A Fairness-Enhanced Federated Learning Scheduling Mechanism for UAV-Assisted Emergency Communication.

As the frequency of natural disasters increases, the study of emergency communication becomes increasingly important. The use of federated learning (FL) in this scenario can facilitate communication collaboration between devices while protecting privacy, greatly improving system performance. Considering the complex geographic environment, the flexible mobility and large communication radius of unmanned aerial vehicles (UAVs) make them ideal auxiliary devices for wireless communication. Using the UAV as a mobile base station can better provide stable communication signals. However, the number of ground-based IoT terminals is large and closely distributed, so if all of them transmit data to the UAV, the UAV will not be able to take on all of the computation and communication tasks because of its limited energy. In addition, there is competition for spectrum resources among many terrestrial devices, and all devices transmitting data will bring about an extreme shortage of resources, which will lead to the degradation of model performance. This will bring indelible damage to the rescue of the disaster area and greatly threaten the life safety of the vulnerable and injured. Therefore, we use user scheduling to select some terrestrial devices to participate in the FL process. In order to avoid the resource waste generated by the terrestrial device resource prediction, we use the multi-armed bandit (MAB) algorithm for equipment evaluation. Considering the fairness issue of selection, we try to replace the single criterion with multiple criteria, using model freshness and energy consumption weighting as reward functions. The state of the art of our approach is demonstrated by simulations on the datasets.

Side Effects of Opioids Are Ameliorated by Regulating TRPV1 Receptors.

Humans have used opioids to suppress moderate to severe pain for thousands of years. However, the long-term use of opioids has several adverse effects, such as opioid tolerance, opioid-induced hyperalgesia, and addiction. In addition, the low efficiency of opioids in controlling neuropathic pain limits their clinical applications. Combining nonopioid analgesics with opioids to target multiple sites along the nociceptive pathway may alleviate the side effects of opioids. This study reviews the feasibility of reducing opioid side effects by regulating the transient receptor potential vanilloid 1 (TRPV1) receptors and summarizes the possible underlying mechanisms. Blocking and activating TRPV1 receptors can improve the therapeutic profile of opioids in different manners. TRPV1 and µ-opioid receptors are bidirectionally regulated by ß-arrestin2. Thus, drug combinations or developing dual-acting drugs simultaneously targeting µ-opioid and TRPV1 receptors may mitigate opioid tolerance and opioid-induced hyperalgesia. In addition, TRPV1 receptors, especially expressed in the dorsal striatum and nucleus accumbens, participate in mediating opioid reward, and its regulation can reduce the risk of opioid-induced addiction. Finally, co-administration of TRPV1 antagonists and opioids in the primary action sites of the periphery can significantly relieve neuropathic pain. In general, the regulation of TRPV1 may potentially ameliorate the side effects of opioids and enhance their analgesic efficacy in neuropathic pain.