It was conjectured by Bouchet that every bidirected graph which admits a nowhere-zero κ flow will admit a nowhere-zero 6-flow. He proved that the conjecture is true when 6 is replaced by 216. Zyka improved the result...It was conjectured by Bouchet that every bidirected graph which admits a nowhere-zero κ flow will admit a nowhere-zero 6-flow. He proved that the conjecture is true when 6 is replaced by 216. Zyka improved the result with 6 replaced by 30. Xu and Zhang showed that the conjecture is true for 6-edge-connected graphs. And for 4-edge-connected graphs, Raspaud and Zhu proved it is true with 6 replaced by 4. In this paper, we show that Bouchet＇s conjecture is true with 6 replaced by 15 for 3-edge-connected graphs.展开更多
Single-atom catalysts(SACs)have garnered significant attention in lithium-sulfur(Li-S)batteries for their potential to mitigate the severe polysulfide shuttle effect and sluggish redox kinetics.However,the development...Single-atom catalysts(SACs)have garnered significant attention in lithium-sulfur(Li-S)batteries for their potential to mitigate the severe polysulfide shuttle effect and sluggish redox kinetics.However,the development of highly efficient SACs and a comprehensive understanding of their structure-activity relationships remain enormously challenging.Herein,a novel kind of Fe-based SAC featuring an asymmetric FeN_(5)-TeN_(4) coordination structure was precisely designed by introducing Te atom adjacent to the Fe active center to enhance the catalytic activity.Theoretical calculations reveal that the neighboring Te atom modulates the local coordination environment of the central Fe site,elevating the d-band center closer to the Fermi level and strengthening the d-p orbital hybridization between the catalyst and sulfur species,thereby immobilizing polysulfides and improving the bidirectional catalysis of Li-S redox.Consequently,the Fe-Te atom pair catalyst endows Li-S batteries with exceptional rate performance,achieving a high specific capacity of 735 mAh g^(−1) at 5 C,and remarkable cycling stability with a low decay rate of 0.038%per cycle over 1000 cycles at 1 C.This work provides fundamental insights into the electronic structure modulation of SACs and establishes a clear correlation between precisely engineered atomic configurations and their enhanced catalytic performance in Li-S electrochemistry.展开更多
Pipelines are extensively used in environments such as nuclear power plants,chemical factories,and medical devices to transport gases and liquids.These tubular environments often feature complex geometries,confined sp...Pipelines are extensively used in environments such as nuclear power plants,chemical factories,and medical devices to transport gases and liquids.These tubular environments often feature complex geometries,confined spaces,and millimeter-scale height restrictions,presenting significant challenges to conventional inspection methods.Here,we present an ultrasonic microrobot(weight,80 mg;dimensions,24 mm×7 mm;thickness,210μm)to realize agile and bidirectional navigation in narrow pipelines.The ultrathin structural design of the robot is achieved through a high-performance piezoelectric composite film microstructure based on MEMS technology.The robot exhibits various vibration modes when driven by ultrasonic frequency signals,its motion speed reaches81 cm s-1 at 54.8 k Hz,exceeding that of the fastest piezoelectric microrobots,and its forward and backward motion direction is controllable through frequency modulation,while the minimum driving voltage for initial movement can be as low as 3 VP-P.Additionally,the robot can effortlessly climb slopes up to 24.25°and carry loads more than 36 times its weight.The robot is capable of agile navigation through curved L-shaped pipes,pipes made of various materials(acrylic,stainless steel,and polyvinyl chloride),and even over water.To further demonstrate its inspection capabilities,a micro-endoscope camera is integrated into the robot,enabling real-time image capture inside glass pipes.展开更多
Accurate and reliable power system data are fundamental for critical operations such as gridmonitoring,fault diagnosis,and load forecasting,underpinned by increasing intelligentization and digitalization.However,data ...Accurate and reliable power system data are fundamental for critical operations such as gridmonitoring,fault diagnosis,and load forecasting,underpinned by increasing intelligentization and digitalization.However,data loss and anomalies frequently compromise data integrity in practical settings,significantly impacting system operational efficiency and security.Most existing data recovery methods require complete datasets for training,leading to substantial data and computational demands and limited generalization.To address these limitations,this study proposes a missing data imputation model based on an improved Generative Adversarial Network(BAC-GAN).Within the BAC-GAN framework,the generator utilizes Bidirectional Long Short-Term Memory(BiLSTM)networks and Multi-Head Attention mechanisms to capture temporal dependencies and complex relationships within power system data.The discriminator employs a Convolutional Neural Network(CNN)architecture to integrate local features with global structures,effectivelymitigating the generation of implausible imputations.Experimental results on two public datasets demonstrate that the BAC-GAN model achieves superior data recovery accuracy compared to five state-of-the-art and classical benchmarkmethods,with an average improvement of 17.7%in reconstruction accuracy.The proposedmethod significantly enhances the accuracy of grid fault diagnosis and provides reliable data support for the stable operation of smart grids,showing great potential for practical applications in power systems.展开更多
Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationall...Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.展开更多
The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable pr...The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.展开更多
Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese...Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.展开更多
Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction pla...Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making.However,deep learning models often falter when processing raw,noisy temporal signals,fail to quantify prediction uncertainty,and face challenges in effectively capturing the nonlinear dynamics of equipment degradation.To address these issues,this study proposes a novel deep learning framework.First,a newbidirectional long short-termmemory network integrated with an attention mechanism is designed to enhance temporal feature extraction with improved noise robustness.Second,a probabilistic prediction framework based on kernel density estimation is constructed,incorporating residual connections and stochastic regularization to achieve precise RUL estimation.Finally,extensive experiments on the C-MAPSS dataset demonstrate that our method achieves competitive performance in terms of RMSE and Score metrics compared to state-of-the-artmodels.More importantly,the probabilistic output provides a quantifiablemeasure of prediction confidence,which is crucial for risk-informed maintenance planning,enabling managers to optimize maintenance strategies based on a quantifiable understanding of failure risk.展开更多
Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a ...Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.展开更多
BACKGROUND Propofol has been widely used in bidirectional gastrointestinal endoscopy sedation;however,it frequently leads to cardiovascular adverse events and respiratory depression.Propofol target-controlled infusion...BACKGROUND Propofol has been widely used in bidirectional gastrointestinal endoscopy sedation;however,it frequently leads to cardiovascular adverse events and respiratory depression.Propofol target-controlled infusion(TCI)can provide safe sedation but may require higher dosages of propofol.On the contrary,etomidate offers hemodynamic stability.AIM To evaluate the effect of different dose etomidate added to propofol TCI sedation during same-visit bidirectional endoscopy.METHODS A total of 330 patients from Fujian Provincial Hospital were randomly divided into three groups:P,0.1EP,and 0.15EP.Patients in the P group received propofol TCI only,with an initial effect-site concentration of the propofol TCI system of 3.0 mg/mL.Patients in the 0.1EP and 0.15EP groups received 0.1 and 0.15 mg/kg etomidate intravenous injection,respectively,followed by propofol TCI.RESULTS Patients in the 0.15EP group had higher mean blood pressure after induction than the other groups(P group:78 mmHg,0.1EP group:82 mmHg,0.15EP group:88 mmHg;P<0.05).Total doses of propofol consumption significantly decreased in the 0.15EP group compared with that in the other groups(P group:260.6 mg,0.1EP group:228.1 mg,0.15EP group:201.2 mg;P<0.05).The induction time was longer in the P group than in the other groups(P group:1.9±0.7 minutes,0.1EP group:1.2±0.4 minutes,0.15EP group:1.1±0.3 minutes;P<0.01).The recovery time was shorter in the 0.15EP group than in the other groups(P group:4.8±2.1 minutes,0.1EP group:4.5±1.6 minutes,0.15EP group:3.9±1.4 minutes;P<0.01).The incidence of hypotension(P group:36.4%,0.1EP group:29.1%,0.15EP group:11.8%;P<0.01)and injection pain was lower in the 0.15EP group than in the other groups(P<0.05).Furthermore,the incidence of respiratory depression was lower in the 0.15EP group than in the P group(P<0.05).Additionally,the satisfaction of the patient,endoscopist,and anesthesiologist was higher in the 0.15EP group than in the other groups(P<0.05).CONCLUSION Our findings suggest that 0.15 mg/kg etomidate plus propofol TCI can significantly reduce propofol consumption,which is followed by fewer cardiovascular adverse events and respiratory depression,along with higher patient,endoscopist,and anesthesiologist satisfaction.展开更多
Lithium-sulfur (Li-S) batteries have gained great attention due to the high theoretical energy density and low cost,yet their further commercialization has been obstructed by the notorious shuttle effect and sluggish ...Lithium-sulfur (Li-S) batteries have gained great attention due to the high theoretical energy density and low cost,yet their further commercialization has been obstructed by the notorious shuttle effect and sluggish redox dynamics.Herein,we supply a strategy to optimize the electron structure of Ni_(2)P by concurrently introducing B-doped atoms and P vacancies in Ni_(2)P (Vp-B-Ni_(2)P),thereby enhancing the bidirectional sulfur conversion.The study indicates that the simultaneous introduction of B-doped atoms and P vacancies in Ni_(2)P causes the redistribution of electron around Ni atoms,bringing about the upward shift of d-band center of Ni atoms and effective d-p orbital hybridization between Ni atoms and sulfur species,thus strengthening the chemical anchoring for lithium polysulfides (LiPSs) as well as expediting the bidirectional conversion kinetics of sulfur species.Meanwhile,theoretical calculations reveal that the incorporation of B-doped atoms and P vacancies in Ni_(2)P selectively promotes Li2S dissolution and nucleation processes.Thus,the Li-S batteries with Vp-B-Ni_(2)P-separators present outstanding rate ability of 777 m A h g^(-1)at 5 C and high areal capacity of 8.03 mA h cm^(-2)under E/S of 5μL mg^(-1)and sulfur loading of 7.20 mg cm^(-2).This work elucidates that introducing heteroatom and vacancy in metal phosphide collaboratively regulates the electron structure to accelerate bidirectional sulfur conversion.展开更多
In the applications such as food production,the environmental temperature should be measured continuously dur-ing the entire process,which requires an ultra-low-power temperature sensor for long-termly monitoring.Conv...In the applications such as food production,the environmental temperature should be measured continuously dur-ing the entire process,which requires an ultra-low-power temperature sensor for long-termly monitoring.Conventional tempera-ture sensors trade the measurement accuracy with power consumption.In this work,we present a battery-free wireless tempera-ture sensing chip for long-termly monitoring during food production.A calibrated oscillator-based CMOS temperature sensor is proposed instead of the ADC-based power-hungry circuits in conventional works.In addition,the sensor chip can harvest the power transferred by a remote reader to eliminate the use of battery.Meanwhile,the system conducts wireless bidirectional communication between the sensor chip and reader.In this way,the temperature sensor can realize both a high precision and battery-free operation.The temperature sensing chip is fabricated in 55 nm CMOS process,and the reader chip is imple-mented in 65 nm CMOS technology.Experimental results show that the temperature measurement error achieves±1.6℃ from 25 to 50℃,with battery-free readout by a remote reader.展开更多
Non-seismically designed(NSD)beam-column joints are susceptible to joint shear failure under seismic loads.Although significant research is available on the seismic behavior of such joints of planar frames,the informa...Non-seismically designed(NSD)beam-column joints are susceptible to joint shear failure under seismic loads.Although significant research is available on the seismic behavior of such joints of planar frames,the information on the seismic behavior of joints of space frames(3D joints)is insufficient.The 3D joints are subjected to bi-directional excitation,which results in an interaction between the shear strength obtained for the joint in the two orthogonal directions separately.The bi-directional seismic behavior of corner reinforced concrete(RC)joints is the focus of this study.First,a detailed finite element(FE)model using the FE software Abaqus,is developed and validated using the test results from the literature.The validated modeling procedure is used to conduct a parametric study to investigate the influence of different parameters such as concrete strength,dimensions of main and transverse beams framing into the joint,presence or absence of a slab,axial load ratio and loading direction on the seismic behavior of joints.By subjecting the models to different combinations of loads on the beams along perpendicular directions,the interaction of the joint shear strength in two orthogonal directions is studied.The comparison of the interaction curves of the joints obtained from the numerical study with a quadratic(circular)interaction curve indicates that in a majority of cases,the quadratic interaction model can represent the strength interaction diagrams of RC beam to column connections with governing joint shear failure reasonably well.展开更多
A medical image encryption is proposed based on the Fisher-Yates scrambling,filter diffusion and S-box substitution.First,chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system,whic...A medical image encryption is proposed based on the Fisher-Yates scrambling,filter diffusion and S-box substitution.First,chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system,which is used for the scrambling,substitution and diffusion processes.The three-dimensional Fisher-Yates scrambling,S-box substitution and diffusion are employed for the first round of encryption.The chaotic sequence is adopted for secondary encryption to scramble the ciphertext obtained in the first round.Then,three-dimensional filter is applied to diffusion for further useful information hiding.The key to the algorithm is generated by the combination of hash value of plaintext image and the input parameters.It improves resisting ability of plaintext attacks.The security analysis shows that the algorithm is effective and efficient.It can resist common attacks.In addition,the good diffusion effect shows that the scheme can solve the differential attacks encountered in the transmission of medical images and has positive implications for future research.展开更多
Following the discovery of bone as an endocrine organ with systemic influence,bone-brain interaction has emerged as a research hotspot,unveiling complex bidirectional communication between bone and brain.Studies indic...Following the discovery of bone as an endocrine organ with systemic influence,bone-brain interaction has emerged as a research hotspot,unveiling complex bidirectional communication between bone and brain.Studies indicate that bone and brain can influence each other’s homeostasis via multiple pathways,yet there is a dearth of systematic reviews in this area.This review comprehensively examines interactions across three key areas:the influence of bone-derived factors on brain function,the effects of brain-related diseases or injuries(BRDI)on bone health,and the concept of skeletal interoception.Additionally,the review discusses innovative approaches in biomaterial design inspired by bone-brain interaction mechanisms,aiming to facilitate bonebrain interactions through materiobiological effects to aid in the treatment of neurodegenerative and bone-related diseases.Notably,the integration of artificial intelligence(AI)in biomaterial design is highlighted,showcasing AI’s role in expediting the formulation of effective and targeted treatment strategies.In conclusion,this review offers vital insights into the mechanisms of bone-brain interaction and suggests advanced approaches to harness these interactions in clinical practice.These insights offer promising avenues for preventing and treating complex diseases impacting the skeleton and brain,underscoring the potential of interdisciplinary approaches in enhancing human health.展开更多
This article explores the bidirectional relationship between type 2 diabetes mellitus(T2DM)and depression,focusing on their shared pathophysiological mechanisms,including immune-inflammatory responses,gut-brain axis d...This article explores the bidirectional relationship between type 2 diabetes mellitus(T2DM)and depression,focusing on their shared pathophysiological mechanisms,including immune-inflammatory responses,gut-brain axis dysregu-lation,metabolic abnormalities,and neuroendocrine modulation.Research indicates that T2DM contributes to anxiety and depression through chronic low-grade inflammation,insulin resistance,gut microbiota imbalance,and hy-peractivation of the hypothalamic-pituitary-adrenal axis.Conversely,depression may increase the risk of T2DM via lifestyle disruption,immune activation,and neurotransmitter imbalance.Additionally,metabolic pathway disturbances-such as reduced adiponectin,impaired insulin signaling,and altered amino acid me-tabolism-may influence mood regulation and cognition.The article further examines emerging therapeutic strategies targeting these shared mechanisms,including anti-inflammatory treatments,gut microbiota modulation,hypothalamic-pituitary-adrenal axis interventions,metabolic therapies(e.g.,glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors),and multidisciplinary integrative management.Emphasizing the multisystem nature of diabetes-depression comorbidity,this work highlights the importance of incorporating mental health strategies into diabetes care to optimize outcomes and enhance patient quality of life.展开更多
Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variat...Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition(VMD)and Channel Attention Mechanism.First,Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power.Second,the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition(VMD).Finally,the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM model.In this model,the convolutional neural network(CNN)and channel attention mechanism dynamically adjust the weights while capturing the spatial features of the input data to improve the discriminative ability of key features.The extracted data is then fed into the bidirectional long short-term memory network(BiLSTM)to capture the time-series features,and the final output is the prediction result.The verification is conducted using a dataset from a distributed photovoltaic power station in the Northwest region of China.The results show that compared with other prediction methods,the method proposed in this paper has a higher prediction accuracy,which helps to improve the proportion of distributed PV access to the grid,and can guarantee the safe and stable operation of the power grid.展开更多
Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a vi...Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.展开更多
Motivated by the early works on bidirectional interaction and the breakthrough to estimate seismic response to bidirectional shaking via unidirectional analysis,it is essential to answer the question:When is the inter...Motivated by the early works on bidirectional interaction and the breakthrough to estimate seismic response to bidirectional shaking via unidirectional analysis,it is essential to answer the question:When is the interaction effect significant?Early works concluded that the effect of interaction is pronounced for stiff systems;consequently,the straightforward method for estimating seismic response to bidirectional excitation by using unidirectional analyses is verified primarily for short period systems.Hence,it is essential to identify the domain of significance for bidirectional interaction before adopting this simple methodology in design.Several parametrically defined systems with elastoplastic and degrading hysteresis models are studied under near-fault motions,assuming strength-independent and strength-dependent stiffness.The force-based and displacement-based analyses,conducted in parallel,reveal that the interaction effect is considerable for stiff systems,especially with degrading characteristics in a relatively low inelasticity range.However,the bidirectional effect may be significant even for highly flexible systems,especially for residual deformation,which in earlier works was shrouded.The range of significance depends on the hysteresis model,system parameters,and response indices.Regression analysis is carried out with the results of the case studies,and the derived regression models may be used for a preliminary assessment of the impact of interaction in advance.展开更多
In addition to its recognized role in providing structural support, bone plays a crucial role in maintaining the functionality and balance of various organs by secreting specific cytokines(also known as osteokines). T...In addition to its recognized role in providing structural support, bone plays a crucial role in maintaining the functionality and balance of various organs by secreting specific cytokines(also known as osteokines). This reciprocal influence extends to these organs modulating bone homeostasis and development, although this aspect has yet to be systematically reviewed. This review aims to elucidate this bidirectional crosstalk, with a particular focus on the role of osteokines. Additionally, it presents a unique compilation of evidence highlighting the critical function of extracellular vesicles(EVs) within bone-organ axes for the first time. Moreover, it explores the implications of this crosstalk for designing and implementing bone-on-chips and assembloids, underscoring the importance of comprehending these interactions for advancing physiologically relevant in vitro models. Consequently, this review establishes a robust theoretical foundation for preventing, diagnosing, and treating diseases related to the bone-organ axis from the perspective of cytokines, EVs, hormones, and metabolites.展开更多
基金Supported by the Fundamental Research Funds for the Central Universitiesthe Research Funds of Renmin University of China Project(Grant No.10XNB054)
文摘It was conjectured by Bouchet that every bidirected graph which admits a nowhere-zero κ flow will admit a nowhere-zero 6-flow. He proved that the conjecture is true when 6 is replaced by 216. Zyka improved the result with 6 replaced by 30. Xu and Zhang showed that the conjecture is true for 6-edge-connected graphs. And for 4-edge-connected graphs, Raspaud and Zhu proved it is true with 6 replaced by 4. In this paper, we show that Bouchet＇s conjecture is true with 6 replaced by 15 for 3-edge-connected graphs.
基金supported by the National Natural Science Foundation(52302284,22002086,22204096)Shanghai Sailing Program(23YF1412200)the Fundamental Research Funds for the Central Universities(22120240314).
文摘Single-atom catalysts(SACs)have garnered significant attention in lithium-sulfur(Li-S)batteries for their potential to mitigate the severe polysulfide shuttle effect and sluggish redox kinetics.However,the development of highly efficient SACs and a comprehensive understanding of their structure-activity relationships remain enormously challenging.Herein,a novel kind of Fe-based SAC featuring an asymmetric FeN_(5)-TeN_(4) coordination structure was precisely designed by introducing Te atom adjacent to the Fe active center to enhance the catalytic activity.Theoretical calculations reveal that the neighboring Te atom modulates the local coordination environment of the central Fe site,elevating the d-band center closer to the Fermi level and strengthening the d-p orbital hybridization between the catalyst and sulfur species,thereby immobilizing polysulfides and improving the bidirectional catalysis of Li-S redox.Consequently,the Fe-Te atom pair catalyst endows Li-S batteries with exceptional rate performance,achieving a high specific capacity of 735 mAh g^(−1) at 5 C,and remarkable cycling stability with a low decay rate of 0.038%per cycle over 1000 cycles at 1 C.This work provides fundamental insights into the electronic structure modulation of SACs and establishes a clear correlation between precisely engineered atomic configurations and their enhanced catalytic performance in Li-S electrochemistry.
基金supported by the National Key Research and Development Program of China(No.2024YFB3212901)National Natural Science Foundation of China(12072189)the Medicine and Engineering Interdisciplinary Research Fund of Shanghai Jiao Tong University(No.YG2025ZD05)。
文摘Pipelines are extensively used in environments such as nuclear power plants,chemical factories,and medical devices to transport gases and liquids.These tubular environments often feature complex geometries,confined spaces,and millimeter-scale height restrictions,presenting significant challenges to conventional inspection methods.Here,we present an ultrasonic microrobot(weight,80 mg;dimensions,24 mm×7 mm;thickness,210μm)to realize agile and bidirectional navigation in narrow pipelines.The ultrathin structural design of the robot is achieved through a high-performance piezoelectric composite film microstructure based on MEMS technology.The robot exhibits various vibration modes when driven by ultrasonic frequency signals,its motion speed reaches81 cm s-1 at 54.8 k Hz,exceeding that of the fastest piezoelectric microrobots,and its forward and backward motion direction is controllable through frequency modulation,while the minimum driving voltage for initial movement can be as low as 3 VP-P.Additionally,the robot can effortlessly climb slopes up to 24.25°and carry loads more than 36 times its weight.The robot is capable of agile navigation through curved L-shaped pipes,pipes made of various materials(acrylic,stainless steel,and polyvinyl chloride),and even over water.To further demonstrate its inspection capabilities,a micro-endoscope camera is integrated into the robot,enabling real-time image capture inside glass pipes.
基金supported by the National Natural Science Foundation of China(No.51977113)the Science and Technology Project of State Grid Zhejiang Electric Power Co.,Ltd.(No.5211JX240001).
文摘Accurate and reliable power system data are fundamental for critical operations such as gridmonitoring,fault diagnosis,and load forecasting,underpinned by increasing intelligentization and digitalization.However,data loss and anomalies frequently compromise data integrity in practical settings,significantly impacting system operational efficiency and security.Most existing data recovery methods require complete datasets for training,leading to substantial data and computational demands and limited generalization.To address these limitations,this study proposes a missing data imputation model based on an improved Generative Adversarial Network(BAC-GAN).Within the BAC-GAN framework,the generator utilizes Bidirectional Long Short-Term Memory(BiLSTM)networks and Multi-Head Attention mechanisms to capture temporal dependencies and complex relationships within power system data.The discriminator employs a Convolutional Neural Network(CNN)architecture to integrate local features with global structures,effectivelymitigating the generation of implausible imputations.Experimental results on two public datasets demonstrate that the BAC-GAN model achieves superior data recovery accuracy compared to five state-of-the-art and classical benchmarkmethods,with an average improvement of 17.7%in reconstruction accuracy.The proposedmethod significantly enhances the accuracy of grid fault diagnosis and provides reliable data support for the stable operation of smart grids,showing great potential for practical applications in power systems.
基金supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.
基金supported by the National Science and Technology Council,Taiwan,with grant numbers NSTC 114-2622-8-992-007-TD1 and 112-2811-E-992-003-MY3.
文摘The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.
基金funded by the Deanship of Scientific Research and Libraries at Princess Nourah bint Abdulrahman University,through the“Nafea”Program,Grant No.(NP-45-082).
文摘Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.
基金funded by scientific research projects under Grant JY2024B011.
文摘Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making.However,deep learning models often falter when processing raw,noisy temporal signals,fail to quantify prediction uncertainty,and face challenges in effectively capturing the nonlinear dynamics of equipment degradation.To address these issues,this study proposes a novel deep learning framework.First,a newbidirectional long short-termmemory network integrated with an attention mechanism is designed to enhance temporal feature extraction with improved noise robustness.Second,a probabilistic prediction framework based on kernel density estimation is constructed,incorporating residual connections and stochastic regularization to achieve precise RUL estimation.Finally,extensive experiments on the C-MAPSS dataset demonstrate that our method achieves competitive performance in terms of RMSE and Score metrics compared to state-of-the-artmodels.More importantly,the probabilistic output provides a quantifiablemeasure of prediction confidence,which is crucial for risk-informed maintenance planning,enabling managers to optimize maintenance strategies based on a quantifiable understanding of failure risk.
文摘Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.
文摘BACKGROUND Propofol has been widely used in bidirectional gastrointestinal endoscopy sedation;however,it frequently leads to cardiovascular adverse events and respiratory depression.Propofol target-controlled infusion(TCI)can provide safe sedation but may require higher dosages of propofol.On the contrary,etomidate offers hemodynamic stability.AIM To evaluate the effect of different dose etomidate added to propofol TCI sedation during same-visit bidirectional endoscopy.METHODS A total of 330 patients from Fujian Provincial Hospital were randomly divided into three groups:P,0.1EP,and 0.15EP.Patients in the P group received propofol TCI only,with an initial effect-site concentration of the propofol TCI system of 3.0 mg/mL.Patients in the 0.1EP and 0.15EP groups received 0.1 and 0.15 mg/kg etomidate intravenous injection,respectively,followed by propofol TCI.RESULTS Patients in the 0.15EP group had higher mean blood pressure after induction than the other groups(P group:78 mmHg,0.1EP group:82 mmHg,0.15EP group:88 mmHg;P<0.05).Total doses of propofol consumption significantly decreased in the 0.15EP group compared with that in the other groups(P group:260.6 mg,0.1EP group:228.1 mg,0.15EP group:201.2 mg;P<0.05).The induction time was longer in the P group than in the other groups(P group:1.9±0.7 minutes,0.1EP group:1.2±0.4 minutes,0.15EP group:1.1±0.3 minutes;P<0.01).The recovery time was shorter in the 0.15EP group than in the other groups(P group:4.8±2.1 minutes,0.1EP group:4.5±1.6 minutes,0.15EP group:3.9±1.4 minutes;P<0.01).The incidence of hypotension(P group:36.4%,0.1EP group:29.1%,0.15EP group:11.8%;P<0.01)and injection pain was lower in the 0.15EP group than in the other groups(P<0.05).Furthermore,the incidence of respiratory depression was lower in the 0.15EP group than in the P group(P<0.05).Additionally,the satisfaction of the patient,endoscopist,and anesthesiologist was higher in the 0.15EP group than in the other groups(P<0.05).CONCLUSION Our findings suggest that 0.15 mg/kg etomidate plus propofol TCI can significantly reduce propofol consumption,which is followed by fewer cardiovascular adverse events and respiratory depression,along with higher patient,endoscopist,and anesthesiologist satisfaction.
基金Institute of Technology Research Fund Program for Young Scholars21C Innovation Laboratory Contemporary Amperex Technology Co.,Limited,Ninde, 352100, China (21C–OP-202314)。
文摘Lithium-sulfur (Li-S) batteries have gained great attention due to the high theoretical energy density and low cost,yet their further commercialization has been obstructed by the notorious shuttle effect and sluggish redox dynamics.Herein,we supply a strategy to optimize the electron structure of Ni_(2)P by concurrently introducing B-doped atoms and P vacancies in Ni_(2)P (Vp-B-Ni_(2)P),thereby enhancing the bidirectional sulfur conversion.The study indicates that the simultaneous introduction of B-doped atoms and P vacancies in Ni_(2)P causes the redistribution of electron around Ni atoms,bringing about the upward shift of d-band center of Ni atoms and effective d-p orbital hybridization between Ni atoms and sulfur species,thus strengthening the chemical anchoring for lithium polysulfides (LiPSs) as well as expediting the bidirectional conversion kinetics of sulfur species.Meanwhile,theoretical calculations reveal that the incorporation of B-doped atoms and P vacancies in Ni_(2)P selectively promotes Li2S dissolution and nucleation processes.Thus,the Li-S batteries with Vp-B-Ni_(2)P-separators present outstanding rate ability of 777 m A h g^(-1)at 5 C and high areal capacity of 8.03 mA h cm^(-2)under E/S of 5μL mg^(-1)and sulfur loading of 7.20 mg cm^(-2).This work elucidates that introducing heteroatom and vacancy in metal phosphide collaboratively regulates the electron structure to accelerate bidirectional sulfur conversion.
基金supported by the National Key R&D Program of China under Grant 2024YFE0203500Xiaomi Young Talents Program。
文摘In the applications such as food production,the environmental temperature should be measured continuously dur-ing the entire process,which requires an ultra-low-power temperature sensor for long-termly monitoring.Conventional tempera-ture sensors trade the measurement accuracy with power consumption.In this work,we present a battery-free wireless tempera-ture sensing chip for long-termly monitoring during food production.A calibrated oscillator-based CMOS temperature sensor is proposed instead of the ADC-based power-hungry circuits in conventional works.In addition,the sensor chip can harvest the power transferred by a remote reader to eliminate the use of battery.Meanwhile,the system conducts wireless bidirectional communication between the sensor chip and reader.In this way,the temperature sensor can realize both a high precision and battery-free operation.The temperature sensing chip is fabricated in 55 nm CMOS process,and the reader chip is imple-mented in 65 nm CMOS technology.Experimental results show that the temperature measurement error achieves±1.6℃ from 25 to 50℃,with battery-free readout by a remote reader.
文摘Non-seismically designed(NSD)beam-column joints are susceptible to joint shear failure under seismic loads.Although significant research is available on the seismic behavior of such joints of planar frames,the information on the seismic behavior of joints of space frames(3D joints)is insufficient.The 3D joints are subjected to bi-directional excitation,which results in an interaction between the shear strength obtained for the joint in the two orthogonal directions separately.The bi-directional seismic behavior of corner reinforced concrete(RC)joints is the focus of this study.First,a detailed finite element(FE)model using the FE software Abaqus,is developed and validated using the test results from the literature.The validated modeling procedure is used to conduct a parametric study to investigate the influence of different parameters such as concrete strength,dimensions of main and transverse beams framing into the joint,presence or absence of a slab,axial load ratio and loading direction on the seismic behavior of joints.By subjecting the models to different combinations of loads on the beams along perpendicular directions,the interaction of the joint shear strength in two orthogonal directions is studied.The comparison of the interaction curves of the joints obtained from the numerical study with a quadratic(circular)interaction curve indicates that in a majority of cases,the quadratic interaction model can represent the strength interaction diagrams of RC beam to column connections with governing joint shear failure reasonably well.
文摘A medical image encryption is proposed based on the Fisher-Yates scrambling,filter diffusion and S-box substitution.First,chaotic sequence associated with the plaintext is generated by logistic-sine-cosine system,which is used for the scrambling,substitution and diffusion processes.The three-dimensional Fisher-Yates scrambling,S-box substitution and diffusion are employed for the first round of encryption.The chaotic sequence is adopted for secondary encryption to scramble the ciphertext obtained in the first round.Then,three-dimensional filter is applied to diffusion for further useful information hiding.The key to the algorithm is generated by the combination of hash value of plaintext image and the input parameters.It improves resisting ability of plaintext attacks.The security analysis shows that the algorithm is effective and efficient.It can resist common attacks.In addition,the good diffusion effect shows that the scheme can solve the differential attacks encountered in the transmission of medical images and has positive implications for future research.
基金financially supported by the Basic Science Center Program(T2288102)the Key Program of the National Natural Science Foundation of China(32230059)+3 种基金the Foundation of Frontiers Science Center for Materiobiology and Dynamic Chemistry(JKVD1211002)the Project supported by the Young Scientists Fund of the National Natural Science Foundation of China(32401128)Postdoctoral Fellowship Program of CPSF(GZC20230793)Shanghai Post-doctoral Excellence Program(2023251).
文摘Following the discovery of bone as an endocrine organ with systemic influence,bone-brain interaction has emerged as a research hotspot,unveiling complex bidirectional communication between bone and brain.Studies indicate that bone and brain can influence each other’s homeostasis via multiple pathways,yet there is a dearth of systematic reviews in this area.This review comprehensively examines interactions across three key areas:the influence of bone-derived factors on brain function,the effects of brain-related diseases or injuries(BRDI)on bone health,and the concept of skeletal interoception.Additionally,the review discusses innovative approaches in biomaterial design inspired by bone-brain interaction mechanisms,aiming to facilitate bonebrain interactions through materiobiological effects to aid in the treatment of neurodegenerative and bone-related diseases.Notably,the integration of artificial intelligence(AI)in biomaterial design is highlighted,showcasing AI’s role in expediting the formulation of effective and targeted treatment strategies.In conclusion,this review offers vital insights into the mechanisms of bone-brain interaction and suggests advanced approaches to harness these interactions in clinical practice.These insights offer promising avenues for preventing and treating complex diseases impacting the skeleton and brain,underscoring the potential of interdisciplinary approaches in enhancing human health.
基金Supported by the Quzhou Science and Technology Plan Project funded by the Quzhou Municipal Science and Technology Bureau,No.2022K67,No.2022K69,and No.2024K076.
文摘This article explores the bidirectional relationship between type 2 diabetes mellitus(T2DM)and depression,focusing on their shared pathophysiological mechanisms,including immune-inflammatory responses,gut-brain axis dysregu-lation,metabolic abnormalities,and neuroendocrine modulation.Research indicates that T2DM contributes to anxiety and depression through chronic low-grade inflammation,insulin resistance,gut microbiota imbalance,and hy-peractivation of the hypothalamic-pituitary-adrenal axis.Conversely,depression may increase the risk of T2DM via lifestyle disruption,immune activation,and neurotransmitter imbalance.Additionally,metabolic pathway disturbances-such as reduced adiponectin,impaired insulin signaling,and altered amino acid me-tabolism-may influence mood regulation and cognition.The article further examines emerging therapeutic strategies targeting these shared mechanisms,including anti-inflammatory treatments,gut microbiota modulation,hypothalamic-pituitary-adrenal axis interventions,metabolic therapies(e.g.,glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors),and multidisciplinary integrative management.Emphasizing the multisystem nature of diabetes-depression comorbidity,this work highlights the importance of incorporating mental health strategies into diabetes care to optimize outcomes and enhance patient quality of life.
基金supported by the Inner Mongolia Power Company 2024 Staff Innovation Studio Innovation Project“Research on Cluster Output Prediction and Group Control Technology for County-Wide Distributed Photovoltaic Construction”.
文摘Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation.This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition(VMD)and Channel Attention Mechanism.First,Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power.Second,the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition(VMD).Finally,the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM model.In this model,the convolutional neural network(CNN)and channel attention mechanism dynamically adjust the weights while capturing the spatial features of the input data to improve the discriminative ability of key features.The extracted data is then fed into the bidirectional long short-term memory network(BiLSTM)to capture the time-series features,and the final output is the prediction result.The verification is conducted using a dataset from a distributed photovoltaic power station in the Northwest region of China.The results show that compared with other prediction methods,the method proposed in this paper has a higher prediction accuracy,which helps to improve the proportion of distributed PV access to the grid,and can guarantee the safe and stable operation of the power grid.
基金National Natural Science Foundation of China(71690233,71971213,71901214)。
文摘Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.
文摘Motivated by the early works on bidirectional interaction and the breakthrough to estimate seismic response to bidirectional shaking via unidirectional analysis,it is essential to answer the question:When is the interaction effect significant?Early works concluded that the effect of interaction is pronounced for stiff systems;consequently,the straightforward method for estimating seismic response to bidirectional excitation by using unidirectional analyses is verified primarily for short period systems.Hence,it is essential to identify the domain of significance for bidirectional interaction before adopting this simple methodology in design.Several parametrically defined systems with elastoplastic and degrading hysteresis models are studied under near-fault motions,assuming strength-independent and strength-dependent stiffness.The force-based and displacement-based analyses,conducted in parallel,reveal that the interaction effect is considerable for stiff systems,especially with degrading characteristics in a relatively low inelasticity range.However,the bidirectional effect may be significant even for highly flexible systems,especially for residual deformation,which in earlier works was shrouded.The range of significance depends on the hysteresis model,system parameters,and response indices.Regression analysis is carried out with the results of the case studies,and the derived regression models may be used for a preliminary assessment of the impact of interaction in advance.
基金supported by the National Natural Science Foundation of China (82230071, 82172098)the Integrated Project of Major Research Plan of National Natural Science Foundation of China (92249303)+2 种基金the Laboratory Animal Research Project of Shanghai Committee of Science and Technology (23141900600)the Shanghai Clinical Research Plan (SHDC2023CRT01)the Young Elite Scientist Sponsorship Program by China Association for Science and Technology (YESS20230049)。
文摘In addition to its recognized role in providing structural support, bone plays a crucial role in maintaining the functionality and balance of various organs by secreting specific cytokines(also known as osteokines). This reciprocal influence extends to these organs modulating bone homeostasis and development, although this aspect has yet to be systematically reviewed. This review aims to elucidate this bidirectional crosstalk, with a particular focus on the role of osteokines. Additionally, it presents a unique compilation of evidence highlighting the critical function of extracellular vesicles(EVs) within bone-organ axes for the first time. Moreover, it explores the implications of this crosstalk for designing and implementing bone-on-chips and assembloids, underscoring the importance of comprehending these interactions for advancing physiologically relevant in vitro models. Consequently, this review establishes a robust theoretical foundation for preventing, diagnosing, and treating diseases related to the bone-organ axis from the perspective of cytokines, EVs, hormones, and metabolites.
