This paper aims to explore the development strategies for subject services in university libraries within the context of the“double first-class”initiative.By examining the relationship between“double first-class”c...This paper aims to explore the development strategies for subject services in university libraries within the context of the“double first-class”initiative.By examining the relationship between“double first-class”construction and university library subject services,the study analyzes the current state of subject services,including resource development,service models,and team building.Drawing on domestic and international case studies,the paper proposes a series of targeted and practical strategies to enhance the quality of subject services in university libraries,thereby providing robust support for the advancement of the“double first-class”initiative.展开更多
NiFe-layered double hydroxides(NiFe-LDHs)are among the most promising earth-abundant electrocatalysts for the oxygen evolution reaction(OER)in alkaline media.However,their practical application is hindered by intrinsi...NiFe-layered double hydroxides(NiFe-LDHs)are among the most promising earth-abundant electrocatalysts for the oxygen evolution reaction(OER)in alkaline media.However,their practical application is hindered by intrinsic activity limitations and poor stability,primarily due to the asymmetric adsorption of oxygen intermediates.To overcome this,the binding strength must be synergistically tuned to a moderate level to optimize catalytic performance.Here,we engineered NiFeCoCr LDH through Co doping to enhance electrical conductivity and controlled Cr leaching to introduce cationic vacancies for modulating intermediate binding strength in NiFe LDH.X-ray absorption near-edge structure and extended X-ray absorption fine structure analyses reveal that NiFe-LDH with Co doping and Cr vacancies modulates the Ni oxidation state and local coordination environment,leading to a balanced electronic structure and enhanced structural complexity around the Ni sites.Additionally,these vacancies can trap OH^(-)/H_(2)O species,which can serve as a reservoir for OH^(-) transfer,facilitating the rapid formation of OER intermediates and enhancing catalytic performance at high current densities.As a result,V_(Cr)-NiFeCo LDH achieves 1.6 A cm^(-2)current density at 1.7 V vs.RHE while maintaining stable operation for over 1000 h at 500 mA cm^(-2).Density functional theory(DFT)calculations validate the synergistic effects of Co doping and Cr-induced vacancies on intermediate binding energies and improved OER kinetics.Overall,this work presents a rational design strategy to simultaneously enhance the activity and durability of NiFe-based OER catalysts for their application in high-performance alkaline water electrolysis.展开更多
The“Double First-Class”construction focuses on the development of disciplinary connotations and the improvement of talent cultivation quality,posing higher demands on the teaching mode and talent supply of public he...The“Double First-Class”construction focuses on the development of disciplinary connotations and the improvement of talent cultivation quality,posing higher demands on the teaching mode and talent supply of public health and preventive medicine disciplines.Environmental health,as a core course in public health and preventive medicine,directly relates to the cultivation of effective composite public health talents.Based on the core orientation of the“Double First-Class”construction and combining the talent cultivation and course characteristics of environmental health,this paper explores the significance of teaching reform and talent cultivation in environmental health from the perspective of“Double First-Class”construction.It also investigates the paths for teaching reform and talent cultivation from multiple dimensions,aiming to provide references for cultivating high-quality professionals who meet the needs of national public health development and ecological environmental protection.展开更多
Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinct...Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations.However,they often neglect the complex contextual dependencies among image patches,resulting in incomplete local representations and limited segmentation accuracy.To address these issues,we propose the Context Patch Fusion with Class Token Enhancement(CPF-CTE)framework,which exploits contextual relations among patches to enrich feature repre-sentations and improve segmentation.At its core,the Contextual-Fusion Bidirectional Long Short-Term Memory(CF-BiLSTM)module captures spatial dependencies between patches and enables bidirectional information flow,yield-ing a more comprehensive understanding of spatial correlations.This strengthens feature learning and segmentation robustness.Moreover,we introduce learnable class tokens that dynamically encode and refine class-specific semantics,enhancing discriminative capability.By effectively integrating spatial and semantic cues,CPF-CTE produces richer and more accurate representations of image content.Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate that CPF-CTE consistently surpasses prior WSSS methods.展开更多
The doubled haploid(DH)technique accelerates homozygosity by inducing chromosome doubling in haploid embryos derived from hybrid plants.This approach offers significant advantages over conventional rice breeding metho...The doubled haploid(DH)technique accelerates homozygosity by inducing chromosome doubling in haploid embryos derived from hybrid plants.This approach offers significant advantages over conventional rice breeding methods by shortening the breeding cycle and enabling rapid development of pure homozygous lines.Anther culture(AC)has been established as an efficient and successful method for producing DH plants via androgenesis in rice.However,despite its success in japonica rice.展开更多
Artificial photosynthesis of hydrogen peroxide(H_(2)O_(2))from earth-abundant water and oxygen is a sustainable approach,however current photocatalysts suffer from low production rate and solar-to-chemical conversion ...Artificial photosynthesis of hydrogen peroxide(H_(2)O_(2))from earth-abundant water and oxygen is a sustainable approach,however current photocatalysts suffer from low production rate and solar-to-chemical conversion efficiency(<1.5%).Herein,we report that nickelchromium layered double hydroxide with intercalated nitrate(NiCrOOH-NO_(3))and a thickness of~4.4 nm is an efficient photocatalyst,enabling a H_(2)O_(2)production yield of 28.7 mmol g^(-1)h^(-1)under visible light irradiation with3.92%solar-to-chemical conversion efficiency.Experimental and computational studies have revealed an inherent facet-dependent reduction-oxidation reaction behavior and spatial separation of photogenerated electrons and holes.An unexpected role of intercalated nitrate is demonstrated,which promotes excited electron—hole spatial separation and facilitates the electron transfer to oxygen intermediate via delocalization.This work provides understandings in the impact of nanostructure and anion in the design of advanced photocatalysts,paving the way toward practical synthesis of H_(2)O_(2)using fully solar-driven renewable energy.展开更多
In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these chall...In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these challenges jointly degrade representation stability,bias learning toward outdated distributions,and reduce the resilience and reliability of detection in dynamic environments.This paper proposes a streaming classincremental learning(SCIL)framework to address these issues.The SCIL framework integrates an autoencoder(AE)with a multi-layer perceptron for multi-class prediction,employs a dual-loss strategy(classification and reconstruction)for prediction and new class detection,uses corrected pseudo-labels for online training,manages classes with queues,and applies oversampling to handle imbalance.The rationale behind the method's structure is elucidated through ablation studies,and a comprehensive experimental evaluation is performed using both real-world and synthetic datasets that feature class imbalance,incremental classes,and concept drifts.Our results demonstrate that SCIL outperforms strong baselines and state-of-the-art methods.In line with our commitment to Open Science,we make our code and datasets available to the community.展开更多
Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classificati...Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.展开更多
Inclusive green growth(IGG)is a crucial pathway to high-quality economic development,with standardization serving as a key enabler.Standardization plays a critical role in reducing coordination costs and improving res...Inclusive green growth(IGG)is a crucial pathway to high-quality economic development,with standardization serving as a key enabler.Standardization plays a critical role in reducing coordination costs and improving resource allocation efficiency by facilitating rule harmonization,factor integration,and collaborative governance.Examining IGG from a standardization perspective helps clarify the mechanisms through which economic,environmental,and social objectives can be jointly realized,and offers new insights into the institutionalized and sustainable pursuit of multiple development goals.However,how standardization promotes IGG by coordinating economic growth,environmental performance,and social equity remains insufficiently explored in the existing literature.Using panel data from 283 prefecture-level Chinese cities(2012-2021),this study treats the comprehensive standardization reform pilot as a quasi-natural experiment and applies a double machine-learning framework to test whether standardization promotes IGG.The analysis further explores the mediating roles of technological innovation,green finance,and employment quality,and examines heterogeneity across geographic location,resource endowment,industrial base,and city hierarchy.It also evaluates the regional coordination effects of standardization.Results show that standardization significantly advances IGG,though its impact varies by regional and structural characteristics.Standardization enhances IGG by strengthening innovation,expanding green finance,and improving job quality.Moreover,it helps bridge geographic divides,narrow interregional disparities,and enhance coordination.These findings offer empirical evidence for policymakers to design targeted standardization strategies that support sustainable and equitable urban development.展开更多
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting...Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.展开更多
In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic per...In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.展开更多
Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from...Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.展开更多
The buoyancy-induced flow constitutes a core scientific issue for thermal management of electronic devices and thermal design of energy systems,where accurate characterization of flow and heat transfer is essential to...The buoyancy-induced flow constitutes a core scientific issue for thermal management of electronic devices and thermal design of energy systems,where accurate characterization of flow and heat transfer is essential to improve thermal efficiency.In this work,buoyancy-induced flow above two heating elements flush-mounted at the bottom of a square enclosure containing air is numerically investigated over a range of Rayleigh numbers(0<Ra≤1.5×10^(8)),with a focus on equal and unequal heat flux conditions under a constraint of constant total thermal energy input.Distinct flow transitions are observed in both cases,leading to the identification of three flow regimes:Steady,periodic unsteady,and chaotic unsteady.Two types of periodic flows are distinguished,in which the first is a periodic flow dominated by a fundamental frequency(FF)and its integer-multiple frequencies(INTMF),while the second is a more complex periodic flow featuring FF,INTMF,and their sub-harmonics.The transitions between these regimes are affected by the relative heat flux of the two heaters.When the heat flux of the two heaters is unequal,the range of Rayleigh numbers corresponding to periodic flow is suppressed.It is also found that the time-averaged maximum temperature of the strong heater increases more rapidly with Ra,while that of the weak heater increases more slowly,reflecting the interaction between buoyancy-driven flow dynamics and asymmetric heat input.Analysis of the time-averaged Nusselt number demonstrates that heat dissipation from the isothermal walls remains roughly equivalent,even when the heat flux of the two heaters differs by a factor of two.These findings highlight the critical roles of Rayleigh number,the number of heaters,and the heat flux ratio of the heaters in determining heat transfer and flow characteristics for buoyancy-driven convection systems,providing important theoretical support and design references for engineering scenarios such as electronic devices and design of new energy systems.展开更多
The electric double layer(EDL)at the electrochemical interface is crucial for ion transport,charge transfer,and surface reactions in aqueous rechargeable zinc batteries(ARZBs).However,Zn anodes routinely encounter per...The electric double layer(EDL)at the electrochemical interface is crucial for ion transport,charge transfer,and surface reactions in aqueous rechargeable zinc batteries(ARZBs).However,Zn anodes routinely encounter persistent dendrite growth and parasitic reactions,driven by the inhomogeneous charge distribution and water-dominated environment within the EDL.Compounding this,classical EDL theory,rooted in meanfield approximations,further fails to resolve molecular-scale interfacial dynamics under battery-operating conditions,limiting mechanistic insights.Herein,we established a multiscale theoretical calculation framework from single molecular characteristics to interfacial ion distribution,revealing the EDL’s structure and interactions between different ions and molecules,which helps us understand the parasitic processes in depth.Simulations demonstrate that water dipole and sulfate ion adsorption at the inner Helmholtz plane drives severe hydrogen evolution and by-product formation.Guided by these insights,we engineered a“water-poor and anion-expelled”EDL using 4,1’,6’-trichlorogalactosucrose(TGS)as an electrolyte additive.As a result,Zn||Zn symmetric cells with TGS exhibited stable cycling for over 4700 h under a current density of 1 mA cm^(−2),while NaV_(3)O_(8)·1.5H_(2)O-based full cells kept 90.4%of the initial specific capacity after 800 cycles at 5 A g^(−1).This work highlights the power of multiscale theoretical frameworks to unravel EDL complexities and guide high-performance ARZB design through integrated theory-experiment approaches.展开更多
NiFe layered double hydroxide(NiFe LDH)has emerged as a promising catalyst for the oxygen evolution reaction(OER);however,its hydrogen evolution reaction(HER)activity remains suboptimal due to unfavorable electronic s...NiFe layered double hydroxide(NiFe LDH)has emerged as a promising catalyst for the oxygen evolution reaction(OER);however,its hydrogen evolution reaction(HER)activity remains suboptimal due to unfavorable electronic structures,particularly the d-electron density of metal sites,which impede water dissociation and lead to poor hydrogen adsorption/desorption capabilities.Herein,we introduce an efficient cooperative d-electron density regulation(CDDR)engineering to comprehensively optimize the delectron density of NiFe LDH by grafting MoO_(x) -modified NiFe LDH nanosheets onto porous nickel particles(PNPs).The PNPs facilitate d-electron density modulation along the edges of the nanosheets,while the MoO_(x) species enable d-electron density modulation across the plane of the nanosheets,thus cooperatively constructing enriched d-electron density in NiFe LDH.Theoretical studies validate the CDDR process and reveal that the enriched d-electron density accelerates water dissociation and optimizes the hydrogen adsorption behavior of NiFe LDH.As a result,the engineered catalyst exhibits significantly improved HER activity,achieving an ultra-low overpotential of 38 mV at 10 mA cm^(-2)in 1 M KOH.Additionally,the CDDR-optimized catalyst also exhibits good OER performance,demonstrating excellent bifunctional performance for overall water splitting in both alkaline freshwater and seawater electrolytes.This work presents a novel CDDR strategy for engineering NiFe LDH into efficient HER catalysts without compromising its OER activity,potentially paving the way for the development of active and robust electrocatalysts for sustainable energy applications.展开更多
Wing design is a critical factor in the aerodynamic performance of flapping-wing(FW)robots.Inspired by the natural wing structures of insects,bats,and birds,we explored how bio-mimetic wing vein morphologies,combined ...Wing design is a critical factor in the aerodynamic performance of flapping-wing(FW)robots.Inspired by the natural wing structures of insects,bats,and birds,we explored how bio-mimetic wing vein morphologies,combined with a bio-inspired double wing clap-and-fling mechanism,affect thrust generation.This study focused on increasing vertical force and payload capacity.Through systematic experimentation with various vein configurations and structural designs,we developed innovative wings optimized for thrust production.Comprehensive tests were conducted to measure aerodynamic forces,power consumption,and wing kinematics across a range of flapping frequencies.Additionally,wings with different aspect ratios,a key factor in wing design,were fabricated and extensively evaluated.The study also examined the role of bio-inspired vein layouts on wing flexibility,a critical component in improving flight efficiency.Our findings demonstrate that the newly developed wing design led to a 20%increase in thrust,achieving up to 30 g-force(gf).This research sheds light on the clap-and-fling effect and establishes a promising framework for bio-inspired wing design,offering significant improvements in both performance and payload capacity for FW robots.展开更多
Time-delayed blasting is widely utilized in engineering to mitigate induced vibration hazards and enhance fragmentation.The underlying vibration reduction principle is the decrease of the charge weight per delay,while...Time-delayed blasting is widely utilized in engineering to mitigate induced vibration hazards and enhance fragmentation.The underlying vibration reduction principle is the decrease of the charge weight per delay,while the potential for further vibration reduction remains debated,largely due to unclear underlying mechanisms.In light of the popularization of electronic detonators and the representativeness of double-hole configurationsfor multiple blastholes,it is essential to investigate the vibration characteristics induced by time-delayed double blastholes.Therefore,a series of doubleborehole experimental blasts was conducted in an underground roadway to clarify the variation in vibration from single-hole to dual-hole conditions.Based on the experimental data and inherent limitations,an exact full-fieldtheoretical model was further employed to systematically analyze the effects of delay time,charge length,and borehole inclination angle on vibrations induced by various doublehole configurations.The experimental data and theoretical analysis reveal that the general scaled distance effectively predicts vibrations in delayed blasting but does not reflectvibration reduction.Increasing delay time causes fluctuatingPPVs,which stabilize slightly above single-hole PPVs as delay times exceed a certain value.The delayed blasting primarily reduces near-fieldfrequencies.Longer charge lengths in double boreholes increase PPV levels and attenuation rates within a certain length,and the vibration behavior of combined long and short charge lengths is governed by the long blasthole.Larger blasthole inclination angles enhance vibration amplitude and reduce PPV attenuation rates.Optimizing inclination angles is more critical than adjusting delay times,and parallel boreholes offer the best vibration control.展开更多
A method for the effective in-situ formation of boron-containing Mg-Al layered double hydroxides(LDHs)was developed for boron removal and stabilization.The influence of the B/Al molar ratio and pH on the formation of ...A method for the effective in-situ formation of boron-containing Mg-Al layered double hydroxides(LDHs)was developed for boron removal and stabilization.The influence of the B/Al molar ratio and pH on the formation of Mg-Al-B–LDHs was investigated.Compared with the adsorption method,under a high B/Al ratio,the coprecipitation method increased the boron sorption density from 0.256 to 0.472 of Al.The Toxicity Characteristic Leaching Procedure showed that the boron-coprecipitated LDHs exhibited higher stability than the boron-adsorption LDHs.The synthesized LDH samples were characterized by X-ray diffraction,X-ray photoelectron spectroscopy,and solid-state 11B-NMR.The results showed that boron was effectively incorporated into the LDH structure for the coprecipitation method.Combined with the experimental results,a potential in-situ formation pathway for Mg-Al-B–LDHs was elucidated through density functional theory calculations.The boron tended to directly incorporate into the LDH structure in the coprecipitation method,whereas it was predominantly adsorbed on the LDH surface in the adsorption method.The adsorption energy demonstrated that boron preferentially bonded to Mg^(2+)sites on the surface.The mechanism of boron incorporation in the LDHs for the coprecipitation method involved precipitation of amorphous aluminum hydroxide,layered boehmite transformation,nucleation,and layer stacking.During these processes,boron formed complexes to enhance its stability.Residual boron underwent further reactions with the LDHs,including surface adsorption and ion exchange.These findings provide theoretical insight into the effective removal and long-term immobilization of boron in landfill leachate self-remediation processes.展开更多
基金Heilongjiang Provincial Library Work Committee Project“Double First-Class Construction Research on the Development Strategy of Subject Services in University Libraries”(2024-016-B)。
文摘This paper aims to explore the development strategies for subject services in university libraries within the context of the“double first-class”initiative.By examining the relationship between“double first-class”construction and university library subject services,the study analyzes the current state of subject services,including resource development,service models,and team building.Drawing on domestic and international case studies,the paper proposes a series of targeted and practical strategies to enhance the quality of subject services in university libraries,thereby providing robust support for the advancement of the“double first-class”initiative.
基金supported by the Natural Science Foundation of China Grant No.52272289 and 5240223,and JSPS(Japan Society for the Promotion of Science)of Grant No.22K19088,23H00313,24H02202,and 24H02205。
文摘NiFe-layered double hydroxides(NiFe-LDHs)are among the most promising earth-abundant electrocatalysts for the oxygen evolution reaction(OER)in alkaline media.However,their practical application is hindered by intrinsic activity limitations and poor stability,primarily due to the asymmetric adsorption of oxygen intermediates.To overcome this,the binding strength must be synergistically tuned to a moderate level to optimize catalytic performance.Here,we engineered NiFeCoCr LDH through Co doping to enhance electrical conductivity and controlled Cr leaching to introduce cationic vacancies for modulating intermediate binding strength in NiFe LDH.X-ray absorption near-edge structure and extended X-ray absorption fine structure analyses reveal that NiFe-LDH with Co doping and Cr vacancies modulates the Ni oxidation state and local coordination environment,leading to a balanced electronic structure and enhanced structural complexity around the Ni sites.Additionally,these vacancies can trap OH^(-)/H_(2)O species,which can serve as a reservoir for OH^(-) transfer,facilitating the rapid formation of OER intermediates and enhancing catalytic performance at high current densities.As a result,V_(Cr)-NiFeCo LDH achieves 1.6 A cm^(-2)current density at 1.7 V vs.RHE while maintaining stable operation for over 1000 h at 500 mA cm^(-2).Density functional theory(DFT)calculations validate the synergistic effects of Co doping and Cr-induced vacancies on intermediate binding energies and improved OER kinetics.Overall,this work presents a rational design strategy to simultaneously enhance the activity and durability of NiFe-based OER catalysts for their application in high-performance alkaline water electrolysis.
文摘The“Double First-Class”construction focuses on the development of disciplinary connotations and the improvement of talent cultivation quality,posing higher demands on the teaching mode and talent supply of public health and preventive medicine disciplines.Environmental health,as a core course in public health and preventive medicine,directly relates to the cultivation of effective composite public health talents.Based on the core orientation of the“Double First-Class”construction and combining the talent cultivation and course characteristics of environmental health,this paper explores the significance of teaching reform and talent cultivation in environmental health from the perspective of“Double First-Class”construction.It also investigates the paths for teaching reform and talent cultivation from multiple dimensions,aiming to provide references for cultivating high-quality professionals who meet the needs of national public health development and ecological environmental protection.
文摘Weakly Supervised Semantic Segmentation(WSSS),which relies only on image-level labels,has attracted significant attention for its cost-effectiveness and scalability.Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations.However,they often neglect the complex contextual dependencies among image patches,resulting in incomplete local representations and limited segmentation accuracy.To address these issues,we propose the Context Patch Fusion with Class Token Enhancement(CPF-CTE)framework,which exploits contextual relations among patches to enrich feature repre-sentations and improve segmentation.At its core,the Contextual-Fusion Bidirectional Long Short-Term Memory(CF-BiLSTM)module captures spatial dependencies between patches and enables bidirectional information flow,yield-ing a more comprehensive understanding of spatial correlations.This strengthens feature learning and segmentation robustness.Moreover,we introduce learnable class tokens that dynamically encode and refine class-specific semantics,enhancing discriminative capability.By effectively integrating spatial and semantic cues,CPF-CTE produces richer and more accurate representations of image content.Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 validate that CPF-CTE consistently surpasses prior WSSS methods.
基金supported by the Science and Technology Innovation Program of Fujian Agriculture and Forestry University,China(Grant No.KFB22045)the General Program of Natural Science Foundation Fujian,China(Grant No.2023J01460).
文摘The doubled haploid(DH)technique accelerates homozygosity by inducing chromosome doubling in haploid embryos derived from hybrid plants.This approach offers significant advantages over conventional rice breeding methods by shortening the breeding cycle and enabling rapid development of pure homozygous lines.Anther culture(AC)has been established as an efficient and successful method for producing DH plants via androgenesis in rice.However,despite its success in japonica rice.
基金support from the National Natural Science Foundation of China(NSFC 21905092,22475072 and 22075085)the Fundamental Research Funds for the Central Universities+1 种基金supported by the Shanghai Frontiers Science Center of Molecule Intelligent SynthesesEast China Normal University Multifunctional Platform for Innovation(004)。
文摘Artificial photosynthesis of hydrogen peroxide(H_(2)O_(2))from earth-abundant water and oxygen is a sustainable approach,however current photocatalysts suffer from low production rate and solar-to-chemical conversion efficiency(<1.5%).Herein,we report that nickelchromium layered double hydroxide with intercalated nitrate(NiCrOOH-NO_(3))and a thickness of~4.4 nm is an efficient photocatalyst,enabling a H_(2)O_(2)production yield of 28.7 mmol g^(-1)h^(-1)under visible light irradiation with3.92%solar-to-chemical conversion efficiency.Experimental and computational studies have revealed an inherent facet-dependent reduction-oxidation reaction behavior and spatial separation of photogenerated electrons and holes.An unexpected role of intercalated nitrate is demonstrated,which promotes excited electron—hole spatial separation and facilitates the electron transfer to oxygen intermediate via delocalization.This work provides understandings in the impact of nanostructure and anion in the design of advanced photocatalysts,paving the way toward practical synthesis of H_(2)O_(2)using fully solar-driven renewable energy.
基金supported by the European Research Council(ERC)under Grant Agreement No.951424(Water-Futures)by the Republic of Cyprus through the Deputy Ministry of Research,Innovation and Digital Policy.
文摘In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these challenges jointly degrade representation stability,bias learning toward outdated distributions,and reduce the resilience and reliability of detection in dynamic environments.This paper proposes a streaming classincremental learning(SCIL)framework to address these issues.The SCIL framework integrates an autoencoder(AE)with a multi-layer perceptron for multi-class prediction,employs a dual-loss strategy(classification and reconstruction)for prediction and new class detection,uses corrected pseudo-labels for online training,manages classes with queues,and applies oversampling to handle imbalance.The rationale behind the method's structure is elucidated through ablation studies,and a comprehensive experimental evaluation is performed using both real-world and synthetic datasets that feature class imbalance,incremental classes,and concept drifts.Our results demonstrate that SCIL outperforms strong baselines and state-of-the-art methods.In line with our commitment to Open Science,we make our code and datasets available to the community.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(RS-2023-00249743).
文摘Most Convolutional Neural Network(CNN)interpretation techniques visualize only the dominant cues that the model relies on,but there is no guarantee that these represent all the evidence the model uses for classification.This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered.This study introduces CasCAM(Cascaded Class Activation Mapping)to address this fundamental limitation through counterfactual reasoning.By asking“if this dominant cue were absent,what other evidence would the model use?”,CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them.Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.
基金supported by the National Social Science Fund of China“Research on the Ecological Symbiotic Development and Dynamic Governance of Manufacturing Entrepreneurship Platform”[Grant No.22BGL048].
文摘Inclusive green growth(IGG)is a crucial pathway to high-quality economic development,with standardization serving as a key enabler.Standardization plays a critical role in reducing coordination costs and improving resource allocation efficiency by facilitating rule harmonization,factor integration,and collaborative governance.Examining IGG from a standardization perspective helps clarify the mechanisms through which economic,environmental,and social objectives can be jointly realized,and offers new insights into the institutionalized and sustainable pursuit of multiple development goals.However,how standardization promotes IGG by coordinating economic growth,environmental performance,and social equity remains insufficiently explored in the existing literature.Using panel data from 283 prefecture-level Chinese cities(2012-2021),this study treats the comprehensive standardization reform pilot as a quasi-natural experiment and applies a double machine-learning framework to test whether standardization promotes IGG.The analysis further explores the mediating roles of technological innovation,green finance,and employment quality,and examines heterogeneity across geographic location,resource endowment,industrial base,and city hierarchy.It also evaluates the regional coordination effects of standardization.Results show that standardization significantly advances IGG,though its impact varies by regional and structural characteristics.Standardization enhances IGG by strengthening innovation,expanding green finance,and improving job quality.Moreover,it helps bridge geographic divides,narrow interregional disparities,and enhance coordination.These findings offer empirical evidence for policymakers to design targeted standardization strategies that support sustainable and equitable urban development.
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.
基金supported by the Funds for Central-Guided Local Science and Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasm.Supported by 2023 Opening Research Fund of Yunnan Key Laboratory of Digital Communications(YNJTKFB-20230686,YNKLDC-KFKT-202304).
文摘In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[RS-2021-II211341,Artificial Intelligence Graduate School Program(Chung-Ang University)],and by the Chung-Ang University Graduate Research Scholarship in 2024.
文摘Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.
基金supported by the Tianjin Education Commission Research Program Project(No.2024KJ105)。
文摘The buoyancy-induced flow constitutes a core scientific issue for thermal management of electronic devices and thermal design of energy systems,where accurate characterization of flow and heat transfer is essential to improve thermal efficiency.In this work,buoyancy-induced flow above two heating elements flush-mounted at the bottom of a square enclosure containing air is numerically investigated over a range of Rayleigh numbers(0<Ra≤1.5×10^(8)),with a focus on equal and unequal heat flux conditions under a constraint of constant total thermal energy input.Distinct flow transitions are observed in both cases,leading to the identification of three flow regimes:Steady,periodic unsteady,and chaotic unsteady.Two types of periodic flows are distinguished,in which the first is a periodic flow dominated by a fundamental frequency(FF)and its integer-multiple frequencies(INTMF),while the second is a more complex periodic flow featuring FF,INTMF,and their sub-harmonics.The transitions between these regimes are affected by the relative heat flux of the two heaters.When the heat flux of the two heaters is unequal,the range of Rayleigh numbers corresponding to periodic flow is suppressed.It is also found that the time-averaged maximum temperature of the strong heater increases more rapidly with Ra,while that of the weak heater increases more slowly,reflecting the interaction between buoyancy-driven flow dynamics and asymmetric heat input.Analysis of the time-averaged Nusselt number demonstrates that heat dissipation from the isothermal walls remains roughly equivalent,even when the heat flux of the two heaters differs by a factor of two.These findings highlight the critical roles of Rayleigh number,the number of heaters,and the heat flux ratio of the heaters in determining heat transfer and flow characteristics for buoyancy-driven convection systems,providing important theoretical support and design references for engineering scenarios such as electronic devices and design of new energy systems.
基金supported by the National Natural Science Foundation of China(52471240)the Natural Science Foundation of Zhejiang Province(LZ23B030003)+2 种基金the Fundamental Research Funds for the Central Universities(226-2024-00075)support from the Engineering and Physical Sciences Research Council(EPSRC,UK)RiR grant-RIR18221018-1EU COST CA23155。
文摘The electric double layer(EDL)at the electrochemical interface is crucial for ion transport,charge transfer,and surface reactions in aqueous rechargeable zinc batteries(ARZBs).However,Zn anodes routinely encounter persistent dendrite growth and parasitic reactions,driven by the inhomogeneous charge distribution and water-dominated environment within the EDL.Compounding this,classical EDL theory,rooted in meanfield approximations,further fails to resolve molecular-scale interfacial dynamics under battery-operating conditions,limiting mechanistic insights.Herein,we established a multiscale theoretical calculation framework from single molecular characteristics to interfacial ion distribution,revealing the EDL’s structure and interactions between different ions and molecules,which helps us understand the parasitic processes in depth.Simulations demonstrate that water dipole and sulfate ion adsorption at the inner Helmholtz plane drives severe hydrogen evolution and by-product formation.Guided by these insights,we engineered a“water-poor and anion-expelled”EDL using 4,1’,6’-trichlorogalactosucrose(TGS)as an electrolyte additive.As a result,Zn||Zn symmetric cells with TGS exhibited stable cycling for over 4700 h under a current density of 1 mA cm^(−2),while NaV_(3)O_(8)·1.5H_(2)O-based full cells kept 90.4%of the initial specific capacity after 800 cycles at 5 A g^(−1).This work highlights the power of multiscale theoretical frameworks to unravel EDL complexities and guide high-performance ARZB design through integrated theory-experiment approaches.
基金financially supported from the National Key Research and Development Program of China(2022YFB3803600)the National Natural Science Foundation of China(52301272,22309168,12564025,and 52472205)+7 种基金the Fundamental Research Funds for the Central Universities(CCNU25ZH006)the National College Student Innovation and Entrepreneurship Training Project(202510513082)the Research Program of HBNU(2025X082 and2025Y145)the Foundation of Hubei Key Laboratory of Photoelectric Materials and Devices(PMD202404)the General Program of Open Project of the State Key Laboratory of Precision Welding and Joining of Materials Structures(MSWJ-25M-18)the Key Research Project of Hubei Provincial Department of Education(No.D20252503)the Key Project of Hubei Provincial Natural Science Foundation of China(2025AFD002)the Foundation of National Laboratory of Solid State Microstructures(M37087)。
文摘NiFe layered double hydroxide(NiFe LDH)has emerged as a promising catalyst for the oxygen evolution reaction(OER);however,its hydrogen evolution reaction(HER)activity remains suboptimal due to unfavorable electronic structures,particularly the d-electron density of metal sites,which impede water dissociation and lead to poor hydrogen adsorption/desorption capabilities.Herein,we introduce an efficient cooperative d-electron density regulation(CDDR)engineering to comprehensively optimize the delectron density of NiFe LDH by grafting MoO_(x) -modified NiFe LDH nanosheets onto porous nickel particles(PNPs).The PNPs facilitate d-electron density modulation along the edges of the nanosheets,while the MoO_(x) species enable d-electron density modulation across the plane of the nanosheets,thus cooperatively constructing enriched d-electron density in NiFe LDH.Theoretical studies validate the CDDR process and reveal that the enriched d-electron density accelerates water dissociation and optimizes the hydrogen adsorption behavior of NiFe LDH.As a result,the engineered catalyst exhibits significantly improved HER activity,achieving an ultra-low overpotential of 38 mV at 10 mA cm^(-2)in 1 M KOH.Additionally,the CDDR-optimized catalyst also exhibits good OER performance,demonstrating excellent bifunctional performance for overall water splitting in both alkaline freshwater and seawater electrolytes.This work presents a novel CDDR strategy for engineering NiFe LDH into efficient HER catalysts without compromising its OER activity,potentially paving the way for the development of active and robust electrocatalysts for sustainable energy applications.
基金Nguyen Tat Thanh University,Ho Chi Minh City,Vietnam for supporting this study。
文摘Wing design is a critical factor in the aerodynamic performance of flapping-wing(FW)robots.Inspired by the natural wing structures of insects,bats,and birds,we explored how bio-mimetic wing vein morphologies,combined with a bio-inspired double wing clap-and-fling mechanism,affect thrust generation.This study focused on increasing vertical force and payload capacity.Through systematic experimentation with various vein configurations and structural designs,we developed innovative wings optimized for thrust production.Comprehensive tests were conducted to measure aerodynamic forces,power consumption,and wing kinematics across a range of flapping frequencies.Additionally,wings with different aspect ratios,a key factor in wing design,were fabricated and extensively evaluated.The study also examined the role of bio-inspired vein layouts on wing flexibility,a critical component in improving flight efficiency.Our findings demonstrate that the newly developed wing design led to a 20%increase in thrust,achieving up to 30 g-force(gf).This research sheds light on the clap-and-fling effect and establishes a promising framework for bio-inspired wing design,offering significant improvements in both performance and payload capacity for FW robots.
基金supported by the National Natural Science Foundation of China(Grant Nos.42407267 and 52374152)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20220975).
文摘Time-delayed blasting is widely utilized in engineering to mitigate induced vibration hazards and enhance fragmentation.The underlying vibration reduction principle is the decrease of the charge weight per delay,while the potential for further vibration reduction remains debated,largely due to unclear underlying mechanisms.In light of the popularization of electronic detonators and the representativeness of double-hole configurationsfor multiple blastholes,it is essential to investigate the vibration characteristics induced by time-delayed double blastholes.Therefore,a series of doubleborehole experimental blasts was conducted in an underground roadway to clarify the variation in vibration from single-hole to dual-hole conditions.Based on the experimental data and inherent limitations,an exact full-fieldtheoretical model was further employed to systematically analyze the effects of delay time,charge length,and borehole inclination angle on vibrations induced by various doublehole configurations.The experimental data and theoretical analysis reveal that the general scaled distance effectively predicts vibrations in delayed blasting but does not reflectvibration reduction.Increasing delay time causes fluctuatingPPVs,which stabilize slightly above single-hole PPVs as delay times exceed a certain value.The delayed blasting primarily reduces near-fieldfrequencies.Longer charge lengths in double boreholes increase PPV levels and attenuation rates within a certain length,and the vibration behavior of combined long and short charge lengths is governed by the long blasthole.Larger blasthole inclination angles enhance vibration amplitude and reduce PPV attenuation rates.Optimizing inclination angles is more critical than adjusting delay times,and parallel boreholes offer the best vibration control.
基金supported by the research equipment (Nos. G1006,G1010 and G1018) shared in MEXT Project for promoting public utilization of advanced research infrastructure (Program for supporting construction of core facilities)(No. JPMXS0440500023)financial support of the China Scholarship Council.
文摘A method for the effective in-situ formation of boron-containing Mg-Al layered double hydroxides(LDHs)was developed for boron removal and stabilization.The influence of the B/Al molar ratio and pH on the formation of Mg-Al-B–LDHs was investigated.Compared with the adsorption method,under a high B/Al ratio,the coprecipitation method increased the boron sorption density from 0.256 to 0.472 of Al.The Toxicity Characteristic Leaching Procedure showed that the boron-coprecipitated LDHs exhibited higher stability than the boron-adsorption LDHs.The synthesized LDH samples were characterized by X-ray diffraction,X-ray photoelectron spectroscopy,and solid-state 11B-NMR.The results showed that boron was effectively incorporated into the LDH structure for the coprecipitation method.Combined with the experimental results,a potential in-situ formation pathway for Mg-Al-B–LDHs was elucidated through density functional theory calculations.The boron tended to directly incorporate into the LDH structure in the coprecipitation method,whereas it was predominantly adsorbed on the LDH surface in the adsorption method.The adsorption energy demonstrated that boron preferentially bonded to Mg^(2+)sites on the surface.The mechanism of boron incorporation in the LDHs for the coprecipitation method involved precipitation of amorphous aluminum hydroxide,layered boehmite transformation,nucleation,and layer stacking.During these processes,boron formed complexes to enhance its stability.Residual boron underwent further reactions with the LDHs,including surface adsorption and ion exchange.These findings provide theoretical insight into the effective removal and long-term immobilization of boron in landfill leachate self-remediation processes.
