Accurate sleep apnea subtype classification is important because obstructive, central, and mixed apnea reflect different physiological mechanisms and may require different clinical management. Although EEG captures cortical responses to respiratory disturbances and microarousal, most existing methods treat each 30s epoch as homogeneous, potentially obscuring brief and localized patterns. This study proposes a transient aware framework for single-channel EEG analysis. It combines event-sensitive intra-epoch segmentation, complementary segment encoding, and structured aggregation, enabling the joint preservation of transient heterogeneity and global spectral context. Multiscale spectral divergence and gamma-band burst cues identify intra-epoch boundaries. Hermite decomposition and Fourier sub-band analysis describe the resulting variable-length segments. Distributional, temporal, and diversity-related statistics then produce a fixed-dimensional representation for classification. On the UCDDB dataset, subject-wise five-fold cross-validation yielded a macro-F1 of 0.8546 ± 0.0231 for four-class classification among Normal, OSA, CSA, and MSA, and an F1 of 0.8608 ± 0.0141 for binary Normal-versus-Apnea detection. In subject-disjoint ablation experiments, adding adaptive local modeling to fixed-epoch spectral features increased the F1 by 6.3 and 5.8 percentage points in the four-class and binary tasks, respectively. Experiments on MIT-BIH further supported the applicability of the representation. These results indicate that transient intra-epoch EEG structure provides complementary information for sleep apnea subtype classification.
Many animals and plants establish intimate symbiotic relationships with specific microorganisms acquired from the environment. Given the immense diversity of environmental microbiomes, selecting appropriate partners from such a vast microbial pool poses a critical challenge for host organisms. To meet this challenge, hosts have evolved sophisticated internal partner-choice mechanisms that ensure stable associations with beneficial microbes. However, because these symbionts primarily inhabit external environments, environmental conditions themselves are also expected to influence the establishment of symbiosis. Despite this expectation, the mechanistic role of external environmental filters in shaping the intended symbiosis remains largely unexplored. Focusing on stink bugs, which acquire their symbiotic bacteria from soil each generation, we investigated how soil properties influence the establishment of gut symbiosis in terrestrial insects. Microbiome analyses confirmed that Burkholderia sensu lato overwhelmingly dominates a specific gut organ in six stink bug species from the superfamilies Coreoidea and Lygaeoidea, including serious agricultural pests (relative abundance ranging from 74.5 to 100
Unmanned aerial vehicles (UAVs) are playing an increasingly pivotal role in modern communication networks,offering flexibility and enhanced coverage for a variety of applica-tions. However, UAV networks pose significant challenges due to their dynamic and distributed nature, particularly when dealing with tasks such as power allocation, channel assignment, caching,and task offloading. Traditional optimization techniques often struggle to handle the complexity and unpredictability of these environments, leading to suboptimal performance. This survey provides a comprehensive examination of how deep reinforcement learning (DRL) can be applied to solve these mathematical optimization problems in UAV communications and networking.Rather than simply introducing DRL methods, the focus is on demonstrating how these methods can be utilized to solve complex mathematical models of the underlying problems. We begin by reviewing the fundamental concepts of DRL, including value-based, policy-based, and actor-critic approaches. Then,we illustrate how DRL algorithms are applied to specific UAV network tasks by discussing from problem formulations to DRL implementation. By framing UAV communication challenges as optimization problems, this survey emphasizes the practical value of DRL in dynamic and uncertain environments. We also explore the strengths of DRL in handling large-scale network scenarios and the ability to continuously adapt to changes in the environment. In addition, future research directions are outlined, highlighting the potential for DRL to further enhance UAV communications and expand its applicability to more complex,multi-agent settings.
We evaluated the performance and environmental robustness of three state-of-the-art gaze classification algorithms designed for infant looking-time research: iCatcher+, OWLET, and an Amazon Rekognition-based model. Gaze classifications for each algorithm were compared to human-coded data using a novel dataset (N = 47), and iCatcher+ demonstrated the highest agreement (78.4–85.4
Air-to-ground communication networks in future sixth-generation (6G) networks are expected to leverage integrated sensing and communication (ISAC) to support the low-altitude economy (LAE). In such networks, a set of unmanned aerial vehicles (UAVs) acting as mobile edge computing (MEC) servers cooperatively process delay-sensitive tasks offloaded by multiple authorised vehicular user equipments (V-UEs). However, the diverse, stringent requirements of ISAC-enabled V-UE services require more intelligent and efficient resource allocation for the LAE-aided vehicle-to-everything (V2X) communications systems. To address this issue, we propose a digital twin (DT)-empowered multi-agent LAE MEC vehicular framework, where the DT technology enables real-time data collection, processing, monitoring, and optimisation in a virtual environment. Meanwhile, each V-UE may offload its delay-sensitive task to a UAV-assisted MEC server. We aim to minimise the long-term average total service delay (which may include the task processing delay and the transmission delay) of all V-UEs, the computation resource allocation at each UAV-assisted MEC server, the transmission power, and the allocation of resource blocks for all V-UEs. To solve the joint optimisation problem, we propose a Multi-agent deep Q-network-based Offloading and Resource allocation Optimisation (MORO) algorithm. Simulation results demonstrate that our proposed algorithm outperforms the benchmarks in terms of the convergence rate and the long-term average total service delay of all V-UEs.
