Training generative adversarial networks (GANs) for noise-to-image synthesis is a challenge task, primarily due to the instability of GANs’ training process. One of the key issues is the generator’s sensitivity to input data, which can cause sudden fluctuations in the generator’s loss value with certain inputs. This sensitivity suggests an inadequate ability to resist disturbances in the generator, causing the discriminator’s loss value to oscillate and negatively impacting the discriminator. Then, the negative feedback of discriminator is also not conducive to updating generator’s parameters, leading to suboptimal image generation quality. In response to this challenge, we present an innovative GANs model equipped with a learnable auxiliary module that processes auxiliary noise. The core objective of this module is to enhance the stability of both the generator and discriminator throughout the training process. To achieve this target, we incorporate a learnable auxiliary penalty and an augmented discriminator, designed to control the generator and reinforce the discriminator’s stability, respectively. We further apply our method to the Hinge and LSGANs loss functions, illustrating its efficacy in reducing the instability of both the generator and the discriminator. The tests we conducted on LSUN, CelebA, Market-1501, and Creative Senz3D datasets serve as proof of our method’s ability to improve the training stability and overall performance of the baseline methods.

The Internet of Things (IoT) requires different but complementary types of wireless network technologies, having their own merits from distance, power consumption, capacity and cost perspectives. This paper introduces a comparative study of LoRa wireless communication network technology for the long-distance and low-power wide-area network scenario deployment. In this paper, we propose a low-power wide-area network construction method based on LoRa technology for an agricultural science and technology park in Ningxia, Western China. Specifically, we design the optimal placement of LoRa sensors in the facility greenhouse by measuring the LoRa signal quality on the spot in the traditional brick wall sunlight greenhouse. In the traditional brick wall solar greenhouse, the success rate of data communication between LoRa terminal and LoRa gateway reaches 100%. Experiment shows that the coverage distance of the LoRa network reaches 3km in the most common conditions in agricultural science and technology park, i.e., the height of the LoRa gateway is 10 meters, and the maximum transmission power is 27dbm.

Wheat pests and diseases threaten food security, but field management still lacks intelligent systems that can connect visual diagnosis with executable treatment recommendations. This study proposes WDPG, a knowledge-enhanced multi-agent framework that integrates a three-layer heterogeneous knowledge graph with a source-traceable prescription knowledge base, and combines DSPy-based diagnostic prompt optimization, DeepResearch-based evidence completion, and agentic prescription verification. The system takes wheat images or symptom descriptions as input and coordinates Diagnostic, Prescription, and Critic agents to generate structured prescriptions with traceable evidence, dosage, application method, and pre-harvest interval. On a 601-sample held-out test set, the integrated WDPG system achieved an end-to-end full-prescription accuracy of 0.9218 and demonstrated better overall performance than GPT-5.5 and GLM-4.6V for wheat pest and disease diagnosis and prescription generation. These results support the effectiveness of the integrated WDPG framework for structured and source-grounded wheat pest and disease diagnosis and prescription generation.