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Predicting energy consumption in large exposition centers presents a significant challenge, primarily due to the limited datasets and fluctuating electricity usage patterns. This study introduces a cutting-edge algorithm, the contrastive transformer network (CTN), to address these issues. By leveraging self-supervised learning, the CTN employs contrastive learning techniques across both temporal and contextual dimensions. Its transformer-based architecture, tailored for efficient feature extraction, allows the CTN to excel in predicting energy consumption in expansive structures, especially when data samples are scarce. Rigorous experiments on a proprietary dataset underscore the potency of the CTN in this domain. [ABSTRACT FROM AUTHOR]

Novel TiO2 nanoparticles-coated CNTs (TiO2@CNTs) fabricated by the alcohol-thermal method were used as the reinforcement to prepare aluminum matrix composites through high-intensity ultrasonic-assisted casting, followed by heat treatment. The results of optical microscope showed that the optimum heat treatment process was solution treatment (495 °C, 2 h) + aging treatment (180 °C, 12 h). Under this condition, the Al2Cu and Al2CuMg phases were uniformly precipitated in the composites, and most of them were in a discrete state. TEM results revealed that TiO2 nanoparticles dispersed on the surface of CNTs played the role of an interlayer structure between CNTs and matrix, locking the CNTs and improving the CNTs–Al interfacial bonding. The ultimate tensile strength (UTS) and yield strength (YS) of 1.0 wt%TiO2@CNTs/2024 composite after T6 heat treatment reach 324 MPa and 219 MPa, respectively, which exhibited enhanced UTS and YS than the monolithic 2024 alloy. This was attributed to the synergistic strengthening of multi-mechanisms, such as grain refinement strengthening, load transfer strengthening, dislocation strengthening and, especially, Orowan strengthening. [ABSTRACT FROM AUTHOR]

Designing and synthesising electrocatalysts with low overpotential, high activity, selectivity, and stability for the CO 2 reduction reaction (CO 2 RR) is crucial for addressing environmental challenges and realising the artificial carbon cycle. In this study, we successfully synthesised a nitrogen-doped carbon-supported diatomic electrocatalyst comprising atomically dispersed CuN 4 and NiN 4 bimetallic centers. This was confirmed through extensive characterisation using high-angle annular dark-field scanning transmission electron microscopy and X-ray absorption spectroscopy techniques. The as-synthesised CBN-CuNi electrocatalyst exhibited exceptional selectivity and stability in CO 2 RR. Over a wide potential range from − 0.60 to − 1.10 V vs RHE, CBN-CuNi maintained over 90% Faraday efficiency for CO (FE CO) production. Notably, at − 0.9 V vs RHE, it achieved a remarkable selectivity with a maximum FE CO of 97.9%, surpassing single metal catalysts, including CBN-Ni and CBN-Cu. Furthermore, CBN-CuNi demonstrated outstanding stability, sustaining over 90% FE CO production for 40 h of continuous electrolysis, surpassing many existing electrocatalysts. These findings highlight the potential of the as-synthesised bimetallic electrocatalyst CBN-CuNi in the electrochemical reduction of CO 2 to CO, contributing to advancing the artificial carbon cycle and addressing environmental concerns. [Display omitted] • A novel nitrogen-doped carbon-supported electrocatalyst CBN-CuNi is synthesized. • The CuN 4 /NiN 4 coordination environment of CBN-CuNi promotes the convert CO 2 into CO. • CBN-CuNi shows over 90% FE CO from − 0.6 to − 1.1 V, especially 97.9% FE CO at − 0.9 V. • CBN-CuNi can maintain FE CO of exceeding 90% at − 0.8 V during 40 h of electrolysis. [ABSTRACT FROM AUTHOR]

Gas emission is a major security risk in coal mining. Seal coating is an effective method for preventing gas from escaping, but the stability of inorganic particles in the coating has great influence on the sealing performance. This research was designed to study the effect of SHMP on the stability of a gas-sealing coating filler based on UFA. The effect of SHMP on powder dispersion behaviors was systematically investigated using a zeta potential tester, particle size analyzer, FT-IR, and SEM- EDS. To explore the dispersion mechanism of SHMP on typical powder materials (such as fly ash, carbon black, and graphite), the interaction energy among particles was calculated according to the classical and extended DLVO theory. The results indicate that the surface hydrophobic effects of graphite and carbon black were the main contributing factors to agglomeration. Further, the dispersant SHMP increases the interparticle electrostatic repulsions and steric hindrance, preventing particle agglomeration and ensuring the stability of fly ash-based filler in gas-sealing coatings. The results of pilot spraying test in a coal mine show that the added dispersant improved the gas-sealing efficiency of the coating and helped in its application in a coal mine. Image 1 • SHMP increases the inter particle electrostatic repulsions and steric hindrance. • Fly ash can be used as a kind of good filler in coating products. • The repulsive energy among particles agreed with extended DLVO theory. • The pilot test verified the gas sealing efficiency of the coating with SHMP. [ABSTRACT FROM AUTHOR]

Luteoloside is a member of the flavonoids family that exhibits several bioactivities including anti-microbial and anti-cancer activities. However, the antiviral activity of luteoloside against enterovirus 71 (EV71) and the potential mechanism(s) responsible for this effect remain unknown. In this study, the antiviral potency of luteoloside against EV71 and its inhibitory effects on 3C protease activity were evaluated. First, we investigated the cytotoxicity of luteoloside against rhabdomyosarcoma (RD) cells, which was the cell line selected for an in vitro infection model. In a subsequent antiviral assay, the cytopathic effect of EV71 was significantly and dose-dependently relieved by the administration of luteoloside (EC50 = 0.43 mM, selection index = 5.3). Using a plaque reduction assay, we administered luteoloside at various time points and found that the compound reduced EV71 viability in RD cells rather than increasing defensive mobilization or viral absorption. Moreover, biochemical studies focused on VP1 (a key structural protein of EV71) mRNA transcript and protein levels also revealed the inhibitory effects of luteoloside on the EV71 viral yield. Finally, we performed inhibition assays using luteoloside to evaluate its effect on recombinant 3C protease activity. Our results demonstrated that luteoloside blocked 3C protease enzymatic activity in a dose-dependent manner (IC50 = 0.36 mM) that was similar to the effect of rutin, which is a well-known C3 protease inhibitor. Collectively, the results from this study indicate that luteoloside can block 3C protease activity and subsequently inhibit EV71 production in vitro. [ABSTRACT FROM AUTHOR]

A set of crosslinked quaternized polybenzimidazole (cQAPBI-nC) membranes with varying carbon chain lengths of crosslinking agents are designed and prepared through grafting and crosslinking reactions for their applications in electrocatalytic CO 2 reduction reaction (CO 2 RR). The cQAPBI-6C membrane stands out among all the cQAPBI-nC membranes due to its ingenious construction of a hydrophilic/hydrophobic micro-phase separation structure. As a result, this membrane exhibits exceptional OH− conductivity and selectivity of 79.40 mS cm−1 and 54.73%, respectively, surpassing those of the commercially available FAA-3-PK-130 membrane (OH− conductivity: 33.38 mS cm−1, selectivity: 39.00%). Moreover, the cQAPBI-6C membrane has excellent mechanical and alkaline stabilities. In the self-made membrane electrode assembly reactor, the cQAPBI-6C membrane demonstrates higher CO Faraday efficiency (FE) compared with FAA-3-PK-130 membrane at −100 ∼ −160 mA cm−2. Furthermore, the cQAPBI-6C membrane can operate continuously and stably for over 32 h at −150 mA cm−2, and the CO FE and voltage maintain at around 95% and −3.7 V. These findings provide strong evidence to support the practical significance of the proposed modification strategy in the development of polybenzimidazole-based anion exchange membranes for CO 2 RR applications. [Display omitted] • cQAPBI membranes were prepared via grafting and crosslinking strategies for CO 2 RR. • The obvious micro-phase separation structure is given to cQAPBI-6C membrane. • cQAPBI-6C membrane has outstanding OH−conductivity and selectivity. • cQAPBI-6C membrane shows excellent CO FE, current density and durability for CO 2 RR. [ABSTRACT FROM AUTHOR]

Lonicera japonica Thunb, rich in chlorogenic acid (CHA), is used for viral upper respiratory tract infection treatment caused by influenza virus, parainfluenza virus, and respiratory syncytial virus, ect in China. It was reported that CHA reduced serum hepatitis B virus level and death rate of influenza virus-infected mice. However, the underlying mechanisms of CHA against the influenza A virus have not been fully elucidated. Here, the antiviral effects and potential mechanisms of CHA against influenza A virus were investigated. CHA revealed inhibitory against A/PuertoRico/8/1934(H1N1) (EC50 = 44.87 μM), A/Beijing/32/92(H3N2) (EC50 = 62.33 μM), and oseltamivir-resistant strains. Time-course analysis showed CHA inhibited influenza virus during the late stage of infectious cycle. Indirect immunofluorescence assay indicated CHA down-regulated the NP protein expression. The inhibition of neuraminidase activity confirmed CHA blocked release of newly formed virus particles from infected cells. Intravenous injection of 100 mg/kg/d CHA possessed effective antiviral activity in mice, conferring 60% and 50% protection from death against H1N1 and H3N2, reducing virus titres and alleviating inflammation in the lungs effectively. These results demonstrate that CHA acts as a neuraminidase blocker to inhibit influenza A virus both in cellular and animal models. Thus, CHA has potential utility in the treatment of the influenza virus infection. [ABSTRACT FROM AUTHOR]

The noise corruption problem commonly exists in hyperspectral images (HSIs) and severely affects the accuracy of hyperspectral unmixing algorithms. The noise formulation existing in HSIs is relatively complex and would change in conjunction with different devices and imaging settings. For real applications, applying denoising approaches without accurate close-to-reality noise modeling before unmixing may not improve, but rather degrade the unmixing performance. This study proposes a robust hyperspectral unmixing method with practical learning-based hyperspectral image denoising. We formulated a close-to-reality noise model for hyperspectral data and provide a calibration approach for the noise parameters. On the basis of the calibrated noise model, synthetic data were generated and used for training a KST-based denoising network. The noisy hyperspectral data were firstly denoised by the trained denoising network and were then used to perform the unmixing process. A variety of unmixing algorithms can be integrated into our method to improve the accuracy of unmixing in noisy situations. In the experiments, several widely used unmixing algorithms were employed to verify the effect of the proposed method. The experimental results on both synthetic and real demonstrated that our proposed method can handle HSI data with various gain settings and helps to improve the unmixing performance effectively. [ABSTRACT FROM AUTHOR]