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The curcumin‐malic acid‐aspartic acid polymer (PCMA) as a new water treatment agent was prepared by solid phase synthesis of curcumin, malic acid and aspartic acid. The static scale inhibition experiments showed that PCMA can inhibit CaCO3 and CaSO4 scale formation by 100.0 % and had excellent scale inhibition effect under various experimental conditions. The mechanism of action of PCMA was obtained by X‐ray diffraction, scanning electron microscope and molecular dynamics simulation. Electrochemical test showed that PCMA is an anodic corrosion inhibitor that achieves 93.1 % corrosion inhibition by forming a protective film on the surface of Q235 carbon steel. Besides, fluorescence spectra proved that PCMA has stable fluorescence intensity. [ABSTRACT FROM AUTHOR]

Despite its high potential, non‐viral gene therapy of cancer remains challenging due to inefficient nucleic acid delivery. Ultrasound (US) with microbubbles (MB) can open biological barriers and thus improve DNA and mRNA passage. Polymeric MB are an interesting alternative to clinically used lipid‐coated MB because of their high stability, narrow size distribution, and easy functionalization. However, besides choosing the ideal MB, it remains unclear whether nanocarrier‐encapsulated mRNA should be administered separately (co‐administration) or conjugated to MB (co‐formulation). Therefore, the impact of poly(n‐butyl cyanoacrylate) MB co‐administration with mRNA‐DOTAP/DOPE lipoplexes or their co‐formulation on the transfection of cancer cells in vitro and in vivo is analyzed. Sonotransfection improved mRNA delivery into 4T1 breast cancer cells in vitro with co‐administration being more efficient than co‐formulation. In vivo, the co‐administration sonotransfection approach also resulted in higher transfection efficiency and reached deeper into the tumor tissue. On the contrary, co‐formulation mainly promoted transfection of endothelial and perivascular cells. Furthermore, the co‐formulation approach is much more dependent on the US trigger, resulting in significantly lower off‐site transfection. Thus, the findings indicate that the choice of co‐administration or co‐formulation in sonotransfection should depend on the targeted cell population, tolerable off‐site transfection, and the therapeutic purpose. [ABSTRACT FROM AUTHOR]

With the rapid growth of multimedia data on the Internet, there has been a rapid rise in the demand for visual–textual cross-media retrieval between images and sentences. However, the heterogeneous property of visual and textual data brings huge challenges to measure the cross-media similarity for retrieval. Although existing methods have achieved great progress with the strong learning ability of the deep neural network, they rely heavily on the scale of training data with manual annotation, that is, either pairwise image–sentence annotation or category annotation as supervised information for visual–textual correlation learning, which are extremely labor and time consuming to collect. Without any pairwise or category annotation, it is highly challenging to construct a correlation between images and sentences due to their inconsistent distributions and representations. But people can naturally understand the correlation between visual and textual data in high-level semantic, and those images and sentences containing the same group of semantic concepts can be easily matched in human brain. Inspired by the above human cognitive process, this article proposes an unsupervised visual–textual correlation learning (UVCL) approach to construct correlations without any manual annotation. The contributions are summarized as follows: 1) unsupervised semantic-guided cross-media correlation mining is proposed to bridge the heterogeneous gap between visual and textual data. We measure the semantic matching degree between images and sentences, and generate descriptive sentences according to the concepts extracted from images to further augment the training data in an unsupervised manner. Therefore, the approach can exploit the semantic knowledge within both visual and textual data to reduce the gap between them for further correlation learning and 2) unsupervised visual–textual fine-grained semantic alignment is proposed to learn cross-media correlation by aligning the fine-grained visual local patches and textual keywords with fine-grained soft attention as well as semantic-guided hard attention, and the results can effectively highlight the fine-grained semantic information within both images and sentences to boost visual–textual alignment. Extensive experiments are conducted to perform visual–textual cross-media retrieval in unsupervised setting without any manual annotation on two widely used datasets, namely, Flickr-30K and MS-COCO, which verify the effectiveness of our proposed UVCL approach. [ABSTRACT FROM AUTHOR]

A novel post-oxidation annealing (POA) process with supercritical N2O (SCN2O) fluid is reported to be highly effective in improving the interface properties of the SiO2/4H–silicon carbide (SiC) (0001) systems. After SCN2O POA, the interface state density reduces to 2.8 × 1011 eV−1cm−2, which is about 3.5 times lower than that after a traditional high-temperature N2O POA process. Meanwhile, the highest oxide critical electric field shows an increase of 18.19% and the near-interfacial oxide traps is reduced by 69.90% compared with that after N2O POA process. The process temperature is as low as 120 °C. The significantly reduced processing temperature avoids additional defect generation while the supercritical state provides a stronger nitridation effect. SCN2O annealing is a promising candidate for POA process toward high-performance SiC power metal-oxide-semiconductor field effect transistors (MOSFETs). [ABSTRACT FROM AUTHOR]

Considering potential applications related to superconductivity and aerospace (typically less than 100 K), in this article, the temperature dependence of silicon carbide (SiC) power diodes is systematically characterized and analyzed over a wide temperature range of 90–478 K, especially focusing on cryogenic temperature. First, the static performance degradation mechanism of SiC diodes is established in an ultrawide temperature range, including forward/reverse I-V characteristics and junction capacitance (Cj) characteristics. Second, the reverse recovery characteristics are achieved, including peak reverse recovery current (Irm), reverse recovery charge (Qrr), and switching energy (Esw), clarifying a clearer internal relationship between reverse recovery and junction temperature. Meanwhile, the aforementioned critical parameters are further analyzed on an electrical scale with normal atmosphere temperature, including switching speed range of 62.3–2054.8 A/μs, load current range of 6–30 A, and dc voltage range of 400–1000 V. Third, based on newly proposed power loss analysis method, the continuous operation performance of SiC diodes is quantified and analyzed in actual cryogenic converters. The excellent temperature dependence indicates that SiC diodes have great superiority for extreme applications. Importantly, SiC mosfet's body diode shows the great potential to operate as a freewheeling diode in the compact converter, especially at cryogenic temperature. [ABSTRACT FROM AUTHOR]

Multi-media data including image, video, text, audio, and 3D model, has been fast emerging on the Internet. Jointly correlating the data of various media types is a challenging task. With the considerable learning ability of deep network, existing works mainly construct multi-pathway network to learn cross-media correlation, where each pathway is for one media type. However, with number of media types increasing, existing methods face the problems of high repetition and complexity, leading to overfitting and poor generalization ability, which makes adverse effect on correlation learning. For addressing the above issues, we propose cross-media deep compression and regularization (CDCR) approach for quintuple-media joint correlation learning: 1) cross-media partial weight-sharing networks is proposed, where a part of parameters are commonly shared among multiple pathways, to exploit common characteristics across different media types for capturing intrinsic cross-media correlation; 2) we propose media-adaptive network pruning to drop connections between weakly-correlated neurons, which can emphasize media-specific characteristics adaptively; and 3) cross-media network regularization is proposed to utilize relationships among quintuple-media data, which can guarantee generalization ability and enhance intra-media and inter-media correlation. The experiments verify the effectiveness of our approach, which outperforms the state-of-the-art methods on two very challenging datasets, including a large-scale dataset PKU XMediaNet with more than 100 000 quintuple-media instances. [ABSTRACT FROM AUTHOR]

Conventional switched-mode power supplies have intrinsic instantaneous power pulsation at the switching frequency thus require bulky filters. To improve the power density, this paper proposes a concept named the mixed analog–digital (MAD) which can be applied as dc–dc converters. By inserting an analog voltage component between the load and source, the output voltage naturally has much smaller fluctuation thereby much smaller passive filter is required. Simulations and experiments validate that the proposed MAD concept can be applied as dc–dc converters to significantly increase the power density. [ABSTRACT FROM AUTHOR]

The rapidly developing information technology leads to a fast growth of visual and textual contents, and it comes with huge challenges to make correlation and perform cross-media retrieval between images and sentences. Existing methods mainly explore cross-media correlation from either global-level instances as the whole images and sentences, or local-level fine-grained patches as the discriminative image regions and key words, which ignore the complementary information from the relation between local-level fine-grained patches. Naturally, relation understanding is highly important for learning cross-media correlation. People focus on not only the alignment between discriminative image regions and key words, but also their relations lying in the visual and textual context. Therefore, in this paper, we propose Multi-level Adaptive Visual-textual Alignment (MAVA) approach with the following contributions. First, we propose cross-media multi-pathway fine-grained network to extract not only the local fine-grained patches as discriminative image regions and key words, but also visual relations between image regions as well as textual relations from the context of sentences, which contain complementary information to exploit fine-grained characteristics within different media types. Second, we propose visual-textual bi-attention mechanism to distinguish the fine-grained information with different saliency from both local and relation levels, which can provide more discriminative hints for correlation learning. Third, we propose cross-media multi-level adaptive alignment to explore global, local and relation alignments. An adaptive alignment strategy is further proposed to enhance the matched pairs of different media types, and discard those misalignments adaptively to learn more precise cross-media correlation. Extensive experiments are conducted to perform image-sentence matching on 2 widely-used cross-media datasets, namely Flickr-30K and MS-COCO, comparing with 10 state-of-the-art methods, which can fully verify the effectiveness of our proposed MAVA approach. [ABSTRACT FROM AUTHOR]

The heterogeneity gap leads to inconsistent distributions and representations between image and text, which rises a challenging task to measure their similarities and construct cross-media correlation between them. The existing works mainly model the cross-media correlation in a common subspace, which causes insufficient correlation modeling in such third-party subspace with intermediate unidirectional transformation. Inspired by the recent advances of neural machine translation, which aims to establish a corresponding relationship between two entirely different languages, we can naturally discover that it has striking common characteristic with cross-media correlation learning to consider image and text as bilingual pairs, where the image is treated as a special kind of language to provide visual description, so that bidirectional transformation can be conducted between image and text to effectively explore cross-media correlation in the feature space of each media type. Thus, we propose a reinforced cross-media bidirectional translation (RCBT) approach to model the correlation between visual and textual descriptions. First, cross-media bidirectional translation mechanism is proposed to conduct direct transformation between the bilingual pairs of visual and textual descriptions bidirectionally, where the cross-media correlation can be effectively captured in both feature spaces of image and text through bidirectional translation training. Second, cross-media context-aware network with residual attention is proposed to exploit the rich spatial and temporal context hints with cross-media convolutional recurrent neural network, which can lead to more precise correlation learning for promoting bidirectional translation process. Third, cross-media reinforcement learning is proposed to perform a two-agent communication game played as a round between image and text to boost the bidirectional translation process, and we further extract inter-media and intra-media reward signals to provide complementary clues for learning cross-media correlation. Extensive experiments are conducted on cross-media retrieval to verify the effectiveness of our proposed RCBT approach, compared with 11 state-of-the-art methods on three cross-media datasets. [ABSTRACT FROM AUTHOR]

In this article, the static, dynamic, and short-circuit properties of 1.2-kV commercial 4H-SiC planar and trench gate metal–oxide–semiconductor field-effect transistors (MOSFETs) are compared and analyzed in a wide temperature range from 90 to 493 K. The temperature-dependent specific ON-resistance (${R}_{\text {sp}- \mathrm{\scriptscriptstyle ON}}$) and threshold voltage (${V}_{\text {th}}$) are analyzed in relation to the density of the interface state. The turn-on rise and turn-off fall times (${T}_{r}$ and ${T}_{f}$) and the corresponding energy loss (${E}_{r}$ and ${E}_{f}$) are extracted from a double-pulse test from cryogenic to high temperature and analyzed. The short-circuit capability of the two structures is studied at low temperature for the first time. The comprehensive comparison and analysis of the planar and trench gate MOSFET versus temperature in this work show the importance to study applications with SiC MOSFETs in a wide temperature range, especially for the cryogenic temperatures. [ABSTRACT FROM AUTHOR]

Due to the superior material properties, SiC mosfet is a promising candidate switching device for high power density and high efficiency power conversion system. The robustness of switching device under extreme temperature condition becomes a crucial factor to ensure power conversion system safely and continuously operating. In this paper, the temperature dependence of dynamic performance of 1.2-kV 4H-SiC power mosfets is systematically characterized over such wide temperature range of 90–493 K and compared with 1.2-kV Si IGBT by a layout optimized double pulse tester (DPT). The degradation of dynamic on-resistance related interface traps is analyzed specially and the energy loss caused by degradation is quantified at cryogenic temperatures. Besides, to validate the performance of SiC mosfet under safely and continuously operating conditions for cryogenic temperature application, a hard switched non-isolated dc–dc buck converter is designed and tested to estimate temperature dependence of conversion efficiency under temperature range of 90–290 K. Moreover, the further characterizations are conducted with gate resistance range of 2–20 Ω, load current range of 3–30 A, and converter output current of 5–22.5 A under different switching frequency (up to 150 kHz) to validate high power and high frequency application potential of SiC mosfet. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to investigate the clinical value of long noncoding RNA growth arrest‐specific transcript 5 (LncRNA GAS5) in the diagnosis of coronary artery disease (CAD) and its protective effect on myocardial injury in rats with CAD. Methods: Patients with CAD and healthy controls were selected to measure the expression of GAS5, and further to perform the correlation analysis and ROC curve. In addition, the rat models of CAD were also established to observe the effect of GAS5 on hyperlipidemia, myocardial injury, cardiomyocyte apoptosis, oxidative stress, and inflammatory injury of rats with CAD, and the effect of the Wnt/β‐catenin signaling pathway was also determined. Results: Overexpression of GAS5 in CAD rats determines improvement of hyperlipidemia, attenuation of myocardial injury, inhibition of cardiomyocyte apoptosis, oxidative stress, inflammatory injury, and abnormal activation of the Wnt/β‐catenin signaling pathway in myocardial tissues. Conclusion: Our study demonstrates that downregulation of GAS5 is found in CAD, and overexpression of GAS5 inhibits abnormal activation of the Wnt/β‐catenin signaling pathway in myocardial tissues of CAD rats. [ABSTRACT FROM AUTHOR]

Multimedia data with various modalities, such as image and text, are huge in quantity but have inconsistent distribution and representation. Many works have been done to break the boundary between image and text to measure their correlation. However, they focus on either the transformation to common subspace or the unidirectional generation from one to another individually, which cannot fully explore their interactions. It is noted that the bidirectional generation between image and text not only can provide complementary hints and mutually boost to learn cross-modal correlation but also cross-modal correlation learning can feed back to give comprehensive clues for promoting the cross-modal generation process. Therefore, we have the motivation that information transmission between image and text should be treated as a circular process, which aims to fully understand their latent correlation, and further realize cross-modal generation to produce both realistic images and text descriptions in a unified framework. In this paper, we propose the cross-modal circular correlation learning approach to perform both cross-modal correlation learning and generation simultaneously through an efficient circular learning training procedure. First, we propose the cross-modal circular learning model to perform an image-to-text caption and text-to-image synthesis circularly and learn common representation as a round-trip bridge, which can realize efficient interactions to fully exploit latent cross-modal correlations. Second, a unified bidirectional framework is proposed to conduct cross-modal mutual generation and is trained in an efficient circular process to enhance the generative ability of common representation, which can feed back circularly to further promote cross-modal correlation learning. In summary, we simultaneously perform cross-modal retrieval, image-to-text caption, and text-to-image synthesis in a unified framework with the circular learning process, which has high scalability and generality to realize universal cognition on the cross-modal data. We conduct extensive experiments to not only evaluate the correlation performance by cross-modal retrieval but also to show the generation effectiveness of both image caption and synthesis on the MS-COCO dataset. [ABSTRACT FROM AUTHOR]

In this paper, an improved 4H-SiC U-shaped trench-gate metal–oxide–semiconductor field-effect transistors (UMOSFETs) structure with low ON-resistance (${R}_{ \mathrm{\scriptscriptstyle ON}}$) and switching energy loss is proposed. The novel structure features an added n-type region, which reduces ON-resistance of the device significantly while maintaining the breakdown voltage (${V}_{\textsf {BR}}$). In addition, the gate of the improved structure is designed as a p-n junction to reduce the switching energy loss. Simulations by Sentaurus TCAD are carried out to reveal the working mechanism of this improved structure. For the static performance, the ON-resistance and the figure of merit (FOM $= {V}_{\textsf {BR}}^{\textsf {2}}/{R}_{ \mathrm{\scriptscriptstyle ON}}$) of the optimized structure are improved by 40% and 44%, respectively, as compared to a conventional trench MOSFET without the added n-type region and modified gate. For the dynamic performance, the turn-on time (${T}_{ \mathrm{\scriptscriptstyle ON}}$) and turn-off time (${T}_{ \mathrm{\scriptscriptstyle OFF}}$) of the proposed structure are both shorter than that of the conventional structure, bringing a 43% and 30% reduction in turn-on energy loss and total switching energy loss (${E}_{\mathbf {SW}}$). [ABSTRACT FROM AUTHOR]

Cross-modal retrieval has become a highlighted research topic for retrieval across multimedia data such as image and text. A two-stage learning framework is widely adopted by most existing methods based on deep neural network (DNN): The first learning stage is to generate separate representation for each modality and the second learning stage is to get the cross-modal common representation. However the existing methods have three limitations: 1) In the first learning stage they only model intramodality correlation but ignore intermodality correlation with rich complementary context. 2) In the second learning stage they only adopt shallow networks with single-loss regularization but ignore the intrinsic relevance of intramodality and intermodality correlation. 3) Only original instances are considered while the complementary fine-grained clues provided by their patches are ignored. For addressing the above problems this paper proposes a cross-modal correlation learning (CCL) approach with multigrained fusion by hierarchical network and the contributions are as follows: 1) In the first learning stage CCL exploits multilevel association with joint optimization to preserve the complementary context from intramodality and intermodality correlation simultaneously. 2) In the second learning stage a multitask learning strategy is designed to adaptively balance the intramodality semantic category constraints and intermodality pairwise similarity constraints. 3) CCL adopts multigrained modeling which fuses the coarse-grained instances and fine-grained patches to make cross-modal correlation more precise. Comparing with 13 state-of-the-art methods on 6 widely-used cross-modal datasets the experimental results show our CCL approach achieves the best performance. [ABSTRACT FROM PUBLISHER]

Fruits provide humans with multiple kinds of nutrients and protect humans against worldwide nutritional deficiency. Therefore, it is essential to understand the nutrient composition of various fruits in depth. In this study, we performed LC-MS-based non-targeted metabolomic analyses with ten kinds of fruit, including passion fruit, mango, starfruit, mangosteen, guava, mandarin orange, grape, apple, blueberry, and strawberry. In total, we detected over 2500 compounds and identified more than 300 nutrients. Although the ten fruits shared 909 common-detected compounds, each species accumulated a variety of species-specific metabolites. Additionally, metabolic profiling analyses revealed a constant variation in each metabolite's content across the ten fruits. Moreover, we constructed a neighbor-joining tree using metabolomic data, which resembles the single-copy protein-based phylogenetic tree. This indicates that metabolome data could reflect the genetic relationship between different species. In conclusion, our work enriches knowledge on the metabolomics of fruits, and provides metabolic evidence for the genetic relationships among these fruits. [ABSTRACT FROM AUTHOR]

The photocatalytic conversion of CO 2 into hydrocarbon fuel holds immense potential for achieving a carbon closed loop and carbon neutrality. Developing efficient photocatalysts plays a pivotal role in enabling the widespread application of photocatalytic CO 2 reduction on a large scale. Herein, a novel S-scheme MnWO 4 /LaTiO 2 N heterojunction composite is successfully synthesized by a hydrothermal method. This composite catalyst demonstrates excellent photocatalytic activity in the reduction of CO 2 to CO and CH 4 using water molecules as electron donors under visible light irradiation, and the optimized 30% MnWO 4 /LaTiO 2 N composite displays significantly enhanced CO and CH 4 yields of 3.94 and 0.81 μmol g -1 h -1 , respectively, and the corresponding utilized photoelectron number reaches 14.7 μmol g -1 h -1 , which is approximately 7.7 and 12.9 times that of LaTiO 2 N and MnWO 4 . The enhancement in photocatalytic activity of the composites can be ascribed to the construction of an S-scheme heterojunction, which exhibits improved charge transfer dynamics, retains the strongest redox capacity, and effectively suppresses back reactions. In situ Fourier-transform infrared imaging provides evidence, to a certain extent, for the existence of a temporal gradient order in the generation of multiple products during the photocatalytic reduction of CO 2 .

Intervertebral disc (IVD) degeneration (IDD) is a worldwide spinal degenerative disease. Low back pain (LBP) is frequently caused by a variety of conditions brought on by IDD, including IVD herniation and spinal stenosis, etc. These conditions bring substantial physical and psychological pressure and economic burden to patients. IDD is closely tied with the structural or functional changes of the IVD tissue and can be caused by various complex factors like senescence, genetics, and trauma. The IVD dysfunction and structural changes can result from extracellular matrix (ECM) degradation, differentiation, inflammation, oxidative stress, mechanical stress, and senescence of IVD cells. At present, the treatment of IDD is basically to alleviate the symptoms, but not from the pathophysiological changes of IVD. Interestingly, the p38 mitogen-activated protein kinase (p38 MAPK) signaling pathway is involved in many processes of IDD, including inflammation, ECM degradation, apoptosis, senescence, proliferation, oxidative stress, and autophagy. These activities in degenerated IVD tissue are closely relevant to the development trend of IDD. Hence, the p38 MAPK signaling pathway may be a fitting curative target for IDD. In order to better understand the pathophysiological alterations of the intervertebral disc tissue during IDD and offer potential paths for targeted treatments for intervertebral disc degeneration, this article reviews the purpose of the p38 MAPK signaling pathway in IDD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Zhang, Zou, Wang, Qi, Han, Xin, Zheng, Wei, Zhang and Zhang.)

The rapidly developing information technology leads to a fast growth of visual and textual contents, and it comes with huge challenges to make correlation and perform crossmedia retrieval between images and sentences. Existing methods mainly explore cross-media correlation from either global-level instances as the whole images and sentences, or local-level fine-grained patches as the discriminative image regions and key words, which ignore the complementary information from the relation between local-level fine-grained patches. Naturally, relation understanding is highly important for learning crossmedia correlation. People focus on not only the alignment between discriminative image regions and key words, but also their relations lying in the visual and textual context. Therefore, in this paper, we propose Multi-level Adaptive Visual-textual Alignment (MAVA) approach with the following contributions. First, we propose cross-media multi-pathway fine-grained network to extract not only the local fine-grained patches as discriminative image regions and key words, but also visual relations between image regions as well as textual relations from the context of sentences, which contain complementary information to exploit fine-grained characteristics within different media types. Second, we propose visual-textual bi-attention mechanism to distinguish the fine-grained information with different saliency from both local and relation levels, which can provide more discriminative hints for correlation learning. Third, we propose cross-media multi-level adaptive alignment to explore global, local and relation alignments. An adaptive alignment strategy is further proposed to enhance the matched pairs of different media types, and discard those misalignments adaptively to learn more precise cross-media correlation. Extensive experiments are conducted to perform image-sentence matching on 2 widely-used cross-media datasets, namely Flickr-30K and MS-COCO, comparing with 10 state-of-the-art methods, which can fully verify the effectiveness of our proposed MAVA approach.

Nowadays, cross-modal retrieval plays an important role to flexibly find useful information across different modalities of data. Effectively measuring the similarity between different modalities of data is the key of cross-modal retrieval. Different modalities such as image and text have imbalanced and complementary relationship, and they contain unequal amount of information when describing the same semantics. For example, images often contain more details that cannot be demonstrated by textual descriptions and vice versa. Existing works based on Deep Neural Network (DNN) mostly construct one common space for different modalities, to find the latent alignments between them, which lose their exclusive modality-specific characteristics. Therefore, we propose modality-specific cross-modal similarity measurement (MCSM) approach by constructing the independent semantic space for each modality, which adopts an endto- end framework to directly generate modality-specific crossmodal similarity without explicit common representation. For each semantic space, modality-specific characteristics within one modality are fully exploited by recurrent attention network, while the data of another modality is projected into this space with attention based joint embedding, which utilizes the learned attention weights for guiding the fine-grained cross-modal correlation learning, and captures the imbalanced and complementary relationship between different modalities. Finally, the complementarity between the semantic spaces for different modalities is explored by adaptive fusion of the modality-specific cross-modal similarities to perform cross-modal retrieval. Experiments on the widely-used Wikipedia, Pascal Sentence, MS-COCO datasets as well as our constructed large-scale XMediaNet dataset verify the effectiveness of our proposed approach, outperforming 9 stateof- the-art methods.