This article delves into the controversial practice of utilizing a student’s first language (L1) as a teaching resource in second language (L2) learning environments. Initially, strategies such as code-switching/code-mixing and translanguaging were considered signs of poor linguistic ability. There was a strong push towards using only the target language in foreign language education, aiming to limit the first language’s interference and foster a deeper immersion in the new language. However, later research has shown the benefits of incorporating the first language in bilingual education and language learning processes. It’s argued that a student’s knowledge in their native language can actually support their comprehension of a second language, suggesting that transferring certain linguistic or conceptual knowledge from L1 to L2 can be advantageous. This perspective encourages the strategic use of this knowledge transfer in teaching methods. Moreover, the text points to positive results from various studies on the positive impact of L1 usage in L2 classrooms. These insights pave the way for further exploration into the application of the first language in adult English as a Second Language (ESL)/English as a Foreign Language (EFL) education, particularly regarding providing corrective feedback.

This study sought an innovative quality management framework for Chinese Prefabricated Buildings (PB) projects. The framework combines TQM, QSP, Reconstruction Engineering, Six Sigma (6Σ), Quality Cost Management, and Quality Diagnosis Theories. A quantitative assessment of a representative sample of Chinese PB projects and advanced statistical analysis using Structural Equation Modeling supported the framework, indicating an excellent model fit (CFI = 0.92, TLI = 0.90, RMSEA = 0.06). The study significantly advances quality management and industrialized building techniques, but it also emphasizes the necessity for ongoing research, innovation, and information exchange to address the changing problems and opportunities in this dynamic area. In addition, this study’s findings and recommendations can help construction stakeholders improve quality performance, reduce construction workload and cost, minimize defects, boost customer satisfaction, boost productivity and efficiency in PB projects, and boost the Chinese construction industry’s growth and competitiveness.

Low enrollment intention threatens the funding pools of rural insurance schemes in developing countries. The purpose of this study is to investigate how social capital enhances the enrollment of health insurance among rural middle-aged and elderly. We propose that social capital directly increases health insurance enrollment, while indirectly influences health insurance through health risk avoidance. We used data from the China Health and Retirement Longitudinal Study (wave 4) dating the year of 2018, instrumental variable estimation was introduced to deal with the endogeneity problem, and the mediation analysis was used to examine the mechanism of social capital on insurance enrollment. The results show that social capital is positively related to social health insurance enrollment, and the relationship between social capital and social health insurance enrollment is mediated by health risk avoidance.

Village Finance System (SISKEUDES) is a village financial reporting application policy. The application of the SISKEUDES is as a form of accountability to be accessible and known by the community. However, communication problems, resources, knowledge and limited internet networks in many regions still cause problems in reporting process. The research used a qualitative descriptive method by conducting in-depth interviews and document analysis of Mamala Negeri SISKEUDES. The policy implementation model according to George Edward III was used as an analysis tool. This research was designed to be carried out for 5 (five) months to explore various data from various information regarding this research problem. The research findings are that the provision of facilities and infrastructure for Mamala Negeri supporting human resources is still limited, making it difficult to apply the SISKEUDES 2.0 application. Besides, the village also needs more systematic transaction planning, which allows each transaction to be recorded completely both planning and realization.

Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.

The present study demonstrates the fabrication of heterogeneous ternary composite photocatalysts consisting of TiO₂, kaolinite, and cement (TKCe),which is essential to overcome the practical barriers that are inherent to currently available photocatalysts. TKCe is prepared via a cost-effective method, which involves mechanical compression and thermal activation as major fabrication steps. The clay-cement ratio primarily determines TKCe mechanical strength and photocatalytic efficiency, where TKCe with the optimum clay-cement ratio, which is 1:1, results in a uniform matrix with fewer surface defects. The composites that have a clay-cement ratio below or above the optimum ratio account for comparatively low mechanical strength and photocatalytic activity due to inhomogeneous surfaces with more defects, including particle agglomeration and cracks. The TKCe mechanical strength comes mainly from clay-TiO₂ interactions and TiO₂-cement interactions. TiO₂-cement interactions result in CaTiO₃ formation, which significantly increases matrix interactions; however, the maximum composite performance is observed at the optimum titanate level; anything above or below this level deteriorates composite performance. Over 90% degradation rates are characteristic of all TKCe, which follow pseudo-first-order kinetics in methylene blue decontamination. The highest rate constant is observed with TKCe 1-1, which is 1.57 h⁻¹ and is the highest among all the binary composite photocatalysts that were fabricated previously. The TKCe 1-1 accounts for the highest mechanical strength, which is 6.97 MPa, while the lowest is observed with TKCe 3-1, indicating that the clay-cement ratio has a direct relation to composite strength. TKCe is a potential photocatalyst that can be obtained in variable sizes and shapes, complying with real industrial wastewater treatment requirements.