Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and v...Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials.展开更多
Human object detection and recognition is essential for elderly monitoring and assisted living however,models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings.To addre...Human object detection and recognition is essential for elderly monitoring and assisted living however,models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings.To address this,we present SCENET-3D,a transformer-drivenmultimodal framework that unifies human-centric skeleton features with scene-object semantics for intelligent robotic vision through a three-stage pipeline.In the first stage,scene analysis,rich geometric and texture descriptors are extracted from RGB frames,including surface-normal histograms,angles between neighboring normals,Zernike moments,directional standard deviation,and Gabor-filter responses.In the second stage,scene-object analysis,non-human objects are segmented and represented using local feature descriptors and complementary surface-normal information.In the third stage,human-pose estimation,silhouettes are processed through an enhanced MoveNet to obtain 2D anatomical keypoints,which are fused with depth information and converted into RGB-based point clouds to construct pseudo-3D skeletons.Features from all three stages are fused and fed in a transformer encoder with multi-head attention to resolve visually similar activities.Experiments on UCLA(95.8%),ETRI-Activity3D(89.4%),andCAD-120(91.2%)demonstrate that combining pseudo-3D skeletonswith rich scene-object fusion significantly improves generalizable activity recognition,enabling safer elderly care,natural human–robot interaction,and robust context-aware robotic perception in real-world environments.展开更多
Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex dataset...Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.展开更多
Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targ...Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targets,complex backgrounds,and small objects in remote sensing.Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot.Therefore,we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture,specifically optimized for the characteristics of large target scale variations,diverse orientations,and numerous small objects in remote sensing images.Our innovations lie in two main aspects:First,a dynamic snake convolution(DSC)is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets.Second,an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information.Finally,we introduce Layer-Adaptive Sparsity for magnitude-based Pruning(LASP)method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios.Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks,and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets(DOTA v1.0,DOTA v1.5,and HRSC2016).展开更多
The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditio...The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.展开更多
Small object detection has been a focus of attention since the emergence of deep learning-based object detection.Although classical object detection frameworks have made significant contributions to the development of...Small object detection has been a focus of attention since the emergence of deep learning-based object detection.Although classical object detection frameworks have made significant contributions to the development of object detection,there are still many issues to be resolved in detecting small objects due to the inherent complexity and diversity of real-world visual scenes.In particular,the YOLO(You Only Look Once)series of detection models,renowned for their real-time performance,have undergone numerous adaptations aimed at improving the detection of small targets.In this survey,we summarize the state-of-the-art YOLO-based small object detection methods.This review presents a systematic categorization of YOLO-based approaches for small-object detection,organized into four methodological avenues,namely attention-based feature enhancement,detection-head optimization,loss function,and multi-scale feature fusion strategies.We then examine the principal challenges addressed by each category.Finally,we analyze the performance of thesemethods on public benchmarks and,by comparing current approaches,identify limitations and outline directions for future research.展开更多
In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free...In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.展开更多
In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often fa...In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.展开更多
Halide solid-state electrolytes have gained significant attention in recent years due to their high ionic conductivity,making them promising candidates for future all-solid-state batteries.Recent studies have identifi...Halide solid-state electrolytes have gained significant attention in recent years due to their high ionic conductivity,making them promising candidates for future all-solid-state batteries.Recent studies have identified numerous crystal structures with the Li_(3)MX_(6)composition,although many remain unexplored across various chemical systems.In this research,we developed a comprehensive method to examine all conceivable space groups and structures within theLi-M-X system,where M includes In,Ga,and La,and X includes F,Cl,Br,and 1.Our findings revealed two metastable structures:Li_(3)InF_(6)with P3c1 symmetry and Li_(3)InI_(6)with C2/c symmetry,exhibiting ionic conductivities of 0.55 and 2.18mS/cm at 300K,respectively.Notably,the trigonal symmetry of Li3InF6 demonstrates that high ionic conductivities are not limited to monoclinic structures but can also be achieved with trigonal symmetries.The electrochemical stability windows,mechanical properties,and reaction energies of these materials with known cathodes suggest their potential for use in all-solid-state batteries.Additionally,we predicted the stability of novel materials,including Li_(5)InCl_(8),Li_(5)InBr_(8),Li_(5)InI_(8),LiIn_(2)Cl_(9),LiIn_(2)Br_(9),and LiIn_(2)I_(9).展开更多
In conventional higher-order topological insulators(HOTIs),the emergence of topological states can be explained by using the nonzero bulk polarization index.However,corner states emerge in HOTIs with incomplete bounda...In conventional higher-order topological insulators(HOTIs),the emergence of topological states can be explained by using the nonzero bulk polarization index.However,corner states emerge in HOTIs with incomplete boundary unit cells(i.e.,boundary defects)even though the bulk polarization is zero,which challenges the conventional understanding of HOTIs.Here,based on a Kekul´e-distorted honeycomb lattice with incomplete unit cells,we reveal that incomplete unit cells exhibit fractional charges through the analysis of Wannier centers by developing a compensation method and creating the concept of Wannier center domain(WCD)which is the smallest region that one Wannier center occupies.This method compensates for the missing parts of these boundary incomplete unit cells with additional WCDs to make them complete.The compensated WCDs automatically carry the corresponding charge,and this charge together with that of the incomplete unit cell constitutes the total charge of the complete unit cell after compensation.We conclude that the emergence of corner states is attributed to the filling anomaly,which is a fundamental mechanism.Our results refresh the understanding of HOTIs,especially those with structural discontinuities,and provide a novel design for topological states which have application value in producing optical functional devices.展开更多
Salient object detection(SOD)models struggle to simultaneously preserve global structure,maintain sharp object boundaries,and sustain computational efficiency in complex scenes.In this study,we propose SPSALNet,a task...Salient object detection(SOD)models struggle to simultaneously preserve global structure,maintain sharp object boundaries,and sustain computational efficiency in complex scenes.In this study,we propose SPSALNet,a task-driven two-stage(macro–micro)architecture that restructures the SOD process around superpixel representations.In the proposed approach,a“split-and-enhance”principle,introduced to our knowledge for the first time in the SOD literature,hierarchically classifies superpixels and then applies targeted refinement only to ambiguous or error-prone regions.At the macro stage,the image is partitioned into content-adaptive superpixel regions,and each superpixel is represented by a high-dimensional region-level feature vector.These representations define a regional decomposition problem in which superpixels are assigned to three classes:background,object interior,and transition regions.Superpixel tokens interact with a global feature vector from a deep network backbone through a cross-attention module and are projected into an enriched embedding space that jointly encodes local topology and global context.At the micro stage,the model employs a U-Net-based refinement process that allocates computational resources only to ambiguous transition regions.The image and distance–similarity maps derived from superpixels are processed through a dual-encoder pathway.Subsequently,channel-aware fusion blocks adaptively combine information from these two sources,producing sharper and more stable object boundaries.Experimental results show that SPSALNet achieves high accuracy with lower computational cost compared to recent competing methods.On the PASCAL-S and DUT-OMRON datasets,SPSALNet exhibits a clear performance advantage across all key metrics,and it ranks first on accuracy-oriented measures on HKU-IS.On the challenging DUT-OMRON benchmark,SPSALNet reaches a MAE of 0.034.Across all datasets,it preserves object boundaries and regional structure in a stable and competitive manner.展开更多
Apparel exports China:The decline in exports widened from January to September(-2.4%,compared to-0.2%in January-June).Exports to the EU increased(+5.9%),though the growth rate moderated.Exports to the US saw a larger ...Apparel exports China:The decline in exports widened from January to September(-2.4%,compared to-0.2%in January-June).Exports to the EU increased(+5.9%),though the growth rate moderated.Exports to the US saw a larger contraction(January-June:-1.6%,January-September:-8.2%).While exports to ASEAN countries still fell by over 10%(-17.7%),shipments to the Philippines(+6.9%),Indonesia(+19.0%),and Cambodia(+64.9%)demonstrated stronger growth performance within the year.Regarding tariffs,on October 30,China and the US agreed to lower the rates on goods subject to additional duties(effectively reducing the average tariff rate on Chinese imports to the US from about 57%to approximately 47%,though this remains significantly higher than the 19.5%overall average rate applied to other countries).展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Surface passivation via two-dimensional(2D)perovskite has emerged as a promising strategy to enhance the performance of perovskite solar cells(PSCs)due to the effective compensation of interfacial states.However,the i...Surface passivation via two-dimensional(2D)perovskite has emerged as a promising strategy to enhance the performance of perovskite solar cells(PSCs)due to the effective compensation of interfacial states.However,the in situ grown 2D perovskite passivation layers typically comprise a mixture of multiple dimensionalities at the interface,where band alignment has only been portrayed qualitatively and empirically.Herein,the interface states for precisely phase-tailored 2D perovskite passivated PSCs are quantitatively investigated.In comparison to traditional passivation molecules,2D perovskite layers based on 4-trifluoromethyl-phenylethylammonium iodide(CF3PEAI)exhibit an increased work function,introducing desirable downward band bending to eliminate the Schottky Barrier.Furthermore,precisely phase-tailored 2D layers could modulate the interface trap density and energetics.The n=1 film delivers optimal performance with a hole extraction efficiency of 95.1%.The optimized n-i-p PSCs in the two-step method significantly improve PCE to 25.40%,along with enhanced photostability and negligible hysteresis.It highlights that tailoring in the composition and phase distribution of the 2D perovskite layer could modulate the interface states at the 2D/3D interface.展开更多
Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate ...Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.展开更多
When two layers of graphene are stacked with a twist angle of approximately 1.1°,strong interlayer coupling gives rise to a pair of flat bands in twisted bilayer graphene(TBG),resulting in pronounced electron–el...When two layers of graphene are stacked with a twist angle of approximately 1.1°,strong interlayer coupling gives rise to a pair of flat bands in twisted bilayer graphene(TBG),resulting in pronounced electron–electron interactions.At half filling of the flat bands,TBG exhibits correlated insulating states.Here,we investigate the electrical transport properties of heterostructures composed of TBG and the antiferromagnetic insulator chromium oxychloride(CrOCl),and propose a strategy to modulate the correlated insulating states in TBG.During the transition from a conventional phase to a strong interfacial coupling phase,kink-like features are observed in the charge neutrality point(CNP),correlated insulating state,and band insulating state.Under a perpendicular magnetic field,the system exhibits broadened quantum Hall plateaus in the strong interfacial coupling regime.Electrons localized in the CrOCl layer screen the bottom gate,rendering the carrier density in TBG less sensitive to variations in the bottom gate voltage.These phenomena are well captured by a charge-transfer model between TBG and CrOCl.Our results provide insights into the control of electronic correlations and topological states in graphene moirésystems via interfacial charge coupling.展开更多
BACKGROUND The prevalence of negative emotional states,such as anxiety and depression,has increased annually.Although personal habits are known to influence emotional regulation,the precise mechanisms underlying this ...BACKGROUND The prevalence of negative emotional states,such as anxiety and depression,has increased annually.Although personal habits are known to influence emotional regulation,the precise mechanisms underlying this relationship remain unclear.AIM To investigate emotion regulation habits impact on students negative emotions during lockdown,using the coronavirus disease 2019 pandemic as a case example.METHODS During the coronavirus disease 2019 lockdown,an online cross-sectional survey was conducted at a Chinese university.Emotional states were assessed using the Depression,Anxiety,and Stress Scale-21(DASS-21),while demographic data and emotion regulation habits were collected concurrently.Data analysis was performed using SPSS version 27.0 and includedχ^(2)-tests for intergroup comparisons,Spearman’s rank-order correlation coefficient analysis to examine associations,and stepwise linear regression modeling to explore the relationships between emotion regulation habits and emotional states.Statistical significance was set atα=0.05.RESULTS Among the 494 valid questionnaires analyzed,the prevalence rates of negative emotional states were as follows:Depression(65.0%),anxiety(69.4%),and stress(50.8%).DASS-21 scores(mean±SD)demonstrated significant symptomatology:Total(48.77±34.88),depression(16.21±12.18),anxiety(14.90±11.91),and stress(17.64±12.07).Significant positive intercorrelations were observed among all DASS-21 subscales(P<0.01).Regression analysis identified key predictors of negative emotions(P<0.05):Risk factors included late-night frequency and academic pressure,while protective factors were the frequency of parental contact and the number of same-gender friends.Additionally,compensatory spending and binge eating positively predicted all negative emotion scores(β>0,P<0.01),whereas appropriate recreational activities negatively predicted these scores(β<0,P<0.01).CONCLUSION High negative emotion prevalence occurred among confined students.Recreational activities were protective,while compensatory spending and binge eating were risk factors,necessitating guided emotion regulation.展开更多
The development of novel quantum many-body computational algorithms relies on robust benchmarking.However,generating such benchmarks is often hindered by the massive computational resources required for exact diagonal...The development of novel quantum many-body computational algorithms relies on robust benchmarking.However,generating such benchmarks is often hindered by the massive computational resources required for exact diagonalization or quantum Monte Carlo simulations,particularly at finite temperatures.In this work,we propose a new algorithm for obtaining thermal pure quantum states,which allows efficient computation of both mechanical and thermodynamic properties at finite temperatures.We implement this algorithm in our open-source C++template library,Physica.Combining the improved algorithm with state-of-the-art software engineering,our implementation achieves high performance and numerical stability.As an example,we demonstrate that for the 4×4 Hubbard model,our method runs approximately 10~3times faster than HΦ3.5.2.Moreover,the accessible temperature range is extended down toβ=32 across arbitrary doping levels.These advances significantly push forward the frontiers of benchmarking for quantum many-body systems.展开更多
The methanol oxidation reaction(MOR)to formic acid offers a promising alternative to the anodic oxygen evolution reaction(OER)in water electrolysis.However,the development of efficient and cost-effective catalysts rem...The methanol oxidation reaction(MOR)to formic acid offers a promising alternative to the anodic oxygen evolution reaction(OER)in water electrolysis.However,the development of efficient and cost-effective catalysts remains a primary challenge.In this study,an enhancement in catalytic MOR performance is achieved through the incorporation of Mn atoms with unsaturated t_(2g)orbitals into Ni_(3)Se_(4).Comprehensive experimental analyses and theoretical calculations reveal that substituting Ni with Mn induces strong electron-withdrawing effects,effectively modulating the local coordination environment of the metal centers.The presence of Mn also elongates Ni–Se(O)bonds,which reduces eg orbital occupancy and modifies the spin state of the material.Electrochemical measurements demonstrate that electrodes based on this optimized material exhibit a high spin state and deliver excellent catalytic activity,achieving a MOR current density up to∼190 mA cm^(−2)at 1.6 V.This performance enhancement is attributed to the favorable electronic configuration and reduced reaction energy barriers associated with the high-spin state.展开更多
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
基金supported by the Major Project for the Integration of ScienceEducation and Industry (Grant No.2025ZDZX02)。
文摘Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Human object detection and recognition is essential for elderly monitoring and assisted living however,models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings.To address this,we present SCENET-3D,a transformer-drivenmultimodal framework that unifies human-centric skeleton features with scene-object semantics for intelligent robotic vision through a three-stage pipeline.In the first stage,scene analysis,rich geometric and texture descriptors are extracted from RGB frames,including surface-normal histograms,angles between neighboring normals,Zernike moments,directional standard deviation,and Gabor-filter responses.In the second stage,scene-object analysis,non-human objects are segmented and represented using local feature descriptors and complementary surface-normal information.In the third stage,human-pose estimation,silhouettes are processed through an enhanced MoveNet to obtain 2D anatomical keypoints,which are fused with depth information and converted into RGB-based point clouds to construct pseudo-3D skeletons.Features from all three stages are fused and fed in a transformer encoder with multi-head attention to resolve visually similar activities.Experiments on UCLA(95.8%),ETRI-Activity3D(89.4%),andCAD-120(91.2%)demonstrate that combining pseudo-3D skeletonswith rich scene-object fusion significantly improves generalizable activity recognition,enabling safer elderly care,natural human–robot interaction,and robust context-aware robotic perception in real-world environments.
基金supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognising human-object interactions(HOI)is a challenging task for traditional machine learning models,including convolutional neural networks(CNNs).Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI.The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity.HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability.This work proposes a Novel quantum gate-enabled hybrid CNN(QEH-CNN)for effectiveHOI recognition.Themodel enhancesCNNperformance by integrating quantumcomputing components.The framework begins with bilateral image filtering,followed bymulti-object tracking(MOT)and Felzenszwalb superpixel segmentation.A watershed algorithm refines object boundaries by cleaning merged superpixels.Feature extraction combines a histogram of oriented gradients(HOG),Global Image Statistics for Texture(GIST)descriptors,and a novel 23-joint keypoint extractionmethod using relative joint angles and joint proximitymeasures.A fuzzy optimization process refines the extracted features before feeding them into the QEH-CNNmodel.The proposed model achieves 95.06%accuracy on the 3D-D3D-HOI dataset and 97.29%on the SYSU3DHOI dataset.Theintegration of quantum computing enhances feature optimization,leading to improved accuracy and overall model efficiency.
基金funded by the Hainan Province Science and Technology Special Fund under Grant ZDYF2024GXJS292.
文摘Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targets,complex backgrounds,and small objects in remote sensing.Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot.Therefore,we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture,specifically optimized for the characteristics of large target scale variations,diverse orientations,and numerous small objects in remote sensing images.Our innovations lie in two main aspects:First,a dynamic snake convolution(DSC)is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets.Second,an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information.Finally,we introduce Layer-Adaptive Sparsity for magnitude-based Pruning(LASP)method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios.Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks,and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets(DOTA v1.0,DOTA v1.5,and HRSC2016).
基金funded by the National Natural Science Foundation of China under Grant No.62371187the Open Program of Hunan Intelligent Rehabilitation Robot and Auxiliary Equipment Engineering Technology Research Center under Grant No.2024JS101.
文摘The ubiquity of mobile devices has driven advancements in mobile object detection.However,challenges in multi-scale object detection in open,complex environments persist due to limited computational resources.Traditional approaches like network compression,quantization,and lightweight design often sacrifice accuracy or feature representation robustness.This article introduces the Fast Multi-scale Channel Shuffling Network(FMCSNet),a novel lightweight detection model optimized for mobile devices.FMCSNet integrates a fully convolutional Multilayer Perceptron(MLP)module,offering global perception without significantly increasing parameters,effectively bridging the gap between CNNs and Vision Transformers.FMCSNet achieves a delicate balance between computation and accuracy mainly by two key modules:the ShiftMLP module,including a shift operation and an MLP module,and a Partial group Convolutional(PGConv)module,reducing computation while enhancing information exchange between channels.With a computational complexity of 1.4G FLOPs and 1.3M parameters,FMCSNet outperforms CNN-based and DWConv-based ShuffleNetv2 by 1%and 4.5%mAP on the Pascal VOC 2007 dataset,respectively.Additionally,FMCSNet achieves a mAP of 30.0(0.5:0.95 IoU threshold)with only 2.5G FLOPs and 2.0M parameters.It achieves 32 FPS on low-performance i5-series CPUs,meeting real-time detection requirements.The versatility of the PGConv module’s adaptability across scenarios further highlights FMCSNet as a promising solution for real-time mobile object detection.
基金supported in part by the by Chongqing Research Program of Basic Research and Frontier Technology under Grant CSTB2025NSCQ-GPX1309.
文摘Small object detection has been a focus of attention since the emergence of deep learning-based object detection.Although classical object detection frameworks have made significant contributions to the development of object detection,there are still many issues to be resolved in detecting small objects due to the inherent complexity and diversity of real-world visual scenes.In particular,the YOLO(You Only Look Once)series of detection models,renowned for their real-time performance,have undergone numerous adaptations aimed at improving the detection of small targets.In this survey,we summarize the state-of-the-art YOLO-based small object detection methods.This review presents a systematic categorization of YOLO-based approaches for small-object detection,organized into four methodological avenues,namely attention-based feature enhancement,detection-head optimization,loss function,and multi-scale feature fusion strategies.We then examine the principal challenges addressed by each category.Finally,we analyze the performance of thesemethods on public benchmarks and,by comparing current approaches,identify limitations and outline directions for future research.
文摘In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.
文摘In modern industrial production,foreign object detection in complex environments is crucial to ensure product quality and production safety.Detection systems based on deep-learning image processing algorithms often face challenges with handling high-resolution images and achieving accurate detection against complex backgrounds.To address these issues,this study employs the PatchCore unsupervised anomaly detection algorithm combined with data augmentation techniques to enhance the system’s generalization capability across varying lighting conditions,viewing angles,and object scales.The proposed method is evaluated in a complex industrial detection scenario involving the bogie of an electric multiple unit(EMU).A dataset consisting of complex backgrounds,diverse lighting conditions,and multiple viewing angles is constructed to validate the performance of the detection system in real industrial environments.Experimental results show that the proposed model achieves an average area under the receiver operating characteristic curve(AUROC)of 0.92 and an average F1 score of 0.85.Combined with data augmentation,the proposed model exhibits improvements in AUROC by 0.06 and F1 score by 0.03,demonstrating enhanced accuracy and robustness for foreign object detection in complex industrial settings.In addition,the effects of key factors on detection performance are systematically analyzed,providing practical guidance for parameter selection in real industrial applications.
基金supported by the Higher Education and Science Committee of Armenia in the frames of the research projects 20TTSG-2F010, 23AA-2F033 and ANSEF (EN-matsc-2660) grant.
文摘Halide solid-state electrolytes have gained significant attention in recent years due to their high ionic conductivity,making them promising candidates for future all-solid-state batteries.Recent studies have identified numerous crystal structures with the Li_(3)MX_(6)composition,although many remain unexplored across various chemical systems.In this research,we developed a comprehensive method to examine all conceivable space groups and structures within theLi-M-X system,where M includes In,Ga,and La,and X includes F,Cl,Br,and 1.Our findings revealed two metastable structures:Li_(3)InF_(6)with P3c1 symmetry and Li_(3)InI_(6)with C2/c symmetry,exhibiting ionic conductivities of 0.55 and 2.18mS/cm at 300K,respectively.Notably,the trigonal symmetry of Li3InF6 demonstrates that high ionic conductivities are not limited to monoclinic structures but can also be achieved with trigonal symmetries.The electrochemical stability windows,mechanical properties,and reaction energies of these materials with known cathodes suggest their potential for use in all-solid-state batteries.Additionally,we predicted the stability of novel materials,including Li_(5)InCl_(8),Li_(5)InBr_(8),Li_(5)InI_(8),LiIn_(2)Cl_(9),LiIn_(2)Br_(9),and LiIn_(2)I_(9).
基金supported by the Natural Science Basic Research Program of Shaanxi Province (Grant Nos.2024JC-JCQN-06 and2025JC-QYCX-006)the National Natural Science Foundation of China (Grant No.12474337)Chinese Academy of Sciences Project (Grant Nos.E4BA270100,E4Z127010F,E4Z6270100,and E53327020D)。
文摘In conventional higher-order topological insulators(HOTIs),the emergence of topological states can be explained by using the nonzero bulk polarization index.However,corner states emerge in HOTIs with incomplete boundary unit cells(i.e.,boundary defects)even though the bulk polarization is zero,which challenges the conventional understanding of HOTIs.Here,based on a Kekul´e-distorted honeycomb lattice with incomplete unit cells,we reveal that incomplete unit cells exhibit fractional charges through the analysis of Wannier centers by developing a compensation method and creating the concept of Wannier center domain(WCD)which is the smallest region that one Wannier center occupies.This method compensates for the missing parts of these boundary incomplete unit cells with additional WCDs to make them complete.The compensated WCDs automatically carry the corresponding charge,and this charge together with that of the incomplete unit cell constitutes the total charge of the complete unit cell after compensation.We conclude that the emergence of corner states is attributed to the filling anomaly,which is a fundamental mechanism.Our results refresh the understanding of HOTIs,especially those with structural discontinuities,and provide a novel design for topological states which have application value in producing optical functional devices.
文摘Salient object detection(SOD)models struggle to simultaneously preserve global structure,maintain sharp object boundaries,and sustain computational efficiency in complex scenes.In this study,we propose SPSALNet,a task-driven two-stage(macro–micro)architecture that restructures the SOD process around superpixel representations.In the proposed approach,a“split-and-enhance”principle,introduced to our knowledge for the first time in the SOD literature,hierarchically classifies superpixels and then applies targeted refinement only to ambiguous or error-prone regions.At the macro stage,the image is partitioned into content-adaptive superpixel regions,and each superpixel is represented by a high-dimensional region-level feature vector.These representations define a regional decomposition problem in which superpixels are assigned to three classes:background,object interior,and transition regions.Superpixel tokens interact with a global feature vector from a deep network backbone through a cross-attention module and are projected into an enriched embedding space that jointly encodes local topology and global context.At the micro stage,the model employs a U-Net-based refinement process that allocates computational resources only to ambiguous transition regions.The image and distance–similarity maps derived from superpixels are processed through a dual-encoder pathway.Subsequently,channel-aware fusion blocks adaptively combine information from these two sources,producing sharper and more stable object boundaries.Experimental results show that SPSALNet achieves high accuracy with lower computational cost compared to recent competing methods.On the PASCAL-S and DUT-OMRON datasets,SPSALNet exhibits a clear performance advantage across all key metrics,and it ranks first on accuracy-oriented measures on HKU-IS.On the challenging DUT-OMRON benchmark,SPSALNet reaches a MAE of 0.034.Across all datasets,it preserves object boundaries and regional structure in a stable and competitive manner.
文摘Apparel exports China:The decline in exports widened from January to September(-2.4%,compared to-0.2%in January-June).Exports to the EU increased(+5.9%),though the growth rate moderated.Exports to the US saw a larger contraction(January-June:-1.6%,January-September:-8.2%).While exports to ASEAN countries still fell by over 10%(-17.7%),shipments to the Philippines(+6.9%),Indonesia(+19.0%),and Cambodia(+64.9%)demonstrated stronger growth performance within the year.Regarding tariffs,on October 30,China and the US agreed to lower the rates on goods subject to additional duties(effectively reducing the average tariff rate on Chinese imports to the US from about 57%to approximately 47%,though this remains significantly higher than the 19.5%overall average rate applied to other countries).
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by the National Natural Science Foundation of China(Nos.62304111,62304110,22579136)the National Key Research and Development Program of China(2024YFE0201800)+6 种基金the China Postdoctoral Science Foundation(No.2024M761492)the Project of State Key Laboratory of Organic Electronics and Information Displays(Nos.GDX2022010009,GZR2023010046)the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(No.NY223053)the Science and Technology Project of Jiangsu(Science and Technology Cooperation Project of HongKong,Macao and Taiwan,No.BZ2023059)Shaanxi Fundamental Science Research Project for Mathematics and Physics(No.22jSY015)Young Talent Fund of Xi'an Association for Science and Technology(No.959202313020)Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems(No.2023B1212010003).
文摘Surface passivation via two-dimensional(2D)perovskite has emerged as a promising strategy to enhance the performance of perovskite solar cells(PSCs)due to the effective compensation of interfacial states.However,the in situ grown 2D perovskite passivation layers typically comprise a mixture of multiple dimensionalities at the interface,where band alignment has only been portrayed qualitatively and empirically.Herein,the interface states for precisely phase-tailored 2D perovskite passivated PSCs are quantitatively investigated.In comparison to traditional passivation molecules,2D perovskite layers based on 4-trifluoromethyl-phenylethylammonium iodide(CF3PEAI)exhibit an increased work function,introducing desirable downward band bending to eliminate the Schottky Barrier.Furthermore,precisely phase-tailored 2D layers could modulate the interface trap density and energetics.The n=1 film delivers optimal performance with a hole extraction efficiency of 95.1%.The optimized n-i-p PSCs in the two-step method significantly improve PCE to 25.40%,along with enhanced photostability and negligible hysteresis.It highlights that tailoring in the composition and phase distribution of the 2D perovskite layer could modulate the interface states at the 2D/3D interface.
基金financial support provided by the Natural Science Foundation of Hebei Province,China(No.E2024105036)the Tangshan Talent Funding Project,China(Nos.B202302007 and A2021110015)+1 种基金the National Natural Science Foundation of China(No.52264042)the Australian Research Council(No.IH230100010)。
文摘Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.52225207 and 52350001)the Shanghai Pilot Program for Basic Research–Fudan University 21TQ1400100(Grant No.21TQ006)the Shanghai Municipal Science and Technology Major Project(Grant No.2019SHZDZX01)。
文摘When two layers of graphene are stacked with a twist angle of approximately 1.1°,strong interlayer coupling gives rise to a pair of flat bands in twisted bilayer graphene(TBG),resulting in pronounced electron–electron interactions.At half filling of the flat bands,TBG exhibits correlated insulating states.Here,we investigate the electrical transport properties of heterostructures composed of TBG and the antiferromagnetic insulator chromium oxychloride(CrOCl),and propose a strategy to modulate the correlated insulating states in TBG.During the transition from a conventional phase to a strong interfacial coupling phase,kink-like features are observed in the charge neutrality point(CNP),correlated insulating state,and band insulating state.Under a perpendicular magnetic field,the system exhibits broadened quantum Hall plateaus in the strong interfacial coupling regime.Electrons localized in the CrOCl layer screen the bottom gate,rendering the carrier density in TBG less sensitive to variations in the bottom gate voltage.These phenomena are well captured by a charge-transfer model between TBG and CrOCl.Our results provide insights into the control of electronic correlations and topological states in graphene moirésystems via interfacial charge coupling.
文摘BACKGROUND The prevalence of negative emotional states,such as anxiety and depression,has increased annually.Although personal habits are known to influence emotional regulation,the precise mechanisms underlying this relationship remain unclear.AIM To investigate emotion regulation habits impact on students negative emotions during lockdown,using the coronavirus disease 2019 pandemic as a case example.METHODS During the coronavirus disease 2019 lockdown,an online cross-sectional survey was conducted at a Chinese university.Emotional states were assessed using the Depression,Anxiety,and Stress Scale-21(DASS-21),while demographic data and emotion regulation habits were collected concurrently.Data analysis was performed using SPSS version 27.0 and includedχ^(2)-tests for intergroup comparisons,Spearman’s rank-order correlation coefficient analysis to examine associations,and stepwise linear regression modeling to explore the relationships between emotion regulation habits and emotional states.Statistical significance was set atα=0.05.RESULTS Among the 494 valid questionnaires analyzed,the prevalence rates of negative emotional states were as follows:Depression(65.0%),anxiety(69.4%),and stress(50.8%).DASS-21 scores(mean±SD)demonstrated significant symptomatology:Total(48.77±34.88),depression(16.21±12.18),anxiety(14.90±11.91),and stress(17.64±12.07).Significant positive intercorrelations were observed among all DASS-21 subscales(P<0.01).Regression analysis identified key predictors of negative emotions(P<0.05):Risk factors included late-night frequency and academic pressure,while protective factors were the frequency of parental contact and the number of same-gender friends.Additionally,compensatory spending and binge eating positively predicted all negative emotion scores(β>0,P<0.01),whereas appropriate recreational activities negatively predicted these scores(β<0,P<0.01).CONCLUSION High negative emotion prevalence occurred among confined students.Recreational activities were protective,while compensatory spending and binge eating were risk factors,necessitating guided emotion regulation.
基金Fu-Zhou Chen for helpful discussions.The work is partly supported by the National Key Research and Development Program of China(Grant No.2022YFA1402704)the National Natural Science Foundation of China(Grant No.12247101)。
文摘The development of novel quantum many-body computational algorithms relies on robust benchmarking.However,generating such benchmarks is often hindered by the massive computational resources required for exact diagonalization or quantum Monte Carlo simulations,particularly at finite temperatures.In this work,we propose a new algorithm for obtaining thermal pure quantum states,which allows efficient computation of both mechanical and thermodynamic properties at finite temperatures.We implement this algorithm in our open-source C++template library,Physica.Combining the improved algorithm with state-of-the-art software engineering,our implementation achieves high performance and numerical stability.As an example,we demonstrate that for the 4×4 Hubbard model,our method runs approximately 10~3times faster than HΦ3.5.2.Moreover,the accessible temperature range is extended down toβ=32 across arbitrary doping levels.These advances significantly push forward the frontiers of benchmarking for quantum many-body systems.
基金financially supported by the Sichuan Science and Technology Program (Grant No. 2025NSFSC0139)the China Postdoctoral Science Foundation (Grant No.2023MD734228)+10 种基金funding from Generalitat de Catalunya 2021SGR00457supported by MCIN with funding from European Union NextGenerationEU(PRTR-C17.I1)by Generalitat de Catalunya (In-CAEM Project)the support from the project AMaDE(PID2023-149158OB-C43)funded by MCIN/AEI/10.13039/501100011033/by “ERDF A way of making Europe”by the “European Union”supported by the Severo Ochoa program from Spanish MCIN/AEI (Grant No.:CEX2021-001214-S)funded by the CERCA Programme/Generalitat de Catalunyaperformed in the framework of Universitat Autònoma de Barcelona Materials Science PhD programfunding from the CSC-UAB PhD scholarship program. ICN2 is founding member of e-DREAM[87]
文摘The methanol oxidation reaction(MOR)to formic acid offers a promising alternative to the anodic oxygen evolution reaction(OER)in water electrolysis.However,the development of efficient and cost-effective catalysts remains a primary challenge.In this study,an enhancement in catalytic MOR performance is achieved through the incorporation of Mn atoms with unsaturated t_(2g)orbitals into Ni_(3)Se_(4).Comprehensive experimental analyses and theoretical calculations reveal that substituting Ni with Mn induces strong electron-withdrawing effects,effectively modulating the local coordination environment of the metal centers.The presence of Mn also elongates Ni–Se(O)bonds,which reduces eg orbital occupancy and modifies the spin state of the material.Electrochemical measurements demonstrate that electrodes based on this optimized material exhibit a high spin state and deliver excellent catalytic activity,achieving a MOR current density up to∼190 mA cm^(−2)at 1.6 V.This performance enhancement is attributed to the favorable electronic configuration and reduced reaction energy barriers associated with the high-spin state.
