Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim ...Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of significant,novel,and high-impact research in the fields of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.展开更多
The hot compression deformation behavior of Mg-6Zn-1Mn-0.5Ca(ZM61-0.5Ca)and Mg-6Zn-1Mn-2Sn-0.5Ca(ZMT612-0.5Ca)alloys was investigated at deformation temperatures ranging from 250℃to 400℃and strain rates varying from...The hot compression deformation behavior of Mg-6Zn-1Mn-0.5Ca(ZM61-0.5Ca)and Mg-6Zn-1Mn-2Sn-0.5Ca(ZMT612-0.5Ca)alloys was investigated at deformation temperatures ranging from 250℃to 400℃and strain rates varying from 0.001 s^(-1) to 1 s^(-1).The results show that the addition of Sn promotes dynamic recrystallization(DRX),and CaMgSn phases can act as nucleation sites during the compression deformation.Flow stress increases with increasing the strain rate and decreasing the temperature.Both the ZM61-0.5Ca and ZMT612-0.5Ca alloys exhibit obvious DRX characteristics.CaMgSn phases can effectively inhibit dislocation motion with the addition of Sn,thus increasing the peak fl ow stress of the alloy.The addition of Sn increases the hot deformation activation energy of the ZM61-0.5Ca alloy from 199.654 kJ/mol to 276.649 kJ/mol,thus improving the thermal stability of the alloy.For the ZMT612-0.5Ca alloy,the optimal hot deformation parameters are determined to be a deformation temperature range of 350–400℃and a strain rate range of 0.001–0.01 s^(-1).展开更多
Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim ...Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of signifi cant,novel,and high-impact research in the fi elds of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.展开更多
In the age of big data,ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge.Traditional searchable encryption schemes face difficulties in handling complex semantic q...In the age of big data,ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge.Traditional searchable encryption schemes face difficulties in handling complex semantic queries.Additionally,they typically rely on honest but curious cloud servers,which introduces the risk of repudiation.Furthermore,the combined operations of search and verification increase system load,thereby reducing performance.Traditional verification mechanisms,which rely on complex hash constructions,suffer from low verification efficiency.To address these challenges,this paper proposes a blockchain-based contextual semantic-aware ciphertext retrieval scheme with efficient verification.Building on existing single and multi-keyword search methods,the scheme uses vector models to semantically train the dataset,enabling it to retain semantic information and achieve context-aware encrypted retrieval,significantly improving search accuracy.Additionally,a blockchain-based updatable master-slave chain storage model is designed,where the master chain stores encrypted keyword indexes and the slave chain stores verification information generated by zero-knowledge proofs,thus balancing system load while improving search and verification efficiency.Finally,an improved non-interactive zero-knowledge proof mechanism is introduced,reducing the computational complexity of verification and ensuring efficient validation of search results.Experimental results demonstrate that the proposed scheme offers stronger security,balanced overhead,and higher search verification efficiency.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
The rapid development of information technology and accelerated digitalization have led to an explosive growth of data across various fields.As a key technology for knowledge representation and sharing,knowledge graph...The rapid development of information technology and accelerated digitalization have led to an explosive growth of data across various fields.As a key technology for knowledge representation and sharing,knowledge graphs play a crucial role by constructing structured networks of relationships among entities.However,data sparsity and numerous unexplored implicit relations result in the widespread incompleteness of knowledge graphs.In static knowledge graph completion,most existing methods rely on linear operations or simple interaction mechanisms for triple encoding,making it difficult to fully capture the deep semantic associations between entities and relations.Moreover,many methods focus only on the local information of individual triples,ignoring the rich semantic dependencies embedded in the neighboring nodes of entities within the graph structure,which leads to incomplete embedding representations.To address these challenges,we propose Two-Stage Mixer Embedding(TSMixerE),a static knowledge graph completion method based on entity context.In the unit semantic extraction stage,TSMixerE leveragesmulti-scale circular convolution to capture local features atmultiple granularities,enhancing the flexibility and robustness of feature interactions.A channel attention mechanism amplifies key channel responses to suppress noise and irrelevant information,thereby improving the discriminative power and semantic depth of feature representations.For contextual information fusion,a multi-layer self-attentionmechanism enables deep interactions among contextual cues,effectively integrating local details with global context.Simultaneously,type embeddings clarify the semantic identities and roles of each component,enhancing the model’s sensitivity and fusion capabilities for diverse information sources.Furthermore,TSMixerE constructs contextual unit sequences for entities,fully exploring neighborhood information within the graph structure to model complex semantic dependencies,thus improving the completeness and generalization of embedding representations.展开更多
This study investigated the effects of xu-argument-based continuation writing on learners’processing of source texts.Seventy-five participants were randomly assigned to three conditions:(1)continuation writing,(2)sum...This study investigated the effects of xu-argument-based continuation writing on learners’processing of source texts.Seventy-five participants were randomly assigned to three conditions:(1)continuation writing,(2)summary writing,or(3)reading comprehension.Eye-tracking data were collected during reading,measuring early(first fixation duration,first pass duration)and late(go-past time,total fixation duration)eye movements.During writing,source-text rereading was tracked via fixation counts and durations.Results showed that task type did not affect initial lexical access,as first fixation duration showed no group differences.However,both production groups exhibited significantly longer first pass durations than the reading comprehension group.Late measures revealed a gradient pattern:the continuation writing group spent significantly longer gopast time and total fixation duration than the summary writing group,which exceeded the reading comprehension group.This indicates that continuation tasks promoted deeper cognitive engagement during reading.During writing,the continuation writing group spent more time rereading the source text with higher fixation counts than the summary writing group.These findings suggest that continuation writing triggers more intensive reader-text interaction during pre-writing and enhances comprehension-production coupling through sustained attention to input during writing.This study sheds light on the cognitive mechanisms underlying the theoretical and pedagogical value of xu-argument.展开更多
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id...Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.展开更多
In this paper,we propose a novel graph signal processing convolution recurrent network(GSP CRN)for signal enhancement against high suppressive interference(HSI)in wireless communications.GSPCRN consists of the short-t...In this paper,we propose a novel graph signal processing convolution recurrent network(GSP CRN)for signal enhancement against high suppressive interference(HSI)in wireless communications.GSPCRN consists of the short-time graph signal processing(SGSP)approach and a modified convolution recurrent network.Similar to the traditional shorttime time-frequency transformation,SGSP frames the complex-valued communication signal and transforms it to the graph-domain representations,where the connection and weight flexibility of each vertex are fully taken into account.In the presence of HSI,SGSP can extract signal features from new graph-domain dimensions and empower neural networks for weak signal enhancement.Two SGSP methods,adjacency singular value decomposition and implicit graph transformation,are designed to capture relationships among the sampling points in the segmented signals.Simulation results demonstrate that our proposed GSPCRN outperforms existing classic methods in extracting weak signals from the HSI environment.When the interference-to-signal ratio exceeds 27dB,only our proposed GSPCRN can achieve the interference mitigation.展开更多
English reading proficiency is essential for university students in a globalized academic environment,yet many L2 learners encounter challenges,such as limited vocabulary,complex syntax,and unclear text organization,l...English reading proficiency is essential for university students in a globalized academic environment,yet many L2 learners encounter challenges,such as limited vocabulary,complex syntax,and unclear text organization,leading to cognitive overload.Grounded in the Cognitive Load Theory(CLT),this study examines the role of the Chunking Reading Processing Strategy-which integrates fragmented linguistic information into meaningful units at lexical,syntactic,and discourse levels-in alleviating cognitive load and improving reading comprehension.Through a mixed-methods approach,the research investigates how learners at different proficiency levels perceive and apply the chunking strategy,and how such application relates to cognitive load management.The results indicate that higher-proficiency learners employ chunking more frequently and report greater benefits,whereas lower-proficiency learners depend more on instructional support.The study confirms the theoretical and pedagogical value of chunk-based reading instruction and suggests that differentiated,cognitively informed teaching of the chunking strategy can enhance both reading efficiency and strategic awareness among L2 learners.展开更多
While the Ordos Basin is recognized for its substantial hydrocarbon exploration prospects,its rugged loess tableland terrain has rendered seismic exploration exceptionally challenging[1-3].Persistent obstacles such as...While the Ordos Basin is recognized for its substantial hydrocarbon exploration prospects,its rugged loess tableland terrain has rendered seismic exploration exceptionally challenging[1-3].Persistent obstacles such as complex 3D survey planning,low signal-tonoise ratio raw data,inadequate near-surface velocity modeling,and imaging inaccuracy have long hindered the advancement of seismic exploration across this region.Through a problem-solving approach rooted in geological target analysis,this research systematically investigates the behavioral patterns of nodal seismometer-based high-density seismic acquisition in loess plateau.Tailored advancements in waveform enhancement and depth velocity modelling methodologies have been engineered.Field validations confirm that the optimized workflow demonstrates marked improvements in amplitude preservation and imaging resolution,offering novel insights for future reservoir characterization endeavors.展开更多
Perception of air pollution is subjective and context-dependent.Previous studies exploring the association between measured air pollution and perceived air quality mainly focused on air pollution levels measured in th...Perception of air pollution is subjective and context-dependent.Previous studies exploring the association between measured air pollution and perceived air quality mainly focused on air pollution levels measured in the residence-based(RB)or regional context,overlooking the mobility-based(MB)context in which people are exposed to air pollution.This study measures air pollution levels in MB,RB,and regional contexts and examines their relationships with perceived air quality across different neighborhoods and gender sub-groups of Hong Kong,China to investigate how people perceive air quality.The results indicate that particulate matter 2.5(PM_(2.5))measured in RB and the regional context significantly contributes to people’s perceived air quality compared to MB PM_(2.5).Individuals in Central and Western district of Hong Kong rely on RB,regional and MB PM_(2.5) to assess air pollution.In Sham Shui Po,RB PM_(2.5) exhibits the highest influence on people’s perceived air quality,followed by regional PM_(2.5).Women’s perceived air quality is strongly related to their RB PM_(2.5) exposure,while men’s perceived air quality is associated with both RB PM_(2.5) and regional PM_(2.5) levels.We conclude that neighborhood effects and mobility levels are the two most important factors influencing the association between meas-ured air pollution and perceived air quality.We reveal that the neighborhood effect averaging problem(NEAP)influences the associ-ation between perceived air quality and measured air pollution levels in a way that differs from health outcome-related studies.Effect-ive measures are needed to improve the public’s awareness of air pollution,and scientific control should be implemented to reduce pub-lic exposure.展开更多
Background:In mental health,recovery is emphasized,and qualitative analyses of service users’narratives have accumulated;however,while qualitative approaches excel at capturing rich context and generating new concept...Background:In mental health,recovery is emphasized,and qualitative analyses of service users’narratives have accumulated;however,while qualitative approaches excel at capturing rich context and generating new concepts,they are limited in generalizability and feasible data volume.This study aimed to quantify the subjective life history narratives of users of psychiatric home-visit nursing using natural language processing(NLP)and to clarify the relationships between linguistic features and recovery-related indicators.Methods:We conducted audio-recorded and transcribed semi-structured interviews on daily life verbatim and collected self-report questionnaires(Recovery Assessment Scale[RAS])and clinician ratings(Global Assessment of Functioning[GAF])from Japanese users of psychiatric home-visit nursing.Using the artificial intelligence-based topic-modeling method BERTopic,we extracted topics from the interview texts and calculated each participant’s topic proportions,and then examined associations between topic proportions and recovery-related indicators using Pearson correlation analyses.Results:“School”showed a significant positive correlation with RAS(r=0.39,p=0.05),whereas“Family”showed a significant negative correlation(r=–0.46,p=0.02).GAF was positively correlated with word count(r=0.44,p=0.02)and“Hospital”(r=0.42,p=0.03),and negatively correlated with“Backchannels”(aizuchi)(r=–0.41,p=0.03).Conclusion:The present results suggest that the quantity,quality,and content of narratives can serve as useful indicators of mental health and recovery,and that objective NLP-based analysis of service users’narratives can complement traditional self-report scales and clinician ratings to inform the design of recovery-oriented care in psychiatric home-visit nursing.展开更多
Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening pa...Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.展开更多
Against the backdrop of integrated development between technical education and higher vocational education,the teaching of Chinese Medicine Processing Technology courses faces new opportunities and challenges.This pap...Against the backdrop of integrated development between technical education and higher vocational education,the teaching of Chinese Medicine Processing Technology courses faces new opportunities and challenges.This paper analyzes the existing problems in the current teaching of Chinese Medicine Processing Technology courses,discusses the necessity of reforming the teaching model under the context of integration,and proposes the construction of a"Dual-Capability Progression,Six-Dimensional Empowerment"teaching model.The aim is to enhance the teaching quality of Chinese Medicine Processing Technology courses and cultivate high-quality skilled talents in Chinese medicine processing who can meet industry demands.展开更多
In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in...In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.展开更多
Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remain...Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.展开更多
Doping in thin-film transistors(TFTs) plays a crucial role in tailoring material properties to enhance device performance, making them essential for advanced electronic applications. This study explores the synthesis ...Doping in thin-film transistors(TFTs) plays a crucial role in tailoring material properties to enhance device performance, making them essential for advanced electronic applications. This study explores the synthesis and characterization of TFTs fabricated using nickel(Ni)-doped indium oxide(In_(2)O_(3)) via a wet-chemical approach. The presented work investigates the effect of "Ni" incorporation in In_(2)O_(3) on the structural and electrical transport properties of In_(2)O_(3), revealing that higher "Ni" content decreases the oxygen vacancies, leading to a reduction in leakage current and a forward shift in threshold potential(V_(th)).Experimental findings reveal that Ni In O-based TFTs(with Ni = 0.5%) showcase enhanced electrical performance, achieving mobility of 7.54 cm^(2)/(V·s), an impressive ON/OFF current ratio of ~10^(7), a V_(th) of 6.26 V, reduced interfacial trap states(D_(it)) of 8.23 ×10^(12) cm^(-2) and enhanced biased stress stability. The efficacy of "Ni" incorporation is attributed to the upgraded Lewis acidity, stable Ni-O bond strength, and small ionic radius of Ni. Negative bias illumination stability(NBIS) measurements further indicate that device stability diminishes with shorter light wavelengths, likely due to the activation of oxygen vacancies. These findings validate the solution-processed techniques' potential for future large-scale, low-cost, energy-efficient, and high-performance electronics.展开更多
The malicious dissemination of hate speech via compromised accounts,automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern.Automatically detecting such content in S...The malicious dissemination of hate speech via compromised accounts,automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern.Automatically detecting such content in Spanish is challenging due to linguistic complexity and the scarcity of annotated resources.In this paper,we compare two predominant AI-based approaches for the forensic detection of malicious hate speech:(1)finetuning encoder-only models that have been trained in Spanish and(2)In-Context Learning techniques(Zero-and Few-Shot Learning)with large-scale language models.Our approach goes beyond binary classification,proposing a comprehensive,multidimensional evaluation that labels each text by:(1)type of speech,(2)recipient,(3)level of intensity(ordinal)and(4)targeted group(multi-label).Performance is evaluated using an annotated Spanish corpus,standard metrics such as precision,recall and F1-score and stability-oriented metrics to evaluate the stability of the transition from zero-shot to few-shot prompting(Zero-to-Few Shot Retention and Zero-to-Few Shot Gain)are applied.The results indicate that fine-tuned encoder-only models(notably MarIA and BETO variants)consistently deliver the strongest and most reliable performance:in our experiments their macro F1-scores lie roughly in the range of approximately 46%–66%depending on the task.Zero-shot approaches are much less stable and typically yield substantially lower performance(observed F1-scores range approximately 0%–39%),often producing invalid outputs in practice.Few-shot prompting(e.g.,Qwen 38B,Mistral 7B)generally improves stability and recall relative to pure zero-shot,bringing F1-scores into a moderate range of approximately 20%–51%but still falling short of fully fine-tuned models.These findings highlight the importance of supervised adaptation and discuss the potential of both paradigms as components in AI-powered cybersecurity and malware forensics systems designed to identify and mitigate coordinated online hate campaigns.展开更多
This study investigates how orthographic,semantic and contextual variables—including word length,concreteness,and contextual support—impact on the processing and learning of new words in a second language(L2)when fi...This study investigates how orthographic,semantic and contextual variables—including word length,concreteness,and contextual support—impact on the processing and learning of new words in a second language(L2)when first encountered during reading.Students learning English as a foreign language(EFL)were recruited to read sentences for comprehension,embedded with unfamiliar L2 words that occurred once.Immediately after this,they received a form recognition test,a meaning recall test,and a meaning recognition test.Eye-movement data showed significant effects of word length on both early and late processing of novel words,along with effects of concreteness only on late-processing eye-tracking measures.Informative contexts were read slower than neutral contexts,yet contextual support did not show any direct influence on the processing of novel words.Interestingly,initial learning of abstract words was better than concrete words in terms of form and meaning recognition.Attentional processing of novel L2 words,operationalized by total reading time,positively predicted L2 learners’recognition of new orthographic forms.Taken together,these results suggest:1)orthographic,semantic and contextual factors play distinct roles for initial processing and learning of novel words;2)online processing of novel words contributes to L2 learners’initial knowledge of unfamiliar lexical items acquired from reading.展开更多
文摘Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of significant,novel,and high-impact research in the fields of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.
基金Sichuan Science and Technology Program(2025ZNSFSC1341)Fundamental Research Funds for the Central Universities(J2022-090,25CAFUC04087)。
文摘The hot compression deformation behavior of Mg-6Zn-1Mn-0.5Ca(ZM61-0.5Ca)and Mg-6Zn-1Mn-2Sn-0.5Ca(ZMT612-0.5Ca)alloys was investigated at deformation temperatures ranging from 250℃to 400℃and strain rates varying from 0.001 s^(-1) to 1 s^(-1).The results show that the addition of Sn promotes dynamic recrystallization(DRX),and CaMgSn phases can act as nucleation sites during the compression deformation.Flow stress increases with increasing the strain rate and decreasing the temperature.Both the ZM61-0.5Ca and ZMT612-0.5Ca alloys exhibit obvious DRX characteristics.CaMgSn phases can effectively inhibit dislocation motion with the addition of Sn,thus increasing the peak fl ow stress of the alloy.The addition of Sn increases the hot deformation activation energy of the ZM61-0.5Ca alloy from 199.654 kJ/mol to 276.649 kJ/mol,thus improving the thermal stability of the alloy.For the ZMT612-0.5Ca alloy,the optimal hot deformation parameters are determined to be a deformation temperature range of 350–400℃and a strain rate range of 0.001–0.01 s^(-1).
文摘Agricultural Products Processing and Storage(ISSN 3059-4510,Owner:Hunan Academy of Agricultural Sciences,China.Production and hosting:Springer Nature)is an international,peer-reviewed open access journal with the aim to offer a platform for the rapid dissemination of signifi cant,novel,and high-impact research in the fi elds of agricultural product processing science,technology,engineering,and nutrition.Additionally,supplemental issues are curated and published to facilitate in-depth discussions on special topics.
基金supported in part by the National Natural Science Foundation of China under Grant 62262073in part by the Yunnan Provincial Ten Thousand People Program for Young Top Talents under Grant YNWR-QNBJ-2019-237in part by the Yunnan Provincial Major Science and Technology Special Program under Grant 202402AD080002.
文摘In the age of big data,ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge.Traditional searchable encryption schemes face difficulties in handling complex semantic queries.Additionally,they typically rely on honest but curious cloud servers,which introduces the risk of repudiation.Furthermore,the combined operations of search and verification increase system load,thereby reducing performance.Traditional verification mechanisms,which rely on complex hash constructions,suffer from low verification efficiency.To address these challenges,this paper proposes a blockchain-based contextual semantic-aware ciphertext retrieval scheme with efficient verification.Building on existing single and multi-keyword search methods,the scheme uses vector models to semantically train the dataset,enabling it to retain semantic information and achieve context-aware encrypted retrieval,significantly improving search accuracy.Additionally,a blockchain-based updatable master-slave chain storage model is designed,where the master chain stores encrypted keyword indexes and the slave chain stores verification information generated by zero-knowledge proofs,thus balancing system load while improving search and verification efficiency.Finally,an improved non-interactive zero-knowledge proof mechanism is introduced,reducing the computational complexity of verification and ensuring efficient validation of search results.Experimental results demonstrate that the proposed scheme offers stronger security,balanced overhead,and higher search verification efficiency.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
基金supported by the National Natural Science Foundation of China(No.62267005)the Chinese Guangxi Natural Science Foundation(No.2023GXNSFAA026493)+1 种基金Guangxi Collaborative Innovation Center ofMulti-Source Information Integration and Intelligent ProcessingGuangxi Academy of Artificial Intelligence.
文摘The rapid development of information technology and accelerated digitalization have led to an explosive growth of data across various fields.As a key technology for knowledge representation and sharing,knowledge graphs play a crucial role by constructing structured networks of relationships among entities.However,data sparsity and numerous unexplored implicit relations result in the widespread incompleteness of knowledge graphs.In static knowledge graph completion,most existing methods rely on linear operations or simple interaction mechanisms for triple encoding,making it difficult to fully capture the deep semantic associations between entities and relations.Moreover,many methods focus only on the local information of individual triples,ignoring the rich semantic dependencies embedded in the neighboring nodes of entities within the graph structure,which leads to incomplete embedding representations.To address these challenges,we propose Two-Stage Mixer Embedding(TSMixerE),a static knowledge graph completion method based on entity context.In the unit semantic extraction stage,TSMixerE leveragesmulti-scale circular convolution to capture local features atmultiple granularities,enhancing the flexibility and robustness of feature interactions.A channel attention mechanism amplifies key channel responses to suppress noise and irrelevant information,thereby improving the discriminative power and semantic depth of feature representations.For contextual information fusion,a multi-layer self-attentionmechanism enables deep interactions among contextual cues,effectively integrating local details with global context.Simultaneously,type embeddings clarify the semantic identities and roles of each component,enhancing the model’s sensitivity and fusion capabilities for diverse information sources.Furthermore,TSMixerE constructs contextual unit sequences for entities,fully exploring neighborhood information within the graph structure to model complex semantic dependencies,thus improving the completeness and generalization of embedding representations.
文摘This study investigated the effects of xu-argument-based continuation writing on learners’processing of source texts.Seventy-five participants were randomly assigned to three conditions:(1)continuation writing,(2)summary writing,or(3)reading comprehension.Eye-tracking data were collected during reading,measuring early(first fixation duration,first pass duration)and late(go-past time,total fixation duration)eye movements.During writing,source-text rereading was tracked via fixation counts and durations.Results showed that task type did not affect initial lexical access,as first fixation duration showed no group differences.However,both production groups exhibited significantly longer first pass durations than the reading comprehension group.Late measures revealed a gradient pattern:the continuation writing group spent significantly longer gopast time and total fixation duration than the summary writing group,which exceeded the reading comprehension group.This indicates that continuation tasks promoted deeper cognitive engagement during reading.During writing,the continuation writing group spent more time rereading the source text with higher fixation counts than the summary writing group.These findings suggest that continuation writing triggers more intensive reader-text interaction during pre-writing and enhances comprehension-production coupling through sustained attention to input during writing.This study sheds light on the cognitive mechanisms underlying the theoretical and pedagogical value of xu-argument.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130719 and 42177173)the Doctoral Direct Train Project of Chongqing Natural Science Foundation(Grant No.CSTB2023NSCQ-BSX0029).
文摘Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.
基金supported by he National Social Science Found of China(2022-SKJJ-B-112).
文摘In this paper,we propose a novel graph signal processing convolution recurrent network(GSP CRN)for signal enhancement against high suppressive interference(HSI)in wireless communications.GSPCRN consists of the short-time graph signal processing(SGSP)approach and a modified convolution recurrent network.Similar to the traditional shorttime time-frequency transformation,SGSP frames the complex-valued communication signal and transforms it to the graph-domain representations,where the connection and weight flexibility of each vertex are fully taken into account.In the presence of HSI,SGSP can extract signal features from new graph-domain dimensions and empower neural networks for weak signal enhancement.Two SGSP methods,adjacency singular value decomposition and implicit graph transformation,are designed to capture relationships among the sampling points in the segmented signals.Simulation results demonstrate that our proposed GSPCRN outperforms existing classic methods in extracting weak signals from the HSI environment.When the interference-to-signal ratio exceeds 27dB,only our proposed GSPCRN can achieve the interference mitigation.
基金funded by the 2024 Shanghai Social Science Planning Annual Project titled“A Study on the Chunk Processing Mechanisms and Cognitive Motivations of ESL Reading”(Fund No.2024BYY012).
文摘English reading proficiency is essential for university students in a globalized academic environment,yet many L2 learners encounter challenges,such as limited vocabulary,complex syntax,and unclear text organization,leading to cognitive overload.Grounded in the Cognitive Load Theory(CLT),this study examines the role of the Chunking Reading Processing Strategy-which integrates fragmented linguistic information into meaningful units at lexical,syntactic,and discourse levels-in alleviating cognitive load and improving reading comprehension.Through a mixed-methods approach,the research investigates how learners at different proficiency levels perceive and apply the chunking strategy,and how such application relates to cognitive load management.The results indicate that higher-proficiency learners employ chunking more frequently and report greater benefits,whereas lower-proficiency learners depend more on instructional support.The study confirms the theoretical and pedagogical value of chunk-based reading instruction and suggests that differentiated,cognitively informed teaching of the chunking strategy can enhance both reading efficiency and strategic awareness among L2 learners.
文摘While the Ordos Basin is recognized for its substantial hydrocarbon exploration prospects,its rugged loess tableland terrain has rendered seismic exploration exceptionally challenging[1-3].Persistent obstacles such as complex 3D survey planning,low signal-tonoise ratio raw data,inadequate near-surface velocity modeling,and imaging inaccuracy have long hindered the advancement of seismic exploration across this region.Through a problem-solving approach rooted in geological target analysis,this research systematically investigates the behavioral patterns of nodal seismometer-based high-density seismic acquisition in loess plateau.Tailored advancements in waveform enhancement and depth velocity modelling methodologies have been engineered.Field validations confirm that the optimized workflow demonstrates marked improvements in amplitude preservation and imaging resolution,offering novel insights for future reservoir characterization endeavors.
基金Under the auspices of the Hong Kong Research Grants Council(No.14605920,14606922,14603724,C4023-20GF,8601219,8601242,3110151)a Grant from the Research Committee on Research Sustainability of Major Research Grants Council Funding Schemes of the Chinese University of Hong Kong(CUHK,No.3133235)the Vice-Chancellor’s One-off Discretionary Fund of CUHK(No.4930787)。
文摘Perception of air pollution is subjective and context-dependent.Previous studies exploring the association between measured air pollution and perceived air quality mainly focused on air pollution levels measured in the residence-based(RB)or regional context,overlooking the mobility-based(MB)context in which people are exposed to air pollution.This study measures air pollution levels in MB,RB,and regional contexts and examines their relationships with perceived air quality across different neighborhoods and gender sub-groups of Hong Kong,China to investigate how people perceive air quality.The results indicate that particulate matter 2.5(PM_(2.5))measured in RB and the regional context significantly contributes to people’s perceived air quality compared to MB PM_(2.5).Individuals in Central and Western district of Hong Kong rely on RB,regional and MB PM_(2.5) to assess air pollution.In Sham Shui Po,RB PM_(2.5) exhibits the highest influence on people’s perceived air quality,followed by regional PM_(2.5).Women’s perceived air quality is strongly related to their RB PM_(2.5) exposure,while men’s perceived air quality is associated with both RB PM_(2.5) and regional PM_(2.5) levels.We conclude that neighborhood effects and mobility levels are the two most important factors influencing the association between meas-ured air pollution and perceived air quality.We reveal that the neighborhood effect averaging problem(NEAP)influences the associ-ation between perceived air quality and measured air pollution levels in a way that differs from health outcome-related studies.Effect-ive measures are needed to improve the public’s awareness of air pollution,and scientific control should be implemented to reduce pub-lic exposure.
文摘Background:In mental health,recovery is emphasized,and qualitative analyses of service users’narratives have accumulated;however,while qualitative approaches excel at capturing rich context and generating new concepts,they are limited in generalizability and feasible data volume.This study aimed to quantify the subjective life history narratives of users of psychiatric home-visit nursing using natural language processing(NLP)and to clarify the relationships between linguistic features and recovery-related indicators.Methods:We conducted audio-recorded and transcribed semi-structured interviews on daily life verbatim and collected self-report questionnaires(Recovery Assessment Scale[RAS])and clinician ratings(Global Assessment of Functioning[GAF])from Japanese users of psychiatric home-visit nursing.Using the artificial intelligence-based topic-modeling method BERTopic,we extracted topics from the interview texts and calculated each participant’s topic proportions,and then examined associations between topic proportions and recovery-related indicators using Pearson correlation analyses.Results:“School”showed a significant positive correlation with RAS(r=0.39,p=0.05),whereas“Family”showed a significant negative correlation(r=–0.46,p=0.02).GAF was positively correlated with word count(r=0.44,p=0.02)and“Hospital”(r=0.42,p=0.03),and negatively correlated with“Backchannels”(aizuchi)(r=–0.41,p=0.03).Conclusion:The present results suggest that the quantity,quality,and content of narratives can serve as useful indicators of mental health and recovery,and that objective NLP-based analysis of service users’narratives can complement traditional self-report scales and clinician ratings to inform the design of recovery-oriented care in psychiatric home-visit nursing.
基金financial support of the National Natural Science Foundation of China(No.52371103)the Fundamental Research Funds for the Central Universities,China(No.2242023K40028)+1 种基金the Open Research Fund of Jiangsu Key Laboratory for Advanced Metallic Materials,China(No.AMM2023B01).financial support of the Research Fund of Shihezi Key Laboratory of AluminumBased Advanced Materials,China(No.2023PT02)financial support of Guangdong Province Science and Technology Major Project,China(No.2021B0301030005)。
文摘Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.
基金Supported by Scientific Research Fund Project of Yunnan Provincial Department of Education(2025J1950).
文摘Against the backdrop of integrated development between technical education and higher vocational education,the teaching of Chinese Medicine Processing Technology courses faces new opportunities and challenges.This paper analyzes the existing problems in the current teaching of Chinese Medicine Processing Technology courses,discusses the necessity of reforming the teaching model under the context of integration,and proposes the construction of a"Dual-Capability Progression,Six-Dimensional Empowerment"teaching model.The aim is to enhance the teaching quality of Chinese Medicine Processing Technology courses and cultivate high-quality skilled talents in Chinese medicine processing who can meet industry demands.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,Grant No.KFU250098.
文摘In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.
基金This study is financed by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001.
文摘Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.
基金funded by the research startup funding of National Research Foundation (NRF) of Korea through the Ministry of Science and ICT 2022R1G1A1009887Part of this study was supported by research start-up funding of Anhui University (S202418001/078)。
文摘Doping in thin-film transistors(TFTs) plays a crucial role in tailoring material properties to enhance device performance, making them essential for advanced electronic applications. This study explores the synthesis and characterization of TFTs fabricated using nickel(Ni)-doped indium oxide(In_(2)O_(3)) via a wet-chemical approach. The presented work investigates the effect of "Ni" incorporation in In_(2)O_(3) on the structural and electrical transport properties of In_(2)O_(3), revealing that higher "Ni" content decreases the oxygen vacancies, leading to a reduction in leakage current and a forward shift in threshold potential(V_(th)).Experimental findings reveal that Ni In O-based TFTs(with Ni = 0.5%) showcase enhanced electrical performance, achieving mobility of 7.54 cm^(2)/(V·s), an impressive ON/OFF current ratio of ~10^(7), a V_(th) of 6.26 V, reduced interfacial trap states(D_(it)) of 8.23 ×10^(12) cm^(-2) and enhanced biased stress stability. The efficacy of "Ni" incorporation is attributed to the upgraded Lewis acidity, stable Ni-O bond strength, and small ionic radius of Ni. Negative bias illumination stability(NBIS) measurements further indicate that device stability diminishes with shorter light wavelengths, likely due to the activation of oxygen vacancies. These findings validate the solution-processed techniques' potential for future large-scale, low-cost, energy-efficient, and high-performance electronics.
基金the research project LaTe4PoliticES(PID2022-138099OB-I00)funded by MCIN/AEI/10.13039/501100011033 and the European Fund for Regional Development(ERDF)-a way to make Europe.Tomás Bernal-Beltrán is supported by University of Murcia through the predoctoral programme.
文摘The malicious dissemination of hate speech via compromised accounts,automated bot networks and malware-driven social media campaigns has become a growing cybersecurity concern.Automatically detecting such content in Spanish is challenging due to linguistic complexity and the scarcity of annotated resources.In this paper,we compare two predominant AI-based approaches for the forensic detection of malicious hate speech:(1)finetuning encoder-only models that have been trained in Spanish and(2)In-Context Learning techniques(Zero-and Few-Shot Learning)with large-scale language models.Our approach goes beyond binary classification,proposing a comprehensive,multidimensional evaluation that labels each text by:(1)type of speech,(2)recipient,(3)level of intensity(ordinal)and(4)targeted group(multi-label).Performance is evaluated using an annotated Spanish corpus,standard metrics such as precision,recall and F1-score and stability-oriented metrics to evaluate the stability of the transition from zero-shot to few-shot prompting(Zero-to-Few Shot Retention and Zero-to-Few Shot Gain)are applied.The results indicate that fine-tuned encoder-only models(notably MarIA and BETO variants)consistently deliver the strongest and most reliable performance:in our experiments their macro F1-scores lie roughly in the range of approximately 46%–66%depending on the task.Zero-shot approaches are much less stable and typically yield substantially lower performance(observed F1-scores range approximately 0%–39%),often producing invalid outputs in practice.Few-shot prompting(e.g.,Qwen 38B,Mistral 7B)generally improves stability and recall relative to pure zero-shot,bringing F1-scores into a moderate range of approximately 20%–51%but still falling short of fully fine-tuned models.These findings highlight the importance of supervised adaptation and discuss the potential of both paradigms as components in AI-powered cybersecurity and malware forensics systems designed to identify and mitigate coordinated online hate campaigns.
文摘This study investigates how orthographic,semantic and contextual variables—including word length,concreteness,and contextual support—impact on the processing and learning of new words in a second language(L2)when first encountered during reading.Students learning English as a foreign language(EFL)were recruited to read sentences for comprehension,embedded with unfamiliar L2 words that occurred once.Immediately after this,they received a form recognition test,a meaning recall test,and a meaning recognition test.Eye-movement data showed significant effects of word length on both early and late processing of novel words,along with effects of concreteness only on late-processing eye-tracking measures.Informative contexts were read slower than neutral contexts,yet contextual support did not show any direct influence on the processing of novel words.Interestingly,initial learning of abstract words was better than concrete words in terms of form and meaning recognition.Attentional processing of novel L2 words,operationalized by total reading time,positively predicted L2 learners’recognition of new orthographic forms.Taken together,these results suggest:1)orthographic,semantic and contextual factors play distinct roles for initial processing and learning of novel words;2)online processing of novel words contributes to L2 learners’initial knowledge of unfamiliar lexical items acquired from reading.
