Natural Language Processing Journal最新文献_第3页

Leveraging large language models to predict unplanned ICU readmissions from electronic health records 利用大型语言模型从电子健康记录中预测非计划的ICU再入院情况

Natural Language Processing Journal Pub Date : 2025-12-01 Epub Date: 2025-10-06 DOI: 10.1016/j.nlp.2025.100182

Hoda Helmy , Ahmed Ibrahim , Maryam Arabi , Aamenah Sattar , Ahmed Serag

{"title":"Leveraging large language models to predict unplanned ICU readmissions from electronic health records","authors":"Hoda Helmy , Ahmed Ibrahim , Maryam Arabi , Aamenah Sattar , Ahmed Serag","doi":"10.1016/j.nlp.2025.100182","DOIUrl":"10.1016/j.nlp.2025.100182","url":null,"abstract":"<div><div>Unplanned readmissions to Intensive Care Units (ICUs) are associated with increased mortality, higher healthcare costs, and significant strain on limited medical resources. Accurate prediction of readmissions can improve patient outcomes and optimize resource allocation. This study investigates the use of large language models (LLMs) for ICU readmission prediction through both classification and explanation tasks. We compare a general-purpose model (Gemma2B) and a medical-domain model (Apollo2B), both open-source and fine-tuned for this task. The models were evaluated on their ability to classify readmission cases and generate clinically meaningful justifications. Gemma2B outperformed Apollo2B, achieving an AUC of 0.9, along with strong performance in explanatory outputs. Its ability to produce accurate, context-aware explanations without hallucinations underscores the value of fine-tuned general-purpose models in healthcare settings. These findings highlight the promise of interpretable LLMs in critical care and support their integration into clinical workflows to enhance patient safety and reduce the burden of unplanned ICU readmissions.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100182"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OPT2CODE: A retrieval-augmented framework for solving linear programming problems OPT2CODE：一个用于求解线性规划问题的检索增强框架

Natural Language Processing Journal Pub Date : 2025-12-01 Epub Date: 2025-10-16 DOI: 10.1016/j.nlp.2025.100185

Tasnim Ahmed, Salimur Choudhury

{"title":"OPT2CODE: A retrieval-augmented framework for solving linear programming problems","authors":"Tasnim Ahmed, Salimur Choudhury","doi":"10.1016/j.nlp.2025.100185","DOIUrl":"10.1016/j.nlp.2025.100185","url":null,"abstract":"<div><div>Mathematical optimization drives decisions across domains such as supply chains, energy grids, and financial systems, among others. Linear programming (LP), a tool for optimizing objectives under constraints, requires domain expertise to translate real-world problems into executable models. We explore automating this translation using Large Language Models (LLMs), generating solver-ready code from textual descriptions to reduce reliance on specialized knowledge. We propose OPT2CODE, a Retrieval-Augmented Generation (RAG) framework that utilizes compact LLMs to transform problem descriptions into optimization solver executable code. OPT2CODE utilizes code documentation for document retrieval and incorporates multiple LLM-as-a-Judge components to improve baseline performance. In addition, OPT2CODE is solver flexible and LLM flexible, and it can generate code for a broad range of mathematical optimization problems such as linear, integer linear, and mixed-integer linear, across different solvers as long as the corresponding solver documentation is available. We show empirical results on two datasets, NL4Opt and EOR, and across two solvers, Gurobi and FICO Xpress, using Llama-3.1-8B and Qwen-2.5-Coder-7B. OPT2CODE consistently improves code generation accuracy, reaching up to <span><math><mrow><mn>67.13</mn><mspace></mspace><mo>%</mo></mrow></math></span> on NL4Opt with FICO Xpress and <span><math><mrow><mn>80.00</mn><mspace></mspace><mo>%</mo></mrow></math></span> on EOR with Gurobi. Finally, our energy analysis shows that these improvements come at reasonable computational cost: OPT2CODE consumes 2,732.91 joules/sample (Llama-3.1-8B) and 1,759.95 joules/sample (Qwen-2.5-Coder-7B).</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100185"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RACHNA: Racial hoax code mixed Hindi–English with novel language augmentation RACHNA：种族恶作剧代码混合了印度语英语和新颖的语言增强

Natural Language Processing Journal Pub Date : 2025-12-01 Epub Date: 2025-09-16 DOI: 10.1016/j.nlp.2025.100183

Shanu SidharthKumar Dhawale , Rahul Ponnusamy , Prasanna Kumar Kumaresan , Sajeetha Thavareesan , Saranya Rajiakodi , Bharathi Raja Chakravarthi

{"title":"RACHNA: Racial hoax code mixed Hindi–English with novel language augmentation","authors":"Shanu SidharthKumar Dhawale , Rahul Ponnusamy , Prasanna Kumar Kumaresan , Sajeetha Thavareesan , Saranya Rajiakodi , Bharathi Raja Chakravarthi","doi":"10.1016/j.nlp.2025.100183","DOIUrl":"10.1016/j.nlp.2025.100183","url":null,"abstract":"<div><div><strong>Warning</strong>: This paper contains derogatory language that may be offensive to some readers. As a type of misinformation, hoaxes seek to propagate incorrect information in order to gain popularity on social media. Racial hoaxes are a particular kind of hoax that is particularly harmful since they falsely link individuals or groups to crimes or incidents. This involves nuanced challenges of identifying false accusations, fabrications, and stereotypes that falsely impact other social, ethnic or out groups in negative actions. On the other hand, social media comments frequently incorporate many languages and are written in scripts that are not native to the user. They also rarely adhere to inflexible grammar norms. Lack of code-mixed racial hoax annotated data for a Low-resource languages like Code-Mixed Hindi and English make this issue more challenging. In order to address this, we collected 210,768 sentences and generated a racial hoax-annotated, code-mixed corpus of 5,105 YouTube comment postings in Hindi–English as HoaxMixPlus corpus. We outline the method of building the corpus and assigning the binary values indicating the presence of racial hoax which fills a critical gap in understanding and combating racialized misinformation along with inter-annotator agreement. We display the results of analysis, training using this corpus as a benchmark, new methodologies which includes dictionary based approach by correctly identifying code-mixed words as well as novel language augmentation strategies like transliteration and language tags. We evaluate several models on this dataset and demonstrate that our augmentation strategies lead to consistent performance gains.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BERT-KAN: Enhancing bilingual sentiment analysis in bangladeshi E-commerce through fine-tuned large language models BERT-KAN：通过微调大型语言模型，加强孟加拉电子商务的双语情感分析

Natural Language Processing Journal Pub Date : 2025-12-01 Epub Date: 2025-10-29 DOI: 10.1016/j.nlp.2025.100190

Mohammad Rifat Ahmmad Rashid, Aritra Das, Kazi Ferdous Hasan, Md. Rakibul Hasan, Mithila Sultana, Mahamudul Hasan, Raihan Ul Islam, Rashedul Amin Tuhin, M. Saddam Hossain Khan

{"title":"BERT-KAN: Enhancing bilingual sentiment analysis in bangladeshi E-commerce through fine-tuned large language models","authors":"Mohammad Rifat Ahmmad Rashid, Aritra Das, Kazi Ferdous Hasan, Md. Rakibul Hasan, Mithila Sultana, Mahamudul Hasan, Raihan Ul Islam, Rashedul Amin Tuhin, M. Saddam Hossain Khan","doi":"10.1016/j.nlp.2025.100190","DOIUrl":"10.1016/j.nlp.2025.100190","url":null,"abstract":"<div><div>Sentiment analysis of code-mixed reviews poses unique challenges due to linguistic variability and contex- tual ambiguity, particularly in multilingual e-commerce environments. In this paper, we introduce BERT- KAN, a novel hybrid architecture that enhances bilingual sentiment analysis in Bangladeshi e-commerce by integrating the deep contextual representations of Bidirectional Encoder Representations from Transform- ers(BERT) with a Kolmogorov-Arnold Network (KAN) layer. The KAN component employs a polynomial expansion to capture complex non-linear relationships within code-mixed Bengali-English text, while an innovative polynomial attention mechanism further refines feature extraction. Extensive ablation studies were conducted on two base models—bert-base-multilingual-uncased and BanglaBERT—using polynomial degrees of 2 and 3. Notably, the best configuration for bert-base-multilingual-uncased (employing KAN, polynomial attention, and feature fusion with polynomial degree 2) achieved a precision of 95.3 %, recall of 97.0 %, and an F1-score of 96.1 %. Comparable performance was observed for polynomial degree 3 (precision 96.2 %, recall 95.8 %, and F1-score 96.0 %), while cross-validation experiments yielded average accuracies exceeding 90 % across multiple folds. Detailed error analyses, supported by confusion matrices and sam- ple predictions, as well as discussions on computational requirements and deployment challenges, further validate the robustness of our approach.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100190"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of google translate for Mandarin Chinese translation using sentiment and semantic analysis 用情感和语义分析评价谷歌翻译对普通话翻译的影响

Natural Language Processing Journal Pub Date : 2025-12-01 Epub Date: 2025-11-08 DOI: 10.1016/j.nlp.2025.100188

Xuechun Wang , Rodney Beard , Rohitash Chandra

{"title":"Evaluation of google translate for Mandarin Chinese translation using sentiment and semantic analysis","authors":"Xuechun Wang , Rodney Beard , Rohitash Chandra","doi":"10.1016/j.nlp.2025.100188","DOIUrl":"10.1016/j.nlp.2025.100188","url":null,"abstract":"<div><div>Machine translation using large language models (LLMs) is having a significant global impact, making communication easier. Mandarin Chinese is the official language used for communication by the government and media in China. In this study, we provide an automated assessment of the translation quality of Google Translate with human experts using sentiment and semantic analysis. In order to demonstrate our framework, we select the classic early twentieth-century novel ’The True Story of Ah Q’ with selected Mandarin Chinese to English translations. We use Google Translate to translate the given text into English and then conduct a chapter-wise sentiment analysis and semantic analysis to compare the extracted sentiments across the different translations. Our results indicate that the precision of Google Translate differs both in terms of semantic and sentiment analysis when compared to human expert translations. We find that Google Translate is unable to translate some of the specific words or phrases in Chinese, such as Chinese traditional idiomatic expressions. The mistranslations may be due to a lack of contextual significance and historical knowledge of China.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A synergistic multi-stage RAG architecture for boosting context relevance in data science literature 一个协同的多阶段RAG架构，用于提高数据科学文献中的上下文相关性

Natural Language Processing Journal Pub Date : 2025-12-01 Epub Date: 2025-09-11 DOI: 10.1016/j.nlp.2025.100179

Ahmet Yasin Aytar, Kamer Kaya, Kemal Kılıç

{"title":"A synergistic multi-stage RAG architecture for boosting context relevance in data science literature","authors":"Ahmet Yasin Aytar, Kamer Kaya, Kemal Kılıç","doi":"10.1016/j.nlp.2025.100179","DOIUrl":"10.1016/j.nlp.2025.100179","url":null,"abstract":"<div><div>Navigating the voluminous and rapidly evolving data science literature presents a significant bottleneck for researchers and practitioners. Standard Retrieval-Augmented Generation (RAG) systems often struggle with retrieving precisely relevant context from this dense academic corpus. This paper introduces a synergistic multi-stage RAG architecture specifically tailored to overcome these challenges. Our approach integrates structured document parsing (GROBID), domain-specific embedding fine-tuning derived from textbooks, semantic chunking for coherence, and proposes a novel ’Abstract First’ retrieval strategy that prioritizes concise, high-signal summaries. Through rigorous evaluation using the RAGAS framework and a custom data science query set, we demonstrate that this integrated architecture significantly boosts Context Relevance by over 15-fold compared to baseline RAG, surpassing configurations using only subsets of these enhancements. These findings underscore the critical importance of multi-stage optimization and highlight the surprising efficacy of the abstract-centric retrieval method for specialized academic domains, offering a validated pathway to more effective literature navigation in data science.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100179"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentiment analysis of Mizo using lexical features in low resource based models 基于低资源模型的词汇特征情感分析

Natural Language Processing Journal Pub Date : 2025-12-01 Epub Date: 2025-09-19 DOI: 10.1016/j.nlp.2025.100181

Mercy Lalthangmawii , Thoudam Doren Singh

引用次数: 0

A multi-class cyberbullying classification on image and text in code-mixed Bangla-English social media content 代码混合孟加拉-英语社交媒体内容中图像和文本的多类网络欺凌分类

Natural Language Processing Journal Pub Date : 2025-12-01 Epub Date: 2025-10-30 DOI: 10.1016/j.nlp.2025.100191

Animesh Chandra Roy , Tanvir Mahmud , Tahlil Abrar

{"title":"A multi-class cyberbullying classification on image and text in code-mixed Bangla-English social media content","authors":"Animesh Chandra Roy , Tanvir Mahmud , Tahlil Abrar","doi":"10.1016/j.nlp.2025.100191","DOIUrl":"10.1016/j.nlp.2025.100191","url":null,"abstract":"<div><div>Social media platforms like Facebook, Instagram, and Twitter are widely used; users frequently share their daily lives by uploading pictures, posts, and videos, which gain significant popularity. However, social media posts often receive a mix of reactions, ranging from positive to negative, and in some instances, negative comments escalate into cyberbullying. Numerous studies have addressed this issue by focusing on cyberbullying classification, primarily through binary classification using multimodal data or targeting either text or image data. This study investigates the identification of multi-class images like No-bullying, Religious, Sexual, and Others using the deep learning pre-trained model MobileNetV2 to detect multiple image labels and achieved an F1-score of 0.86. For categorizing hate comments, we consider multiple classes, including Not Hate, Slang, Sexual, Racial, and Religious-related content. Extensive experiments were conducted on a novel Bengali-English code-mixed dataset, utilizing a combination of advanced transformer models, traditional machine learning techniques, and deep learning approaches to detect multiple hate comment labels. Bangla BERT achieved the highest F1-score of 0.79, followed closely by SVM at 0.78 and BiLSTM with attention at 0.73. These findings underscore the effectiveness of these models in capturing the complexities of code-mixed Bengali-English, offering valuable insights into cyberbullying detection in diverse linguistic contexts. This research contributes essential strategies for improving online safety and fostering respectful digital interactions.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trusted knowledge extraction for operations and maintenance intelligence 为运维智能提取可信知识

Natural Language Processing Journal Pub Date : 2025-12-01 Epub Date: 2025-10-13 DOI: 10.1016/j.nlp.2025.100187

Kathleen P. Mealey, Jonathan A. Karr Jr, Priscila Saboia Moreira, Paul R. Brenner, Charles F. Vardeman II

{"title":"Trusted knowledge extraction for operations and maintenance intelligence","authors":"Kathleen P. Mealey, Jonathan A. Karr Jr, Priscila Saboia Moreira, Paul R. Brenner, Charles F. Vardeman II","doi":"10.1016/j.nlp.2025.100187","DOIUrl":"10.1016/j.nlp.2025.100187","url":null,"abstract":"<div><div>Deriving operational intelligence from organizational data repositories is a key challenge due to the dichotomy of data confidentiality vs data integration objectives, as well as the limitations of Natural Language Processing (NLP) tools relative to the specific knowledge structure of domains such as operations and maintenance. In this work, we discuss Knowledge Graph construction and break down the Knowledge Extraction process into its Named Entity Recognition, Coreference Resolution, Named Entity Linking, and Relation Extraction functional components. We then evaluate sixteen NLP tools in concert with or in comparison to the rapidly advancing capabilities of Large Language Models (LLMs). We focus on the operational and maintenance intelligence use case for trusted applications in the aircraft industry. A baseline dataset is derived from a rich public domain US Federal Aviation Administration dataset focused on equipment failures or maintenance requirements. We assess the zero-shot performance of NLP and LLM tools that can be operated within a controlled, confidential environment (no data is sent to third parties). Based on our observation of significant performance limitations, we discuss the challenges related to trusted NLP and LLM tools as well as their Technical Readiness Level for wider use in mission-critical industries such as aviation. We conclude with recommendations to enhance trust and provide our open-source curated dataset to support further baseline testing and evaluation.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MAEVa: A hybrid approach for matching agroecological experiment variables MAEVa：一种匹配农业生态试验变量的混合方法

Natural Language Processing Journal Pub Date : 2025-12-01 Epub Date: 2025-09-11 DOI: 10.1016/j.nlp.2025.100180

Oussama Mechhour , Sandrine Auzoux , Clément Jonquet , Mathieu Roche

{"title":"MAEVa: A hybrid approach for matching agroecological experiment variables","authors":"Oussama Mechhour , Sandrine Auzoux , Clément Jonquet , Mathieu Roche","doi":"10.1016/j.nlp.2025.100180","DOIUrl":"10.1016/j.nlp.2025.100180","url":null,"abstract":"<div><div>Source variables or observable properties used to describe agroecological experiments are heterogeneous, nonstandardized, and multilingual, which makes them challenging to understand, explain, and use in cropping system modeling and multicriteria evaluations of agroecological system performance. Data annotation via a controlled vocabulary, known as candidate variables from the agroecological global information system (AEGIS), offers a solution. Text similarity measures play crucial roles in tasks such as word-sense disambiguation, schema matching in databases, and data annotation. Commonly used measures include (1) string-based similarity, (2) corpus-based similarity, (3) knowledge-based similarity, and (4) hybrid-based similarity, which combine two or more of these measures. This work presents a hybrid approach called Matching Agroecological Experiment Variables (MAEVa), which combines well-known techniques (PLMs, multi-head attention, TF–IDF) tailored to the challenges of aligning source and candidate variables in agroecology. MAEVa integrates the following components: (1) Our key innovation, which consists of extending pretrained language models (PLMs) (i.e., BERT, SBERT, SimCSE) with an external multi-head attention layer for matching variable names; (2) An analysis of the relevance and impact of various data collection techniques (snippet extraction, scientific articles) and prompt-based data augmentation on TF–IDF for matching variable descriptions; (3) A linear combination of components (1) and (2); and (4) A voting-based method for selecting the final matching results. Experimental results demonstrate that extending PLMs with an external multi-head attention layer improves the matching of variable names. Furthermore, TF–IDF benefits consistently from the presence of an enriched corpus, regardless of the specific enrichment technique employed.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100180"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0