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Abstract Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditional methods rely on Computed Tomography Pulmonary Angiography (CTPA), which requires contrast agents with potential health risks. Non-contrast CT, a safer and more widely available approach, however, has long been considered impossible for this task. Here we propose High-abundant Pulmonary Artery-vein Segmentation (HiPaS), enabling accurate segmentation across both non-contrast CT and CTPA at multiple resolutions. HiPaS integrates spatial normalization with an iterative segmentation strategy, leveraging lower-level vessel segmentations as priors for higher-level segmentations. Trained on a multi-center dataset comprising 1073 CT volumes with manual annotations, HiPaS achieves superior performance (dice score: 91.8%, sensitivity: 98.0%) and demonstrates non-inferiority on non-contrast CT compared to CTPA. Furthermore, HiPaS enables large-scale analysis of 11,784 participants, revealing associations between vessel abundance and sex, age, and diseases, under lung-volume control. HiPaS represents a promising, non-invasive approach for clinical diagnostics and anatomical research.

The mantle transition zone (MTZ) plays a key role in the deep global material cycle, while the water content in MTZ is debated from saturated to dry. Since the electrical conductivity is highly sensitive to water, its accurate estimation will greatly help reveal the water content. The high quality and plenty of data are crucial for global-scale conductivity recovery. In this article, we use the magnetic vector data of China seismo-electromagnetic satellite (CSES) to estimate the global mantle electrical structure, accompanying with the Swarm satellite and observatories. In particular, we correct the latitude effect of CSES Level 2 data. The radial conductivity model and uncertainty information of the Earth are obtained by using Bayesian inversion. It is found that large changes in the electrical results of MTZ occur when using the CSES magnetic field data. The conductivity is higher than that inverted from Swarm data, but lower than that from the observatory data. Finally, we, respectively, invert the resistivity structure of the MTZ with two years and nearly nine years of database of CSES, Swarm, and observatories, and analyze the laboratory conductivity model. The results indicate that the water content of the MTZ is less than 0.01 weight%.

Metal interference has always been one of the key factors affecting the reliability of transient electromagnetic (TEM) data in mines. Not only does it cause misjudgment in data interpretation, but it also can lead to ineffective signal. It is crucial to comprehensively study the TEM response characteristics under the influence of metal environment. Thus, it is necessary to adopt the 3D vector finite-element (FE) method to realize a TEM forward modeling. Using refined tetrahedral grids for the spatial discretization of the computational domain, utilizing the second-order backward Euler scheme to discretize the governing equation, and applying the direct solver MUMPS for the solution of the linear equations, so that the high-precision numerical solutions can be obtained. After verifying the accuracy of the algorithm, comparing the results of mine TEM responses in metal interference environments, it proves that the unstructured FE method can get good results for the metal-containing full-space models even with significant resistivity contrast and large permeability parameters. To analyze the TEM responses under a metal interference environment, typical block-shaped metal and I-beam models that are suitable for actual sizes are designed, the EM diffusion profiles are displayed for different models with refined tetrahedral mesh to fit interface, and the influences of the distance between the metal object and receiving coil and the size of the metal on the response are discussed. The numerical results of unstructured FE method indicate that the metal in the roadway distorts the EM fields, the electromotive force (EMF) curve generates abnormally high values in the late time. The larger the metal size is, or the closer the metal is from the coils, the larger the amplitude of the EMF and the influence area, even submerging the signal of the target water bearing geological body. For the block metal and the continuous I-beam having similar response characteristics, its influence can be weakened by adjusting their distance.

Abstract Postoperative abdominal adhesion is one of the most common complications after abdominal surgery. The Toll-like receptor 4 (TLR4) signaling pathway is one of the most common inflammation-related pathways, and it has been demonstrated that TLR4 is highly expressed in adhesive tissues; however, the function of TLR4 in adhesion formation has not yet been studied. In the present study, the expression of TLR4 was first detected by immunohistochemical (IHC) and double-immunofluorescence staining in 40 mice, which were randomly divided into four groups, and sacrificed at 1, 3, 5 and 7 days after surgery. Subsequently, another 40 mice were randomly divided into five groups; with the exception of the sham group, the other groups were modeled and treated with saline that contained DMSO, sodium hyaluronate (HA), TAK-242 or TAK-242 + HA (applied to damaged peritoneal wounds). A total of 7 days after surgery, the mice were sacrificed and specimens were collected. Inflammation was detected by hematoxylin and eosin staining, and ELISA of transforming growth factor- β1 (TGF-β1) and interleukin-6 (IL-6); collagen deposition was examined by Masson staining and IHC staining of α-SMA; and reactive oxygen species (ROS) were detected by ROS staining and malondialdehyde (MDA) assay. The results revealed that TLR4 was highly expressed in the adhesive tissues at 3, 5 and 7 days after surgery. In addition, TAK-242 + HA treatment could reduce abdominal adhesion formation, exhibiting lower Nair’s score and inflammation scores, lower TGF-β1 and IL-6 levels, and lower collagen thickness and α-SMA levels compared with those in the control group. In addition, the TAK-242 + HA group had lower levels of ROS and MDA compared with those in the control group. The present study revealed that TLR4 was highly expressed in the process of adhesion formation and its inhibitor, TAK-242, combined with HA, could reduce adhesion formation by reducing inflammation and ROS, and alleviating collagen deposition.

Abstract A widely employed energy technology, known as reverse electrodialysis (RED), holds the promise of delivering clean and renewable electricity from water. This technology involves the interaction of two or more bodies of water with varying concentrations of salt ions. The movement of these ions across a membrane generates electricity. However, the efficiency of these systems faces a challenge due to membrane performance degradation over time, often caused by channel blockages. One potential solution to enhance system efficiency is the use of nanofluidic membranes. These specialized membranes offer high ion exchange capacity, abundant ion sources, and customizable channels with varying sizes and properties. Graphene oxide (GO)‐based membranes have emerged as particularly promising candidates in this regard, garnering significant attention in recent literature. This work provides a comprehensive overview of the literature surrounding GO membranes and their applications in RED systems. It also highlights recent advancements in the utilization of GO membranes within these systems. Finally, it explores the potential of these membranes to play a pivotal role in electricity generation within RED systems.

The rheological properties of red blood cells (RBCs) are crucial for human health. Combining optical tweezers with microfluidics provides a non-contact, sensitive, and high-throughput method for studying RBC rheology. However, the limited trapping capacity of optical tweezers restricts RBC flow within microchannels, reducing individual RBC capture efficiency. To address this, we developed a multichannel microfluidic chip with a pressure relief structure. Integrating this with optical tweezers using time-division multiplexing enabled simultaneous capture of RBCs across multiple microchannels. This method not only enhances sample throughput during optical tweezer measurements but also allows individual capture and analysis of multiple flowing RBCs in the same timeframe. Image recognition analysis of RBCs captured by optical tweezers revealed distinct morphological differences between normal and diseased RBCs, consistent with finite element method simulations of RBC rheological behavior. This approach provides quantitative characterization of RBC rheology and enables effective detection.

A series of triazolopyridine-based dual JAK/HDAC inhibitors were rationally designed and synthesised by merging different pharmacophores into one molecule. All triazolopyridine derivatives exhibited potent inhibitory activities against both targets and the best compound 4-(((5-(benzo[d][1, 3]dioxol-5-yl)-[1, 2, 4]triazolo[1, 5-a]pyridin-2-yl)amino)methyl)-N-hydroxybenzamide (19) was dug out. 19 was proved to be a pan-HDAC and JAK1/2 dual inhibitor and displayed high cytotoxicity against two cancer cell lines MDA-MB-231 and RPMI-8226 with IC50 values in submicromolar range. Docking simulation revealed that 19 fitted well into the active sites of HDAC and JAK proteins. Moreover, 19 exhibited better metabolic stability in vitro than SAHA. Our study demonstrated that compound 19 was a promising candidate for further preclinical studies.

In contemporary society, individuals are inundated with a vast amount of redundant information, and recommendation systems have undoubtedly opened up new avenues for managing irrelevant data. Graph convolutional networks (GCNs) have demonstrated remarkable performance in the field of recommendation systems by iteratively performing node convolutions to capture information from neighboring nodes, thereby enhancing recommendation efficacy. However, most existing models fail to distinguish the importance of different nodes, which limits their performance. To address this issue, we propose the asGCL model. To mitigate the prevalent issue of popularity bias and to learn more uniform embedding representations, we have integrated a lightweight contrastive learning module into our model. Finally, extensive experiments conducted on four real-world datasets demonstrate the effectiveness of our model. Notably, on the Amazon-Books dataset, our asGCL model achieved improvements of 4.21% and 8.74% in recall@20 and NDCG@20, respectively.

Data sparsity caused by limited interactions makes it challenging for recommendation to accurately capture user preferences. Contrastive learning effectively alleviates this issue by enriching embedding information through the learning of diverse contrastive views. The effectiveness of contrastive learning in uncovering users’ and items’ latent preferences largely depends on the construction of data augmentation strategies. Structure and feature perturbations are commonly used augmentation strategies in graph-based contrastive learning. Since graph structure augmentation is time consuming and can disrupt interaction information, this paper proposes a novel feature augmentation contrastive learning method. This approach leverages preference prototypes to guide user and item embeddings in acquiring augmented information. By generating refined prototypes for each user and item based on existing prototypes to better approximate true preferences, it effectively alleviates the over-smoothing issue within similar preferences. To balance feature augmentation, a prototype filtering network is employed to control the flow of prototype information, ensuring consistency among different embeddings. Compared with existing prototype-based methods, ProtoRec achieves maximum gains of up to 16.8% and 20.0% in recall@k and NDCG@k on the Yelp2018, Douban-Book, and Amazon-Book datasets.

Propagation properties represent a critical aspect of laser beams utilized in free space optical (FSO) communications. We examined the evolution characteristics of the electric field associated with partially coherent flat-topped beam rectangular arrays propagating bidirectionally through the turbulent atmosphere and plasma links. Utilizing the optical transmission matrix, alongside the second moment theory and Wigner distribution functions, we derived analytical expressions for both the intensity distribution and propagation factors of the partially coherent flat-topped beam rectangular arrays affected by the atmospheric turbulence and plasma disturbances. The numerical results indicate that appropriately selecting parameters such as beam order, transverse spatial coherence width, and beam width can effectively mitigate the adverse effects on propagation properties caused by the turbulent atmosphere and plasma. Our results have significant implications for FSO communications within specific environmental contexts.

As an effective geophysical tool, ground penetrating radar (GPR) is widely used for environmental and engineering detections. Numerous numerical simulation algorithms have been developed to improve the computational efficiency of GPR simulations, enabling the modeling of complex structures. The discontinuous Galerkin method is a high efficiency numerical simulation algorithm which can deal with complex geometry. This method uses numerical fluxes to ensure the continuity between elements, allowing Maxwell’s equations to be solved within each element without the need to assemble a global matrix or solve large systems of linear equations. As a result, memory consumption can be significantly reduced, and parallel solvers can be applied at the element level, facilitating the construction of high-order schemes to enhance computational accuracy. In this paper, we apply the discontinuous Galerkin (DG) method based on unstructured meshes to 3D GPR simulation. To verify the accuracy of our algorithm, we simulate a full-space vacuum and a cuboid in a homogeneous medium and compare results, respectively, with the analytical solutions and those from the finite-difference method. The results demonstrate that, for the same error level, the proposed DG method has significant advantages over the FDTD method, with less than 20% of the memory consumption and calculation time. Additionally, we evaluate the effectiveness of our method by simulating targets in an undulating subsurface, and further demonstrate its capability for simulating complex models.

Abstract Employee scheduling aims to assign employees to shifts to satisfy daily workload and constraints. Some employee scheduling problems and their variants have been proven NP-hard, and a series of works have been done. However, the existing algorithms consider the fixed work time, which may cause plenty of overstaffing and understaffing phenomenons. Hence, this paper proposes a fast-flexible strategy based approach (FFS) to solve it. FFS introduces the idea of soft work time, which allows the work time of employees can be adjusted in a range. Based on this, we set the flextime strategy to decide the specific work time of each employee every day. Besides, FFS adopts a pairwise-allocated strategy and proficiency average matrix to boost its efficiency and effectiveness. Finally, the extensive experimental evaluation shows that FFS is more effective and efficient than the baselines for solving the employee scheduling problem considering soft work time.

Purpose: To evaluate the myopia risk in school-aged children one year after lifting a pandemic-related lockdown and develop a tool to identify high-risk groups. Methods: In total, 38,079 children without myopia from 38 schools were included. The outcomes were myopia incidence and progression in 1 year after the COVID-19 lockdown was lifted, both obtained by the spherical equivalent refraction (SER). We separated the population into an exploratory (75%) and a validation sample (25%) to construct the risk score model. Results: In total, 9811 (29.57%) students became myopic, and the overall myopia progression was 0.22 ± 0.62 D. Even less myopia progression was noted in the pre-myopia group at baseline (All: P = 0.045, Boy: P = 0.005). The risk score model included seven predictors: gender, grade, SER at baseline, residence, parental myopia, eye discomfort symptoms, and online courses. The model had a score range of 0–46 and an optimal cutoff of 34. The area under the receiver operating curve of the model was 0.726 (0.719–0.732) for the exploratory sample and 0.731 (0.720–0.742) for the validation sample. Conclusions: The risk score can serve as a practical tool for classifying the risk of myopia in school-aged children.

Efficient genetic transformation approaches play pivotal roles in both gene function research and crop breeding. However, stable transformation in mustard, particularly for different horticultural types, has not been systematically studied and well-established so far. In this study, we optimized the key factors in the genetic transformation of mustard, including the optical density value of Agrobacterium suspension, the age of explants, and the combination of phytohormones at different concentrations. As a result, the optimal conditions for the genetic transformation of leaf and stem mustard included hypocotyl explants derived from 4-day-old seedlings, infection by 0.8 OD600nm Agrobacterium suspension, and then re-differentiation on a medium containing 4 mg/L trans-zeatin (tZ) and 0.1 mg/L indoleacetic acid (IAA) for leaf mustard, and for stem mustard, re-differentiation on a medium containing 2 mg/L tZ and 0.4 mg/L IAA, with positive rates of 4.74% and 5.26%, respectively. Those for root mustard were hypocotyl explants derived from 8-day-old seedlings, infection by 0.2 OD600nmAgrobacterium suspension, and a medium containing 2 mg/L tZ and 0.1 mg/L IAA, with a positive rate of 4.42%. Overall, this work provides an effective tool for both the theoretical study and genetic improvement of Brassica juncea.

Abstract Pedestrian trajectory prediction is essential for self-driving vehicles, social robots, and intelligent monitoring applications. Diverse trajectory data is critical for high-accuracy trajectory prediction. However, the trajectory data is captured in scattered scenes, which can cause the problem of data island. Furthermore, artificial aggregation of trajectory data suffers from the risk of data leakage, ignoring the rule of privacy protection. We propose a multi-scene federated trajectory prediction (Fed-TP) method to solve the above problems. As our key contribution, a destination-oriented LSTM (Long Short-Term Memory)-based trajectory prediction (DO-TP) network is proposed in each scene to forecast future trajectories in an encoder-decoder manner. The independent training using trajectory data in each scene can prevent data leakage and achieves high privacy security. As another key contribution, a federated learning framework is introduced to break the scene limitation by conducting distributed collaborative training. The performance of different federated learning methods is compared on public datasets, including ETH, UCY, and Stanford Drone Dataset (SDD). Compared with FedAvg and FedProx, FedAtt is more suitable for pedestrian trajectory prediction. Experimental results demonstrate that the proposed method has better data privacy security than directly training on multiple scenes and superior prediction performance than training on a single scene.

In mineral, environmental, and engineering explorations, we frequently encounter geological bodies with varied sizes, depths, and conductivity contrasts with surround rocks and try to interpret them with single survey data. The conventional three-dimensional (3-D) inversions significantly rely on the size of the grids, which should be smaller than the smallest geological target to achieve a good recovery to anomalous electric conductivity. However, this will create a large amount of unknowns to be solved and cost significant time and memory. In this paper, we present a multi-scale (MS) stochastic inversion scheme based on shearlet transform for airborne electromagnetic (AEM) data. The shearlet possesses the features of multi-direction and multi-scale, allowing it to effectively characterize the underground conductivity distribution in the transformed domain. To address the practical implementation of the method, we use a compressed sensing method in the forward modeling and sensitivity calculation, and employ a preconditioner that accounts for both the sampling rate and gradient noise to achieve a fast stochastic 3-D inversion. By gradually updating the coefficients from the coarse to fine scales, we obtain the multi-scale information on the underground electric conductivity. The synthetic data inversion shows that the proposed MS method can better recover multiple geological bodies with different sizes and depths with less time consumption. Finally, we conduct 3-D inversions of a field dataset acquired from Byneset, Norway. The results show very good agreement with the geological information.

Collaborative Filtering Recommender Systems face data sparsity and cold-start issues, leading to a decrease in their recommendation performance. Therefore, numerous researchers have integrated knowledge graphs and graph convolutional networks into recommender systems to enhance their performance. Through an analysis of previous knowledge graph convolutional network recommendation systems, the following problems have been identified: (1) Some graph convolutional networks only consider the neighborhood aggregation of items while neglecting the neighborhood aggregation of users. (2) User rating differences are not taken into account. To tackle these problems, a Bilateral Attention Mechanism Graph Convolutional Network Recommender Model is proposed. This model effectively captures high-order neighborhood information for both items and users by utilizing bipartite attention mechanisms on the knowledge graph and interaction graph. Additionally, it incorporates the influence of user ratings on items. Finally, it aggregates item and user information with neighborhood information to predict user ratings for items. The validity of this model was verified through experimental analysis and comparative evaluation of its performance against SPGCN, CACF on the MovieLens-20M, Book-Crossing, and Last.FM datasets.

Abstract The accurate diagnosis and treatment of cancer cell lesions need a high standard of detection technology. Fluorescent probes to perform cancer biomarker detection have become a popular research issue. However, fluorescent probes still face enormous challenges of complex design and difficult detection. In this work, we propose a novel composite material UCNP@SiO2 + QDs based on the combination of rare earth upconversion (UCNPs) and perovskite quantum dots (QDs) and design a new fluorescent probe MB-UCNP@SiO2 + QDs with molecular beacon (MB) as the carrier, that can be excited by near-infrared light, emitted in the visible wavelength, specifically identified and highly sensitive. Under the excitation of 980 nm near-infrared light, the UCNPs and QDs in the composite produced the maximum efficiency of energy transfer through fluorescence resonance, and the multi-emission light of UCNPs synergistically excited the re-emission of QDs, and the energy transfer efficiency is 70.6%. By changing the doping ratio of QDs halogen elements in UCNP@SiO2 + QDs, it is possible to modulate the precise luminescence of UCNP@SiO2 + QDs in the entire wavelength range of visible light at different positions. The novel fluorescent probe is obtained using UCNP@SiO2 + QDs and Black Hole Quencher-1 (BHQ1) quenching groups linked to the two respective sides of MB, selecting as the target of detection the myeloma cancer biomarker miRNA-155, a difficult diagnostic and complex developmental type, and have achieved specific recognition and low concentration of miRNA-155 and a detection limit of 73.5 pM. This fluorescent probe design can provide new ideas for the early diagnosis and treatment of cancer, tumors, and cardiovascular diseases. Graphical Abstract

Abstract The increase in car ownership has brought convenience to people's lives, but it has also led to a sharp increase in pedestrian–vehicle conflicts. Such conflicts can be avoided by braking when pedestrians in front of the car are detected. However, frequent braking reduces driving comfort and travel efficiency. Considering that pedestrian–vehicle conflicts always occur when pedestrians cross the road, this study proposes a multi‐factor fusion network (MFFN) to anticipate pedestrians' crossing intentions. MFFN contains a dual‐channel action recognition sub‐network to robustly identify pedestrians' actions by adaptively fusing pedestrians' skeleton and representation characteristics. Afterward, object‐level and semantic‐level perception of traffic scenes are realised by a hierarchical graph attention network and a lightweight semantic segmentation method, respectively. These hierarchical environmental contexts can provide critical clues for identifying pedestrians' crossing intentions. Then, a self‐attention mechanism is used to integrate various factors and produce intention identification results. The proposed MFFN is evaluated on public datasets PIE and JAAD. Experimental results show that the proposed method can accurately and reliably identify pedestrian crossing intentions. The accuracy, F1 score, AUC, and precision indicators on JAAD/PIE datasets are 0.912/0.876, 0.813/0.806, 0.896/0.889, and 0.802/0.788, respectively.

In this era of information overload, to better provide personalized content services to users, recommendation systems have greatly improved the efficiency of information distribution. Graph Convolution Network(GCN), which is one of the representative works of graph structure aggregation processing, works by node convolution with the help of the Laplacian matrix of the graph and weighted combination of neighbor node information according to the outgoing and incoming degrees of neighbor nodes to obtain the representation of the current node. However, the mainstream GCN models nowadays do not take into account data augmentation of metadata and the fact that each node plays different roles with different importance and weights, thus making the recommendation performance limited. To better solve the above problems, we propose the IcaGCN model, which can perform data augmentation and calculate node weights in modules, and is a convenient plug-and-play method. Finally, extensive experimental results on four real-world datasets have shown the effectiveness and robustness of the proposed model. Especially on the Amazon-Book dataset, our IcaGCN has improved by 6.32%, 42.29%, and 12.38% in Recall@20, MRR@20, and NDCG@20, respectively, compared to other existing state-of-the-art models. We also provide source code and data to reproduce the experimental results available at https://github.com/PersonZ1223/IcaGCN.git

Existing self-supervised sequential recommendations face the problem of noisy interactions and sparse sequence data, and train models based only on item prediction losses, so they usually fail to learn an appropriate sequential representation. In this paper, to address the above problem, we propose long and short-term interest contrastive learning under filter-enhanced sequential recommendation (FLSCSR). Specifically, a filtering algorithm is used on the user’s interaction sequences to attenuate the noisy information in the sequence data. Two independent encoders are used to model the user’s long-term and short-term interests separately on the filter-based enhanced interaction sequences. Then user-specific gating mechanisms are constructed to capture the long-term and short-term interests tailored to the user’s personalized preferences, which are incorporated into the attention network to achieve better learning of interest representations in sequence recommendations. In addition, representation alignment learning goals are proposed to minimize the discrepancy between long-term and short-term interest representations in personalized global contexts and local sequence representations. Experiments were conducted on three public and industrial datasets, where the FLSCSR model could obtain superior performance compared to the benchmark model: AUC improves by 0.76%-2.02%, GAUC improves by 0.55%-1.01%, MRR improves by 1.19%-2.09%, and NDCG@2 improves by 1.07%-2.26%.

This paper presents a rapidly direct algorithm (RDA) to calculate explicit Fréchet derivatives (EFD) for marine controlled-source electromagnetic measurement (MCSEM) in a three-dimensional (1-D) transversely isotropic (TI) formation. By discretizing the Helmholtz equations about coupled potentials on Yee’s staggered grids by the 3-D finite volume method (FVM), we obtain a complex linear system about the unknown potentials excited by a mass of moving electric current sources. To efficiently determine electromagnetic (EM) fields, we introduce an interpolation operator and projection operator per receiver by using the direct solver PARDISO and 3-D Newtown interpolation. Based on this, the perturbation in goal conductivity is expressed as a piece-wise constant function according to block or pixel model. The spatial scattered electric currents will be decomposed into a series of electric current elements distributed on Yee’s grids due to the conductivity perturbation. We then discretize the scattered electric currents by 3-D FVM and obtain the new right-hand terms about the unknown scattered potentials. This allows for fast production of the linear relationship between the changes in the EM fields and the relative conductivity perturbation per block or pixel, ultimately resulting in the EFD of MCSEM responses. Numerical results demonstrate the efficiency and accuracy of this method. The 3-D pixel sensitivities are presented to further investigate the response characteristics of MCSEM in several cases.

Highly sensitive and reliable detection of multiple myeloma remains a major challenge in liquid biopsy. Herein, for the first time, quantum dot-molecular beacon (QD-MB) functionalized MoS2 (QD-MB @MoS2) fluorescent probes were designed for the dual detection of multiple myeloma (MM)-related miRNA-155 and miRNA-150. The results indicate that the two probes can effectively detect miRNA-155 and miRNA-150 simultaneously with satisfactory recovery rates, and the limit of detections (LODs) of miRNA-155 and miRNA-150 in human serum are low to 7.19 fM and 5.84 fM, respectively. These results indicate that our method is the most sensitive detection so far reported and that the designed fluorescent probes with signal amplification strategies can achieve highly sensitive detection of MM-related miRNAs for MM diagnosis.

Abstract Understanding agents’ motion behaviors under complex scenes is crucial for intelligent autonomous moving systems (like delivery robots and self-driving cars). It is challenging duo to the inherent uncertain of future trajectories and the large variation in the scene layout. However, most recent approaches ignored or underutilized the scenario information. In this work, a Multi-Granularity Scenarios Understanding framework, MGSU, is proposed to explore the scene layout from different granularity. MGSU can be divided into three modules: (1) A coarse-grained fusion module uses the cross-attention to fuse the observed trajectory with the semantic information of the scene. (2) The inverse reinforcement learning module generates optimal path strategy through grid-based policy sampling and outputs multiple scene paths. (3) The fine-grained fusion module integrates the observed trajectory with the scene paths to generate multiple future trajectories. To fully explore the scene information and improve the efficiency, we present a novel scene-fusion Transformer, whose encoder is used to extract scene features and the decoder is used to fuse scene and trajectory features to generate future trajectories. Compared with the current state-of-the-art methods, our method decreases the ADE errors by 4.3% and 3.3% by gradually integrating different granularity of scene information on SDD and NuScenes, respectively. The visualized trajectories demonstrate that our method can accurately predict future trajectories after fusing scene information.

Photoacoustic tomography (PAT) is a newly developed medical imaging modality, which combines the advantages of pure optical imaging and ultrasound imaging, owning both high optical contrast and deep penetration depth. Very recently, PAT is studied in human brain imaging. Nevertheless, while ultrasound waves are passing through the human skull tissues, the strong acoustic attenuation and aberration will happen, which causes photoacoustic signals’ distortion. In this work, we use 180 T1 weighted magnetic resonance imaging (MRI) human brain volumes along with the corresponding magnetic resonance angiography (MRA) brain volumes, and segment them to generate the 2D human brain numerical phantoms for PAT. The numerical phantoms contain six kinds of tissues, which are scalp, skull, white matter, gray matter, blood vessel and cerebrospinal fluid. For every numerical phantom, Monte-Carlo based optical simulation is deployed to obtain the photoacoustic initial pressure based on optical properties of human brain. Then, two different k-wave models are used for the skull-involved acoustic simulation, which are fluid media model and viscoelastic media model. The former one only considers longitudinal wave propagation, and the latter model takes shear wave into consideration. Then, the PA sinograms with skull-induced aberration is taken as the input of U-net, and the skull-stripped ones are regarded as the supervision of U-net to train the network. Experimental result shows that the skull’s acoustic aberration can be effectively alleviated after U-net correction, achieving conspicuous improvement in quality of PAT human brain images reconstructed from the corrected PA signals, which can clearly show the cerebral artery distribution inside the human skull.

BackgroundGiven the key role of integrins in maintaining intestinal homeostasis, anti-integrin biologics in inflammatory bowel disease (IBD) are being investigated in full swing. However, the unsatisfactory efficacy and safety of current anti-integrin biologics in clinical trials limit their widespread use in clinic. Therefore, it is particularly important to find a target that is highly and specifically expressed in the intestinal epithelium of patients with IBD.MethodsThe function of integrin αvβ6 in IBD and colitis-associated carcinoma (CAC) with the underlying mechanisms has been less studied. In the present study, we detected the level of integrin β6 within inflammation including colitis tissues in human and mouse. To investigate the role of integrin β6 in IBD and CAC, integrin β6 deficient mice were hence generated based on the construction of colitis and CAC model.ResultsWe noted that integrin β6 was significantly upregulated in inflammatory epithelium of patients with IBD. Integrin β6 deletion not only reduced infiltration of pro-inflammatory cytokines, but also attenuated disruption of tight junctions between colonic epithelial cells. Meanwhile, lack of integrin β6 affected macrophage infiltration in mice with colitis. This study further revealed that lack of integrin β6 could inhibit tumorigenesis and tumor progression in CAC model by influencing macrophage polarization, which was also involved in attenuating the degree of intestinal symptoms and inflammatory responses in mice suffering from colitis.ConclusionsThe present research provides a potentially new perspective and option for the treatment of IBD and CAC.

The fatigue problem of orthotropic steel bridge decks of urban rail transit steel bridges has gradually become one of the hot research topics. And it is also a key problem that restricts the further development of rail transit steel bridges. In this paper, the orthotropic steel bridge deck structure of a long-span urban rail transit cable-stayed bridge is studied. Based on the segmental finite element model and full-scale model, the fatigue details of the joint weld between an orthotropic steel bridge deck and U-rib were studied theoretically and experimentally. The theoretical model of the segment is analyzed to obtain the hot spot stress characteristics. On this basis, the full-scale model fatigue test and the fatigue performance evaluation are completed based on the S-N curve. The results show that the fatigue performance of the bridge deck and U-rib joints of the orthotropic steel bridge deck structure model meets the design requirements and has a certain safety reserve. The joint fatigue details of the bridge deck, the U-rib joint weld, and the diaphragm plate are the sensitive areas that are most likely to occur fatigue failure first and need to be paid attention to in the later bridge maintenance and inspection.

The standard reconstruction of Photoacoustic (PA) computed tomography (PACT) image could cause the artifacts due to interferences or ill-posed setup. Recently, deep learning has been used to reconstruct the PA image with ill-posed conditions. In this paper, we propose a jointed feature fusion framework (JEFF-Net) based on deep learning to reconstruct the PA image using limited-view data. The cross-domain features from limited-view position-wise data and the reconstructed image are fused by a backtracked supervision. A quarter position-wise data (32 channels) is fed into model, which outputs another 3-quarters-view data (96 channels). Moreover, two novel losses are designed to restrain the artifacts by sufficiently manipulating superposed data. The experimental results have demonstrated the superior performance and quantitative evaluations show that our proposed method outperformed the ground-truth in some metrics by 135% (SSIM for simulation) and 40% (gCNR for in-vivo) improvement.

As an efficient geophysical exploration tool, the airborne electromagnetic (AEM) method has been widely used in mineral exploration, geological mapping, environmental and engineering investigation, etc. Currently, the imaging and 1D inversions are the mainstream means for AEM interpretation as the amount of AEM data is huge and 2D and 3D inversions are not efficient. In this paper, we propose a 2D fast imaging method for frequency-domain AEM data based on U-net network. The U-net is a symmetric full-convolution neural network, in which the partial pooling operation between the convolution layers is replaced by the up-sampling operation, while the target location is achieved by skipping connection. This method does not need to consider the complex coupling between the EM responses and underground structures, but instead it establishes a mapping relationship between EM responses and the resistivity model and can quickly achieve accurate imaging of AEM data. We use this network to image both synthetic and field survey data and compare the results with the traditional inversion algorithms. The results show that the U-net imaging have high resolution at high speed that provides a new way for interpreting large amounts of AEM data.

Landslides are common geological hazards that often result in significant casualties and economic losses. Due to their occurrence in complex terrain areas, conventional geophysical techniques face challenges in early detection and warning of landslides. Semi-airborne electromagnetic (SAEM) technology, utilizing unmanned aerial platforms for rapid unmanned remote sensing, can overcome the influence of complex terrain and serve as an effective approach for landslide detection and monitoring. In response to the low computational efficiency of conventional semi-airborne EM 3D forward modeling, this study proposes an efficient forward modeling method. To handle arbitrarily complex topography and geologic structures, the unstructured tetrahedron mesh is adopted to discretize the earth. Based on the vector finite element formula, the Dual–Primal Finite Element Tearing and Interconnecting (FETI-DP) method is further applied to enhance modeling efficiency. It obtains a reduced order subsystem and avoids directly solving huge overall linear equations by converting the entirety problem into the interface problem. We check our algorithm by comparing it with 1D semi-analytical solutions and the conventional finite element method. The numerical experiments reveal that the FETI-DP method utilities less memory and exhibits higher computation efficiency than the conventional methods. Additionally, we compare the electromagnetic responses with the topography model and flat earth model. The comparison results indicate that the effect of topography cannot be ignored. Further, we analyze the characteristic of electromagnetic responses when the thickness of the aquifer changes in a landslide area. We demonstrate the effectiveness of the 3D SAEM method for landslide detection and monitoring.

Due to the temperature changes caused by seasonal changes and extreme weather, asphalt pavement suffers from rutting, cracking, and other damage. With commonly used pavement additives, the high-temperature performance and the low-temperature performance of asphalt pavement show opposite trends, with related research endeavoring to find a balance between the two. In this study, a dual-phase-change material (DPCM) with both high- and low-temperature effects was prepared. The chemical stability and rheological properties of modified asphalt binders were characterized using Fourier transform infrared spectroscopy (FTIR) and a dynamic shear rheometer (DSR). Temperature control tests of the DPCM-modified asphalt binders were carried out with an indoor simulation device. The results show that the DPCMs could improve the rutting resistance of the asphalt binders at a high temperature, but the fatigue performance of the modified asphalt binder with different DPCM contents was reduced. The FTIR results showed that no chemical reaction occurred in the mixing of the asphalt binder and the DPCM. In the indoor simulation temperature control test, the 40% DPCM-content-modified asphalt binder reduced the high-temperature extreme value by 4.2 °C and increased the low-temperature extreme value by 2.5 °C, showing a good temperature control effect and practical application value.

The study of the numerical simulation of seismoelectric effects is very helpful for understanding the theory and mechanism of seismoelectric activities. Quasi-static approximation is widely used in the numerical simulation of seismoelectric fields. However, numerical errors occur when the model domain is not within the near-field area of EM waves or the medium is of high salinity. To solve this problem, we propose a time-domain finite-element algorithm (FETD) based on the full-wave electromagnetic (EM) equation to simulate seismoelectric waves in 2D PSVTM mode. By decomposing the electrokinetic coupling equations into two independent ones, we can solve the seismoelectric waves separately. In our implementation, we focus our attention on the solution of EM waves based on vector–scalar potentials, while using the open-source code SPECFEM2D to explicitly solve Biot’s equations and obtain the relative fluid–solid displacement, which is taken as the source for the complete Maxwell’s equations. In the solution of EM wave fields, we use an unconditionally stable implicit method for time discretization. Computation efficiency can be improved by combining explicit and implicit recursions. After conducting the mathematical formulation, we first validate our method by comparing its results with the analytic solutions for a half-space and a two-layer model, as well as with a quasi-static approximation method. Moreover, we run numerical simulations and wavefield analyses on an elliptical hydrocarbon reservoir, and reveal that the interface responses are promising for the identification of underground interfaces and hydrocarbon reservoir exploration.

The finite-element (FE) method for three-dimensional (3D) airborne electromagnetic (AEM) modeling can flexibly simulate complex geological structures at high accuracy. However, it has low efficiency and high computational requirements. To solve these problems, one needs to generate meshes more reasonably. In view of this, we develop an adaptive octree meshing scheme for frequency-domain AEM modeling. The octree meshes have the characteristics of regularity and flexibility, while the adaptive algorithm can effectively refine the mesh locally. In our adaptive mesh generation, the posterior errors and weighted coefficients are used to construct the final weighted posterior errors. We verify the accuracy of our method by comparing its results with semi-analytical solutions for a half-space model. Furthermore, we use the spectral-element (SE) method and our method to calculate EM responses for an abnormal block model and compare their computational costs. The results show that our adaptive scheme has obviously technical advantages over SE method for AEM modeling with multiple frequencies and multiple survey stations. Finally, we calculate a model with complex geological structures to verify the feasibility of our algorithm in complex geological circumstances.

Rare earth elements have been widely utilized in material manufacturing to enhance properties in various ways. In order to obtain the WC-10Co4Cr coating with uniform distribution of rare earths, CeO2-modified powder was prepared by mixing 1 wt.% nano-sized CeO2 during the initial ball-milling of the powder fabrication process. Bare and CeO2-modified WC-10Co4Cr coatings were deposited via high velocity oxygen fuel spraying to investigate the impact of CeO2 modification on the coating’s microstructure, mechanical properties and abrasive wear performance. The results show that the addition of CeO2 increased the interface energy, inhibiting the formation of the Co3W3C phase during the powder sintering process, as well as the W2C phase and CoCr alloy during the high-velocity oxy-fuel (HVOF) process. This led to a significantly decreased porosity and higher concentration of undissolved Cr-rich areas. The microhardness and fracture toughness of the CeO2-modified coating were 1230 HV0.3 and 5.77 MPam1/2, respectively. The abrasive wear resistance of the CeO2-modified coating was only 70.9% of that of the unmodified coating. Due to the weak cohesive strength between WC and Cr, Cr-rich areas were preferentially removed, resulting in an increased wear rate in the CeO2-modified coating.

Abstract Organic compounds bearing redox‐active ionic pairs as electrode materials for high‐performance rechargeable batteries have gained growing attention owing to the properties of synthetic tunability, high theoretical capacity, and low solubility. Herein, an innovative biredox‐ionic composite, i.e., ethylviologen dianthraquinone‐2‐sulfonate (EV‐AQ2), affording multiple and reversible active sites as a cathode material in lithium‐organic batteries is reported. EV‐AQ2 exhibits a high initial capacity of 199.2 mAh g−1 at 0.1 C rate, which corresponds to the transfer of two electrons from one redox couple EV2+/EV0 and four electrons from two redox‐active AQ− anions. It is notable that EV‐AQ2 shows remarkably improved cyclability compared to the precursors. The capacity retention is 89% and the Coulombic efficiency approaches 100% over 120 cycles at 0.5 C rate. The results offer evidence that AQ− into the EV2+ scaffold with multiple redox sites are promising in developing high‐energy‐density organic rechargeable batteries.

The study of feature interactions in deep neural network-based recommender systems has been a popular research area in industry and academic circles. However, the vast majority of parallel CTR prediction models do not classify the input features but instead feed them into the model. This way not only reduces the accuracy of the model but also ignores the effectiveness of learning individual feature interactions. In addition, the majority of parallel CTR prediction models only focus on the submodel intersections of their parallel models, ignoring the importance of the external intersection. To address the shortcomings, this paper proposes the CCPIN model on the basis of the XdeepFM model. In the CCPIN model, it can not only learn different category feature interactions but also learn individual feature interactions. Through the classification gate, adaptive features are maximized to improve the performance of the submodel. Through the Combine layer, the interaction of submodel results can be learned while retaining the original output. Through comparison experiments with other models on two datasets, it is demonstrated that the CCPIN model has an average increase of 0.93% in AUC and a decrease of 0.47% in Logloss compared to other models.

The surface-to-borehole transient electromagnetic (SBTEM) method can provide images at higher resolution for deep earth because its receivers are close to targets. However, as usually the boreholes distribute sparsely, the limited EM data can result in an “equivalent trap” in SBTEM inversions, i.e., the data are well-fitted, but the model is not properly recovered. To overcome this non-unique problem, we propose a dual-mesh three-dimensional (3D) SBTEM inversion scheme. We first use a coarse mesh to obtain a rough resistivity distribution near the borehole, and then we map the coarse mesh attribute into a fine one and capture details from the fine mesh inversion. We test our method on both synthetic data and survey data acquired in Daye, Hubei Province, China. Numerical experiments show that our dual-mesh inversion strategy can better recover the location and resistivity of targets.

In order to alleviate the cyclic attenuation caused by the dissolution of poly-selenides in lithium/sodium storage devices, quantitative selenium was slowly evaporated on the surface of sodium citrate derived carbon (SCDC) at low temperature, and simultaneously the element Se was doped. Benefiting from the synergistic effects of the domain-limiting effect of embedded nanopores on Se nanoparticles and the stability of SCDC with Se doped during the embedding and stripping of Na ions, Se@C versus sodium metal exhibits high second specific capacity of 485 mAh·g−1 and unexpected stability at 0.1 A g−1 and 1 A g−1. Se@C versus lithium metal exhibits high second specific capacity of 1185 mAh·g−1 at 0.1 A g−1 and excellent stability. Together with the simple and application of synthesis method, Se@C composite is expected to become an anode material for large sodium/lithium storage devices.

The traditional three-dimensional (3D) magnetotelluric (MT) forward modeling using Krylov subspace algorithms has the problem of low modeling efficiency. To improve the computational efficiency of 3D MT forward modeling, we present a novel geometric multigrid algorithm for the finite element method. We use the vector finite element to discretize Maxwell’s equations in the frequency domain and apply the Dirichlet boundary conditions to obtain large sparse complex linear equations for the solution of EM responses. To improve the convergence of the solution at low frequencies we use the divergence correction to correct the electric field. Then, we develop a V-cycle geometric multigrid algorithm to solve the linear equations system. To demonstrate the efficiency and effectiveness of our geometric multigrid method, we take three synthetic models (COMMEMI 3D-2 model, Dublin test model 1, modified SEG/EAEG salt dome model) and compare our results with the published ones. Numerical results show that the geometric multigrid algorithm proposed in this paper is much better than the commonly used Krylov subspace algorithms (such as SOR-GMRES, ILU-BICGSTAB, SOR-BICGSTAB) in terms of the iteration number, the solution time, and the stability, and thus is more suitable for large-scale 3D MT forward modeling.

Abstract Background Papaya (Carica papaya L.) is a popular climacteric fruit, undergoing various physico-chemical changes during ripening. Although papaya is widely cultivated and consumed, few studies on the changes in metabolism during its ripening process at the proteasome level have been performed. Using a newly developed TMT-LCMS analysis, proteomes of papaya fruit at different ripening stages were investigated. Results In total, 3220 proteins were identified, of which 2818 proteins were quantified. The differential accumulated proteins (DAPs) exhibited various biological functions and diverse subcellular localizations. The KEGG enrichment analysis showed that various metabolic pathways were significantly altered, particularly in flavonoid and fatty acid metabolisms. The up-regulation of several flavonoid biosynthesis-related proteins may provide more raw materials for pigment biosynthesis, accelerating the color variation of papaya fruit. Variations in the fatty acid metabolism- and cell wall degradation-related proteins were investigated during the ripening process. Furthermore, the contents of several important fatty acids were determined, and increased unsaturated fatty acids may be associated with papaya fruit volatile formation. Conclusions Our data may give an intrinsic explanation of the variations in metabolism during the ripening process of papaya fruit.

In the blockchain system, mining pools are popular for miners to work collectively and obtain more revenue. Nowadays, there are consensus attacks that threaten the efficiency and security of mining pools. As a new type of consensus attack, the Fork After Withholding (FAW) attack can cause huge economic losses to mining pools. Currently, there are a few evaluation tools for FAW attacks, but it is still difficult to evaluate the FAW attack protection capability of target mining pools. To address the above problem, this paper proposes a novel evaluation framework for FAW attack protection of the target mining pools in blockchain systems. In this framework, we establish the revenue model for mining pools, including honest consensus revenue, block withholding revenue, successful fork revenue, and consensus cost. We also establish the revenue functions of target mining pools and other mining pools, respectively. In particular, we propose an efficient computing power allocation optimization algorithm (CPAOA) for FAW attacks against multiple target mining pools. We propose a model-solving algorithm based on improved Aquila optimization by improving the selection mechanism in different optimization stages, which can increase the convergence speed of the model solution and help find the optimal solution in computing power allocation. Furthermore, to greatly reduce the possibility of falling into local optimal solutions, we propose a solution update mechanism that combines the idea of scout bees in an artificial bee colony optimization algorithm and the constraint of allocating computing power. The experimental results show that the framework can effectively evaluate the revenue of various mining pools. CPAOA can quickly and accurately allocate the computing power of FAW attacks according to the computing power of the target mining pool. Thus, the proposed evaluation framework can effectively help evaluate the FAW attack protection capability of multiple target mining pools and ensure the security of the blockchain system.

A new algorithm is developed to accurately compute the electromagnetic (EM) fields in the layered biaxial anisotropic media. We enclose the computational region in an infinitely long rectangular region by four vertical truncation planes and establish the corresponding algorithm to approximate the EM fields in the entire space. The EM fields in this region are expanded as a two-dimensional (2-D) Fourier series of the transverse variables. By using the spectral state variable method, the generalized reflection coefficient matrices and transmission matrices are then derived to determine the Fourier coefficients per layer. Therefore, we can obtain the spatial-domain EM fields by summing the 2-D Fourier series. To enhance the accuracy and efficiency of this algorithm, we apply the method of images to estimate the influence of the artificial boundaries on the EM fields at the observer. We then further develop a quantitative principle to choose the proper size of the region according to the desired error tolerance. With the proper choice, the summation of the series can achieve satisfactory accuracy. This algorithm is finally applied to simulate the responses of the triaxial logging tool in transversely isotropic and biaxial anisotropic media and is verified through comparisons to the other method.

Blockchain has become one of the key techniques for the security of the industrial internet. However, the blockchain is vulnerable to FAW (Fork after Withholding) attacks. To protect the industrial internet from FAW attacks, this paper proposes a novel FAW attack protection algorithm (FAWPA) based on the behavior of blockchain miners. Firstly, FAWPA performs miner data preprocessing based on the behavior of the miners. Then, FAWPA proposes a behavioral reward and punishment mechanism and a credit scoring model to obtain cumulative credit value with the processed data. Moreover, we propose a miner’s credit classification mechanism based on fuzzy C-means (FCM), which combines the improved Aquila optimizer (AO) with strong solving ability. That is, FAWPA combines the miner’s accumulated credit value and multiple attack features as the basis for classification, and optimizes cluster center selection by simulating Aquila’s predation behavior. It can improve the solution update mechanism in different optimization stages. FAWPA can realize the rapid classification of miners’ credit levels by improving the speed of identifying malicious miners. To evaluate the protective effect of the target mining pool, FAWPA finally establishes a mining pool and miner revenue model under FAW attack. The simulation results show that FAWPA can thoroughly and efficiently detect malicious miners in the target mining pool. FAWPA also improves the recall rate and precision rate of malicious miner detection, and it improves the cumulative revenue of the target mining pool. The proposed algorithm performs better than ND, RSCM, AWRS, and ICRDS.

Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began to surpass other established mature models on some important benchmarks. Later, it was widely used in academia and industry. Ranging from image analysis to natural language processing, it fully exerted its magic and now become the state-of-the-art machine learning models. Deep neural networks have great potential in medical imaging technology, medical data analysis, medical diagnosis and other healthcare issues, and is promoted in both pre-clinical and even clinical stages. In this review, we performed an overview of some new developments and challenges in the application of machine learning to medical image analysis, with a special focus on deep learning in photoacoustic imaging.The aim of this review is threefold: (i) introducing deep learning with some important basics, (ii) reviewing recent works that apply deep learning in the entire ecological chain of photoacoustic imaging, from image reconstruction to disease diagnosis, (iii) providing some open source materials and other resources for researchers interested in applying deep learning to photoacoustic imaging.

Conventional reconstruction algorithms (e.g., delay-and-sum) used in photoacoustic imaging (PAI) provide a fast solution while many artifacts remain, especially for limited-view with ill-posed problem. In this paper, we propose a new convolutional neural network (CNN) framework Y-Net: a CNN architecture to reconstruct the initial PA pressure distribution by optimizing both raw data and beamformed images once. The network combines two encoders with one decoder path, which optimally utilizes more information from raw data and beamformed image. We compared our result with some ablation studies, and the results of the test set show better performance compared with conventional reconstruction algorithms and other deep learning method (U-Net). Both in-vitro and in-vivo experiments are used to validated our method, which still performs better than other existing methods. The proposed Y-Net architecture also has high potential in medical image reconstruction for other imaging modalities beyond PAI.

Summary: Non-oxidative propane dehydrogenation (PDH) is an attractive reaction from both an industrial and a scientific viewpoint because it allows direct large-scale production of propene and fundamental analysis of C-H activation respectively. The main challenges are related to achieving high activity, selectivity, and on-stream stability of environment-friendly and cost-efficient catalysts without non-noble metals. Here, we describe an approach for the preparation of supported ultrasmall ZnO nanoparticles (2–4 nm, ZnO NPs) for high-temperature applications. The approach consists of encapsulation of NPs into a nitrogen-doped carbon (NC) layer in situ grown from zeolitic imidazolate framework-8 on a Silicalite-1 support. The NC layer was established to control the size of ZnO NPs and to hinder their loss to a large extent at high temperatures. The designed catalysts exhibited high activity, selectivity, and on-stream stability in PDH. Propene selectivity of about 90% at 44.4% propane conversion was achieved at 600°C after nearly 6 h on stream. : Chemistry; Catalysis; Nanoparticles Subject Areas: Chemistry, Catalysis, Nanoparticles

With the advancement of society, the museum has exhibited more and more cultural relics, the number of visitors has also increased rapidly, and more and more criminals have stolen cultural relics. The traditional anti-theft methods cannot completely block their pace. This paper proposes a museum anti-theft scheme based on the Internet of Things (IoT) technology, which identifies whether the cultural relics are within the safe range through the passive RFID readers/writers. Once stolen, the cultural relics will leave the effective RFID identification range, which results in immediately alarming, then the system starts the anti-theft plan. The method is free from the drawbacks of the traditional infrared anti-theft, door magnetic detection and the like, the proposed anti-theft method monitoring has the immediacy and the safety factor is higher. Finally, in this paper, hardware circuit designs, software development and a series of tests are carried out to achieve the desired results.

In this paper, a novel rare-earth-doped upconverted nanomaterial NaYF4:Yb,Tm fluorescent probe is reported, which can detect cancer-related specific miRNAs in low abundance. The detection is based on an upconversion of nanomaterials NaYF4:Yb,Tm, with emissions at 345, 362, 450, 477, 646, and 802 nm, upon excitation at 980 nm. The optimal Yb3+:Tm3+ doping ratio is 40:1, in which the NaYF4:Yb,Tm nanomaterials have the strongest fluorescence. The NaYF4:Yb, Tm nanoparticles were coated with carboxylation or carboxylated protein, in order to improve their water solubility and biocompatibility. The two commonly expressed proteins, miRNA-155 and miRNA-150, were detected by the designed fluorescent probe. The results showed that the probes can distinguish miRNA-155 well from partial and complete base mismatch miRNA-155, and can effectively distinguish miRNA-155 and miRNA-150. The preliminary results indicate that these upconverted nanomaterials have good potential for protein detection in disease diagnosis, including early cancer detection.