Nationalist Trends in the Transformation of New Zealand’s Maori Language Policy

Language is an important carrier of human culture and a specific method for communication between humans.It is one of the most important symbols of human identity and flags of dividing group identity.At present,in the modern national state,the government is paying more attention to language policy and being more and more prudent to it.In the process of making and implementing language policy,the government must take into account a range of factors such as the country’s national history,politics,culture,economy,and relations between different social groups.From the mid-nineteenth century,when New Zealand became a British colony,until the mid-twentieth century,the Maori people were inspired to defend their rights and respect for themselves by the rise of Maori nationalist ideology and Maori political radicalism in a struggle that lasted more than 100 years.Until 1994,there were more than 800 kohanga reo,early childhood institutions that established Maori language instruction independent of the school system,and the nationalism drives to revitalize the Maori national language was at its peak.The process of revolution on Maori language policies in New Zealand reflects a trend of nationalist thoughts from Maori becoming more deep.Exploring the process of change in New Zealand’s Maori language policy will help to develop the study of the social history of indigenous peoples during the colonial period and will contribute to the study of the development of colonized countries and regions in the post-colonial period.The change in New Zealand’s Maori language policy has provided an important example of the preservation and revitalization of the indigenous languages of the world’s former colonies,which is of great significance for the preservation of linguistic and cultural diversity.

Unraveling the significance of cobalt on transformation kinetics,crystallography and impact toughness in high-strength steels

This work reveals the significant effects of cobalt(Co)on the microstructure and impact toughness of as-quenched highstrength steels by experimental characterizations and thermo-kinetic analyses.The results show that the Co-bearing steel exhibits finer blocks and a lower ductile-brittle transition temperature than the steel without Co.Moreover,the Co-bearing steel reveals higher transformation rates at the intermediate stage with bainite volume fraction ranging from around 0.1 to 0.6.The improved impact toughness of the Co-bearing steel results from the higher dense block boundaries dominated by the V1/V2 variant pair.Furthermore,the addition of Co induces a larger transformation driving force and a lower bainite start temperature(BS),thereby contributing to the refinement of blocks and the increase of the V1/V2 variant pair.These findings would be instructive for the composition,microstructure design,and property optimization of high-strength steels.

Soliton Solutions of a Coupled KdV System via Backlund Transformation

In this paper we use Böcklund transformation to construct soliton solutions for a coupled KdV system.This system was first proposed by Wang in 2010.First we generalize the well-known Bäcklund transformation for the KdV equation to such coupled KdV system.Then from a trivial seed solution,we construct soliton solutions.We also give a nonlinear superposition formula,which allows us to generate multi-soliton solutions.

A Study of Students’Informal Digital Learning of Foreign Language in the Context of Digital Transformation of Vocational Education

Against the backdrop of the digital transformation of vocational education,informal digital learning of foreign language(hereinafter referred to as IDLFL)is an emerging learning model with significant potential for enabling individualized and autonomous learning and improving workplace English proficiency among Higher Vocational Colleges(hereinafter referred to as HVC)students.This study explores how Chinese foreign language teachers in HVC perceive and understand HVC students’IDLFL.Primary data are collected through questionnaires and interviews,and qualitative analysis is conducted based on grounded theory to reveal effective ways to improve the foreign language learning outcomes of HVC students through IDLFL and the corresponding strategies that HVC language teachers should adopt.However,this study also emphasizes the need to be wary of overstating the impact and role of IDLFL in teaching practice.

Xiamen after 20 Years of Ecological Transformation

The efforts Xiamen has made in ecological protection over the last 20 years have helped it become a beautiful and clean city.TWENTY years ago,I had the opportunity to travel to the city of Xiamen in southeastern China’s Fujian Province.Unfortunately,due to the fact that I was in a rush,I did not have the chance to see the whole city.My memory of Xiamen was its pleasant climate and unforgettable beauty-I knew if I ever got the opportunity,I would revisit this place.Twenty years went by until I was able to return to this city again.After emerging from the airport,I realized that I had grown older,while Xiamen was becoming increasingly youthful and vibrant.What exactly had given this city such vitality?

Stage IV malignant transformation of mature cystic teratoma palliatively treated with concurrent chemoradiotherapy:A case report

BACKGROUND Malignant transformation(MT)of mature cystic teratoma(MCT)has a poor prognosis,especially in advanced cases.Concurrent chemoradiotherapy(CCRT)has an inhibitory effect on MT.CASE SUMMARY Herein,we present a case in which CCRT had a reduction effect preoperatively.A 73-year-old woman with pyelonephritis was referred to our hospital.Computed tomography revealed right hydronephrosis and a 6-cm pelvic mass.Endoscopic ultrasound-guided fine-needle biopsy(EUS-FNB)revealed squamous cell carci-noma.The patient was diagnosed with MT of MCT.Due to her poor general con-dition and renal malfunction,we selected CCRT,expecting fewer adverse effects.After CCRT,her performance status improved,and the tumor size was reduced;surgery was performed.Five months postoperatively,the patient developed dis-semination and lymph node metastases.Palliative chemotherapy was ineffective.She died 18 months after treatment initiation.CONCLUSION EUS-FNB was useful in the diagnosis of MT of MCT;CCRT suppressed the disea-se and improved quality of life.

Assessing the possibility of using large language models in ocular surface diseases

AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases:ChatGPT-4,ChatGPT-3.5,Claude 2,PaLM2,and SenseNova.METHODS:A group of experienced ophthalmology professors were asked to develop a 100-question singlechoice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions.The exam includes questions on the following topics:keratitis disease(20 questions),keratoconus,keratomalaciac,corneal dystrophy,corneal degeneration,erosive corneal ulcers,and corneal lesions associated with systemic diseases(20 questions),conjunctivitis disease(20 questions),trachoma,pterygoid and conjunctival tumor diseases(20 questions),and dry eye disease(20 questions).Then the total score of each LLMs and compared their mean score,mean correlation,variance,and confidence were calculated.RESULTS:GPT-4 exhibited the highest performance in terms of LLMs.Comparing the average scores of the LLMs group with the four human groups,chief physician,attending physician,regular trainee,and graduate student,it was found that except for ChatGPT-4,the total score of the rest of the LLMs is lower than that of the graduate student group,which had the lowest score in the human group.Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers,giving very little chance of an incorrect answer.ChatGPT-4 showed higher credibility when answering questions,with a success rate of 59%,but gave the wrong answer to the question 28% of the time.CONCLUSION:GPT-4 model exhibits excellent performance in both answer relevance and confidence.PaLM2 shows a positive correlation(up to 0.8)in terms of answer accuracy during the exam.In terms of answer confidence,PaLM2 is second only to GPT4 and surpasses Claude 2,SenseNova,and GPT-3.5.Despite the fact that ocular surface disease is a highly specialized discipline,GPT-4 still exhibits superior performance,suggesting that its potential and ability to be applied in this field is enormous,perhaps with the potential to be a valuable resource for medical students and clinicians in the future.

Evaluating large language models as patient education tools for inflammatory bowel disease:A comparative study

BACKGROUND Inflammatory bowel disease(IBD)is a global health burden that affects millions of individuals worldwide,necessitating extensive patient education.Large language models(LLMs)hold promise for addressing patient information needs.However,LLM use to deliver accurate and comprehensible IBD-related medical information has yet to be thoroughly investigated.AIM To assess the utility of three LLMs(ChatGPT-4.0,Claude-3-Opus,and Gemini-1.5-Pro)as a reference point for patients with IBD.METHODS In this comparative study,two gastroenterology experts generated 15 IBD-related questions that reflected common patient concerns.These questions were used to evaluate the performance of the three LLMs.The answers provided by each model were independently assessed by three IBD-related medical experts using a Likert scale focusing on accuracy,comprehensibility,and correlation.Simultaneously,three patients were invited to evaluate the comprehensibility of their answers.Finally,a readability assessment was performed.RESULTS Overall,each of the LLMs achieved satisfactory levels of accuracy,comprehensibility,and completeness when answering IBD-related questions,although their performance varies.All of the investigated models demonstrated strengths in providing basic disease information such as IBD definition as well as its common symptoms and diagnostic methods.Nevertheless,when dealing with more complex medical advice,such as medication side effects,dietary adjustments,and complication risks,the quality of answers was inconsistent between the LLMs.Notably,Claude-3-Opus generated answers with better readability than the other two models.CONCLUSION LLMs have the potential as educational tools for patients with IBD;however,there are discrepancies between the models.Further optimization and the development of specialized models are necessary to ensure the accuracy and safety of the information provided.

The Main Theories of Transformational-Generative Grammar and Its Guide Upon Language Teaching

This essay is showing an opinion that despite the many disadvantages of T.G grammar,its main theories about human language learning can give valuable guide to language teaching,which will be illustrated in the following from three main aspects.

Hemorrhagic transformation in patients with large-artery atherosclerotic stroke is associated with the gut microbiota and lipopolysaccharide

Hemorrhagic transformation is a major complication of large-artery atheroscle rotic stroke(a major ischemic stro ke subtype)that wo rsens outcomes and increases mortality.Disruption of the gut microbiota is an important feature of stroke,and some specific bacteria and bacterial metabolites may contribute to hemorrhagic transformation pathogenesis.We aimed to investigate the relationship between the gut microbiota and hemorrhagic transformation in largearte ry atheroscle rotic stro ke.An observational retrospective study was conducted.From May 2020 to September 2021,blood and fecal samples were obtained upon admission from 32 patients with first-ever acute ischemic stroke and not undergoing intravenous thrombolysis or endovascular thrombectomy,as well as 16 healthy controls.Patients with stro ke who developed hemorrhagic transfo rmation(n=15)were compared to those who did not develop hemorrhagic transformation(n=17)and with healthy controls.The gut microbiota was assessed through 16S ribosomal ribonucleic acid sequencing.We also examined key components of the lipopolysaccharide pathway:lipopolysaccharide,lipopolysaccharide-binding protein,and soluble CD14.We observed that bacterial diversity was decreased in both the hemorrhagic transformation and non-hemorrhagic transfo rmation group compared with the healthy controls.The patients with ischemic stro ke who developed hemorrhagic transfo rmation exhibited altered gut micro biota composition,in particular an increase in the relative abundance and dive rsity of members belonging to the Enterobacteriaceae family.Plasma lipopolysaccharide and lipopolysaccharide-binding protein levels were higher in the hemorrhagic transformation group compared with the non-hemorrhagic transfo rmation group.lipopolysaccharide,lipopolysaccharide-binding protein,and soluble CD14 concentrations were associated with increased abundance of Enterobacte riaceae.Next,the role of the gut microbiota in hemorrhagic transformation was evaluated using an experimental stroke rat model.In this model,transplantation of the gut microbiota from hemorrhagic transformation rats into the recipient rats triggered higher plasma levels of lipopolysaccharide,lipopolysaccharide-binding protein,and soluble CD14.Ta ken togethe r,our findings demonstrate a noticeable change in the gut microbiota and lipopolysaccharide-related inflammatory response in stroke patients with hemorrhagic transformation.This suggests that maintaining a balanced gut microbiota may be an important factor in preventing hemorrhagic transfo rmation after stro ke.

Formation and transformation of metastable LPSO building blocks clusters in Mg-Gd-Y-Zn-Zr alloys by spinodal decomposition and heterogeneous nucleation

To study the formation and transformation mechanism of long-period stacked ordered(LPSO)structures,a systematic atomic scale analysis was conducted for the structural evolution of long-period stacked ordered(LPSO)structures in the Mg-Gd-Y-Zn-Zr alloy annealed at 300℃~500℃.Various types of metastable LPSO building block clusters were found to exist in alloy structures at different temperatures,which precipitate during the solidification and homogenization process.The stability of Zn/Y clusters is explained by the first principles of density functional theory.The LPSO structure is distinguished by the arrangement of its different Zn/Y enriched LPSO structural units,which comprises local fcc stacking sequences upon a tightly packed plane.The presence of solute atoms causes local lattice distortion,thereby enabling the rearrangement of Mg atoms in the different configurations in the local lattice,and local HCP-FCC transitions occur between Mg and Zn atoms occupying the nearest neighbor positions.This finding indicates that LPSO structures can generate necessary Schockley partial dislocations on specific slip surfaces,providing direct evidence of the transition from 18R to 14H.Growth of the LPSO,devoid of any defects and non-coherent interfaces,was observed separately from other precipitated phases.As a result,the precipitation sequence of LPSO in the solidification stage was as follows:Zn/Ycluster+Mg layers→various metastable LPSO building block clusters→18R/24R LPSO;whereas the precipitation sequence of LPSO during homogenization treatment was observed to be as follows:18R LPSO→various metastable LPSO building block clusters→14H LPSO.Of these,14H LPSO was found to be the most thermodynamically stable structure.

Phase transformation in titanium alloys:A review

Due to a series of exceptional properties,titanium and titanium alloys have received extensive attention in recent years.Different from other alloy systems,there are two allotropes and a sequence of metastable phases in titanium alloys.By summarizing the recent investigations,the phase transformation processes corresponding to the common phases and also some less reported phases are reviewed.For the phase transformation only involvingαandβphases,it can be divided intoβ→αtransformation and a reverse transformation.The former one has been demonstrated from the orientation relationship betweenαandβphases and the regulation ofαmorphology.For the latter transformation,the role of the stress has been discussed.In terms of the metastable phases,the mechanisms of phase formation and their effects on microstructure and mechanical properties have been discussed.Finally,some suggestions about the development of titanium alloys have been proposed.

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

Evaluating Privacy Leakage and Memorization Attacks on Large Language Models (LLMs) in Generative AI Applications

The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. We describe different black-box attacks from potential adversaries and study their impact on the amount and type of information that may be recovered from commonly used and deployed LLMs. Our research investigates the relationship between PII leakage, memorization, and factors such as model size, architecture, and the nature of attacks employed. The study utilizes two broad categories of attacks: PII leakage-focused attacks (auto-completion and extraction attacks) and memorization-focused attacks (various membership inference attacks). The findings from these investigations are quantified using an array of evaluative metrics, providing a detailed understanding of LLM vulnerabilities and the effectiveness of different attacks.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Development of sustainable and efficient recycling technology for spent Li-ion batteries: Traditional and transformation go hand in hand

Clean and efficient recycling of spent lithium-ion batteries(LIBs)has become an urgent need to promote sustainable and rapid development of human society.Therefore,we provide a critical and comprehensive overview of the various technologies for recycling spent LIBs,starting with lithium-ion power batteries.Recent research on raw material collection,metallurgical recovery,separation and purification is highlighted,particularly in terms of all aspects of economic efficiency,energy consumption,technology transformation and policy management.Mechanisms and pathways for transformative full-component recovery of spent LIBs are explored,revealing a clean and efficient closed-loop recovery mechanism.Optimization methods are proposed for future recycling technologies,with a focus on how future research directions can be industrialized.Ultimately,based on life-cycle assessment,the challenges of future recycling are revealed from the LIBs supply chain and stability of the supply chain of the new energy battery industry to provide an outlook on clean and efficient short process recycling technologies.This work is designed to support the sustainable development of the new energy power industry,to help meet the needs of global decarbonization strategies and to respond to the major needs of industrialized recycling.

A new insight into LPSO phase transformation and mechanical properties uniformity of large-scale Mg-Gd-Y-Zn-Zr alloy prepared by multi-pass friction stir processing

A large-scale fine-grained Mg-Gd-Y-Zn-Zr alloy plate with high strength and ductility was successfully prepared by multi-pass friction stir processing(MFSP)technology in this work.The structure of grains and long period stacking ordered(LPSO)phase were characterized,and the mechanical properties uniformity was investigated.Moreover,a quantitative relationship between the microstructure and tensile yield strength was established.The results showed that the grains in the processed zone(PZ)and interfacial zone(IZ)were refined from 50μm to 3μm and 4μm,respectively,and numerous original LPSO phases were broken.In IZ,some block-shaped 18R LPSO phases were transformed into needle-like 14H LPSO phases due to stacking faults and the short-range diffusion of solute atoms.The severe shear deformation in the form of kinetic energy caused profuse stacking fault to be generated and move rapidly,greatly increasing the transformation rate of LPSO phase.After MFSP,the ultimate tensile strength,yield strength and elongation to failure of the large-scale plate were 367 MPa,305 MPa and 18.0% respectively.Grain refinement and LPSO phase strengthening were the major strengthening mechanisms for the MFSP sample.In particularly,the strength of IZ was comparable to that of PZ because the strength contribution of the 14H LPSO phase offsets the lack of grain refinement strengthening in IZ.This result opposes the widely accepted notion that IZ is a weak region in MFSP-prepared large-scale fine-grained plate.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

Intrahepatic portal venous systems in adult patients with cavernous transformation of portal vein: Imaging features and a new classification

Background: Cavernous transformation of the portal vein(CTPV) due to portal vein obstruction is a rare vascular anomaly defined as the formation of multiple collateral vessels in the hepatic hilum. This study aimed to investigate the imaging features of intrahepatic portal vein in adult patients with CTPV and establish the relationship between the manifestations of intrahepatic portal vein and the progression of CTPV. Methods: We retrospectively analyzed 14 CTPV patients in Beijing Tsinghua Changgung Hospital. All patients underwent both direct portal venography(DPV) and computed tomography angiography(CTA) to reveal the manifestations of the portal venous system. The vessels measured included the left portal vein(LPV), right portal vein(RPV), main portal vein(MPV) and the portal vein bifurcation(PVB). Results: Nine males and 5 females, with a median age of 40.5 years, were included in the study. No significant difference was found in the diameters of the LPV or RPV measured by DPV and CTA. The visualization in terms of LPV, RPV and PVB measured by DPV was higher than that by CTA. There was a significant association between LPV/RPV and PVB/MPV in term of visibility revealed with DPV( P = 0.01), while this association was not observed with CTA. According to the imaging features of the portal vein measured by DPV, CTPV was classified into three categories to facilitate the diagnosis and treatment. Conclusions: DPV was more accurate than CTA for revealing the course of the intrahepatic portal vein in patients with CTPV. The classification of CTPV, that originated from the imaging features of the portal vein revealed by DPV, may provide a new perspective for the diagnosis and treatment of CTPV.

Evaluating the role of large language models in inflammatory bowel disease patient information

This letter evaluates the article by Gravina et al on ChatGPT’s potential in providing medical information for inflammatory bowel disease patients.While promising,it highlights the need for advanced techniques like reasoning+action and retrieval-augmented generation to improve accuracy and reliability.Emphasizing that simple question and answer testing is insufficient,it calls for more nuanced evaluation methods to truly gauge large language models’capabilities in clinical applications.