Densely deployed Wi Fi networks will play a crucial role in providing the capacity for next generation mobile internet. However, due to increasing interference, overlapped channels in Wi Fi networks and throughput eff...Densely deployed Wi Fi networks will play a crucial role in providing the capacity for next generation mobile internet. However, due to increasing interference, overlapped channels in Wi Fi networks and throughput efficiency degradation, densely deployed Wi Fi networks is not a guarantee to obtain higher throughput. An emergent challenge is how to effi ciently utilize scarce spectrum resources, by matching physical layer resources to traffi c demand. In this aspect, access control allocation strategies play a pivotal role but remain too coarse-grained. As a solution, this research proposes a flexible framework for fine-grained channel width adaptation and multi-channel access in Wi Fi networks. This approach, named SFCA(Subcarrier Fine-grained Channel Access), adopts DOFDM(Discontinuous Orthogonal Frequency Division Multiplexing) at the PHY layer. It allocates the frequency resource with a subcarrier granularity, which facilitates the channel width adaptation for multi-channel access and thus brings more fl exibility and higher frequency efficiency. The MAC layer uses a frequencytime domain backoff scheme, which combines the popular time-domain BEB scheme with a frequency-domain backoff to decrease access collision, resulting in higher access probability for the contending nodes. SFCA is compared with FICA(an established access scheme)showing significant outperformance. Finally we present results for next generation 802.11 ac Wi Fi networks.展开更多
We propose molten polymer's entanglement network deformation to be nonaffine and use transient network structural theory with the revised Liu's kinetics rate equation and the revised upper convected Maxwell co...We propose molten polymer's entanglement network deformation to be nonaffine and use transient network structural theory with the revised Liu's kinetics rate equation and the revised upper convected Maxwell constitutive equation to establish a nonaffine network structural constitutive model for studying the rheological behavior of molten Low Density Polyethylene (LDPE) and High Density Polyethylene (HDPE) in oscillatory shear. As a result, when the strain amplitude or frequency increases, the shear stress amplitude increases. At the same time, the accuracy of the nonaffine network model is higher than that of affine network model. It is clear that there is a small amount of nonaffine network deformation for LDPE melts which have long chain branches, and there is a larger amount of nonaffine network deformation in oscillatory shear for HDPE melts which has no long chain branches. So we had better consider the network deformation nonaffine when we establish the constitutive equations of polymer melts in oscillatory shear.展开更多
In the context of rapid urbanization,high-density construction areas face significant challenges,including the reduction of ecological spaces and the deterioration of their functions.Planning and managing ecological s...In the context of rapid urbanization,high-density construction areas face significant challenges,including the reduction of ecological spaces and the deterioration of their functions.Planning and managing ecological spaces have emerged as essential strategies to address the conflict between urban development and ecological conservation.Using Jinjiang City,Fujian Province as the case study,this paper systematically examines the significance and primary challenges of ecological space planning in highdensity construction areas.It also identifies prevailing issues within the current research domain,including“an overemphasis on top-level design at the expense of implementation,a focus on isolated aspects rather than systemic integration,and prioritization of control over coordination”.This study proposes the key aspects of ecological space planning and management in high-density construction areas,focusing on three fundamental dimensions:human-centered demand orientation,the integration of top-down and bottomup linkage mechanisms,and a differentiated control system.Drawing on the full-element assessment of the ecosystem,ecological network construction,and full-process control system implemented in Jinjiang City,an integrated approach to ecological space governance,encompassing assessment,planning,and control,has been developed.This approach offers both theoretical insights and practical guidance for optimizing ecological spaces in comparable urban contexts.展开更多
Stroke survivors often face significant challenges when performing daily self-care activities due to upper limb motor impairments.Traditional surface electromyography(sEMG)analysis typically focuses on isolated hand p...Stroke survivors often face significant challenges when performing daily self-care activities due to upper limb motor impairments.Traditional surface electromyography(sEMG)analysis typically focuses on isolated hand postures,overlooking the complexity of object-interactive behaviors that are crucial for promoting patient independence.This study introduces a novel framework that combines high-density sEMG(HD-sEMG)signals with an improved Whale Optimization Algorithm(IWOA)-optimized Long Short-Term Memory(LSTM)network to address this limitation.The key contributions of this work include:(1)the creation of a specialized HD-sEMG dataset that captures nine continuous self-care behaviors,along with time and posture markers,to better reflect real-world patient interactions;(2)the development of a multi-channel feature fusion module based on Pascal’s theorem,which enables efficient signal segmentation and spatial–temporal feature extraction;and(3)the enhancement of the IWOA algorithm,which integrates optimal point set initialization,a diversity-driven pooling mechanism,and cosine-based differential evolution to optimize LSTM hyperparameters,thereby improving convergence and global search capabilities.Experimental results demonstrate superior performance,achieving 99.58%accuracy in self-care behavior recognition and 86.19%accuracy for 17 continuous gestures on the Ninapro db2 benchmark.The framework operates with low latency,meeting the real-time requirements for assistive devices.By enabling precise,context-aware recognition of daily activities,this work advances personalized rehabilitation technologies,empowering stroke patients to regain autonomy in self-care tasks.The proposed methodology offers a robust,scalable solution for clinical applications,bridging the gap between laboratory-based gesture recognition and practical,patient-centered care.展开更多
Metal-support interactions and hydrogen spillover effects in heterogeneous catalysts play a crucial role in aromatic hydrogenation reactions;however,these effects are limited by the metal dispersion on the catalyst an...Metal-support interactions and hydrogen spillover effects in heterogeneous catalysts play a crucial role in aromatic hydrogenation reactions;however,these effects are limited by the metal dispersion on the catalyst and the number of acceptable H*receptors.This study prepares highly dispersed Ni nanoparticles(NPs)catalysts on a Beta substrate via precursor structure topology transformation.In contrast to traditional support materials,the coordination and electronic structure changes between the Ni NPs and the support were achieved,further optimizing the active interface sites and enhancing hydrogen activation and hydrogenation performance.Additionally,the-OH groups at the strong acid sites in zeolite effectively intensified the hydrogen spillover effect as receptors for H^(*)migration and anchoring,accelerating the hydrogenation rate of aromatic rings.Under solvent-free conditions,this catalyst was used for the hydrogenation reaction of aromatic-rich oils,directly producing a C_(8)-C_(14)branched cycloalkanes mixture with an aromatic conversion rate of>99%.The cycloalkanes mixture produced by this method features high density(0.92 g/mL)and a low freezing point(<-60℃),making it suitable for use as high-density aviation fuel or as an additive to enhance the volumetric heat value of conventional aviation fuels in practical applications.展开更多
Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy o...Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.展开更多
In rotationally extruded fittings,high-density polyethylene(HDPE)pipes prepared using conventional processing methods often suffer from poor pressure resistance and low toughness.This study introduces an innovative ro...In rotationally extruded fittings,high-density polyethylene(HDPE)pipes prepared using conventional processing methods often suffer from poor pressure resistance and low toughness.This study introduces an innovative rotary shear system(RSS)to address these deficiencies through controlled mandrel rotation and cooling rates.We successfully prepared self-reinforced HDPE pipes with a three-layer structure combining spherical and shish-kebab crystals.Rotational processing aligned the molecular chains in the ring direction and formed shish-kebab crystals.As a result,the annular tensile strength of the rotationally processed three-layer shish-kebab structure(TSK)pipe increased from 26.7 MPa to 76.3 MPa,an enhancement of 185.8%.Notably,while maintaining excellent tensile strength(73.4 MPa),the elongation at break of the spherulite shishkebab spherulite(SKS)tubes was improved to 50.1%,as compared to 33.8%in the case of shish-kebab spherulite shish-kebab(KSK)tubes.This improvement can be attributed to the changes in the micro-morphology and polymer structure within the SKS tubes,specifically due to the formation of small-sized shish-kebab crystals and the low degrees of interlocking.In addition,2D-SAXS analysis revealed that KSK tubes have higher tensile strength due to smaller crystal sizes and larger shish dimensions,forming dense interlocking structures.In contrast,the SKS and TSK tubes had thicker amorphous regions and smaller shish sizes,resulting in reduced interlocking and mechanical performance.展开更多
Evaluation of backfilling effectiveness plays a crucial role in the geological environment management and restoration of abandoned open-pit quarries,providing a scientific basis for subsequent greening efforts.Backfil...Evaluation of backfilling effectiveness plays a crucial role in the geological environment management and restoration of abandoned open-pit quarries,providing a scientific basis for subsequent greening efforts.Backfill soil,predominantly composed of silty clay,demonstrates high water retention capacity and elevated moisture content,leading to a pronounced resistivity contrast with the bedrock exposed by quarrying activities.To investigate the distribution of backfill soil subsurface and assess backfilling effectiveness in the study area,this study conducted a comprehensive geophysical investigation utilizing the high-density electrical resistivity tomography(ERT).A total of 19 ERT survey lines were deployed across three distinct areas in Liuyao Village,Huaibei City,Anhui Province,China.The inversion results,derived from both two-dimensional(2D)and three-dimensional(3D),reveal distinct electrical properties of the subsurface materials:the backfill soil layer shows low resistivity features,the fill stone layer exhibits medium to high resistivity,and the bedrock shows the highest resistivity.The 2D inversion results,from the data measured using the Wenner array effectively capture the spatial distribution and structural features of the backfill soil layer.The findings indicate a gradual east-west thinning of the clay layer within the quarry.Furthermore,the northern pit area exhibits a uniform distribution of backfill soil layer,indicative of effective backfilling operations.In contrast,the southern pit area lacks a well-defined clay layer,suggesting suboptimal backfilling effectiveness.展开更多
High-density interconnect(HDI)soft electronics that can integrate multiple individual functions into one miniaturized monolithic system is promising for applications related to smart healthcare,soft robotics,and human...High-density interconnect(HDI)soft electronics that can integrate multiple individual functions into one miniaturized monolithic system is promising for applications related to smart healthcare,soft robotics,and human-machine interactions.However,despite the recent advances,the development of three-dimensional(3D)soft electronics with both high resolution and high integration is still challenging because of the lack of efficient manufacturing methods to guarantee interlayer alignment of the high-density vias and reliable interlayer electrical conductivity.Here,an advanced 3D laser printing pathway,based on femtosecond laser direct writing(FLDW),is demonstrated for preparing liquid metal(LM)-based any layer HDI soft electronics.FLDW technology,with the characteristics of high spatial resolution and high precision,allows the maskless fabrication of high-resolution embedded LM microchannels and high-density vertical interconnect accesses for 3D integrated circuits.High-aspect-ratio blind/through LM microstructures are formed inside the elastomer due to the supermetalphobicity induced during laser ablation.The LM-based HDI circuit featuring high resolution(~1.5μm)and high integration(10-layer electrical interconnection)is achieved for customized soft electronics,including various customized multilayer passive electric components,soft multilayer circuit,and cross-scale multimode sensors.The 3D laser printing method provides a versatile approach for developing chip-level soft electronics.展开更多
Dislocation strengthening,as one of the methods to simultaneously enhance the room temperature strength and ductility of alloys,does not achieve the desired strengthening and plasticity effect during elevated-temperat...Dislocation strengthening,as one of the methods to simultaneously enhance the room temperature strength and ductility of alloys,does not achieve the desired strengthening and plasticity effect during elevated-temperature deformation.Here,we report a novel strategy to boost the dislocation multiplication and accumulation during deformation at elevated temperatures through dynamic strain aging(DSA).With the introduction of the rare-earth element Ho in Mg-Y-Zn alloy,Ho atoms diffuse toward dislocations during deformation at elevated temperatures,provoking the DSA effect,which increases the dislocation density significantly via the interactions of mobile dislocations and Ho atoms.The resulting alloy achieves a great enhancement of dislocation hardening and obtains the dual benefits of high strength and good ductility simultaneously at high homologous temperatures.The present work provides an effective strategy to enhancing the strength and ductility for elevated-temperature materials.展开更多
BACKGROUND The association between the uric acid-to-high-density lipoprotein cholesterol ratio(UHR)and mental health among individuals with type 2 diabetes mellitus(T2DM)has not been thoroughly investigated.AIM To exa...BACKGROUND The association between the uric acid-to-high-density lipoprotein cholesterol ratio(UHR)and mental health among individuals with type 2 diabetes mellitus(T2DM)has not been thoroughly investigated.AIM To examine the link between UHR and symptoms of depression and anxiety in patients with T2DM.METHODS A cross-sectional analysis was carried out from March 2023 to April 2024,involving participants diagnosed with T2DM.Data on sociodemographic characteristics,clinical parameters,and UHR values were systematically gathered.The Self-Rating Depression Scale(SDS)and Self-Rating Anxiety Scale(SAS)were utilized to evaluate depressive and anxiety symptoms,respectively.To assess the relationships between UHR and SDS/SAS scores,linear regression models were employed,incorporating adjustments for potential confounding variables.Additionally,smooth curve fitting and threshold effect analyses were conducted to explore potential nonlinear relationships.RESULTS A total of 285 patients with T2DM were included.Initial univariate analysis demonstrated a significant positive correlation between elevated UHR levels and higher SDS and SAS scores.Multivariate regression analysis revealed that a one-unit rise in UHR was associated with a 1.13-point increase in SDS scores(95%CI:0.69-1.58)and a 0.57-point increase in SAS scores(95%CI:0.20-0.93).After controlling for confounders,UHR remained positively correlated with SDS(β=1.55,95%CI:0.57-2.53)and SAS(β=0.72,95%CI:0.35-1.09).Nonlinear analysis identified critical thresholds at UHR values of 5.02 for SDS and 4.00 for SAS,beyond which the relationships between UHR and psychological symptom scores became markedly stronger(P<0.05).CONCLUSION Higher UHR levels are significantly linked to exacerbated depressive and anxiety symptoms in patients with T2DM.These results indicate that UHR may function as a promising biomarker to identify individuals at greater risk of mental health complications within this population.展开更多
Rapeseed mustard(Brassica juncea L.) is the third most important oilseed crop in the world, but the geneticmechanism underlying its massive phenotypic variation remains largely unexplored. In this study, specific leng...Rapeseed mustard(Brassica juncea L.) is the third most important oilseed crop in the world, but the geneticmechanism underlying its massive phenotypic variation remains largely unexplored. In this study, specific length amplified fragment sequencing(SLAF-Seq) was used to resequence a population comprising 197 F8recombinantinbred lines(RILs) derived from a cross between vegetable-type Qichi881 and oilseed-type YufengZC of B. juncea. In total, 438,895 high-quality SLAFs were discovered, 47,644 of which were polymorphic, and 3,887 of the polymorphic markers met the requirements for genetic map construction. The final map included 3,887 markers on 18 linkage groups and was 1,830.23 centiMorgan(cM) in length, with an average distance of 0.47 cM between adjacent markers. Using the newly constructed high-density genetic map, a total of 53 QTLs for erucicacid(EA), oleic acid(OA), and linolenic acid(LNA) were detected and integrated into eight consensus QTLswith two for each of these traits. For each of these three traits, two candidate genes were cloned and sequence analysis indicated colocalization with their respective consensus QTLs. The co-dominant allele-specific markers for Bju.FAD3.A03 and Bju.FAD3.B07 were developed and showed co-localization with their consensus QTLs andco-segregation with LNA content, further supporting the results of QTL mapping and bioinformatic analysis. Theexpression levels of the cloned homologous genes were also determined, and the genes were tightly correlatedwith the EA, OA and LNA contents of different lines. The results of this study will facilitate the improvement offatty acid traits and molecular breeding of B. juncea. Further uses of the high-density genetic map created in this study are also discussed.展开更多
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results i...In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.展开更多
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.展开更多
BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes tha...BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.展开更多
This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential g...This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY o...Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY on AD.Methods:The DNFB-induced mouse models of AD were established to investigate the therapeutic effects of WQY on AD.The symptoms of AD in the ears and backs of the mice were assessed,while inflammatory factors in the ear were quantified using quantitative real-time-polymerase chain reaction(qRT-PCR),and the percentages of CD4^(+)and CD8^(+)cells in the spleen were analyzed through flow cytometry.The compounds in WQY were identified using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis and the key targets and pathways of WQY to treat AD were predicted by network pharmacology.Subsequently,the key genes were tested and verified by qRT-PCR,and the potential active components and target proteins were verified by molecular docking.Results:WQY relieved the AD symptoms and histopathological injuries in the ear and back skin of mice with AD.Meanwhile,WQY significantly reduced the levels of inflammatory factors IL-6 and IL-1βin ear tissue,as well as the ratio of CD4^(+)/CD8^(+)cells in spleen.Additionally,a total of 142 compounds were identified from the water extract of WQY by UPLC-Orbitrap-MS/MS.39 key targets related to AD were screened out by network pharmacology methods.The KEGG analysis indicated that the effects of WQY were primarily mediated through pathways associated with Toll-like receptor signaling and T cell receptor signaling.Moreover,the results of qRT-PCR demonstrated that WQY significantly reduced the mRNA expressions of IL-4,IL-10,GATA3 and FOXP3,and molecular docking simulation verified that the active components of WQY had excellent binding abilities with IL-4,IL-10,GATA3 and FOXP3 proteins.Conclusion:The present study demonstrated that WQY effectively relieved AD symptoms in mice,decreased the inflammatory factors levels,regulated the balance of CD4^(+)and CD8^(+)cells,and the mechanism may be associated with the suppression of Th2 and Treg cell immune responses.展开更多
基金supported by National Natural Science Foundation of China(No.61471376)the 863 project(No.2014AA01A701)
文摘Densely deployed Wi Fi networks will play a crucial role in providing the capacity for next generation mobile internet. However, due to increasing interference, overlapped channels in Wi Fi networks and throughput efficiency degradation, densely deployed Wi Fi networks is not a guarantee to obtain higher throughput. An emergent challenge is how to effi ciently utilize scarce spectrum resources, by matching physical layer resources to traffi c demand. In this aspect, access control allocation strategies play a pivotal role but remain too coarse-grained. As a solution, this research proposes a flexible framework for fine-grained channel width adaptation and multi-channel access in Wi Fi networks. This approach, named SFCA(Subcarrier Fine-grained Channel Access), adopts DOFDM(Discontinuous Orthogonal Frequency Division Multiplexing) at the PHY layer. It allocates the frequency resource with a subcarrier granularity, which facilitates the channel width adaptation for multi-channel access and thus brings more fl exibility and higher frequency efficiency. The MAC layer uses a frequencytime domain backoff scheme, which combines the popular time-domain BEB scheme with a frequency-domain backoff to decrease access collision, resulting in higher access probability for the contending nodes. SFCA is compared with FICA(an established access scheme)showing significant outperformance. Finally we present results for next generation 802.11 ac Wi Fi networks.
文摘We propose molten polymer's entanglement network deformation to be nonaffine and use transient network structural theory with the revised Liu's kinetics rate equation and the revised upper convected Maxwell constitutive equation to establish a nonaffine network structural constitutive model for studying the rheological behavior of molten Low Density Polyethylene (LDPE) and High Density Polyethylene (HDPE) in oscillatory shear. As a result, when the strain amplitude or frequency increases, the shear stress amplitude increases. At the same time, the accuracy of the nonaffine network model is higher than that of affine network model. It is clear that there is a small amount of nonaffine network deformation for LDPE melts which have long chain branches, and there is a larger amount of nonaffine network deformation in oscillatory shear for HDPE melts which has no long chain branches. So we had better consider the network deformation nonaffine when we establish the constitutive equations of polymer melts in oscillatory shear.
文摘In the context of rapid urbanization,high-density construction areas face significant challenges,including the reduction of ecological spaces and the deterioration of their functions.Planning and managing ecological spaces have emerged as essential strategies to address the conflict between urban development and ecological conservation.Using Jinjiang City,Fujian Province as the case study,this paper systematically examines the significance and primary challenges of ecological space planning in highdensity construction areas.It also identifies prevailing issues within the current research domain,including“an overemphasis on top-level design at the expense of implementation,a focus on isolated aspects rather than systemic integration,and prioritization of control over coordination”.This study proposes the key aspects of ecological space planning and management in high-density construction areas,focusing on three fundamental dimensions:human-centered demand orientation,the integration of top-down and bottomup linkage mechanisms,and a differentiated control system.Drawing on the full-element assessment of the ecosystem,ecological network construction,and full-process control system implemented in Jinjiang City,an integrated approach to ecological space governance,encompassing assessment,planning,and control,has been developed.This approach offers both theoretical insights and practical guidance for optimizing ecological spaces in comparable urban contexts.
基金supported by the National Natural Science Foundation of China(72061006)the research on the auxiliary diagnosis system of chronic injury of levator scapulae based on the concept of digital twin(Contract No:Qian Kehe Support[2023]General 117)Research on indoor intelligent assisted walking robot for the rehabilitation of walking ability of the elderly(Contract No:Qian kehe Support[2023]General 124).
文摘Stroke survivors often face significant challenges when performing daily self-care activities due to upper limb motor impairments.Traditional surface electromyography(sEMG)analysis typically focuses on isolated hand postures,overlooking the complexity of object-interactive behaviors that are crucial for promoting patient independence.This study introduces a novel framework that combines high-density sEMG(HD-sEMG)signals with an improved Whale Optimization Algorithm(IWOA)-optimized Long Short-Term Memory(LSTM)network to address this limitation.The key contributions of this work include:(1)the creation of a specialized HD-sEMG dataset that captures nine continuous self-care behaviors,along with time and posture markers,to better reflect real-world patient interactions;(2)the development of a multi-channel feature fusion module based on Pascal’s theorem,which enables efficient signal segmentation and spatial–temporal feature extraction;and(3)the enhancement of the IWOA algorithm,which integrates optimal point set initialization,a diversity-driven pooling mechanism,and cosine-based differential evolution to optimize LSTM hyperparameters,thereby improving convergence and global search capabilities.Experimental results demonstrate superior performance,achieving 99.58%accuracy in self-care behavior recognition and 86.19%accuracy for 17 continuous gestures on the Ninapro db2 benchmark.The framework operates with low latency,meeting the real-time requirements for assistive devices.By enabling precise,context-aware recognition of daily activities,this work advances personalized rehabilitation technologies,empowering stroke patients to regain autonomy in self-care tasks.The proposed methodology offers a robust,scalable solution for clinical applications,bridging the gap between laboratory-based gesture recognition and practical,patient-centered care.
基金financially supported by the National Natural Science Foundation of China(Grant 22278439,21776313)the Shandong Province Higher Education Youth Innovation Technology Support Program(Grant 2022KJ074)。
文摘Metal-support interactions and hydrogen spillover effects in heterogeneous catalysts play a crucial role in aromatic hydrogenation reactions;however,these effects are limited by the metal dispersion on the catalyst and the number of acceptable H*receptors.This study prepares highly dispersed Ni nanoparticles(NPs)catalysts on a Beta substrate via precursor structure topology transformation.In contrast to traditional support materials,the coordination and electronic structure changes between the Ni NPs and the support were achieved,further optimizing the active interface sites and enhancing hydrogen activation and hydrogenation performance.Additionally,the-OH groups at the strong acid sites in zeolite effectively intensified the hydrogen spillover effect as receptors for H^(*)migration and anchoring,accelerating the hydrogenation rate of aromatic rings.Under solvent-free conditions,this catalyst was used for the hydrogenation reaction of aromatic-rich oils,directly producing a C_(8)-C_(14)branched cycloalkanes mixture with an aromatic conversion rate of>99%.The cycloalkanes mixture produced by this method features high density(0.92 g/mL)and a low freezing point(<-60℃),making it suitable for use as high-density aviation fuel or as an additive to enhance the volumetric heat value of conventional aviation fuels in practical applications.
基金Supported by the National Natural Science Foundation of China (61074153, 61104131)the Fundamental Research Fundsfor Central Universities of China (ZY1111, JD1104)
文摘Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.
基金supported by the National Natural Science Foundation of China(Nos.52373045 and 52033005).
文摘In rotationally extruded fittings,high-density polyethylene(HDPE)pipes prepared using conventional processing methods often suffer from poor pressure resistance and low toughness.This study introduces an innovative rotary shear system(RSS)to address these deficiencies through controlled mandrel rotation and cooling rates.We successfully prepared self-reinforced HDPE pipes with a three-layer structure combining spherical and shish-kebab crystals.Rotational processing aligned the molecular chains in the ring direction and formed shish-kebab crystals.As a result,the annular tensile strength of the rotationally processed three-layer shish-kebab structure(TSK)pipe increased from 26.7 MPa to 76.3 MPa,an enhancement of 185.8%.Notably,while maintaining excellent tensile strength(73.4 MPa),the elongation at break of the spherulite shishkebab spherulite(SKS)tubes was improved to 50.1%,as compared to 33.8%in the case of shish-kebab spherulite shish-kebab(KSK)tubes.This improvement can be attributed to the changes in the micro-morphology and polymer structure within the SKS tubes,specifically due to the formation of small-sized shish-kebab crystals and the low degrees of interlocking.In addition,2D-SAXS analysis revealed that KSK tubes have higher tensile strength due to smaller crystal sizes and larger shish dimensions,forming dense interlocking structures.In contrast,the SKS and TSK tubes had thicker amorphous regions and smaller shish sizes,resulting in reduced interlocking and mechanical performance.
基金Supported by National Key Research and Development Program of China(No.2023YFC3707901)。
文摘Evaluation of backfilling effectiveness plays a crucial role in the geological environment management and restoration of abandoned open-pit quarries,providing a scientific basis for subsequent greening efforts.Backfill soil,predominantly composed of silty clay,demonstrates high water retention capacity and elevated moisture content,leading to a pronounced resistivity contrast with the bedrock exposed by quarrying activities.To investigate the distribution of backfill soil subsurface and assess backfilling effectiveness in the study area,this study conducted a comprehensive geophysical investigation utilizing the high-density electrical resistivity tomography(ERT).A total of 19 ERT survey lines were deployed across three distinct areas in Liuyao Village,Huaibei City,Anhui Province,China.The inversion results,derived from both two-dimensional(2D)and three-dimensional(3D),reveal distinct electrical properties of the subsurface materials:the backfill soil layer shows low resistivity features,the fill stone layer exhibits medium to high resistivity,and the bedrock shows the highest resistivity.The 2D inversion results,from the data measured using the Wenner array effectively capture the spatial distribution and structural features of the backfill soil layer.The findings indicate a gradual east-west thinning of the clay layer within the quarry.Furthermore,the northern pit area exhibits a uniform distribution of backfill soil layer,indicative of effective backfilling operations.In contrast,the southern pit area lacks a well-defined clay layer,suggesting suboptimal backfilling effectiveness.
基金supported by the National Science Foundation of China under the Grant Nos.12127806 and 62175195the International Joint Research Laboratory for Micro/Nano Manufacturing and Measurement Technologies。
文摘High-density interconnect(HDI)soft electronics that can integrate multiple individual functions into one miniaturized monolithic system is promising for applications related to smart healthcare,soft robotics,and human-machine interactions.However,despite the recent advances,the development of three-dimensional(3D)soft electronics with both high resolution and high integration is still challenging because of the lack of efficient manufacturing methods to guarantee interlayer alignment of the high-density vias and reliable interlayer electrical conductivity.Here,an advanced 3D laser printing pathway,based on femtosecond laser direct writing(FLDW),is demonstrated for preparing liquid metal(LM)-based any layer HDI soft electronics.FLDW technology,with the characteristics of high spatial resolution and high precision,allows the maskless fabrication of high-resolution embedded LM microchannels and high-density vertical interconnect accesses for 3D integrated circuits.High-aspect-ratio blind/through LM microstructures are formed inside the elastomer due to the supermetalphobicity induced during laser ablation.The LM-based HDI circuit featuring high resolution(~1.5μm)and high integration(10-layer electrical interconnection)is achieved for customized soft electronics,including various customized multilayer passive electric components,soft multilayer circuit,and cross-scale multimode sensors.The 3D laser printing method provides a versatile approach for developing chip-level soft electronics.
基金supported by the National Key Research and Development Project(2023YFA1609100)the NSFC Funding(U2141207,52171111,52001083)+6 种基金Natural Science Foundation of Heilongjiang(YQ2023E026)China Postdoctoral Science foundation(2024M754149)Postdoctoral Fellowship Program of CPSF(GZC20242192)support from the National Science Foundation(DMR-1611180 and 1809640)with the program directors,DrsHKU Seed Fund for Collaborative Research(#2207101618)support by Croucher Senior Research Fellowship and City U Project(Project No.9229019)Shenzhen Science and Technology Program(Project No.JCYJ20220818101203007)。
文摘Dislocation strengthening,as one of the methods to simultaneously enhance the room temperature strength and ductility of alloys,does not achieve the desired strengthening and plasticity effect during elevated-temperature deformation.Here,we report a novel strategy to boost the dislocation multiplication and accumulation during deformation at elevated temperatures through dynamic strain aging(DSA).With the introduction of the rare-earth element Ho in Mg-Y-Zn alloy,Ho atoms diffuse toward dislocations during deformation at elevated temperatures,provoking the DSA effect,which increases the dislocation density significantly via the interactions of mobile dislocations and Ho atoms.The resulting alloy achieves a great enhancement of dislocation hardening and obtains the dual benefits of high strength and good ductility simultaneously at high homologous temperatures.The present work provides an effective strategy to enhancing the strength and ductility for elevated-temperature materials.
基金Supported by Science and Technology Program of Quzhou,China,No.2022K67Zhejiang Medical Association Clinical Research Fund Project,No.2024ZYC-A526and the Research Project of Quzhou People’s Hospital,No.KYQD2024-006.
文摘BACKGROUND The association between the uric acid-to-high-density lipoprotein cholesterol ratio(UHR)and mental health among individuals with type 2 diabetes mellitus(T2DM)has not been thoroughly investigated.AIM To examine the link between UHR and symptoms of depression and anxiety in patients with T2DM.METHODS A cross-sectional analysis was carried out from March 2023 to April 2024,involving participants diagnosed with T2DM.Data on sociodemographic characteristics,clinical parameters,and UHR values were systematically gathered.The Self-Rating Depression Scale(SDS)and Self-Rating Anxiety Scale(SAS)were utilized to evaluate depressive and anxiety symptoms,respectively.To assess the relationships between UHR and SDS/SAS scores,linear regression models were employed,incorporating adjustments for potential confounding variables.Additionally,smooth curve fitting and threshold effect analyses were conducted to explore potential nonlinear relationships.RESULTS A total of 285 patients with T2DM were included.Initial univariate analysis demonstrated a significant positive correlation between elevated UHR levels and higher SDS and SAS scores.Multivariate regression analysis revealed that a one-unit rise in UHR was associated with a 1.13-point increase in SDS scores(95%CI:0.69-1.58)and a 0.57-point increase in SAS scores(95%CI:0.20-0.93).After controlling for confounders,UHR remained positively correlated with SDS(β=1.55,95%CI:0.57-2.53)and SAS(β=0.72,95%CI:0.35-1.09).Nonlinear analysis identified critical thresholds at UHR values of 5.02 for SDS and 4.00 for SAS,beyond which the relationships between UHR and psychological symptom scores became markedly stronger(P<0.05).CONCLUSION Higher UHR levels are significantly linked to exacerbated depressive and anxiety symptoms in patients with T2DM.These results indicate that UHR may function as a promising biomarker to identify individuals at greater risk of mental health complications within this population.
基金funded by the Scientific and Technological Key Program of Guizhou Province, China (Qiankehezhicheng [2022] Key 031)the National Natural Science Foundation of China (32160483 and 32360497)+2 种基金the Post-Funded Project for the National Natural Science Foundation of China from Guizhou University ([2023]093)the Key Laboratory of Molecular Breeding for Grain and Oil Crops in Guizhou Province, China (Qiankehezhongyindi [2023]008)the Key Laboratory of Functional Agriculture of Guizhou Provincial Higher Education Institutions, China (Qianjiaoji [2023] 007)。
文摘Rapeseed mustard(Brassica juncea L.) is the third most important oilseed crop in the world, but the geneticmechanism underlying its massive phenotypic variation remains largely unexplored. In this study, specific length amplified fragment sequencing(SLAF-Seq) was used to resequence a population comprising 197 F8recombinantinbred lines(RILs) derived from a cross between vegetable-type Qichi881 and oilseed-type YufengZC of B. juncea. In total, 438,895 high-quality SLAFs were discovered, 47,644 of which were polymorphic, and 3,887 of the polymorphic markers met the requirements for genetic map construction. The final map included 3,887 markers on 18 linkage groups and was 1,830.23 centiMorgan(cM) in length, with an average distance of 0.47 cM between adjacent markers. Using the newly constructed high-density genetic map, a total of 53 QTLs for erucicacid(EA), oleic acid(OA), and linolenic acid(LNA) were detected and integrated into eight consensus QTLswith two for each of these traits. For each of these three traits, two candidate genes were cloned and sequence analysis indicated colocalization with their respective consensus QTLs. The co-dominant allele-specific markers for Bju.FAD3.A03 and Bju.FAD3.B07 were developed and showed co-localization with their consensus QTLs andco-segregation with LNA content, further supporting the results of QTL mapping and bioinformatic analysis. Theexpression levels of the cloned homologous genes were also determined, and the genes were tightly correlatedwith the EA, OA and LNA contents of different lines. The results of this study will facilitate the improvement offatty acid traits and molecular breeding of B. juncea. Further uses of the high-density genetic map created in this study are also discussed.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
文摘In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
基金Supported by Key Research and Development Program of Shaanxi Province,China,No.2024SF-YBXM-078.
文摘BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.
文摘This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金supported by grants from the National Natural Science Foundation of China(82004252)the Project of Administration of Traditional Chinese Medicine of Guangdong Province(202405112017596500)the Basic and Applied Basic Research Foundation of Guangzhou Municipal Science and Technology Bureau(202102020533).
文摘Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY on AD.Methods:The DNFB-induced mouse models of AD were established to investigate the therapeutic effects of WQY on AD.The symptoms of AD in the ears and backs of the mice were assessed,while inflammatory factors in the ear were quantified using quantitative real-time-polymerase chain reaction(qRT-PCR),and the percentages of CD4^(+)and CD8^(+)cells in the spleen were analyzed through flow cytometry.The compounds in WQY were identified using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis and the key targets and pathways of WQY to treat AD were predicted by network pharmacology.Subsequently,the key genes were tested and verified by qRT-PCR,and the potential active components and target proteins were verified by molecular docking.Results:WQY relieved the AD symptoms and histopathological injuries in the ear and back skin of mice with AD.Meanwhile,WQY significantly reduced the levels of inflammatory factors IL-6 and IL-1βin ear tissue,as well as the ratio of CD4^(+)/CD8^(+)cells in spleen.Additionally,a total of 142 compounds were identified from the water extract of WQY by UPLC-Orbitrap-MS/MS.39 key targets related to AD were screened out by network pharmacology methods.The KEGG analysis indicated that the effects of WQY were primarily mediated through pathways associated with Toll-like receptor signaling and T cell receptor signaling.Moreover,the results of qRT-PCR demonstrated that WQY significantly reduced the mRNA expressions of IL-4,IL-10,GATA3 and FOXP3,and molecular docking simulation verified that the active components of WQY had excellent binding abilities with IL-4,IL-10,GATA3 and FOXP3 proteins.Conclusion:The present study demonstrated that WQY effectively relieved AD symptoms in mice,decreased the inflammatory factors levels,regulated the balance of CD4^(+)and CD8^(+)cells,and the mechanism may be associated with the suppression of Th2 and Treg cell immune responses.
