A Contrastive Analysis of English and Chinese Idioms About Color Words & Their Cultural Connotations

There are many idioms related to color words in English and Chinese.The use of color words in idioms adds beauty and vividness to the language.Due to the cultural differences,“color idioms”have gained different cultural connotations with the development of English and Chinese languages.It is of great significance to accurately understand and grasp the meanings and differences of color-related idioms in Chinese and English.This paper intends to analyze and expound the cultural connotations of English and Chinese idioms related to several widely used basic color words with the aim of helping English learners know and use the idioms about color words better.

Dynamic contrast enhanced ultrasound in differential diagnosis of hepatocellular carcinoma: A systematic review and meta-analysis

BACKGROUND Non-invasive differential diagnosis between hepatocellular carcinoma(HCC)and other liver cancer(i.e.cholangiocarcinoma or metastasis)is highly challenging and definitive diagnosis still relies on histological exam.The patterns of enhancement and wash-out of liver nodules can be used to stratify the risk of malignancy only in cirrhotic patients and HCC frequently shows atypical features.Dynamic contrast-enhanced ultrasound(DCEUS)with standardized software could help to overcome these obstacles,providing functional and quantitative parameters and potentially improving accuracy in the evaluation of tumor perfusion.AIM To explore clinical evidence regarding the application of DCEUS in the differential diagnosis of liver nodules.METHODS A comprehensive literature search of clinical studies was performed to identify the parameters of DCEUS that could relate to histological diagnosis.In accordance with the study protocol,a qualitative and quantitative analysis of the evidence was planned.RESULTS Rise time was significantly higher in HCC patients with a standardized mean difference(SMD)of 0.83(95%CI:0.48-1.18).Similarly,other statistically significant parameters were mean transit time local with a SMD of 0.73(95%CI:0.20-1.27),peak enhancement with a SMD of 0.37(95%CI:0.03-0.70),area wash-in area under the curve with a SMD of 0.47(95%CI:0.13-0.81),wash-out area under the curve with a SMD of 0.55(95%CI:0.21-0.89)and wash-in and wash-out area under the curve with SMD of 0.51(95%CI:0.17-0.85).SMD resulted not significant in fall time and wash-in rate,but the latter presented a trend towards greater values in HCC compared to intrahepatic cholangiocarcinoma.CONCLUSION DCEUS could improve non-invasive diagnosis of HCC,leading to less liver biopsy and early treatment.This quantitative analysis needs to be applied on larger cohorts to confirm these preliminary results.

Accuracy and Utility of Vessel Analysis Using Non-Contrast CT for Planning Endovascular Aortic Repair

Objectives: This study aimed to determine whether errors in vascular measurements would affect device selection in endovascular aortic repair (EVAR) by comparing measurements obtained using non-contrast computed tomography (NCT) with those obtained using contrast-enhanced CT (CECT). Materials and Methods: This single-center, retrospective study included 25 patients who underwent EVAR for abdominal aortic aneurysm at our institution. A 1-mm horizontal cross-sectional slice of NCT and CECT from each patient was retrospectively reviewed. The area from the abdominal aorta to the common iliac artery was divided into four zones. A centerline was created using the NCT by manually plotting the center points. Subsequently, the centerlines were automatically extracted and manually corrected during the arterial phase of CECT. The diameter and length of each zone were measured for each modality. The mean diameters and lengths of the target vessels were compared between NCT and CECT. Results: The measurements obtained using both methods were reproducible and demonstrated good agreement. The mean differences in vessel length and diameter measurements for each segment between NCT and CECT were not statistically significant, indicating good consistency. Conclusion: NCT may be useful for preoperative EVAR evaluation in patients with renal dysfunction or allergies to contrast agents.

A Rapid Adaptation Approach for Dynamic Air‑Writing Recognition Using Wearable Wristbands with Self‑Supervised Contrastive Learning

Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.

Text-Image Feature Fine-Grained Learning for Joint Multimodal Aspect-Based Sentiment Analysis

Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.

Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.

Research status and prospects of the fractal analysis of metal material surfaces and interfaces

As a mathematical analysis method,fractal analysis can be used to quantitatively describe irregular shapes with self-similar or self-affine properties.Fractal analysis has been used to characterize the shapes of metal materials at various scales and dimensions.Conventional methods make it difficult to quantitatively describe the relationship between the regular characteristics and properties of metal material surfaces and interfaces.However,fractal analysis can be used to quantitatively describe the shape characteristics of metal materials and to establish the quantitative relationships between the shape characteristics and various properties of metal materials.From the perspective of two-dimensional planes and three-dimensional curved surfaces,this paper reviews the current research status of the fractal analysis of metal precipitate interfaces,metal grain boundary interfaces,metal-deposited film surfaces,metal fracture surfaces,metal machined surfaces,and metal wear surfaces.The relationship between the fractal dimensions and properties of metal material surfaces and interfaces is summarized.Starting from three perspectives of fractal analysis,namely,research scope,image acquisition methods,and calculation methods,this paper identifies the direction of research on fractal analysis of metal material surfaces and interfaces that need to be developed.It is believed that revealing the deep influence mechanism between the fractal dimensions and properties of metal material surfaces and interfaces will be the key research direction of the fractal analysis of metal materials in the future.

Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their products.Automating the analysis of these reviews is vital for efficient review management.While traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior performance.This research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and satisfaction.We propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification accuracy.Comparative analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,respectively.Thesignificant contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews dataset.These advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

Antioxidant and lipoxygenase inhibitory properties of a novel flavonoid from Pistacia chinensis Bunge and its molecular docking analysis

Background:Pistacia chinensis Bunge has been traditionally used to manage various conditions,including asthma,pain,inflammation,hepatoprotection,and diabetes.The study was conducted to investigate the antioxidant and anti-lipoxygenase(LOX)properties of the isolated compound 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one from Pistacia chinensis.Methods:LOX assay and antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl(DPPH)assay were performed.Molecular docking studies were conducted using a molecular operating environment.Results:The LOX assay revealed significant inhibitory effects at 0.2µM concentration,with an IC50 value of 37.80µM.The antioxidant effect demonstrated dose-dependency across 5 to 100µg/mL concentrations,reaching 93.09%at 100µg/mL,comparable to ascorbic acid’s 95.43%effect.Molecular docking studies highlighted strong interactions with the lipoxygenase enzyme,presenting an excellent docking score of-10.98 kcal/mol.Conclusion:These findings provide valuable insights into Pistacia chinensis’chemical components and biological effects,reinforcing its traditional medicinal applications.

Optimizing Fine-Tuning in Quantized Language Models:An In-Depth Analysis of Key Variables

Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.

Fatigue Strength Analysis of Dissimilar Aluminum Alloy TIG Welds

The constant amplitude loading fatigue tests were carried out on the 6061/7075 aluminum alloy TIG fillet welded lap specimens in this study,and the weld seam cross-section hardness was measured.The experimental results show that most specimens mainly failed at the 7075 side weld toes even though the base material tensile strength of 7075 is higher than that of 6061.The maximum stress-strain concentration in the two finite element models is located at the 7075 side weld toe,which is basically consistent with the actual fracture location.The weld zone on the 7075 side experiences severe material softening,with a large gradient.However,the Vickers hardness value on the 6061 side negligibly changes and fluctuates around 70 HV.No obvious defects are found on the fatigue fracture,but a large number of secondary cracks appear.Cracks germinate from the weld toe and propagate in the direction of the plate thickness.Weld reinforcement has a serious impact on fatigue life.Fatigue life will decrease exponentially as the weld reinforcement increases under low stress.It is found that the notch stress method can give a better fatigue life prediction for TIG weldments,and the errors of the predicted results are within the range of two factors,while the prediction accuracy decreases under low stress.The equivalent structural stress method can also be used for fatigue life prediction of TIG weldments,but the errors of prediction results are within the range of three factors,and the accuracy decreases under high stress.

Low-concentration atropine(0.01%)on quantitative contrast sensitivity function in Chinese children with myopia

AIM:To investigate the effect of 0.01%low-concentration atropine(LA)on quantitative contrast sensitivity function(qCSF)in children with myopia.METHODS:This paired case-control study included 90 eyes of 58 children who were sex-,age-,and refractionmatched and equally divided into two groups:the 0.01%LA group had undergone 6mo use of daily 0.01%atropine and control group was naïve to LA.Routine ophthalmic examinations and qCSF test without refractive correction were performed.Two groups were compared in monocular and binocular qCSF parameters,including the area under logCSF,CSF acuity,and contrast sensitivity(CS)at 1.0-18.0 cycle per degree(cpd).RESULTS:In the monocular comparison,the CSF acuity of the LA group was significantly higher than that of the control group(7.58±5.51 vs 6.37±4.22 cpd,P<0.05).The subgroup analysis showed that in the 6-9y group,CSF acuity was significantly higher in the LA group than the control group(8.76±6.19 vs 6.54±4.25 cpd,P<0.05),and in the Female group,low refraction sphere group,and high refraction cylinder group,the CS at high spatial frequencies(12.0 and 18.0 cpd)were significantly higher in the LA group than in the control group(all P<0.05).In the binocular test,CSF acuity and CS at 12.0 cpd were significantly higher in the LA group than in the control group(10.95±7.00 vs 8.65±5.12 cpd;0.17±0.33 vs 0.06±0.16,respectively;both P<0.05).CONCLUSION:Use of LA may result in improved CS in children with early onset myopia.

Enhancing Solar Photovoltaic Efficiency:A Computational Fluid Dynamics Analysis

The growing need for sustainable energy solutions,driven by rising energy shortages,environmental concerns,and the depletion of conventional energy sources,has led to a significant focus on renewable energy.Solar energy,among the various renewable sources,is particularly appealing due to its abundant availability.However,the efficiency of commercial solar photovoltaic(PV)modules is hindered by several factors,notably their conversion efficiency,which averages around 19%.This efficiency can further decline to 10%–16%due to temperature increases during peak sunlight hours.This study investigates the cooling of PV modules by applying water to their front surface through Computational fluid dynamics(CFD).The study aimed to determine the optimal conditions for cooling the PV module by analyzing the interplay between water film thickness,Reynolds number,and their effects on temperature reduction and heat transfer.The CFD analysis revealed that the most effective cooling condition occurred with a 5 mm thick water film and a Reynolds number of 10.These specific parameters were found to maximize the heat transfer and temperature reduction efficiency.This finding is crucial for the development of practical and efficient cooling systems for PV modules,potentially leading to improved performance and longevity of solar panels.Alternative cooling fluids or advanced cooling techniques that might offer even better efficiency or practical benefits.

Multilevel analysis of the central-peripheral-target organ pathway:contributing to recovery after peripheral nerve injury

Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites,neglecting multilevel pathological analysis of the overall nervous system and target organs.This has led to restrictions on current therapeutic approaches.In this paper,we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective,covering the central nervous system,peripheral nervous system,and target organs.After peripheral nerve injury,the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves;changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord.The nerve will undergo axonal regeneration,activation of Schwann cells,inflammatory response,and vascular system regeneration at the injury site.Corresponding damage to target organs can occur,including skeletal muscle atrophy and sensory receptor disruption.We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury.The main current treatments are conducted passively and include physical factor rehabilitation,pharmacological treatments,cell-based therapies,and physical exercise.However,most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway.Therefore,we should further explore multilevel treatment options that produce effective,long-lasting results,perhaps requiring a combination of passive(traditional)and active(novel)treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.

Contrastive thematic analysis in research articles

Based on the theory of thematic complexity, a contrastive analysis on 2 sections selected from research articles-method and discussion, was conducted. The differences and similarities between the 2 sections in simple theme, multiple theme, textual theme, interpersonal theme, topical theme and thematic progression were analyzed and concluded. The author believes that the causes of the differences and similarities are the different genre features of the 2 sections and the academic features of the materials selected.

Contrastive Analysis:A Phonetic Case

This paper is to make a comparison on Chinese and English sounds under contrastive analysis with the consideration of that Chinese teachers of English would be better able to foresee their students' difficulty and consequently be better able to do their teaching plan in practice.

Contrastive Analysis of Conversational Implicature between English and Chinese

Implicature is a universal feature of human communication.In conversations,it is common to see examples of something unsaid but communicated.After introducing the theory of conversational impilcature and its four maxims,this paper presents English and Chinese examples to compare their conversational implicature,and finds conversational implicature exists in both Chinese and English conversation.

A Study of E-C and C-E Translation in the Light of Lexical Contrastive Analysis and Cultural Differences

The essay briefly illustrates E-C and C-E Translation strategies in the light of lexical similarity and differences of Lexical Contrastive Analysis, and explores lexical translation skills under the theory of Cultural Differences.

A Brief Analysis of Contrastive Analysis Hypothesis

This paper makes a brief analysis on the contrastive analysis hypothesis, and it is hoped to be helpful for the English teachers.

A Contrastive Analysis of Two Chinese Versions of Women in Love

There are many differences between English and Chinese in terms of grammar,form,vocabulary,tense and so on.This paper mainly discusses the differences of form,voice,and vocabulary of the two languages based on the original work and the two Chinese versions of Women in Love by applying a contrastive method.It is not a paper discussing translation methods but analyzing the linguistically differences of English and Chinese.The aim of this paper is to study the differences of the two languages in the aspect of contrastive linguistics in order to help the students to have a clear concept of what is language learning in their mind and how to learn English in an effective way.And at the same time,this paper shows the teachers some applicable means of English teaching.Therefore,the study of the differences between English and Chinese benefits both the students and the teachers.