The multi-dimensional interactive teaching model significantly enhances the effectiveness of college English instruction by emphasizing dynamic engagement between teachers and students,as well as among students themse...The multi-dimensional interactive teaching model significantly enhances the effectiveness of college English instruction by emphasizing dynamic engagement between teachers and students,as well as among students themselves.This paper explores practical strategies for implementing this model,focusing on four key aspects:deepening teachers’understanding of the model through continuous learning,innovating interactive methods such as questioning techniques and practical activities,leveraging modern technology to integrate resources and track learning progress,and establishing a communication platform that centers on student participation.By adopting these approaches,the model fosters a student-centered classroom environment,improves comprehensive English application skills,and optimizes overall teaching quality.展开更多
Luminescent metal-organic frameworks(MOFs)have garnered significant attention due to their structural tunability and potential applications in solid-state lighting,bioimaging,sensing,anticounterfeiting,and other field...Luminescent metal-organic frameworks(MOFs)have garnered significant attention due to their structural tunability and potential applications in solid-state lighting,bioimaging,sensing,anticounterfeiting,and other fields.Nevertheless,due to the tendency of1,4-benzenedicarboxylic acid(BDC)to rotate within the framework,MOFs composed of it exhibit significant non-radiative energy dissipation and thus impair the emissive properties.In this study,efficient luminescence of MIL-140A nanocrystals(NCs)with BDC rotors as ligands is achieved by pressure treatment strategy.Pressure treatment effectively modulates the pore structure of the framework,enhancing the interactions between the N,N-dimethylformamide vip molecules and the BDC ligands.The enhanced host-vip interaction contributes to the structural rigidity of the MOF,thereby suppressing the rotation-induced excited-state energy loss.As a result,the pressure-treated MIL-140A NCs displayed bright blue-light emission,with the photoluminescence quantum yield increasing from an initial 6.8%to 69.2%.This study developed an effective strategy to improve the luminescence performance of rotor ligand MOFs,offers a new avenue for the rational design and synthesis of MOFs with superior luminescent properties.展开更多
Dielectric-magnetic integrated absorbers have attracted arousing attention in microwave absorption,however,it still remains a great challenge to simultaneously achieve superior dielectric polarization and strong magne...Dielectric-magnetic integrated absorbers have attracted arousing attention in microwave absorption,however,it still remains a great challenge to simultaneously achieve superior dielectric polarization and strong magnetic loss.Herein,we propose a multi-scale structure optimization strategy to anchor CoNiMOFs derived OD CoNi alloy onto 1 D core-shell Ni@C surface.By decorating with the poly-dopamine layer,the connection between 1 D NiO and CoNi-MOFs precursors was greatly improved via the electrostatic interaction.Benefiting from the overlapping conductive networks,enhanced interfacial polarization among the multi-dimensional heterogeneous interfaces and strong magnetic interaction,the fabricated multi-dimensional Ni@C-CoNi composites exhibit outstanding microwave absorption.Typically,the optimal reflection loss is as high as-51.4 dB at 1.9 mm,and the effective absorption bandwidth achieves 4.6 GHz with a thickness of only 1.3 mm.This multi-scale structure optimization strategy inspires us with an efficient method to fabricate ideal microwave absorbers and the obtained multi-dimensional composites can be used as promising candidates in electromagnetic radiation protection.展开更多
Haptic interaction plays an important role in the virtual reality technology,which let a person not only view the 3D virtual environment but also realistically touch the virtual environment.As a key part of haptic int...Haptic interaction plays an important role in the virtual reality technology,which let a person not only view the 3D virtual environment but also realistically touch the virtual environment.As a key part of haptic interaction,force feedback has become an essential function for the haptic interaction.Therefore,multi-dimensional force sensors are widely used in the fields of virtual reality and augmented reality.In this paper,some conventional multi-dimensional force sensors based on different measurement principles,such as resistive,capacitive,piezoelectric,are briefly introduced.Then the mechanical structures of the elastic body of multi-dimensional force sensors are reviewed.It is obvious that the performance of the multi-dimensional force sensor is mainly dependent upon the mechanical structure of elastic body.Furthermore,the calibration process of the force sensor is analyzed,and problems in calibration are discussed.Interdimensional coupling error is one of the main factors affecting the measurement precision of the multi-dimensional force sensors.Therefore,reducing or even eliminating dimensional coupling error becomes a fundamental requirement in the design of multi-dimensional force sensors,and the decoupling state-of-art of the multi-dimensional force sensors are introduced in this paper.At last,the trends and current challenges of multi-dimensional force sensing technology are proposed.展开更多
The primary mechanism of secondary injury after cerebral ischemia may be the brain inflammation that emerges after an ischemic stroke,which promotes neuronal death and inhibits nerve tissue regeneration.As the first i...The primary mechanism of secondary injury after cerebral ischemia may be the brain inflammation that emerges after an ischemic stroke,which promotes neuronal death and inhibits nerve tissue regeneration.As the first immune cells to be activated after an ischemic stroke,microglia play an important immunomodulatory role in the progression of the condition.After an ischemic stroke,peripheral blood immune cells(mainly T cells)are recruited to the central nervous system by chemokines secreted by immune cells in the brain,where they interact with central nervous system cells(mainly microglia)to trigger a secondary neuroimmune response.This review summarizes the interactions between T cells and microglia in the immune-inflammatory processes of ischemic stroke.We found that,during ischemic stroke,T cells and microglia demonstrate a more pronounced synergistic effect.Th1,Th17,and M1 microglia can co-secrete proinflammatory factors,such as interferon-γ,tumor necrosis factor-α,and interleukin-1β,to promote neuroinflammation and exacerbate brain injury.Th2,Treg,and M2 microglia jointly secrete anti-inflammatory factors,such as interleukin-4,interleukin-10,and transforming growth factor-β,to inhibit the progression of neuroinflammation,as well as growth factors such as brain-derived neurotrophic factor to promote nerve regeneration and repair brain injury.Immune interactions between microglia and T cells influence the direction of the subsequent neuroinflammation,which in turn determines the prognosis of ischemic stroke patients.Clinical trials have been conducted on the ways to modulate the interactions between T cells and microglia toward anti-inflammatory communication using the immunosuppressant fingolimod or overdosing with Treg cells to promote neural tissue repair and reduce the damage caused by ischemic stroke.However,such studies have been relatively infrequent,and clinical experience is still insufficient.In summary,in ischemic stroke,T cell subsets and activated microglia act synergistically to regulate inflammatory progression,mainly by secreting inflammatory factors.In the future,a key research direction for ischemic stroke treatment could be rooted in the enhancement of anti-inflammatory factor secretion by promoting the generation of Th2 and Treg cells,along with the activation of M2-type microglia.These approaches may alleviate neuroinflammation and facilitate the repair of neural tissues.展开更多
Deficiency or restriction of Zn absorption in soils is one of the most common micronutrients deficient in cereal plants. To investigate critical micronutrient interaction in zinc deficiency and zinc sufficient in soil...Deficiency or restriction of Zn absorption in soils is one of the most common micronutrients deficient in cereal plants. To investigate critical micronutrient interaction in zinc deficiency and zinc sufficient in soil, a factorial experiment based on completely randomized design (CRD) with three replications was conducted in 2023. Six wheat cultivars with different Zn efficiency were used. The cultivars were grown under Zn deficiency and adequate conditions. Results showed that in Zn deficiency conditions, with increasing Zn concentration in the roots, Fe concentrations were increased too, while the Cu and Mn concentrations decreased. In the same condition and with increasing Zn concentration in shoots, the concentrations of Fe and Mn decreased, while Cu were increased. However, by increasing Zn concentration, Fe, Cu, and Mn concentrations were increased in Zn deficiency condition in grains, as well as Zn sufficient conditions. RST (root to shoot micronutrient translocation) comparison of cultivars showed that in lack of Zn, the ability of translocation of Zn, Fe, and Mn in Zn-inefficient cultivar from root to shoot was higher than inefficient cultivar. In the same conditions, the capability of Zn-inefficient cultivar in Cu translocation from root to shoot was lower than other cultivars. In general, it seems that in Zn deficiency conditions, there are antagonistic effects among Zn, Cu and Mn and synergistic effects between Zn and Fe in the root. Also, in Zn sufficient conditions, there were synergistic effects among all studies micronutrients which include Zn, Fe, Cu, and Mn.展开更多
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effe...Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effective rockburst control. In this study, the squeezing behavior of the surrounding rock is analyzed in rockburst roadways, and a mechanical model of rockbursts is established considering the dynamic support stress, thus deriving formulas and providing characteristic curves for describing the interaction between the support and surrounding rock. Design principles and parameters of supports for rockburst control are proposed. The results show that only when the geostress magnitude exceeds a critical value can it drive the formation of rockburst conditions. The main factors influencing the convergence response and rockburst occurrence around roadways are geostress, rock brittleness, uniaxial compressive strength, and roadway excavation size. Roadway support devices can play a role in controlling rockburst by suppressing the squeezing evolution of the surrounding rock towards instability points of rockburst. Further, the higher the strength and the longer the impact stroke of support devices with constant resistance, the more easily multiple balance points can be formed with the surrounding rock to control rockburst occurrence. Supports with long impact stroke allow adaptation to varying geostress levels around the roadway, aiding in rockburst control. The results offer a quantitative method for designing support systems for rockburst-prone roadways. The design criterion of supports is determined by the intersection between the convergence curve of the surrounding rock and the squeezing deformation curve of the support devices.展开更多
This work demonstrates experimentally the close relation between return currents from relativistic laser-driven target polarization and the quality of the relativistic laser–plasma interaction for laser-driven second...This work demonstrates experimentally the close relation between return currents from relativistic laser-driven target polarization and the quality of the relativistic laser–plasma interaction for laser-driven secondary sources,taking as an example ion acceleration by target normal sheath acceleration.The Pearson linear correlation of maximum return current amplitude and proton spectrum cutoff energy is found to be in the range from~0.70 to 0.94.kA-scale return currents rise in all interaction schemes where targets of any kind are charged by escaping laser-accelerated relativistic electrons.Their precise measurement is demonstrated using an inductive scheme that allows operation at high repetition rates.Thus,return currents can be used as a metrological online tool for the optimization of many laser-driven secondary sources and for diagnosing their stability.In particular,in two parametric studies of laser-driven ion acceleration,we carry out a noninvasive online measurement of return currents in a tape target system irradiated by the 1 PW VEGA-3 laser at Centro de Láseres Pulsados:first the size of the irradiated area is varied at best compression of the laser pulse;second,the pulse duration is varied by means of induced group delay dispersion at best focus.This work paves the way to the development of feedback systems that operate at the high repetition rates of PW-class lasers.展开更多
Food systems are deeply affected by climate change and air pollution,while being key contributors to these environmental challenges.Understanding the complex interactions among food systems,climate change,and air poll...Food systems are deeply affected by climate change and air pollution,while being key contributors to these environmental challenges.Understanding the complex interactions among food systems,climate change,and air pollution is crucial for mitigating climate change,improving air quality,and promoting the sustainable development of food systems.However,the literature lacks a comprehensive review of these interactions,particularly in the current phase of rapid development in the field.To address this gap,this study systematically reviews recent research on the impacts of climate change and air pollution on food systems,as well as the greenhouse gas and air pollutant emissions from agri-food systems and their contribution to global climate change and air pollution.In addition,this study summarizes various strategies for mitigation and adaptation,including adjustments in agricultural practices and food supply chains.Profound changes in food systems are urgently needed to enhance adaptability and reduce emissions.This review offers a critical overview of current research on the interactions among food systems,climate change,and air pollution and highlights future research directions to support the transition to sustainable food systems.展开更多
Lithium-ion batteries are widely recognized as prime candidates for energy storage devices.Ethylene carbonate(EC)has become a critical component in conventional commercial electrolytes due to its exceptional film-form...Lithium-ion batteries are widely recognized as prime candidates for energy storage devices.Ethylene carbonate(EC)has become a critical component in conventional commercial electrolytes due to its exceptional film-forming properties and high dielectric constant.However,the elevated freezing point,high viscosity,and strong solvation energy of EC significantly hinder the transport rate of Li^(+)and the desolvation process at low temperatures.This leads to substantial capacity loss and even lithium plating on graphite anodes.Herein,we have developed an efficient electrolyte system specifically designed for lowtemperature conditions,which consists of 1.0 M lithium bis(fluorosulfonyl)imide(LiFSI)in isoxazole(IZ)with fluorobenzene(FB)as an uncoordinated solvent and fluoroethylene carbonate(FEC)as a filmforming co-solvent.This system effectively lowers the desolvation energy of Li^(+)through dipole-dipole interactions.The weak solvation capability allows more anions to enter the solvation sheath,promoting the formation of contact ion pairs(CIPs)and aggregates(AGGs)that enhance the transport rate of Li^(+)while maintaining high ionic conductivity across a broad temperature range.Moreover,the formation of inorganic-dominant interfacial phases on the graphite anode,induced by fluoroethylene carbonate,significantly enhances the kinetics of Li^(+)transport.At a low temperature of-20℃,this electrolyte system achieves an impressive reversible capacity of 200.9 mAh g^(-1)in graphite half-cell,which is nearly three times that observed with conventional EC-based electrolytes,demonstrating excellent stability throughout its operation.展开更多
Plasmonic nanoantennas provide unique opportunities for precise control of light–matter coupling in surface-enhanced infrared absorption(SEIRA)spectroscopy,but most of the resonant systems realized so far suffer from...Plasmonic nanoantennas provide unique opportunities for precise control of light–matter coupling in surface-enhanced infrared absorption(SEIRA)spectroscopy,but most of the resonant systems realized so far suffer from the obstacles of low sensitivity,narrow bandwidth,and asymmetric Fano resonance perturbations.Here,we demonstrated an overcoupled resonator with a high plasmon-molecule coupling coefficient(μ)(OC-Hμresonator)by precisely controlling the radiation loss channel,the resonator-oscillator coupling channel,and the frequency detuning channel.We observed a strong dependence of the sensing performance on the coupling state,and demonstrated that OC-Hμresonator has excellent sensing properties of ultra-sensitive(7.25%nm^(−1)),ultra-broadband(3–10μm),and immune asymmetric Fano lineshapes.These characteristics represent a breakthrough in SEIRA technology and lay the foundation for specific recognition of biomolecules,trace detection,and protein secondary structure analysis using a single array(array size is 100×100μm^(2)).In addition,with the assistance of machine learning,mixture classification,concentration prediction and spectral reconstruction were achieved with the highest accuracy of 100%.Finally,we demonstrated the potential of OC-Hμresonator for SARS-CoV-2 detection.These findings will promote the wider application of SEIRA technology,while providing new ideas for other enhanced spectroscopy technologies,quantum photonics and studying light–matter interactions.展开更多
Oil shale is characterized by a dense structure,low proportion of pores and fissures,and low permeability.Pore-fracture systems serve as crucial channels for shale oil migration,directly influencing the production eff...Oil shale is characterized by a dense structure,low proportion of pores and fissures,and low permeability.Pore-fracture systems serve as crucial channels for shale oil migration,directly influencing the production efficiency of shale oil resources.Effectively stimulating oil shale reservoirs remains a challenging and active research topic.This investigation employed shale specimens obtained from the Longmaxi Formation.Scanning electron microscopy,fluid injection experiments,and fluid-structure interaction simulations were used to comprehensively analyze structural changes and fluid flow behavior under high temperatures from microscopic to macroscopic scales.Experimental results indicate that the temperature has little effect on the structure and permeability of shale before 300℃.However,there are two threshold temperatures within the range of 300 to 600℃that have significant effects on the structure and permeability of oil shale.The first threshold temperature is between 300 and 400℃,which causes the oil shale porosity,pore-fracture ratio,and permeability begin to increase.This is manifested by the decrease in micropores and mesopores,the increase in macropores,and the formation of a large number of isolated pores and fissures within the shale.The permeability increases but not significantly.The second threshold temperature is between 500 and 600℃,which increases the permeability of oil shale significantly.During this stage,micropores and mesopores are further reduced,and macropores are significantly enlarged.A large number of connected and penetrated pores and fissures are formed.More numerous and thicker streamlines appear inside the oil shale.The experimental results demonstrate that high temperatures significantly alter the microstructure and permeability of oil shale.At the same time,the experimental results can provide a reference for the research of in-situ heating techniques in oil shale reservoir transformation.展开更多
Catalyst–support interaction plays a crucial role in improving the catalytic activity of oxygen evolution reaction(OER).Here we modulate the catalyst–support interaction in polyaniline-supported Ni_(3)Fe oxide(Ni_(3...Catalyst–support interaction plays a crucial role in improving the catalytic activity of oxygen evolution reaction(OER).Here we modulate the catalyst–support interaction in polyaniline-supported Ni_(3)Fe oxide(Ni_(3)Fe oxide/PANI)with a robust hetero-interface,which significantly improves oxygen evolution activities with an overpotential of 270 mV at 10 mA cm^(-2)and specific activity of 2.08 mA cm_(ECSA)^(-2)at overpotential of 300 mV,3.84-fold that of Ni_(3)Fe oxide.It is revealed that the catalyst–support interaction between Ni_(3)Fe oxide and PANI support enhances the Ni–O covalency via the interfacial Ni–N bond,thus promoting the charge and mass transfer on Ni_(3)Fe oxide.Considering the excellent activity and stability,rechargeable Zn-air batteries with optimum Ni_(3)Fe oxide/PANI are assembled,delivering a low charge voltage of 1.95 V to cycle for 400 h at 10 mA cm^(-2).The regulation of the effect of catalyst–support interaction on catalytic activity provides new possibilities for the future design of highly efficient OER catalysts.展开更多
Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug devel...Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,offering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summarizes the application of AI in drug development,particularly in drug-target prediction,and offers recommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.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
Identifying drug-drug interactions(DDIs)is essential to prevent adverse effects from polypharmacy.Although deep learning has advanced DDI identification,the gap between powerful models and their lack of clinical appli...Identifying drug-drug interactions(DDIs)is essential to prevent adverse effects from polypharmacy.Although deep learning has advanced DDI identification,the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits.Here,we developed a Multi-Dimensional Feature Fusion model named MDFF,which integrates one-dimensional simplified molec-ular input line entry system sequence features,two-dimensional molecular graph features,and three-dimensional geometric features to enhance drug representations for predicting DDIs.MDFF was trained and validated on two DDI datasets,evaluated across three distinct scenarios,and compared with advanced DDI prediction models using accuracy,precision,recall,area under the curve,and F1 score metrics.MDFF achieved state-of-the-art performance across all metrics.Ablation experiments showed that integrating multi-dimensional drug features yielded the best results.More importantly,we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs.Among 12 real-world adverse drug reaction reports,the predictions of 9 reports were supported by relevant evidence.Additionally,MDFF demon-strated the ability to explain adverse DDI mechanisms,providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.展开更多
Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI pre...Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.展开更多
Deep-sea mining has emerged as a critical solution to address global resource shortages;however,the mechanical interaction between tracked mining vehicles(TMVs)and soft seabed sediments presents fundamental engineerin...Deep-sea mining has emerged as a critical solution to address global resource shortages;however,the mechanical interaction between tracked mining vehicles(TMVs)and soft seabed sediments presents fundamental engineering challenges.This study establishes a multiscale modelling framework coupling the discrete element method(DEM)with multi-body dynamics(MBD)to investigate track-seabed dynamic interactions across three operational modes:flat terrain,slope climbing,and ditch surmounting.The simulation framework,validated against laboratory experiments,systematically evaluates the influence of grouser geometry(involute,triangular,and pin-type)and traveling speed(0.2–1.0 m/s)on traction performance,slip rate,and ground pressure distribution.Results reveal rate-dependent traction mechanisms governed by soil microstructural responses:higher speeds enhance peak traction but exacerbate slip instability on complex terrain.Critical operational thresholds are established—0.7 m/s for flat terrain,≤0.5 m/s for slopes and ditches—with distinct grouser optimization strategies:involute grousers achieve 35%–40%slip reduction on slopes through progressive soil engagement,while triangular grousers provide optimal impact resistance during ditch crossing with 30%–35%performance improvement.These findings provide quantitative design criteria and operational guidelines for optimizing TMV structural parameters and control strategies,offering a robust theoretical foundation for enhancing the performance,safety,and reliability of deep-sea mining equipment in complex submarine environments.展开更多
This article describes a pilot study aiming at generating social interactions between a humanoid robot and adolescents with autism spectrum disorder (ASD), through the practice of a gesture imitation game. The partici...This article describes a pilot study aiming at generating social interactions between a humanoid robot and adolescents with autism spectrum disorder (ASD), through the practice of a gesture imitation game. The participants were a 17-year-old young lady with ASD and intellectual deficit, and a control participant: a preadolescent with ASD but no intellectual deficit (Asperger syndrome). The game is comprised of four phases: greetings, pairing, imitation, and closing. Field educators were involved, playing specific roles: visual or physical inciter. The use of a robot allows for catching the participants’ attention, playing the imitation game for a longer period of time than with a human partner, and preventing the game partner’s negative facial expressions resulting from tiredness, impatience, or boredom. The participants’ behavior was observed in terms of initial approach towards the robot, positioning relative to the robot in terms of distance and orientation, reactions to the robot’s voice or moves, signs of happiness, and imitation attempts. Results suggest a more and more natural approach towards the robot during the sessions, as well as a higher level of social interaction, based on the variations of the parameters listed above. We use these preliminary results to draw the next steps of our research work as well as identify further perspectives, with this aim in mind: improving social interactions with adolescents with ASD and intellectual deficit, allowing for better integration of these people into our societies.展开更多
文摘The multi-dimensional interactive teaching model significantly enhances the effectiveness of college English instruction by emphasizing dynamic engagement between teachers and students,as well as among students themselves.This paper explores practical strategies for implementing this model,focusing on four key aspects:deepening teachers’understanding of the model through continuous learning,innovating interactive methods such as questioning techniques and practical activities,leveraging modern technology to integrate resources and track learning progress,and establishing a communication platform that centers on student participation.By adopting these approaches,the model fosters a student-centered classroom environment,improves comprehensive English application skills,and optimizes overall teaching quality.
基金supported by the National Key R&D Program of China(Grant No.2023YFA1406200)the National Natural Science Foundation of China(No.12274177 and 12304261)the China Postdoctoral Science Foundation(No.2024M751076)。
文摘Luminescent metal-organic frameworks(MOFs)have garnered significant attention due to their structural tunability and potential applications in solid-state lighting,bioimaging,sensing,anticounterfeiting,and other fields.Nevertheless,due to the tendency of1,4-benzenedicarboxylic acid(BDC)to rotate within the framework,MOFs composed of it exhibit significant non-radiative energy dissipation and thus impair the emissive properties.In this study,efficient luminescence of MIL-140A nanocrystals(NCs)with BDC rotors as ligands is achieved by pressure treatment strategy.Pressure treatment effectively modulates the pore structure of the framework,enhancing the interactions between the N,N-dimethylformamide vip molecules and the BDC ligands.The enhanced host-vip interaction contributes to the structural rigidity of the MOF,thereby suppressing the rotation-induced excited-state energy loss.As a result,the pressure-treated MIL-140A NCs displayed bright blue-light emission,with the photoluminescence quantum yield increasing from an initial 6.8%to 69.2%.This study developed an effective strategy to improve the luminescence performance of rotor ligand MOFs,offers a new avenue for the rational design and synthesis of MOFs with superior luminescent properties.
基金financially supported by the National Natural Science Foundation of China (Nos.52173254,11975124)the Natural Science Foundation of Jiangsu Province (BK20211200)+4 种基金the Natural Science Foundation of Shaanxi Province (No.2022JM-260)the China Postdoctoral Science Foundation (No.2020M681601)the Fundamental Research Funds for the Central Universities (Nos.30920041103,30920021107)the Shanghai Key Laboratory of R&D for Metallic Functional Materials (2021-01)the Open Fund from Henan University of Science and Technology。
文摘Dielectric-magnetic integrated absorbers have attracted arousing attention in microwave absorption,however,it still remains a great challenge to simultaneously achieve superior dielectric polarization and strong magnetic loss.Herein,we propose a multi-scale structure optimization strategy to anchor CoNiMOFs derived OD CoNi alloy onto 1 D core-shell Ni@C surface.By decorating with the poly-dopamine layer,the connection between 1 D NiO and CoNi-MOFs precursors was greatly improved via the electrostatic interaction.Benefiting from the overlapping conductive networks,enhanced interfacial polarization among the multi-dimensional heterogeneous interfaces and strong magnetic interaction,the fabricated multi-dimensional Ni@C-CoNi composites exhibit outstanding microwave absorption.Typically,the optimal reflection loss is as high as-51.4 dB at 1.9 mm,and the effective absorption bandwidth achieves 4.6 GHz with a thickness of only 1.3 mm.This multi-scale structure optimization strategy inspires us with an efficient method to fabricate ideal microwave absorbers and the obtained multi-dimensional composites can be used as promising candidates in electromagnetic radiation protection.
基金Supported by Natural Science Foundation of China(U1713210).
文摘Haptic interaction plays an important role in the virtual reality technology,which let a person not only view the 3D virtual environment but also realistically touch the virtual environment.As a key part of haptic interaction,force feedback has become an essential function for the haptic interaction.Therefore,multi-dimensional force sensors are widely used in the fields of virtual reality and augmented reality.In this paper,some conventional multi-dimensional force sensors based on different measurement principles,such as resistive,capacitive,piezoelectric,are briefly introduced.Then the mechanical structures of the elastic body of multi-dimensional force sensors are reviewed.It is obvious that the performance of the multi-dimensional force sensor is mainly dependent upon the mechanical structure of elastic body.Furthermore,the calibration process of the force sensor is analyzed,and problems in calibration are discussed.Interdimensional coupling error is one of the main factors affecting the measurement precision of the multi-dimensional force sensors.Therefore,reducing or even eliminating dimensional coupling error becomes a fundamental requirement in the design of multi-dimensional force sensors,and the decoupling state-of-art of the multi-dimensional force sensors are introduced in this paper.At last,the trends and current challenges of multi-dimensional force sensing technology are proposed.
基金supported by the National Natural Science Foundation of China,Nos.82104560(to CL),U21A20400(to QW)the Natural Science Foundation of Beijing,No.7232279(to XW)the Project of Beijing University of Chinese Medicine,No.2022-JYB-JBZR-004(to XW)。
文摘The primary mechanism of secondary injury after cerebral ischemia may be the brain inflammation that emerges after an ischemic stroke,which promotes neuronal death and inhibits nerve tissue regeneration.As the first immune cells to be activated after an ischemic stroke,microglia play an important immunomodulatory role in the progression of the condition.After an ischemic stroke,peripheral blood immune cells(mainly T cells)are recruited to the central nervous system by chemokines secreted by immune cells in the brain,where they interact with central nervous system cells(mainly microglia)to trigger a secondary neuroimmune response.This review summarizes the interactions between T cells and microglia in the immune-inflammatory processes of ischemic stroke.We found that,during ischemic stroke,T cells and microglia demonstrate a more pronounced synergistic effect.Th1,Th17,and M1 microglia can co-secrete proinflammatory factors,such as interferon-γ,tumor necrosis factor-α,and interleukin-1β,to promote neuroinflammation and exacerbate brain injury.Th2,Treg,and M2 microglia jointly secrete anti-inflammatory factors,such as interleukin-4,interleukin-10,and transforming growth factor-β,to inhibit the progression of neuroinflammation,as well as growth factors such as brain-derived neurotrophic factor to promote nerve regeneration and repair brain injury.Immune interactions between microglia and T cells influence the direction of the subsequent neuroinflammation,which in turn determines the prognosis of ischemic stroke patients.Clinical trials have been conducted on the ways to modulate the interactions between T cells and microglia toward anti-inflammatory communication using the immunosuppressant fingolimod or overdosing with Treg cells to promote neural tissue repair and reduce the damage caused by ischemic stroke.However,such studies have been relatively infrequent,and clinical experience is still insufficient.In summary,in ischemic stroke,T cell subsets and activated microglia act synergistically to regulate inflammatory progression,mainly by secreting inflammatory factors.In the future,a key research direction for ischemic stroke treatment could be rooted in the enhancement of anti-inflammatory factor secretion by promoting the generation of Th2 and Treg cells,along with the activation of M2-type microglia.These approaches may alleviate neuroinflammation and facilitate the repair of neural tissues.
文摘Deficiency or restriction of Zn absorption in soils is one of the most common micronutrients deficient in cereal plants. To investigate critical micronutrient interaction in zinc deficiency and zinc sufficient in soil, a factorial experiment based on completely randomized design (CRD) with three replications was conducted in 2023. Six wheat cultivars with different Zn efficiency were used. The cultivars were grown under Zn deficiency and adequate conditions. Results showed that in Zn deficiency conditions, with increasing Zn concentration in the roots, Fe concentrations were increased too, while the Cu and Mn concentrations decreased. In the same condition and with increasing Zn concentration in shoots, the concentrations of Fe and Mn decreased, while Cu were increased. However, by increasing Zn concentration, Fe, Cu, and Mn concentrations were increased in Zn deficiency condition in grains, as well as Zn sufficient conditions. RST (root to shoot micronutrient translocation) comparison of cultivars showed that in lack of Zn, the ability of translocation of Zn, Fe, and Mn in Zn-inefficient cultivar from root to shoot was higher than inefficient cultivar. In the same conditions, the capability of Zn-inefficient cultivar in Cu translocation from root to shoot was lower than other cultivars. In general, it seems that in Zn deficiency conditions, there are antagonistic effects among Zn, Cu and Mn and synergistic effects between Zn and Fe in the root. Also, in Zn sufficient conditions, there were synergistic effects among all studies micronutrients which include Zn, Fe, Cu, and Mn.
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
基金funded by the National Natural Science Foundation of China (No. 52304133)the National Key R&D Program of China (No. 2022YFC3004605)the Department of Science and Technology of Liaoning Province (No. 2023-BS-083)。
文摘Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effective rockburst control. In this study, the squeezing behavior of the surrounding rock is analyzed in rockburst roadways, and a mechanical model of rockbursts is established considering the dynamic support stress, thus deriving formulas and providing characteristic curves for describing the interaction between the support and surrounding rock. Design principles and parameters of supports for rockburst control are proposed. The results show that only when the geostress magnitude exceeds a critical value can it drive the formation of rockburst conditions. The main factors influencing the convergence response and rockburst occurrence around roadways are geostress, rock brittleness, uniaxial compressive strength, and roadway excavation size. Roadway support devices can play a role in controlling rockburst by suppressing the squeezing evolution of the surrounding rock towards instability points of rockburst. Further, the higher the strength and the longer the impact stroke of support devices with constant resistance, the more easily multiple balance points can be formed with the surrounding rock to control rockburst occurrence. Supports with long impact stroke allow adaptation to varying geostress levels around the roadway, aiding in rockburst control. The results offer a quantitative method for designing support systems for rockburst-prone roadways. The design criterion of supports is determined by the intersection between the convergence curve of the surrounding rock and the squeezing deformation curve of the support devices.
基金funding from the European Union’s Horizon 2020 research and innovation program through the European IMPULSE project under Grant Agreement No.871161from LASERLAB-EUROPE V under Grant Agreement No.871124+6 种基金from the Grant Agency of the Czech Republic(Grant No.GM23-05027M)Grant No.PDC2021120933-I00 funded by MCIN/AEI/10.13039/501100011033by the European Union Next Generation EU/PRTRsupported by funding from the Ministerio de Ciencia,Innovación y Universidades in Spain through ICTS Equipment Grant No.EQC2018-005230-Pfrom Grant No.PID2021-125389O A-I00 funded by MCIN/AEI/10.13039/501100011033/FEDER,UEby“ERDF A Way of Making Europe”by the European Unionfrom grants of the Junta de Castilla y León with Grant Nos.CLP263P20 and CLP087U16。
文摘This work demonstrates experimentally the close relation between return currents from relativistic laser-driven target polarization and the quality of the relativistic laser–plasma interaction for laser-driven secondary sources,taking as an example ion acceleration by target normal sheath acceleration.The Pearson linear correlation of maximum return current amplitude and proton spectrum cutoff energy is found to be in the range from~0.70 to 0.94.kA-scale return currents rise in all interaction schemes where targets of any kind are charged by escaping laser-accelerated relativistic electrons.Their precise measurement is demonstrated using an inductive scheme that allows operation at high repetition rates.Thus,return currents can be used as a metrological online tool for the optimization of many laser-driven secondary sources and for diagnosing their stability.In particular,in two parametric studies of laser-driven ion acceleration,we carry out a noninvasive online measurement of return currents in a tape target system irradiated by the 1 PW VEGA-3 laser at Centro de Láseres Pulsados:first the size of the irradiated area is varied at best compression of the laser pulse;second,the pulse duration is varied by means of induced group delay dispersion at best focus.This work paves the way to the development of feedback systems that operate at the high repetition rates of PW-class lasers.
基金supported by the National Natural Science Foundation of China(42277087,42130708,42471021,42277482,and 42361144876)the Natural Science Foundation of Guangdong Province(2024A1515012550)+3 种基金the Hainan Institute of National Park grant(KY-23ZK01)the Tsinghua Shenzhen International Graduate School Cross-disciplinary Research and Innovation Fund Research Plan(JC2022011)the Shenzhen Science and Technology Program(JCYJ20240813112106009 and ZDSYS20220606100806014)the Scientific Research Start-up Funds(QD2021030C)from Tsinghua Shenzhen International Graduate School。
文摘Food systems are deeply affected by climate change and air pollution,while being key contributors to these environmental challenges.Understanding the complex interactions among food systems,climate change,and air pollution is crucial for mitigating climate change,improving air quality,and promoting the sustainable development of food systems.However,the literature lacks a comprehensive review of these interactions,particularly in the current phase of rapid development in the field.To address this gap,this study systematically reviews recent research on the impacts of climate change and air pollution on food systems,as well as the greenhouse gas and air pollutant emissions from agri-food systems and their contribution to global climate change and air pollution.In addition,this study summarizes various strategies for mitigation and adaptation,including adjustments in agricultural practices and food supply chains.Profound changes in food systems are urgently needed to enhance adaptability and reduce emissions.This review offers a critical overview of current research on the interactions among food systems,climate change,and air pollution and highlights future research directions to support the transition to sustainable food systems.
基金financial support from the Department of Science and Technology of Jilin Province(20240304104SF,20240304103SF)the Research and Innovation Fund of the Beihua University for the Graduate Student(Major Project 2023012)。
文摘Lithium-ion batteries are widely recognized as prime candidates for energy storage devices.Ethylene carbonate(EC)has become a critical component in conventional commercial electrolytes due to its exceptional film-forming properties and high dielectric constant.However,the elevated freezing point,high viscosity,and strong solvation energy of EC significantly hinder the transport rate of Li^(+)and the desolvation process at low temperatures.This leads to substantial capacity loss and even lithium plating on graphite anodes.Herein,we have developed an efficient electrolyte system specifically designed for lowtemperature conditions,which consists of 1.0 M lithium bis(fluorosulfonyl)imide(LiFSI)in isoxazole(IZ)with fluorobenzene(FB)as an uncoordinated solvent and fluoroethylene carbonate(FEC)as a filmforming co-solvent.This system effectively lowers the desolvation energy of Li^(+)through dipole-dipole interactions.The weak solvation capability allows more anions to enter the solvation sheath,promoting the formation of contact ion pairs(CIPs)and aggregates(AGGs)that enhance the transport rate of Li^(+)while maintaining high ionic conductivity across a broad temperature range.Moreover,the formation of inorganic-dominant interfacial phases on the graphite anode,induced by fluoroethylene carbonate,significantly enhances the kinetics of Li^(+)transport.At a low temperature of-20℃,this electrolyte system achieves an impressive reversible capacity of 200.9 mAh g^(-1)in graphite half-cell,which is nearly three times that observed with conventional EC-based electrolytes,demonstrating excellent stability throughout its operation.
基金supported by A*STAR under the“Nanosystems at the Edge”program(Grant No.A18A4b0055)Ministry of Education(MOE)under the research grant of R-263-000-F18-112/A-0009520-01-00+1 种基金National Research Foundation Singapore grant CRP28-2022-0038the Reimagine Re-search Scheme(RRSC)Project(Grant A-0009037-02-00&A0009037-03-00)at National University of Singapore.
文摘Plasmonic nanoantennas provide unique opportunities for precise control of light–matter coupling in surface-enhanced infrared absorption(SEIRA)spectroscopy,but most of the resonant systems realized so far suffer from the obstacles of low sensitivity,narrow bandwidth,and asymmetric Fano resonance perturbations.Here,we demonstrated an overcoupled resonator with a high plasmon-molecule coupling coefficient(μ)(OC-Hμresonator)by precisely controlling the radiation loss channel,the resonator-oscillator coupling channel,and the frequency detuning channel.We observed a strong dependence of the sensing performance on the coupling state,and demonstrated that OC-Hμresonator has excellent sensing properties of ultra-sensitive(7.25%nm^(−1)),ultra-broadband(3–10μm),and immune asymmetric Fano lineshapes.These characteristics represent a breakthrough in SEIRA technology and lay the foundation for specific recognition of biomolecules,trace detection,and protein secondary structure analysis using a single array(array size is 100×100μm^(2)).In addition,with the assistance of machine learning,mixture classification,concentration prediction and spectral reconstruction were achieved with the highest accuracy of 100%.Finally,we demonstrated the potential of OC-Hμresonator for SARS-CoV-2 detection.These findings will promote the wider application of SEIRA technology,while providing new ideas for other enhanced spectroscopy technologies,quantum photonics and studying light–matter interactions.
基金supported by the Chongqing Natural Science Foundation of Chongqing,China(No.CSTB2022NSCQ-MSX0333)the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZD-K202401205)+1 种基金Chongqing Three Gorges University Graduate Research and Innovation Project Funding(No.YJSKY24045)Chongqing Engineering Research Center of Disaster Prevention&Control for Banks and Structures in Three Gorges Reservoir Area(No.SXAPGC24YB14,No.SXAPGC24YB03,No.SXAPGC24YB12)。
文摘Oil shale is characterized by a dense structure,low proportion of pores and fissures,and low permeability.Pore-fracture systems serve as crucial channels for shale oil migration,directly influencing the production efficiency of shale oil resources.Effectively stimulating oil shale reservoirs remains a challenging and active research topic.This investigation employed shale specimens obtained from the Longmaxi Formation.Scanning electron microscopy,fluid injection experiments,and fluid-structure interaction simulations were used to comprehensively analyze structural changes and fluid flow behavior under high temperatures from microscopic to macroscopic scales.Experimental results indicate that the temperature has little effect on the structure and permeability of shale before 300℃.However,there are two threshold temperatures within the range of 300 to 600℃that have significant effects on the structure and permeability of oil shale.The first threshold temperature is between 300 and 400℃,which causes the oil shale porosity,pore-fracture ratio,and permeability begin to increase.This is manifested by the decrease in micropores and mesopores,the increase in macropores,and the formation of a large number of isolated pores and fissures within the shale.The permeability increases but not significantly.The second threshold temperature is between 500 and 600℃,which increases the permeability of oil shale significantly.During this stage,micropores and mesopores are further reduced,and macropores are significantly enlarged.A large number of connected and penetrated pores and fissures are formed.More numerous and thicker streamlines appear inside the oil shale.The experimental results demonstrate that high temperatures significantly alter the microstructure and permeability of oil shale.At the same time,the experimental results can provide a reference for the research of in-situ heating techniques in oil shale reservoir transformation.
基金Research Institute for Smart Energy(CDB2)the grant from the Research Institute for Advanced Manufacturing(CD8Z)+4 种基金the grant from the Carbon Neutrality Funding Scheme(WZ2R)at The Hong Kong Polytechnic Universitysupport from the Hong Kong Polytechnic University(CD9B,CDBZ and WZ4Q)the National Natural Science Foundation of China(22205187)Shenzhen Municipal Science and Technology Innovation Commission(JCYJ20230807140402006)Start-up Foundation for Introducing Talent of NUIST and Natural Science Foundation of Jiangsu Province of China(BK20230426).
文摘Catalyst–support interaction plays a crucial role in improving the catalytic activity of oxygen evolution reaction(OER).Here we modulate the catalyst–support interaction in polyaniline-supported Ni_(3)Fe oxide(Ni_(3)Fe oxide/PANI)with a robust hetero-interface,which significantly improves oxygen evolution activities with an overpotential of 270 mV at 10 mA cm^(-2)and specific activity of 2.08 mA cm_(ECSA)^(-2)at overpotential of 300 mV,3.84-fold that of Ni_(3)Fe oxide.It is revealed that the catalyst–support interaction between Ni_(3)Fe oxide and PANI support enhances the Ni–O covalency via the interfacial Ni–N bond,thus promoting the charge and mass transfer on Ni_(3)Fe oxide.Considering the excellent activity and stability,rechargeable Zn-air batteries with optimum Ni_(3)Fe oxide/PANI are assembled,delivering a low charge voltage of 1.95 V to cycle for 400 h at 10 mA cm^(-2).The regulation of the effect of catalyst–support interaction on catalytic activity provides new possibilities for the future design of highly efficient OER catalysts.
基金supported by grants from the National Natural Science Foundation of China(Grant No.:T2341008)Intelligent and Precise Research on TCM for Spleen and Stomach Diseases(20233930063).
文摘Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,offering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summarizes the application of AI in drug development,particularly in drug-target prediction,and offers recommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.
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.
基金supported by the National Key R&D Program of China(Grant No.:2023YFC2604400)the National Natural Science Foundation of China(Grant No.:62103436).
文摘Identifying drug-drug interactions(DDIs)is essential to prevent adverse effects from polypharmacy.Although deep learning has advanced DDI identification,the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits.Here,we developed a Multi-Dimensional Feature Fusion model named MDFF,which integrates one-dimensional simplified molec-ular input line entry system sequence features,two-dimensional molecular graph features,and three-dimensional geometric features to enhance drug representations for predicting DDIs.MDFF was trained and validated on two DDI datasets,evaluated across three distinct scenarios,and compared with advanced DDI prediction models using accuracy,precision,recall,area under the curve,and F1 score metrics.MDFF achieved state-of-the-art performance across all metrics.Ablation experiments showed that integrating multi-dimensional drug features yielded the best results.More importantly,we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs.Among 12 real-world adverse drug reaction reports,the predictions of 9 reports were supported by relevant evidence.Additionally,MDFF demon-strated the ability to explain adverse DDI mechanisms,providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.
基金supported by the National Natural Science Foundation of China(Nos.82173746 and U23A20530)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission)。
文摘Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.
基金financially supported by the National Key Research and Development Program of China-Young Scientist Project(No.2024YFC2815400)the National Natural Science Foundation of China(No.52588202).
文摘Deep-sea mining has emerged as a critical solution to address global resource shortages;however,the mechanical interaction between tracked mining vehicles(TMVs)and soft seabed sediments presents fundamental engineering challenges.This study establishes a multiscale modelling framework coupling the discrete element method(DEM)with multi-body dynamics(MBD)to investigate track-seabed dynamic interactions across three operational modes:flat terrain,slope climbing,and ditch surmounting.The simulation framework,validated against laboratory experiments,systematically evaluates the influence of grouser geometry(involute,triangular,and pin-type)and traveling speed(0.2–1.0 m/s)on traction performance,slip rate,and ground pressure distribution.Results reveal rate-dependent traction mechanisms governed by soil microstructural responses:higher speeds enhance peak traction but exacerbate slip instability on complex terrain.Critical operational thresholds are established—0.7 m/s for flat terrain,≤0.5 m/s for slopes and ditches—with distinct grouser optimization strategies:involute grousers achieve 35%–40%slip reduction on slopes through progressive soil engagement,while triangular grousers provide optimal impact resistance during ditch crossing with 30%–35%performance improvement.These findings provide quantitative design criteria and operational guidelines for optimizing TMV structural parameters and control strategies,offering a robust theoretical foundation for enhancing the performance,safety,and reliability of deep-sea mining equipment in complex submarine environments.
文摘This article describes a pilot study aiming at generating social interactions between a humanoid robot and adolescents with autism spectrum disorder (ASD), through the practice of a gesture imitation game. The participants were a 17-year-old young lady with ASD and intellectual deficit, and a control participant: a preadolescent with ASD but no intellectual deficit (Asperger syndrome). The game is comprised of four phases: greetings, pairing, imitation, and closing. Field educators were involved, playing specific roles: visual or physical inciter. The use of a robot allows for catching the participants’ attention, playing the imitation game for a longer period of time than with a human partner, and preventing the game partner’s negative facial expressions resulting from tiredness, impatience, or boredom. The participants’ behavior was observed in terms of initial approach towards the robot, positioning relative to the robot in terms of distance and orientation, reactions to the robot’s voice or moves, signs of happiness, and imitation attempts. Results suggest a more and more natural approach towards the robot during the sessions, as well as a higher level of social interaction, based on the variations of the parameters listed above. We use these preliminary results to draw the next steps of our research work as well as identify further perspectives, with this aim in mind: improving social interactions with adolescents with ASD and intellectual deficit, allowing for better integration of these people into our societies.
