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This paper investigates the energy transfer and data collection problem of multiple unmanned aerial vehicle (UAV)-assisted wireless-powered Internet of Things (IoT) networks. To ensure information freshness for IoT devices and reduce UAVs’ energy consumption, we minimize the average Age of Information (AoI) of IoT devices by jointly optimizing the energy harvesting (EH) and data collection time for IoT devices, the selection of data collection points (DCPs), DCP-IoT associations, and task assignment, flight speed, and trajectories of UAVs, subject to the limited endurance of UAVs. As this problem is nonconvex, we propose a novel DCP association and trajectory-planning scheme that seeks age-optimal solutions through an iterative three-step process. First, we calculate the EH and data collection time for IoT devices using Karush–Kuhn–Tucker (KKT) conditions. Then, we introduce an optimal hovering time allocation-based affinity propagation (OHTAP) clustering algorithm to determine optimal DCP locations and establish DCP-IoT associations. Finally, we develop two algorithms to optimize UAVs’ trajectories: an improved partheno-genetic algorithm with enhancement mechanisms (EIPGA) and a hybrid algorithm that combines improved MinMax k-means clustering with EIPGA. Numerical results confirm that our scheme consistently outperforms benchmark schemes in AoI performance and solution stability across diverse scenarios.

Returning straw to the field is a crucial practice for enhancing soil quality and increasing efficient use of secondary crop products. However, maize straw has a higher carbon-to-nitrogen ratio compared to other crops. This can result in crop nitrogen loss when the straw is returned to the field. Therefore, it is crucial to explore how different methods of straw return affect maize (Zea mays L.) farmland. In this study, a field experiment was performed with three treatments (I, no straw returned, CK; II, direct straw return, SR; and III, straw returned in deep furrows, ISR) to explore the effects of the different straw return modes on soil carbon and nitrogen content and greenhouse gas emissions. The results indicated that the SR and ISR treatments increased the dissolved organic carbon (DOC) content in the topsoil (0–15 cm). Additionally, the ISR treatment boosted the contents of total nitrogen (TN), nitrate nitrogen (NO3−-N), ammonium nitrogen (NH4+-N), dissolved organic nitrogen (DON), and DOC in the subsurface soil (15–30 cm) compared with CK. When it comes to greenhouse gas emissions, the ISR treatment led to an increase in CO2 emissions. However, SR and ISR reduced N2O emissions, with ISR showing a more pronounced reduction. The ISR treatment significantly increased leaf and grain biomass compared to CK and SR. The correlation analyses showed that the yield was positively correlated with soil DOC, and soil greenhouse gas emission was correlated with soil NO3−-N. The ISR technology has great potential in sequestering soil organic matter, improving soil fertility, and realizing sustainable agricultural development.

Sound event localization and detection (SELD) is a crucial component of machine listening that aims to simultaneously identify and localize sound events in multichannel audio recordings. This task demands an integrated analysis of spatial, temporal, and frequency domains to accurately characterize sound events. The spatial domain pertains to the varying acoustic signals captured by multichannel microphones, which are essential for determining the location of sound sources. However, the majority of recent studies have focused on time-frequency correlations and spatio-temporal correlations separately, leading to inadequate performance in real-life scenarios. In this paper, we propose a novel SELD method that utilizes the newly developed Spatio-Temporal-Frequency Fusion Network (STFF-Net) to jointly learn comprehensive features across spatial, temporal, and frequency domains of sound events. The backbone of our STFF-Net is the Enhanced-3D (E3D) residual block, which combines 3D convolutions with a parameter-free attention mechanism to capture and refine the intricate correlations among these domains. Furthermore, our method incorporates the multi-ACCDOA format to effectively handle homogeneous overlaps between sound events. During the evaluation, we conduct extensive experiments on three de facto benchmark datasets, and our results demonstrate that the proposed SELD method significantly outperforms current state-of-the-art approaches.

Polyethylene film mulch (PM) is a kind of widely used technology to improve crop yields worldwide; however, because of a problem related with plastic residual pollution, it has gradually been replaced by biodegradable plastic film mulch (BDP). Although BDP has helped to solve the plastic residual pollution, its consequences in terms of greenhouse gas (GHG) emissions have rarely been revealed. Related knowledge is important for forming low-carbon development strategies for the plastic industry and agriculture. The objective of this study is to evaluate the influence of BDP on GHG emissions at different stages of its life cycle, and determine whether replacing polyethylene (PE) film with BDP film is a helpful way to reduce national GHG emissions. The results of this study suggest that the application of BDP improved the GHG emissions associated with agricultural inputs, but induced lower GHG emissions at the growing stage and the waste disposal stage, and resulted in lower total area-scale GHG emissions. Compared to the no mulch (NM) cultivation system, the yield-scale carbon footprint was reduced in both the PM and BDP cultivation systems, which meant that both PM and BDP produced lower GHG emissions than NM for the production of the same amount of grain. It was concluded that BDP is not only a measure to control the problem of plastic residue pollution in agriculture, but it can also mitigate the GHG emissions.