Search

Recently, with the remarkable advancements of deep learning in the field of image processing, convolutional neural networks (CNNs) have garnered widespread attention from researchers in the domain of hyperspectral image (HSI) classification. Moreover, due to the high performance demonstrated by the transformer architecture in classification tasks, there has been a proliferation of neural networks combining CNNs and transformers for HSI classification. However, the majority of the current methods focus on extracting spatial–spectral features from the HSI data of a single size for a pixel, overlooking the rich multi-scale feature information inherent to the data. To address this problem, we designed a novel transformer network with a CNN-enhanced cross-attention (TNCCA) mechanism for HSI classification. It is a dual-branch network that utilizes different scales of HSI input data to extract shallow spatial–spectral features using a multi-scale 3D and 2D hybrid convolutional neural network. After converting the feature maps into tokens, a series of 2D convolutions and dilated convolutions are employed to generate two sets of Q (queries), K (keys), and V (values) at different scales in a cross-attention module. This transformer with CNN-enhanced cross-attention explores multi-scale CNN-enhanced features and fuses them from both branches. Experimental evaluations conducted on three widely used hyperspectral image (HSI) datasets, under the constraint of limited sample size, demonstrate excellent classification performance of the proposed network. [ABSTRACT FROM AUTHOR]

Simple Summary: Cas9 is limited by protospacer adjacent motif (PAM) sequences during the application process, which is particularly evident in insect genome editing. Researchers have discovered Cas9 variants with a wider range of recognition due to the development of genome-editing technology. However, the application of these variants in insects has been ineffective and time-consuming. To tackle this issue, this study established a high-throughput assay system capable of rapidly detecting recognizable PAM sequences of different Cas9 variants in Bombyx mori and the corresponding editing efficiency of the sites. We utilized this system to evaluate the editing ability of two protein variants, xCas9 and Cas9-NG, in Bombyx mori cells. Based on the editing ability of these two proteins, the editable range of the Bombyx mori genome can be greatly expanded, accelerating research on the genome's function. This system is also applicable to other insects for detecting the editing ability of Cas9 variants, thus accelerating the study of silkworm genome function. Genome editing provides novel opportunities for the precise genome engineering of diverse organisms. Significant progress has been made in the development of genome-editing tools for Bombyx mori (B. mori) in recent years. Among these, CRISPR/Cas9, which is currently the most commonly used system in lepidopteran insects, recognizes NGG protospacer adjacent motif (PAM) sequences within the target locus. However, Cas9 lacks the ability to target all gene loci in B. mori, indicating the need for Cas9 variants with a larger editing range. In this study, we developed a high-throughput screening platform to validate Cas9 variants at all possible recognizable and editable PAM sites for target sequences in B. mori. This platform enabled us to identify PAM sites that can be recognized by both xCas9 3.7 and SpCas9-NG variants in B. mori and to assess their editing efficiency. Cas9 shows PAM sites every 13 base pairs in the genome, whereas xCas9 3.7 and SpCas9-NG have an average distance of 3.4 and 3.6 base pairs, respectively, between two specific targeting sites. Combining the two Cas9 variants could significantly expand the targeting range of the genome, accelerate research on the B. mori genome, and extend the high-throughput rapid screening platform to other insects, particularly those lacking suitable NGG PAM sequences. [ABSTRACT FROM AUTHOR]

Thermal conductivity and viscosity are important properties for nanofluids as they significantly affect the flow and heat transfer process. To date, the rheological properties of water-based nanofluids have been well studied, while the results are scarce for non-aqueous nanofluids. In this study, the thermal conductivity and rheological properties of two different kinds of oxide nanofluids (CuO and Al2O3) in a typical commercial data center focusing on liquid coolants were systematically investigated at different mass fractions and temperatures. The results showed that the addition of nanoparticles can significantly improve the heat conduction capacity of mineral oil coolants. There is an average increase in thermal conductivity of up to 20–25%. The shear rate–shear stress and shear rate–viscosity curves all showed that mineral oil coolant-based oxide nanofluids behaved as Newtonian fluids and that nanoparticles did not cause the increment in viscosity. The effect of temperature on rheological properties was also studied, and the result showed that high temperatures resulted in low viscosity and shear stress. Finally, the effect of particle type was investigated, and it was found that no matter what kind of nanoparticles were added, their effects on the rheological behaviors were the same. [ABSTRACT FROM AUTHOR]

Microtia-atresia is a rare type of congenital craniofacial malformation causing severe damage to the appearance and hearing ability of affected individuals. The genetic factors associated with microtia-atresia have not yet been determined. The AMER1 gene has been identified as potentially pathogenic for microtia-atresia in two twin families. An amer1 mosaic knockdown zebrafish model was constructed using CRISPR/Cas9. The phenotype and the development process of cranial neural crest cells of the knockdown zebrafish were examined. Components of the Wnt/β-catenin pathway were examined by qPCR, Western blotting, and immunofluorescence assay. IWR-1-endo, a reversible inhibitor of the Wnt/β-catenin pathway, was applied to rescue the abnormal phenotype. The present study showed that the development of mandibular cartilage in zebrafish was severely compromised by amer1 knockdown using CRISPR/Cas9. Specifically, amer1 knockdown was found to affect the proliferation and apoptosis of cranial neural crest cells, as well as their differentiation to chondrocytes. Mechanistically, amer1 exerted an antagonistic effect on the Wnt/β-catenin pathway. The application of IWR-1-endo could partially rescue the abnormal phenotype. We demonstrated that amer1 was essential for the craniofacial development of zebrafish by interacting with the Wnt/β-catenin pathway. These findings provide important insight into the role of amer1 in zebrafish mandibular development and the pathology of microtia-atresia caused by AMER1 gene mutations in humans. [ABSTRACT FROM AUTHOR]

The borosilicate scales with different B 2 O 3 contents were prepared on MoSi 2 ceramics by cold pressing, pressureless sintering and pre-oxidation. Hot corrosion with Na 2 SO 4 was carried out at 1000 °C for 8 h. The pre-oxidized borosilicate scale on ceramics with 10 wt% B 2 O 3 has an excellent hot corrosion resistance. B 2 O 3 –SiO 2 barrier scales mainly contained amorphous borosilicate glass which can reacted with Na 2 SO 4 producing sodium borosilicate glass to cover and heal the corroded surface. The Mo-based particles under the barrier scale were isolated to avoid severe reactions producing Na 2 MoO 4 , so the borosilicate barrier scale had an excellent corrosion resistance to Na 2 SO 4 molten salt. [ABSTRACT FROM AUTHOR]

Time series reconstruction of astronomical catalogues is an important part of data archiving and a basis for time-domain astronomical analysis in the era of time-domain astronomy. As the field of view and sampling frequency of various time-domain telescopes increase, the amount of data to be processed becomes larger and larger. How to optimize the spatial and temporal efficiency of this process with the aid of computer technology becomes a hot issue. To address the problem of spatial efficiency, in this paper, we propose a time series data compression algorithm based on the negative database and dynamic programming, and on this basis, we design a multi-level storage and access query architecture for hot data and non-hot data, which greatly compresses the storage space of data while ensuring the query efficiency. To address the issue of time efficiency, this paper proposes a spatio-temporal data partitioning and layout algorithm suitable for distributed architecture, whose nested round-robin strategy has a wide range of load balancing effects on different spatial locations, temporal locations, and different ranges of temporal data queries, which can effectively ensure the execution efficiency of the distributed system. Experimental results show that the proposed optimization algorithm can keep the system at a low load skewness level of about 4% and save about 83% of storage space. [ABSTRACT FROM AUTHOR]

Background: In vertebrates, the development of the inner ear is a delicate process, whereas its relating molecular pathways are still poorly understood. LMO4, an LIM domain-only transcriptional regulator, is drawing an increasing amount of interest for its multiple roles regarding human embryonic development and the modulation of ototoxic side effects of cisplatin including cochlear apoptosis and hearing loss. The aim of the present study is to further explore the role of lmo4a in zebrafish inner ear development and thus explore its functional role. Methods: The Spatial Transcript Omics DataBase was referred to in order to evaluate the expression of lmo4a during the first 24 h of zebrafish development. In situ hybridization was applied to validate and extend the expression profile of lmo4a to 3 days post-fertilization. The morpholino (MO) knockdown and CRISPR/Cas9 knockout (KO) of lmo4a was applied. Morphological analyses of otic vesical, hair cells, statoacoustic ganglion and semicircular canals were conducted. The swimming pattern of lmo4a KO and MO zebrafish was tracked. In situ hybridization was further applied to verify the expression of genes of the related pathways. Rescue of the phenotype was attempted by blockage of the bmp pathway via heat shock and injection of Dorsomorphin. Results: lmo4a is constitutively expressed in the otic placode and otic vesicle during the early stages of zebrafish development. Knockdown and knockout of lmo4a both induced smaller otocysts, less hair cells, immature statoacoustic ganglion and malformed semicircular canals. Abnormal swimming patterns could be observed in both lmo4a MO and KO zebrafish. eya1 in preplacodal ectoderm patterning was downregulated. bmp2 and bmp4 expressions were found to be upregulated and extended in lmo4a morphants, and blockage of the Bmp pathway partially rescued the vestibular defects. Conclusions: We concluded that lmo4a holds a regulative effect on the Bmp pathway and is required for the normal development of zebrafish inner ear. Our study pointed out the conservatism of LMO4 in inner ear development between mammals and zebrafish as well as shed more light on the molecular mechanisms behind it. Further research is needed to distinguish the relationships between lmo4 and the Bmp pathway, which may lead to diagnostic and therapeutic approaches towards human inner ear malformation. [ABSTRACT FROM AUTHOR]

A permanent magnet synchronous machine (PMSM) temperature estimation method fusing the classic lumped‐parameter thermal network (LPTN) and the stator sensor temperature information is investigated. With respect to the industrial servo applications, it is important to monitor the rotor magnet temperature to extend the machine thermal utilisation. A simple LPTN is developed and the quadratic programming is applied to identify the model parameters. The research work shows that the rotor temperature is sensitive to the rotor speed even at low speeds, and this simple estimation network can provide an accurate estimation. The winding sensor information can further improve the performance. A compound mechanism is built, by which the winding temperature estimation error is used as feedback to suppress the model‐based temperature estimation error accumulation. The estimation performance is compared with a regular constant LPTN‐based method. Experimental results based on a PMSM are presented to validate the proposed method. [ABSTRACT FROM AUTHOR]

CO is an important gas signal molecule and plays an indispensable role in the maintenance of cell homeostasis. Herein, photoinduced CO-releasing molecules (photoCORMs), that combine the effects of zinc(ii) and different ligands including flavonol derivatives and tripod pyridyl compounds, are reported. The photoCORMs can release about one equivalent of CO, and the solid samples are stabile for more than 90 days in air. Cytotoxicity tests suggest that photoCORMs possess low toxicity and have the potential to be used in organisms. The intracellular uptake and photoreactivity of photoCORM 3a , with low toxicity and a rapid CO-release rate, were studied in HeLa cells. The results indicate that 3a could successfully penetrate the cell membrane and enter the cytoplasm. More importantly, it is further demonstrated that 3a can successfully release CO in HeLa cells, which is detected using intracellular CO sensors. Based on the cell study, the same result was found when the photoinduced CO release of 3a in Kunming mice was studied utilizing a carboxyhemoglobin kit. This study is of great significance for the development of new valuable CO donors that can be applied to organisms to exert their biological effects. Flavonols have an active regulatory effect on organisms, and they have been used to form new types of photoinduced CO-releasing molecules (photoCORMs) with superior application prospects. A range of photoCORMs based on flavonol derivatives were synthesized and screened. PhotoCORM 3a , with a fast CO-release rate and low cytotoxicity, can penetrate the cell membrane well into the cytoplasm, and release 0.03 moles of CO in mice. PhotoCORM 3a can potenitally be utilized as a molecular tool to study the biological effects of CO in organisms, with applications in biomedicine. [ABSTRACT FROM AUTHOR]

The development of medical sensors and the Internet of Things (IoT) offers many opportunities for research on disease diagnosis and prognosis in the electronic healthcare (eHealth) industry. IoT medical applications use wearable medical sensor devices that can be connected to the Internet for remote monitoring. However, cloud computing technology cannot meet the real-time and low-latency requirements of IoT applications in eHealth. As an intermediate layer between things and clouds, fog computing has features such as enhanced low latency, mobility, network bandwidth, security and privacy. Therefore, fog computing is very useful for the diagnosis and prognosis of diseases in the eHealth industry. In this paper, we undertake a comprehensive survey on fog computing used in eHealth. We summarize the main challenges in the eHealth industry and analyze the corresponding solutions proposed by the existing works. [ABSTRACT FROM AUTHOR]

The dual-motor drive is common in many heavy-duty swinging arm servo systems. A well-tuned dual-motor drive can provide better stability and durability than the regular single motor servo. However, the backlash is usually unavoidable in the heavy transmission chains. In this article, the issues provoked by the backlash are resolved by a virtual average motor model and a simple anti-backlash torsion torque. The transient performance is guaranteed by the dual-motor synchronous drive, and the anti-backlash strategy is only enabled when the load is approaching the reference position. The challenge is that the motion system structure is transforming during this transition. The model predictive control combined with the extended state observer is applied to get adaptive in this scenario. The expected effect is that the servo can track the desired position with high precision and high response. The scheme is comprehensively validated and evaluated in an experimental servo setup with a dual-motor drive. [ABSTRACT FROM AUTHOR]

Spectral unmixing is among one of the major hyperspectral image analysis tasks that aims to extract basic features (endmembers) at the subpixel level and estimate their corresponding proportions (fractional abundances). Recently, the rapid development of deep learning networks has provided us with a new method to solve the problem of spectral unmixing. In this paper, we propose a spatial-information-assisted spectral information learning unmixing network (SISLU-Net) for hyperspectral images. The SISLU-Net consists of two branches. The upper branch focuses on the extraction of spectral information. The input of the upper branch is a number of pixels randomly extracted from the hyperspectral image. The data are fed into the network as a random combination of different pixel blocks each time. The random combination of batches can boost the network to learn global spectral information. Another branch focuses on learning spatial information from the entire hyperspectral image and transmitting it to the upper branch through the shared weight strategy. This allows the network to take into account the spectral information and spatial information of HSI at the same time. In addition, according to the distribution characteristics of endmembers, we employ Wing loss to solve the problem of uneven distributions of endmembers. Experimental results on one synthetic and three real hyperspectral data sets show that SISLU-Net is effective and competitive compared with several state-of-the-art unmixing algorithms in terms of the spectral angle distance (SAD) of the endmembers and the root mean square error (RMSE) of the abundances. [ABSTRACT FROM AUTHOR]

Low-rank tensor representation philosophy has enjoyed a reputation in many hyperspectral image (HSI) low-level vision applications, but previous studies often failed to comprehensively exploit the low-rank nature of HSI along different modes in low-dimensional subspace, and unsurprisingly handled only one specific task. To address these challenges, in this paper, we figured out that in addition to the spatial correlation, the spectral dependency of HSI also implicitly exists in the coefficient tensor of its subspace, this crucial dependency that was not fully utilized by previous studies yet can be effectively exploited in a cascaded manner. This led us to propose a unified subspace low-rank learning regime with a new tensor cascaded rank minimization, named STCR, to fully couple the low-rankness of HSI in different domains for various low-level vision tasks. Technically, the high-dimensional HSI was first projected into a low-dimensional tensor subspace, then a novel tensor low-cascaded-rank decomposition was designed to collapse the constructed tensor into three core tensors in succession to more thoroughly exploit the correlations in spatial, nonlocal, and spectral modes of the coefficient tensor. Next, difference continuity-regularization was introduced to learn a basis that more closely approximates the HSI’s endmembers. The proposed regime realizes a comprehensive delineation of the self-portrait of HSI tensor. Extensive evaluations conducted with dozens of state-of-the-art (SOTA) baselines on eight datasets verified that the proposed regime is highly effective and robust to typical HSI low-level vision tasks, including denoising, compressive sensing reconstruction, inpainting, and destriping. The source code of our method is released at https://github.com/CX-He/STCR.git. [ABSTRACT FROM AUTHOR]

The quality change of shrimp is inevitable during storage, while shrimp preservation is extremely important in the fishery and food industries. Many efforts have been devoted to explore preservation strategies to maintain shrimp quality and develop sensitive freshness indicators to monitor shrimp quality during storage in the last decade. This review provides an update on the current knowledge of the mechanisms of shrimp quality changes, active films/coatings used to maintain shrimp quality, and smart films used to indicate the shrimp freshness during low-temperature storage. The enzymatic activities of trypsin-like protease, cathepsin B, polyphenoloxidase, prophenoloxidase, and ATPase play important roles in protein hydrolysis, formation of melanin and degradation of ATP in shrimp, resulting in muscle softening, melanosis and loss of umami. More importantly, the microbial activity is the main reason for forming off-flavor in shrimp during low-temperature storage. In addition, lipid and protein oxidation, astaxanthin degradation and cell wall damage caused by ice crystals during frozen storage can also contribute to the quality changes of shrimp. The use of active coatings/films is an effective method to maintain the shrimp quality during storage, because the encapsulated functional compounds (e.g., protein hydrolysates, essential oil and plant extracts) can act as anti -melanosis, antibacterial and antioxidant agents. In addition, smart pH-responsive films, as a simple and effective technique to monitor the quality changes of shrimp, have received increased interest in recent years, and natural pigments (e.g., anthocyanin) can be used as freshness sensors in such films to enhance its convenience for application. • Spoilage mechanisms, active and intelligent films/coatings for shrimp are reviewed. • Quality degradation in shrimp is caused by various mechanisms during storage. • Various quality assessment methods can be carried out for shrimp during storage. • Packaging films/coatings can effectively maintain shrimp quality during storage. • pH-sensitive films containing pigment compounds can monitor shrimp quality. [ABSTRACT FROM AUTHOR]

Studies on peptide-based emulsifiers in the field of foods have progressed rapidly in the last decade, due to the higher anti-oxidative activity, faster adsorption to the interfaces, reduced allergenicity and higher nutritional values, compared to proteins used as emulsifiers. Peptides therefore have the potential for broader applications in emulsion-based foods. This paper provides an update on the current knowledge of the surface behavior of surface-active peptides and peptide assemblies. Factors affecting physical and oxidative stability of peptide-stabilized emulsions and the fabrication and utilization of peptide-associated conjugates in emulsions have also been summarized. The potential applications of peptide-stabilized emulsions in the food industry are finally discussed. Peptides with different primary and secondary structures show different surface behavior. Surface-active peptides with facial amphiphilicity can orient horizontally at the interfaces, while those with half hydrophobic-half hydrophilic residues orient perpendicularly at the interfaces. In addition, peptide assemblies with supermolecular structures can embed in the oil-water interfaces. The long-chain peptides with molecular weights of higher than 5 kDa usually exhibit better emulsifying capacity than short-chain peptides, and many factors (e.g., pH, temperature and ionic strength) can influence the physical stability of peptide-stabilized emulsions. Moreover, the presence of peptides can improve the oxidative stability of emulsions by repulsing or chelating metals. The fabrication of peptides into conjugates with polysaccharides, flavonoids, or phenols can further improve their emulsifying capacity. Finally, peptide-stabilized emulsions have been reported to be used as encapsulation materials or stabilizers/antioxidants in real emulsion-based food systems. • Protein hydrolysis is the mostly used method to prepare emulsifying peptide. • Primary and secondary structures of peptide affect their orientation at interfaces. • Environmental conditions influence the physical and oxidative stability of peptide-stabilized emulsions. • The stability of peptide-stabilized emulsions can be improved by using peptide conjugates. • Peptide-based emulsions can be used in delivery systems and real food systems. [ABSTRACT FROM AUTHOR]

High-quality semen with high viability is critical to improving the in-vitro fertilization efficiency. This study aimed to understand the effect of ambient temperature and humidity on semen quality and seminal plasma biochemical parameters of Mediterranean buffalo in March and July. The metabolites of seminal plasma in two seasons were detected using the UPLC-MS/MS method. The results showed that temperature and humidity index (THI) in March were 66.86 ± 2.98, and 82.94 ± 3.52 in July. Compared with in March, breath frequency, rectal temperature, and heat shock protein 70 expressions of seminal plasma were significantly increased in July (p < 0.05), motility of sperm was dramatically reduced, and sperm deformity rate was significantly increased (p < 0.05). Fructose, acid phosphatase and α-glucosidase in seminal plasma were significantly increased (p < 0.05) in July, while testosterone level was significantly reduced (p < 0.05). Six different metabolites were found in the two groups, which involved in three metabolic pathways, the tricarboxylic acid cycle, glycerophospholipid, glyoxylic acid and dicarboxylic acid. The above results indicate that the increased ambient temperature has obvious side effects on the semen quality of Mediterranean buffalo, and the compromised quality is associated with the change of metabolites related to male hormone secretion, energy metabolism and fatty acid oxidation. [ABSTRACT FROM AUTHOR]

Spatiotemporal modal analysis and prediction of tropospheric atmospheric CO concentration in the world and East Asia from 2002 to 2021 using the inversion data of airs sounder. The results show that: The CO concentration in the northern hemisphere is higher than that in the southern hemisphere; from the upper troposphere to the lower troposphere, the CO concentration changes from " ∧ " to "√"; the fluctuation range of near surface CO concentration in the northern hemisphere is relatively intense, and the fluctuation range in the southern hemisphere is relatively small. Using MK, Sen slope estimation, and EOF analysis, it is found that CO concentration in the convective middle layer tends to decline in more than 90% of the global area, and the decline rate in the northern hemisphere is significantly higher than that in the southern hemisphere. In East Asia, the CO concentration in the lower tropospheric marine area is significantly lower than that in the land area. The average concentration and decline rate of CO in East Asia is always higher than that in the world; the CO concentration in East Asia is the highest in spring and winter in the lower troposphere; and the CO concentration in East Asia is lower in the northeast and higher in the southeast in the upper troposphere in spring, autumn, and winter, and higher in the northeast and Central Plains in summer. Compared with the three-exponential smoothing model, the prediction error of the VMD-LSTM hybrid model for atmospheric CO concentration is significantly reduced, which indicates that the improved neural network prediction model has higher prediction accuracy. The factors affecting the change of tropospheric CO concentration are not only affected by the ground factors, but also related to indirect factors such as water vapor, methane, and atmospheric temperature in the atmosphere. [ABSTRACT FROM AUTHOR]

Digital inclusive finance not only reduces the threshold of rural financial services but also plays an important role in promoting the rural economy. Under the background of the digital economy in China, we studied how digital inclusive finance affects the high-quality development of the rural economy. For this purpose, we assembled the provincial panel data from 2009 to 2018 and constructed an index system of rural high-quality development and then calculated its index using the entropy weight method. FE regression model and RE regression model were employed for empirical analysis, and we constructed a nonlinear regression model to investigate the nonlinear relationship as well. Furthermore, systematic GMM and iterative GMM were used to solve the endogenous problem. We found that digital inclusive finance plays a significant and positive role in promoting rural high-quality development, mainly through the channel of economic efficiency, urban and rural structure, green ecological development, harmony of people's livelihood, and innovative development potential. Each subdimension of digital inclusive finance has a positive impact on rural high-quality development to different degrees. There is also a direct nonlinear relationship between the core variables, which act as a disincentive in the initial stage and boost after reaching a certain inflection point. Therefore, it is recommended to increase the construction of rural digital infrastructure, improve the digitalization of inclusive finance with the help of digital technologies such as cloud computing, and expand the breadth and depth of digital inclusive finance services in rural areas, especially in economically underdeveloped provinces and cities, to effectively promote the comprehensive and high-quality development of rural areas. [ABSTRACT FROM AUTHOR]

Infrared and visible image fusion aims to generate informative images by leveraging the distinctive strengths of infrared and visible modalities. These fused images play a crucial role in subsequent downstream tasks, including object detection, recognition, and segmentation. However, complementary information is often difficult to extract. Existing generative adversarial network-based methods generate fused images by modifying the distribution of source images' features to preserve instances and texture details in both infrared and visible images. Nevertheless, these approaches may result in a degradation of the fused image quality when the original image quality is low. Considering the balance of information from different modalities can improve the quality of the fused image. Hence, we introduce CABnet, a Channel Attention dual adversarial Balancing network. CABnet incorporates a channel attention mechanism to capture crucial channel features, thereby, enhancing complementary information. It also employs an adaptive factor to control the mixing distribution of infrared and visible images, which ensures the preservation of instances and texture details during the adversarial process. To enhance efficiency and reduce reliance on manual labeling, our training process adopts a semi-supervised learning strategy. Through qualitative and quantitative experiments across multiple datasets, CABnet surpasses existing state-of-the-art methods in fusion performance, notably achieving a 51.3% enhancement in signal to noise ratio and a 13.4% improvement in standard deviation. • Enhancing feature extraction capability and eliminating redundant features by channel attention module. • Controlling the mixed distribution by adaptive balance module. • Converging faster by an enhanced loss function. [ABSTRACT FROM AUTHOR]

With the advancement in digital communication and artificial intelligence-based applications, the emphasis on information security has intensified. Traditional authentication techniques like passwords, fingerprints, and facial recognition present risks of unauthorized access and duplication. The distinctive nature of electrocardiogram (ECG) signals is drawing considerable attention as an identity verification approach. However, current ECG-based identification methods tend to possess intricate preprocessing phases and excessively rely on the accuracy of fiducial point detection. This prevents a comprehensive capture of the information within the ECG signals, thereby affecting the accuracy of identification. To address these issues, we introduce RandSaD (Random-window Split attention Dual-path), an innovative ECG identity recognition model. RandSaD combines rand om window extraction with a d ual-path residual neural network and integrates the s plit a ttention (SpAt) mechanism. During the preprocessing stage, RandSaD operates directly on wavelet-denoised ECG signals. It extracts random windows with the objective of preserving the intrinsic characteristics of the ECG signal while minimizing information loss. Subsequently, we integrate a dual-path residual network integrated with SpAt. Each path can independently extract features of a specific scale or type, enhancing the capability of the model to extract features. The SpAt amplifies the capacity of the model to distinguish and merge information from different channels. Experimental results indicate that the RandSaD model achieves a 99.6% accuracy rate in ECG identity recognition tasks. [ABSTRACT FROM AUTHOR]

With the development of neural networks, identity recognition techniques utilizing human physiological signals have found applications in healthcare systems. However, many current physiological signal datasets have to spend a lot of resources in the pre-labeling phase, which leads to longer training times. To reduce the cost of labeling, we propose a self-supervised time-series representation learning framework based on contrastive positive-unlabeled learning (TS − CPUL), to learn the representations of temporal neighborhood sequences. To obtain the representations, the unit root test divides the unlabeled time series into neighborhood samples and unlabeled samples. Secondly, we use neighborhood sample representations and unlabeled sample representations to do self-supervised contrastive learning and positive-unlabeled learning with a negative sample selector. Both approaches can deepen the model's understanding of the sample representation. Lastly, to enhance the model's capability in discriminating between ambiguous sample pairs, we design the distance-polarized matrix. The proposed TS-CPUL is evaluated through experiments on three physiological signal datasets. The results demonstrate its effectiveness in understanding time-series representations and performing well in classification. For the identity recognition task, we achieve accuracy comparable to the supervised model by fine-tuning the model with only 10% of the labeled data. [ABSTRACT FROM AUTHOR]

Traffic forecasting plays a vital role in the management of urban road networks and the development of intelligent transportation systems. To effectively capture spatial and temporal dependencies within traffic data, Graph Neural Networks (GNNs) emerge by combining recurrent neural networks and dynamic graph learning. However, traditional GNNs face challenges in efficiently memorizing patterns at different levels of these dependencies, including long-term shared patterns and short-term specific patterns. We propose a traffic forecasting model, the Multi-level Graph Memory Network Cluster Convolutional Recurrent Network (MMNCCRN). The MMNCCRN consists of four modules: Encoder Module (EM), Attention Module (AM), Memory Network Cluster Module (MNCM), and Decoder Module (DM). To enhance the model's performance, AM incorporates a concise yet efficient attention mechanism that augments important information in the output of EM. This alleviates the pressure on MNCM to identify patterns at different levels. Meanwhile, in MNCM, we extensively leverage memory networks and introduce the concept of clustering. By storing and memorizing patterns implicit in spatial and temporal dependencies, MNCM assists the model in learning underlying graph structures and discovering new hidden patterns. Experimental results show that our model outperforms others in most metrics on four datasets. • Specific patterns and shared patterns are implicit in traffic data. • Memory network cluster can memorize, and utilize patterns at multiple levels. • An effective attention machanism helps alleviate the memory burden of the cluster. • Memory items are collectively used to represent the graph structure. • Memory network cluster is more effective than simply expanding one single network. [ABSTRACT FROM AUTHOR]

A smart city is an intelligent space, in which large amounts of data are collected and analyzed using low-cost sensors and automatic algorithms. The application of artificial intelligence and Internet of Things (IoT) technologies in electronic health (E-health) can efficiently promote the development of sustainable and smart cities. The IoT sensors and intelligent algorithms enable the remote monitoring and analyzing of the healthcare data of patients, which reduces the medical and travel expenses in cities. Existing deep learning-based methods for healthcare sensor data classification have made great achievements. However, these methods take much time and storage space for model training and inference. They are difficult to be deployed in small devices to classify the physiological signal of patients in real time. To solve the above problems, this paper proposes a micro time series classification model called the micro neural network (MicroNN). The proposed model is micro enough to be deployed on tiny edge devices. MicroNN can be applied to long-term physiological signal monitoring based on edge computing devices. We conduct comprehensive experiments to evaluate the classification accuracy and computation complexity of MicroNN. Experiment results show that MicroNN performs better than the state-of-the-art methods. The accuracies on the two datasets (MIT-BIH-AR and INCART) are 98.4% and 98.1%, respectively. Finally, we present an application to show how MicroNN can improve the development of sustainable and smart cities. [ABSTRACT FROM AUTHOR]

The stator-PM machine concept has been practiced on the recently proposed stator-PM variable reluctance (VR) resolver. When inheriting the simple and sturdy rotor structure of the conventional VR resolver, the novel stator-PM VR resolver provides a chance to avoid the high-frequency excitation by the unique PM back electromotive force decoding solution. This article extends this stator-PM VR resolver category with the axial magnetic field technology. The axial slotless structure makes the VR resolver compact and much smaller than the radial field prototypes. The thin and light leaf-style rotor is suitable for the super-high-speed applications, as the additional inertia due to a sensor is not welcome in those scenarios. Additionally, the printed circuit board replaces the winding coils in the regular VR resolvers, significantly simplifying the manufacturing. The design with even and odd poles cannot share the same principle, so they are discussed separately. The prototypes with 4-X and 5-X configurations are tested in the experiment setup respectively to validate the analysis of the proposed structure. Moreover, the 4-X resolver is integrated with a permanent magnet synchronous machine, and a motor driver with the space vector modulation on the basis of the specific resolver decoder is built. [ABSTRACT FROM AUTHOR]

Due to the limited hardware conditions, hyperspectral image (HSI) has a low spatial resolution, while multispectral image (MSI) can gain higher spatial resolution. Therefore, derived from the idea of fusion, we reconstructed HSI with high spatial resolution and spectral resolution from HSI and MSI and put forward an HSI Super-Resolution model based on Spectral Smoothing prior and Tensor tubal row-sparse representation, termed SSTSR. Foremost, nonlocal priors are applied to refine the super-resolution task into reconstructing each nonlocal clustering tensor. Then per nonlocal cluster tensor is decomposed into two sub tensors under the tensor t-prodcut framework, one sub-tensor is called tersor dictionary and the other is called tensor coefficient. Meanwhile, in the process of dictionary learning and sparse coding, spectral smoothing constraint is imposed on the tensor dictionary, and L 1 , 1 , 2 norm based tubal row-sparse regularizer is enforced on the tensor coefficient to enhance the structured sparsity. With this model, the spatial similarity and spectral similarity of the nonlocal cluster tensor are fully utilized. Finally, the alternating direction method of multipliers (ADMM) was employed to optimize the solution of our method. Experiments on three simulated datasets and one real dataset show that our approach is superior to many advanced HSI super-resolution methods. [ABSTRACT FROM AUTHOR]

In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover category. In the recent past, convolutional neural network (CNN)-based HSI classification methods have greatly improved performance due to their superior ability to represent features. However, these methods have limited ability to obtain deep semantic features, and as the layer’s number increases, computational costs rise significantly. The transformer framework can represent high-level semantic features well. In this article, a spectral–spatial feature tokenization transformer (SSFTT) method is proposed to capture spectral–spatial features and high-level semantic features. First, a spectral–spatial feature extraction module is built to extract low-level features. This module is composed of a 3-D convolution layer and a 2-D convolution layer, which are used to extract the shallow spectral and spatial features. Second, a Gaussian weighted feature tokenizer is introduced for features transformation. Third, the transformed features are input into the transformer encoder module for feature representation and learning. Finally, a linear layer is used to identify the first learnable token to obtain the sample label. Using three standard datasets, experimental analysis confirms that the computation time is less than other deep learning methods and the performance of the classification outperforms several current state-of-the-art methods. The code of this work is available at https://github.com/zgr6010/HSI%5fSSFTT for the sake of reproducibility. [ABSTRACT FROM AUTHOR]

The thermal performance of the magnetic‐geared dual‐rotor machine (MGDRM) is investigated. Because the effective component in the outer airgap magnetic field harmonics is low, the torque production of MGDRM cannot match the regular permanent magnetic synchronous machine (PMSM). Although the analysis shows that the torque generation of the MGDRM is weak, this conclusion is limited in the electromagnetic field. The MGDRM outer rotor should also be considered from the thermal aspect, and the effect on the torque generation should be valued in particular. The outer rotor thermal buffer effect blocks the heating flow from the winding to the inner rotor magnets. Thus, the MGDRM can withstand a heavy electrical load in a short period, producing higher torque. With this understanding, the torque production ability of the MGDRM can extend to a higher level, even close to the regular PMSM. To validate the analysis, a MGDRM machine is prototyped, and the dual‐rotor temperature information acquisition is realised by a wireless data collection. The rotor temperature estimation technology on the dual‐rotor is then investigated as an auxiliary function. The estimation error can be limited around 5°C, which is good for warning the thermal risk. And thus the MGDRM overheating can be safely adopted to extend the torque generation. [ABSTRACT FROM AUTHOR]

Summary: Acid‐soluble collagens (ASCs) were extracted and characterised from different tissues of large yellow croaker (Larimichthys crocea). The yields of ASCs in bones, skins, scales and muscles were 3.22 ± 0.47%, 42.30 ± 1.15%, 2.82 ± 0.31% and 0.89 ± 0.12%, respectively. SDS‐PAGE and ATR‐FTIR analysis showed that all ASCs were type I collagen with intact triple helical structure and consisted of α1,3‐chain, α2‐chain, β‐chain and γ‐chain. Quantitative analysis of SDS‐PAGE revealed that the ratio of β‐chains in ASC from scales was lower than other tissues. The imino acid contents of ASC from bones, skins, scales and muscles were 169, 167, 162 and 173 residues/1000 residues, respectively. The maximum transition temperature (Tm) of ASC from scales was 30.15 °C, lower than the other three ASCs, indicated that the thermal stability of collagen was affected by both the content of imino acid and β‐chains. The results also implied that thermal stability of ASC might exist the tissue specificity among different fish species. [ABSTRACT FROM AUTHOR]

In the maglev train electric drive system, the linear electric machine is a welcome solution. However, for long railways, the mover position measurement is an issue, especially when the train speed is very high, e.g., 600 km/h. The position measurement method of the mover must be simple and precise at high speeds while maintaining a low cost for industrial applications. The principle of the stator permanent magnet (stator-PM) machine provides evidence that the magnets and windings can be mounted on the mover of the linear resolver, leaving a simple secondary-side structure on the railway. With this idea, this article presents a PM linear resolver that generates orthogonal back-EMFs as the sinusoidal and cosine (SIN/COS) signals. The train position can then be measured by decoding the PM back-EMF. On the other hand, the high-frequency injection is imposed to solve the mover position measurement at zero speed and low speed. This PM linear resolver is prototyped with printed circuit board windings instead of regular coil windings. The dedicated decoding firmware is developed to evaluate the measurement performance up to 60 km/h in the lab setup. [ABSTRACT FROM AUTHOR]

Objective support on positive coping styles was significantly stronger. Conclusion: We revealed the underlying mechanisms between social support and mental health literacy. The present study has profound implications for mental health literacy services for parents who have children with special needs. [ABSTRACT FROM AUTHOR]

The present study was intended to investigate the performance dependence of the solid oxide fuel cells on the pore structure of the supporting cathode. The Sr-doped lanthanum manganite/yttria-stabilized zirconia electrodes with different pore structures were prepared, and their inner surfaces were modified with nano-scale Sm 0.2 Ce 0.8 O 1.95-δ (SDC). The cell supported on the electrode with a straight pore structure demonstrated much higher maximum power densities than the one supported on the electrode with a tortuous pore structure, e.g. , 652 vs 308 mW cm−2 at 800 °C. Impedance analysis revealed that the straight pore structure together with SDC-modified pore surface not only promoted the surface oxygen exchange, but also enhanced the transport of oxygen species to the reaction sites and conduction of oxide ions and electrons in the electrode. The ceramic cathode-supported cell survived 20 redox cycles, showing much better stability than the cermet anode-supported cell. Further research on the supporting cathode is needed to increase the power output density required for practical applications. The SOFC supported on a LSM-YSZ supporting electrode with a straight pore structure demonstrated a much increased maximum power density over the one on the electrode with a tortuous pore structure, which was attributed to the reduced polarization resistance and ohmic resistance of the electrode. [Display omitted] • A cathode with a straight pore structure and catalyst-modified pore surface was prepared. • The cathode showed much reduced interfacial polarization and ohmic resistance. • The cell supported on the cathode demonstrated much increased power densities. • The cell survived 20 redox cycles, showing excellent stability. [ABSTRACT FROM AUTHOR]

The stator-PM variable reluctance (VR) resolver has been proposed as a new position sensor for a wide speed range. The high-frequency (HF) signal injection is only required to measure the rotor position at low-speeds. Alternatively, at higher speeds, without requiring the HF excitation, the position measurement can be achieved by the back-EMF generated by the stator-PM. This novel resolver has a simple rotor structure and the analog signals can be efficiently decoded at high-speeds. In the first proposed stator-PM VR resolver, the prototype must have even number of rotor salient poles. However, the resolvers with odd number of rotor poles are also useful in many scenarios. To complete the design scheme of this resolver category, this article practices a solution on the basis of the theory of field modulation. The design issue and solution are illustrated and addressed in this paper. The winding function approach is applied to analyze the signals. With the low-cost materials, the proposed resolver measurement system is prototyped. [ABSTRACT FROM AUTHOR]

Peptides exhibit various functional properties and bioactivities, and have been widely investigated and used in food area. However, using peptides alone may show some drawbacks, such as the undesirable stability of peptide-stabilized emulsions during storage, and the diminished bioactivities and the occurrence of peptide-induced celiac disease during digestion. The addition of polyphenols may help to solve above issues by the formation of peptide-polyphenol complexes. This paper provides a comprehensive review of covalent and non-covalent interactions between peptides and polyphenols. The influence of such interactions in bioactivity, antimicrobial property, sensitivity (i.e., causing celiac disease), and emulsifying property of peptides is also discussed. Hydrophobic interactions, hydrogen bonds, electrostatic interaction, and van der Waals interactions are the main non-covalent bondings involving in the formation of peptide-polyphenol complexes. The properties of peptides and polyphenols, the ratio of peptide/polyphenol, and the environmental conditions are the main factors influencing such non-covalent interactions. Oxidized peptides or reactive peptide radicals can also interact with polyphenols trough covalent interactions, and the properties of peptides and polyphenols can influence these interactions. In addition, the presence of polyphenols in peptide-polyphenol complexes can show synergetic or subtractive effect on bioactive peptides through various mechanisms. In contrary, the inhibiting effect of polyphenols on the digestion, absorption and recognition of peptides can help to improve the safety of celiac disease-related peptides. Moreover, the formation of peptide-polyphenol complexes can also improve the physical and oxidative stability of peptide-stabilized emulsions. • The impact of forming peptide-polyphenol complexes on the properties of peptides is reviewed. • The peptide-polyphenol complexes can be obtained by covalent or non-covalent bonds. • The presence of polyphenols enhances or diminishes the bioactivities of peptides. • The presence of polyphenols improves the safety of celiac disease-related peptides. • The formation of complexes can also improve the emulsifying properties of peptides. [ABSTRACT FROM AUTHOR]

Recently, the magneto-optical properties of magnetic nanofluids have received increasing attention due to their wide range of applications in solar energy conversions and also as smart fluids in tunable photonic devices, optical switch, optical fiber sensor, etc. This review discusses up-to-date developments in magneto-optical transmission in ferrofluids. The magnetic nanoparticle would undergo various structural transitions under external magnetic field, including chainlike formation and lateral coalescence which, in turn, leads to various interesting light transmission phenomena. It is thus possible to alter the properties of the transmitted light by adjusting the orientation of the magnetic field with respect to that of the incident light. The polarization direction of light has a negligible effect on the magneto-optical transmission while light passing along the magnetic field. However, it makes a significant difference for light normal to the field direction. These characteristics are comprehensively reviewed, and their potential applications are presented. It is supposed to provide a general view on the research trends, existing problems and future work for the investigation of magneto-optical transmission in magnetic nanofluids. [ABSTRACT FROM AUTHOR]

The stator-permanent magnet (PM) motor has an advantage with its simple and firm rotor, which is suitable when being applied to automotive applications. This article proposes a novel structure for a variable reluctance (VR) resolver by using the concept of the stator PM. This novel VR resolver shares a similar operation principle to the conventional VR resolver by injecting a 10-kHz excitation signal at low speeds. Alternatively, at higher speeds, the stator PM can produce motional back electromotive force (EMF) to support position measurement without the need for electrical excitation. The motional back-EMF can also reduce decoding computation. In addition, higher frequency signal excitation is not required for position measurement at higher speeds. This feature is beneficial for extremely high-speed motor applications. As a sensor, an inexpensive and low-amount ferrite magnet is used in the prototype to satisfy the low-cost requirements of the automotive industry. [ABSTRACT FROM AUTHOR]

Kernel methods, e.g., composite kernels (CKs) and spatial-spectral kernels (SSKs), have been demonstrated to be an effective way to exploit the spatial-spectral information nonlinearly for improving the classification performance of hyperspectral image (HSI). However, these methods are always conducted with square-shaped window or superpixel techniques. Both techniques are likely to misclassify the pixels that lie at the boundaries of class, and thus a small target is always smoothed away. To alleviate these problems, in this paper, we propose a novel patch-based low rank component induced spatial-spectral kernel method, termed LRCISSK, for HSI classification. First, the latent low-rank features of spectra in each cubic patch of HSI are reconstructed by a low rank matrix recovery (LRMR) technique, and then, to further explore more accurate spatial information, they are used to identify a homogeneous neighborhood for the target pixel (i.e., the centroid pixel) adaptively. Finally, the adaptively identified homogenous neighborhood which consists of the latent low-rank spectra is embedded into the spatial-spectral kernel framework. It can easily map the spectra into the nonlinearly complex manifolds and enable a classifier (e.g., support vector machine, SVM) to distinguish them effectively. Experimental results on three real HSI datasets validate that the proposed LRCISSK method can effectively explore the spatial-spectral information and deliver superior performance with at least 1.30% higher OA and 1.03% higher AA on average when compared to other state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]

Duplications of MECP2 -containing genomic segments led to severe autistic symptoms in male. Transgenic mice overexpressing the human MECP2 gene exhibit autistic-like behaviors. Neural circuits underlying social defects in MECP2 transgenic (MECP2 -TG) mice remain unknown. To observe neural activity of MECP2 -TG mice in vivo , we performed calcium imaging by implantation of microendoscope in the hippocampal CA1 regions of MECP2 -TG and wild type (WT) mice. We identified neurons whose activities were tightly associated with social interaction, which activity patterns were compromised in MECP2 -TG mice. Strikingly, we rescued the social-related neural activity in CA1 and social defects in MECP2 -TG mice by deleting the human MECP2 transgene using the CRISPR/Cas9 method during adulthood. Our data points to the neural circuitry responsible for social interactions and provides potential therapeutic targets for autism in adulthood. [ABSTRACT FROM AUTHOR]

Modeling the processes of neuronal progenitor proliferation and differentiation to produce mature cortical neuron subtypes is essential for the study of human brain development and the search for potential cell therapies. We demonstrated a novel paradigm for the generation of vascularized organoids (vOrganoids) consisting of typical human cortical cell types and a vascular structure for over 200 days as a vascularized and functional brain organoid model. The observation of spontaneous excitatory postsynaptic currents (sEPSCs), spontaneous inhibitory postsynaptic currents (sIPSCs), and bidirectional electrical transmission indicated the presence of chemical and electrical synapses in vOrganoids. More importantly, single-cell RNA-sequencing analysis illustrated that vOrganoids exhibited robust neurogenesis and that cells of vOrganoids differentially expressed genes (DEGs) related to blood vessel morphogenesis. The transplantation of vOrganoids into the mouse S1 cortex resulted in the construction of functional human-mouse blood vessels in the grafts that promoted cell survival in the grafts. This vOrganoid culture method could not only serve as a model to study human cortical development and explore brain disease pathology but also provide potential prospects for new cell therapies for nervous system disorders and injury. This study establishes a method to generate vascularized cortical organoids. This shows that in addition to reducing hypoxia and cell death, the vascular system promotes neural development in organoids. When transplanting these organoids into host mice, a graft-host vascular system could be reconstructed. [ABSTRACT FROM AUTHOR]

Hydrate has been considered as a promising and cost-effective route for massive storage of natural gas. However, the main problem for this technique to be commercially available is its poor chemical kinetics. In the present study, aiming to reveal the underlying mechanism dominating the hydrate formation kinetics, we investigated combined effects of surface tension and thermal conductivity on the methane hydrate formation. The induction time, gas uptake, and storage capacity for hydrate formation was investigated in detail in the presence of TiO2 nanoparticles and SDS surfactant. The optimal condition for methane hydrate growth was found to be 500 ppm SDS in the presence of 1.0 wt% TiO2 nanoparticles. In this case, the induction time significantly reduced from 183 to 14 min compared to that in pure water. With the addition of SDS alone the surface tension decreased from 72.4 mN/m for pure water to 38.4 mN/m which further decreased to 33.8mN/m, when 1.0% TiO2 was added into the SDS solution. On the other hand, the thermal conductivity of solution showed a continuous increase with the increasing addition of TiO2 while there was an optimal thermal conductivity value for the hydrate growth. It turns out that the optimal addition of TiO2 in SDS solution can result in the lowest surface tension and suitable thermal conductivity, which, in turn, leads to the favorable environment for methane hydrate growth. Our results are supposed to be valuable for the precise control of methane hydrate growth kinetics. [ABSTRACT FROM AUTHOR]

This paper presents a practical convex-optimization-based sensorless scheme for interior permanent magnet synchronous motor (IPMSM) drives. The proposed sensorless scheme simplifies the optimization process that employs only one variable as compared with two variables in the existing optimization-based methods. The reduction in variables substantially reduces the computational burden in the proposed scheme and, hence, is suitable for practical applications. Additionally, the proposed algorithm is able to estimate the rotor position regardless of the pattern of the high-frequency (HF) injected signal under low-speed conditions. This characteristic has been investigated in this paper where three typical HF injection patterns are compared. The performance of the proposed scheme is experimentally validated. [ABSTRACT FROM AUTHOR]

Abstract To clarify the gel formation mechanism of isolated proteins recovered from blue round scads by isoelectric solubilization/precipitation, the physiochemical, conformational and gelation properties of the major protein myosin treated by acidic-(AM) or alkaline-(KM) pH-shifting were investigated and compared with native myosin (NM). The solubility in all myosin samples dropped significantly at 40 °C, with remarkable increases of particle size, turbidity and surface hydrophobicity. Among the samples, AM exhibited the lowest solubility and highest turbidity than NM and KM as the temperature was higher than 40 °C, indicating that AM is easily to be denatured and aggregated. Circular dichroism (CD) analysis showed that both acidic and alkaline pH treatments led to conformational changes of myosin, together with α-helix content variations. When pH was readjusted to neutral, KM recovered its structure similar to NM, while AM failed to refold to its native state. NM exhibited the highest storage modulus (G ′) at 90 °C, while AM revealed the lowest G ′ value. Furthermore, AM did not change obviously during heating, especially at the gelling temperature range (60–90 °C). These results strongly suggested that acidic treatment led to an irreversible conformational change in myosin, consequently induced a fierce denaturation and aggregation at the initial stage of heating, and eventually led to a complete loss of gel-forming ability. Alkaline pH-shifting, on the other hand is thus more feasible for protein recovery from blue round scads. Graphical abstract Image 108 Highlights • Myosin was isolated from blue round scads and treated by acidic or alkaline pH-shifting. • Acidic pH-shifting caused irreversible conformational change in myosin and the loss of its gel-forming ability. • Alkaline pH-shifting is more feasible for protein recovery from blue round scads. [ABSTRACT FROM AUTHOR]

This paper presents a novel adaptive spectral-spatial kernel-based low-rank approximation method for spectral-spatial hyperspectral image (HSI) classification. In the first of three steps of the proposed method, superpixel and image patch are used together to calculate the weights in the homogeneous region. Second, an adaptive spectral-spatial kernel is defined to capture the spectral and spatial feature of HSIs. In the final step, an adaptive spectral-spatial kernel and low-rank approximation are integrated into a decision model to perform HSI classification. Extensive experimental results on Indian Pines and Pavia University demonstrate the superiority of the proposed classifier when compared with other competing classifiers. [ABSTRACT FROM AUTHOR]

In the hyperspectral image (HSI) classification task, every HSI pixel is labeled as a specific land cover category. Although convolutional neural network (CNN)-based HSI classification methods have made significant progress in enhancing classification performance in recent years, they still have limitations in acquiring deep semantic features and face the challenges of escalating computational costs with increasing network depth. In contrast, the Transformer framework excels in expressing high-level semantic features. This study introduces a novel classification network by extracting spectral–spatial features with an enhanced Transformer with Large-Kernel Attention (ETLKA). Specifically, it utilizes distinct branches of three-dimensional and two-dimensional convolutional layers to extract more diverse shallow spectral–spatial features. Additionally, a Large-Kernel Attention mechanism is incorporated and applied before the Transformer encoder to enhance feature extraction, augment comprehension of input data, reduce the impact of redundant information, and enhance the model's robustness. Subsequently, the obtained features are input to the Transformer encoder module for feature representation and learning. Finally, a linear layer is employed to identify the first learnable token for sample label acquisition. Empirical validation confirms the outstanding classification performance of ETLKA, surpassing several advanced techniques currently in use. This research provides a robust and academically rigorous solution for HSI classification tasks, promising significant contributions in practical applications. [ABSTRACT FROM AUTHOR]

Object detection (OD) in remote sensing (RS) images is an important task in the field of computer vision. OD techniques have achieved impressive advances in recent years. However, complex background interference, large-scale variations, and dense instances pose significant challenges for OD. These challenges may lead to misalignment between features extracted by OD models and the features of real objects. To address these challenges, we explore a novel single-stage detection framework for the adaptive fusion of multiscale features and propose a novel adaptive edge aggregation and multiscale feature interaction detector (AEAMFI-Det) for OD in RS images. AEAMFI-Det consists of an adaptive edge aggregation (AEA) module, a feature enhancement module (FEM) embedded in a context-aware cross-attention feature pyramid network (2CA-FPN), and a pyramid squeeze attention (PSA) module. The AEA module employs an edge enhancement mechanism to guide the network to learn spatial multiscale nonlocal dependencies and solve the problem of feature misalignment between the network's focus and the real object. The 2CA-FPN employs level-by-level feature fusion to enhance multiscale feature interactions and effectively mitigate the misalignment between the scales of the extracted features and the scales of real objects. The FEM is designed to capture the local and nonlocal contexts as auxiliary information to enhance the feature representation of information interaction between multiscale features in a cross-attention manner. We introduce the PSA module to establish long-term dependencies between multiscale spaces and channels for better interdependency refinement. Experimental results obtained using the NWPU VHR-10 and DIOR datasets demonstrate the superior performance of AEAMFI-Det in object classification and localization. [ABSTRACT FROM AUTHOR]

The magnetostrictive strain-applied field curve typical of giant magnetostrictive materials is nonlinear and it is affected notably by prestress and temperature. A constitutive model to describe these properties is suggested in this paper. The model considered here is built on the Gibbs free energy function G(σij,Mk,T). Thermodynamic relations are used to obtain the constitutive expressions. Results based on the model show that the nonlinear property of magnetostrictive strain, the magnetization, and Young’s modulus under different prestress and temperature are in excellent agreement with experimental data. Moreover, the proposed model is convenient to be used in engineering applications since the parameters referred to the model can all be easily determined by experiments. [ABSTRACT FROM AUTHOR]

Surface adsorption behavior and electron transfer determine the catalytic activity in hydrogen evolution reaction (HER). However, the inevitable surface oxidation of NbC hinders electron transfer and hydrogen conversion. The surface adsorption behavior and electron transfer of NbC are optimized by doping Cu+ as well as modifying the orbital hybridizations. Benefiting from the introduced electron interaction of Cu+, the lower p-d hybridization between Nb and O reduces the Nb-O bond and affords the Cu-NbC thinner surface oxidation layer. The reduced surface oxidation degree favors electron transfer. Further, the electron orbital hybridization modulates the electron states and optimizes the hydrogen adsorption behavior of Cu-NbC for efficient hydrogen conversion. As the electrochemical results, the obtained Cu-NbC demands 271 mV to drive 10 mA cm−2, which is 50 times higher than that of NbC (only 0.2 mA cm−2 at the same overpotential). Hence, we believe the orbital hybridization manipulation strategy renders a valuable solution for designing efficient HER catalysts. A orbital hybridization engineering for boosting the HER kinetics by doping Cu+ in NbC is first proposed. The orbital hybridization engineering optimizes the electron structure of Cu-NbC, reducing the surface oxidation layer and optimizing the hydrogen adsorption strength. Then thus, the boosting electron transfer and intermediates conversion promote the HER kinetic. This work paves a new way for rationally designing efficient catalysts for alkaline HER. [Display omitted] • Cu+ is doped into NbC by a one-pot molten salt method. • Introduced electron interaction of Cu+ modify p-d hybridization between Nb and O. • The reducing surface oxidation degree favors the electron transfer. • Orbital hybridization engineering optimizes the electron state at Fermi Level. • Efficient electron transfer and optimized adsorption behavior boost the reaction kinetics. [ABSTRACT FROM AUTHOR]

Herein, a novel fluorescence probe Fla-DNP based on flavonol has been designed and synthesized for rapid, specific detection of H2S. With the addition of H2S, Fla-DNP triggered thiolysis and released Fla displaying the “turn-on” fluorescence response at 566 nm, which is consistent with the reaction site predicted by calculating Electrostatic potential and ADCH charges. As an easily available H2S probe, Fla-DNP has the advantages of high selectivity, anti-interference, low detection limit (0.834 μM), short response time (6 min), and large Stokes shift (124 nm). The sensing mechanism of H2S was determined by HRMS analysis and DFT calculation. Moreover, Fla-DNP processes a wide range of multiple applications, including the detection of H2S in environmental water samples with good recovery rates ranging from 89.6% to 102.0%, as well as tracking the production of H2S during food spoilage. Meanwhile, the probe exhibits superior biocompatibility and can not only be available used for H2S detection in living cells but be further designed as an H2S-activated CO photoreleaser, based on which it can be developed as a targeted anti-cancer drug.A novel fluorescence probe Fla-DNP was synthesized utilizing 4-dimethylaminobenzoxanthone fluorescent dye (Fla) as the fluorophore, 2, 4-dinitrobenzenether group (DNP) as the recognition group, which can rapidly respond to H2S with high selectivity, anti-interference, low detection limit (0.834 μM), short response time (6 min), and large Stokes shift (124 nm) characteristics. The practical applications of Fla-DNP were further explored in water, foodstuffs samples and living cells. It is reflected that Fla-DNP can not only track H2S in complex environment water, but also can detect H2S produced during foodstuffs spoilage to monitor food freshness. More importantly, Fla-DNP can be available used for H2S detection in living cells and utilize the properties of the photoinduced release of CO from flavonols to be designed as a bifunctional platform for H2S detection and CO release. It is demonstrated that H2S-activated CO photoreleaser Fla-DNP has promise for development as an anti-cancer drug.Graphical Abstract: Herein, a novel fluorescence probe Fla-DNP based on flavonol has been designed and synthesized for rapid, specific detection of H2S. With the addition of H2S, Fla-DNP triggered thiolysis and released Fla displaying the “turn-on” fluorescence response at 566 nm, which is consistent with the reaction site predicted by calculating Electrostatic potential and ADCH charges. As an easily available H2S probe, Fla-DNP has the advantages of high selectivity, anti-interference, low detection limit (0.834 μM), short response time (6 min), and large Stokes shift (124 nm). The sensing mechanism of H2S was determined by HRMS analysis and DFT calculation. Moreover, Fla-DNP processes a wide range of multiple applications, including the detection of H2S in environmental water samples with good recovery rates ranging from 89.6% to 102.0%, as well as tracking the production of H2S during food spoilage. Meanwhile, the probe exhibits superior biocompatibility and can not only be available used for H2S detection in living cells but be further designed as an H2S-activated CO photoreleaser, based on which it can be developed as a targeted anti-cancer drug.A novel fluorescence probe Fla-DNP was synthesized utilizing 4-dimethylaminobenzoxanthone fluorescent dye (Fla) as the fluorophore, 2, 4-dinitrobenzenether group (DNP) as the recognition group, which can rapidly respond to H2S with high selectivity, anti-interference, low detection limit (0.834 μM), short response time (6 min), and large Stokes shift (124 nm) characteristics. The practical applications of Fla-DNP were further explored in water, foodstuffs samples and living cells. It is reflected that Fla-DNP can not only track H2S in complex environment water, but also can detect H2S produced during foodstuffs spoilage to monitor food freshness. More importantly, Fla-DNP can be available used for H2S detection in living cells and utilize the properties of the photoinduced release of CO from flavonols to be designed as a bifunctional platform for H2S detection and CO release. It is demonstrated that H2S-activated CO photoreleaser Fla-DNP has promise for development as an anti-cancer drug. [ABSTRACT FROM AUTHOR]