Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that c...Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that confer cold and UV resistance on the crop also facilitate human adaptation to high-altitude stresses.Specifically,β-glucans regulate blood glucose primarily via short-chain fatty acids(SCFAs)produced through gut microbiota fermentation,which directly mediate glucose homeostasis.Phenolamides accumulate via the phenylpropanoid pathway,with chalcone isomerase(CHI)serving as a key enzyme in flavonoid biosynthesis and enhancing UV-B resistance.Under low temperatures,β-glucans improve frost tolerance by modulating osmotic balance and inhibiting ice-nucleating proteins,while lipids maintain membrane fluidity to sustain cellular function during cold stress.Importantly,we explore the hypothesis that these same metabolites,upon consumption,may facilitate human adaptation to high-altitude stresses.This hypothesis is supported by preliminary epidemiological associations between Qingke consumption and favorable health outcomes in high-altitude populations,as well as established bioactivities of the implicated metabolites in vitro and in animal models.However,direct causal evidence in humans and a comprehensive understanding of the underlying molecular mechanisms remain key knowledge gaps that warrant future investigation.Qingke as a unique resource at the interface of agricultural resilience and human nutrition.Understanding its metabolic blueprint will inform the development of functional foods and climate-resilient crops.展开更多
It is essential to understand how adaptation needs and options differ among stakeholders in protected areas(PAs)to effectively implement climate change(CC)adaptation strategies.Using the Qiangtang PA in Xizang as a ca...It is essential to understand how adaptation needs and options differ among stakeholders in protected areas(PAs)to effectively implement climate change(CC)adaptation strategies.Using the Qiangtang PA in Xizang as a case study,this research examines CC adaptation needs and options from the perspectives of stakeholders across multiple administrative levels,including provincial,prefectural,county authorities,73 protection stations,and 13364 pastoralists residing within the PA.The findings show that stakeholders at the provincial level,as well as those from the Ali and Naqu prefectures and six counties,place greater emphasis on institutional and resource-related needs than on other categories(attention score:7.0-9.3 vs.5.0-7.0).In contrast,stakeholders from the 73 protection stations prioritize technological and capacity-building needs more strongly than other types(attention score:8.0-9.0 vs.4.0-8.0).The 13364 pastoralists assign the highest importance to social needs relative to other categories(attention score:9.0-9.5 vs.3.0-8.0).Most of the eight existing protection measures were found to indirectly support broader climate adaptation efforts.In particular,protective actions addressing fire,pests,and weather-related disasters can be classified as autonomous adaptation,while other measures generate outcomes that enhance adaptation capacity under specific conditions.Adaptation options,grouped into three main types and 13 subcategories,differ across stakeholder groups,although substantial overlap exists between these options and current protective actions,including ecosystem based adaptation strategies,adaptation-related practices,autonomous adaptation measures,and emergency interventions.Overall,these findings highlight the critical role of all stakeholders-especially staff from the 73 protection stations and the 13364 pastoralists-in the effective implementation of adaptation actions within the PA.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
Climate change poses a profound threat to mountain agro-ecosystems,particularly in the Himalayan region of West Bengal,India,by disrupting precipitation patterns,increasing temperature variability,and intensifying ext...Climate change poses a profound threat to mountain agro-ecosystems,particularly in the Himalayan region of West Bengal,India,by disrupting precipitation patterns,increasing temperature variability,and intensifying extreme weather events.Despite growing evidence of climate change impacts,there remains a critical research gap in understanding how socioeconomic factors drive farmers' adaptation strategies to climate change in this vulnerable region.This study examines how farmers in the Himalayan region of West Bengal,India,perceived and responded to the growing impacts of climate change on mountain agro-ecosystems.Drawing on cross-sectional data from 370 farm households selected through multistage sampling,the research employs a combination of analytical tools,including the severity index(SI) to assess farmers' perceptions to climate change,the adaptation index(AI) to evaluate adaptive responses,the Garrett's ranking technique to prioritize constraints,and the ordered logistic regression to identify key socioeconomic drivers of adaptation.Findings reveal a high level of climate awareness among farmers,particularly regarding the increase in weather extremes(SI=74.87%),increase in temperature(SI=72.31%),and irregular rainfall patterns and highly erratic rainfall(SI=62.52%).The most commonly adopted strategies include adopting intercropping and mixed cropping systems(AI=0.613),adoption of the integrated farming system model(AI=0.600),and shift towards non-farm employment(AI=0.608),while the adoption of climate-resilient crop varieties and improved irrigation remains limited.Regression analysis highlights that education(regression coefficient=0.38),average landholding size(regression coefficient=1.21),and access to daily weather forecast information(regression coefficient=1.92) significantly promote adaptive behaviour,whereas age(regression coefficient= –0.09) and gender(regression coefficient= –0.76) are negatively associated.Institutional constraints,particularly unavailability of institutional credit,emerge as primary barriers.The study underscores the urgent need for region-specific,inclusive policy frameworks that enhance climate advisory services,support technology dissemination,and empower marginalized groups in the Himalayan region of West Bengal.By fostering informed,equitable,and resilient agricultural systems,these strategies can significantly strengthen the adaptive capacity of mountain farming communities and contribute to sustainable development under a changing climate.展开更多
Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-...Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition.展开更多
In a multiple voltage source converter(VSC)system,the nonlinear characteristics of phase-locked loops(PLLs)and their interactions have a significant influence on the synchronization stability of converters.In this pap...In a multiple voltage source converter(VSC)system,the nonlinear characteristics of phase-locked loops(PLLs)and their interactions have a significant influence on the synchronization stability of converters.In this paper,these influences are investigated from the perspective of the time domain.First,a novel time-domain model of the multi-VSC system is obtained by using a multi-scale method.On this basis,a stability criterion is proposed to assess the synchronization stability of the system.Then,the accuracy of the time-domain model and its stability criterion in various conditions are discussed.Moreover,the negative impact of the interaction on the system is quantified.Finally,the above theoretical analysis is also verified in the controller hardware-in-the-loop(CHIL)experiments.展开更多
Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the backgroun...Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal ...1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal evidence that a lack of physical activity,not only has direct effects on the prevalence of non-contagious diseases(NCDs)but has profound additive effects of other risk factors for NCD such as obesity and hypertension.1 The articles in this special topic of Journal of Sport and Health Science(JSHS)are dedicated to research on Exercise biochemistry&metabolism.展开更多
Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet th...Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.展开更多
Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posi...Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posing a significant challenge for accurate low-light object detection.Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images.For this purpose,this study proposes a DL-YOLO model specially tailored for low-light detection.To mitigate target feature attenuation introduced by repeated downsampling,we design aMulti-Scale FeatureConvolution(MSF-Conv)module that captures rich,multi-level details via multi-scale feature learning,thereby reducing model complexity and computational cost.For feature fusion,we integrated the C3k2-DWRmodule by embedding the Dilation-wise Residual(DWR)mechanism into the 2-core optimized Cross Stage Partial(C3)framework,achieving efficient feature integration.In addition,we replace conventional localization losses with WIoU(Weighted Intersection over Union),which dynamically adjusts gradient gain according to sample quality,thereby improving localization robustness and precision.Experiments on the ExDark dataset demonstrate that DL-YOLO delivers strong low-light detection performance.The relevant code is published at https://github.com/cym0997/DL-YOLO.展开更多
Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made re...Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.展开更多
The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a mo...The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a modified remote sensing ecological index(MRSEI)was developed by incorporating evapotranspiration.Spatial and temporal patterns were analyzed using the coefficient of variation,spatial autocorrelation,and semi-variogram methods,while influencing factors were explored via the optimal parameter geographical detector model.The MRSEI’s first principal component loadings and rankings aligned with those of RSEI(average contribution:81.31%),effectively reflecting spatiotemporal variations.At sub-irrigation district and landscape scales,ecological quality was slightly lower than at the district level but remained stable.Moderate and good ecological grades accounted for 36.28%and 33.38%of the area,respectively,at the district scale,and the moderate grade reached 70.48%on smaller scales.Spatial heterogeneity intensified with decreasing scale,and human activity lost explanatory power below a 5 km range.Human factors mainly drove ecological differentiation at the district scale,while natural factors dominated at finer scales.The MRSEI offers a novel tool for ecological assessment in arid/semi-arid areas and supports scale-adapted ecological protection strategies.展开更多
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric n...Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.展开更多
Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To ...Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.展开更多
The pore structure of shale oil reservoir significantly affects the occurrence and mobility of hydrocarbons.The potential of a new type of alkaline lake shale oil has been demonstrated,but there are few reports on the...The pore structure of shale oil reservoir significantly affects the occurrence and mobility of hydrocarbons.The potential of a new type of alkaline lake shale oil has been demonstrated,but there are few reports on the pore system of alkaline lake shale,which restricts the efficient exploration and development of shale oil.This study investigates the Fengcheng Formation shale in the Mahu sag of the Junggar Basin,employing methods such as low-temperature nitrogecn adsorption(LTNA),mercury intrusion capillary pressure(MICP),and nuclear magnetic resonance(NMR)to quantitatively characterize the multi-scale pore structure and fractal characteristics of shale,while evaluating the applicability of these methods.Based on a comprehensive analysis of material composition,different pore types,and fractal dimensions,the controlling factors for the development of different pore types and their seepage capacity are discussed.The results indicate that inorganic mineral pores are the main development in alkaline lake shale,with the pore morphology being characterized by slit-like and ink-bottle shapes.The multi-scale pore size distribution(PSD)shows that Ⅱ-micropores(10-100 nm)and mesopores(100–1000 nm)are the main contributors to the pore system.The development of Ⅱ-micropores is associated with feldspar and calcareous minerals,the development of Ⅰ-micropores(<10 nm)and mesopores is related to quartz content,while large pores are mainly found in interlayer fissures of clay minerals.The development of Ⅰ-micropores increases the roughness of pore surface and enhances the adsorption capacity of the pores,while the development of Ⅱ-micropores associated with calcareous minerals hinders pore seepage capacity.Mesopores and macropores(>1000 nm)exhibit good flowability.The high content of siliceous minerals plays a positive role in the pore system of alkaline lake shale.The shale with higher fractal dimension Dmin exhibits greater adsorption capacity,which hinders the accumulation of free-state shale oil.Different types of pore space play different roles in the occurrence of shale oil,with free-state shale oil primarily occurring in micro-fractures and inorganic mineral pores,and the pore size is exceeding 10 nm.展开更多
Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activ...Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activities.Long-term poor understanding of their genetics and evolution undermines effective conservation efforts.In this study,we analyse comparative population genomic data of four Nomascus species.Our results reveal strong genetic differentiation and gene flow among Nomascus species.Additionally,we identify genomic features that are potentially related to natural selection linked to vocalization,fructose metabolism,motor balance,and body size,consistent with the unique phenotype and adaptability of gibbons.Inbreeding,coupled with population declines due to climate change and historical human activities,leads to reduced genetic diversity and the accumulation of deleterious variations that likely affect cardiovascular disease and the reproductive potential of gibbons and further reduce their fitness,highlighting the urgent need for effective conservation strategies.展开更多
Conventionally,foundations have been classified as shallow or deep in routine civil engineering practice.However,due to recent developments,two other approaches,semi-deep and ground modification foundations,are now av...Conventionally,foundations have been classified as shallow or deep in routine civil engineering practice.However,due to recent developments,two other approaches,semi-deep and ground modification foundations,are now available,complicating foundation categorization.Accordingly,a new concept for foundation categorization is introduced in this paper based on insights into the theory of structure analysis.Based on the form aspect,foundation systems can be categorized as one-dimensional(linear),two-dimensional(planar),and threedimensional(volumetric).Based on the load transfer aspect,foundations can also be categorized as vector-acting(piles),section or surface-acting(rafts and shells),and block-acting(piled rafts).As a step toward implementing this new categorization scheme,a database of 22 cases has been compiled,symbolizing novel introduced foundation systems.This compilation involves structures such as offshore jackets,high-rise buildings,towers and storages,and diverse geomaterials.Among them,a few have been selected for detailed evaluation,emphasizing influential factors in foundation selection,comprising superstructure,subsoil condition,foundation system,circumferential conditions,and supplementary considerations,that is,constructional and sustainability-based issues.Lessons learned from experience and these knowledge-based cases have described for foundation selection and implementation.Geotechnical and practical aspects with critical components have been realized as major performance assessment and comparison factors.Foundation systems have been compared and ranked using the improved analytic hierarchy process approach.Finally,four categories of buildings,from low-rise to towers and four prevailing levels of soil strength,from soft to very hard,have been considered to propose a perspective for building substructure implementation,adapted via relevant cases.Overall,the introduced categorization is recognized as an efficient algorithm for the experimentation of appropriate foundations for specific structures and subsoil conditions.展开更多
This paper proposes an approach to determing the optimal cluster spacing for volume fracturing in shale oil reservoirs based on three scales,i.e.microscopic capillary displacement,large-scale core imbibition,and macro...This paper proposes an approach to determing the optimal cluster spacing for volume fracturing in shale oil reservoirs based on three scales,i.e.microscopic capillary displacement,large-scale core imbibition,and macroscopic reservoir nuclear magnetic resonance(NMR)logging.Through flow experiments using capillary with different diameters and lengths,and large-scale core counter-current and dynamic imbibition tests,and combing with the NMR logging data of single wells,a graded optimization criterion for cluster spacing is established.The proposed approach was tested in the shale oil reservoir in the seventh member of the Triassic Yanchang Formation(Change 7 Member),the Ordos Basin.The following findings are obtained.First,in the Chang 7 reservoir,oil in pores smaller than 8μm requires a threshold pressure,and for 2-8μm pores,the movable drainage distance ranges from 0.7 m to 4.6 m under a pressure difference of 27 mPa.Second,the large-scale core imbibition tests show a counter-current imbibition distance of only 10 cm,but a dynamic imbibition distance up to 30 cm.Third,in-situ NMR logging results verified that the post-fracturing matrix drainage radius around fractures is 0-4 m,which is consistent with those of capillary flow experiments and large-scale core imbibition tests.The main pore-size range(2-8μm)of the Chang 7 reservoir corresponds to a permeability interval of(0.1-0.4)×10^(-3)μm^(2).Accordingly,a graded optimization criterion for cluster spacing is proposed as follows:for reservoirs with permeability less than 0.20×10^(-3)μm^(2),the cluster spacing should be reduced to smaller than 4.2 m;for reservoirs with permeability of(0.2-0.4)×10^(-3)μm^(2),the cluster spacing should be designed as 4.2-9.2 m.Field application on a pilot platform,where the cluster spacing was reduced to 4.0-6.0 m,yielded an increased initial oil production by approximately 36.6%over a 100-m horizontal reservoir section as compared with untested similar platforms.展开更多
基金supported by the Financial Special Fund,grant number XZ202401JD0027National Barley Industry Technology System(CARS-05-01A-08)+3 种基金the Xizang Agri-Tech Innovation Project(XZNKY-2025-CXGC-T01)the Joint Funds of the National Natural Science Foundation of China(No.U20A2026)the Financial Special Fund,grant number(32401784,2017CZZX001/2,XZNKY-2018-C-021 and NYSTC202401)the China Agriculture Research System of Barley(CARS-05).
文摘Qingke,a staple crop grown on the high-altitude Tibetan Plateau,has evolved a metabolomic profile providing both environmental stress resilience and human nutrition.We review the hypothesis that the metabolites that confer cold and UV resistance on the crop also facilitate human adaptation to high-altitude stresses.Specifically,β-glucans regulate blood glucose primarily via short-chain fatty acids(SCFAs)produced through gut microbiota fermentation,which directly mediate glucose homeostasis.Phenolamides accumulate via the phenylpropanoid pathway,with chalcone isomerase(CHI)serving as a key enzyme in flavonoid biosynthesis and enhancing UV-B resistance.Under low temperatures,β-glucans improve frost tolerance by modulating osmotic balance and inhibiting ice-nucleating proteins,while lipids maintain membrane fluidity to sustain cellular function during cold stress.Importantly,we explore the hypothesis that these same metabolites,upon consumption,may facilitate human adaptation to high-altitude stresses.This hypothesis is supported by preliminary epidemiological associations between Qingke consumption and favorable health outcomes in high-altitude populations,as well as established bioactivities of the implicated metabolites in vitro and in animal models.However,direct causal evidence in humans and a comprehensive understanding of the underlying molecular mechanisms remain key knowledge gaps that warrant future investigation.Qingke as a unique resource at the interface of agricultural resilience and human nutrition.Understanding its metabolic blueprint will inform the development of functional foods and climate-resilient crops.
基金supported by the National Key Research and Development Project[Grand No.2022YFF0802304]Key Research and Development and Transformation Project of the Xizang Autonomous Region[Grand No.XZ202501ZY0119].
文摘It is essential to understand how adaptation needs and options differ among stakeholders in protected areas(PAs)to effectively implement climate change(CC)adaptation strategies.Using the Qiangtang PA in Xizang as a case study,this research examines CC adaptation needs and options from the perspectives of stakeholders across multiple administrative levels,including provincial,prefectural,county authorities,73 protection stations,and 13364 pastoralists residing within the PA.The findings show that stakeholders at the provincial level,as well as those from the Ali and Naqu prefectures and six counties,place greater emphasis on institutional and resource-related needs than on other categories(attention score:7.0-9.3 vs.5.0-7.0).In contrast,stakeholders from the 73 protection stations prioritize technological and capacity-building needs more strongly than other types(attention score:8.0-9.0 vs.4.0-8.0).The 13364 pastoralists assign the highest importance to social needs relative to other categories(attention score:9.0-9.5 vs.3.0-8.0).Most of the eight existing protection measures were found to indirectly support broader climate adaptation efforts.In particular,protective actions addressing fire,pests,and weather-related disasters can be classified as autonomous adaptation,while other measures generate outcomes that enhance adaptation capacity under specific conditions.Adaptation options,grouped into three main types and 13 subcategories,differ across stakeholder groups,although substantial overlap exists between these options and current protective actions,including ecosystem based adaptation strategies,adaptation-related practices,autonomous adaptation measures,and emergency interventions.Overall,these findings highlight the critical role of all stakeholders-especially staff from the 73 protection stations and the 13364 pastoralists-in the effective implementation of adaptation actions within the PA.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
文摘Climate change poses a profound threat to mountain agro-ecosystems,particularly in the Himalayan region of West Bengal,India,by disrupting precipitation patterns,increasing temperature variability,and intensifying extreme weather events.Despite growing evidence of climate change impacts,there remains a critical research gap in understanding how socioeconomic factors drive farmers' adaptation strategies to climate change in this vulnerable region.This study examines how farmers in the Himalayan region of West Bengal,India,perceived and responded to the growing impacts of climate change on mountain agro-ecosystems.Drawing on cross-sectional data from 370 farm households selected through multistage sampling,the research employs a combination of analytical tools,including the severity index(SI) to assess farmers' perceptions to climate change,the adaptation index(AI) to evaluate adaptive responses,the Garrett's ranking technique to prioritize constraints,and the ordered logistic regression to identify key socioeconomic drivers of adaptation.Findings reveal a high level of climate awareness among farmers,particularly regarding the increase in weather extremes(SI=74.87%),increase in temperature(SI=72.31%),and irregular rainfall patterns and highly erratic rainfall(SI=62.52%).The most commonly adopted strategies include adopting intercropping and mixed cropping systems(AI=0.613),adoption of the integrated farming system model(AI=0.600),and shift towards non-farm employment(AI=0.608),while the adoption of climate-resilient crop varieties and improved irrigation remains limited.Regression analysis highlights that education(regression coefficient=0.38),average landholding size(regression coefficient=1.21),and access to daily weather forecast information(regression coefficient=1.92) significantly promote adaptive behaviour,whereas age(regression coefficient= –0.09) and gender(regression coefficient= –0.76) are negatively associated.Institutional constraints,particularly unavailability of institutional credit,emerge as primary barriers.The study underscores the urgent need for region-specific,inclusive policy frameworks that enhance climate advisory services,support technology dissemination,and empower marginalized groups in the Himalayan region of West Bengal.By fostering informed,equitable,and resilient agricultural systems,these strategies can significantly strengthen the adaptive capacity of mountain farming communities and contribute to sustainable development under a changing climate.
基金supported,in part,by the National Nature Science Foundation of China under Grant 62272236,62376128in part,by the Natural Science Foundation of Jiangsu Province under Grant BK20201136,BK20191401.
文摘Video emotion recognition is widely used due to its alignment with the temporal characteristics of human emotional expression,but existingmodels have significant shortcomings.On the one hand,Transformermultihead self-attention modeling of global temporal dependency has problems of high computational overhead and feature similarity.On the other hand,fixed-size convolution kernels are often used,which have weak perception ability for emotional regions of different scales.Therefore,this paper proposes a video emotion recognition model that combines multi-scale region-aware convolution with temporal interactive sampling.In terms of space,multi-branch large-kernel stripe convolution is used to perceive emotional region features at different scales,and attention weights are generated for each scale feature.In terms of time,multi-layer odd-even down-sampling is performed on the time series,and oddeven sub-sequence interaction is performed to solve the problem of feature similarity,while reducing computational costs due to the linear relationship between sampling and convolution overhead.This paper was tested on CMU-MOSI,CMU-MOSEI,and Hume Reaction.The Acc-2 reached 83.4%,85.2%,and 81.2%,respectively.The experimental results show that the model can significantly improve the accuracy of emotion recognition.
基金supported by the Science and Technology Project of State Grid Corporation of China(5400-202199281A-0-0-00).
文摘In a multiple voltage source converter(VSC)system,the nonlinear characteristics of phase-locked loops(PLLs)and their interactions have a significant influence on the synchronization stability of converters.In this paper,these influences are investigated from the perspective of the time domain.First,a novel time-domain model of the multi-VSC system is obtained by using a multi-scale method.On this basis,a stability criterion is proposed to assess the synchronization stability of the system.Then,the accuracy of the time-domain model and its stability criterion in various conditions are discussed.Moreover,the negative impact of the interaction on the system is quantified.Finally,the above theoretical analysis is also verified in the controller hardware-in-the-loop(CHIL)experiments.
基金financially supported byChongqingUniversity of Technology Graduate Innovation Foundation(Grant No.gzlcx20253267).
文摘Camouflaged Object Detection(COD)aims to identify objects that share highly similar patterns—such as texture,intensity,and color—with their surrounding environment.Due to their intrinsic resemblance to the background,camouflaged objects often exhibit vague boundaries and varying scales,making it challenging to accurately locate targets and delineate their indistinct edges.To address this,we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network(EGMFNet),which leverages edge-guided multi-scale integration for enhanced performance.The model incorporates two innovative components:a Multi-scale Fusion Module(MSFM)and an Edge-Guided Attention Module(EGA).These designs exploit multi-scale features to uncover subtle cues between candidate objects and the background while emphasizing camouflaged object boundaries.Moreover,recognizing the rich contextual information in fused features,we introduce a Dual-Branch Global Context Module(DGCM)to refine features using extensive global context,thereby generatingmore informative representations.Experimental results on four benchmark datasets demonstrate that EGMFNet outperforms state-of-the-art methods across five evaluation metrics.Specifically,on COD10K,our EGMFNet-P improves F_(β)by 4.8 points and reduces mean absolute error(MAE)by 0.006 compared with ZoomNeXt;on NC4K,it achieves a 3.6-point increase in F_(β).OnCAMO and CHAMELEON,it obtains 4.5-point increases in F_(β),respectively.These consistent gains substantiate the superiority and robustness of EGMFNet.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
文摘1.Introduction The field of exercise science is experiencing a renaissance,with recent research illuminating the molecular,cellular,and systemic effects of physical activity.This is largely due to the now unequivocal evidence that a lack of physical activity,not only has direct effects on the prevalence of non-contagious diseases(NCDs)but has profound additive effects of other risk factors for NCD such as obesity and hypertension.1 The articles in this special topic of Journal of Sport and Health Science(JSHS)are dedicated to research on Exercise biochemistry&metabolism.
基金Tianmin Tianyuan Boutique Vegetable Industry Technology Service Station(Grant No.2024120011003081)Development of Environmental Monitoring and Traceability System for Wuqing Agricultural Production Areas(Grant No.2024120011001866)。
文摘Tomato is a major economic crop worldwide,and diseases on tomato leaves can significantly reduce both yield and quality.Traditional manual inspection is inefficient and highly subjective,making it difficult to meet the requirements of early disease identification in complex natural environments.To address this issue,this study proposes an improved YOLO11-based model,YOLO-SPDNet(Scale Sequence Fusion,Position-Channel Attention,and Dual Enhancement Network).The model integrates the SEAM(Self-Ensembling Attention Mechanism)semantic enhancement module,the MLCA(Mixed Local Channel Attention)lightweight attention mechanism,and the SPA(Scale-Position-Detail Awareness)module composed of SSFF(Scale Sequence Feature Fusion),TFE(Triple Feature Encoding),and CPAM(Channel and Position Attention Mechanism).These enhancements strengthen fine-grained lesion detection while maintaining model lightweightness.Experimental results show that YOLO-SPDNet achieves an accuracy of 91.8%,a recall of 86.5%,and an mAP@0.5 of 90.6%on the test set,with a computational complexity of 12.5 GFLOPs.Furthermore,the model reaches a real-time inference speed of 987 FPS,making it suitable for deployment on mobile agricultural terminals and online monitoring systems.Comparative analysis and ablation studies further validate the reliability and practical applicability of the proposed model in complex natural scenes.
文摘Driven by rapid advances in deep learning,object detection has been widely adopted across diverse application scenarios.However,in low-light conditions,critical visual cues of target objects are severely degraded,posing a significant challenge for accurate low-light object detection.Existing methods struggle to preserve discriminative features while maintaining semantic consistency between low-light and normal-light images.For this purpose,this study proposes a DL-YOLO model specially tailored for low-light detection.To mitigate target feature attenuation introduced by repeated downsampling,we design aMulti-Scale FeatureConvolution(MSF-Conv)module that captures rich,multi-level details via multi-scale feature learning,thereby reducing model complexity and computational cost.For feature fusion,we integrated the C3k2-DWRmodule by embedding the Dilation-wise Residual(DWR)mechanism into the 2-core optimized Cross Stage Partial(C3)framework,achieving efficient feature integration.In addition,we replace conventional localization losses with WIoU(Weighted Intersection over Union),which dynamically adjusts gradient gain according to sample quality,thereby improving localization robustness and precision.Experiments on the ExDark dataset demonstrate that DL-YOLO delivers strong low-light detection performance.The relevant code is published at https://github.com/cym0997/DL-YOLO.
基金supported by the National Key Research and Development of China(No.2022YFB2503400).
文摘Semantic segmentation for mixed scenes of aerial remote sensing and road traffic is one of the key technologies for visual perception of flying cars.The State-of-the-Art(SOTA)semantic segmentation methods have made remarkable achievements in both fine-grained segmentation and real-time performance.However,when faced with the huge differences in scale and semantic categories brought about by the mixed scenes of aerial remote sensing and road traffic,they still face great challenges and there is little related research.Addressing the above issue,this paper proposes a semantic segmentation model specifically for mixed datasets of aerial remote sensing and road traffic scenes.First,a novel decoding-recoding multi-scale feature iterative refinement structure is proposed,which utilizes the re-integration and continuous enhancement of multi-scale information to effectively deal with the huge scale differences between cross-domain scenes,while using a fully convolutional structure to ensure the lightweight and real-time requirements.Second,a welldesigned cross-window attention mechanism combined with a global information integration decoding block forms an enhanced global context perception,which can effectively capture the long-range dependencies and multi-scale global context information of different scenes,thereby achieving fine-grained semantic segmentation.The proposed method is tested on a large-scale mixed dataset of aerial remote sensing and road traffic scenes.The results confirm that it can effectively deal with the problem of large-scale differences in cross-domain scenes.Its segmentation accuracy surpasses that of the SOTA methods,which meets the real-time requirements.
基金National Key Research&Development Program of China,No.2021YFC3201201Ningxia Key Research and Development Program(Special Talents),No.2023BSB03021+1 种基金Natural Science Foundation of Ningxia,No.2023AAC05014University First-Class Discipline Construction Project of Ningxia,No.NXYLXK2021A03。
文摘The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a modified remote sensing ecological index(MRSEI)was developed by incorporating evapotranspiration.Spatial and temporal patterns were analyzed using the coefficient of variation,spatial autocorrelation,and semi-variogram methods,while influencing factors were explored via the optimal parameter geographical detector model.The MRSEI’s first principal component loadings and rankings aligned with those of RSEI(average contribution:81.31%),effectively reflecting spatiotemporal variations.At sub-irrigation district and landscape scales,ecological quality was slightly lower than at the district level but remained stable.Moderate and good ecological grades accounted for 36.28%and 33.38%of the area,respectively,at the district scale,and the moderate grade reached 70.48%on smaller scales.Spatial heterogeneity intensified with decreasing scale,and human activity lost explanatory power below a 5 km range.Human factors mainly drove ecological differentiation at the district scale,while natural factors dominated at finer scales.The MRSEI offers a novel tool for ecological assessment in arid/semi-arid areas and supports scale-adapted ecological protection strategies.
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
基金financial support from the National Key Research and Development Program of China(2022YFB3804905)National Natural Science Foundation of China(22375047,22378068,and 22378071)+1 种基金Natural Science Foundation of Fujian Province(2022J01568)111 Project(No.D17005).
文摘Advanced healthcare monitors for air pollution applications pose a significant challenge in achieving a balance between high-performance filtration and multifunctional smart integration.Electrospinning triboelectric nanogenerators(TENG)provide a significant potential for use under such difficult circumstances.We have successfully constructed a high-performance TENG utilizing a novel multi-scale nanofiber architecture.Nylon 66(PA66)and chitosan quaternary ammonium salt(HACC)composites were prepared by electrospinning,and PA66/H multiscale nanofiber membranes composed of nanofibers(≈73 nm)and submicron-fibers(≈123 nm)were formed.PA66/H multi-scale nanofiber membrane as the positive electrode and negative electrode-spun PVDF-HFP nanofiber membrane composed of respiration-driven PVDF-HFP@PA66/H TENG.The resulting PVDF-HFP@PA66/H TENG based air filter utilizes electrostatic adsorption and physical interception mechanisms,achieving PM_(0.3)filtration efficiency over 99%with a pressure drop of only 48 Pa.Besides,PVDF-HFP@PA66/H TENG exhibits excellent stability in high-humidity environments,with filtration efficiency reduced by less than 1%.At the same time,the TENG achieves periodic contact separation through breathing drive to achieve self-power,which can ensure the long-term stability of the filtration efficiency.In addition to the air filtration function,TENG can also monitor health in real time by capturing human breathing signals without external power supply.This integrated system combines high-efficiency air filtration,self-powered operation,and health monitoring,presenting an innovative solution for air purification,smart protective equipment,and portable health monitoring.These findings highlight the potential of this technology for diverse applications,offering a promising direction for advancing multifunctional air filtration systems.
文摘Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.
基金financially supported by the National Natural Science Foundation of China(Nos.42272137,42202160)AAPG Foundation Grants-in-Aid and the Strategic Cooperation Technology Projecti of CNPC and CUPB(No.ZLZX2020-01-05)。
文摘The pore structure of shale oil reservoir significantly affects the occurrence and mobility of hydrocarbons.The potential of a new type of alkaline lake shale oil has been demonstrated,but there are few reports on the pore system of alkaline lake shale,which restricts the efficient exploration and development of shale oil.This study investigates the Fengcheng Formation shale in the Mahu sag of the Junggar Basin,employing methods such as low-temperature nitrogecn adsorption(LTNA),mercury intrusion capillary pressure(MICP),and nuclear magnetic resonance(NMR)to quantitatively characterize the multi-scale pore structure and fractal characteristics of shale,while evaluating the applicability of these methods.Based on a comprehensive analysis of material composition,different pore types,and fractal dimensions,the controlling factors for the development of different pore types and their seepage capacity are discussed.The results indicate that inorganic mineral pores are the main development in alkaline lake shale,with the pore morphology being characterized by slit-like and ink-bottle shapes.The multi-scale pore size distribution(PSD)shows that Ⅱ-micropores(10-100 nm)and mesopores(100–1000 nm)are the main contributors to the pore system.The development of Ⅱ-micropores is associated with feldspar and calcareous minerals,the development of Ⅰ-micropores(<10 nm)and mesopores is related to quartz content,while large pores are mainly found in interlayer fissures of clay minerals.The development of Ⅰ-micropores increases the roughness of pore surface and enhances the adsorption capacity of the pores,while the development of Ⅱ-micropores associated with calcareous minerals hinders pore seepage capacity.Mesopores and macropores(>1000 nm)exhibit good flowability.The high content of siliceous minerals plays a positive role in the pore system of alkaline lake shale.The shale with higher fractal dimension Dmin exhibits greater adsorption capacity,which hinders the accumulation of free-state shale oil.Different types of pore space play different roles in the occurrence of shale oil,with free-state shale oil primarily occurring in micro-fractures and inorganic mineral pores,and the pore size is exceeding 10 nm.
基金supported by Science and Technology Program from the Forestry Administration of Guangdong Province(2024KJQT0012)the Guangdong Provincial Key R&D Program(2022B1111040001)+2 种基金the National Forestry Administration rare and endangered species field rescue and breeding project(Gui lin hu yu O10)the National Natural Science Foundation of China(32200337)a fellowship from the China Postdoctoral Science Foundation(2022M712003).
文摘Gibbons are small,arboreal apes that play a critical role in tropical biodiversity and ecosystem ecology.However,nearly all species of gibbons are threatened by habitat loss,illegal trade,hunting,and other human activities.Long-term poor understanding of their genetics and evolution undermines effective conservation efforts.In this study,we analyse comparative population genomic data of four Nomascus species.Our results reveal strong genetic differentiation and gene flow among Nomascus species.Additionally,we identify genomic features that are potentially related to natural selection linked to vocalization,fructose metabolism,motor balance,and body size,consistent with the unique phenotype and adaptability of gibbons.Inbreeding,coupled with population declines due to climate change and historical human activities,leads to reduced genetic diversity and the accumulation of deleterious variations that likely affect cardiovascular disease and the reproductive potential of gibbons and further reduce their fitness,highlighting the urgent need for effective conservation strategies.
文摘Conventionally,foundations have been classified as shallow or deep in routine civil engineering practice.However,due to recent developments,two other approaches,semi-deep and ground modification foundations,are now available,complicating foundation categorization.Accordingly,a new concept for foundation categorization is introduced in this paper based on insights into the theory of structure analysis.Based on the form aspect,foundation systems can be categorized as one-dimensional(linear),two-dimensional(planar),and threedimensional(volumetric).Based on the load transfer aspect,foundations can also be categorized as vector-acting(piles),section or surface-acting(rafts and shells),and block-acting(piled rafts).As a step toward implementing this new categorization scheme,a database of 22 cases has been compiled,symbolizing novel introduced foundation systems.This compilation involves structures such as offshore jackets,high-rise buildings,towers and storages,and diverse geomaterials.Among them,a few have been selected for detailed evaluation,emphasizing influential factors in foundation selection,comprising superstructure,subsoil condition,foundation system,circumferential conditions,and supplementary considerations,that is,constructional and sustainability-based issues.Lessons learned from experience and these knowledge-based cases have described for foundation selection and implementation.Geotechnical and practical aspects with critical components have been realized as major performance assessment and comparison factors.Foundation systems have been compared and ranked using the improved analytic hierarchy process approach.Finally,four categories of buildings,from low-rise to towers and four prevailing levels of soil strength,from soft to very hard,have been considered to propose a perspective for building substructure implementation,adapted via relevant cases.Overall,the introduced categorization is recognized as an efficient algorithm for the experimentation of appropriate foundations for specific structures and subsoil conditions.
基金Supported by the China National Oil and Gas Major Project(2025ZD1404800)PetroChina Science and Technology Major Project(2023ZZ15YJ03)CNPC Changqing Oilfield Company Major Special Project(2023DZZ04)。
文摘This paper proposes an approach to determing the optimal cluster spacing for volume fracturing in shale oil reservoirs based on three scales,i.e.microscopic capillary displacement,large-scale core imbibition,and macroscopic reservoir nuclear magnetic resonance(NMR)logging.Through flow experiments using capillary with different diameters and lengths,and large-scale core counter-current and dynamic imbibition tests,and combing with the NMR logging data of single wells,a graded optimization criterion for cluster spacing is established.The proposed approach was tested in the shale oil reservoir in the seventh member of the Triassic Yanchang Formation(Change 7 Member),the Ordos Basin.The following findings are obtained.First,in the Chang 7 reservoir,oil in pores smaller than 8μm requires a threshold pressure,and for 2-8μm pores,the movable drainage distance ranges from 0.7 m to 4.6 m under a pressure difference of 27 mPa.Second,the large-scale core imbibition tests show a counter-current imbibition distance of only 10 cm,but a dynamic imbibition distance up to 30 cm.Third,in-situ NMR logging results verified that the post-fracturing matrix drainage radius around fractures is 0-4 m,which is consistent with those of capillary flow experiments and large-scale core imbibition tests.The main pore-size range(2-8μm)of the Chang 7 reservoir corresponds to a permeability interval of(0.1-0.4)×10^(-3)μm^(2).Accordingly,a graded optimization criterion for cluster spacing is proposed as follows:for reservoirs with permeability less than 0.20×10^(-3)μm^(2),the cluster spacing should be reduced to smaller than 4.2 m;for reservoirs with permeability of(0.2-0.4)×10^(-3)μm^(2),the cluster spacing should be designed as 4.2-9.2 m.Field application on a pilot platform,where the cluster spacing was reduced to 4.0-6.0 m,yielded an increased initial oil production by approximately 36.6%over a 100-m horizontal reservoir section as compared with untested similar platforms.
