in garbage-collected languages as it enables the allocation of non-escaping variables on the stack by identifying the dynamic lifetimes of objects and pointers. This helps in reducing heap allocations and alleviating garbage collection pressure. However, Go, as a garbage-collected language, employs a fast yet conservative escape analysis, which is field-insensitive and omits point-to-set calculation to expedite compilation. This results in more variables being allocated on the heap. Empirical statistics reveal that field access and indirect memory access are prevalent in real-world Go programs, suggesting potential opportunities for escape analysis to enhance program performance. In this paper, we propose MEA 2 , an escape analysis framework atop GoLLVM (an LLVM-based Go compiler), which combines field sensitivity and points-to analysis. Moreover, a novel generic function summary representation is designed to facilitate fast inter-procedural analysis. We evaluated it by using MEA 2 to perform stack allocation in 12 wildly-use open-source projects. The results show that, compared to Go’s escape analysis, MEA 2 can reduce heap allocation sites by 7.9 % on average (up to 25.7 % ) while reducing the dynamic memory allocation size by 11.6 % on average (up to 35.5 % ). All this is achieved while keeping the time overhead of escape analysis within 1 % of the compilation process.

Five-hundred-meter aperture spherical radio telescope (FAST) is a very famous large-scale scientific facility with excellent performance for astronomical observation in the world, but it currently fails to observe the center of the Milky Way Galaxy due to the limited observation angle that is affected by the heavy weight of the feed cabin. To improve this problem, an upgraded feed mechanism (UFM) with a lighter cable structure is designed and employed to replace the existing heavy rigid A-B rotator and Stewart platform in the feed cabin of FAST. The structural dimension of the UFM is analyzed and optimized under cable tension constraints to meet the requirements of the observation angle. Then, a novel disturbance increment method is proposed to analyze the anti-disturbance ability of the UFM, where a gradually increased disturbance wrench is applied to the UFM with the stiffness matrix iteratively updated. Through the dimensional optimization and further anti-disturbance analysis, the newly-designed UFM can indeed meet the higher demand for astronomical observation with the larger observation angle, which benefits from the lightweight cable structure. Besides, the UFM also has the appreciable anti-disturbance ability for long-term stable operation of FAST.

CGO is a foreign function interface mechanism that enables the creation of Go packages that call C code. It provides a way to reuse legacy C code or high-performance C libraries. However, it is tedious and error-prone to manually write the bindings of C libraries in Go. To make better use of CGO to realize the use of C libraries in Go, we propose CGORewritter in this paper. For a given Go library and corresponding C library, the internal code of the Go API function can be rewritten while keeping the Go API unchanged, and the C API function can be called through CGO in a semi-automatic way so that the application layer program can upgrade without any change. We use this method to rewrite the go/crypto with the OpenSSL library. Experimental results show that the rewritten code maintains full functionality and can be used by application code without modification. Besides, the rewritten code gains a 0.97-3.07× speedup.

Multiple-input multiple-output (MIMO) technique is a key enabling technology for today's high-rate wireless communications. The sphere decoding algorithm (SDA) has been used for maximum likelihood (ML) detection in MIMO systems. However, it suffers from variable computation complexity and non-fixed throughput. Therefore, the k-best sphere decoding algorithm is proposed for MIMO detections for its less complexity and fixed throughput. The disadvantage for k-best SDA is that it has performance loss due to the reason that ML detection is not guaranteed. In this paper, we propose some improved k-best sphere decoding algorithms which improve the MIMO detection performance. Simulation results show that by applying sorted QR decomposition for the channel matrix, and/or introducing dynamic K values for different layers, our improved algorithms can achieve 1-2 dB detection performance gain for 4 /spl times/ 4 64QAM MIMO systems over the traditional k-best SDA without introducing extra computational complexity.