Murun Yang^1,*, Fuxue Li²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2061-2075, 2025, DOI:10.32604/cmc.2024.058248 - 17 February 2025

Abstract Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lack formality. In this paper, we propose how to improve NMT formality with large language models (LLMs), which combines the style transfer and evaluation capabilities of an LLM and the high-quality translation generation ability of NMT models to improve NMT formality. The proposed method (namely INMTF) encompasses two approaches. The first involves a revision approach using an LLM to revise the NMT-generated translation, ensuring a… More >

AUGUSTO FUSCO¹, STEFANO BONOMI^2,*, LUCA PADUA^1,2

BIOCELL, Vol.49, No.1, pp. 1-5, 2025, DOI:10.32604/biocell.2024.058418 - 24 January 2025

Abstract Connective tissue is a dynamic structure that reacts to environmental cues to maintain homeostasis, including mechanical properties. Mechanical load influences extracellular matrix (ECM)—cell interactions and modulates cellular behavior. Mechano-regulation processes involve matrix modification and cell activation to preserve tissue function. The ECM remodeling is crucial for force transmission. Cytoskeleton components are involved in force sensing and transmission, affecting cellular adhesion, motility, and gene expression. Proper mechanical loading helps to maintain tissue health, while imbalances may lead to pathological processes. Active and passive movement, including manual mobilization, improves connective tissue elasticity, promotes ECM-cell homeostasis, and More > Graphic Abstract

Yuriy E. Kolupaev^1,2,*, Tetiana O. Yastreb^1,*, Alexander P. Dmitriev³

Phyton-International Journal of Experimental Botany, Vol.93, No.12, pp. 3151-3195, 2024, DOI:10.32604/phyton.2024.057922 - 31 December 2024

Abstract Stomatal closure, which serves to limit water loss, represents one of the most rapid and critical reactions of plants, occurring not only in response to drought but also to a range of other stressors, including salinity, extreme temperatures, heavy metals, gaseous toxicants, and pathogen infection. ABA is considered to be the main regulator of stomatal movements in plants under abiotic stress. In the last two decades, however, the list of plant hormones and other physiologically active substances that affect stomatal status has expanded considerably. It is believed that stomata are regulated by a complex multicomponent… More >

Peichao Xie^1,#, Kexu Chen^1,#, Yuxuan Ji¹, Qi Wang¹, Kaiyun Li^1,*, Fanlu Jia¹, Ting Peng²

International Journal of Mental Health Promotion, Vol.26, No.11, pp. 887-895, 2024, DOI:10.32604/ijmhp.2024.056305 - 28 November 2024

Abstract Background: Coping self-efficacy can help individuals mitigate the adverse emotional impacts of stress, anxiety, and other negative emotions, and it also influences individuals’ academic performance, including school adjustment and academic burnout. It is an important factor affecting the mental health of adolescents. However, there is no measurement tool specifically designed for adolescent populations in China. Therefore, the purpose of this study is to assess the applicability of the Coping Self-Efficacy Scale (CSES) among Chinese adolescents. Methods: In September 2023, this study collected data through online questionnaires and ultimately conducted item analysis, exploratory factor analysis, confirmatory… More >

Muhammad Naeem Ul Hassan^1,2, Zhengtao Yu^1,2,*, Jian Wang^1,2, Ying Li^1,2, Shengxiang Gao^1,2, Shuwan Yang^1,2, Cunli Mao^1,2

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 951-969, 2024, DOI:10.32604/cmc.2024.054673 - 15 October 2024

Abstract Thanks to the strong representation capability of pre-trained language models, supervised machine translation models have achieved outstanding performance. However, the performances of these models drop sharply when the scale of the parallel training corpus is limited. Considering the pre-trained language model has a strong ability for monolingual representation, it is the key challenge for machine translation to construct the in-depth relationship between the source and target language by injecting the lexical and syntactic information into pre-trained language models. To alleviate the dependence on the parallel corpus, we propose a Linguistics Knowledge-Driven Multi-Task (LKMT) approach to… More >

Liqing Wang^*, Yiheng Xiao

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 619-631, 2024, DOI:10.32604/iasc.2024.052971 - 06 September 2024

Abstract Neural Machine Translation is one of the key research directions in Natural Language Processing. However, limited by the scale and quality of parallel corpus, the translation quality of low-resource Neural Machine Translation has always been unsatisfactory. When Reinforcement Learning from Human Feedback (RLHF) is applied to low-resource machine translation, commonly encountered issues of substandard preference data quality and the higher cost associated with manual feedback data. Therefore, a more cost-effective method for obtaining feedback data is proposed. At first, optimizing the quality of preference data through the prompt engineering of the Large Language Model (LLM), More >

Qi Wang^1,#, Qi Wang^1,#, Yuxuan Ji¹, Kexu Chen¹, Kaiyun Li^1,*, Fanlu Jia¹, Ting Peng²

International Journal of Mental Health Promotion, Vol.26, No.8, pp. 671-677, 2024, DOI:10.32604/ijmhp.2024.053127 - 30 August 2024

Abstract Background: In this study, the Universal Mental Health Literacy Scale for Adolescents (UMHL-A) was revised and tested for its reliability and validity in Chinese middle school students, thus establishing a useful tool for assessing the mental health of individuals in this occupation. Methods: Our sample comprised 1208 junior high school students (58.85% male), aged between 11 and 15 years old. The Chinese version of the scale includes a mental health attitude subscale and mental health knowledge subscale, including attitudes towards seeking help, attitudes related to stigma, general mental health knowledge, and knowledge about specific mental… More >

Khushal Das¹, Fazeel Abid², Jawad Rasheed^3,4,*, Kamlish⁵, Tunc Asuroglu^6,*, Shtwai Alsubai⁷, Safeeullah Soomro⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 689-711, 2024, DOI:10.32604/cmes.2024.051335 - 20 August 2024

Abstract Deaf people or people facing hearing issues can communicate using sign language (SL), a visual language. Many works based on rich source language have been proposed; however, the work using poor resource language is still lacking. Unlike other SLs, the visuals of the Urdu Language are different. This study presents a novel approach to translating Urdu sign language (UrSL) using the UrSL-CNN model, a convolutional neural network (CNN) architecture specifically designed for this purpose. Unlike existing works that primarily focus on languages with rich resources, this study addresses the challenge of translating a sign language… More >

Tianzhe Jiao, Chaopeng Guo, Xiaoyue Feng, Yuming Chen, Jie Song^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1-35, 2024, DOI:10.32604/cmc.2024.053204 - 18 July 2024

Abstract Multi-modal fusion technology gradually become a fundamental task in many fields, such as autonomous driving, smart healthcare, sentiment analysis, and human-computer interaction. It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities. Under complex scenes, multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions. However, achieving outstanding performance is challenging because of equipment performance limitations, missing information, and data noise. This paper comprehensively reviews existing methods based on multi-modal fusion techniques and completes a detailed and in-depth analysis.… More >

Siyuan Bi¹, Junfeng Yuan^1,2, Lin Luo^1,2,3,*

International Journal of Mental Health Promotion, Vol.26, No.1, pp. 51-60, 2024, DOI:10.32604/ijmhp.2023.041614 - 05 February 2024

Abstract Purpose: The core component theory of addiction behavior provides a multidimensional theoretical model for measuring social networking addiction. Based on this theoretical model, the Social Networking Addiction Scale (SNAS) was developed. The aim of this study was to test the psychometric properties of the Chinese version of the SNAS (SNAS-C). Methods: This study used a sample of 3383 Chinese university students to conduct confirmatory factor analysis (CFA) to explore the structural validity of the SNAS-C. This study examined the Pearson correlations between the six subscales of the SNAS-C (i.e., salience, mood modification, tolerance, withdrawal symptoms,… More >