Abstract ：

Soft sensing techniques have been extensively employed to monitor water quality in the Urban Wastewater Treatment Process. Wastewater data often exhibit complex features, including nonlinearity, time correlation, and non-Gaussianity. Therefore, to establish an accurate soft sensing model, extracting complex features from wastewater data during the modeling process is essential. Considering the characteristics of wastewater data, a feature-augmented extraction broad learning system (FAE-BLS) is proposed for soft sensing applications. Inspired by the structure of recurrent networks, a recurrent cascading improvement of the feature window in the broad learning system (BLS) is implemented by FAE-BLS to extract the time correlation features of wastewater data. Furthermore, a feature window based on overcomplete independent component analysis (OICA) is proposed to extract the inherent non-Gaussian features of wastewater data. Finally, a method for fast online model updating is developed to address the decline in model accuracy under the harsh conditions of wastewater treatment processes. Experimental results on the wastewater simulation platform validate the effectiveness and superiority of the proposed approach.

Keyword ：

Accuracy soft sensor Wastewater treatment feature-augmented extraction Data models Feature extraction Broad learning system (BLS) Windows wastewater treatment process Soft sensors water quality Correlation Data mining Wastewater Training

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Abstract ：

Thanks to the development of 2D keypoint detectors, monocular 3D human pose estimation (HPE) via 2D-to-3D lifting approaches have achieved remarkable improvements. However, monocular 3D HPE is still a challenging problem due to the inherent depth ambiguities and occlusions. Recently, diffusion models have achieved great success in the field of image generation. Inspired by this, we transform 3D human pose estimation problem into a reverse diffusion process, and propose a dual-branch diffusion model so as to handle the indeterminacy and uncertainty of 3D pose and fully explore the global and local correlations between joints. Furthermore, we propose conditional dual-branch diffusion model to enhance the performance of 3D human pose estimation, in which the joint-level semantic information are regarded as the condition of the diffusion model, and integrated into the joint-level representations of 2D pose to enhance the expression of joints. The proposed method is verified on two widely used datasets and the experimental results have demonstrated the superiority.

Keyword ：

Human pose estimation Diffusion model Joint semantics Dual-branch

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Nonmuscle channels are able to enhance voluntary movement control or improve rehabilitation efficacy, and analyzing electroencephalogram (EEG) signals yields valuable insights into functional neural activity during motor imagery (MI). However, most existing MI-EEG datasets primarily focus on exploring more discriminative tasks to control external devices, making it difficult to meet the actual needs of motor function rehabilitation. In addition, the expensive acquisition of labeled data hinders the practical application of EEG-based rehabilitation. In this study, we aim to collect EEG signals from 13 subjects performing four MI tasks of the unilateral upper limb: arm lifting/lowering and forearm pronation/supination. Moreover, we propose a Contrastive representation learning framework with an Attention Spatiotemporal Convolutional Encoder (CASCE) for MI-EEG decoding. In the pretraining phase, the unlabeled data from background samples and label-erased samples are applied to the noise addition module and the scaling module to generate pairs of positive and negative samples. These sample pairs are input into the encoder to learn temporal and spatial information, and the encoder parameters are further adjusted by using the contrastive loss function to measure the similarity of the feature information in the projection space. During the fine-tuning phase, the transferred encoder and the classification head are specifically adapted to the labeled MI-EEG data. The CASCE framework achieves a classification accuracy of 51.58% on the refined upper limb MI dataset. In addition, CASCE outperforms state-of-the-art (SOTA) methods with accuracies of 88.51% and 90.34% on the brain-computer interface (BCI) Competition IV 2a and 2b datasets, respectively.

Keyword ：

Contrastive learning Motors upper limb decoding Brain-computer interface (BCI) Convolutional neural networks contrastive learning Representation learning Data mining Brain modeling Electroencephalography electroencephalogram (EEG) Decoding Convolution motor imagery (MI) Feature extraction

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Abstract ：

Utilizing fake data (simulated based on mechanism models or generated through data-driven models) for data enhancement is a popular approach to solve the problem of fault diagnosis with small samples. Consequently, the quality of such fake data impacts fault diagnosis accuracy. This article proposes a data model fusion (DMF)-driven framework for small sample fault diagnosis. This framework integrates the digital twin model (DTM) and the conditional deep convolutional generative adversarial network (C-DCGAN). Digital twin data (DTD) under various fault conditions is first obtained in the data generation stage based on DTM simulation. Then, a data generation method based on DTM-C-DCGAN is proposed. The method adopts DTD as the soft-physics constraint input to the generator of C-DCGAN. Hence, the generator is induced to generate data that is more consistent with the failure mechanism and closer to the real data. During the fault diagnosis stage, the generated data (GD) are used to enhance the training process of the fault diagnosis model, improving its generalization ability. Finally, the effectiveness of the proposed method is comprehensively verified via two publicly rolling bearing datasets. Compared with the existing single data-driven and physics-based methods, the experimental results demonstrate that the proposed DMF method can significantly enhance the quality of the GD and improve the accuracy of fault identification, achieving an average accuracy of 97.31%.

Keyword ：

Accuracy digital twin model (DTM) generated data (GD) Generators small sample fault diagnosis Data collection Data models Conditional deep convolutional generative adversarial network (C-DCGAN) data model fusion (DMF) Rolling bearings Digital twins Generative adversarial networks Fault diagnosis Feature extraction Training

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Abstract ：

The cobalt-free Mn-based Li-rich layered oxide material has the advantages of low cost, high energy density, and good performance at low temperatures, and is the promising choice for energy storage batteries. However, the long-cycling stability of batteries needs to be improved. Herein, the Mn-based Li-rich cathode materials with small amounts of Li2MnO3 crystal domains and gradient doping of Al and Ti elements from the surface to the bulk have been developed to improve the structure and interface stability. Then the batteries with a high energy density of 600 Wh kg(-1), excellent capacity retention of 99.7 % with low voltage decay of 0.03 mV cycle(-1) after 800 cycles, and good rates performances can be achieved. Therefore, the structure and cycling stability of low voltage Mn-based Li-rich cathode materials can be significantly improved by the bulk structure design and interface regulation, and this work has paved the way for developing low-cost and high-energy Mn-based energy storage batteries with long lifetime. (c) 2024 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.

Keyword ：

Lithium-ion batteries Elemental gradient Li2MnO3 crystal domain Mn-based Li-rich layered oxide cathode Energy storage

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Nano-light-emitting diodes (LEDs) are ideal for ultra-high resolution displays due to their small size and high pixel density. However, traditional photolithography techniques fall short in meeting the requirements for nanoscale LED fabrication. Besides, as the size decreases and the specific surface area increases, non-radiative recombination generated by sidewalls defects becomes a significant issue, affecting the efficiency of nano-LEDs. To address this challenge, a nano-LED array with a single nanorod size of 800 nm was fabricated in this work by using nanosphere lithography and etching technology. Meanwhile, localized surface plasmons (LSPs) coupling technology was employed to enhance the PL efficiency of these nano-LEDs. By comparing with bare nano-LEDs, the PL intensity was boosted by about 43% and 129% when Ag and Ag@SiO2 nanoparticles were added separately. The existence of LSPs coupling process has been further confirmed through time-resolved photoluminescence measurement and finite element simulation analysis of different samples. The results provide compelling evidence for the LSPs coupling technology in enhancing the efficiency of nanoscale LEDs.

Keyword ：

nanorod photoluminescence micro-LED

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We propose a concept of a superconducting (SC) photodiode-a device that transforms the energyand 'spin' of an external electromagnetic (EM) field into the rectified steady-state supercurrent anddevelop a microscopic theory describing its properties. For this, we consider a two-dimensionalthin film cooled down below the temperature of SC transition with the injected dc supercurrentand exposed to an external EM field with a frequency smaller than the SC gap. As a result, wepredict the emergence of a photoexcited quasiparticle current, and, as a consequence, oppositelyoriented stationary flow of Cooper pairs. The strength and direction of this photoinducedsupercurrent depend on (i) such material properties as the effective impurity scattering time andthe nonequilibrium quasiparticles' energy relaxation time and (ii) such EM field properties as itsfrequency and polarization.

Keyword ：

transition-metal dichalcogenides superconducting diode effect superconducting photodiode light-matter interaction in superconductors light-induced superconductivity in 2D materials 2D materials

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Abstract ：

In addition to energy harvesting and self-powered sensing, there is growing interest in expanding the capabilities of cellulose-based triboelectric nanogenerators. Here, we report the development of a pectin-lignin nanoparticle-based triboelectric nanogenerator (PL-TENG) with advanced features, such as antibacterial, ultraviolet (UV) protection, green degradation, closed-loop recycling, and reuse. When the PL-TENG with an effective contact area of 3 cm × 3 cm reaches an open circuit voltage of 245 V and a short circuit current of 28 µA, the power density is as high as 15.42 W · m−2, and the PL-TENG is stable in 1200 contact separation cycles. Moreover, the PL-TENG not only exhibits remarkable antibacterial activity against Escherichia coli ( E. coli) and Staphylococcus aureus (S.A.) but also exhibits remarkable UV-protection capabilities with an ultraviolet protection factor (UPF) value of up to 315. Additionally, the flexible PL-TENG demonstrated excellent self-powered haptic sensor capability with real-time electrical signal feedback capability. Patients can use Morse code for medical emotion expression and real-time monitoring of human movement and help patients improve their exercise habits. The design of the versatile PL-TENG technology shows the great potential of wearable electronics, including healthcare, human–machine interfaces, and charging microelectronic devices. © 2024 Elsevier B.V.

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Escherichia coli Nanoclay

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Salsolinol (1-methyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline, Sal) is a catechol isoquinoline that causes neurotoxicity and shares structural similarity with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, an environmental toxin that causes Parkinson's disease. However, the mechanism by which Sal mediates dopaminergic neuronal death remains unclear. In this study, we found that Sal significantly enhanced the global level of N-6-methyladenosine (m(6)A) RNA methylation in PC12 cells, mainly by inducing the downregulation of the expression of m(6)A demethylases fat mass and obesity-associated protein (FTO) and alkB homolog 5 (ALKBH5). RNA sequencing analysis showed that Sal downregulated the Hippo signaling pathway. The m(6)A reader YTH domain-containing family protein 2 (YTHDF2) promoted the degradation of m(6)A-containing Yes-associated protein 1 (YAP1) mRNA, which is a downstream key effector in the Hippo signaling pathway. Additionally, downregulation of YAP1 promoted autophagy, indicating that the mutual regulation between YAP1 and autophagy can lead to neurotoxicity. These findings reveal the role of Sal on m(6)A RNA methylation and suggest that Sal may act as an RNA methylation inducer mediating dopaminergic neuronal death through YAP1 and autophagy. Our results provide greater insights into the neurotoxic effects of catechol isoquinolines compared with other studies and may be a reference for assessing the involvement of RNA methylation in the pathogenesis of Parkinson's disease.

Keyword ：

YTHDF2 FTO salsolinol RNA methylation YAP1 Hippo pathway autophagy ALKBH5 m(6)A Parkinson's disease

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