bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pengembangan E-Book Panduan Mendesain Bahan Ajar E-Learning Di Pusdiklat Keuangan Umum
Authors: Husnul Haibah  , Khaerudin  , Ariani Diana 
Published: January 2025

ABSTRAK
Penelitian pengembangan ini bertujuan untuk menghasilkan produk berupa E-Book Panduan Mendesain Bahan Ajar E-learning. Pengembangan produk ini ditujukan untuk staf Pengembang Teknologi Pembelajaran di Pusat Pendidikan dan Pelatihan Keuangan Umum. Penelitian pengembangan ini dilakukan dengan mengacu pada model pengembangan Rowntree. Model ini terdiri dari tiga tahapan utama yaitu tahap perencanaan, tahap persiapan penulisan, serta tahap penulisan dan penyuntingan. Evaluasi pada penelitian ini menggunakan metode evaluasi formatif dengan melibatkan ahli (expert review) dan pengguna (face-to-face tryouts). Penelitian pengembangan ini melibatkan ahli materi, ahli desain pembelajaran dan ahli media dalam rangka melakukan review dan memberi masukkan terhadap produk yang dikembangkan dengan metode wawancara. Tahap uji coba pengguna dilakukan dengan face to face tryouts melalui wawancara yang melibatkan 2 staf Pengembang Teknologi Pembelajaran. Hasil penelitian ini menunjukkan bahwa produk e-book panduan yang dihasilkan memiliki kualitas yang baik dan dapat digunakan untuk memfasilitasi kegiatan mendesain bahan ajar e-learning di Pusdiklat Keuangan Umum.

Kata Kunci: E-Book, Mendesain Bahan Ajar E-Learning, Model Pengembangan Rowntree, Panduan, Penelitian Pengembangan.

ABSTRACT
This development research aims to produce an E-Book Guide for Designing E-Learning Teaching Materials. The product is intended for Learning Technology Developer staff at the General Finance Education and Training Center. The development research was conducted by referring to the Rowntree development model. This model comprises three main stages: the planning stage, the writing preparation stage, and the writing and editing stage. The evaluation in this study used the formative evaluation method, which involved experts (expert review) and users (face-to-face tryouts). This development research involved material experts, learning design experts, and media experts who reviewed and provided input on the products developed using the interview method. The user trial stage was conducted through face-to-face interviews with 2 Learning Technology Developer staff. The results of this study indicate that the resulting e-book guide product has good quality and can be used to facilitate the activities of designing e-learning teaching materials at the General Finance Training Center.

Keywords: Designing E-Learning Teaching Materials, Development Research, E-Book, Guidebook, Rowntree Development Model.
Longitudinal study of foot-and-mouth disease virus in Northern Nigeria: implications for the roles of small ruminants and environmental contamination in endemic settings
Authors: Gubbins Simon , Brown Emma , Wungak Yiltawe , Oyekan Olumuyiwa , Adedeji Adeyinka J. , Ijoma Sandra I. , Atai Rebecca B. , Oguche Moses O. , Samson Mark , Dogonyaro Banenat B. , Rosso Fabrizio , Hicks Hayley , Wood Britta A. , Wadsworth Jemma , Knowles Nick , King Donald P. , Ludi Anna B. , Colenutt Claire , Shaw Andrew E. , Limon Georgina , Ehizibolo David O. 
Published: January 2025

Abstract

Foot-and-mouth disease (FMD) is a highly contagious disease affecting cloven-hoofed ungulates. This study aimed to enhance our understanding of the role of small ruminants and environmental contamination in the epidemiology and endemicity of FMD. A longitudinal study was conducted between March 2021 and October 2021 in northern Nigeria, where monthly samples were collected from five households, one livestock market and one transhumance location in two local government areas (LGA) identified as being at high risk of FMD. Serum samples (n = 783), oral swabs (n = 424) and environmental swabs (n = 458) were collected and tested for the presence of foot-and-mouth disease virus (FMDV) RNA by rRT-PCR. Serum samples (n = 780) were also tested for the presence of antibodies against FMDV non-structural proteins. The proportion of FMDV RNA positive samples increased in all sample types collected in one LGA during the period when an FMD outbreak was reported in the same LGA. In contrast, sero-positive samples did not differ by month but differed between LGAs and amongst species. The force of infection estimated from age-seroprevalence data for each household was significantly lower in goats compared with both cattle or sheep. Five O/EA-3 topotype sequences were obtained from selected FMDV RNA positive samples; findings which support the use of environmental swabs to detect circulating FMDV strains in endemic settings. These results show oral and environmental swabs are suitable sampling methods for early detection at animal and herd level, respectively and provide insights on the role of small ruminants on FMD epidemiology.
Exam Timetabling Problem Using Cooperative Hyper-heuristics: A Case Study of the ITC2007 Dataset
Authors: Han Kate , Oginni Evelyn , Chen Yun 
Published: January 2025

Abstract

In recent years, selection-based hyper-heuristics have shown success across various problem domains, significantly enhancing performance in complex optimization tasks. These hyper-heuristics rely on an effective ensemble of heuristic selection and move acceptance strategies to achieve optimal results. To further advance this field, we propose a novel cooperative hyper-heuristic approach that introduces a three-level agent-based framework, which incorporates multiple cooperative schemes to enhance solution exploration. Our evaluation focuses on exam timetabling problems, where collaboration among agents enables a broader investigation of feasible solutions within the same computational time constraints. Experimental results from the ITC2007 dataset reveal that our approach not only improves hyper-heuristic performance but also maintains robustness compared to existing state-of-the-art methods.
An in-field automatic fruits recognition and classification system
Authors: Gill Harmandeep Singh , Kaur sumeet , Khan Shakir , Gupta Gaurav , Bhatt Abhishek 
Published: January 2025

Abstract

Fruit is an important part of daily diet around the globe. Automatic fruit classification and recognition is an ill-posed problem. Till now many machines learning models have been developed to classification fruits. However, the classification performance of these techniques is reduced during poor weather and environmental conditions in real-time processing applications. It is quite challenging to automatically classify the fruits from images, when the images are captured from a different viewing angle. This paper proposes an in-field automatic fruit recognition and classification model. Convolution Neural Network (CNN) used to extract image features, Recurrent Neural Network (RNN) used to label and sequence the extracted by CNN. Long-Short Term Memory (LSTM) used to categorize the fruits by using image features that have already been selected and extracted. Experimental and simulation results demonstrate that the proposed system outperforms conventional Machine learning applications. Moreover, the proposed system has been packed into real-time support for the recognition of fruits.
Detecting AI Generated Content in Short Form Text
Authors: Jadhwani Somesh , Jain Shreya , Doshi Pankti 
Published: January 2025

Abstract

As the use of AI-powered language models continues to grow exponential-ly, the ability to distinguish human generated text from AI generated text has become critical to combat misinformation and uphold trust on online platforms. This study addresses the novel challenge of classifying casual, short-form user reviews on e-commerce websites as either human generated or AI generated. A custom dataset of over 60,000 authentic reviews and 17,645 AI generated reviews across diverse product categories was engi-neered by web scraping and prompting state-of-the-art large language mod-els. Through rigorous feature engineering, model development, and statisti-cal analysis, this research demonstrates the feasibility of accurately dis-criminating between human generated and AI generated content in informal contexts.
Divergent Effects of the Enfacement Illusion on Face and Body Image Disturbance Across Female Eating Disorder Risk Groups
Authors: Portingale Jade , Krug Isabel , Rheenen Tamsyn E Van , Kiropoulos Litza , Bartholomeusz Cali F , Nasser Helen , Butler David 
Published: January 2025

Abstract

Embodiment illusion research suggests impaired multisensory integration in individuals with eating disorders (EDs), whilst offering potential therapeutic applications. However, face-related illusions remain unexplored. This study investigated the relationship between ED risk, susceptibility to the enfacement illusion, and improvements in face and body image disturbance after experiencing enfacement. Female Caucasian and Asian participants (N = 226), categorised as high ED risk (n = 102, 45.1%) or low ED risk (n= 124, 54.9%), completed an online assessment featuring a novel enfacement illusion task involving synchronous and asynchronous facial mimicry. We assessed subjective (self-report) and objective (self-face recognition task) enfacement, alongside pre- and post-task self-reported face and body image disturbance. Multilevel modelling revealed successful enfacement induction across participants, with models explaining 3–11% unique variance in enfacement measures. While both groups showed similar illusion susceptibility, the effects of enfacement diverged significantly: high ED-risk participants experienced increased body and head dissatisfaction, whereas low ED-risk participants demonstrated reduced body dissatisfaction and dysmorphic concern (20-29% unique variance explained). These findings suggest a potential dissociation between face and body perception processes, whereby face-related multisensory integration processes may remain intact in ED populations, despite potentially adverse effects of enfacement on face and body image in high-ED-risk individuals.
Impact Classification within and beyond Academia: Domain-Robust Annotation and the Capacity of Large Language Models
Authors: Becker Maria , Han Kanyao , Rezapour Rezvaneh , Diesner Jana , Witt Andreas 
Published: January 2025

Abstract

Prior analyses and assessments of the impact of scientific research has mainly relied on analyzing its scope within academia and its influence within scholarly circles. However, by not considering the broader societal, economic, and policy implications of research projects, these studies overlook the ways in which scientific discoveries contribute to technological innovation, public health improvements, environmental sustainability, and other areas of real-world application. We expand upon this prior work by developing and validating a conceptual and computational solution to automatically identify and categorize the impact of scientific research within and especially beyond academia based on text data. We first empirically develop and evaluate an annotation schema to capture and classify the impact of research projects based on research reports from different scientific domains. We then annotate a large dataset of more than 45k sentences extracted from research reports for the developed impact categories. We examine the annotated dataset for patterns in the distribution of impact categories across different scientific domains, co-occurrences of impact categories, and signal words of impact. Using the annotated texts and the novel classification schema, we investigate the performance of large language models (LLMs) for automated impact classification. Our results show that fine-tuning the models on our annotated datasets statistically significantly outperforms zero- and fewshot prompting approaches. This indicates that state-of-the-art LLMs without fine-tuning may not work well for novel classification schemas such as our impact classification schema, and in turn highlights the importance of diligent manual annotations as empirical basis in the field of computational social science.
Design of High Voltage Power Supply System for Application of Electrohydrodynamic Drying
Authors: Shaikh Anas Ejaz Yasmeen , Mishra Hari Niwas 
Published: January 2025

Abstract

Electrohydrodynamic (EHD) drying is an emerging technique for energy-efficient, non-thermal, and high-quality drying of food and biomaterials. It is based on enhancement of moisture removal from food material under action of electric wind resulting from corona discharge, which is generated when high voltage is applied across two electrodes with different radii of curvature. A high voltage power supply (HVPS) is an important component in experimental set-up for studying EHD drying process. In the present study, an HVPS for EHD drying of food has been developed based on its design, measurement, and control requirements. A flyback or line output transformer was used to generate high voltage up to range of 10 kV (peak-to-peak output voltage). Performance of HVPS was evaluated using current-voltage characteristics and power consumption. Maximum peak-to-peak output current obtained was ~ 450 mA under load of 20 MΩ. Average and root mean-squared power of HVPS ranged from 9 to 26 W and 13 to 35 W, respectively. Cost and weight of HVPS were 5,588 INR and 2.4 kg, respectively. Thus, the designed HVPS offered low-cost and portable solution to generate high voltage for electrohydrodynamic drying. This research will expedite the process for development of electrohydrodynamic dryers for food industry.
Exploration of Geothermal Potential in Mbeya Region by Using Remotely Sensed Data
Authors: Masanga Mercy , Ijumulana Julian 
Published: January 2025

Abstract

Amid a global push for sustainable energy solutions, Tanzania is emerging as a frontrunner in exploring renewable energy resources. The nation's strong commitment to combating climate change has driven extensive research into renewable energy alternatives sources such as geothermal, solar, and wind energy. Traditionally dependent on hydropower, Tanzania's energy sector has faced disruptions due to decreasing water levels and technical difficulties with hydropower plants. Geothermal energy, however, has emerged as a promising alternative. While conventional geological methods for detecting geothermal zones are known for their accuracy, they come with substantial costs. Integrating these methods with remotely sensed data has led to significant improvements in efficiency and precision. In response to the challenges the government of Tanzania has launched ambitious plans to advance geothermal exploration and extraction by establishing Tanzania Geothermal Development Company (TGDC) in 2014. This study employs advanced Geographical Information System (GIS) tools and remotely sensed data to identify geothermal potential zones in Mbeya, Rungwe, and Kyela districts. The methodology includes spatial analysis, by generating flow direction maps with major and minor faults, then overlaying hot springs to create a geological suitability factor. The Normalized Difference Vegetation Index (NDVI) threshold method was used to generate thermal elements. Additionally, the Random Forest method was then applied to create a land cover suitability map. Thereafter classifying regions into three primary categories: most suitable, moderately suitable, and least suitable. The study’s results were compared with existing field survey data to validate the effectiveness of the GIS based approach. To ensure high reliability, this research proposes validating remotely detected potential zones using various models, aiming for a confidence level of at least 95%. These efforts lay the foundation for unlocking Tanzania's geothermal potential, paving the way for a transformative shift towards sustainable energy leadership both within Africa and globally.
Single-cell resolution transcriptional atlases of aerial and subterranean pegs reveals auxin signaling associated with peg development in peanut
Authors: Jin Weicai , Xu Mengyun , Huang Lu , Zhou Yueni , Xiao Yuan , Zhong Wenhao , Liang Yanyue , Zheng Zihao , Li Haifen , Yu Qianxia , Li Shaoxiong , Wang Runfeng , Varshney Rajeev K. , Lu Qing , Hong Yanbin , Chen Xiaoping , Wang Wenyi , Liu Hao 
Published: January 2025

Abstract

Pegging is a unique characteristic of peanut plants in which chasmogamous flowers are produced aerially that penetrate into the soil and develop subterranean pods. However, the gene networks that regulate peg development in peanut remain largely unknown. To investigate the spatiotemporal regulation of gene regulatory networks during peg growth and development. Here, we constructed a single-nucleus resolution transcriptomic atlas of both aerial and subterranean pegs comprising 50,903 individual cells and annotated nine major cell types using cell markers and in situ hybridization. Transcription factors (TFs) developmental differentiation trajectories and predicted interaction networks were identified during peg development. Combination of single-nucleus RNA-sequencing (snRNA-seq) and bulk RNA-seq in aerial and subterranean pegs highlighted the potential involvement of phytohormone signals in pegging. In addition, our findings suggest that auxin signal transduction pathways are crucial for gravity-regulated peg formation. This study provides new insights into the development of aerial and subterranean pegs in peanut at cell-type resolution, and could also serve as the basis for future investigation of the molecular mechanisms underlying cell maintenance and differentiation during pod formation.
Screening for Atrial Fibrillation in Older Adults in Primary Care: A Cross-Sectional Pilot Study in Qatar
Authors: Alnuaimi Ahmed Sameer , Ahmad Shahid , Abdulla Syed , Khan Babur , Ahmed Zaheer , Hameed Tahir , Syed Mohamed Ahmed 
Published: January 2025

Abstract

Background
Atrial fibrillation (AF) is the most common arrhythmia in clinical practice, and its prevalence is increasing in aging populations. It is a leading cause of acute stroke, heart failure, sudden death, and other cardiovascular morbidities. AF is often asymptomatic and is frequently diagnosed late, typically after a patient presents with acute stroke. Early detection and treatment can prevent such life-threatening events, making AF screening a public health priority. However, diagnosing asymptomatic AF remains challenging. While international guidelines recommend AF screening, Qatar lacks documented prevalence data and formal screening programs. This study aimed to assess the feasibility of an AF screening program for older adults in Qatar.
Methods
A cross-sectional study was conducted among individuals aged ≥65 years registered with the Primary Health Care Corporation (PHCC)-Qatar, which serves approximately 70% of Qatar’s population. A simple random sample of 139 to 385 individuals was determined to ensure a sampling error of 5% to 3%, assuming a 10% AF prevalence. The participants underwent screening via standard ECG tracing that were reviewed independently by both the family physicians and a cardiologist.
Results
The prevalence of AF among the study population was 5.4%. Newly diagnosed cases accounted for 2% of the 249 participants screened. All newly detected patients had a CHA2DS2VASc scores indicating the need for anticoagulant therapy to reduce stroke risk. Furthermore, compared with cardiologists, the family physicians in primary care successfully identified 75% of AF cases through ECG.
Conclusions
If implemented nationally among individuals aged ≥65 years, the screening program could identify 747 undiagnosed AF cases among the 37,371. AF screening for individuals aged ≥75 years appears to offer a favorable cost‒benefit ratio, supporting the implementation of a targeted screening strategy in Qatar.
Understanding the Interplay Between Stress, Anxiety, and Depression and Their Impact on Health in Traffic Police Officers
Authors: Ramos-Galarza Carlos , Flores Fiamma , Argoti Taysha , Díaz-Guerra Diego , Hernández-Lugo Marena , Broche-Pérez Yunier 
Published: January 2025

Abstract

Background
Traffic police officers are frequently exposed to stress, anxiety, and depression, which adversely impact their health. This study hypothesized that these factors influence the hormonal, muscular, digestive, and cognitive systems of traffic police officers.
Methods
The sample comprised 146 traffic police officers (42 women and 104 men) aged 30 to 38. Gender differences in symptoms were examined, and model fit was assessed using indicators such as chi-square, comparative fit index, root mean square error of approximation (RMSEA), and standardized root means residual (SRMR).
Results
Gender differences were observed, with women reporting more significant issues related to muscular, hormonal, and cognitive symptoms. The models demonstrated a good fit, particularly in the model examining digestive symptoms.
Conclusions
The findings indicate a substantial impact of stress, anxiety, and depression on traffic police officers' health. Psychological support and monitoring are recommended at traffic police stations. The results align with previous research and underscore the need for targeted interventions to address the health challenges faced by these specialized police groups exposed to daily stressors.
Rhizobial plant probiotics enhance yield, heavy metal resistance, and impact the rhizosphere bacterial community in tomato (Solanum lycopersicum) under conventional agriculture system
Authors: Manzano-Gómez Luis Alberto , Rincón-Molina Clara Ivette , Stopol-Martínez Simón Samuel , Martínez-Romero Esperanza , Villalobos-Maldonado Juan José , Ruíz-Lau Nancy , Ruíz-Valdiviezo Víctor Manuel , Rincón-Rosales Reiner 
Published: January 2025

Abstract

Background and Aims
This study investigates the potential of native elite rhizobial strains Rhizobium sp. ACO-34A, Sinorhizobium mexicanum ITTG-R7T, and S. chiapasense ITTG-S70T to enhance tomato crop productivity under conventional open-field agricultural management. It evaluates their effects on yield, plant growth, nutrient uptake, and rhizosphere bacterial community dynamics.
Methods
Tomato plants were inoculated with rhizobial strains under conventional cultivation practices and compared to a control group treated with chemical fertilizers. Agronomic and biological parameters, nutrient content in plant tissue, and rhizosphere bacterial diversity (alpha and beta) were assessed. Soil properties were analyzed to determine their influence on plant and microbial performance.
Results
Inoculation with Rhizobium sp. ACO-34A resulted in a significant 2.6-fold increase in tomato yield compared to chemically fertilized controls. Enhanced fruit size, plant height, stem width, and dry weight were also observed. Additionally, inoculated plants showed a statistically significant reduction in Fe and Cu content in plant tissue. However, no substantial changes were detected in rhizosphere bacterial alpha or beta diversity, nor a significant association between soil properties and crop performance.
Conclusions
The findings suggest that the observed improvements in tomato productivity are primarily due to inoculation with native elite strains, particularly ACO-34A, rather than soil characteristics. This study underscores the potential of native rhizobial strains as sustainable biotechnological tools to boost crop productivity and support global sustainable agriculture.