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This article analyzes the role of sound in the transformation of social space and organization of noncentered group action through a case study of the series of radio ballets programmed by the German artist collective LIGNA. Typically, LIGNA uses portable audio devices and wireless technology to disseminate instructions to the radio ballet participants, directing them to make synchronized body gestures but not giving them fixed movement paths or identities. This process creates a conceptualized acoustic space parallel to the physical space, challenges the regulations that come with the privatization of public spaces, and dismantles the dichotomies between inside and outside, public and private, and physical and mental. I argue that the radio ballet participants, who form a decentralized, temporary, and mobile community, can explore new possibilities for political intervention in the public sphere through silent collective performances in which sound not only serves the important function of sidestepping visual censorship and avoiding physical conflict but also provides a new methodology for reshaping the order of social space.

In this research, the composition of free phenols, bound phenols, and anthocyanins and their in vitro antioxidant activity and in vitro α-glucosidase inhibiting activity were observed in different barley colors. The outcomes revealed that the contents of total phenols (570.78 mg/100 gDW), total flavonoids (47.08 mg/100 gDW), and anthocyanins (48.07 mg/100 g) were the highest in purple barley. Furthermore, the structure, composition, and concentration of phenolics differed depending on the colors of barley. The types and contents of bound total phenolic acids and flavonoids were greater than those of free total phenolic acids and flavonoids. The main phenolic acids in blue barley were cinnamic acid polyphenols, whereas in black, yellow, and purple barley, benzoic acid polyphenols were the main phenolic acids, and the main types of flavonoids in black and blue barley were chalcones and flavanones, respectively, whereas flavonol was the main type of flavonoid in yellow and purple barley. Moreover, cornflower pigment-3-glucoside was the major anthocyanin in blue, yellow, and purple barley, whereas the main anthocyanin in black barley was delphinidin-3-glucoside. The dark color of barley indicated richness in the anthocyanins. In addition, the free polyphenol fractions had stronger DPPH and ABTS radical scavenging capacity as compared to the bound ones. In vitro α-glucosidase-inhibiting activity was greater in bound polyphenols than in free polyphenols, with differences between different varieties of barley. Purple barley phenolic fractions had the greatest ABTS radical scavenging and iron ion reduction capacities, as well as the highest α-glucosidase-inhibiting activity. The strongest DPPH radical scavenging capacity was found in yellow barley, while the strongest in vitro α-glucosidase-inhibiting activity was found in anthocyanins isolated from black barley. Furthermore, in different colors of barley, there was a strong association between the concentration of specific phenolic compounds and antioxidant and α-glucosidase-inhibiting activities. The outcomes of this study revealed that all colored barley seeds tested were high in phenolic compounds, and had a good antioxidant impact and α-glucosidase-inhibiting activity. As a result, colored barley can serve as an antioxidant and hypoglycemic food. Polyphenols extracted from purple barley and anthocyanins extracted from black barley stand out among them.

There are many influencing factors present in different situations of power load. There is also a strong imbalance in the number of load samples. In addition to examining the problem of low training efficiency of existing algorithms, this paper proposes a short-term power load prediction method based on feature selection and error compensation under imbalanced samples. After clustering the load data, we expand some sample data to balance the sample categories and input the load data and filtered feature sequences into the improved GRU for prediction. At the same time, the errors generated during the training process are used as training data. An error correction model is constructed and trained, and the results are used for error compensation to further improve prediction accuracy. The experimental results show that the overall prediction accuracy of the model has increased by 80.24%. After expanding a few samples, the prediction accuracy of the region where the samples are located increased by 59.41%. Meanwhile, due to the improvement of the algorithms, the running time was reduced by approximately 14.92%.

Global warming has significantly reduced summer ice coverage in the Arctic region, providing long-awaited opportunities for the shipping industry to open new routes through a region known for its harsh navigational conditions. If a shortcut between Asia and Europe via the Northern Sea Route (NSR) is adopted, significant energy saving and pollution reduction are possible compared with conventional southern routes. However, opinions in literature differ regarding this shortcut’s economic viability. We present an analysis from the perspective of COSCO, China’s largest container sea freight operator. We perform a cost–benefit analysis under several scenarios considering the following current realities: (1) declining oil prices not seen for decades, even lower than the lowest prices assumed in previous studies; (2) declining Russian NSR tariff as an effort to attract shipping traffic; (3) possible emission control areas along a northern route may require much cleaner energy and thus impact costs not studied in previous models; and (4) the capital cost difference between a hired and a self-owned vessel. Classical case studies of shipping routes between Shanghai and Rotterdam are adopted for comparison. We explain how different factors impact the shipping costs and to what extent can the NSR be economically viable. Occasional usage of NSR (e.g., one time transit) is unlikely to be more profitable given the higher unit transportation cost, but the route could be economically competitive in terms of the total profits earned for continuous usage. A more aggressive scenario which requires ships on the NSR to switch to much cleaner fuel would erode this route’s competitiveness, but extra environmental benefits should be taken into consideration if future carbon emission trading schemes include the shipping industry.

The prevalence of ship deficiencies continues to be a significant issue. Data from the Tokyo Memorandum of Understanding reveals that ship detentions in 2023 surged by more than 80% compared with the previous year. The significant number of detained ships not only disrupts ships’ daily operations but also strains port resources, leading to increased additional costs. In light of this issue, predicting the duration of ship detention becomes crucial, as accurate predictions can assist port managers in resource allocation and provide shipping companies with critical information for operational planning. This study is the first to predict ship detention duration, specifically distinguishing between long-term and short-term detained ships. Initially, key deficiency types influencing the ship detention duration were identified using an improved entropy weight–grey relational analysis. Subsequently, in consideration of the imbalance in data distribution between long-term and short-term detentions, a random forest model capable of handling imbalanced data was applied to classify these two types. The study found that fire safety, propulsion and auxiliary machinery, and pollution prevention are the three most critical deficiency types impacting detention duration; and the random forest model sampled and processed from the data level possessed the best model performance, achieving prediction accuracies of 0.71, 0.72, and 0.85 for bulk carriers, containers, and oil tankers, respectively. This research offers a comprehensive analysis of ship detention duration, making a significant contribution to both the theoretical understanding and practical applications in the maritime industry. Accurately predicting ship detention duration provides valuable insights for stakeholders, enabling them to anticipate potential detention scenarios and thus supporting shipping companies in effective fleet management while assisting port authorities in the optimal allocation of berth resources.

To dissect the antibiotic role of nanostructures from chemical moieties belligerent to both bacterial and mammalian cells, here we show the antimicrobial activity and cytotoxicity of nanoparticle-pinched polymer brushes (NPPBs) consisting of chemically inert silica nanospheres of systematically varied diameters covalently grafted with hydrophilic polymer brushes that are non-toxic and non-bactericidal. Assembly of the hydrophilic polymers into nanostructured NPPBs doesn’t alter their amicability with mammalian cells, but it incurs a transformation of their antimicrobial potential against bacteria, including clinical multidrug-resistant strains, that depends critically on the nanoparticle sizes. The acquired antimicrobial potency intensifies with small nanoparticles but subsides quickly with large ones. We identify a threshold size (d silica ~ 50 nm) only beneath which NPPBs remodel bacteria-mimicking membrane into 2D columnar phase, the epitome of membrane pore formation. This study illuminates nanoengineering as a viable approach to develop nanoantibiotics that kill bacteria upon contact yet remain nontoxic when engulfed by mammalian cells. Developing antibacterial agents which don’t have cytotoxic effects against mammalian cells is of interest for biomedical applications. Here, the authors explore how attaching inert polymer brushes to different sized nanoparticles can result in toxicity to bacteria but not to mammalian cells in a size dependent manner.

In the next-generation wireless communications system of Beyond 5G networks, video streaming services have held a surprising proportion of the whole network traffic. Furthermore, the user preference and demand towards a specific video might be different because of the heterogeneity of users’ processing capabilities and the variation of network condition. Thus, it is a complicated decision problem with high-dimensional state spaces to choose appropriate quality videos according to users’ actual network condition. To address this issue, in this paper, a Content Distribution Network and Cluster-based Mobile Edge Computing framework has been proposed to enhance the ability of caching and computing and promote the collaboration among edge severs. Then, we develop a novel deep reinforcement learning-based framework to automatically obtain the intracluster collaborative caching and transcoding decisions, which are executed based on video popularity, user requirement prediction, and abilities of edge servers. Simulation results demonstrate that the quality of video streaming service can be significantly improved by using the designed deep reinforcement learning-based algorithm with less backhaul consumption and processing costs.

The gut microbiota not only constitutes intestinal microenvironment homeostasis and human health but also exerts indispensable roles in the occurrence and progression of multiple liver diseases, including alcohol-related liver disease, nonalcoholic fatty liver disease, autoimmune liver disease and liver cancer. Given the therapeutic status of these diseases, their prevention and early therapy are crucial, and the detailed mechanism of gut microbiota in liver disease urgently needs to be explored. Meanwhile, multiple studies have shown that various traditional Chinese medicines, such as Si Miao Formula, Jiangzhi Granules, Liushen Capsules, Chaihu-Shugan Power, Cassiae Semen and Gynostemma, as well as some natural products, including Costunolide, Coprinus comatus polysaccharide, Antarctic krill oil, Oridonin and Berberine, can repair liver injury, improve fatty liver, regulate liver immunity, and even inhibit liver cancer through multiple targets, links, and pathways. Intriguingly, the aforementioned effects demonstrated by these traditional Chinese medicines and natural products have been shown to be closely related to the gut microbiota, directly driving the strategy of traditional Chinese medicines and natural products to regulate the gut microbiota as one of the breakthroughs in the treatment of liver diseases. Based on this, this review comprehensively summarizes and discusses the characteristics, functions and potential mechanisms of these medicines targeting gut microbiota during liver disease treatment. Research on the potential effects on gut microbiota and the regulatory mechanisms of traditional Chinese medicine and natural products provides novel insights and significant references for developing liver disease treatment strategies. In parallel, such explorations will enhance the comprehension of traditional Chinese medicine and natural products modulating gut microbiota during disease treatment, thus facilitating their clinical investigation and application.

Background Disulfidptosis refers to cell death caused by the accumulation and bonding of disulfide in the cytoskeleton protein of SLC7A11-high level cells under glucose deprivation. However, the role of disulfidptosis-related genes (DRGs) in prostate cancer (PCa) classification and regulation of the tumor microenvironment remains unclear. Methods Firstly, we analyzed the expression and mutation landscape of DRGs in PCa. We observed the expression levels of SLC7A11 in PCa cells through in vitro experiments and assessed the inhibitory effect of the glucose transporter inhibitor BAY-876 on SLC7A11-high cells using CCK-8 assay. Subsequently, we performed unsupervised clustering of the PCa population and analyzed the differentially expressed genes (DEGs) between clusters. Using machine learning techniques to select a minimal gene set and developed disulfidoptosis-related risk signatures for PCa. We analyzed the tumor immune microenvironment and the sensitivity to immunotherapy in different risk groups. Finally, we validated the accuracy of the prognostic signatures genes using single-cell sequencing, qPCR, and western blot. Results Although SLC7A11 can increase the migration ability of tumor cells, BAY-876 effectively suppressed the viability of prostate cancer cells, particularly those with high SLC7A11 expression. Based on the DRGs, PCa patients were categorized into two clusters (A and B). The risk label, consisting of a minimal gene set derived from DEGs, included four genes. The expression levels of these genes in PCa were initially validated through in vitro experiments, and the accuracy of the risk label was confirmed in an external dataset. Cluster-B exhibited higher expression levels of DRG, representing lower risk, better prognosis, higher immune cell infiltration, and greater sensitivity to immune checkpoint blockade, whereas Cluster A showed the opposite results. These findings suggest that DRGs may serve as targets for PCa classification and treatment. Additionally, we constructed a nomogram that incorporates DRGs and clinical pathological features, providing clinicians with a quantitative method to assess the prognosis of PCa patients. Conclusion This study analyzed the potential connection between disulfidptosis and PCa, and established a prognostic model related to disulfidptosis, which holds promise as a valuable tool for the management and treatment of PCa patients.

A prediction model of the welding process of Ti-6Al-4V titanium alloy was established by using the finite element method, which was used to evaluate the phase composition, residual stress and deformation of the welded joints of Ti-6Al-4V sheets with different processes (including tungsten inert gas welding, TIG, and laser beam welding, LBW). The Ti-6Al-4V structures of TIG welding and LBW are widely used in marine engineering. In order to quantitatively study the effects of different welding processes (including TIG welding and LBW) on the microstructure evolution, macro residual stress and deformation of Ti6Al4V titanium alloy sheets during welding, a unified prediction model considering solid-state phase transformation was established based on the ABAQUS subroutine. In this paper, LBW and TIG welding experiments of 1.6 mm thick Ti-6Al-4V titanium alloy sheets were designed. The microstructure distribution of the welded joints observed in the experiment was consistent with the phase composition predicted by the model, and the hardness measurement experiment could also verify the phase composition and proportion. From the residual stress measured by experiment and the residual stress and deformation calculated by finite element simulation of LBW and TIG weldments, it is concluded that the effect of phase transformation on residual stress is mainly in the weld area, which has an effect on the distribution of tensile and compressive stress in the weld area. The overall deformation of the welded joint is mainly related to the welding process, and the phase transformation only affects the local volume change of the weld seam. Importantly, the phase composition and residual stress, which are scalar fields, calculated by the established model can be introduced into the numerical analysis of structural fracture failure as input influence factors.