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Global climate change has intensified the frequency and severity of extreme drought and heat events, significantly impacting vegetation growth, particularly in the arid and semi-arid regions of Central Asia. This study employs the Vine Copula conditional probability model, incorporating vegetation (NDVI), drought (SPEI), temperature (STI), and cropland intensity (CI) indices, to quantify the impact of compound dry-hot events on vegetation under different agricultural intensities in Central Asia from 1983 to 2022. The results indicate significant variations in vegetation vulnerability under different levels of cropland coverage. Under low cropland intensity (CI = 0.2), vegetation vulnerability shows an increasing trend from June to August (S3: 70.31 % to 77.45 %), with high-probability areas gradually expanding eastward. Under high cropland intensity (CI = 0.8), vulnerability increases from June to July and then slightly decreases in August (S3: 67.82 % to 71.10 % to 71.60 %), showing a similar eastward progression. Different vegetation types exhibit distinct vulnerability patterns: grasslands generally show the highest sensitivity initially, while croplands demonstrate increasing vulnerability throughout summer, exceeding grassland probabilities by August (0.627 vs 0.596 under CI = 0.2, 0.570 vs 0.525 under CI = 0.8). Forests, predominantly distributed in northern mountainous regions (around 50⁰N), maintain the lowest vulnerability across all scenarios. The findings demonstrate the effectiveness of the Vine Copula model in assessing vegetation vulnerability to compound climate extremes under different cropland intensities, providing a scientific basis for ecosystem assessment and climate adaptation strategies in Central Asia.

In the west of China, a rarely seen black storm, with a high intensity of dust aerosols and a large area of influence, occurred from April 26–29, 2015, for the first time, after more than 30 years. Based on the regional climate model (RegCM version 4.6), combined with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite retrieval, meteorological, and environmental data, this work presents the pollution situation, weather background, and backward trajectory during the black storm process. In addition, we analyzed the temporal–spatial distribution of aerosol optical depth (AOD) and the impacts of dust aerosols on solar radiation and near-surface temperature under this extreme weather condition. We discovered that this black storm process was caused by the surface cold high pressure and frontal transit under the background of the upper-air circulation of “two troughs and two ridges.” The pollutants primarily from Xinjiang and the Central Asia region, along with the airflow, entered northern Xinjiang almost simultaneously with the southwest airflow, piled up along the Tianshan Mountains, and then crossed the mountains into southern Xinjiang. In addition, the areas with high AOD were mostly in the desert regions and basins, whereas the low-value areas were mountainous areas with relatively high altitudes due to the effect of geographical and climatic conditions. The AOD from RegCM 4.6 was generally lower, unlike the MODIS AOD. Moreover, the dust aerosols from this black storm caused a significant decline in net short-wave radiation (NSR) both at the top of the atmosphere (TOA) and surface. The cooling effect was more significant in the region with high AOD. For the areas where the AOD was higher than 0.7, the net short-wave radiative forcing of dust aerosols (ADRF) at the surface was above −70 W•m−2; on the other hand, the cooling effect at the TOA was not as significant as that at the surface, with the ADRFTOA being only about a quarter of the ADRFSUR. The ADRFSUR could reduce the near-surface temperature, and the region with a large temperature drop corresponded to the high-value areas of ADRFSUR/AOD. During this black storm, the near-surface temperature response to dust aerosols reached 0.40°C–2.9°C with a significant temperature drop because of cold air.

To mitigate the adverse effects of climate warming, the Paris Agreement proposed the goal to reduce global warming up to an increase of 1.5 °C above the preindustrial level. Study of the population and GDP exposure to precipitation extreme events under the 1.5 °C warming target is fundamental for disaster risk mitigation and adaptation on the Loess Plateau. This study projected the population and GDP exposure to extreme precipitation events under the 1.5 °C global warming level on the Loess Plateau using daily precipitation data from CMIP6 outputs and population and GDP data under a Shared Socioeconomic Pathway 1(SSP1) 2.6 scenario. The population and GDP exposure were evaluated by combing the frequency and the areal coverage of the extreme precipitation events. Results show that population and GDP exposure to extreme precipitation events on the Loess Plateau will increase under the 1.5 °C global warming level. The population exposure was projected from 1.32 × 106 to 2.68 × 106 person-year. The population exposure of eastern and southern Loess Plateau is significantly higher than that of the northern region. The annual exposure of GDP ranges from USD 2.9 to 12.3 billion, and the regions with the highest GDP exposure are Zhengzhou, Xi’an, Taiyuan, and Lanzhou. Our results reveal that limiting the increase of global mean temperature to 1.5 °C warming level is of great significance to reduce the social and economic exposure to extreme precipitation events on the Loess Plateau.

Future changes in drought characteristics in Central Asia are projected at the regional scale using 21 climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Based on the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), drought characteristics were characterized by drought frequency at 1-, 3-, and 12-month timescales. The drought duration was analyzed based on SPI1 and SPEI1. Drought indices were calculated by the multimodel ensemble (MME) from 21 CMIP5 models. The varimax rotation method was used to identify drought conditions for the entire area and seven drought subregions. In general, the projection results of future drought in Central Asia are related to the choice of drought index, and SPI and SPEI show different results. The drought frequency based on SPEI1, SPEI3, and SPEI12 showed an increasing trend in the future periods, that is, the drought frequency based on monthly, seasonal, and annual timescales will show an increase trend in the future periods. However, for SPI1, SPI3, and SPI12, the drought frequency will decrease in the future. SPI projected that the duration of drought will decrease in the future, while SPEI mainly showed an increasing trend. The results of the study should be of sufficient concern to policymakers to avoid land degradation, crop loss, water resource deficit, and economic loss.

Using historical data compiled by the Climate Research Unit, spatial and temporal analysis, trend analysis, empirical orthogonal function (EOF) analysis, and Taylor diagram analysis were applied to test the ability of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models to accurately simulate the annual mean surface air temperature in central Asia from the perspective of the average climate state and climate variability. Results show that each model can reasonably capture the spatial distribution characteristics of the surface air temperature in central Asia but cannot accurately describe the regional details of climate change impacts. Some of the studied models, including CNRM-CM5, GFDL-CM3, and GISS-E2-H, could better simulate the high- and low-value centers and the contour distribution of the surface air temperature. Taylor diagram analysis showed that the root mean square errors of all models were less than 3, the standard deviations were between 8.36 and 13.45, and the spatial correlation coefficients were greater than 0.96. EOF analysis showed that the multi-model ensemble can accurately reproduce the surface air temperature characteristics in central Asia from 1901 to 2005, including the rising periods and the fluctuations of the north and south inversion phases. Overall, this study provides a valuable reference for future climate prediction studies in central Asia.

In recent years, there has been significant progress in the use of organic–inorganic hybrid perovskites for photovoltaic applications. Engineering the compositions and/or morphology of perovskites has become the most effective method to address the challenges related to photovoltaic efficiency and stability. Herein, an amino‐functionalized covalent organic framework (NH2‐COF) as a precursor additive to modulate the crystallization of perovskites is incorporated. The NH2‐COF is found to decrease the defect concentration, reduce the nonradiative recombination within the perovskite layer, and further promote carrier transport. Correspondingly, the solar cells based on the NH2‐COF‐modified perovskites deliver a champion power conversion efficiency of 22.13% with a fill factor of 0.773 under AM 1.5G 100 mW cm−2 illumination. Furthermore, the device retains approximately 81% of its initial efficiency after 1000 h of aging under ambient conditions at a temperature of 30 °C and relative humidity ranging from 45% to 55%. It is believed that this work would provide a facile and efficient strategy to prepare high‐quality perovskite films for efficient and stable solar cells.

The rapid growth of on-demand ride service platforms has made it increasingly important for these platforms to efficiently match services by understanding driver characteristics and consumer preferences. This paper aims to investigate the pricing strategy by considering the impact of consumer preference heterogeneity and the different service types offered by drivers. The findings of this study reveal the need for the platform to strike a balance between service cost and the benefits of high-quality drivers, which can be referred to as the "cost-performance ratio". If the "cost-performance ratio" that attracts high-quality drivers is high, the platform will attract high-quality drivers or drivers of all types to participate while offering differentiated services. Otherwise, the platform will only provide services through low-quality drivers. Furthermore, the platform will also consider when to offer differentiated services based on network externalities and service quality. When the network externalities of the two types of services are similar, the platform will differentiate them based on service quality differences. Overall, considering consumer preference heterogeneity, drivers of service types, and network externalities, this paper provides guidance for platforms to make optimal decisions that enhance their service offerings and improve overall customer satisfaction.

The genus Uncaria is famous for its high medicinal value. However, the high morphological similarities and unclear interspecific genetic relationships have posed challenges to the classification and identification of Uncaria species. Here, we newly sequenced six chloroplast genomes of Uncaria species: U. hirsuta, U. rhynchophylla, U. rhynchophylloides, U. homomalla, U. sinensis, and U. lancifolia. Comparisons among the chloroplast genomes of Uncaria species showed their conservation in structure, gene content, and order. Ten highly variable loci could be potentially used as specific molecular markers in the identification of Uncaria species. The third position of codons tended to use A/U base, and natural selection contributed more to the formation of codon usage bias in comparison to mutation pressure. Four genes (rbcL, ndhF, rps8, and ycf2) were detected to be subjected to positive selection. Phylogenetic analysis showed that the genus Uncaria was a monophyletic group, belonging to the tribe Naucleeae. Moreover, U. sinensis was not a variant of U. rhynchophylla. U. rhynchophylloides and U. rhynchophylla were not the same species. The results of the comparative and phylogenetic analysis provide valuable references for further research studies of classification, identification, breeding improvement, and phylogenetic relationships in Uncaria species.

Background In recent years, numerous guidelines and expert consensuses have recommended that digital technologies and products, such as digital therapeutics (DTx), be incorporated into cardiac rehabilitation (CR). Objective The study aims to compare the cost-effectiveness of DTx for home-based CR (HBCR) with that of the conventional HBCR for patients with atrial fibrillation (AF) after catheter ablation in a real-world clinical setting. Methods Based on a single-center, prospective, blinded, randomized, parallel controlled trial of DTx-based HBCR for AF patients after catheter ablation, 100 AF patients undergoing catheter ablation were selected and randomized at a 1:1 ratio into a UC group (conventional HBCR) and DT group (DTx-based HBCR). The intervention lasted for 12 weeks. Medical cost data and clinical efficacy data (VO 2 max) were acquired from trial data. Effect data (QALYs, anxiety and depression status, health beliefs related to cardiovascular disease, and exercise self-efficacy) were obtained from a patient questionnaire. The cost-effectiveness analysis was conducted to assess the economic benefits of DTx-based HBCR versus conventional HBCR, and the robustness of the results was verified by sensitivity analysis. Results In the DT group (n = 49), the mean QALYs increased 0.119 ± 0.09, VO 2 max increased 9.3 ± 8.0 mL/(min*kg), anxiety, depression, health belief, and exercise self-efficacy had improved, and the total medical cost was 75,080.55 ± 19,746.62 CNY. In the UC group (n = 48), the mean QALYs increased 0.077 ± 0.06, VO 2 max increased 4.9 ± 6.6 mL/(min*kg), anxiety, depression, health belief, and exercise self-efficacy had improved, and the total medical cost was 73,972.66 ± 16,582.04 CNY. The difference in the medical cost was 1107.89 CNY, and the incremental cost-effectiveness ratio was 33,572.42 CNY/QALY, which was lower than the willingness-to-pay threshold of 85,698 CNY (China's per capita GDP in 2022). Conclusion DTx-based HBCR is more effective and cost-effective than conventional HBCR.

Colorectal cancer (CRC) is a major health burden, accounting for approximately 10% of all new cancer cases worldwide. Accumulating evidence suggests that the crosstalk between the host mucins and gut microbiota is associated with the occurrence and development of CRC. Mucins secreted by goblet cells not only protect the intestinal epithelium from microorganisms and invading pathogens but also provide a habitat for commensal bacteria. Conversely, gut dysbiosis results in the dysfunction of mucins, allowing other commensals and their metabolites to pass through the intestinal epithelium, potentially triggering host responses and the subsequent progression of CRC. In this review, we summarize how gut microbiota and bacterial metabolites regulate the function and expression of mucin in CRC and novel treatment strategies for CRC.

BackgroundRadiofrequency catheter ablation (RFCA) for patients with atrial fibrillation (AF) can generate considerable physical and psychological discomfort under conscious sedation. App-based mindfulness meditation combined with an electroencephalography (EEG)-based brain-computer interface (BCI) shows promise as effective and accessible adjuncts in medical practice. ObjectiveThis study aimed to investigate the effectiveness of a BCI-based mindfulness meditation app in improving the experience of patients with AF during RFCA. MethodsThis single-center pilot randomized controlled trial involved 84 eligible patients with AF scheduled for RFCA, who were randomized 1:1 to the intervention and control groups. Both groups received a standardized RFCA procedure and a conscious sedative regimen. Patients in the control group were administered conventional care, while those in the intervention group received BCI-based app–delivered mindfulness meditation from a research nurse. The primary outcomes were the changes in the numeric rating scale, State Anxiety Inventory, and Brief Fatigue Inventory scores. Secondary outcomes were the differences in hemodynamic parameters (heart rate, blood pressure, and peripheral oxygen saturation), adverse events, patient-reported pain, and the doses of sedative drugs used in ablation. ResultsBCI-based app–delivered mindfulness meditation, compared to conventional care, resulted in a significantly lower mean numeric rating scale (mean 4.6, SD 1.7 [app-based mindfulness meditation] vs mean 5.7, SD 2.1 [conventional care]; P=.008), State Anxiety Inventory (mean 36.7, SD 5.5 vs mean 42.3, SD 7.2; P

Protein based adhesive is limited by poor mechanical properties and water resistance. Inspired by the structure of muscle tissue, a complex structure of layered double hydroxide (LDH) anchored chicken feather fiber (CFF) and tannic acid (TA) was constructed by self-assembly process, forming a novel organic–inorganic hybrid reinforcement structure (CFF-LDH-TA). CFF-LDH-TA was closely combined with soybean meal (SM) through covalent and hydrogen bond multiple interactions. TA simulated the function of connective tissue around fibers, significantly improving the cohesion of SM based adhesive through effective energy dissipation and load transfer. Due to the increased crosslinking density, the dry and wet shear strengths of SM/CFF-LDH-TA reached 2.12 and 1.60 MPa, respectively, which were 50 % and 214 % higher than original SM adhesive, and significantly exceeded the strength of several commercialized formaldehyde adhesives. The existence of bridging effect and sacrificial structure improved toughness of the adhesive, the work of adhesion increased by 802 %, reached 830 mJ. In addition, the SM/CFF-LDH-TA adhesive had better anti-mold and flame retardant ability. More importantly, the cost was lower than the commercial SM based adhesive. This simple and green preparation method provided a new strategy for the production of high-performance, multifunctional and economically effective SM based adhesive.

Abstract Background The carbon pools of forest soils play a vital role in global carbon sequestration and emissions. Forest management can regulate the sequestration and output of forest soil carbon pools to a certain extent; however, the kinetics of the effects of forest density on soil carbon pools require further investigation. Methods We established sample plots with stand density gradients in three different aged Larix principis-rupprechtii plantations and quantified the soil respiration, soil organic carbon (SOC), soil dissolved organic carbon (DOC), microbial biomass carbon (MBC), light fraction organic carbon (LFOC), and readily oxidized carbon (ROC). Results and conclusions During the growth and development of plantations, stand density is an essential factor that impacts soil respiration and its associated elements. Moderate density was observed to promote both the soil and heterotrophic respiration rates and the sequestration of MBC and LFOC, whereas it inhibited the sequestration of ROC. The soil, heterotrophic, and autotrophic respiration rates of older forest stands were relatively rapid, whereas the contents of SOC, MBC, LFOC, DOC, and ROC were higher and more sensitive to changes in stand density. The MBC, LFOC, and ROC in soil labile organic carbon were closely related to both the soil and heterotrophic respiration, but not the SOC. Among them, the LFOC and MBC played the roles of “warehouse” and “tool” and were significantly correlated with soil and heterotrophic respiration. The ROC, as a “raw material”, exhibited a significantly negative correlation with the soil and heterotrophic respiration. When the soil and heterotrophic respiration rates were rapid, the ROC content in the soil maintained the low level of a “dynamically stabilized” state. The stand density regulated heterotrophic respiration by affecting the soil labile organic carbon, which provided an essential path for the stand density to regulate soil respiration.

In recent years, there has been an increasing interest in waste fiber reinforced asphalt mixes. Waste betel nut fiber (BNF) can improve the tensile strength of asphalt mixture, while the hydrophilic groups on its surface can reduce its water stability. Therefore, this study investigated the effect of alkali-treated BNF on the water stability performance of asphalt mixtures. The effects of shear time, fiber content, and asphalt aggregate ratio on the water stability of BNF-enhanced asphalt mixtures were investigated separately by response surface methodology (RSM). The road performance of the RSM method with optimal solution and without fiber, fiber without alkali treatment, and fiber without shear was also compared. The results showed that under the AC13 gradation, the optimal solutions of multi response variables are fiber content of 0.2561%, fiber shear time of 12.78 min and asphalt aggregate ratio of 5.82%. The corresponding predicted Rdry value is 1.48 MPa, Rsaturated value is 1.27 MPa and TSR value is 88.41%. The final test results are within the 95% confidence interval, and the prediction model has good reliability. The optimal solution has the best low-temperature flexural strength and water stability. This study helps to promote the application and promotion of BNF in road construction.

Abstract Environmental natural organic matters (NOMs) have great effects on the physicochemical properties of engineering nanoparticles, which may impact the transport of nanoparticles across plasma membrane and the cytotoxicity. Therefore, the kinetics, uptake pathway and mass of transporting into A549 cell membrane of silver nanoparticles (AgNPs) coated with citric acid (CA), tartaric acid (TA) and fulvic acid (FA) were investigated, respectively. CA, FA and TA enhanced the colloidal stability of AgNPs in culture medium and have greatly changed the surface plasmon resonance spectrum of AgNPs due to the absorption of CA, FA and TA on surface of AgNPs. Internalizing model showed that velocity of CA-, TA- and FA-nAg transporting into A549 cell were 5.82-, 1.69- and 0.29-fold higher than those of the control group, respectively. Intracellular mass of Ag was dependent on mass of AgNPs delivered to cell from suspension, which obeyed Logistic model and was affected by NOMs that CA- and TA-nAg showed a large promotion on intracellular mass of Ag. The lipid raft/caveolae-mediated endocytosis (LME) of A549 cell uptake of AgNPs were susceptible to CA, TA and FA that uptake of CA-, TA- and FA-nAg showed lower degree of dependent on LME than that of the control (uncoated AgNPs). Actin-involved uptake pathway and macropinocytosis would have less contribution to uptake of FA-nAg. Overall, transmembrane transport of NOMs-coated AgNPs differs greatly from that of the pristine AgNPs.

Microbial induced carbonate precipitation (MICP) in coordination with vegetation protection is a novel technology in the field of slope reinforcement. Urea in MICP can promote vegetation growth and change the root content in the soil. However, existing studies often ignore the influence of MICP on root growth, while root content has a significant impact on the ability of vegetation to cement soil and protect slopes. In this paper, through the laboratory direct shear test, the strength variation characteristics of root-soil composites with different root contents before and after reinforcement were analyzed, and the influence of root content changes on the strength of root-soil composites in the MICP collaborative slope protection project was studied. The results showed that: (1) the strength of the root-soil composite was improved significantly after MICP treatment. When the root content was 1.5%, the maximum strength peak value was increased by 32.6%, and the cohesive force was increased by 49.2%. (2) MICP reaction has no effect on the root content of the peak intensity, indicating that MICP has no negative effect on vegetation growth and can be combined with vegetation protection. The results show that MICP synergistic slope protection has strong engineering application value.

Although the responses of forests to the regulation of stand density are better understood, a further elucidation is required as to how it impacts understory vegetation and soil properties. The relationships between understory soil properties and vegetation also warrants attention; thus, for this study we endeavored to develop new strategies to explore changes in understory vegetation. To investigate the effects of stand density on the diversity of understory vegetation and soil properties of a secondary Pinus tabulaeformis forest, fifteen sample plots with different stand densities were established. Furthermore, the soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), nitrate nitrogen (NN), ammonium nitrogen (AN), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) contents were determined. The Simpson, Shannon Weiner, and Pielou indices, C:N, C:P, and N:P ratios, microbial entropy carbon (qMBC), and microbial entropy nitrogen (qMBN) were calculated. It was observed that stand density substantially influenced soil attributes and the variety of understory vegetation, whereas the soil nutrient content and the diversity index of different stand density levels had significant differences. Moderate stand density promoted the retention of soil nutrients and the organic decomposition rate of the soil in a secondary Pinus tabulaeformis forest. Excessive forest stand density limited the availability of soil P; thus, microorganisms and plants transitioned to a P-restricted state, and the diversity index was reduced with higher stand densities. There was a strong correlation between soil properties and the diversity index of understory vegetation. Soil properties were one of the primary drivers toward the formation of understory vegetation diversity, whereas those of herbs were more affected by soil properties. Stand density influenced the diversity of understory vegetation by altering the nature of the soil, which was also a critical factor for how stand density influenced understory vegetation.

A new variable stiffness four-finger soft gripper with a combination of rigid and soft structures is designed. The combination of rigid and soft structures is one of the methods to improve the performance of soft grippers. Grasping motion is achieved by the rigid structure of the screw and the connecting rod. Soft gripper uses human finger-like structure made of silicone, and the air pressure and the rigidity of the soft fingers can be adjusted by the air pump. The soft gripper overcomes the inability of a rigid gripper to easily and safely grasp soft and brittle objects and the inability of a completely soft gripper to exert sufficiently high forces to achieve effective grasping. Grasping force can be improved by increasing the stiffness of the finger and the driving stroke of screw. The variable grasping force allows the soft gripper to grasp different shape objects, specially soft and brittle objects.

In this article, a flexible tactile sensor that made of conductive silicone rubber for dexterous robot hand is designed. The tactile sensor is made up of four microsensors. The maximum gripping force is simulated when the degree of a robot finger joint is 138. Meanwhile, a control system to analyze the creep and hysteresis characteristics and a processing system of the tactile sensor is designed. We also demonstrated an experiment for the application of robot grasp object, showing the finger’s flexibility and sensitivity. Then the feedback data is sent to control system to provide precise grasp action changes for the robot hand.

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm’s monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects’ left and right wrists.

Fishes can avoid colliding with obstacles and track baits depending on the lateral distributed sense nodes which can sense the pressure variances of the surrounding flow field. Fish often uses the lateral-line system as their only means for navigation, especially under poor visual conditions. This sensing mechanism provides a new perspective for researchers and engineers to build such a sensing system that could be applied to control and near field navigation for underwater robots and vehicles. In this article, a pressure-sensing-based is proposed, with 10 pressure sensors acting as lateral line and use the three-dimensional printer to print the fish structure and install the artificial lateral line on it. Through preliminary experiments and numerical simulation, we obtain the pressure sensor data. By comparing the experimental data with the numerical simulation data, it can be verified that the pressure variation of the pressure sensor in the numerical simulation data is consistent with that in the experimental data. The artificial lateral line provides a new sense to man-made underwater vehicles and marine robots, so that they can sense like fish.

Traditional malware classification relies on known malware types and significantly large datasets labeled manually which limits its ability to recognize new malware classes. For unknown malware types or new variants of existing malware containing only a few samples each class, common classification methods often fail to work well due to severe overfitting. In this paper, we propose a new neural network structure called ConvProtoNet which employs few-shot learning to address the problem of scarce malware samples while prevent from overfitting. We design a convolutional induction module to replace the insufficient prototype reduction in most few-shot models and generates more appropriate class-level malware prototypes for classification. We also adopt meta-learning scheme to make classifier robust enough to adapt unseen malware classes without fine-tuning. Even in extreme conditions where only 5 samples in each class are provided, ConvProtoNet still achieves more than 70% accuracy on average and outperforms other traditional malware classification methods or existed few-shot models in experiments conducted on several datasets. Extra experiments across datasets illustrate that ConvProtoNet learns general knowledge of malware which is dataset-invariant and careful model analysis proves effectiveness of ConvProtoNet in few-shot malware classification.