Achieving optimal alignment in total knee arthroplasty(TKA) is a critical factor in ensuring optimal outcomes and long-term implant survival. Traditionally, mechanical alignment has been favored to achieve neutral pos...Achieving optimal alignment in total knee arthroplasty(TKA) is a critical factor in ensuring optimal outcomes and long-term implant survival. Traditionally, mechanical alignment has been favored to achieve neutral postoperative joint alignment. However, contemporary approaches, such as kinematic alignments and hybrid techniques including adjusted mechanical, restricted kinematic, inverse kinematic, and functional alignments, are gaining attention for their ability to restore native joint kinematics and anatomical alignment, potentially leading to enhanced functional outcomes and greater patient satisfaction. The ongoing debate on optimal alignment strategies considers the following factors: long-term implant durability, functional improvement, and resolution of individual anatomical variations. Furthermore, advancements of computer-navigated and robotic-assisted surgery have augmented the precision in implant positioning and objective measurements of soft tissue balance. Despite ongoing debates on balancing implant longevity and functional outcomes, there is an increasing advocacy for personalized alignment strategies that are tailored to individual anatomical variations. This review evaluates the spectrum of various alignment techniques in TKA, including mechanical alignment, patient-specific kinematic approaches, and emerging hybrid methods. Each technique is scrutinized based on its fundamental principles, procedural techniques, inherent advantages, and potential limitations, while identifying significant clinical gaps that underscore the need for further investigation.展开更多
Large language models(LLMs)represent significant advancements in artificial intelligence.However,their increasing capabilities come with a serious challenge:misalignment,which refers to the deviation of model behavior...Large language models(LLMs)represent significant advancements in artificial intelligence.However,their increasing capabilities come with a serious challenge:misalignment,which refers to the deviation of model behavior from the designers’intentions and human values.This review aims to synthesize the current understanding of the LLM misalignment issue and provide researchers and practitioners with a comprehensive overview.We define the concept of misalignment and elaborate on its various manifestations,including generating harmful content,factual errors(hallucinations),propagating biases,failing to follow instructions,emerging deceptive behaviors,and emergent misalignment.We explore the multifaceted causes of misalignment,systematically analyzing factors from surface-level technical issues(e.g.,training data,objective function design,model scaling)to deeper fundamental challenges(e.g.,difficulties formalizing values,discrepancies between training signals and real intentions).This review covers existing and emerging techniques for detecting and evaluating the degree of misalignment,such as benchmark tests,red-teaming,and formal safety assessments.Subsequently,we examine strategies to mitigate misalignment,focusing on mainstream alignment techniques such as RLHF,Constitutional AI(CAI),instruction fine-tuning,and novel approaches that address scalability and robustness.In particular,we analyze recent advances in misalignment attack research,including system prompt modifications,supervised fine-tuning,self-supervised representation attacks,and model editing,which challenge the robustness of model alignment.We categorize and analyze the surveyed literature,highlighting major findings,persistent limitations,and current contentious points.Finally,we identify key open questions and propose several promising future research directions,including constructing high-quality alignment datasets,exploring novel alignment methods,coordinating diverse values,and delving into the deep philosophical aspects of alignment.This work underscores the complexity and multidimensionality of LLM misalignment issues,calling for interdisciplinary approaches to reliably align LLMs with human values.展开更多
Nanocrystals have emerged as cutting-edge functional materials benefiting from the increased surface and enhanced coupling of electronic states.High-resolution imaging in transmission electron microscope can provide i...Nanocrystals have emerged as cutting-edge functional materials benefiting from the increased surface and enhanced coupling of electronic states.High-resolution imaging in transmission electron microscope can provide invaluable structural information of crystalline materials,albeit it remains greatly challenging to nanocrystals due to the arduousness of accurate zone axis adjustment.Herein,a homemade software package,called SmartAxis,is developed for rapid yet accurate zone axis alignment of nanocrystals.Incident electron beam tilt is employed as an eccentric goniometer to measure the angular deviation of a crystal to a zone axis,and then serves as a linkage to calculate theαandβtilts of goniometer based on an accurate quantitative relationship.In this way,high-resolution imaging of one identical small Au nanocrystal,as well as electron beam-sensitive MIL-101 metal-organic framework crystals,along multiple zone axes,was performed successfully by using this accurate,time-and electron dose-saving zone axis alignment software package.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
Bismuth oxyselenide(Bi_(2)O_(2)Se),a novel quasi-two-dimensional charge-carrying semiconductor,is recognized as one of the most promising emerging platforms for next-generation semiconductor devices.Recent advancement...Bismuth oxyselenide(Bi_(2)O_(2)Se),a novel quasi-two-dimensional charge-carrying semiconductor,is recognized as one of the most promising emerging platforms for next-generation semiconductor devices.Recent advancements in the development of diverse Bi_(2)O_(2)Se heterojunctions have unveiled extensive potential applications in both electronics and optoelectronics.However,achieving an in-depth understanding of band alignment and particularly interface dynamics remains a significant challenge.In this study,we conduct a comprehensive experimental investigation into band alignment utilizing high-resolution X-ray photoelectron spectroscopy(HRXPS),while also thoroughly discussing the properties of interface states.Our findings reveal that ultrathin films of Bi_(2)O_(2)Se grown on SrTiO_(3)(with TiO_(2)(001)termination)exhibit Type-I(straddling gap)band alignment characterized by a valence band offset(VBO)of approximately 1.77±0.04 eV and a conduction band offset(CBO)around 0.68±0.04 eV.Notably,when accounting for the influence of interface states,the bands at the interface display a herringbone configuration due to substantial built-in electric fields,which markedly deviate from conventional band alignments.Thus,our results provide valuable insights for advancing high-efficiency electronic and optoelectronic devices,particularly those where charge transfer is highly sensitive to interface states.展开更多
To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities...To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.展开更多
A high pattern resolution is critical for fabricating roll-to-roll printed electronics(R2RPE)products.For enhanced overlay alignment accuracy,position errors between the printer and the substrate web must be eliminate...A high pattern resolution is critical for fabricating roll-to-roll printed electronics(R2RPE)products.For enhanced overlay alignment accuracy,position errors between the printer and the substrate web must be eliminated,particularly in inkjet printing applications.This paper proposes a novel five-degree-of-freedom(5-DOF)flexure-based alignment stage to adjust the posture of an inkjet printer head.The stage effectively compensates for positioning errors between the actuation mechanism and manipulated objects through a series-parallel combination of compliant substructures.Voice coil motors(VCMs)and linear motors serve as actuators to achieve the required motion.Theoretical models were established using a pseudo-rigid-body model(PRBM)methodology and were validated through finite element analysis(FEA).Finally,an alignment stage prototype was fabricated for an experiment.The prototype test results showed that the developed positioning platform attains 5-DOF motion capabilities with 335.1μm×418.9μm×408.1μm×3.4 mrad×3.29 mrad,with cross-axis coupling errors below 0.11%along y-and z-axes.This paper pro-poses a novel 5-DOF flexure-based alignment stage that can be used for error compensation in R2RPE and effectively improves the interlayer alignment accuracy of multi-layer printing.展开更多
The multiple oligopeptides have been regarded as promising alignment media due to their structural diverseness and tendency for self-assembly in solution.Herein,an assembled amphiphilic peptide alignment medium,i.e.,C...The multiple oligopeptides have been regarded as promising alignment media due to their structural diverseness and tendency for self-assembly in solution.Herein,an assembled amphiphilic peptide alignment medium,i.e.,C15eCONH-Phg-Phg-IIIKK-CONH2 with un-natural amino acids for the determination of anisotropic parameters of NMR is introduced.The amphiphilic peptide can be self-assembled at low concentrations in DMSO and is stable and highly homogeneous.The NMR spectrum collected with the addition of the medium had fewer background signals.The utility of the acquired RDC data is demon-strated to determine relative configuration of three natural products,Helminthosporic acid,Estrone,and a-Santonin.展开更多
Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely us...Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely used in pulmonary disease diagnosis,such as pneumonia and tuberculosis.However,traditional feature fusion methods often suffer from feature disparity,information loss,redundancy,and increased complexity,hindering the further extension of DL algorithms.To solve this problem,we propose a Graph-Convolution Fusion Network with Self-Supervised Feature Alignment(Self-FAGCFN)to address the limitations of traditional feature fusion methods in deep learning-based medical image classification for respiratory diseases such as pneumonia and tuberculosis.The network integrates Convolutional Neural Networks(CNNs)for robust feature extraction from two-dimensional grid structures and Graph Convolutional Networks(GCNs)within a Graph Neural Network branch to capture features based on graph structure,focusing on significant node representations.Additionally,an Attention-Embedding Ensemble Block is included to capture critical features from GCN outputs.To ensure effective feature alignment between pre-and post-fusion stages,we introduce a feature alignment loss that minimizes disparities.Moreover,to address the limitations of proposed methods,such as inappropriate centroid discrepancies during feature alignment and class imbalance in the dataset,we develop a Feature-Centroid Fusion(FCF)strategy and a Multi-Level Feature-Centroid Update(MLFCU)algorithm,respectively.Extensive experiments on public datasets LungVision and Chest-Xray demonstrate that the Self-FAGCFN model significantly outperforms existing methods in diagnosing pneumonia and tuberculosis,highlighting its potential for practical medical applications.展开更多
The efficiency of Cu2ZnSnS4(CZTS)solar cells is limited due to interfacial band misalignment and severe non-radiative recombination.ZnSnO(ZTO)is a promising Cd-free buffer layer,offering a potential for favorable band...The efficiency of Cu2ZnSnS4(CZTS)solar cells is limited due to interfacial band misalignment and severe non-radiative recombination.ZnSnO(ZTO)is a promising Cd-free buffer layer,offering a potential for favorable band alignment with CZTS absorber.Here,we demonstrate that optimizing the temperature-dependent deposition during reactive magnetron sputtering significantly promotes elemental interdiffusion.For the proposed CZTS/ZTO interface,a favorable“spike-like”band alignment is achieved,effectively enhancing the carrier transport efficiency and reducing the interfacial defect density.Furthermore,Zn diffusion mitigates CuZn(that is,copper atoms sit at sites normally occupied by zinc atoms)antisite defects,reducing the non-radiative recombination and improving the absorber quality.Finally,the champion device achieved the highest power conversion efficiency(PCE)of 10.90%by sputtering ZTO as buffer layer in CZTS solar cell so far,with a high open circuit voltage(VOC)of 740 mV and a fill factor(FF)of 61.79%.This strategy highlights the potential of sputtered ZTO as a scalable and eco-friendly buffer layer for Cd-free CZTS solar cells.展开更多
With the advancements in parameter-efficient transfer learning techniques,it has become feasible to leverage large pre-trained language models for downstream tasks under low-cost and low-resource conditions.However,ap...With the advancements in parameter-efficient transfer learning techniques,it has become feasible to leverage large pre-trained language models for downstream tasks under low-cost and low-resource conditions.However,applying this technique to multimodal knowledge transfer introduces a significant challenge:ensuring alignment across modalities while minimizing the number of additional parameters required for downstream task adaptation.This paper introduces UniTrans,a framework aimed at facilitating efficient knowledge transfer across multiple modalities.UniTrans leverages Vector-based Cross-modal Random Matrix Adaptation to enable fine-tuning with minimal parameter overhead.To further enhance modality alignment,we introduce two key components:the Multimodal Consistency Alignment Module and the Query-Augmentation Side Network,specifically optimized for scenarios with extremely limited trainable parameters.Extensive evaluations on various cross-modal downstream tasks demonstrate that our approach surpasses state-of-the-art methods while using just 5%of their trainable parameters.Additionally,it achieves superior performance compared to fully fine-tuned models on certain benchmarks.展开更多
In order to investigate the effect of different doping types on the band alignment of heterojunctions,we prepared PtSe_(2)/n-GaN,PtSe_(2)/p-GaN,and PtSe_(2)/u-GaN heterojunctions by wet transfer technique.The valence ...In order to investigate the effect of different doping types on the band alignment of heterojunctions,we prepared PtSe_(2)/n-GaN,PtSe_(2)/p-GaN,and PtSe_(2)/u-GaN heterojunctions by wet transfer technique.The valence band offsets(VBO)of the three heterojunctions were measured by x-ray photoelectron spectroscopy(XPS),while the PtSe_(2)/n-GaN is 3.70±0.15 eV,PtSe_(2)/p-GaN is 0.264±0.15 eV,and PtSe_(2)/u-GaN is 3.02±0.15 eV.The conduction band offset(CBO)of the three heterojunctions was calculated from the material bandgap and VBO,while the PtSe_(2)/n-GaN is 0.61±0.15 eV,PtSe_(2)/p-GaN is 2.83±0.15 eV,and PtSe_(2)/u-GaN is 0.07±0.15 eV.This signifies that both PtSe_(2)/u-GaN and PtSe_(2)/p-GaN exhibit type-Ⅰband alignment,but the PtSe_(2)/n-GaN heterojunction has type-Ⅲband alignment.This signifies that the band engineering of PtSe_(2)/GaN heterojunction can be achieved by manipulating the concentration and type of doping,which is significantly relevant for the advancement of related devices through the realization of band alignment and the modulation of the material properties of the PtSe_(2)/GaN heterojunction.展开更多
Just a few days after the astronomical spectacle of the“seven planets in alignment”on February 28,2025,the weather in many places underwent a dramatic change.In Shanghai,which was still at the beginning of spring(Ma...Just a few days after the astronomical spectacle of the“seven planets in alignment”on February 28,2025,the weather in many places underwent a dramatic change.In Shanghai,which was still at the beginning of spring(March 1),the temperature suddenly soared to 29℃,the temperature of summer,while Shandong was hit by a sudden heavy snowstorm.There are various opinions and no consensus on this inexplicable weather change.For this reason,based on the principle of the role of planets in the luminescence and heat generation of stars,the author of this article reveals the significant impact of the“Seven planets in alignment”on global climate change,and also points out that the melting of polar glaciers and the approach of the moon to the Earth is another important cause of global climate change.Therefore,countermeasures to save the abnormal changes in global climate are proposed.展开更多
Ionic covalent organic framework(COF)lamellar membranes are the alternative materials as promising Li^(+)conductors for all-solid-state lithium batteries.However,COF lamellar membrane suffers from poor structural stab...Ionic covalent organic framework(COF)lamellar membranes are the alternative materials as promising Li^(+)conductors for all-solid-state lithium batteries.However,COF lamellar membrane suffers from poor structural stability and inevitable cross-layer transfer resistance due to the weak interaction at interface of adjacent nanosheets.Herein,a lamellar polymer-threaded ionic COF(PEI@TpPa-SO_(3)Li)composite electrolyte with single Li^(+)conduction was prepared by assembling lithium sulfonated COF(TpPa-SO_(3)Li)nanosheets and then threading them with polyethyleneimine(PEI)chains.It reveals that the threaded PEI chains induce the oriented permutation of pore channel of PEI@TpPa-SO_(3)Li electrolyte through electrostatic interaction between-NH_(2)/-NH-and-SO_(3)Li groups.This enables the construction of continuous and aligned-SO_(3)^(-)...Li^(+)...-NH_(2)/-NH-pairs along pore channels,which act as efficient Li^(+)conducting sites and afford high Li^(+)hopping conduction(1.4×10^(-4)S cm^(-1)at 30℃)with a high Young's modulus of 408.7 MP and wide electrochemical stability window of 0~4.7 V.The assembled LiFePO_(4)‖Li and LiNi_(0.8)Mn_(0.1)Co_(0.1)O_(2)‖Li half-cells achieve high discharge capacities of 155.0 mAh g^(-1)and 167.2 mAh g^(-1)at 30℃under0.2 C,respectively,with high capacity retention of 98%after 300 cycles.This study provides an alternative route to highly ion-conductive lamellar porous electrolytes for high-performance energy devices.展开更多
Multimodal Aspect-Based Sentiment Analysis(MABSA)aims to detect sentiment polarity toward specific aspects by leveraging both textual and visual inputs.However,existing models suffer from weak aspectimage alignment,mo...Multimodal Aspect-Based Sentiment Analysis(MABSA)aims to detect sentiment polarity toward specific aspects by leveraging both textual and visual inputs.However,existing models suffer from weak aspectimage alignment,modality imbalance dominated by textual signals,and limited reasoning for implicit or ambiguous sentiments requiring external knowledge.To address these issues,we propose a unified framework named Gated-Linear Aspect-Aware Multimodal Sentiment Network(GLAMSNet).First of all,an input encoding module is employed to construct modality-specific and aspect-aware representations.Subsequently,we introduce an image–aspect correlation matching module to provide hierarchical supervision for visual-textual alignment.Building upon these components,we further design a Gated-Linear Aspect-Aware Fusion(GLAF)module to enhance aspect-aware representation learning by adaptively filtering irrelevant textual information and refining semantic alignment under aspect guidance.Additionally,an External Language Model Knowledge-Guided mechanism is integrated to incorporate sentimentaware prior knowledge from GPT-4o,enabling robust semantic reasoning especially under noisy or ambiguous inputs.Experimental studies conducted based on Twitter-15 and Twitter-17 datasets demonstrate that the proposed model outperforms most state-of-the-art methods,achieving 79.36%accuracy and 74.72%F1-score,and 74.31%accuracy and 72.01%F1-score,respectively.展开更多
Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a t...Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a typical virtual reality game that entails multi-user collaboration.When a user approaches and interacts with a target user in the VE,the user is expected to approach and interact with the target user in the corresponding PE as well.Existing methods of multi-user RDW mainly focus on obstacle avoidance,which does not account for the relative positional relationship between the users in both VE and PE.Methods To enhance the user experience and facilitate potential interaction,this paper presents a novel dynamic alignment algorithm for multi-user collaborative redirected walking(DA-RDW)in a shared PE where the target user and other users are moving.This algorithm adopts improved artificial potential fields,where the repulsive force is a function of the relative position and velocity of the user with respect to dynamic obstacles.For the best alignment,this algorithm sets the alignment-guidance force in several cases and then converts it into a constrained optimization problem to obtain the optimal direction.Moreover,this algorithm introduces a potential interaction object selection strategy for a dynamically uncertain environment to speed up the subsequent alignment.To balance obstacle avoidance and alignment,this algorithm uses the dynamic weightings of the virtual and physical distances between users and the target to determine the resultant force vector.Results The efficacy of the proposed method was evaluated using a series of simulations and live-user experiments.The experimental results demonstrate that our novel dynamic alignment method for multi-user collaborative redirected walking can reduce the distance error in both VE and PE to improve alignment with fewer collisions.展开更多
Presetting tensile twins(TTs)can enhance the mechanical properties of magnesium(Mg)alloys.Two as-received(AR)sheets,as-received state-A(AR-A)with fiber texture and nonuniform grains and as-received state-B with basal ...Presetting tensile twins(TTs)can enhance the mechanical properties of magnesium(Mg)alloys.Two as-received(AR)sheets,as-received state-A(AR-A)with fiber texture and nonuniform grains and as-received state-B with basal texture and uniform equiaxial grains are selected to induce TTs via a novel method called corrugated wide limit alignment(CWLA),and the corresponding CWLA-processed sheets are denoted as CWLA-processed state-A(C-A)and CWLA-processed state-B(C-B).The results demonstrate that a larger initial average grain size correlates with a higher fraction of TTs induced in Mg sheets,thereby refining the grains and forming a new rolling direction(RD)tilted texture during CWLA.The ultimate tensile strength increases by 32%from AR-A to C-A,primarily due to refinement strengthening and twinning-induced strain hardening.The recrystallization mechanism of C-A is dominated by twinning-induced dynamic recrystallization(DRX),where DRX grains prefer to inherit the orientation of TTs,resulting in an enhanced RD-tilted texture and the formation of multi-modal texture.The recrystallization mechanism of C-B is mainly discontinuous DRX and continuous DRX,and the DRX grains prefer to inherit the orientation of matrix grains,ultimately forming a basal texture.In summary,the tensile mechanical behavior of pre-twinned Mg sheets significantly depends on the grain size and texture of the AR sheets,so they present similar changing trends during tensile deformation.展开更多
This paper deals with the problem of recreating horizontal alignments of existing railway lines.The main objective is to propose a simple method for automatically obtaining optimized recreated alignments located as cl...This paper deals with the problem of recreating horizontal alignments of existing railway lines.The main objective is to propose a simple method for automatically obtaining optimized recreated alignments located as close as possible to an existing one.Based on a previously defined geometric model,two different constrained optimization problems are formulated.The first problem uses only the information provided by a set of points representing the track centerline while the second one also considers additional data about the existing alignment.The proposed methodology consists of a two-stage process in which both problems are solved consecutively using numerical techniques.The main results obtained applying this methodology are presented to show its performance and to prove its practical usefulness:an academic example used to compare with other methods,and a case study of a railway section located in Parga(Spain)in which the geometry of its horizontal alignment is successfully recovered.展开更多
The band alignment between silicon and high-k dielectrics,which is a key factor in device operation and reliability,still suffers from uncontrolled fluctuations and ambiguous understanding.In this study,by conducting ...The band alignment between silicon and high-k dielectrics,which is a key factor in device operation and reliability,still suffers from uncontrolled fluctuations and ambiguous understanding.In this study,by conducting atomic-level ab initio calculations on realistic Si/SiO_(2)/HfO_(2)stacks,we reveal the physical origin of band alignment fluctuations,i.e.,the oxygen density-dependent interface and surface dipoles,and demonstrate that band offsets can be tuned without introducing other materials.This is instructive for reducing the gate tunneling current,alleviating device-to-device variation,and tuning the threshold voltage.Additionally,this study indicates that significant attention should be focused on model construction in emerging atomistic studies on semiconductor devices.展开更多
Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective metho...Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.展开更多
文摘Achieving optimal alignment in total knee arthroplasty(TKA) is a critical factor in ensuring optimal outcomes and long-term implant survival. Traditionally, mechanical alignment has been favored to achieve neutral postoperative joint alignment. However, contemporary approaches, such as kinematic alignments and hybrid techniques including adjusted mechanical, restricted kinematic, inverse kinematic, and functional alignments, are gaining attention for their ability to restore native joint kinematics and anatomical alignment, potentially leading to enhanced functional outcomes and greater patient satisfaction. The ongoing debate on optimal alignment strategies considers the following factors: long-term implant durability, functional improvement, and resolution of individual anatomical variations. Furthermore, advancements of computer-navigated and robotic-assisted surgery have augmented the precision in implant positioning and objective measurements of soft tissue balance. Despite ongoing debates on balancing implant longevity and functional outcomes, there is an increasing advocacy for personalized alignment strategies that are tailored to individual anatomical variations. This review evaluates the spectrum of various alignment techniques in TKA, including mechanical alignment, patient-specific kinematic approaches, and emerging hybrid methods. Each technique is scrutinized based on its fundamental principles, procedural techniques, inherent advantages, and potential limitations, while identifying significant clinical gaps that underscore the need for further investigation.
基金supported by National Natural Science Foundation of China(62462019,62172350)Guangdong Basic and Applied Basic Research Foundation(2023A1515012846)+6 种基金Guangxi Science and Technology Major Program(AA24263010)The Key Research and Development Program of Guangxi(AB24010085)Key Laboratory of Equipment Data Security and Guarantee Technology,Ministry of Education(GDZB2024060500)2024 Higher Education Scientific Research Planning Project(No.24NL0419)Nantong Science and Technology Project(No.JC2023070)the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(GrantNo.SKLACSS-202407)sponsored by the Cultivation of Young andMiddle-aged Academic Leaders in the“Qing Lan Project”of Jiangsu Province and the 2025 Outstanding Teaching Team in the“Qing Lan Project”of Jiangsu Province.
文摘Large language models(LLMs)represent significant advancements in artificial intelligence.However,their increasing capabilities come with a serious challenge:misalignment,which refers to the deviation of model behavior from the designers’intentions and human values.This review aims to synthesize the current understanding of the LLM misalignment issue and provide researchers and practitioners with a comprehensive overview.We define the concept of misalignment and elaborate on its various manifestations,including generating harmful content,factual errors(hallucinations),propagating biases,failing to follow instructions,emerging deceptive behaviors,and emergent misalignment.We explore the multifaceted causes of misalignment,systematically analyzing factors from surface-level technical issues(e.g.,training data,objective function design,model scaling)to deeper fundamental challenges(e.g.,difficulties formalizing values,discrepancies between training signals and real intentions).This review covers existing and emerging techniques for detecting and evaluating the degree of misalignment,such as benchmark tests,red-teaming,and formal safety assessments.Subsequently,we examine strategies to mitigate misalignment,focusing on mainstream alignment techniques such as RLHF,Constitutional AI(CAI),instruction fine-tuning,and novel approaches that address scalability and robustness.In particular,we analyze recent advances in misalignment attack research,including system prompt modifications,supervised fine-tuning,self-supervised representation attacks,and model editing,which challenge the robustness of model alignment.We categorize and analyze the surveyed literature,highlighting major findings,persistent limitations,and current contentious points.Finally,we identify key open questions and propose several promising future research directions,including constructing high-quality alignment datasets,exploring novel alignment methods,coordinating diverse values,and delving into the deep philosophical aspects of alignment.This work underscores the complexity and multidimensionality of LLM misalignment issues,calling for interdisciplinary approaches to reliably align LLMs with human values.
基金supported by the National Key R&D Program of China(No.2021YFA1501002)Thousand Talents Program for Distinguished Young Scholars.X.Li thanks the National Natural Science Foundation of China(No.22309021).
文摘Nanocrystals have emerged as cutting-edge functional materials benefiting from the increased surface and enhanced coupling of electronic states.High-resolution imaging in transmission electron microscope can provide invaluable structural information of crystalline materials,albeit it remains greatly challenging to nanocrystals due to the arduousness of accurate zone axis adjustment.Herein,a homemade software package,called SmartAxis,is developed for rapid yet accurate zone axis alignment of nanocrystals.Incident electron beam tilt is employed as an eccentric goniometer to measure the angular deviation of a crystal to a zone axis,and then serves as a linkage to calculate theαandβtilts of goniometer based on an accurate quantitative relationship.In this way,high-resolution imaging of one identical small Au nanocrystal,as well as electron beam-sensitive MIL-101 metal-organic framework crystals,along multiple zone axes,was performed successfully by using this accurate,time-and electron dose-saving zone axis alignment software package.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
基金supported by the National Natural Science Foundation of China(Nos.52072059,12304078,12274061 and 11774044)the Natural Science Foundation of Sichuan Province(No.2024NSFSC1384).
文摘Bismuth oxyselenide(Bi_(2)O_(2)Se),a novel quasi-two-dimensional charge-carrying semiconductor,is recognized as one of the most promising emerging platforms for next-generation semiconductor devices.Recent advancements in the development of diverse Bi_(2)O_(2)Se heterojunctions have unveiled extensive potential applications in both electronics and optoelectronics.However,achieving an in-depth understanding of band alignment and particularly interface dynamics remains a significant challenge.In this study,we conduct a comprehensive experimental investigation into band alignment utilizing high-resolution X-ray photoelectron spectroscopy(HRXPS),while also thoroughly discussing the properties of interface states.Our findings reveal that ultrathin films of Bi_(2)O_(2)Se grown on SrTiO_(3)(with TiO_(2)(001)termination)exhibit Type-I(straddling gap)band alignment characterized by a valence band offset(VBO)of approximately 1.77±0.04 eV and a conduction band offset(CBO)around 0.68±0.04 eV.Notably,when accounting for the influence of interface states,the bands at the interface display a herringbone configuration due to substantial built-in electric fields,which markedly deviate from conventional band alignments.Thus,our results provide valuable insights for advancing high-efficiency electronic and optoelectronic devices,particularly those where charge transfer is highly sensitive to interface states.
基金partially supported by the National Natural Science Foundation of China under Grants 62471493 and 62402257(for conceptualization and investigation)partially supported by the Natural Science Foundation of Shandong Province,China under Grants ZR2023LZH017,ZR2024MF066,and 2023QF025(for formal analysis and validation)+1 种基金partially supported by the Open Foundation of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)under Grant 2023ZD010(for methodology and model design)partially supported by the Russian Science Foundation(RSF)Project under Grant 22-71-10095-P(for validation and results verification).
文摘To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.
基金Supported by Natural Science Research Project of Anhui Educational Committee(Grant No.2024AH040010).
文摘A high pattern resolution is critical for fabricating roll-to-roll printed electronics(R2RPE)products.For enhanced overlay alignment accuracy,position errors between the printer and the substrate web must be eliminated,particularly in inkjet printing applications.This paper proposes a novel five-degree-of-freedom(5-DOF)flexure-based alignment stage to adjust the posture of an inkjet printer head.The stage effectively compensates for positioning errors between the actuation mechanism and manipulated objects through a series-parallel combination of compliant substructures.Voice coil motors(VCMs)and linear motors serve as actuators to achieve the required motion.Theoretical models were established using a pseudo-rigid-body model(PRBM)methodology and were validated through finite element analysis(FEA).Finally,an alignment stage prototype was fabricated for an experiment.The prototype test results showed that the developed positioning platform attains 5-DOF motion capabilities with 335.1μm×418.9μm×408.1μm×3.4 mrad×3.29 mrad,with cross-axis coupling errors below 0.11%along y-and z-axes.This paper pro-poses a novel 5-DOF flexure-based alignment stage that can be used for error compensation in R2RPE and effectively improves the interlayer alignment accuracy of multi-layer printing.
基金supported by the National Natural Science Foundation of China(21874158)the Science and Technology Major Program of Gansu Province of China(22ZD6FA006 and 23ZDFA015)+1 种基金We are also grateful for the financial support from the Science and Technology Program of Henan Province(232102311180)the foundation for the University Young Key Teacher of Henan Province(2024GGJS116).
文摘The multiple oligopeptides have been regarded as promising alignment media due to their structural diverseness and tendency for self-assembly in solution.Herein,an assembled amphiphilic peptide alignment medium,i.e.,C15eCONH-Phg-Phg-IIIKK-CONH2 with un-natural amino acids for the determination of anisotropic parameters of NMR is introduced.The amphiphilic peptide can be self-assembled at low concentrations in DMSO and is stable and highly homogeneous.The NMR spectrum collected with the addition of the medium had fewer background signals.The utility of the acquired RDC data is demon-strated to determine relative configuration of three natural products,Helminthosporic acid,Estrone,and a-Santonin.
基金supported by the National Natural Science Foundation of China(62276092,62303167)the Postdoctoral Fellowship Program(Grade C)of China Postdoctoral Science Foundation(GZC20230707)+3 种基金the Key Science and Technology Program of Henan Province,China(242102211051,242102211042,212102310084)Key Scientiffc Research Projects of Colleges and Universities in Henan Province,China(25A520009)the China Postdoctoral Science Foundation(2024M760808)the Henan Province medical science and technology research plan joint construction project(LHGJ2024069).
文摘Feature fusion is an important technique in medical image classification that can improve diagnostic accuracy by integrating complementary information from multiple sources.Recently,Deep Learning(DL)has been widely used in pulmonary disease diagnosis,such as pneumonia and tuberculosis.However,traditional feature fusion methods often suffer from feature disparity,information loss,redundancy,and increased complexity,hindering the further extension of DL algorithms.To solve this problem,we propose a Graph-Convolution Fusion Network with Self-Supervised Feature Alignment(Self-FAGCFN)to address the limitations of traditional feature fusion methods in deep learning-based medical image classification for respiratory diseases such as pneumonia and tuberculosis.The network integrates Convolutional Neural Networks(CNNs)for robust feature extraction from two-dimensional grid structures and Graph Convolutional Networks(GCNs)within a Graph Neural Network branch to capture features based on graph structure,focusing on significant node representations.Additionally,an Attention-Embedding Ensemble Block is included to capture critical features from GCN outputs.To ensure effective feature alignment between pre-and post-fusion stages,we introduce a feature alignment loss that minimizes disparities.Moreover,to address the limitations of proposed methods,such as inappropriate centroid discrepancies during feature alignment and class imbalance in the dataset,we develop a Feature-Centroid Fusion(FCF)strategy and a Multi-Level Feature-Centroid Update(MLFCU)algorithm,respectively.Extensive experiments on public datasets LungVision and Chest-Xray demonstrate that the Self-FAGCFN model significantly outperforms existing methods in diagnosing pneumonia and tuberculosis,highlighting its potential for practical medical applications.
基金supported by the National Natural Science Foundation of China(No.52472225)Guangdong Basic and Applied Basic Research Foundation(No.2025A1515012041)+1 种基金Shenzhen University 2035 Program for Excellent Research(No.2024B003)China and the Special Fund for the Cultivation of Independent Innovation Achievements of Postgraduate Students at Shenzhen University.
文摘The efficiency of Cu2ZnSnS4(CZTS)solar cells is limited due to interfacial band misalignment and severe non-radiative recombination.ZnSnO(ZTO)is a promising Cd-free buffer layer,offering a potential for favorable band alignment with CZTS absorber.Here,we demonstrate that optimizing the temperature-dependent deposition during reactive magnetron sputtering significantly promotes elemental interdiffusion.For the proposed CZTS/ZTO interface,a favorable“spike-like”band alignment is achieved,effectively enhancing the carrier transport efficiency and reducing the interfacial defect density.Furthermore,Zn diffusion mitigates CuZn(that is,copper atoms sit at sites normally occupied by zinc atoms)antisite defects,reducing the non-radiative recombination and improving the absorber quality.Finally,the champion device achieved the highest power conversion efficiency(PCE)of 10.90%by sputtering ZTO as buffer layer in CZTS solar cell so far,with a high open circuit voltage(VOC)of 740 mV and a fill factor(FF)of 61.79%.This strategy highlights the potential of sputtered ZTO as a scalable and eco-friendly buffer layer for Cd-free CZTS solar cells.
文摘With the advancements in parameter-efficient transfer learning techniques,it has become feasible to leverage large pre-trained language models for downstream tasks under low-cost and low-resource conditions.However,applying this technique to multimodal knowledge transfer introduces a significant challenge:ensuring alignment across modalities while minimizing the number of additional parameters required for downstream task adaptation.This paper introduces UniTrans,a framework aimed at facilitating efficient knowledge transfer across multiple modalities.UniTrans leverages Vector-based Cross-modal Random Matrix Adaptation to enable fine-tuning with minimal parameter overhead.To further enhance modality alignment,we introduce two key components:the Multimodal Consistency Alignment Module and the Query-Augmentation Side Network,specifically optimized for scenarios with extremely limited trainable parameters.Extensive evaluations on various cross-modal downstream tasks demonstrate that our approach surpasses state-of-the-art methods while using just 5%of their trainable parameters.Additionally,it achieves superior performance compared to fully fine-tuned models on certain benchmarks.
基金Project supported by the National Natural Science Foundation of China(Grant No.61874108)the Fundamental Research Funds for the Central Universities(Grant No.lzujbky-2024-04)the Gansu Provincial Scientific and Technologic Planning Program(Grant No.22ZD6GE016).
文摘In order to investigate the effect of different doping types on the band alignment of heterojunctions,we prepared PtSe_(2)/n-GaN,PtSe_(2)/p-GaN,and PtSe_(2)/u-GaN heterojunctions by wet transfer technique.The valence band offsets(VBO)of the three heterojunctions were measured by x-ray photoelectron spectroscopy(XPS),while the PtSe_(2)/n-GaN is 3.70±0.15 eV,PtSe_(2)/p-GaN is 0.264±0.15 eV,and PtSe_(2)/u-GaN is 3.02±0.15 eV.The conduction band offset(CBO)of the three heterojunctions was calculated from the material bandgap and VBO,while the PtSe_(2)/n-GaN is 0.61±0.15 eV,PtSe_(2)/p-GaN is 2.83±0.15 eV,and PtSe_(2)/u-GaN is 0.07±0.15 eV.This signifies that both PtSe_(2)/u-GaN and PtSe_(2)/p-GaN exhibit type-Ⅰband alignment,but the PtSe_(2)/n-GaN heterojunction has type-Ⅲband alignment.This signifies that the band engineering of PtSe_(2)/GaN heterojunction can be achieved by manipulating the concentration and type of doping,which is significantly relevant for the advancement of related devices through the realization of band alignment and the modulation of the material properties of the PtSe_(2)/GaN heterojunction.
文摘Just a few days after the astronomical spectacle of the“seven planets in alignment”on February 28,2025,the weather in many places underwent a dramatic change.In Shanghai,which was still at the beginning of spring(March 1),the temperature suddenly soared to 29℃,the temperature of summer,while Shandong was hit by a sudden heavy snowstorm.There are various opinions and no consensus on this inexplicable weather change.For this reason,based on the principle of the role of planets in the luminescence and heat generation of stars,the author of this article reveals the significant impact of the“Seven planets in alignment”on global climate change,and also points out that the melting of polar glaciers and the approach of the moon to the Earth is another important cause of global climate change.Therefore,countermeasures to save the abnormal changes in global climate are proposed.
基金supported by the financial support from the National Key Research and Development Program(2022YFB3805204,2022YFB3805201)National Natural Science Foundation of China(22478362)+2 种基金Joint Foundation for Science and Technology Research&Development Plan of Henan Province(222301420003 and 232301420038)Key Scientific and Technological Project of Henan Province(242102321032)Foundation of Henan Educational Committee(22A530003)。
文摘Ionic covalent organic framework(COF)lamellar membranes are the alternative materials as promising Li^(+)conductors for all-solid-state lithium batteries.However,COF lamellar membrane suffers from poor structural stability and inevitable cross-layer transfer resistance due to the weak interaction at interface of adjacent nanosheets.Herein,a lamellar polymer-threaded ionic COF(PEI@TpPa-SO_(3)Li)composite electrolyte with single Li^(+)conduction was prepared by assembling lithium sulfonated COF(TpPa-SO_(3)Li)nanosheets and then threading them with polyethyleneimine(PEI)chains.It reveals that the threaded PEI chains induce the oriented permutation of pore channel of PEI@TpPa-SO_(3)Li electrolyte through electrostatic interaction between-NH_(2)/-NH-and-SO_(3)Li groups.This enables the construction of continuous and aligned-SO_(3)^(-)...Li^(+)...-NH_(2)/-NH-pairs along pore channels,which act as efficient Li^(+)conducting sites and afford high Li^(+)hopping conduction(1.4×10^(-4)S cm^(-1)at 30℃)with a high Young's modulus of 408.7 MP and wide electrochemical stability window of 0~4.7 V.The assembled LiFePO_(4)‖Li and LiNi_(0.8)Mn_(0.1)Co_(0.1)O_(2)‖Li half-cells achieve high discharge capacities of 155.0 mAh g^(-1)and 167.2 mAh g^(-1)at 30℃under0.2 C,respectively,with high capacity retention of 98%after 300 cycles.This study provides an alternative route to highly ion-conductive lamellar porous electrolytes for high-performance energy devices.
基金supported in part by the National Nature Science Foundation of China under Grants 62476216 and 62273272in part by the Key Research and Development Program of Shaanxi Province under Grant 2024GX-YBXM-146+1 种基金in part by the Scientific Research Program Funded by Education Department of Shaanxi Provincial Government under Grant 23JP091the Youth Innovation Team of Shaanxi Universities.
文摘Multimodal Aspect-Based Sentiment Analysis(MABSA)aims to detect sentiment polarity toward specific aspects by leveraging both textual and visual inputs.However,existing models suffer from weak aspectimage alignment,modality imbalance dominated by textual signals,and limited reasoning for implicit or ambiguous sentiments requiring external knowledge.To address these issues,we propose a unified framework named Gated-Linear Aspect-Aware Multimodal Sentiment Network(GLAMSNet).First of all,an input encoding module is employed to construct modality-specific and aspect-aware representations.Subsequently,we introduce an image–aspect correlation matching module to provide hierarchical supervision for visual-textual alignment.Building upon these components,we further design a Gated-Linear Aspect-Aware Fusion(GLAF)module to enhance aspect-aware representation learning by adaptively filtering irrelevant textual information and refining semantic alignment under aspect guidance.Additionally,an External Language Model Knowledge-Guided mechanism is integrated to incorporate sentimentaware prior knowledge from GPT-4o,enabling robust semantic reasoning especially under noisy or ambiguous inputs.Experimental studies conducted based on Twitter-15 and Twitter-17 datasets demonstrate that the proposed model outperforms most state-of-the-art methods,achieving 79.36%accuracy and 74.72%F1-score,and 74.31%accuracy and 72.01%F1-score,respectively.
基金Supported by STI 2030 Major Projects of China(2021ZD0200400).
文摘Background The redirected walking(RDW)method for multi-user collaboration requires maintaining the relative position between users in a virtual environment(VE)and physical environment(PE).A chasing game in a VE is a typical virtual reality game that entails multi-user collaboration.When a user approaches and interacts with a target user in the VE,the user is expected to approach and interact with the target user in the corresponding PE as well.Existing methods of multi-user RDW mainly focus on obstacle avoidance,which does not account for the relative positional relationship between the users in both VE and PE.Methods To enhance the user experience and facilitate potential interaction,this paper presents a novel dynamic alignment algorithm for multi-user collaborative redirected walking(DA-RDW)in a shared PE where the target user and other users are moving.This algorithm adopts improved artificial potential fields,where the repulsive force is a function of the relative position and velocity of the user with respect to dynamic obstacles.For the best alignment,this algorithm sets the alignment-guidance force in several cases and then converts it into a constrained optimization problem to obtain the optimal direction.Moreover,this algorithm introduces a potential interaction object selection strategy for a dynamically uncertain environment to speed up the subsequent alignment.To balance obstacle avoidance and alignment,this algorithm uses the dynamic weightings of the virtual and physical distances between users and the target to determine the resultant force vector.Results The efficacy of the proposed method was evaluated using a series of simulations and live-user experiments.The experimental results demonstrate that our novel dynamic alignment method for multi-user collaborative redirected walking can reduce the distance error in both VE and PE to improve alignment with fewer collisions.
基金supported by the National Natural Science Foundation of China(No.52005362)the Fundamental Research Program of Shanxi Province(Nos.202303021221005 and 202303021211045)+1 种基金the Patent Commercialization Program of Shanxi Province(No.202402003)the Key Research and Development Plan of Xinzhou City.
文摘Presetting tensile twins(TTs)can enhance the mechanical properties of magnesium(Mg)alloys.Two as-received(AR)sheets,as-received state-A(AR-A)with fiber texture and nonuniform grains and as-received state-B with basal texture and uniform equiaxial grains are selected to induce TTs via a novel method called corrugated wide limit alignment(CWLA),and the corresponding CWLA-processed sheets are denoted as CWLA-processed state-A(C-A)and CWLA-processed state-B(C-B).The results demonstrate that a larger initial average grain size correlates with a higher fraction of TTs induced in Mg sheets,thereby refining the grains and forming a new rolling direction(RD)tilted texture during CWLA.The ultimate tensile strength increases by 32%from AR-A to C-A,primarily due to refinement strengthening and twinning-induced strain hardening.The recrystallization mechanism of C-A is dominated by twinning-induced dynamic recrystallization(DRX),where DRX grains prefer to inherit the orientation of TTs,resulting in an enhanced RD-tilted texture and the formation of multi-modal texture.The recrystallization mechanism of C-B is mainly discontinuous DRX and continuous DRX,and the DRX grains prefer to inherit the orientation of matrix grains,ultimately forming a basal texture.In summary,the tensile mechanical behavior of pre-twinned Mg sheets significantly depends on the grain size and texture of the AR sheets,so they present similar changing trends during tensile deformation.
基金founded by project TED2021129324B-I00 of the Ministerio de Ciencia e Innovación(Spain)and NextGenerationEU(European Union)the Collaboration Agreement between Consellería de Educación,Formación Profesional e Universidades(Xunta de Galicia,Spain)and Universidade de Santiago de Compostela(Spain)which regulates the Specialization Campus Campus Terra under Grant number 2022-PU014support given by Xunta de Galicia(Spain)by means of the research projects 2023 GPC GI-2084 ED431B2023/17 and GRC GI-1563-ED431C 2021/15,respectively.
文摘This paper deals with the problem of recreating horizontal alignments of existing railway lines.The main objective is to propose a simple method for automatically obtaining optimized recreated alignments located as close as possible to an existing one.Based on a previously defined geometric model,two different constrained optimization problems are formulated.The first problem uses only the information provided by a set of points representing the track centerline while the second one also considers additional data about the existing alignment.The proposed methodology consists of a two-stage process in which both problems are solved consecutively using numerical techniques.The main results obtained applying this methodology are presented to show its performance and to prove its practical usefulness:an academic example used to compare with other methods,and a case study of a railway section located in Parga(Spain)in which the geometry of its horizontal alignment is successfully recovered.
基金supported by the National Natural Science Foundation of China(Grant Nos.62174155,12334005,and T2293702)the CAS Project for Young Scientists in Basic Research(Grant No.YSBR-056)the MIND Project(Grant No.MINDKT202403)。
文摘The band alignment between silicon and high-k dielectrics,which is a key factor in device operation and reliability,still suffers from uncontrolled fluctuations and ambiguous understanding.In this study,by conducting atomic-level ab initio calculations on realistic Si/SiO_(2)/HfO_(2)stacks,we reveal the physical origin of band alignment fluctuations,i.e.,the oxygen density-dependent interface and surface dipoles,and demonstrate that band offsets can be tuned without introducing other materials.This is instructive for reducing the gate tunneling current,alleviating device-to-device variation,and tuning the threshold voltage.Additionally,this study indicates that significant attention should be focused on model construction in emerging atomistic studies on semiconductor devices.
基金supported by the Fund of Key Laboratory of Biomedical Engineering of Hainan Province(No.BME20240001)the STI2030-Major Projects(No.2021ZD0200104)the National Natural Science Foundations of China under Grant 61771437.
文摘Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.
