Wireless Powered Metaverse: Joint Task Scheduling and Trajectory Design for Multi-Devices and Multi-UAVs

To support the running of human-centric metaverse applications on mobile devices, Unmanned Aerial Vehicle (UAV)-assisted Wireless Powered Mobile Edge Computing (WPMEC) is promising to compensate for limited computational capabilities and energy supplies of mobile devices. The high-speed computational processing demands and significant energy consumption of metaverse applications require joint resource scheduling of multiple devices and UAVs, but existing WPMEC solutions address either device or UAV scheduling due to the complexity of combinatorial optimization. To solve the above challenge, we propose a two-stage alternating optimization algorithm based on multi-task Deep Reinforcement Learning (DRL) to jointly allocate charging time, schedule computation tasks, and optimize trajectory of UAVs and mobile devices in a wireless powered metaverse scenario. First, considering energy constraints of both UAVs and mobile devices, we formulate an optimization problem to maximize the computation efficiency of the system. Second, we propose a heuristic algorithm to efficiently perform time allocation and charging scheduling for mobile devices. Following this, we design a multi-task DRL scheme to make charging scheduling and trajectory design decisions for UAVs. Finally, theoretical analysis and performance results demonstrate that our algorithm exhibits significant advantages over representative methods in terms of convergence speed and average computation efficiency

Image restoration with point-spread function regularization and active learning

Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal the intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and point-spread functions can hamper the accuracy and efficiency of information extraction from these images. To mitigate these effects, we propose a novel image restoration algorithm that connects a deep-learning-based restoration algorithm with a high-fidelity telescope simulator. During the training stage, the simulator generates images with different levels of blur and noise to train the neural network based on the quality of restored images. After training, the neural network can restore images obtained by the telescope directly, as represented by the simulator. We have tested the algorithm using real and simulated observation data and have found that it effectively enhances fine structures in blurry images and increases the quality of observation images. This algorithm can be applied to large-scale sky survey data, such as data obtained by the Large Synoptic Survey Telescope (LSST), Euclid, and the Chinese Space Station Telescope (CSST), to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research

Lomerizine attenuates LPS-induced acute lung injury by inhibiting the macrophage activation through reducing Ca2+ influx

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are life-threatening lung diseases with high mortality rates, predominantly attributable to acute and severe pulmonary inflammation. Lomerizine (LMZ) is a calcium channel blocker previously used in preventing and treating migraine. Here, we found that LMZ inhibited inflammatory responses and lung pathological injury by reducing pulmonary edema, neutrophil infiltration and pro-inflammatory cytokine production in lipopolysaccharide (LPS)-induced ALI mice. In vitro experiments, upon treating with LMZ, the expression of interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α was attenuated in macrophages. The phosphorylation of p38 MAPK, ERK1/2, JNK, and NF-κB p65 was inhibited after LMZ treatment. Furthermore, LPS-induced Ca2+ influx was reduced by treating with LMZ, which correlated with inhibition of pro-inflammatory cytokine production. And L-type Ca2+ channel agonist Bay K8644 (BK) could restore cytokine generation. In conclusion, our study demonstrated that LMZ alleviates LPS-induced ALI and is a potential agent for treating ALI/ARDS

Recent Progress in Phage Therapy to Modulate Multidrug-Resistant Acinetobacter baumannii, Including in Human and Poultry

Acinetobacter baumannii is a multidrug-resistant and invasive pathogen associated with the etiopathology of both an increasing number of nosocomial infections and is of relevance to poultry production systems. Multidrug-resistant Acinetobacter baumannii has been reported in connection to severe challenges to clinical treatment, mostly due to an increased rate of resistance to carbapenems. Amid the possible strategies aiming to reduce the insurgence of antimicrobial resistance, phage therapy has gained particular importance for the treatment of bacterial infections. This review summarizes the different phage-therapy approaches currently in use for multiple-drug resistant Acinetobacter baumannii, including single phage therapy, phage cocktails, phage–antibiotic combination therapy, phage-derived enzymes active on Acinetobacter baumannii and some novel technologies based on phage interventions. Although phage therapy represents a potential treatment solution for multidrug-resistant Acinetobacter baumannii, further research is needed to unravel some unanswered questions, especially in regard to its in vivo applications, before possible routine clinical use

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

MgF2-Modified Hydrotalcite-Derived Composites Supported Pt-In Catalysts for Isobutane Direct Dehydrogenation

Here, a simple method was developed to prepare an MgF2-modified hydrotalcite-derived composite, which was used as support for the Pt-In catalyst for isobutane direct dehydrogenation. The catalysts, composites, and their precursors were characterized by numerous characterization techniques. The results provided evidence for the MgF2 promoter effect on the physical–chemical properties and dehydrogenation performance of the supported Pt-In catalysts. The catalyst with MgF2 shows exceptional isobutene selectivity that can be stabilized at 95%, and the conversion increases from 50% to 58% during the reaction process. Moreover, the existence of MgF2 plays an important role in the resistance to coke formation and Pt sintering by improving the Pt dispersion, inhibiting the reduction of the In3+ species, and adjusting the acidity of the catalyst

Combining and Comparing Coalescent, Distance and Character-Based Approaches for Barcoding Microalgaes: A Test with Chlorella-Like Species (Chlorophyta)

Several different barcoding methods of distinguishing species have been advanced, but which method is the best is still controversial. Chlorella is becoming particularly promising in the development of second-generation biofuels. However, the taxonomy of Chlorella–like organisms is easily confused. Here we report a comprehensive barcoding analysis of Chlorella-like species from Chlorella, Chloroidium, Dictyosphaerium and Actinastrum based on rbcL, ITS, tufA and 16S sequences to test the efficiency of traditional barcoding, GMYC, ABGD, PTP, P ID and character-based barcoding methods. First of all, the barcoding results gave new insights into the taxonomic assessment of Chlorella-like organisms studied, including the clear species discrimination and resolution of potentially cryptic species complexes in C. sorokiniana, D. ehrenbergianum and C. Vulgaris. The tufA proved to be the most efficient barcoding locus, which thus could be as potential “specific barcode” for Chlorella-like species. The 16S failed in discriminating most closely related species. The resolution of GMYC, PTP, P ID, ABGD and character-based barcoding methods were variable among rbcL, ITS and tufA genes. The best resolution for species differentiation appeared in tufA analysis where GMYC, PTP, ABGD and character-based approaches produced consistent groups while the PTP method over-split the taxa. The character analysis of rbcL, ITS and tufA sequences could clearly distinguish all taxonomic groups respectively, including the potentially cryptic lineages, with many character attributes. Thus, the character-based barcoding provides an attractive complement to coalescent and distance-based barcoding. Our study represents the test that proves the efficiency of multiple DNA barcoding in species discrimination of microalgaes.Waller Creek Working Grou

Chemical structure of hollow carbon spheres and polyaniline nanocomposite

In this data article, the chemical data of hollow carbon spheres and polyaniline (HCS@PANI) nanocomposite are presented for the research article entitled “Novel electrochemical biosensor based on core-shell nanostructured composite of hollow carbon spheres and polyaniline for sensitively detecting malathion” (He et al., 2018) [1]. The data includes chemical structure and components obtained by Raman spectra, X-ray photoelectron spectroscopy (XPS), and nitrogen adsorption and desorption isotherms