High Quality Research Articles

Towards Complex-query Referring Image Segmentation: A Novel Benchmark

Referring Image Segmentation (RIS) has been extensively studied over the past decade, leading to the development of advanced algorithms. However, there has been a lack of research investigating how existing algorithms should be benchmarked with complex language queries, which include more informative descriptions of surrounding objects and backgrounds ( e.g. “the black car.” vs. “the black car is parking on the road and beside the bus.” ). Given the significant improvement in the semantic understanding capability of large pre-trained models, it is crucial to take a step further in RIS by incorporating complex language that resembles real-world applications. To close this gap, building upon the existing RefCOCO and Visual Genome datasets, we propose a new RIS benchmark with complex queries, namely RIS-CQ . The RIS-CQ dataset is of high quality and large scale, which challenges the existing RIS with enriched, specific and informative queries, and enables a more realistic scenario of RIS research. Besides, we present a nichetargeting method to better task the RIS-CQ, called dual-modality graph alignment model ( DuMoGa ), which outperforms a series of RIS methods. To provide a valuable foundation for future advancements in the field of RIS with complex queries, we release the datasets, preprocessing and synthetic scripts, and the algorithm implementations at https://github.com/lili0415/DuMoGa .

Overseas Smart City Short Course of Airlangga and International Community Service: The Role of Science and Technology Towards Smart City

The smart city concept emerged as an alternative solution to overcome the negative impacts of urbanization in big cities, based on the concept of sustainable development. A smart city is a city that can utilize human resources, social capital, and modern telecommunications infrastructure to achieve sustainable economic growth and high quality of life through wise management of resources by the government based on community participation. Jombang has not been managed optimally due to a lack of digital exposure and promotion. Based on these problems, the Faculty of Science and Technology, Airlangga University will organize an international community service program in Jombang to introduce culture and tourism in Jombang to the world through international students and transfer knowledge regarding the application of smart tourism to increase tourism promotion in Jombang. This community service program was carried out together with international students who will first be given an introduction and knowledge of the smart city concept and its application at Universitas Airlangga and the City of Surabaya through a short course. Then the students will be invited to visit the tourist and cultural village in Jombang to transfer knowledge to the local community regarding the application of the smart tourism concept to promote tourism potential in Jombang.

Forecasting techniques based on the use of time series models for forecasting expenditures on social protection and social security

In the context of a military conflict, it is growing the number of people who are in difficult life circumstances and need state assistance. Therefore, the issue of forecasting spending on social protection and social security is becoming increasingly relevant. The aim of the paper is to consider modern methods of analyzing and forecasting time series and propose their application to determine the need for spending on social protection and social security. The article provides a brief overview of regression models, their advantages and features of use for analyzing and predicting nonlinear and linear processes described by time series of statistical indicators used in the population social protection system. The paper offers a combination of predictions that can be made based on locally linear and nonlinear models, such as threshold autoregression (TAR) and exponential autoregression (ExAR), which have known quality characteristics. This approach primarily concerns time series that contain data on structural changes in the analyzed processes. Using combined predictions, it is possible to estimate the contribution of each component for both different and selected time points using variable weights. To build forecasts, depending on the scenario of distribution of social protection and social security expenditures, it is proposed to use Bayes networks. Their advantages are flexibility, high quality of results, the possibility of structural and parametric optimization and adaptation to new data and conditions, support for quality assessment by appropriate sets of statistical criteria, which makes it possible to analyze thoroughly intermediate and final results. It helps to increase the adequacy of models and the high quality of the final result of their application.

Bird flock effect-based dynamic community detection: Unravelling network patterns over time

Community structure is essential for topological analysis, function study, and pattern detection in complex networks. As establishing community structure in a dynamic network is difficult, it gives a unique perspective in many interdisciplinary fields. Many researchers have explored the challenging technique that requires parameter specification and optimization for quality result. This study proposed an eco-system conceptual framework based on bird flock effect. Relying on the natural law of rule, we designed a dynamic community detection named DCDBFE. The design of algorithm was based on the three basic rules of bird flock: separation, alignment, and cohesion phase. Then, we provide an explanation of similarity measure used between vertices to identify the modules attraction. DCDBFE employs an incremental community detection approach to repeatedly detect communities in each network snapshot or time step. The contributions are obtained for high quality community detected, free-parameter and well stability. To test its performance, extensive experiments were conducted on both synthetic and real-world networks. The outcomes demonstrate that our approach can effectively find satisfaction from each time step by comparison with the other well-known algorithms.

Professional Development of Teachers in the Context of STEM Education as a Relevant Psychological and Pedagogical Issue

The article analyzes scientific research related to the issues of professional development of teachers in the context of STEM education, which is determined as a prerequisite for improving the quality of education. It is determined that the professional development of teachers in the context of STEM education involves mastering the knowledge and skills necessary for the implementation of STEM programs and methods, adaptability and cognitive flexibility, professional self-improvement, etc. This process should be systematic and continuous and requires training and professional development during courses, scientific and practical seminars, conferences, workshops, summer schools, and other educational events. The article presents the results of a study that highlights the psychological and pedagogical aspects of teachers' professional development in the context of STEM education, and identifies a number of problems with professional support for educators. The authors of the publication substantiate the relevance of teacher training to ensure the competitiveness and high quality of the educational process. The steps for effective professional pedagogical development in modern conditions are highlighted and a list of resources for the professional development of the teaching community in the context of STEM education is proposed.

Lipid nanoparticles as adjuvant of norovirus VLP vaccine augment cellular and humoral immune responses in a TLR9- and type I IFN-dependent pathway.

Norovirus (NoV) virus-like particles (VLPs) adjuvanted with aluminum hydroxide (Alum) are common vaccine candidates in clinical studies. Alum adjuvants usually inefficiently assist recombinant proteins to induce cellular immune responses. Thus, novel adjuvants are required to develop NoV vaccines that could induce both efficient humoral and robust cellular immune responses. Lipid nanoparticles (LNPs) are well-known mRNA delivery vehicles. Increasing evidence suggests that LNPs may have intrinsic adjuvant activity and can be used as adjuvants for recombinant protein vaccines; however, the underlying mechanism remains poorly understood. In this study, we compared the adjuvant effect of LNPs and Alum for a bivalent GI.1/GII.4 NoV VLP vaccine. Compared with Alum, LNP-adjuvanted vaccines induced earlier production of binding, blocking, and neutralizing antibodies and promoted a more balanced IgG2a/IgG1 ratio. It is crucial that LNP-adjuvanted vaccines induced stronger Th1-type cytokine-producing CD4+ T cell and CD8+ T cell responses than Alum. The adjuvant activity of LNPs depended on the ionizable lipid components. Mechanistically, LNPs activated innate immune responses in a type I IFN-dependent manner and were partially dependent on Toll-like receptor (TLR) 9, thus affecting the adaptive immune responses of the vaccine. This conclusion was supported by RNA-seq analysis and in vitro cell experiments and by the deeply blunted T cell responses in IFNαR1-/- mice immunized with LNP-adjuvanted vaccines. This study not only identified LNPs as a high quality adjuvant for NoV VLP vaccines, but also clarified the underlying mechanism of LNPs as a potent immunostimulatory component for improving protein subunit vaccines.IMPORTANCEWith the rapid development of mRNA vaccines, recurrent studies show that lipid nanoparticles (LNPs) have adjuvant activity. However, the mechanism of its adjuvant effect in protein vaccines remains unknown. In this study, we found that the LNP-adjuvanted norovirus bivalent virus-like particle vaccines led to durable antibody responses as well as Th1-type cytokine-producing CD4+ T cell and CD8+ T cell responses, which exceeded the efficiency of the conventional adjuvant aluminum hydroxide. Mechanistically, LNPs activated innate immune responses in a type I IFN-dependent manner and were partially dependent on Toll-like receptor 9, thus affecting the adaptive immune responses of the vaccine. This work unveils that LNPs as a potent immunostimulatory component may be ideal for generating CD8+ T cell and B cell responses for recombinant protein vaccines.

Quality of clinical practice guidelines for frozen shoulder: a systematic review.

Perform a systematic critical appraisal of current clinical practice guidelines (CPGs) for frozen shoulder. Systematic review of CPGs (PROSPERO number CRD42022368775). Inclusion criteria- CPGs written in English providing guidance on the evaluation and or treatment for frozen shoulder, traumatic injury and neurologic CPGs were excluded.Relevant studies were assessed for inclusion and selected studies were identified from PubMed, EMBASE, Scopus and CINAHL databases. The search strategy was developed by a biomedical librarian, performed on October 9, 2024. Data were extracted from the selected CPGs and underwent quality assessment using the Appraisal of Guidelines for Research and Evaluation (AGREE) II. The search resulted in 38,428 studies and 2 CPGs were retained for appraisal. The mean overall AGREE II score was 75% (SD = 5.7). Lowest mean scores were found in the applicability (27% SD = 24.0) and editorial independence (48% SD = 14.1) domains. The highest domain scores were found in scope and purpose (92% SD = 7.8) and clarity and presentation (79% SD = 9.9). One CPG was rated as low quality based on a priori criteria and ultimately one higher quality CPG was recommended. Given the advances in research that have developed over the last decade pertaining to the evaluation and treatment of frozen shoulder there is a critical need for an up to date, evidence informed, high quality CPG in order to identify gaps in our knowledge that the global research community should address.

Product development of sweets chocolate cube using value engineering method

Cocoa is one of the plantation commodities that has an important role in economic activities as a foreign exchange earner. Indonesia is ranked first in Asia with the highest consumption of chocolate, which is 7.3 kg per capita. Chocolates are seen by consumers as a comfort food or stress relief. For consumers who buy chocolate for their own consumption, consuming chocolate in a small size causes consumers to act more impulsively to consume larger quantities. Consumption of chocolate in small sizes (one bite size) gives the impression that the product has a premium image and higher quality. The purpose of this study was to identify the characteristics of chocolate that influence the decisions of product purchasing and to develop one-bite-sized chocolate cubes using the value engineering method. The study used 433 respondents with the criteria of chocolate consumers aged 15-40 years. Based on the research, chocolate bars and molded chocolate are the types of chocolate most often consumed with a frequency of 2-3 times a month. Consumers have the highest complaints when consuming chocolate, namely melted chocolate which makes hands dirty due to its big size and is too hard to bite. Based on the consumer needs analysis, consumers want the development of milk chocolate cube candy with nuts filling. Four alternative cube milk chocolate concepts with a cocoa mass percentage of 50% were prepared using 2 variations in the number of cocoa beans and cocoa fat (40%:10% and 35%:15%) and 2 variations of nuts (almonds and cashews). Based on the hedonic test results, milk chocolate cubes with a formula of 10% cocoa butter and cashew nut filling had the highest product value of 0.013 compared to other formulas. Chocolate color and ease of eating are the attribute weights with the highest weights. It can be concluded that the milk chocolate cube formula is 40% cocoa beans; 10% cocoa butter, cashew nut filling is the selected formula with a production cost of IDR 985.00/g.

Warming Leads to Lower Rice Quality in East Asia

AbstractRice quality, which is intricately connected to market value and human nutrition, is sensitive to weather conditions. However, the relative importance of the different climatic factors is poorly understood, and the impact of climate change on rice quality has been little studied on a large scale. Here, using more than 35 years of rice quality data, we present the first effort to determine the key climate variables driving rice quality in China and Japan. Results show a significant decline in high quality rice rate (HRR, an indicator of rice quality), mainly driven by warm nighttime temperatures when they exceed a critical threshold estimated at 18°C and 12°C in China and Japan, respectively. Climate projections suggest a continuing decreasing trend in HRR under moderate and high emission scenarios by 2100. These findings emphasize the importance of breeding new heat tolerant cultivars to maintain stable rice quality in the future.

Tailored Growth of Transition Metal Dichalcogenides' Monolayers by Chemical Vapor Deposition.

Here, results on the tailored growth of monolayers (MLs) of transition metal dichalcogenides (TMDs) are presented using chemical vapor deposition (CVD) techniques. To enable reproducible growth, the flow of chalcogen precursors is controlled by Knudsen cells providing an advantage in comparison to the commonly used open crucible techniques. It is demonstrated that TMD MLs can be grown by CVD on large scale with structural, and therefore electronic, photonic and optoelectronic properties similar to TMD MLs are obtained by exfoliating bulk crystals. It is shown that besides the growth of the "standard" TMD MLs also the growth of MLs that are not available by the exfoliation is possible including examples like lateral TMD1-TMD2 ML heterostructures and Janus TMDs. Moreover, the CVD technique enables the growth of TMD MLs on various 3D substrates on large scale and with high quality. The intrinsic properties of the grown MLs are analyzed by complementary microscopy and spectroscopy techniques down to the nanoscale with a particular focus on the influence of structural defects. Their functional properties are studied in devices including field-effect transistors, photodetectors, wave guides and excitonic diodes. Finally, an outlook of the developed methodology in both applied and fundamental research is given.

A Theoretical Study of the Impact of ESG Performance on the Cost of Corporate Debt Financing

With the increasing global attention to environmental protection, social responsibility and corporate governance (ESG) issues, the ESG performance of enterprises has become an important indicator for creditors to assess their non-financial performance. This paper analyzes the development status quo of ESG ratings in China and the situation of corporate debt financing, combines information asymmetry theory, principal-agent theory, contract theory and other in-depth discussion of the impact of corporate ESG performance on its debt financing costs, so as to put forward targeted recommendations to further enhance the attention of China's listed enterprises to ESG ratings, as well as to reduce the cost of corporate debt financing measures put forward to lay a more solid theoretical foundation, to help enterprises better achieve high-quality and high quality of corporate governance. It lays a more solid theoretical foundation for further improving China's listed enterprises' attention to ESG ratings and proposing measures to reduce the cost of corporate debt financing, and helps enterprises better realize high quality and sustainable development.

Chromosome-level assembly for the complex genome of land hermit crab Coenobita brevimanus

Land hermit crabs are a group of shell-carrying crabs that have evolved remarkable terrestrial adaptations in behavior, morphology, physiology, and biochemistry. However, the genetic mechanisms underlying these adaptations remain unclear. In addition, usually it is very difficult to get good genome assemblies for crustaceans. In this study, we managed to assemble the first chromosome-level genome for a land hermit crab (Coenobita brevimanus) with careful manual curation. The final assembly spans 4.74 Gb, with the contig N50 of 1.75 Mb and scaffold N50 of 42.95 Mb, encompassing 117 chromosomes that account for 96.54% of the genome. The evaluations including genome BUSCO (95.26%), Merqury qv (35.88) and the mapping ratio of pair-end short reads (99.48%) showed the high-continuity of C. brevimanus genome assembly, making it the genome with the highest quality in crustaceans with genome size bigger than 3 Gb. The availability of this chromosome-scale genome of crustaceans represents a valuable resource for the land hermit crab, which represents an independent water-to-land transition evolutionary event in the animal kingdom.

An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication

Semantic communication (SC) is a communication paradigm that has gained significant attention, as it offers a potential solution to move beyond Shannon’s formulation in bandwidth-limited communication channels by delivering the semantic meaning of the message rather than its exact form. In this paper, we propose an autoencoder-based SC system for transmitting images between two machines over error-prone channels to support emerging applications such as VIoT, XR, M2M, and M2H communications. The proposed autoencoder architecture, with a semantically modeled encoder and decoder, transmits image data as a reduced-dimension vector (latent vector) through an error-prone channel. The decoder then reconstructs the image to determine its M2M implications. The autoencoder is trained for different noise levels under various channel conditions, and both image quality and classification accuracy are used to evaluate the system’s efficacy. A CNN image classifier measures accuracy, as no image quality metric is available for SC yet. The simulation results show that all proposed autoencoders maintain high image quality and classification accuracy at high SNRs, while the autoencoder trained with zero noise underperforms other trained autoencoders at moderate SNRs. The results further indicate that all other proposed autoencoders trained under different noise levels are highly robust against channel impairments. We compare the proposed system against a comparable JPEG transmission system, and results reveal that the proposed system outperforms the JPEG system in compression efficiency by up to 50% and in received image quality with an image coding gain of up to 17 dB.

Evaluating process parameters of SLS part of polyamide-12 recycled powder using RSM

Abstract Selective laser sintering (SLS) is a highly flexible rapid prototyping (RP) technology which can use a wide range of powdered materials to generate high quality complex geometries directly from digital models. The SLS process uses a laser to sinter selected areas of powdered materials to produce solid objects layer by layer, according to the 2D data obtained from CT scan or MRI imaging techniques. This paper presents research work for evaluating the effect of process parameters on impact toughness and surface roughness of the part manufactured on Farsoon SS402P SLS from recycled ageing FS 3300PA which is PA-12 based powder and standard operating parameters using Response Surface Methodology (RSM) design of experiments. Impact tests have been performed as per the ASTM D6110 standard using recommendations from ISO 179-2. According to the results, effect of process parameters are successfully analyzed by employing impact toughness and surface roughness tests. Based on the results of the ANOVA analysis, impact toughness increases with increasing laser power (4.467 j/cm2 at 72.5 watts). Similarly, it is also clear from the results that the region with the lowest laser power, high layer thickness, and 90-degree orientation had the lowest surface roughness (3.46 μm).

PURCHASE BEHAVIOUR OF PADDY FARMERS TOWARDS HYBRID PADDY SEEDS IN DHARAMPUR TALUKA OF VALSAD DISTRICT

Seed is the initial point of cropping cycle. Seeds play a vital role in agriculture and the seeds industry is rapidly growing due to the rising demand for increased food production and high quality seeds. The primary aim of this study was to explore the purchase behaviour of farmers regarding hybrid paddy seeds. To fulfill the objective descriptive research design was adopted with Multi-stage sampling method. Initially, Dharampur taluka was purposefully selected from 6 talukas in Valsad district. In the next stage, 10 villages were randomly selected within Dharampur taluka. Finally, 10 paddy farmers were conveniently selected from each village, resulting in a total sample size of 100 farmers. The structured interview schedule was used to collect the primary data from farmers. The collected data was analyzed with the help of statistical tools as simple percentage analysis, weighted average mean, rating scale, etc. The study found that most farmers (56%) were belonged to 41 to 50 year age group, primarily had secondary education (45%) and commonly combined farming with animal husbandry (58%). Most of the farmers (56%) owned small, fragmented land under 1 hectare. Hybrid paddy seed purchase was influenced by past experience and product benefits. KEY WORDS: Socio-economic profile, Purchase behaviour, Paddy farmers, Hybrid paddy seeds, Valsad district

Nurse led models of care for outpatient substance use disorder treatment: A scoping review.

To map key characteristics and describe nurse led models of care for the treatment of persons with substance use disorders (SUDs) in the outpatient setting. A scoping review. Conducted in accordance with the JBI methodology. The PRISMA-ScR checklist was used. Pubmed, CINAHL Complete (EBSCOhost), Cochrane Library, APA PsycNet and Scopus were searched from 1999 to May 2022 and updated on 28 November 2023. A handsearch and a grey literature search was conducted. Title and abstract screening was performed on 774 articles resulting in 88 articles for full text screening. Full text screening yielded 13 articles that met inclusion criteria. Existing nurse-led models of care for SUDS are scarce and limited in scope, with the majority focused on treating opioid use disorder. Additional research is needed to develop, test, and implement efficacious nurse-led models of care for the treatment and management of SUDs. Nurse led models of care have demonstrated their efficacy and quality in the management of other chronic diseases. As we move forward with innovative solutions for individuals with addiction, nurse led models of care can be a mechanism to deliver high quality, evidence-based care for SUDs. SUDs are chronic diseases that impact individuals, families, and communities. SUDs require a biopsychosocial approach to treatment. Globally, nurses are well positioned to provide high quality care to mitigate the impact of SUDs. This scoping review mapped the extant literature on nurse led models of care for substance use disorder treatment in the outpatient setting finding that additional research is needed to develop, test and implement evidence-based interventions to care for individuals, families, and communities experiencing SUDs. PRISMA checklist for scoping reviews. No patient or public contribution were part of this study. Open Science Framework accessible at: https://doi.org/10.17605/OSF.IO/NSW7V.

Dynamic Multiobjective Optimization Based on Multi-Environment Knowledge Selection and Transfer

Background: Dynamic multiobjective optimization problems (DMOPs) involve multiple conflicting and time-varying objectives, and dynamic multiobjective algorithms (DMOAs) aim to find Pareto optima that are closer to the real one in the new environment as soon as possible. In particular, the introduction of transfer learning in DMOAs has led to good results in solving DMOPs. However, the selection of valuable historical knowledge and the mitigation of negative transfer remain important problems in existing transfer learning-based DMOAs. Method: A DMOA based on multi-environment knowledge selection and transfer (MST-DMOA) is proposed in this article. First, by clustering historical Pareto optima, some representative solutions that can reflect the main evolutionary information are selected as knowledge of the environment. Second, the similarity between the historical and current environments is evaluated, and then the knowledge of multiple similar environments is selected as valuable historical knowledge to construct the source domain. Third, solutions with high quality in the new environment are obtained to form the target domain, which can better help historical knowledge to adapt to the current environment, thus effectively alleviating negative transfer. Conclusions: We compare the proposed MST-DMOA with five state-of-the-art DMOAs on fourteen benchmark test problems, and the experimental results verify the excellent performance of MST-DMOA in solving DMOPs.

Integrating VGG 19 U‐Net for Breast Thermogram Segmentation and Hybrid Enhancement With Optimized Classifier Selection: A Novel Approach to Breast Cancer Diagnosis

ABSTRACTEarly diagnosis of breast cancer is essential for improving patient survival rates and reducing treatment costs. Despite breast thermogram images having high quality, doctors in developing countries often struggle with early diagnosis due to difficulties in interpreting subtle details. Implementing a Computer‐Aided Diagnosis (CAD) system can assist doctors in accurately analyzing these details. This article presents an innovative approach to breast cancer diagnosis using thermal images. The proposed method enhances the quality and clarity of relevant features while preserving sharp and curved edges through U‐Net‐based segmentation for automatic selection of the ROI, advanced hybrid image enhancement techniques, and a machine learning classifier. Subjective analysis compares the processed images with five conventional enhancement techniques, demonstrating the efficiency of the proposed method. The quantitative analysis further validates the effectiveness of the proposed method against five conventional methods using four quality measures. The proposed method achieves superior performance with PSNR of 15.27 for normal and 14.31 for malignant images, AMBE of 6.594 for normal and 7.46 for malignant images, SSIM of 0.829 for normal and 0.80 for malignant images, and DSSIM of 0.084 for normal and 0.14 for malignant images. The classification phase evaluates four classifiers using 13 features from three categories. The Random Forest (RF) classifier with Discrete Wavelet Transform (DWT) based features initially outperformed other classifier features but had limited performance, with accuracy, sensitivity and specificity of 81.8%, 88.8%, and 91%, respectively. To improve this, three categories of features were normalized and converted into two principal components using Principal Component Analysis (PCA) to train the RF classifier, which then showed superior performance with 97.7% accuracy, 96.5% sensitivity, and 98.2% specificity. The dataset utilized in this article is obtained from the Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam, India. The entire proposed model is implemented in a Jupyter notebook.

A Review of Pluralistic Image Completion

Abstract. Image inpainting has always been a major problem in the field of scientific research. How to fill in the damaged area of the image to make the image look realistic is the main goal of the image inpainting task. In recent years, with the rapid development of machine learning, the researchers started using machine learning to assist in image inpainting. The most representative one is image inpainting based on generative models. The classic idea of restoration is to generate a restoration result with the highest quality and the most realistic appearance. However, as long as the result is reasonable, image inpainting allows for multiple restoration results. For this reason, the field of pluralistic image completion was born. This paper introduces pluralistic image completion and reviews past research in this field. This paper divides pluralistic image completion methods into three categories: VAE-based, GAN-based, and Transformer-based, and gives examples of various representative methods and the latest research in this field. This paper also introduces the available datasets and evaluation metrics. A discussion is given based on the performance of these three categories of methods and the prospects for the development of the field of pluralistic image completion. This review can serve as a reference for researchers in the field of image inpainting. It provides a table of datasets that can be used to evaluate and compare the listed methods and looks forward to possible developments and applications in this field in the future.

Natural variation of WBR7 confers rice high yield and quality by modulating sucrose supply in sink organs.

Grain chalkiness is an undesirable trait that negatively regulates grain yield and quality in rice. However, the regulatory mechanism underlying chalkiness is complex and remains unclear. We identified a positive regulator of white-belly rate (WBR). The WBR7 gene encodes sucrose synthase 3 (SUS3). A weak functional allele of WBR7 is beneficial in increasing grain yield and quality. During the domestication of indica rice, a functional G/A variation in the coding region of WBR7 resulted in an E541K amino acid substitution in the GT-4 glycosyltransferase domain, leading to a significant decrease in decomposition activity of WBR7A (allele in cultivar Jin23B) compared with WBR7G (allele in cultivar Beilu130). The NIL(J23B) and knockout line NIL(BL130)KO exhibited lower WBR7 decomposition activity than that of NIL(BL130) and NIL(J23B)COM, resulting in less sucrose decomposition and metabolism in the conducting organs. This caused more sucrose transportation to the endosperm, enhancing the synthesis of storage components in the endosperm and leading to decreased WBR. More sucrose was also transported to the anthers, providing sufficient substrate and energy supply for pollen maturation and germination, ultimately leading to an increase rate of seed setting and increased grain yield. Our findings elucidate a mechanism for enhancing rice yield and quality by modulating sucrose metabolism and allocation, and provides a valuable allele for improved rice quality.