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The abnormal energy metabolism level of a tumor reduces the efficiency of chemotherapy. Metal–organic nanomaterials (MONs) with high drug loading efficiency, easy processes of synthesis, and controlled drug release have shown great potential in metabolic blocking and enhancement of tumor therapy. These metal–organic nanomedicines have been reported to modulate glycolysis or oxidative phosphorylation to provide monotherapy or combined therapies in tumorous treatments. In addition, the encapsulation or coordination of fluorescent dyes into MONs endowed them with the imaging ability of tumor metabolism. Herein, this Perspective summarizes the progress of MONs as therapeutic agents or imaging probes for application during tumor metabolic blocking or imaging, providing solid inspiration for biomedical applications of effective biomaterials. In addition, the current drawbacks of MONs for further biological applications in the future were discussed, giving stimulation of innovation and development in biomedical applications of MONs.

Memristors have been widely studied as artificial synapses in neuromorphic circuits, due to their functional similarity with biological synapses, low operating power, and high integration density. Currently, the synaptic weight symbolic limitation and weight update inaccuracy are two challenging issues to be solved. In this work, a novel memristive synapse and a matched mixed-signal neuron circuit are designed to implement robust yet accurate spike-timing-dependent plasticity learning in excitatory and inhibitory synapses. To break through the weight symbolic limitation, a four memristors and two resistors (4M2R) synapse composed of 4M2R for spiking neural network (SNN) is designed. The proposed synapse can be either excitatory or inhibitory (E/I) by rationally arranging the resistors in the circuit, and it is the first of its kind, enabling Hebbian and anti-Hebbian training without additional adjusting of neural signals. In addition, the high symmetricity, linearity, and stability against device variation of the 4M2R synapse can also greatly improve the weight update accuracy. To further address the inaccurate weight update issue caused by signal complexity, a neuron circuit is designed to generate square-wave pulses for spike transmission and synaptic weight modulation. Simulations are carried out in the MATLAB Simscape as well as Virtuoso using SMIC $0.18~\mu $ m process and a specially developed memristor model for SNN synapse simulation. The simulating results show good agreement with the weight change derived from the algorithmic methods, and the influence of weak signal-induced weight variation on circuit performance can be rigorously assessed.

Nuclear fission energy is the low-carbon resource that helps manage the cost of deep decarbonization. Safety is the basis of deploying nuclear power plants near load centers on a large scale. The inherent safety of nuclear reactors depends solely on the laws of nature. The world’s first demonstration plant of a high-temperature reactor with a pebble-bed module (HTR-PM) entered its commercial operation on December 6, 2023. Two safety tests were conducted on the two reactor modules of the HTR-PM plant, each at a power of 200 MWt. During the tests, the active power supply was totally switched off to see if the decay heat can be removed passively. The responses of nuclear power and temperatures within different reactor structures show that the reactors can be cooled down naturally without active intervention. The results of the tests manifest the existence of commercial-scale inherent safety for the first time.

Dual/multimodal imaging strategies are increasingly recognized for their potential to provide comprehensive diagnostic insights in cancer imaging by harnessing complementary data. This study presents an innovative probe that capitalizes on the synergistic benefits of afterglow luminescence and magnetic resonance imaging (MRI), effectively eliminating autofluorescence interference and delivering a superior signal-to-noise ratio. Additionally, it facilitates deep tissue penetration and enables noninvasive imaging. Despite the advantages, only a limited number of probes have demonstrated the capability to simultaneously enhance afterglow luminescence and achieve high-resolution MRI and afterglow imaging. Herein, we introduce a cutting-edge imaging platform based on semiconducting polymer nanoparticles (PFODBT) integrated with NaYF4@NaGdF4(Y@Gd@PFO-SPNs), which can directly amplify afterglow luminescence and generate MRI and afterglow signals in tumor tissues. The proposed mechanism involves lanthanide nanoparticles producing singlet oxygen (1O2) upon white light irradiation, which subsequently oxidizes PFODBT, thereby intensifying afterglow luminescence. This innovative platform paves the way for the development of high signal-to-background ratio imaging modalities, promising noninvasive diagnostics for cancer.

Flexible OLED displays have become a development trend. CPI film in a flexible panel can improve the bending performance of the panel. However, CPI film is prone to plastic deformation in the bending process, which will affect the reliability and service life of OLED panel. Finite element analysis is employed in this paper to analyze how CPI film affects the bending performance of a flexible OLED panel. The simulation results show that the optimization of the structure of water‐drop‐shaped bending can improve the plastic deformation of CPI film. The reasonable position of CPI film in the stacked structure can also improve its plastic deformation. The increase of the thickness of the OCA located below the CPI film can significantly reduce its plastic deformation. The analysis method used in this paper can help improve the bending performance of a flexible OLED panel.

X‐ray digital imaging equipment is widely used in medical, nondestructive testing, security and other fields. X‐ ray imaging device consists of x‐ray generator, flat panel detector, image processing system and display. The imaging sensor, as the key component of the flat panel detector, is mainly made of amorphous silicon, which has the disadvantages of small aperture ratio, slow imaging speed and low resolution.Indium gallium zinc oxide (IGZO) based TFT attract much attentions because of high field effect mobility, good uniformity, and low off‐current. The paper firstly establishes the simulation model of IGZO TFT and makes the deep analysis of the effects of different aspect ratios, different active layer thicknesses, and temperatures on the IGZO TFT performance.

Memories are encoded in neural ensembles during learning1–6and are stabilized by post-learning reactivation7–17. Integrating recent experiences into existing memories ensures that memories contain the most recently available information, but how the brain accomplishes this critical process remains unclear. Here we show that in mice, a strong aversive experience drives offline ensemble reactivation of not only the recent aversive memory but also a neutral memory formed 2 days before, linking fear of the recent aversive memory to the previous neutral memory. Fear specifically links retrospectively, but not prospectively, to neutral memories across days. Consistent with previous studies, we find that the recent aversive memory ensemble is reactivated during the offline period after learning. However, a strong aversive experience also increases co-reactivation of the aversive and neutral memory ensembles during the offline period. Ensemble co-reactivation occurs more during wake than during sleep. Finally, the expression of fear in the neutral context is associated with reactivation of the shared ensemble between the aversive and neutral memories. Collectively, these results demonstrate that offline ensemble co-reactivation is a neural mechanism by which memories are integrated across days.

Recent advancements in remote sensing foundation models have unveiled their tremendous potential in addressing Earth observation tasks. Presently, when large-scale foundation models are transferred to downstream tasks, the prevalent approach is to adopt the full-tuning strategy, resulting in significant increases in storage demands and computational costs. Although the introduction of parameter-efficient fine-tuning (PEFT) has mitigated this issue to some extent, mainstream PEFT methods are primarily designed for classification tasks and often prove insufficient to meet the demands of dense prediction tasks. To overcome the aforementioned limitations, we propose a unified PEFT framework UPetu, encompassing two essential and complementary modules: the efficient quantization adapter module (EQAM) and the context-aware prompt module (CAPM). EQAM is specifically designed to enhance the correlation between fine-grained feature information and task-specific knowledge through the introduction of quantization linear (Q-Linear) layers and nonlinear activation functions. In addition, CAPM is introduced to acquire rich contextual features by incorporating trainable prompts into multiscale features. The synergistic integration of both the modules enhances the representation learning capability and generalization transferability of the foundation model. Extensive experiments on three remote sensing scene classification datasets demonstrate the superiority of UPetu over other fine-tuning methods. With the update of only 0.73% of ConvNeXt-B parameters, our UPetu achieves superior performance compared with full-tuning on the UCM-55, AID-28, and AID-55 datasets. Furthermore, experiments conducted on semantic segmentation and change detection tasks provide additional evidence of the effectiveness and generalization capabilities of the proposed UPetu.

Foundation models offer a highly versatile and precise solution for intelligent interpretation of remote sensing images, thus greatly facilitating various remote sensing applications. Nevertheless, conventional remote sensing foundational models based on generative transformers neglect the consideration of multiscale features and frequency information, limiting their potential for dense prediction tasks in remote sensing scenarios. In this article, we make the first attempt to propose a generative convolutional neural network (ConvNet) foundation model tailored for remote sensing scenarios, which comprises two key components: First, a large dataset named GeoSense, containing approximately nine million diverse remote sensing images, is constructed to enhance the robustness and generalization of the foundation model during the pretraining phase. Second, a sparse modeling and low-frequency reconstruction (SMLFR) framework is designed for self-supervised representation learning of the ConvNet foundation model. Specifically, a sparse modeling strategy is proposed in masked image modeling (MIM), which allows ConvNet to process variable-length sequences by treating unmasked patches as voxels and sparsifying the encoder. In addition, a low-frequency reconstruction target is designed to guide the model’s attention toward essential ground object features in remote sensing images, while mitigating unnecessary detail interference. To evaluate the general performance of our proposed foundation model, comprehensive experiments have been carried out on five datasets across three downstream tasks. Experimental results demonstrate that our method consistently achieves state-of-the-art performance across all the benchmark datasets and downstream tasks. The code and pretrained models will be available at https://github.com/HIT-SIRS/SMLFR.

Background: Although the majority of members belonging to the small GTPase Ras superfamily have been studied in several malignancies, the function of RBJ has remained unclear, particularly in non-small cell lung cancer (NSCLC).Objective: The research aims to determine the function of RBJ in NSCLC.Methods: The levels of RBJ protein in tumor tissue and para-carcinoma normal tissue were ascertained via immunohistochemistry (IHC). The growth, migration, and invasion of NSCLC cells were assessed by 5- ethynyl-2-deoxyuridine (EdU) assay, colony formation, cell counting kit-8 (CCK8), transwell and wound healing assays. Furthermore, a nude mouse xenograft model was established to study the function of RBJ in tumorigenesis in vivo.Results: The IHC analysis revealed that the protein levels of RBJ were notably increased in tumor tissue and positively associated with the clinical stage. In addition, the knockdown of RBJ restrained the growth, invasion, and migration of NSCLC cell lines by inhibiting the epithelial-mesenchymal transition (EMT) through the MEK/ERK signaling pathway. Accordingly, opposite results were observed when RBJ was overexpressed. In addition, the overexpression of RBJ accelerated tumor formation by A549 cells in nude mice.Conclusion: RBJ promoted cancer progression in NSCLC by activating EMT via the MEK/ERK signaling. Thus, RBJ could be used as a potential therapeutic against NSCLC.

Mobile edge computing is a promising computing paradigm enabling mobile devices to offload computation-intensive tasks to nearby edge servers. However, within small-cell networks, the user mobilities can result in uneven spatio-temporal loads, which have not been well studied by considering adaptive load balancing, thus limiting the system performance. Motivated by the data analytics and observations on a real-world user association dataset in a large-scale WiFi system, in this paper, we investigate the mobility-aware online task offloading problem with adaptive load balancing to minimize the total computation costs. However, the problem is intractable directly without prior knowledge of future user mobility behaviors and spatio-temporal computation loads of edge servers. To tackle this challenge, we transform and decompose the original task offloading optimization problem into two sub-problems, i.e., task offloading control (ToC) and server grouping (SeG). Then, we devise an online control scheme, named MOTO (i.e., Mobility-aware Online Task Offloading), which consists of two components, i.e., Long Short Term Memory based algorithm and Dueling Double DQN based algorithm, to efficiently solve the ToC and SeG sub-problems, respectively. Extensive trace-driven experiments are carried out and the results demonstrate the effectiveness of MOTO in reducing computational costs of mobile devices and achieving load balancing when compared to the state-of-the-art benchmarks.

Chemotherapeutic resistance poses a significant challenge in cancer treatment, resulting in the reduced efficacy of standard chemotherapeutic agents. Abnormal metabolism, particularly increased anaerobic glycolysis, has been identified as a major contributing factor to chemotherapeutic resistance. To address this issue, noninvasive imaging techniques capable of visualizing tumor glycolysis are crucial. However, the currently available methods (such as PET, MRI, and fluorescence) possess limitations in terms of sensitivity, safety, dynamic imaging capability, and autofluorescence. Here, we present the de novo design of a unique afterglow molecular scaffold based on hemicyanine and rhodamine dyes, which holds promise for low-background optical imaging. In contrast to previous designs, this scaffold exhibits responsive “OFF-ON” afterglow signals through spirocyclization, thus enabling simultaneous control of photodynamic effects and luminescence efficacy. This leads to a larger dynamic range, broader detection range, higher signal enhancement ratio, and higher sensitivity. Furthermore, the integration of multiple functionalities simplifies probe design, eliminates the need for spectral overlap, and enhances reliability. Moreover, we have expanded the applications of this afterglow molecular scaffold by developing various probes for different molecular targets. Notably, we developed a water-soluble pH-responsive afterglow nanoprobe for visualizing glycolysis in living mice. This nanoprobe monitors the effects of glycolytic inhibitors or oxidative phosphorylation inhibitors on tumor glycolysis, providing a valuable tool for evaluating the tumor cell sensitivity to these inhibitors. Therefore, the new afterglow molecular scaffold presents a promising approach for understanding tumor metabolism, monitoring chemotherapeutic resistance, and guiding precision medicine in the future.

Dear Editor, This letter investigates the output tracking control issue of networked control systems (NCSs) with communication constraints and denial-of-service (DoS) attacks in the sensor-to-controller channel, both of which would induce random network delays. A dual-prediction-based compensation control (DPCC) scheme, consisting of a predictive observer and a predictive controller, is proposed to actively compensate for the adverse effect of network delays on NCSs. Compared with existing networked predictive control (NPC) methods, the DPCC scheme only requires the sensor to send a single measurement output to the controller at each sampling instant, and also does not need to know the upper bound of random network delays in advance. The stability condition of the closed-loop system is derived. Finally, numerical simulations are carried out to validate the effectiveness of the proposed scheme.

Ferroptosis, as a type of programmed cell death process, enables effective damage to various cancer cells. However, we discovered that persistent oxidative stress during ferroptosis can upregulate the apurinic/apyrimidinic endonuclease 1 (APE1) protein that induces therapeutic resistance (“ferroptosis resistance”), resulting in an unsatisfactory treatment outcome. To address APE1-induced therapeutic resistance, we developed a GSH/APE1 cascade activated therapeutic nanoplatform (GAN). Specifically, the GAN is self-assembled by DNA-functionalized ultrasmall iron oxide nanoparticles and further loaded with drug molecules (drug-GAN). GSH-triggered GAN disassembly can “turn on” the catalysis of GAN to induce efficient lipid peroxidation (LPO) for ferroptosis toward the tumor, which could upregulate APE1 expression. Subsequently, upregulated APE1 can further trigger accurate drug release for overcoming ferroptosis resistance and inducing the recovery of near-infrared fluorescence for imaging the dynamics of APE1. Importantly, adaptive drug release can overcome the adverse effects of APE1 upregulation by boosting intracellular ROS yield and increasing DNA damage, to offset APE1’s functions of antioxidant and DNA repair, thus leading to adaptive ferroptosis. Moreover, with overexpressed GSH and upregulated APE1 in the tumor as stimuli, the therapeutic specificity of ferroptosis toward the tumor is greatly improved, which minimized nonspecific activation of catalysis and excessive drug release in normal tissues. Furthermore, a switchable MRI contrast from negative to positive is in sync with ferroptosis activation, which is beneficial for monitoring the ferroptosis process. Therefore, this adapted imaging and therapeutic nanoplatform can not only deliver GSH/APE1-activated lipid peroxide mediated adaptive synergistic therapy but also provided a switchable MRI/dual-channel fluorescence signal for monitoring ferroptosis activation, drug release, and therapy resistance dynamics in vivo, leading to high-specificity and high-efficiency adaptive ferroptosis therapy.

Chiral amines are commonly used in the pharmaceutical and agrochemical industries1. The strong demand for unnatural chiral amines has driven the development of catalytic asymmetric methods1,2. Although the N-alkylation of aliphatic amines with alkyl halides has been widely adopted for over 100 years, catalyst poisoning and unfettered reactivity have been preventing the development of a catalyst-controlled enantioselective version3–5. Here we report the use of chiral tridentate anionic ligands to enable the copper-catalysed chemoselective and enantioconvergent N-alkylation of aliphatic amines with α-carbonyl alkyl chlorides. This method can directly convert feedstock chemicals, including ammonia and pharmaceutically relevant amines, into unnatural chiral α-amino amides under mild and robust conditions. Excellent enantioselectivity and functional-group tolerance were observed. The power of the method is demonstrated in a number of complex settings, including late-stage functionalization and in the expedited synthesis of diverse amine drug molecules. The current method indicates that multidentate anionic ligands are a general solution for overcoming transition-metal-catalyst poisoning.

The weakly supervised semantic segmentation (WSSS) method aims to assign semantic labels to each image pixel from weak (image-level) instead of strong (pixel-level) labels, which can greatly reduce human labor costs. However, there are some problems in WSSS of remote sensing images, such as how to locate labels accurately and how to get precise segmentation edges. To address these issues, we propose a novel framework directly transferring the scene classification model to perform semantic segmentation. We first train a multilabel scene classification network as the encoder to obtain the pretrained model, and then, the feature learned by the model is transferred to the decoder. Different from other methods, we propose a saliency map generator (SMG) instead of the class activation map (CAM) for more accurate location information by making pixels belonging to the same class lie close together while different classes are separated in feature space. Meanwhile, we take the superpixel patch as processing unit to provide precise boundary inhibition for the saliency map. To assign semantic labels for each patch, combined with extracted salient region, we propose a module responsible for exploiting the consistency of spatial and semantic similarity between different patches. Finally, we incorporate the above two modules to supervise the training process of the decoder without generating pseudolabels as most methods do, thus simplifying the training process. Experimental results show that our method outperforms other weakly supervised approaches on dense labeling remote sensing dataset (DLRSD) and Wuhan dense labeling dataset (WHDLD) with at least a 3% improvement on mean intersection over union (mIoU).

Semantic segmentation is a crucial task in remote sensing and has been predominantly performed using convolutional neural networks (CNNs) for the past decade. Recently, transformers with self-attention mechanisms have demonstrated superior performance compared with CNNs. However, due to the locality of CNN and the high computational complexity and massive data resource requirements of transformer, neither of them can be well applied in resource-constrained practical remote sensing scenarios. Motivated by the limitations of using either CNNs or transformers alone in the task of semantic segmentation of remote sensing images, a novel cross-model knowledge distillation (KD) framework, named distilling segmenters from CNNs and transformers (DSCTs), is proposed in this article to harness the complementary advantages of both the models. The framework uses a channel-weighted attention-guided feature distillation (CAFD) module to condense the feature from the teacher model and enhance the student model’s focus on the teacher-focused regions. In addition, a target–nontarget KD (TNKD) module is proposed that decouples logit distillation into target and nontarget KD to guide the student model in learning the underlying representations and decision boundaries from the teacher model. By learning the complementary knowledge from the teacher, our proposed DSCT framework improves the student’s segmentation performance without adding trainable parameters. Experiments on four available remote sensing datasets (ISPRS Potsdam, Vaihingen, GID, and LoveDA) indicate that the proposed DSCT outperforms the state-of-the-art KD methods and demonstrates its effectiveness and robustness.

Aiming at improving the accuracy of motion gesture recognition and reducing the time-consuming recognition, this paper proposes an athlete's body motion gesture recognition method based on improved deep neural network. According to the movement characteristics and basic structural characteristics of the human body, the movement posture recognition standard is designed and set in the model. Collect athletes' human motion images, and perform motion image extraction, residual compensation, filtering, normalisation, and morphological processing. On this basis, the features of human motion images are extracted and fused. The recognition classifier is constructed based on the improved deep neural network, the fused feature vector is input into the classifier, and the recognition result is output. The results of the comparative experiment show that the proposed method has a high recognition rate and short overhead time only at 1.2 s.

A critical requirement for the success of supervised deep learning lies in having numerous annotated images, which is often challenging to fulfill in remote sensing semantic segmentation tasks. Self-supervised contrastive learning (CL) offers a strategy for learning general feature representations by pretraining neural networks on vast amounts of unlabeled data and subsequently fine-tuning them on downstream tasks with limited annotations. However, the vast majority of CL methods are designed based on instance discriminative pretext tasks, focusing solely on learning the global representation of the entire image while disregarding the essential spatial and semantic correlations crucial for semantic segmentation tasks. To address the above issues, in this article, we propose a spatial and semantic consistency CL (SSCCL) framework for the semantic segmentation task of remote sensing images. Specifically, a consistency branch in SSCCL is designed to learn feature representations with spatial and semantic consistency by maximizing the similarity of the overlapping regions of the two augmented views. In addition, an instance branch is introduced to learn global representations by enforcing the similarity of two augmented views from one image. Through the integration of the consistency branch and instance branch, the proposed SSCCL framework can learn robust and informative feature representations for semantic segmentation in remote sensing scenarios. The proposed method was evaluated on three publicly available remote sensing semantic segmentation datasets, and the experiment results show that our method achieves superior segmentation performance with limited annotations compared to state-of-the-art CL methods as well as the ImageNet pretraining method.

In a previous study, this laboratory produced and tested a lactic acid biosensor using copper-doped zinc oxide (CZO) sensing film modified with graphite carbon nitride (g-C3N4) and iron platinum nanoparticles (FePt NPs). The previous sensor exhibited two non-ideal effects during persistent use and when alternating between high and low lactose concentrations. Using a low unity-gain frequency and low power consumption instrumentation amplifier (UGFPCIA) in conjunction with a calibration readout circuit, it was possible to reduce the previously observed drift and hysteresis effects. The UGFPCIA and calibration readout circuit were connected to a voltage–time ( $V$ – $T$ ) measurement system so the effects could be accurately quantified. The influence of the UGFPCIA on the measurement characteristics of the lactate biosensor was recorded and compared to a similar device using a commercial LT1167. Results showed that the UGFPCIA produced a more stable measurement, where linearity increased from 0.985 to 0.998. When measuring interference effects, the fluctuation in response voltage became smaller, and the time drift effect was successfully reduced by 89.7%. The hysteresis effect reduced the forward period by 0.13 mV, and the reverse period by 4.37 mV.

Type XI collagen plays a fundamental role in fibrillogenesis, through formation of cartilage collagen fibrils and contributions to the cohesive properties of cartilage. The α-1 chain of type XI collagen is encoded by the COL11A1. Pathogenic variants of COL11A1, have been identified in several genetic conditions, including Marshall syndrome (MRSHS), Stickler syndrome type II, and Fibrochondrogenesis 1. We investigated genetic etiology of an induced labor fetus displaying micrognathia, cleft palate, hypertelorism and polydactyly. Whole-exome sequencing was performed in DNA samples of the proband and parents. Sanger sequencing was then performed as a confirmatory experiment and in silicoevaluation was conducted on suspected variant. A novel de novomissense variant in exon62 of the COL11A1, NM_001854.3 COL11A1: c.4583G>T (p.Gly1528Val), was identified and verified by Sanger sequencing. In silicoanalysis demonstrated that the amino-acid p.Gly1528 was evolutionarily conservative, and that p.Gly1528Val was potentially damaging. This data provided reliable evidence for the subsequent genetic counseling to the affected family.

Stress can have profound consequences on mental health. While much is known about the neural circuits supporting associative memories of stressful events, our understanding of the circuits underlying the non-associative impacts of stress, such as heightened stress sensitivity and anxiety-related behavior, is limited. Here, we demonstrate that the ventral hippocampus (vHC) and basolateral amygdala (BLA) support distinct non-associative behavioral changes following stress. Inhibiting stress-induced protein synthesis in the BLA blocked subsequent increases in stress sensitivity but not anxiety-related behaviors. Conversely, inhibiting stress-induced protein synthesis in the vHC blocked subsequent increases in anxiety-related behavior but not stress sensitivity. Inhibiting neuronal activity in the BLA and vHC during the assessment of stress sensitivity or anxiety-related behavior recapitulated these structures’ dissociable contributions to defensive behavior. Lastly, blocking the associative memory of a stressor had no impact on stress-induced changes in anxiety-related behavior. These findings highlight that multiple memory systems support the long-lasting effects of stress.

This paper proposes an adaptive super-twisting control (ASTC) for the deployment of space-tethered systems with the consideration of uncertainty of external and internal disturbances with unknown boundaries. The main advantage of the ASTC scheme is that it can deal with the unknown bounds of uncertainties and disturbances. The proposed control law consists of two adaptive control gains that ensure the establishment, in a finite time, of a real second-order sliding mode. This, in turn, guarantees a convergence to a small domain and without overestimating the control gains. The stability of the control law is demonstrated theoretically. Compared with the sliding mode control algorithm with power reaching law, the newly proposed adaptive super-twisting control method performs better in the settling time, the maximum in-plane angle, and angular velocity control and suppressing oscillations. Numerical simulations are presented to validate the effectiveness and robustness of the proposed ASTC scheme.

Building detection is an important topic in remote sensing image applications. This study proposes a multiscale building detection method based on boundary preservation, to detect building roofs from a large number of high-resolution remote sensing images at a fast speed. A lightweight network extracts a feature map and a feature pyramid network (FPN) aggregates low- and high-level features. A multidimensional attention network (MDA) enhances building features and weakens the complex background information. The method uses four branches to extract buildings: classification, box, direction box, and mask. We manually labeled approximately 870,000 buildings of different types and selected about 300 1 ​km ​× ​1 ​km image plots of different ground objects without buildings, to construct positive and negative sample sets for 27 provinces in China. The accuracy and recall of test results of the proposed method are 12.4% and 3.6% higher than those of Mask R–CNN, respectively, while its accuracy and detection time of segmentation results are 6% and about 30% higher than those of Mask R–CNN, respectively. Pre-disaster buildings were extracted using the proposed method in several key provinces across the country, which were applied to quick assessment in emergency work for Yangbi earthquake. The method was used on post-disaster UAV images as well, achieving 95.61% precision and 91.36% recall of detection results. Experiments show that: the detection method and its results are beneficial to reduce manual interpretation time significantly, and detections on pre- and post-disaster images can be compared to help identification of damaged buildings.

Studies show that two dimensional (2D) nanomaterial and its hybrid have a great promise in tribology for the special laminar microstructure. However, the majority of performed investigations about 2D graphitic carbon nitride (g-C3N4) nanosheets are most focused on energy storage, catalysis, adsorption, rarely tribology. In the present study, g-C3N4supporting mono-dispersed Ag nanoparticle hybrid (g-C3N4/Ag) is prepared, and its microstructure and chemical composition are determined. More specifically, the tribological performance as the lubricating additive of poly phthalazinone ether sulfone ketone (PPESK) composite is investigated using the ball-on-disc friction tester. Moreover, the corresponding enhancement mechanism is well proposed based on the experimental analysis and theoretical simulation. Obtained results show that Ag nanoparticles with a size of about 10–20 nm are homogeneously anchored on g-C3N4nanosheets, favoring for good compatibility between g-C3N4/Ag and PPESK. It is found that when 0.3 wt% of g-C3N4/Ag is added to PPESK, the friction coefficient and wear rate of PPESK decrease by 68.9% and 97.1%, respectively. These reductions are mainly attributed to the synergistic self-lubricating effect of Ag nanoparticles and g-C3N4nanosheet, the formation of transfer film, as well as the limited effect of g-C3N4/Ag on the shear deformation of PPESK composite film. Furthermore, it is found that the proposed g-C3N4/Ag-PPESK composite still keeps reasonable friction-reducing and wear-resistant properties under heavy loads and high rotating speeds. The present study demonstrates that the proposed g-C3N4/Ag hybrid is an excellent lubricating additive for polymer composites.

A mussel-inspired coating based on catecholamine chemistry can serve as a versatile platform for covalent enzyme immobilization. However, its application was limited by the weak salt/acid/alkali tolerance. To tackle this issue, three silane coupling agents were screened to construct a coating layer on a nylon membrane with tannic acid (TA) for enzyme immobilization. It was found that TA/3-aminopropyltriethoxysilane (APTES), and TA/3-(2-aminoethylamino) propyltriethoxysilane (AEAPTES) coatings had higher enzyme loading and activity than TA/3-glycidyloxypropyltrimethoxysilane (KH-560), because TA/APTES and TA/AEAPTES coatings formed more nanoparticles and thus provided more active sites for enzyme attachment (taking glucose oxidase as an example). Then, the pH tolerance of the immobilized enzyme was greatly enhanced by reinforcing the interfacial interactions of membrane–coating and coating–enzyme, which was realized by membrane pretreatment, ternary additive, and metal ions chelation posttreatment. The optimized TA/AEAPTES/tetraethoxysilane (TEOS)-Fe3+coating showed excellent pH stability and reusability, and the immobilized enzyme retained 82%, 83%, and 79% of its initial activity even after 7 h of incubation in buffer solutions at pH 3, 7, and 11, respectively. Such a coating layer was also evaluated for zearalenone hydrolase (ZHD) immobilization, and the immobilized ZHD exhibited boosted activity and robust reusability in the degradation of zearalenone (ZEN, a refractory and hypertoxic mycotoxin). This work not only offered a green and practical strategy for enzyme immobilization but also provided fundamental data in the design of a mussel-inspired coating layer toward versatile applications.

Glyphosate and glufosinate ammonium are non-selective, broad spectrum herbicides for controlling grasses in agriculture and forestry. Rapid and sensitive analytical methods for effective monitoring and subsequent risk control of glyphosate and glufosinate ammonium and their metabolites in crops are not available. In this paper, an analytical method for the simultaneous determination of glyphosate, glufosinate ammonium, and their metabolites in maize and soybean by ultra-performance liquid chromatography with tandem mass spectrometry was established. The seven compounds were well separated on an anion exchange analytical column within 10 min, with the mean recoveries of the target compounds ranging from 78.2 to 110.9%. The method showed good linearity (R2≥ 0.9819) with a limit of quantification for glyphosate, glufosinate ammonium, and their metabolites of 0.01 mg kg−1in both maize and soybean. The method simplifies and expedites the sample preparation procedures, overcomes the traditional disadvantages including derivatization, weak retention, and low sensitivity, and has been successfully validated in actual maize and soybean samples.

In order to overcome the problem that the traditional recognition method has poor statistical performance on the regularity of body feature data before recognising the throwing angle, which leads to the deviation of javelin flight trajectory judgment results, this paper proposes an automatic recognition method of javelin athletes' throwing angle based on the recognisable statistical characteristics. Firstly, the technical characteristics of javelin throwers of different genders are extracted by using the statistical process of distinguishing features. Then, the angle of recognition equipment is calibrated and the position of trigger signal is combined to realise the automatic recognition of javelin throw angle. Experimental results show that: the javelin flight trajectory identified by this method is the closest to the actual trajectory; the recognition accuracy of the throwing angle can reach more than 98%. It shows that the method can effectively realise the accurate recognition of javelin athletes' throwing angle.

In order to solve the problems of low calculation accuracy and poor control accuracy of the traditional velocity control methods for water supply and drainage in gymnasiums, a new speed control method for water supply and drainage of multi-functional gymnasiums based on predictive function algorithm is proposed in this paper. In this method, the basis function of water supply and drainage velocity control of multi-functional stadium is constructed, and the reference trajectory of water control is drawn by using predictive function algorithm. The multi-function water supply and drainage model is built to predict and optimise the water supply and drainage capacity of the stadium. The experimental results show that the proposed water supply and drainage velocity control method has a high accuracy of water supply and drainage velocity control, and can accurately calculate the water supply and drainage velocity.

Metal-catalysed cross-couplings are a mainstay of organic synthesis and are widely used for the formation of C–C bonds, particularly in the production of unsaturated scaffolds1. However, alkyl cross-couplings using native sp3-hybridized functional groups such as alcohols remain relatively underdeveloped2. In particular, a robust and general method for the direct deoxygenative coupling of alcohols would have major implications for the field of organic synthesis. A general method for the direct deoxygenative cross-coupling of free alcohols must overcome several challenges, most notably the in situ cleavage of strong C–O bonds3, but would allow access to the vast collection of commercially available, structurally diverse alcohols as coupling partners4. We report herein a metallaphotoredox-based cross-coupling platform in which free alcohols are activated in situ by N-heterocyclic carbene salts for carbon–carbon bond formation with aryl halide coupling partners. This method is mild, robust, selective and most importantly, capable of accommodating a wide range of primary, secondary and tertiary alcohols as well as pharmaceutically relevant aryl and heteroaryl bromides and chlorides. The power of the transformation has been demonstrated in a number of complex settings, including the late-stage functionalization of Taxol and a modular synthesis of Januvia, an antidiabetic medication. This technology represents a general strategy for the merger of in situ alcohol activation with transition metal catalysis.

Poor cyclic stability and low rate performance due to dramatic volume change and low intrinsic electronic conductivity are the two key issues needing to be urgently solved in silicon (Si)-based anodes for lithium-ion batteries. Herein, a novel tin (Sn)-bonded Si anode is proposed for the first time. Sn, which has a high electronic conductivity, is used to bond the Si-anode material and copper (Cu) current collector together using a hot-pressed method with a temperature slightly above the melting point of Sn. The cycling performance of the electrode is studied using a galvanostatic method. Nanoindentation and peeling tests are conducted to measure the mechanical strength of the electrodes. Direct current polarization and galvanostatic intermittent titration techniques are applied to assess the conductivity of the composites. Electrochemical impedance spectroscopy and X-ray photoelectron spectroscopy are conducted to evaluate the effect of the coating layer on the cycling ability of the composites. The Sn-bonded Si anodes show superior cycling stability and high rate performance with an improved initial Coulombic efficiency. Analyses reveal that the low-melting-point Sn helps to markedly improve the electronic conductivity of the electrodes and serves as a metallic binder as well to enhance the adhesive strength of the electrode. It is hopeful that this novel Sn-bonded Si anode provides a new insight for the development of advanced Si-based anodes for LIBs.

Endogenous ceramide is considered to be associated with the progress of insulin resistance. However, the effects of dietary exogenous glucosylceramides and ceramides on insulin resistance are unclear. A model of fructose-induced male Sprague Dawley rats was used to compare the effects of sea-cucumber-derived glucosylceramides and ceramides on insulin resistance. Both glucosylceramides and ceramides significantly improved glucose tolerance, reduced the concentrations of serum glucose and glycosylated hemoglobin, and alleviated the accompanied hypertension. Ceramides significantly enhanced glycogen levels in skeletal muscle, whereas glucosylceramides significantly increased the hepatic glycogen levels. Moreover, glucosylceramides alleviated insulin resistance by inhibiting gluconeogenesis, promoting glycogen synthesis and insulin signal transduction in the liver; meanwhile, ceramides were mainly due to the promotion of glycogen synthesis and insulin signal transduction in skeletal muscle. Additionally, glucosylceramides and ceramides effectively attenuated inflammation in adipose tissue. These results indicate that glucosylceramides and ceramides have potential value in the prevention and alleviation of insulin resistance.