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Facing wireless charging systems, this article proposes an innovative analytical model of self-inductance (SI) and mutual inductance (MI) for the magnetic couplers, using joint analytical calculation (JAC) method. The JAC method is extended to addresses the challenges of the construction of mirror model and the solution of mirror coefficient for source coils with bilateral finite ferrite cores. The influence of ferrite cores on inductance is divided into three parts, with each aspect being replaced by equivalent first, second, and third mirror coils. The constructed mirror model strictly satisfies the boundary value conditions at medium interfaces. Regardless of whether the magnetic couplers are perfectly aligned or not, mirror coefficients can be calculated by combining the double truncation region eigenfunction expansion method and the mirror method. After translating ferrite cores into air-core mirror coils, both SI and MI can be determined when horizontal and angular misalignments occur. Experimental verification confirms that the constructed model can accurately calculate SI and MI under various misalignment conditions.

Thermoelectric materials traditionally incorporate heavy metals to achieve low lattice thermal conductivity. However, elements such as Te, Bi, and Pb are costly and pose environmental hazards. In this study, we introduce a novel design strategy for thermoelectric materials, focusing on room-temperature, light-element, and high-ZT materials such as coronene-cyclobutadienoid graphene nanoribbons (cor4GNRs). This material demonstrates a ZT value exceeding 2.1, attributed to its exceptionally low phonon thermal conductivity resulting from its unique edge structure. Importantly, because its electrical conductance and Seebeck coefficient are nearly unaffected by the edge structure, they remain relatively high. This distinct behavior in phonon and electronic transport properties leads to a remarkably high ZT value. Additionally, we discover that applying strain can significantly reduce phonon thermal conductivity, potentially increasing the ZT value to over 3.0. Our findings provide innovative insights for the design and application of advanced thermoelectric materials.

Purpose: In voxel-based dosimetry, dose-voxel kernels (DVKs) represent the absorbed dose in a target voxel per decay in a source voxel. In this work, we aimed to validate DVKs generated using Particle and Heavy Ion Transport Code System (PHITS) as a first step in establishing a quantitative SPECT/CT-based dosimetry framework for selective internal radiation therapy (SIRT). Methods: A 1-MBq source of 90Y, 153Sm, 177Lu, 188Re, and 166Ho located in central cubic voxels of 1 mm, 3 mm, and 6 mm dimension within soft tissue (1.04 g⋅cm−³), was simulated using 2.5 × 10⁷ histories in PHITS. The DVK results were compared to published data (Lanconelli et al., 2012). In a pilot study, 17 MBq of 153Sm microspheres were injected into a liver tumor-bearing rat and imaged 24 h later using SPECT/CT. SPECT/CT images were then converted to cumulated activity and convolved with a 153Sm DVK using Fast Fourier Transform in MATLAB to create an absorbed dose map. Results: As expected, DVKs for the radionuclides showed a steep decrease from the source voxel to the maximum continuous slowing down approximation (CSDA) of beta components. Beyond the CSDA, gamma and bremsstrahlung contributed minimally (10⁻⁶ to 10⁻⁴) to voxel doses. Observed DVK changes as voxel size increased were in good agreement with published data, with differences in source voxel ranging from 1 to 24%. Rat imaging revealed 153Sm microspheres localized at injection sites, with mean and maximum doses of 45 Gy and 194 Gy, respectively. Conclusion: The DVKs calculated in PHITS were successfully validated. 153Sm dose maps were produced from SPECT/CT images in a rat model. Further studies on the application of DVKs in heterogeneous media will be conducted.

Background: The impact of postoperative sarcopenia on the Textbook Oncological Outcome (TOO) in locally advanced gastric cancer (LAGC) remains uncertain. This study investigates the relationship between sarcopenia and TOO, explores its long-term prognostic value, and develops a prognostic model incorporating sarcopenia and TOO for survival prediction. Methods: We performed a retrospective analysis of clinical and pathological data from patients with LAGC who underwent radical surgery at two Chinese tertiary referral hospitals. Sarcopenia was defined as an SMI < 36.4 cm2/m2in males and < 28.4 cm2/m2in females. TOO was defined as the addition of perioperative chemotherapy to the textbook outcomes (TO). A nomogram was developed to predict postoperative overall survival (OS) and recurrence-free survival (RFS) in LAGC patients. Results: The study included 972 patients with LAGC. The overall TOO achievement rate was 67.1%. The TOO achievement rate was significantly higher in patients non-sarcopenia compared to those with sarcopenia (68.9% vs. 61.1%, P= 0.031). Logistic regression revealed that age ≥ 65, high ASA score, and sarcopenia were independent risk factors for TOO failure. Cox regression analysis identified TOO, sarcopenia, tumor size, differentiation, vascular invasion, pT stage, and pN stage as independent predictors of OS and RFS. Nomogram models based on sarcopenia and TOO accurately predicted the 3-year and 5-year OS and RFS. Conclusion: Preoperative sarcopenia was an independent predictor of TOO implementation. A prognostic prediction model that integrates preoperative sarcopenia and TOO, which outperforms the current staging system, can aid clinicians in effectively assessing the prognosis of patients with LAGC.

Wind power is of strategic importance for reducing carbon dioxide emissions, minimizing environmental pollution, and enhancing the sustainability of energy supply. Health monitoring of wind turbines is a crucial technology to ensure the quality of grid-connected power. Insufficient labeled data and class imbalance problems are two critical issues for intelligent fault diagnosis of wind turbines. In this article, an intelligent fault diagnosis method based on stacked capsule autoencoders is proposed to address the issues of inadequate labeled data and class imbalance. A prior knowledge-based convolution layer is applied to optimize the initialization of capsules, making it more conducive to learning spectral information. The pose representations of parts and objects can be improved, and a method for embedding spectral templates is proposed. The stacked capsule autoencoder in this study can learn partial templates unsupervised through likelihood estimation and establish the mapping between capsules and fault types. The experimental results, obtained from the CWRU dataset and a private dataset from a wind turbine drive-train simulation platform, demonstrate that the proposed method is robust to imbalanced and small-sized datasets. It can perform stable and effective unsupervised training by utilizing a sufficient amount of normal class data to expedite learning convergence.

The key components of the rotating machinery, such as gears and bearings, are prone to damage owing to long-term complex and harsh working situations. This study investigates the weight distribution of neural networks, and finds that the response of the network to the input is uneven, indicating that data-driven models tend to learn more information from certain local parts of the input. Based on this discovery, a novel attention mechanism, namely dynamic focusing, is proposed. The dynamic focusing mechanism highlights local information with important features to extract the discriminative features of key frequency bands. In addition, insufficient labeled data presents challenges for fault diagnosis in the practical. A semisupervised learning method based on mutual information is proposed to solve this problem. The effectiveness of the proposed method is verified by the Case Western Reserve University public dataset as well as the Gearbox Dynamic Simulator dataset obtained in our laboratory. The experimental results show that the proposed method has considerable advantages compared to existing deep learning methods, with test accuracy ranging from 95.31% to 100%.

Globally, B‐mode ultrasound is the most common modality used for the diagnosis of hepatic steatosis. We aimed to assess the correlation between qualitative liver ultrasound parameters, attenuation imaging (ATI) and histopathology‐diagnosed steatosis grade obtained from liver biopsy. Our secondary aim was to examine the interobserver variability of qualitative ultrasound features. A retrospective cohort study was performed which included adult patients (age ≥ 18 years) who had same‐day liver ultrasound, ATI and liver biopsy for grading hepatic steatosis severity between 2018 and 2022. The qualitative US features for hepatic steatosis were independently scored by three radiologists and interobserver variability was examined. Histologic steatosis grade, ATI and qualitative ultrasound parameters were compared. Ninety patients were included; 67% female with a median age of 54 (IQR 39–65) years. The radiologist's overall impression had the highest correlation (very strongly correlated) with histologic steatosis grade (r = 0.82, P < 0.001). ATI coefficient and all qualitative ultrasound B‐mode features except for liver echotexture and focal fat sparing were strongly correlated with histologic steatosis grade (r ≥ 0.70, P < 0.001). Most qualitative ultrasound features had good agreement between observers (Kappa statistic 0.61–1.0, P < 0.001), (Kendall coefficient 0.92, P < 0.001). The examined qualitative ultrasound parameters and ATI had good‐excellent performance for diagnosing clinically significant hepatic steatosis; however, the radiologist's overall impression had the best correlation with histologic steatosis grade. Our findings suggest an ongoing role for qualitative liver ultrasound assessment of hepatic steatosis despite the emergence of newer quantitative measures.

Current trackers only rely on a fixed target template to localize the target in each frame, which is however prone to fail in case of fast appearance changes or the presence of distractor objects. Having some historical knowledge about the tracked targets as well as their surrounding scenes can be highly beneficial for robust tracking. This historical information can be propagated through the sequence and used to timely perceive the change in target appearance and explicitly avoid distractor objects. In this work, we propose a Spatial-Temporal Context Attention (STCA) model which utilizes the appearance and state information of previously tracked targets as well as their surrounding scenes to more accurately localize the real target in the current frame. We embed an improved position encoder into the STCA, which enables the target template, context template and search patch to perform extensive interactional fusion through simultaneously self-attention and cross-attention calculation. By embedding the STCA module into Transformer, we construct a target-aware based online tracking network (named TATrack) that has a backbone to extract features better suited to the tracking task, a neck to further suppress distractors and highlight target, and a classification-regression head to make the tracking scores consistently reflect the quality of the bounding boxes. In addition, we also design a simple yet effective online updating approach to select high-quality context templates. Our tracker reaches the latest level on several benchmarks, including LaSOT, TrackingNet, GOT10k, OTB100 and UAV123. The code and trained models are available at https://github.com/hekaijie123/TATrack.

Efficient tumor-targeted drug delivery is still a challenging and currently unbreakable bottleneck in chemotherapy for tumors. Nanomedicines based on passive or active targeting strategy have not yet achieved convincing chemotherapeutic benefits in the clinic due to the tumor heterogeneity. Inspired by the efficient inflammatory-cell recruitment to acute clots, we constructed a two-component nanosystem, which is composed of an RGD-modified pyropheophorbide-a (Ppa) micelle (PPRM) that mediates the tumor vascular-targeted photodynamic reaction to activate local coagulation and subsequently transmits the coagulation signals to the circulating clot-targeted CREKA peptide-modified camptothecin (CPT)-loaded nanodiscs (CCNDs) for amplifying tumor targeting. PPRM could effectively bind with the tumor vasculature and induce sufficient local thrombus by a photodynamic reaction. Local photodynamic reaction-induced tumor target amplification greatly increased the tumor accumulation of CCND by 4.2 times, thus significantly enhancing the chemotherapeutic efficacy in the 4T1 breast tumor model. In other words, this study provides a powerful platform to amplify tumor-specific drug delivery by taking advantage of the efficient crosstalk between the PPRM-activated coagulation cascade and clot-targeted CCND.

TiZrTaNb-based high-entropy alloys (HEAs) are research frontier of biomedical materials due to their high hardness, good yield strength, excellent wear resistance and corrosion resistance. Sn, as an essential trace element in the human body that plays a significant role in physiological process. It has stable chemical properties and a low elastic modulus. In this study, a new material, TiZrTaNbSn HEAs, was proposed as a potential biomedical alloy. The Ti35Zr25Ta15Nb15Sn10biomedical high-entropy alloys (BHEAs) were successfully prepared through an arc melting furnace and then remelted using a German high-temperature and high-pressure apparatus under GPa-level (4 GPa and 7 GPa). The precipitation behavior of the needle-like HCP-Zr5Sn3phase that precipitates discontinuously at the grain boundary was successfully controlled. The phase constitution, microstructure, and corrosion resistance of the alloy were studied. The results show that the needle-like HCP-Zr5Sn3phase is eliminated and the (Zr, Sn)-rich nanoprecipitated phase is precipitated in the microstructure under high pressure, which leads to the narrowing of grain boundaries and consequently improves the corrosion resistance of the alloy. In addition, the formation mechanisms of (Zr, Sn)-rich nanoprecipitates in BHEAs were discussed. More Zr and Sn dissolve in the matrix due to the effect of high pressure, during the cooling process, they precipitate to form a (Zr, Sn)-rich nano-precipitated phase.

To address the universal challenge that the mirror coefficient (MC) cannot be analytically expressed, the joint analytical calculation (JAC) method is initially proposed in this article. Selecting the magnetic vector potential (MVP) as a uniform analytical target, the JAC method constructs a bridge, linking the truncated region eigenfunction expansion (TREE) method and the mirror method. Furthermore, an analytical expression of MC is derived based on the uniqueness principle of the magnetic field. The JAC method comprehensively reflects the effect of magnetic medium parameters, coil parameters, and field point coordinates on MC. Furthermore, the double TREE method is proposed to further improve the analytical accuracy of MVP. The experiment indicates that the analytical error of MVP is shrunk by a maximum of 3.16%. Then, magnetic flux density (MFD) is solved according to the analytical expressions of MC. The experiment shows that the JAC method and the mirror method have a maximum error of 4.08% and 27.99% for calculating MFD, respectively.

Objective: Textbook outcome (TO) is widely recognized as a comprehensive prognostic indication for patients with gastric cancer (GC). This study aims to develop a modified TO (mTO) for elderly patients with GC. Methods: Data from the elderly patients (aged ≥ 65 years) in two Chinese tertiary referral hospitals were analyzed. 1389 patients from Fujian Medical University Union Hospital were assigned as the training cohort and 185 patients from Affiliated Hospital of Putian University as the validation cohort. Nomogram was developed by the independent prognostic factors of Overall Survival (OS) based on Cox regression. Results: In the training cohort, laparoscopic surgery was significantly correlated with higher TO rate (P&lt; 0.05). Cox regression analysis revealed that surgical approach was also an independent factor of OS (P&lt; 0.001), distinct from the traditional TO. In light of these findings, TO parameters were enhanced by the inclusion of surgical approach, rendering a modified TO (mTO). Further analysis showed that mTO, tumor size, pTNM staging, and adjuvant chemotherapy were independent prognostic factors associated with OS (all P&lt; 0.05). Additionally, the nomogram incorporating these four indicators accurately predicted 1-, 3-, and 5-year OS in the training cohort, with AUC values of 0.793, 0.814, and 0.807, respectively, and exhibited outstanding predictive performance within the validation cohort. Conclusion: mTO holds a robust association with the prognosis of elderly patients with GC, meriting intensified attention in efforts aimed at enhancing surgical quality. Furthermore, the predictive model incorporating mTO demonstrates excellent predictive performance for elderly patients with GC.

The microstructure and mechanical properties of tailor-rolled blank (TRB) of medium-manganese steel during annealing were investigated. The annealing process of austenite-reverted transformation of the experimental steel was formulated. The effects of different morphologies on properties with different annealing temperatures in different thickness zones of TRB were analyzed. In the thin zone, the morphology is blocky, and in the thick zone, the morphology is lamellar. At the same annealing temperature, the tensile strength of the thick zone is lower than that of the thin zone, and the elongation and product of strength and elongation (PSE) of the thick zone are higher than those of the thin zone. With the increase in annealing temperature, the tensile strength increases, while the yield strength, the elongation and the PSE all decrease in the same thickness zone. Because the stability of lamellar austenite is higher than that of blocky austenite, the comprehensive mechanical properties in the thick zone are good. At the annealing temperature of 640 °C, the experimental steel has the best comprehensive mechanical properties, and the maximum PSE is more than 40.0 GPa% in different thickness zones. Particularly, PSE is up to 45.6 GPa% in the thickness zone of 2.0 mm.

The reverse water–gas shift (RWGS) reaction has tremendous practical significance for solving energy shortage problems. However, its harsh reaction conditions inevitably lead to the sintering of an active metal, which results in the loss of interface sites. Therefore, the construction of efficient and stable catalysts with uniform interfaces for the RWGS reaction is a persisting challenge. In this work, sintered Cu species were applied to fabricate an inverse Y2O3/Cu catalyst with a notable RWGS reaction performance. This inverse Y2O3/Cu catalyst sustained a high CO2conversion (45.6%) for up to 100 h at 600 °C (GHSV = 400 000 mL gcat−1h−1), exceeding the CO2conversion of a conventional Cu/Y2O3catalyst (24.4% for up to 40 h). The CO2and H2adsorption and activation ability of the inverse Y2O3/Cu catalyst were greatly optimized, which strikingly accelerated the catalytic reaction. Y2O3/CuOx/Cu interfaces constructed using the sintered Cu species promoted the metal–support interaction of the inverse Y2O3/Cu catalyst to achieve excellent catalytic stability. This strategy of using sintering Cu species to construct a stable interface provides new insights into the study of efficient and stable catalytic materials in the RWGS reaction.

Recently, memory-based methods have exhibited remarkable performance in Video Object Segmentation (VOS) by employing non-local pixel-wise matching between the query and memory. Nevertheless, these methods suffer from two limitations: 1) Non-local pixel-wise matching can result in the incorrect segmentation of background distractor objects, and 2) memory features with substantial temporal redundancy consume significant computing resources and reduce the inference speed. To address the limitations, we first propose a local attention mechanism to suppress background features, and we introduce a novel training framework based on contrast learning to ensure the network learns reliable and robust pixel-wise correspondence between query and memory. We adaptively determine whether to update the memory based on the variation of foreground objects. Next, we propose a dynamic memory bank, which utilizes a lightweight and differentiable soft modulation gate to determine the number of memory features to remove along the temporal dimension. This allows efficient and flexible management of memory features. Our network achieves competitive results (e.g., 92.1% on DAVIS 2016 val, 87.6%/81.3% on DAVIS 2017 val/test, 87.0% on YouTube-VOS 2018 val) compared with the state-of-the-art methods while maintaining a faster inference speed of 25+FPS. Moreover, our network demonstrates a favorable balance between performance and speed when dealing with the long-time video dataset.

Background: Textbook outcome (TO) has been widely employed as a comprehensive indicator to assess the short-term prognosis of patients with cancer. Preoperative malnutrition is a potential risk factor for adverse surgical outcomes in patients with gastric cancer (GC). This study aimed to compare the TO between robotic-assisted gastrectomy (RAG) and laparoscopic-assisted gastrectomy (LAG) in malnourished patients with GC. Methods: According to the diagnostic consensus of malnutrition proposed by Global Leadership Initiative on Malnutrition (GLIM) and Nutrition Risk Index (NRI), 895 malnourished patients with GC who underwent RAG (n= 115) or LAG (n= 780) at a tertiary referral hospital between January 2016 and May 2021 were included in the propensity score matching (PSM, 1:2) analysis. Results: After PSM, no significant differences in clinicopathological characteristics were observed between the RAG (n= 97) and LAG (n= 194) groups. The RAG group had significantly higher operative time and lymph nodes harvested, as well as significantly lower blood loss and hospital stay time compared to the LAG group. More patients in the RAG achieved TO. Logistic regression analysis revealed that RAG was an independent protective factor for achieving TO. There were more adjuvant chemotherapy (AC) cycles in the RAG group than in the LAG group. After one year of surgery, a higher percentage of patients (36.7% vs. 22.8%; P&lt; 0.05) in the RAG group recovered from malnutrition compared to the LAG group. Conclusions: For malnourished patients with GC, RAG performed by experienced surgeons can achieved a higher rate of TO than those of LAG, which directly contributed to better AC compliance and a faster restoration of nutritional status.

The advantage of intelligent fault diagnosis (IFD) based on industrial big data lies in the powerful feature extraction ability of machine learning models. However, it has become extremely difficult to apply machine learning-based fault diagnosis models to the actual industry due to the problem of labeled data insufficiency and class imbalance. Ensemble learning, which leverages the aggregation of multiple base classifiers to effectively utilize data, is regarded as a promising approach to address this issue. In this study, we propose an ensemble learning framework that integrates multiple stacked capsule autoencoders (SCAEs) for accurate fault diagnosis. The proposed ensemble framework introduces a novel method for evaluating intrinsic templates based on a symmetric graph Laplacian with the aim of selecting capsules that can effectively reduce information redundancy. Finally, a new decision fusion method is proposed to achieve the decoupling of composite fault labels by DS evidence. The proposed method is validated to achieve fault classification accuracy of up to 100% and 91% on datasets with sufficient and insufficient samples. In addition, the accuracy is higher than 94% on four imbalanced datasets. The experimental results demonstrate that the proposed method exhibits enhanced resilience against dataset defects, thereby offering more adaptable and reliable fault diagnosis services in real-world industry.

Background: Surgical quality control is a crucial determinant of evaluating the tumor efficacy. Objective: To assess the ClassIntra grade for quality control and oncological outcomes of robotic radical surgery for gastric cancer (GC). Methods: Data of patients undergoing robotic radical surgery for GC at a high-volume center were retrospectively analyzed. Patients were categorized into two groups, the intraoperative adverse event (iAE) group and the non-iAE group, based on the occurrence of intraoperative adverse events. The iAEs were further classified into five sublevels (ranging from I to V according to severity) based on the ClassIntra grade. Surgical performance was assessed using the Objective Structured Assessment of Technical Skill (OSATS) and the General Error Reporting Tool. Results: This study included 366 patients (iAE group: n= 72 [19.7%] and non-iAE group: n= 294 [80.3%]). The proportion of ClassIntra grade II patients was the highest in the iAE group (54.2%). In total and distal gastrectomies, iAEs occurred most frequently in the suprapancreatic area (50.0% and 54.8%, respectively). In total gastrectomy, grade IV iAEs were most common during lymph node dissection in the splenic hilum area (once for bleeding [grade IV] and once for injury [grade IV]). The overall survival (OS) and disease-free survival of the non-iAE group were significantly better than those of the iAE group (Log rank P&lt; 0.001). Uni- and multi-variate analyses showed that iAEs were key prognostic indicators, independent of tumor stage and adjuvant chemotherapy (P&lt; 0.001). Conclusion: iAEs in patients who underwent robotic radical gastrectomy significantly correlated with the occurrence of postoperative complications and a poor long-term prognosis. Therefore, utilization and inclusion of ClassIntra grading as a crucial surgical quality control and prognostic indicator in the routine surgical quality evaluation system are recommended.

LIDAR and camera fusion techniques are promising for achieving 3-D object detection in autonomous driving (AD). Most multimodal 3-D object detection frameworks integrate semantic knowledge from 2-D images into 3-D LiDAR point clouds to enhance detection accuracy. Nevertheless, the restricted resolution of 2-D feature maps impedes accurate reprojection and often induces a pronounced boundary-blurring effect, which is primarily attributed to erroneous semantic segmentation. To address these limitations, we present the multi-sem fusion (MSF) framework, a versatile multimodal fusion approach that employs 2-D/3-D semantic segmentation methods to generate parsing results for both modalities. Subsequently, the 2-D semantic information undergoes reprojection into 3-D point clouds utilizing calibration parameters. To tackle misalignment challenges between the 2-D and 3-D parsing results, we introduce an adaptive attention-based fusion (AAF) module to fuse them by learning an adaptive fusion score. Then, the point cloud with the fused semantic label is sent to the following 3-D object detectors. Furthermore, we propose a deep feature fusion (DFF) module to aggregate deep features at different levels to boost the final detection performance. The effectiveness of the framework has been verified on two public large-scale 3-D object detection benchmarks by comparing them with different baselines. And the experimental results show that the proposed fusion strategies can significantly improve the detection performance compared to the methods using only point clouds and the methods using only 2-D semantic information. Moreover, our approach seamlessly integrates as a plug-in within any detection framework.

The electrochemical and stress corrosion cracking behavior of 9Cr ferritic–martensitic steel is investigated in the chloride environment by using the traditional electrochemical method, the scanning vibrating electrode technique and the slow strain rate test (SSRT). Results of the static corrosion tests and corrosion morphology show that the prior austenite grain boundaries and martensite lath boundaries are the preferred sites for pit nucleation and growth in chloride environment. Results of SSRT coupled with in situ electrochemical test show that the transition from pitting corrosion to uniform corrosion, as well as the nucleation of stress corrosion crack, is the synergistic effects of the chloride and applied load. Stress corrosion cracking of the steel in the chloride environment can be divided into three different regions as follows: fast and uniform corrosion activity, microcrack nucleation and propagation, and active crack growth regions.

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17–34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.

Accurate visual detection under poor illuminance is arguably an important and challenging practice for robot monitoring. The purpose of this study is to build detectors for inspection robot under nonuniform and poor lighting conditions. To this end, we propose an unsupervised lighting reflectance estimation framework for dark image detection unsupervised lighting reflectance estimation network (ULRE-Net). Based on lightness-color consistency, the “true color” of objects merely depends on the reflectance regardless of the variations of illuminance. The proposed ULRE-Net extracts robust features for detection by estimating a simple and differentiable expression of reflectance, and the method formulates reflectance estimation in an unsupervised learning manner without relying on image pairs. Thorough experiments are conducted to validate the veracity and generality of the proposed method over existing ones.

Strong second-harmonic generation (SHG) and phase-matchable capability at specific transmittance windows are crucial to the real application of nonlinear optical crystals. Herein, an efficient strategy of introducing Pb2+with stereochemically active lone-pair electrons into non-phase-match-able Ga2Se3viaenhancing the anisotropy microstructure is first applied to meet phase-matchable SHG intensity requirements, offering a selenide, namely, PbGa4Se7, which is designed and synthesized with a diamond-like anionic framework, where Pb2+cations reside in a parallel arrangement. As expected, PbGa4Se7possesses a large SHG coefficient of 3.3 times that of AgGaS2under the incident laser of 1910 nm. Moreover, PbGa4Se7displays a high laser-induced damage threshold of 7.0 times that of AgGaS2at 2090 nm, which originates from the low anisotropy of thermal expansion coefficient (0.36) and suitable optical band gap (2.1 eV). Theoretical calculations demonstrate that the considerable phase-matchable SHG response of PbGa4Se7can be attributed to the synergies of tetrahedral GaSe4and pyramid PbSe3microstructural blocks with additional anisotropy and polarizability.

We present a lightweight and efficient semi-supervised video object segmentation network based on the space-time memory framework. To some extent, our method solves the two difficulties encountered in traditional video object segmentation: one is that the single frame calculation time is too long, and the other is that the current frame’s segmentation should use more information from past frames. The algorithm uses a global context (GC) module to achieve high-performance, real-time segmentation. The GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real time. Moreover, the prediction mask of the previous frame is helpful for the segmentation of the current frame, so we input it into a spatial constraint module (SCM), which constrains the areas of segments in the current frame. The SCM effectively alleviates mismatching of similar targets yet consumes few additional resources. We added a refinement module to the decoder to improve boundary segmentation. Our model achieves state-of-the-art results on various datasets, scoring 80.1% on YouTube-VOS 2018 and a J&Fscore of 78.0% on DAVIS 2017, while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

Catalytic asymmetric transformations of ynamides have attracted considerable attention in recent years. However, most of them were limited to intramolecular reactions or required metal catalysts. Herein, a chiral Brønsted acid-catalyzed asymmetric intermolecular [4 + 2] annulation of ynamides with para-quinone methides (p-QMs) is disclosed, which not only represents the first metal-free protocol for catalytic asymmetric nucleophilic addition of ynamides to electrophiles, but also constitutes the first enantioselective annulation between p-QMs and alkynes. This methodology leads to the practical synthesis of biologically important chiral 4-aryl-3,4-dihydrocoumarins and 4-aryl-coumarins. Preliminary control experiments indicate that the ortho-hydroxyphenyl substituted p-QMs could isomerize into ortho-quinone methides (o-QMs) in the presence of chiral catalyst, which further react with ynamides viaenantioselective [4 + 2] annulation, to generate the chiral product.

Multi-laser powder bed fusion (ML_PBF) can improve the efficiency of traditional single-laser process, but the study on the potential defects causing by the introduction of multiple lasers is still open. In this paper, the particle-scale computational fluid dynamics (CFD) method is used to simulate the molten pool behavior of nickel-based superalloy in triple-laser PBF (TL_PBF) process with low energy density. The processing parameters on the flow and temperature fields and balling phenomenon in the TL_PBF process are mainly studied. The comparison between the simulation and the experimental results shows that the proposed model is reliable and can reproduce the balling phenomenon. The simulation results show that in the competition of three melt flows, the molten track of the main laser is randomly deviated to one of the molten tracks of the auxiliary lasers due to the uneven distribution of the powder bed. In the TL_PBF process, the larger the vertical offset is, the more serious the balling phenomenon is. When the parallel offset is larger than the sum of the radii of the two adjacent lasers, the larger the parallel offset is, the more the pits generated on the surface of the molten tracks are, leading to the extreme degradation of surface quality. It can be concluded that the synchronous TL_PBF with low energy density can obtain better surface quality when the parallel offset is about the sum of the radii of two adjacent lasers. This work can be used to guide multi-laser additive manufacturing to improve products with better surface quality.

To cope with the urgent demands of the aerospace industry for lightweight and excellent high-temperature performance titanium alloy components, the microstructure evolution, silicide precipitation and mechanical behaviour of a novel high-temperature Ti65 titanium alloy ultrathin sheet with different heat treatments were systematically investigated. Submicron (Ti,Zr)6Si3silicides are observed after solution treatments followed by air cooling, leading to a decline and significant fluctuations in tensile properties. For furnace cooling samples, (Ti,Zr)5Si3silicide crossing grain boundaries and (Ti,Zr)6Si3silicide near grain boundaries are observed after solution treatment at 950 °C. When the solution temperature increases to 1010 °C, the third silicide (Ti,Zr)3Si appears near dislocations. The combined effect of the strong size effect of the alloy ultrathin sheet and silicide precipitation results in great performance fluctuation, sharp drop of ductility and even brittle fracture. In addition, silicide precipitation differs little with various holding time during low-temperature solution treatment. Moreover, novel {101¯1}twins appear after the rolling process at 990 °C and are only inside coarse basal texture α grains, with an axis—angle pair of 65° 〈844¯3〉.

Developing new low-cost and efficient proton-conducting materials remains an attractive and challenging task. Herein, sodium molybdate dihydrate is used as the source of molybdenum, mixed with transition metal chloride and 2-methylimidazole (2-MI), using the "one-pot method" to synthesize two crystalline proton conducting materials based on {P4Mo6} units: H14[C4H6N2]2[M(H2O)5] [M(H2O)2]2{M[(PO3)3(PO4)Mo6O15]2}·4H2O (M=Co and Fe) (1–2). Different from the common {P4Mo6}, we use H3PO3to adjust the pH value, resulting in two different coordination modes of P atoms in the crystal structure. The structure is expanded into three-dimensional network by metal ions. At 75 °C and 98% relative humidity, the proton conductivity of compounds 1and 2are 1.33 × 10–2S·cm−1and 1.03 × 10–2S·cm−1, respectively. The high proton conductivity is mainly attributed to the free state of 2-methylimidazole as the proton carrier, which has a fast migration rate. At the same time, 2-methylimidazole, coordination water, and {P4Mo6} anion form a hydrogen bond network to provide multiple pathways for the transmission of protons.

The temperature field distribution and thermal history of Fe-9Cr2WVTa reduced activation steel prepared by laser melting deposition (LMD) have been calculated with Gaussian and Ring laser beams, and the nucleation and growth behaviors of M23C6precipitates in the 1st, 7th and 19th layers have been calculated using the modified classical nucleation theory and Svoboda Fischer Fratzl Kozeschnik model. The energy distribution shows W-shape with Ring laser beam while it shows V-shape with Gaussian laser beam, which results in the more uniform M23C6size in the same layer with Ring laser beam. Precipitates in the bottom (i.e., the 1st layer) have the minimum size and the size increases with the layer number with Gaussian and Ring laser beams. The temperature history, the instantaneous nucleation rate and the size evolution of M23C6have been systematically discussed. The results indicate that the nucleation, growth and re-dissolution of precipitates in reduced activation steel depend on the amount of energy absorbed in the thermal cycle during LMD. The continuous accumulation of energy during the thermal cycle leads to larger M23C6at the top area. The unsteady state precipitation dynamics of M23C6carbides during thermal cycling are consistent with the simulation results.

Several viroids in the genus Pospiviroidcan infect tomato (Solanum lycopersicum) and cause severe diseases, posing a serious threat to tomato production. For simultaneous detection of six tomato-infecting pospiviroids - columnea latent viroid (CLVd), pepper chat fruit viroid (PCFVd), potato spindle tuber viroid (PSTVd), tomato apical stunt viroid (TASVd), tomato chlorotic dwarf viroid (TCDVd), and tomato planta macho viroid (TPMVd), we developed a universal probe based on a highly conserved 61 nt long sequence shared among them. Compared with their specific probes, the universal probe has a similar, though slightly reduced, detection sensitivity and has the advantages of simple and cost-effective preparation and simultaneous detection of the six pospiviroids. In addition, the universal probe was used in dot-blot hybridization assays for a large-scale survey of viroid(s) in tomato plantings in China. Only PSTVd was detected in a few greenhouse-planted tomato plants. Sequence analysis revealed that these tomato PSTVd isolates may have been introduced from tomato seeds imported from abroad.

Astaxanthin (AST), a keto carotenoid, is widely recognized for its antioxidant, anti-inflammatory, and apoptosis-regulating properties, but its effects on intestinal health have not been elucidated. Therefore, this experiment aimed to investigate the effects of astaxanthin on intestinal morphology, barrier function, mucosal immunity, and cecal flora in yellow-feathered broilers, and to explore the potential mechanisms. A total of 288 male yellow-feathered broilers (1-day-old) were randomly allocated to four groups with six replicates of 12 birds each. The control group (CON) was fed a basal diet, the test groups were fed a basal diet added with 20 mg/kg, 40 mg/kg, and 80mg/kg of astaxanthin (AST20, AST40, and AST80), respectively. The results showed that compared with the CON group, the villus height and the villus-to-crypt ratio of broiler jejunum in the astaxanthin-added group increased, while the crypt depth decreased (P<0.05). In addition, IL-1β content and gene expression in broiler jejunal mucosa decreased, IL-6 gene expression decreased, and IL-4 content and gene expression increased (P<0.05). The tight junction protein ZO-1 expression level in the jejunum mucosa of broilers was up-regulated, and the content of serum D-lactic acid was decreased (P<0.05). Besides, the α-diversity and β-diversity analyses showed that astaxanthin regulated both the diversity and the structure of the intestinal flora of broilers. After multiple comparative analyses, the relative abundance of norank_f__Eubacterium_coprostanoligenes_group, Lachnospiraceae NC2004_group, and unclassified_p_Firmicutesincreased in the AST20 group compared to the CON group, while that of Ruminococcus_torques_groupin the AST80 group decreased (P<0.05). Therefore, AST can regulate the immune function of broiler intestinal mucosa, improve intestinal morphology and structure, and then influence intestinal permeability. It also has a considerable regulatory effect on the diversity and structure of broiler intestinal flora and plays a multifaceted role in maintaining broiler intestinal health.

Background: The survival benefit of venovenous extracorporeal membrane oxygenation (VV-ECMO) in adult patients with severe acute respiratory distress syndrome (ARDS) remains controversial. This study aimed to investigate the efficiency and potential prognostic factors of VV-ECMO for severe ARDS in adults by evaluating our institutional experience and results. Materials and methods: This research studied ARDS patients receiving VV-ECMO between June 2011 and May 2023. The inclusion criteria were PaO2/FiO2&lt; 100 mmHg at FiO2of 1.0. Retrospective data was analyzed to identify factors associated with successful ECMO weaning and hospital discharge survival. Results: A total of 18 patients were included in this study, with 7 cases (38.9%) successfully weaned from ECMO and 5 cases (27.8%) surviving hospital discharge. The overall complication rate was 77.8%. After treatment with VV ECMO, there were statistically significant improvements in both PaO2and PaCO2(P&lt; 0.05). Patients in the successful weaning group had a lower pTB value, less accumulative volume of sodium bicarbonate during ECMO, and lower accumulative volume of intravenous immunoglobulin in the hospital compared to the unsuccessful weaning group (all P&lt; 0.05). Furthermore, compared to the non-survivors, the survivors had less severe acidosis, higher mean arterial pressure before ECMO, a lower level of pCr, and a lower pTB value during ECMO (all P&lt; 0.05). Conclusion: ECMO can effectively promote oxygenation and carbon dioxide (CO2) removal in patients with severe ARDS. Early initiation of ECMO with appropriate management could benefit in reducing comorbidities and mortality.

The carboxylation of readily available organo halides with CO2represents a practical strategy to afford valuable carboxylic acids. However, efficient carboxylation of inexpensive unactivated alkyl chlorides is still underdeveloped. Herein, we report the electro-reductive carboxylation of CCl bonds in unactivated chlorides and polyvinyl chloride with CO2. A variety of alkyl carboxylic acids are obtained in moderate to good yields under mild conditions with high chemoselectivity. Importantly, the utility of this electro-reductive carboxylation is demonstrated with great potential in polyvinyl chloride (PVC) upgrading, which could convert discarded PVC from hydrophobic to hydrophilic functional products. Mechanistic experiments support the successive single electron reduction of unactivated chlorides to generate alkyl anion species and following nucleophilic attack on CO2to give desired products.

Objective: The integration of training in theory and practice across the medical education spectrum is being encouraged to increase student understanding and skills in the sciences. This study aimed to determine the deciding factors that drive students’ perceived advantages in class to improve precision education and the teaching model. Methods: A mixed strategy of an existing flipped classroom (FC) and a case-based learning (CBL) model was conducted in a medical morphology curriculum for 575 postgraduate students. The subjective learning evaluation of the individuals (learning time, engagement, study interest and concentration, and professional integration) was collected and analyzed after FC-CBL model learning. Results: The results from the general evaluation showed promising results of the medical morphology in the FC-CBL model. Students felt more engaged by instructors in person and benefited in terms of time-saving, flexible arrangements, and professional improvement. Our study contributed to the FC-CBL model in Research Design in postgraduate training in 4 categories: 1) advancing a guideline of precision teaching according to individual characteristics; 2) revealing whether a learning background is needed for a Research Design course to guide setting up a preliminary course; 3) understanding the perceived advantages and their interfaces; and 4) barriers and/or improvement to implement the FC-CBL model in the Research Design class, such as a richer description of e-learning and hands-on practice. Conclusion: Undertaking a FC-CBL combined model could be a useful addition to pedagogy for medical morphology learning in postgraduate training.

Rotating machinery is typical complex electromechanical equipment under nonstationary working conditions and hazardous environments, which is thus vulnerable to an unexpected fault. The currently wide-applied deep learning-based fault diagnosis methods for rotating machines require a huge amount of training data and can only extract features from independent samples. In this study, a semi-supervised self-correcting graph neural network (SSGNN) is proposed for fault diagnosis, which effectively extracts features from vibrational signals and generates a graph-structured representation of fault knowledge. First, a preprocessing layer is introduced to mine the propinquity of vibration samples and construct a graph, where circle loss is applied to enhance convergence. Second, the state transform algorithm is improved by proposing a graph convolutional layer for efficiently implementing state propagation. Finally, an alternate learning method is proposed based on the expectation maximization (EM) algorithm, in which the feature extraction network and graph structure are optimized alternately to reduce the training complexity. The proposed method is validated through experiments conducted on a publicly available dataset and a dynamic condition dataset obtained from a drivetrain dynamic simulator. The results show that the proposed method has higher accuracy and faster convergence speed compared to the state-of-the-art methods.

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2–5are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6and satellite-derived approaches2,7,8to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species1,2. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies3,4. Here, leveraging global tree databases5–7, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.

Rotating machinery, such as ventilators and water pumps, are crucial components in modern industry, of which safety monitoring requires intelligent fault diagnosis. Feature representation learning is essential in the intelligent fault diagnosis of rotating machinery. In this study, a fast robust capsule network augmented with a dynamic pruning technique and a mutual information loss is proposed. The capsule layer overcomes limitations in pooling layers and scale-invariant feature transformation by learning tensor representations of features. The dynamic pruning method employs a dropout-like strategy to prevent repeated calculations and reduce the scale of parameters to simplify the network topology while increasing robustness. The enhanced agreement function limits the similarity of capsules in the same layer to avoid homogeneous features. The local and global discriminators are designed to learn and obtain mutual information in two aspects. The resulting multiscale mutual information loss for the proposed model successfully increases the model's representation learning capacity by integrating local and global information. The performance of the proposed method is successfully verified on several datasets with various noise levels obtained from a simulation platform.

Deep learning models, such as convolutional neural networks (CNNs), have made significant progress in hyperspectral image (HSI) classification. However, these models require a large number of parameters, which occupy a lot of storage space and suffer from overfitting, thus resulting in performance loss. To solve the above problems, in this article, we propose a new compression network [namely, a Hybrid Fully Connected Tensorized Compression Network (HybridFCTCN)] by considering the high dimensionality of HSI data. First, using the low-rank fully connected tensor network decomposition (FCTND), three novel units, i.e., FCTN-FC, FCTNConv2D, and FCTNConv3D, are designed to compress the weight tensor of standard fully connected (FC) layer and kernel tensor of convolutional layer, reducing their parameters. In the novel units, the intrinsic correlation of the decomposed factors is adequately exploited by the FC structures, which enhances their feature extraction and classification abilities. Then, benefiting from the hybrid network backbone composed of the FCTNConv3D and FCTNConv2D units, HybridFCTCN can extract more discriminative features with fewer parameters, while it has great generalization capability and robustness, enabling better HSI classification. Finally, the rank of above-designed units is defined, and its determination is discussed to facilitate the application of the proposed model. Extensive experiments on three widely used HSI datasets reveal that the proposed model achieves state-of-the-art classification performance for different training sample sizes with a very small number of parameters.

This article presents an efficient learning-based method to solve the inverse kinematic (IK) problem on soft robots with highly nonlinear deformation. The major challenge of efficiently computing IK for such robots is due to the lack of analytical formulation for either forward or inverse kinematics. To address this challenge, we employ neural networks to learn both the mapping function of forward kinematics and also the Jacobian of this function. As a result, Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real training transfer strategy is conducted to make this approach more practical. We first generate a large number of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. Thereafter, a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from a very limited number of training samples generated on the hardware.

Precise control of the chemoselectivity of the halogenation of a substrate equipped with multiple nucleophilic sites is highly demanding and challenging. Most reported chlorinations of methyl ketones show poor compatibility or even exclusive selectivity toward electron-rich arene, olefin, and alkyne residues. This is attributed to the direct or in situ employment of electrophilic Cl2/Cl+species. Here, we reported that, even bearing those competitive residues, methyl ketones can still undergo dichlorination to afford α,α-dichloroketones in a chemo-specific manner. Enabled by the I2-dimethyl sulfoxide catalytic system, in which hydrochloric acid only acts as a nucleophilic Cl–donor, this straightforward dichlorination reaction is safe and operator-friendly and has high atomic economy, giving access to structurally diverse α,α-dichloroketones in good yields and with good functional-group tolerance.

A facile method was proposed to prepare stretchable silver-based composite coatings with excellent conductivity and stability for flexible electronics. Silver coating was firstly deposited on thermoplastic polyurethane (TPU) elastomer rubber surface via two-component spraying technique, then the superhydrophobic surface was obtained by one-step electrodeposition of cerium compounds (CeM) and graphene nanosheets (GNS) to produce Ag/CeM/GNS composite coatings. The obtained Ag/CeM/GNS composite coatings maintained high conductivity after experiencing bending cycles and stretching cycles. Furthermore, the as-prepared Ag/CeM/GNS composite coatings showed excellent self-cleaning and anti-fouling properties, and the corrosion resistance has improved significantly compared to the original Ag coating. In addition, the Ag/CeM/GNS composite coatings could drive the circuit normally in the states of tensile, bending and twisting deformation, showing excellent mechanical stability and applicability. As a result, it is believed that the prepared Ag/CeM/GNS composite coatings with excellent conductivity and stability have promising applications for flexible electronics in harsh conditions.

The back-streaming white-neutron beamline (Back-n) of the China Spallation Neutron Source is an essential neutron-research platform built for the study of nuclear data, neutron physics, and neutron applications. Many types of cross-sectional neutron-reaction measurements have been performed at Back-n since early 2018. These measurements have shown that a significant number of gamma rays can be transmitted to the experimental stations of Back-n along with the neutron beam. These gamma rays, commonly referred to as in-beam gamma rays, can induce a non-negligible experimental background in neutron-reaction measurements. Studying the characteristics of in-beam gamma rays is important for understanding the experimental background. However, measuring in-beam gamma rays is challenging because most gamma-ray detectors are sensitive to neutrons; thus, discriminating between neutron-induced signals and those from in-beam gamma rays is difficult. In this study, we propose the use of the black resonance filter method and a CeBr3scintillation detector to measure the characteristics of the in-beam gamma rays of Back-n. Four types of black resonance filters, 181Ta, 59Co, natAg, and natCd, were used in this measurement. The time-of-flight (TOF) technique was used to select the detector signals remaining in the absorption region of the TOF spectra, which were mainly induced by in-beam gamma rays. The energy distribution and flux of the in-beam gamma rays of Back-n were determined by analyzing the deposited energy spectra of the CeBr3scintillation detector and using Monte Carlo simulations. Based on the results of this study, the background contributions from in-beam gamma rays in neutron-reaction measurements at Back-n can be reasonably evaluated, which is beneficial for enhancing both the experimental methodology and data analysis.

Due to its high flexibility and expansibility, nanomaterials-based systems have been constructed and applied from sensing, and medicine to molecular information technology. However, the current challenges include tedious and harsh nanomaterials preparation, the need for a wider range of response channels, new information paradigms, higher information density and length. Herein, fish-scale derived carbon nanomaterials (FSCN) were used as reducing and stabilizing agents for direct, green, and facile synthesis of carbon-bimetallic nanocomposites and applications in six-channel colorimetric sensing of Hg2+, and multi-combination and multi-round information encoding and protection for long text. First, the reaction sequence of three precursors (FSCN, Au3+, Ag+) was optimized to prepare FSCN–Au–Ag nanocomposites with double plasmonic absorption peaks of Au/Ag nanoparticles (NPs). The addition of Hg2+causes the aggregation of nanoparticles and the formation of Ag/Hg amalgam or Au/Hg amalgam, FSCN–Au–Ag NPs were capable of multi-channel sensing (color, absorbance, and peak shift) of Hg2+. Especially, the detection limit of Hg2+was 8.9 nM based on absorption response in wide wavelength range (456–546 nm), which was at least 6 times more sensitive than that at single wavelength. Moreover, different long text information can be encrypted and hidden in six-channel selective responses of FSCN–Au–Ag NPs-based colorimetric sensing system. By setting the threshold, these selective responses can be abstracted as a series of binary strings and then divided into shorter strings. Utilizing multi-combination and multi-round information coding, these short binary strings can be decrypted and merged into various long text content. This research promotes preparation and multi-purpose application of carbon-bimetallic nanomaterials, which provides new way to improve the sensing sensitivity of nanosystems and also offers new direction for molecular digitization.

Mitochondrial dynamics and quality control play a central role in the maintenance of the proliferation–apoptosis balance, which is closely related to the progression of pulmonary arterial hypertension (PAH). However, the exact mechanism of this balance remains unknown. Pulmonary artery smooth muscle cells (PASMCs) were cultured in hypoxia condition for constructing a PAH model in vitro. The expression of genes and proteins were determined by qRT-PCR and western bolt assays. Cell proliferation–apoptosis balance were tested by MTT, EdU and TUNEL assays. The mitochondrial functions were assessed by flow cytometry, JC-1, Mito tracker red staining, and corresponding kits. Besides, the molecular interaction was validated by dual-luciferase reporter assay. MFF was overexpressed in hypoxia-treated PAMSCs. Knockdown of MFF significantly repressed the excessive proliferation but enhanced cell apoptosis in hypoxia-treated PAMSCs. Moreover, MFF silencing improved mitochondrial function of hypoxia-treated PAMSCs by increasing ATP production and decreasing ROS release and mitochondrial fission. Mechanistically, MFF was a directly target of miR-340-5p, and could negatively regulate SIRT1/3 expression. Subsequently, functional rescue assays showed that the biological effects of MFF in hypoxia-treated PAMSCs were negatively regulated by miR-340-5p and depended on the regulation on SIRT1/3 pathway. These results provided evidences that miR-340-5p regulated MFF-SIRT1/3 axis to improve mitochondrial homeostasis and proliferation–apoptosis imbalance of hypoxia-treated PAMSCs, which provided a theoretical basis for the prevention and treatment of PAH.