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Correction to: Organic Matter DYnamic model (OMDY) predicts the wood debris decomposition of ten Mediterranean species

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Plant and Soil 2025年 1-2页

作者： Spigno, Sabrina Cartenì, Fabrizio Idbella, Mohamed Piscitelli, Alfonso Staiano, Michele Cangemi, Silvana Spaccini, Riccardo Mazzoleni, Stefano Bonanomi, Giuliano Giannino, Francesco Department of Agricultural Science University of Naples Federico II Portici Italy College of Agriculture and Environmental Sciences AgroBioSciences Program Mohammed VI Polytechnic University (UM6P) Ben Guerir Morocco Statistics Technology and Analysis of Data Department of Industrial Engineering University of Naples Federico II Naples Italy CERMANU-Centro Interdipartimentale di Ricerca sulla Risonanza Magnetica Nucleare per l’Ambiente l’Agro-Alimentare ed i Nuovi Materiali Portici Italy

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Some granular structures for weighted digraphs

Some granular structures for weighted digraphs

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Lin, Tse-Yu National Taiwan University Data Science Degree Program Taiwan

ISBN: (纸本)9798350324174

Graphs are non-Euclidean data structures used to model interaction relationships between abstract objects. When additional structures, such as graph orientation, weighted vertices, or weight functions on edges, are imposed on the graph, additional graph properties can be applied to discover hidden patterns within the structure of the graph. In this paper, we discuss possible granular structures for graphs, with a focus on granular structure for edge-weighted digraphs derived from pairwise comparison problems. These structures have been applied in various fields such as recommender systems, graph databases, and social network analysis. © 2023 IEEE.

关键词： Graph databases

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Uncovering Sentiments Expressed on GPT in Academic Papers 17

Uncovering Sentiments Expressed on GPT in Academic Papers

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17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Hong, Alicia Nwoji, Stanley C Murungi, Joyce K. Tatikonda, Pavan K. Kaluvakolli, Anandhakumar Harrisburg University of Science and Technology Data Science Program HarrisburgPA United States

ISBN: (纸本)9798350381320

The growing hype surrounding GPT models has generated both fear and excitement, with concerns about job replacement and admiration for its capabilities. This study investigated the prevailing sentiments within the academic field. The paper aimed to conduct a more comprehensive and objective analysis focusing on academia. We conducted sentiment analysis on a corpus of both peer-reviewed and preprinted article abstracts published between January 2022 and March 2023 to determine the early prevailing sentiments toward GPT models. We collected and processed 400+academic papers on GPT models, extracting the abstracts and keywords to gain insights into authors' perspectives. The study focused on identifying these articles' positive, negative, and neutral sentiments. The study considered various approaches, including RoBERTa, and traditional Machine Learning models such as Naïve Bayes, Random Forest, and Support Vector Machine, to analyze the collected data and compare performance results. The results demonstrated that the predominant sentiments expressed in scholarly paper abstracts toward GPT models are neutral (60.2% of the sample), instead of polarized. This observation holds even when the confidence score of the model output is limited to $\gt0.5$. The significance of this study lies in its novelty, as limited articles have examined these sentiments. Understanding the various sentiments expressed in scholarly discourse on GPT models can contribute to further research on the ethical implications of generative AI. © 2023 IEEE.

关键词： Sentiment analysis

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Leveraging LLMs for Integrated Sentiment and Topic Analysis on African Social Media 23

Leveraging LLMs for Integrated Sentiment and Topic Analysis ...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Sibitenda, Harriet Hu, Ruofan Rundensteiner, Elke Diattara, Awa Traore, Assitan Ba, Cheikh University of Gaston Berger Computer Science Department Saint Louis Senegal Data Science Program Worcester Polytechnic Institute Worcester United States Data Science Program Worcester Polytechnic Institute Worcester United States Actroll Data Science Department France

ISBN: (纸本)9798350374889

Leveraging AI to analyze key topics on African social media can enhance public governance. Our study analyzes social media discourse within African society on development concerns by (1) evaluating AI techniques for sentiment, topic, and theme extraction, comparing the accuracy of these methods with human annotations, and (2) extracting key insights from the data to provide policymakers with actionable recommendations for sustainable development. For this study, we utilized a data corpus of 22,036 posts from Twitter and YouTube, all focused on development issues in Africa. We applied topic modeling to extract relevant topics from the corpus and used similarity analysis, powered by Large Language Models, to link these topics to prevalent development themes. Additionally, we leveraged unsupervised models such as VADER and Large Language Models to extract sentiment related to the identified topics. To validate these model-generated sentiments, we conducted a small crowdsourced study to gather human-annotated labels as ground truth. Our sentiment analysis findings show improvements with models like TextBlob, VADER, and Llama. Fine-tuning, partic-ularly with BERT, achieved an impressive Fl score of 0.988. Meanwhile, Llama demonstrated strong precision (0.72) and balanced accuracy (0.55) in capturing contextual sentiment. We identified 304 topics using BERTopic and Llama, with robust coherence (0.81 C-v) and divergence (0.58 IRBO). In theme analysis, the One-vs-Rest classification with ensemble voting performed exceptionally well, with 'Poverty' achieving the highest F1 score of 0.89. Our results suggest that African policymakers prioritize addressing corruption, unemployment, drought, and instability, while closely monitoring the positive impacts of policy interventions. © 2024 IEEE.

关键词： Tweets

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A Bayesian-based approach for extracting the pion charge radius from electron-electron scattering data

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Chinese Physics C 2021年第8期45卷 12-27页

作者： Alam A Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara UniversityJakarta 11480Indonesia Computer Science Department BINUS Graduate Program-Master of Computer ScienceBina Nusantara UniversityJakarta 11480Indonesia

In this study,we utilize a potentially versatile Bayesian parameter approach to compute the value of the pion charge radius and quantify its uncertainty from several experimental e^(+)e^(-) datasets for the pion vector form *** employ dispersion relations to model the pion vector form factor to extract the *** model selection is used to determine the order of polynomial appearing in the form factor formulation that can be supported by the data,adapting the computation of Bayes evidence and Bayesian effective complexity based on Occam's *** findings indicate that five out of six used datasets favor the nine-parameter model for radius extraction,and accordingly,we average the radii from the *** some inconsistencies with the most updated radius values,our approach may serve as a more intuitive method of addressing parameter estimations in dispersion theory.

关键词： Bayesian complexity dispersion hadron pion

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Hyperbolic Metric Learning Embedding for User Behavior Sequence

Hyperbolic Metric Learning Embedding for User Behavior Seque...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Lin, Tse-Yu National Taiwan University Data Science Degree Program Taipei Taiwan

ISBN: (纸本)9798350324136

The sequential behavior history is an important data in the field of consumer electronics, A common way is to model all browser history as a graph, thus, each sequence of user behavior can be view as a walk on the certain graph. Further, we could model individual user's historical behavior as a tree sub-graph. Recently, tree-structure data is widely studied, and various hyperbolic metric learning methods are proposed to find a nice representation into a hyperbolic space for the usage of downstream tasks. In this work, we propose a framework to deal with sequential behavior history defined by tags of website pages, then apply hyperbolic metric learning models to obtain an appropriate representation each user. © 2024 IEEE.

关键词： hierarchical representations Learning hyperbolic metric learning user behavior

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Sensor Selection for Activity Classification at Smart Home Environments 42

Sensor Selection for Activity Classification at Smart Home E...

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42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020

作者： Bolleddula, Nithish Chun Hung, Geoffrey Yau Ma, Daren Noorian, Hoda Woodbridge, Diane Myung-Kyung Ms in Data Science Program University of San Francisco United States

ISBN: (纸本)9781728119908

As the world's older population grows dramatically, the needs of continuing care retirement communities increases. Studies show that privacy can be a major concern for adopting technologies, while the older population prefers smart homes [1]. In order to minimize the number of sensors to be installed in each house, we performed Principal Component Analysis (PCA) to filter out the relatively unimportant sensors. We applied a machine learning model to classify residents' activity types, using a different set of sensors chosen by PCA. Then, we validated the trade-off between the classification model accuracy and the number of sensors used in classification. Our experiment shows that feature engineering helps reduce accuracy degradation for activity type classification when using fewer sensors in smart homes. © 2020 IEEE.

关键词： Machine learning

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py_ped_sim: a flexible forward pedigree and genetic simulator for complex family pedigree analysis

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BMC Bioinformatics 2025年第1期26卷 1-18页

作者： Guardado, Miguel Perez, Cynthia Campana, Sthen Chavez Rojas, Berenice Magaña, Joaquín Jackson, Shalom Samperio, Emily Hernandez, Selena Syas, Kaela Hernandez, Ryan D. Zavala, Elena I. Rohlfs, Rori V. Department of Mathematics San Francisco State University San FranciscoCA94132 United States Biological and Medical Informatics Graduate Program University of California San Francisco San FranciscoCA94158 United States Bioengineering and Therapeutic Sciences University of California San FranciscoCA94134 United States Department of Data Science University of Oregon EugeneOR97403 United States Department of Biology San Francisco State University San FranciscoCA94132 United States Department of Molecular and Cell Biology University of California Berkeley BerkeleyCA94720 United States

Background: Large-scale family pedigrees are commonly used across medical, evolutionary, and forensic genetics. These pedigrees are tools for identifying genetic disorders, tracking evolutionary patterns, and establishing familial relationships via forensic genetic identification. However, there is a lack of software to accurately simulate different pedigree structures along with genomes corresponding to those individuals in a family pedigree. This limits simulation-based evaluations of methods that use pedigrees. Results: We have developed a python command-line-based tool called py_ped_sim that facilitates the simulation of pedigree structures and the genomes of individuals in a pedigree. py_ped_sim represents pedigrees as directed acyclic graphs, enabling conversion between standard pedigree formats and integration with the forward population genetic simulator, SLiM. Notably, py_ped_sim allows the simulation of varying numbers of offspring for a set of parents, with the capacity to shift the distribution of sibship sizes over generations. We additionally add simulations for events of misattributed paternity, which offers a way to simulate half-sibling relationships, and simulations to extend the breadth of a family pedigree. We validated the accuracy of both our genome simulator and pedigree simulator. We show that we can simulate genomes onto family pedigrees with levels of expected kinship. Conclusions: py_ped_sim is a user-friendly and open-source solution for simulating pedigree structures and conducting pedigree genome simulations. It empowers medical, forensic, and evolutionary genetics researchers to gain deeper insights into the dynamics of genetic inheritance and relatedness within families. © The Author(s) 2025.

关键词： Open source software

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Risks of liver cirrhosis,hepatocellular carcinoma,hepatic-related complications,and mortality in patients with type 2 diabetes in Taiwan

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World Journal of Diabetes 2025年第5期16卷 202-215页

作者： Hua-Fen Chen Yung-Yueh Chang Peter Chen Xiao-Han Shen Chin-Huan Chang Wan-Lun Hsu Department of Endocrinology Far Eastern Memorial HospitalNew Taipei City 220TaiwanChina School of Medicine College of MedicineFu Jen Catholic UniversityNew Taipei City 242TaiwanChina Department of Public Health College of MedicineFu Jen Catholic UniversityNew Taipei City 242TaiwanChina Institute of Epidemiology and Preventive Medicine College of Public HealthNational Taiwan UniversityTaipei City 100TaiwanChina Department of Gastroenterology Choninn HospitalChoninn Medical GroupNew Taipei City 220TaiwanChina Master Program of Big Data in Medical Healthcare Industry College of MedicineFu Jen Catholic UniversityNew Taipei City 242TaiwanChina Data Science Center Fu Jen Catholic UniversityNew Taipei City 242TaiwanChina

BACKGROUND Hepatitis B and C and alcoholic liver disease are the principal causes of hepaticrelated morbidity and ***,evidence of the associations between diabetes without the above risk factors and hepatic-related study endpoints is not well *** addition,the effects of associated metabolic dysfunction and exercise on hepatic outcomes are still not *** To investigate the incidence and relative hazards of cirrhosis of the liver,hepato cellular carcinoma(HCC),hepatic-related complications and mortality in patients with type 2 diabetes(T2D)who were nonalcoholic and serologically negative for hepatitis B and C in *** A total of 33184 T2D patients and 648746 nondiabetic subjects selected from Taiwan’s adult preventive health care service were linked to various National Health Insurance databases,cancer registry,and death registry to identify cirrhosis of the liver,HCC,hepatic-related complications,and *** Poisson assumption and Cox proportional hazard regression model were used to estimate the incidences and relative hazards of all hepatic-related study endpoints,*** also compared the risk of hepatic outcomes stratified by age,sex,associated metabolic dysfunctions,and regular exercise between T2D patients and nondiabetic *** Compared with nondiabetic subjects,T2D patients had a significantly greater incidence(6.32 vs 17.20 per 10000 person-years)and greater risk of cirrhosis of the liver[adjusted hazard ratio(aHR)1.45;95%CI:1.30-1.62].The aHRs for HCC,hepatic complications,and mortality were 1.81,1.87,and 2.08,*** older age,male sex,obesity,hypertension,and dyslipidemia further increased the risks of all hepatic-related study endpoints,and regular exercise decreased the risk,irrespective of diabetes *** Patients with T2D are at increased risk of cirrhosis of the liver,HCC,hepatic-related complications,and mortality,and associated metabolic dysfunctions provide additional

关键词： Type 2 diabetes mellitus Liver cirrhosis Hepatocellular carcinoma Hepatic-related complications Mortality Metabolic dysfunction-associated steatotic liver disease Metabolic dysfunction-associated steatohepatitis

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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