Electronics

This paper proposes a power-of-two-based quantization technique aimed at improving the hardware efficiency of artificial neural networks (ANNs) implemented on field-programmable gate arrays (FPGAs). The effectiveness of the proposed approach is validated using gated recurrent unit (GRU) models. The resulting architecture, referred to as $2^{Q}GRU$, exploits parallelism, optimized operation scheduling, and fine-grained data bit-width management to achieve efficient hardware realization. Compared with state-of-the-art FPGA implementations based on sparsity compression, $2^{Q}GRU$ demonstrates superior performance in terms of resource utilization and power consumption, while eliminating the need for dedicated DSP blocks. Furthermore, area and power efficiency can be further improved by trading latency for reduced hardware cost through an integrated implementation reduction strategy, enabling deployment on highly resource-constrained devices. Finally, the $2^{Q}GRU$ model is integrated into an automated ANN framework, allowing the proposed quantization and hardware optimization techniques to be readily extended to other ANN models and FPGA-based deployments.

As the composition of energy markets becomes increasingly diverse and distributed in character, it is difficult for traditional vertically integrated energy system (IES) structures and centralized optimization methods to stimulate coupled interactions and interactive synergies among multiple subjects. Consequently, a collaborative low-carbon scheduling strategy utilizing a leader–follower game framework is introduced for the distributed IES. Making the integrated energy system operator (IESO) a leader, distributed integrated energy supply system (DIESS) and smart user terminal (SUT) as followers, the optimal interaction operation strategy of each subject in the game process can be solved. Firstly, the overall energy interaction process of the system and the game objectives of each participant are introduced to construct a distributed collaborative optimization model with one leader and multiple followers. Secondly, the integrated demand response (IDR) and the ladder-type carbon trading scheme are considered, the two-stage operation process of the electrical gas technology (P₂G) equipment is analyzed in detail, and the genetic algorithm nested CPLEX solver is used to solve the model. Finally, the results show that this paper can provide guarantee and theoretical support for the optimal operation of the integrated energy market in terms of trading model and algorithm.

The light-trap attraction rate (LTARI) is an important metric for characterizing diel activity patterns and supports studies in insect behavioral ecology and pest management. However, conventional automatic light-trap devices often rely on lethal methods (e.g., high-voltage grids or infrared heating), causing high mortality of non-target insects and severe image obstruction due to stacking of insect bodies. These issues disturb natural populations and bias attempts to quantify LTARI. Our primary objective is to develop and evaluate a non-lethal monitoring system as a methodological basis for future LTARI research, rather than to provide head-to-head quantitative comparisons with conventional traps. To address the above limitations, we propose a live-insect monitoring instrument that integrates a wind-suction trap with a Water-Flow Dispersion and Transport Structure (WF-DTS). The non-destructive trapping–dispersion–release process limits body stacking, allows captured insects to be released, and yields a community-level post-capture survival rate of 94% under the conditions tested. Experimental results show that the prototype maintains image integrity with clearly isolated single insects and achieves a detection performance of 95.6% (mAP@0.5) using the YOLOv8s model. At the inference stage, only the standard resizing and normalization operations of YOLOv8s are applied, without additional denoising, background subtraction, or data augmentation. These observations suggest that the WF-DTS generates images that are easier to segment and classify than those from conventional devices. The high detection accuracy is largely attributable to the physical dispersion of specimens and the uniform white matte background provided by the hardware design. Overall, the system constitutes a non-lethal hardware–software platform that may reduce backend processing complexity and provide a methodological basis for more accurate LTARI estimation in future, dedicated field studies.