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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]

• A novel gram-scale (around 1.66 g) diatomic sites catalyst CN-NiZn is synthesized. • The NiN 3 /ZnN 3 sites of CN-NiZn own synergistic effect for converting CO 2 to CO. • CN-NiZn shows over 92 % FE CO from −0.57 to −1.07 V, especially 97.5 % FE CO at −0.77 V. • CN-NiZn can maintain FE CO of exceeding 90 % at −0.77 V during 30 h of electrolysis. Diatomic catalysts inherit the advantages of single atom catalysts and use the adjacent metal-N sites for functional complementary and synergistic effects in electrochemical CO 2 reduction (ECR). However, preparation of diatomic catalysts with high yield, Faradaic efficiency and stability together with low overpotential still remains challenging. In this work, we develop a nitrogen-doped carbon supported Ni-Zn diatomic catalyst, whose CO Faradaic efficiency of is higher than 92.0 % in the wide potential range of −0.57 ∼ −1.07 V vs RHE, and the maximum CO Faradaic efficiency is 97.5 % at −0.77 V vs RHE. And also, CN-NiZn performs an extraordinary stability for 30 h in the long-time durability test. More importantly, large-quantities (around 1.66 g) of Ni-Zn diatomic catalysts with remarkable catalytic performance can be prepared by this method. Synchrotron radiation X-ray absorption spectroscopy was tested to reflect the coordination environment and valence states around Ni and Zn atoms. Theoretical calculations further reveal that the synergistic effect between Ni and Zn binary metal sites largely reduces the energy barrier of the reaction to produce the intermediate *COOH, thus effectively increasing the selectivity of ECR. This work indicates that the introduction of another coordination metal can significantly affect the electronic structure of monatomic catalysts, thus improving the electrocatalytic activity and selectivity of ECR. [Display omitted] [ABSTRACT FROM AUTHOR]

Hyperspectral classification is a fundamental problem for applying hyperspectral technology, and unsupervised learning is a promising direction to address the issue. Unsupervised feature learning algorithm extracts representative features, which help efficiently classify the hyperspectral pixels. This paper combines the popular contrastive learning method (prototypical contrastive learning) and the classic representation learning method (autoencoder) to design an unsupervised feature learning network for hyperspectral classification. First, two different encoders are used to extract different features as augmentation function in a contrastive network (ContrastNet). Second, the prototypical contrastive learning method is adopted to train a contrastive network. Third, features extracted by the contrastive network are used for classification. Experiments have proved that our two proposed autoencoder networks show good feature learning capabilities by themselves and the designed contrastive learning network can further learn better representative features from the two modules. The three modules compose an efficient framework for unsupervised feature learning. Our method also performs reasonably fast in the testing phase, which implies that it is applicable in practice under specific conditions. [ABSTRACT FROM AUTHOR]

Hyperspectral classification is a fundamental problem for applying hyperspectral technology, and unsupervised learning is a promising direction to address the issue. Unsupervised feature learning algorithm extracts representative features, which help efficiently classify the hyperspectral pixels. This paper combines the popular contrastive learning method (prototypical contrastive learning) and the classic representation learning method (autoencoder) to design an unsupervised feature learning network for hyperspectral classification. First, two different encoders are used to extract different features as augmentation function in a contrastive network (ContrastNet). Second, the prototypical contrastive learning method is adopted to train a contrastive network. Third, features extracted by the contrastive network are used for classification. Experiments have proved that our two proposed autoencoder networks show good feature learning capabilities by themselves and the designed contrastive learning network can further learn better representative features from the two modules. The three modules compose an efficient framework for unsupervised feature learning. Our method also performs reasonably fast in the testing phase, which implies that it is applicable in practice under specific conditions. [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]

Botulinum toxin type A (BoNT-A) has been recommended in various neurological disorders as a useful tool for alleviating dystonia. In Wilson disease (WD) patients with dystonia, BoNT-A injection can be used as a treatment modality when conventional treatment is ineffective for alleviating symptoms. The purpose of this study was to thoroughly evaluate the efficacy of BoNT-A injection in treating WD complicated by lower extremity dystonia. The efficacy of these injections was assessed by clinical scales, surface electromyography (EMG), and gait analysis. A comparative analysis of all gait parameters, EMG parameters, and clinical scales revealed a significant increase in velocity, decrease in integrated EMG (iEMG), and improvement in modified Ashworth scale (MAS), Burke Fahn Marsden (BFM), and activities of daily living (ADL) scores (all P < 0.05). Overall, our findings indicated that BoNT-A injection led to marked relief of symptoms in patients with WD with lower extremity dystonia. [Display omitted] • Single-blind, pre-and post-intervention study were initially performed in Wilson disease patients with lower limb dystonia. • Botulinum toxin type A injection improves the muscle tone, as evident by the decrease in integrated electromyogram. • Botulinum toxin type A injection improves walking function, as evident by the increase in velocity. • Botulinum toxin type A injection alleviates the symptoms of lower limb dystonia in Wilson disease patients. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are widely recognized as low-carbon transportation that adopts green and clean technology with contributing to mitigating global climate change. However, it appears that the practice in using EVs has not gained widespread popularity despite the promotion of various incentives with the presence of various restricting factors and challenges. In conducting this study in the context of China, we hypothesize that restricting factors have varying impacts on promoting EVs in China, where the adoption of EVs is of great potential. This paper identifies these restricting factors by applying AHP-FCE integrated approach, and these factors are classified into five dimensions, namely, technical, infrastructural, economic, governance and social. The results indicate that the top five restricting factors are security issues, limited driving range, long charging time, improper distribution of charging stations and lack of sharing between charging piles respectively. Our research contributes to a better understanding of EV restricting factors and their significance, which offers the assistance in the formulation of policies and decisions in order to promote effectively EVs in China. • The promotion of EVs is of great potential in China for mitigating global climate change. • Explore the RFPEVs in China using AHP-FCE integrated approach. • The RFPEVs in China can be classified into five dimensions, namely, technical, infrastructural, economic, governance and social. • The technical and infrastructural related are the most important constraints. • The main three important restricting factors are security issues, limited driving range, long charging time. [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]

• Introducing the extended transient current region by adopting short-time overcurrent capacity of grid-side converter. • Current relaxation region (CRR) is proposed for description of transient current boundary and look-up table design. • A novel CRR-based LVRT strategy is proposed to alleviate active current reduction while maintaining the reactive current ability. This paper proposes a current relation region (CRR)-based low-voltage ride-through (LVRT) strategy for offshore wind turbines (OWTs). The proposed strategy seeks to minimize the detrimental shortfall of OWT's active current on the premise of maintaining the reactive support ability by considering short-time overcurrent capacity of grid-side converter (GSC) during low-voltage fault. Firstly, the extended transient current region (ETCR) is introduced to improve the transient current ability of OWTs with comparison to conventional transient current region (CTCR) based on rated current capacity of GSC. Combined with the introduced ETCR, grid code constraint and wind speed constraint are investigated for detailed description of OWT's transient current boundary of CRR. Furthermore, we design the CRR-based LVRT strategy with look-up tables to facilitate dispatching the transient current instructions to OWTs' controllers. Finally, extensive case studies based on PSCAD are performed and the results show that the proposed strategy can improve active current and reactive current simultaneously over the conventional strategy. In the scenario of the rated wind speed (11.4 m/s) and critical voltage (0.2 p.u.), this achievement can be clearly seen by the value improvement in the active current and reactive current by 0.328 p.u. and 0.05 p.u., respectively. [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]

Reduning injection (RDNI) is a patented Traditional Chinese medicine that contains three Chinese herbal medicines, respectively are the dry aboveground part of Artemisia annua L. , the flower of Lonicera japonica Thunb. , and the fruit Gardenia jasminoides J.Ellis. RDNI has been recommended for treating Coronavirus Disease 2019 (COVID-19) in the "New Coronavirus Pneumonia Diagnosis and Treatment Plan". To elucidate and verify the underlying mechanisms of RDNI for the treatment of COVID-19. This study firstly performed anti-SARS-CoV-2 experiments in Vero E6 cells. Then, network pharmacology combined with molecular docking was adopted to explore the potential mechanisms of RDNI in the treatment for COVID-19. After that, western blot and a cytokine chip were used to validate the predictive results. We concluded that half toxic concentration of drug CC50 (dilution ratio) = 1:1280, CC50 = 2.031 mg crude drugs/mL (0.047 mg solid content/mL) and half effective concentration of drug (EC50) (diluted multiples) = 1:25140.3, EC50 = 103.420 μg crude drugs/mL (2.405 μg solid content/mL). We found that RDNI can mainly regulate targets like carbonic anhydrases (CAs), matrix metallopeptidases (MMPs) and pathways like PI3K/AKT, MAPK, Forkhead box O s and T cell receptor signaling pathways to reduce lung damage. We verified that RDNI could effectively inhibit the overexpression of MAPKs, PKC and p65 nuclear factor-κB. The injection could also affect cytokine levels, reduce inflammation and display antipyretic activity. RDNI can regulate ACE2, Mpro and PLP in COVID-19. The underlying mechanisms of RDNI in the treatment for COVID-19 may be related to the modulation of the cytokine levels and inflammation and its antipyretic activity by regulating the expression of MAPKs, PKC and p65 nuclear factor NF-κB. [Display omitted] [ABSTRACT FROM AUTHOR]