bluuebunny/crossref_metadata_2025_split huggingface dataset:
Protein Function Prediction Using GO Similarity-based Heterogeneous Network Propagation
Authors: Hu Sai , Zhao Bihai 
Published: January 2025

Abstract

Background:
Protein function prediction serves as a fundamental cornerstone in bioinformatics, offering critical insights into the intricate biological processes and molecular mechanisms that form the basis of life. Precise annotation of protein functions is indispensable for unraveling disease mechanisms, identifying drug targets, and propelling forward synthetic biology applications. Nevertheless, this task remains complex due to the diverse characteristics of multi-omics data and the hierarchical structure of Gene Ontology (GO) annotations.
Results:
To tackle these challenges, we have developed an innovative approach that seamlessly integrates the topological structure of protein-protein interaction networks, a wide array of biological data including protein domain profile and protein complex information, and Gene Ontology into a heterogeneous network based on GO similarity. This integrated network encapsulates the multifaceted relationships between proteins and their functional annotations, setting the stage for a comprehensive protein function prediction framework. Building on this heterogeneous network, we have devised a protein function prediction method named GOHPro, which leverages the strength of network propagation algorithms. To evaluate the effectiveness of GOHPro, we conducted rigorous experiments on two model organisms, yeast and human, using the most up-to-date GO annotations dataset and the CAFA3 dataset. Our method was compared against several state-of-the-art approaches, and the results unequivocally showed that GOHPro outperforms its competitors, highlighting its superiority in predicting protein functions. The code and dataset of GOHPro are freely available at https://github.com/husaiccsu/GOHPro.
Conclusions:
The proposed GOHPro method, which seamlessly combines multi-omics data, protein interaction networks, and GO annotations within a heterogeneous network framework, significantly enhances protein function prediction accuracy. The outstanding performance of GOHPro in our experiments underscores its potential as a powerful tool for annotating protein functions, facilitating a more profound understanding of biological processes and contributing to advancements in bioinformatics and computational biology.
Deep Learning Applications for Shunt Compensation Using LCL Filter Supported D-Statcom
Authors: Mangaraj Mrutyunjaya , Sabat Jogeswara 
Published: January 2025

Abstract

In low and medium voltage distribution networks, the LCL integrated conventional converter based distributed static compensator (D-Statcom) has shown to be a practical solution for shunt compensation. Despite numerous efforts in this area, the traditional control approach still has a number of issues. This article describes the development of LCL integrated D-Statcom for shunt compensation utilizing a deep learning technique. The voltage source converter (VSC) and LCL filter are included in the new framework. The operation and control of the distribution network are directly impacted by the proposed system's performance. MATLAB Simulink software and an experimental research based on d-SPACE are used to demonstrate the synchronization, which solves current related power quality (PQ) issues such as poor power factor (p.f.), current harmonics, unbalanced voltage at point of common coupling (PCC) and poor voltage regulation. In order to provide precise reference currents for control, the deep learning technique is used to monitor the essential active and reactive components of load currents. Furthermore, it precisely ascertains the remaining constituents, attaining a swifter transient reaction and superior system stability. Comparisons are then made between VSC and LCL integrated VSC using deep learning technique by considering the implementation procedure. With a lower DC-link voltage and a smaller converter power rating, the LCL integrated VSC system improves PQ of distribution network more than the VSC.
Deep Reinforcement Learning Double-LayerEco-City Vehicle-Road Cooperative Control inLarge-Scale Road Networks
Authors: Yan Liping , Wu Kanglai , Tang Renjie , Xu Jiayue , Jia Haojie , Song Kai 
Published: January 2025

Abstract

As Connected and Automated Vehicle (CAV) technology progresses, optimizing vehicle-road cooperative control becomes critical for alleviating traffic congestion. Existing approaches often involve complex computations that are unsuitable for real-time control and are typically limited to isolated intersections, restricting their scalability. To address these limitations, this paper introduces a Mixed Platoon Dual-Layer (MPDL) model designed for large-scale road networks. The model leverages mixed platoon arrival time predictions combined with Deep Reinforcement Learning (DRL) for cooperative control at signalized intersections. To enhance decision-making, this study proposes the Multi-Distributed Proximal Policy Optimization (MDPPO) algorithm, which efficiently manages dynamic vehicle-traffic interactions. This algorithm optimizes mixed platoon trajectories and determines the optimal signal phases. Furthermore, the model incorporates eco-friendly traffic strategies aimed at reducing emissions across the network. Extensive simulations using the SUMO traffic simulator on both synthetic and real-world networks demonstrate that the MPDL model achieves higher training rewards compared to existing methods such as MA2C and IA2C. The MPDL model consistently outperforms these methods in key performance metrics, including waiting time, speed, delay, and pollutant emissions.
Deep Insights Into The Coupled Optoelectronic Analysis Of ETL Thin Films And Photovoltaic Analysis Of CsPbI 3 -Based Perovskite Solar Cell Using SCAPS-1D Simulations
Authors: BOUSSAADA Salah Eddine , MOUCHAAL Younes , RIANE Houaria , KHELIL Abdelbacet 
Published: January 2025

Abstract

Cesium lead iodide (CsPbI3) is a type of perovskite compound used in solar cells. CsPbI3 has a unique structure that efficiently absorbs sunlight, making it highly efficient for generating power. It can be made using low-cost methods and adjusted to capture different parts of sunlight. However, its stability in varying conditions is a challenge that researchers are working to overcome. CsPbI3 perovskite shows promise for creating efficient and affordable solar cells, though stability remains an area of focus.In this study, the thicknesses, optical gaps and electron mobility of the electron transport layer (ETL) derived from a mixture of oxides: SnO2 and CoO (SnCoOx), were calculated using experimental UV-Vis spectrometry and Hall Effect measurements. The results were then used as input data for the simulation of CsPbI3-based s using SCAPS 1-D software. In addition, several materials were compared as electron transport layers (ETLs), including C60, CdS, IGZO, PCBM, ZnO, CdZnS and TiO2, comparing them initially with SnCoOx as well as organic and inorganic hole transport materials (HTLs) such as Spiro-OMeTAD, PEDOT:PSS, P3HT, CuO, CuI and CuO2. The results showed that SnCoOx as ETL and Cu2O as HTL are the most suitable materials among those studied. In addition, device performance was enhanced by optimizing various parameters such as back electrode work function, absorber thickness, doping density, defect density, series and shunt resistances, and temperature. Under optimal conditions, a conversion efficiency of 21.34% was achieved for the FTO/(75%)SnO2(25%)Co/CsPbI3/Cu2O/Au solar cell. This investigation illustrates the potential of SnCoOx as an ETL for the production of renewable energy that is free of toxicity.
Unveiling the Complex Mechanism of Short Video Addiction on English Learning Engagement and Burnout among College EFL Students
Authors: Kuilong Huang 
Published: January 2025

Abstract

In the digital era, short-video apps offer students copious learning resources and entertainment. However, short-video addiction has become a notable concern. Based on Media Dependence Theory and Social Cognitive Theory, this research explored how short-video addiction affects English learning engagement and burnout, with English academic self-efficacy as a mediator. Electronic questionnaires were administered to 500 college EFL students in Guangxi, China, and data analysis was conducted with SPSS and SmartPLS. Results showed that : (1) short-video addiction had no direct impact on English learning engagement but significantly and directly contributed to English learning burnout;(2) English academic self-efficacy mediated the relationships between short-video addiction and both learning engagement and burnout. Overall, this study uncovers the intricate connections among short-video addiction, English learning engagement, burnout, and English academic self-efficacy. Theoretically, it enriches the two theories by offering fresh perspectives on how emerging media influence learning behaviors. Practically, to boost college students' English learning engagement and reduce burnout, we should not only address short-video addiction but also focus on cultivating their English academic self-efficacy.
Optimizing Energy Efficiency and Comfort in Rural Self-Built Housing: A Comparative Study of GA and EA Algorithms
Authors: Chen Chen , Wang Huifang , Li Dayao 
Published: January 2025

Abstract

With the growing global focus on sustainable building design, reducing energy consumption and carbon emissions in buildings has become a critical research topic. This study explores the variations in electricity consumption, carbon emissions, and comfort levels influenced by building design parameters, and compares the applications of Genetic Algorithm (GA) and Evolutionary Algorithm (EA) in building optimization. The research is based on a Private residence in Huzhou, Zhejiang Province, where actual data were collected, including wall-to-window ratio, heating set point, cooling set point, roof shading coefficient, equipment power density, airtightness, and thermal transmittance of external walls, to construct a detailed building model. During the optimization process, DesignBuilder software was utilized for modeling, and both GA and EA optimization algorithms were implemented. The simulation results show that GA outperforms EA in terms of convergence speed, stability, and balancing multiple objectives. Specifically, GA reduced the annual electricity consumption from 862.26 kWh to 825.32 kWh, a 4.28% decrease, with the standard deviation dropping from 287.61 to 275.43 (a 4.25% improvement in stability). Discomfort time was significantly reduced by 31.56%, from 1030.2 hours to 705.12 hours, with substantial reductions in both standard deviation and interquartile range (IQR). Carbon emissions decreased from 552.62 kg to 533.86 kg, a 3.39% reduction, highlighting the potential for emission reduction. The results indicate significant differences between the two algorithms in optimizing building energy consumption, reducing carbon emissions, and enhancing comfort levels. GA demonstrates advantages in convergence speed and result accuracy, while EA excels in solution diversity and global search capability. Ultimately, this paper presents optimization recommendations based on both algorithms, aimed at providing a reference for future sustainable building design.
The Effects of Government’s Role on Convergence Acceleration in Indonesia
Authors: Vania Mia , Santoso Dwi Budi 
Published: January 2025

This study aims to determine the influence of government roles through expenditures in education, health, and development sectors on the acceleration of convergence in Indonesia. The data used is panel data covering 33 provinces from 2010 to 2022. The method employed in this research is panel data regression with a convergence model. The variables used include education expenditure, health expenditure, and development expenditure. The results of the study indicate that the variables of education expenditure, health expenditure, and development expenditure have no effect on accelerating convergence. In addition, the three variables have no effect on economics growth.

Abstrak
Penelitian ini bertujuan untuk mengetahui pengaruh dari peran pemerintah melalui pengeluaran di bidang pendidikan, kesehatan, dan pembangunan terhadap percepatan konvergensi di Indonesia. Data yang digunakan merupakan data panel meliputi 33 provinsi dari tahun 2010-2022. Metode yang digunakan dalam penelitian ini merupakan regresi data panel model konvergensi. Variabel yang digunakan antara lain belanja pendidikan, kesehatan, dan pembangunan. Hasil dari penelitian menunjukkan variabel belanja pendidikan, belanja kesehatan, dan belanja pembangunan tidak berpengaruh terhadap percepatan konvergensi. Selain itu, ketiga variabel tersebut tidak berpengaruh terhadap pertumbuhan ekonomi.
The Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets
Authors: Willem Theresa , Wollek Alessandro , Cheslerean-Boghiu Theodor , Kenney Martha , Buyx Alena 
Published: January 2025

Background
In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models’ outputs. As a standard, categorical data, such as patients’ gender, socioeconomic status, or skin color, are used to train models in fusion with other data types, such as medical images and text-based medical information. However, the effects of including categorical data features for model training in such data-scarce areas are underexamined, particularly regarding models intended to serve individuals equitably in a diverse population.

Objective
This study aimed to explore categorical data’s effects on machine learning model outputs, rooted the effects in the data collection and dataset publication processes, and proposed a mixed methods approach to examining datasets’ data categories before using them for machine learning training.

Methods
Against the theoretical background of the social construction of categories, we suggest a mixed methods approach to assess categorical data’s utility for machine learning model training. As an example, we applied our approach to a Brazilian dermatological dataset (Dermatological and Surgical Assistance Program at the Federal University of Espírito Santo [PAD-UFES] 20). We first present an exploratory, quantitative study that assesses the effects when including or excluding each of the unique categorical data features of the PAD-UFES 20 dataset for training a transformer-based model using a data fusion algorithm. We then pair our quantitative analysis with a qualitative examination of the data categories based on interviews with the dataset authors.

Results
Our quantitative study suggests scattered effects of including categorical data for machine learning model training across predictive classes. Our qualitative analysis gives insights into how the categorical data were collected and why they were published, explaining some of the quantitative effects that we observed. Our findings highlight the social constructedness of categorical data in publicly available datasets, meaning that the data in a category heavily depend on both how these categories are defined by the dataset creators and the sociomedico context in which the data are collected. This reveals relevant limitations of using publicly available datasets in contexts different from those of the collection of their data.

Conclusions
We caution against using data features of publicly available datasets without reflection on the social construction and context dependency of their categorical data features, particularly in data-sparse areas. We conclude that social scientific, context-dependent analysis of available data features using both quantitative and qualitative methods is helpful in judging the utility of categorical data for the population for which a model is intended.
Health and Experiences During the COVID-19 Pandemic Among Children and Young People: Analysis of Free-Text Responses From the Children and Young People With Long COVID Study
Authors: Rojas Natalia K , Martin Sam , Cortina-Borja Mario , Shafran Roz , Fox-Smith Lana , Stephenson Terence , Ching Brian C F , d'Oelsnitz Anaïs , Norris Tom , Xu Yue , McOwat Kelsey , Dalrymple Emma , Heyman Isobel , Ford Tamsin , Chalder Trudie , Simmons Ruth ,   CLoCk Consortium, Pinto Pereira Snehal M 
Published: January 2025

Background
The literature is equivocal as to whether the predicted negative mental health impact of the COVID-19 pandemic came to fruition. Some quantitative studies report increased emotional problems and depression; others report improved mental health and well-being. Qualitative explorations reveal heterogeneity, with themes ranging from feelings of loss to growth and development.

Objective
This study aims to analyze free-text responses from children and young people participating in the Children and Young People With Long COVID study to get a clearer understanding of how young people were feeling during the pandemic.

Methods
A total of 8224 free-text responses from children and young people were analyzed using InfraNodus, an artificial intelligence–powered text network analysis tool, to determine the most prevalent topics. A random subsample of 411 (5%) of the 8224 responses underwent a manual sentiment analysis; this was reweighted to represent the general population of children and young people in England.

Results
Experiences fell into 6 main overlapping topical clusters: school, examination stress, mental health, emotional impact of the pandemic, social and family support, and physical health (including COVID-19 symptoms). Sentiment analysis showed that statements were largely negative (314/411, 76.4%), with a small proportion being positive (57/411, 13.9%). Those reporting negative sentiment were mostly female (227/314, 72.3%), while those reporting positive sentiment were mostly older (170/314, 54.1%). There were significant observed associations between sentiment and COVID-19 status as well as sex (P=.001 and P<.001, respectively) such that the majority of the responses, regardless of COVID-19 status or sex, were negative; for example, 84.1% (227/270) of the responses from female individuals and 61.7% (87/141) of those from male individuals were negative. There were no observed associations between sentiment and all other examined demographics. The results were broadly similar when reweighted to the general population of children and young people in England: 78.52% (negative), 13.23% (positive), and 8.24% (neutral).

Conclusions
We used InfraNodus to analyze free-text responses from a large sample of children and young people. The majority of responses (314/411, 76.4%) were negative, and many of the children and young people reported experiencing distress across a range of domains related to school, social situations, and mental health. Our findings add to the literature, highlighting the importance of specific considerations for children and young people when responding to national emergencies.
The Development of an Instagram Reel-Based Bystander Intervention Message Among College Students: Formative Survey and Mixed Methods Pilot Study
Authors: Couto Leticia 
Published: January 2025

Abstract

Background
Bystander intervention is a common method to address the ubiquitous issue that is sexual violence across college campuses. Short messages that incentivize bystander intervention behavior can be another tool to fight sexual violence.

Objective
This study aimed to conduct formative research surrounding social norms and bystander barriers to pilot and develop Instagram (Meta) reel-based messages addressing bystander intervention among college students.

Methods
The first step was to conduct a formative survey to identify peer norms and actual behavior of the intended population. Once that data were collected, a mixed methods message pilot was conducted by a survey where participants randomly saw 5 of the 12 messages developed, assessing them for credibility, perceived message effect, and intended audience.

Results
The formative survey was conducted among 195 college students from the same institution, and the pilot test was conducted among 107 college students. The formative survey indicated a discrepancy between perceived peer behavior and actual behavior of the participants in all 3 measures, allowing for the development of normative messaging. The pilot testing indicated the credibility was acceptable (eg, mean 3.94, SD 1.15 on a 5-point scale) as well as the perceived message effect (eg, mean 4.26, SD 0.94 on a 5-point scale). Intended audiences were also identified and reached. Qualitative results indicated that the messages may have lacked credibility, although the quantitative results suggest otherwise.

Conclusions
Participants understood the messages concerned bystander intervention, and perceived message effects results indicated the messages to be effective in assisting bystander intervention engagement by normative messaging. Messages were considered credible and reached the intended audience. The qualitative results provided further insights on how the messages can be adapted before being tested for effects. Future research should focus on further adapting the messages and testing their effects among the studied population.