bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantum Visual Feature Encoding Revisited
Authors: Nguyen Xuan-Bac , Nguyen Hoang-Quan , Churchill Hugh , Khan Samee U. , Luu Khoa 
Published: June 2024

Abstract

Although quantum machine learning has been introduced for a while, its applications in computer vision are still limited. This paper, therefore, revisits the quantum visual encoding strategies, the initial step in quantum machine learning. Investigating the root cause, we uncover that the existing quantum encoding design fails to ensure information preservation of the visual features after the encoding process, thus complicating the learning process of the quantum machine learning models. In particular, the problem, termed ”Quantum Information Gap” (QIG), leads to a gap of information between classical and corresponding quantum features. We provide theoretical proof and practical examples with visualization for that found and underscore the significance of QIG, as it directly impacts the performance of quantum machine learning algorithms. To tackle this challenge, we introduce a simple but efficient new loss function named Quantum Information Preserving (QIP) to minimize this gap, resulting in enhanced performance of quantum machine learning algorithms. Extensive experiments validate the effectiveness of our approach, showcasing superior performance compared to current methodologies and consistently achieving state-of-the-art results in quantum modeling.
Environmental and socio-economic impact comparison of solar and hydroelectric systems: A case of a real investment
Authors: Aytac Ayca 
Published: June 2024

Abstract

Most of the energy produced from renewable energy sources in the world is generated from hydraulic and solar energy sources. This study compares the socio-economic and environmental impacts of hydroelectric power plants, which have the largest share in total energy production, and solar power plants, which have recently increased their installed capacity rapidly and will soon have the largest energy potential in the world. For this purpose, Cardakli Hydroelectric Power Plant (HEPP) and Ekinozu Solar Power Plant (SPP) projects, which have same installed capacity in Turkey, were analyzed in terms of (i) initial investment cost, (ii) energy production values, (iii) operating costs and (iv) socioeconomic and environmental impacts. As a result, it is concluded that solar power plants in Turkey are more advantageous than hydroelectric power plants due to ease of construction and operation, low environmental impacts, more secure energy income, low initial investment costs, high electricity sales prices, and short payback period.
Mineral Dissociation and Enrichment Characteristics of Apatite Pulverized by High-pressure Gas Rapid Unloading and High-Speed Impact Combined Effect
Authors: yongbo fan , Genghao Zhang , Shihai Li , Li Zhang , Jianqiang Guo , Chun Feng 
Published: June 2024

Abstract

Mineral dissociation and enrichment have always been the core issues in ore processing. The goal of multi-stage crushing and ball milling is dissociation because mineral dissociation is the foundation of beneficiation. High energy consumption and environmental pollution have always been unavoidable topics. We propose a novel method involving continuously graded apatite particles produced by high-pressure gas rapid unloading and high-speed impact. Particle size follows MR-R distribution. The scanning electron microscopy data shows that the apatite particles smaller than 4 mm produced by rapid unloading of high-pressure gas have completely dissociated, and high-grade apatite enriched in the particle size range of 0.5 to 4 mm. The average grade of the preferred particle size interval is 3–5% higher than the original ore because most impurity minerals become fine particles and separate from apatite. The quality proportion of particle size range of 0.5 to 4 mm is higher than that of particle size less than 0.5 mm. Dissociation degrees of apatite less than 4 mm are above 88%, which is beneficial for mineral processing.
Factors associated with anemia among pregnant and lactating women during the COVID-19 pandemic in the targeted municipalities of five selected provinces in Nepal: a cross-sectional study
Authors: Niraula Pratik , Upadhyay Atul , Karn Sanjeev Kumar , Karki Roman 
Published: June 2024

Abstract

Background: Anemia poses significant health risks, especially affecting women and children, leading to increased morbidity, mortality, and adverse birth outcomes. The COVID-19 pandemic has further exacerbated economic challenges and food insecurity, and weakened immune systems affecting vulnerable populations like pregnant, and lactating women, and children. This study aimed to assess factors associated with anemia among pregnant and lactating women during the covid-19 pandemic in the targeted municipalities of five provinces of Nepal.
Methods: A cross-sectional study was conducted from August, 2022-April, 2023 among 844 pregnant and lactating women (mean age 26.8±4.96 years) from five provinces of Nepal selected through cluster sampling. A semi-structured electronic questionnaire, a paper-based 24-hour dietary recall, and hemoglobin concentration in the blood were recorded. Bivariate and multivariate binary logistic regressions were used to estimate the crude odds ratios (cOR), and adjusted odds ratios (aOR) with 95% confidence intervals (CIs).
Results: The overall anemia prevalence was determined to be 41.2% with a mean hemoglobin level of 11.58 ± 1.59 g/dl. In the multivariate model, women aged 30-39 years (aOR = 0.667, 95% CI: 0.172-2.59) and pregnant women had lower anemia odds (aOR = 0.866, 95% CI: 0.645-1.162). Unskilled manual workers (aOR = 1.666, 95% CI: 0.708-3.924) and those with no formal education were more likely to be anemic (aOR = 3.251, 95% CI: 1.728-6.115). Anemia odds were higher for low-income (aOR = 1.601, 95% CI: 0.933-2.749, Muslim (aOR = 1.051, 95% CI: 0.577-1.916), Madhesi (aOR = 1.051, 95% CI: 0.577-1.916), Christian (aOR = 1.645, 95% CI: 0.683-3.960), and Buddhist (aOR = 1.497, 95% CI: 0.810-2.674) women. Women with low dietary diversity (aOR = 3.054, 95% CI: 2.248-4.15), not consuming fortified foods (aOR = 1.192, 95% CI: 0.689-2.065) and not consuming eggs (aOR = 1.23, 95% CI: 0.841-1.799) had higher likelihood of anemia. The study also revealed that the consumption of cereals and legumes remained unchanged, but a majority reported having a reduced intake of fruits, vegetables, milk, meat, and eggs during the pandemic.
Conclusions: A high prevalence of anemia, a severe public health problem, among the low-income pregnant and lactating women, with limited access to education and dietary diversity pointed out the urgent need for intervention to alleviate their nutritional and health status in targeted municipalities.
Entropy Based Blending of Policies for Multi-Agent Coexistence
Authors: David Rother , Franziska Herbert , Fabian Kalter , Dorothea Koert , Joni Pajarinen , Jan Peters , Thomas H. Weisswange 
Published: June 2024

Abstract

Research on multi-agent interaction involving humans is still in its infancy. Most approaches have focused on environments with collaborative human behavior or a small, defined set of situations. When deploying robots in human-inhabited environments in the future, the diversity of interactions surpasses the capabilities of pre-trained collaboration models. "Coexistence" environments, characterized by agents with varying or partially aligned objectives, present a unique challenge for robotic collaboration. Traditional reinforcement learning methods fall short in these settings. These approaches lack the flexibility to adapt to changing agent counts or task requirements without undergoing retraining. Moreover, existing models do not adequatelysupport scenarios where robots should exhibit helpful behavior toward others without compromising their primary goals.To tackle this issue, we introduce a novel framework that decomposes interaction and task-solving into separate learning problems and blends the resulting policies at inference time using a theory of mind model for task estimation. We create impact-aware agents and linearly scale the cost of training agents with the number of agents and available tasks. To this end, a weighting function blending action distributions for individual interactions with the original task action distribution is proposed. To support our claims we demonstrate that our framework scales in task and agent count across several environments and considers collaboration opportunities when present.The new learning paradigm opens the path to more complex multi-robot, multi-human interactions.
Evaluation of standard precaution training for healthcare trainees
Authors: Zhang Xing , Li Hongwei , Li Ning , Li Yusen , Kong Li , Wang Peng , Ma Yonghui , Ni Leiping , Liu Lili , Meng Xiujuan 
Published: June 2024

Abstract

Introduction: Blood-borne occupational exposure (OBE) has become a major public health problem for healthcare workers globally. Effective education and training on post-exposure management and standard precautions for healthcare trainees, are important measures for reducing OBE and infection rates. The aim of this study was to evaluate the effectiveness of OBE and standard precaution training for healthcare trainees.
Methods: This study is designed as a randomized controlled study. Healthcare trainees who were undergoing training at the hospital from July 2020 to July 2023 were selected as the research subjects. They were randomized into theoretical teaching group and situational simulation group. The two groups taught the same knowledge using different methods. Both groups were tested the same content before and after the training.
Results: The accuracy rates before and after training in different groups were 71.71% and 78.08%, respectively (P<0.001). The lowest accuracy rate before training was for standard precaution knowledge (70.66%), while the highest accuracy rate after training was for OBE follow-up knowledge (82.39%) (P<0.001). The accuracy rate for trainees who experienced OBE was 69.87% before training and 77.11% after training. For trainees who had not experienced OBE, the accuracy rates were 71.38% before training and 77.25% after training. The accuracy rates were higher after training compared to before training, with statistically significant differences (P<0.001).
Conclusion: This study demonstrated that the self-protection awareness, knowledge on OBE management, and standard precaution among healthcare trainees were enhanced through the training, and operational content can achieve better results through scenario simulation training.
Predicting Alzheimer's disease in imbalanced datasets focusing on cardiovascular risk scales with machine learning models
Authors: García-Lluch Gemma , Mora Angélica Resendiz , Royo Lucrecia Moreno , Cháfer-Pericás Consuelo , Baquero Miquel , Pardo Juan 
Published: June 2024

Abstract

Purpose
Considering the aging population, the prevalence of Alzheimer's disease (AD) is on the rise. As there is currently no cure for AD, it is crucial to identify the key factors contributing to its progression. Cardiovascular risk is believed to play a significant role in the advancement of AD, potentially leading to neurodegenerative changes in the brain. Therefore, this project seeks to demonstrate the effectiveness of using machine learning models (ML) to develop non-invasive and cost-effective screening tools incorporating various cardiovascular risk scores.
Methods
We gathered data from the electronic health records (EHR) of a hospital of reference in Spain. This process yielded a highly imbalanced dataset of 177 diagnosed subjects and 48 controls aged 50 to 75. To address this common issue, we employed a range of ML models, along with balancing techniques and metrics, to overcome such a typical problem, leading to the development of highly accurate models.
Results
Several bagging, boosting, linear, and stacked models resulted in better F1-Score, and cardiovascular risk scales, such as SCORE2, were essential for such prediction algorithms. Glucose levels seemed important in AD prediction, and drugs such as anticholinergics, antidepressants, or angiotensin-converting enzyme inhibitors were positively related to AD prediction. In contrast, nonsteroidal anti-inflammatory drugs and angiotensin receptor blockers had the opposite effect.
Conclusion
Our research demonstrates the potential of machine learning techniques to improve the screening of AD patients before they undergo invasive and costly diagnosis tests, allowing personalized rationalization of healthcare costs and improving patient care.
Enhancing Multiple-Style Image Colorization through Context-Aware Codebook and Multi-Stage Learning
Authors: Liu Zheyuan , Xu Hanning , Mu Pan 
Published: June 2024

Abstract

Single image colorization is an inherently ill-posed problem that has attracted more research interest recently. It challenges to learn a luminance-chroma mapping and manage multi-modal uncertainty. However, directly learning a luminance-chroma mapping often results in issues such as semantic mismatch and color bleeding while reconstructing the chroma channels. Furthermore, the problem of output diversity remains unaddressed. To this end, we propose CodeColorist, a novel multi-stage network that effectively tackles the colorization process. To enhance the reconstruction of the chroma channels and overcome issues like semantic mismatch and color bleeding, in the first stage, we focus on learning a sophisticated and context-aware codebook by leveraging the interplay between the features extracted from luminance and chroma channels. In the second stage, we reframe the colorization problem as a code prediction task and learn to predict the code sequence for accurate codebook lookup. In addition, we address the issue of stochastic diverse output in colorization by navigating through an expanded hidden representation space in the final stage. This allows us to produce a wide range of diverse and visually appealing colorized images. Thanks to our tailored feature extraction architecture, expressive codebook, and designed lookup scheme, we are able to achieve vibrant and realistic colorized images that outperform previous works. Source code will be released in https://github.com/OHaiYo-lzy/CodeColorist.
ACUM And ATUM: Adaptive Multi-pixel Aggregation Upsample Modules for Lightweight Arbitrary Scale Super-Resolution
Authors: Tan Xinlei , Wu Fupei 
Published: June 2024

Abstract

While image super-resolution (SR) has made significant breakthroughs with the application of vision transformer (ViT) framework, researchers have primarily focused on enhancing model backbones, ignoring the influence of upsample modules. Mainstream upsample modules predominantly relying on pixshuffle method which lacks spatial and channel interactions among multiple pixels. Moreover, they are limited to specific scale factors (e.g., ×2/×3/×4) rather than accommodating non-integer or asymmetric SR, constraining their reconstructive capabilities. To address these limitations, this paper proposes an innovative adaptive upsample strategy. This strategy aims to effectively aggregate the extracted features and reconstruct high-resolution (HR) image by learning the location and content correlations among feature map pixels. Based on this strategy, we develop two plug-in modules called Adaptive Convolution Upsample Module (ACUM) and Adaptive Transformer Upsample Module (ATUM) for existing lightweight SR networks to perform scale-arbitrary SR. For any low-resolution (LR) images, the two modules can learn to establish the dependencies between each HR image pixel and multiple LR feature pixels to zoom in it with arbitrary scale factors within a single model. Coupling with different kinds of SR backbones, including CNN-based and Vit-based networks, extensive experiments demonstrate that ACUM and ATUM achieves better performance compared to traditional method at specific scale and more efficient at arbitrary scale.
Density-wave-like gap evolution in La3Ni2O7 under high pressure revealed by ultrafast optical spectroscopy
Authors: Yu Xiao Hui , Meng Yanghao , Yang Yi , Sun Hualei , Zhang Sasa , Luo Jianlin , Chen Liucheng , Ma Xiaoli , Wang Meng , Hong Fang , Wang Xinbo 
Published: June 2024

Abstract

Density-wave-like (DW) order is believed to be correlated with superconductivity in the recently discovered high-temperature superconductor La3Ni2O7. However, experimental investigations of its evolution under high pressure are still lacking. Here, we investigate the quasiparticle dynamics in bilayer nickelate La3Ni2O7 single crystals using ultrafast optical pump-probe spectroscopy under high pressures up to 34.2 GPa. Near ambient pressure, the temperature-dependent relaxation dynamics demonstrate a phonon bottleneck effect due to the opening of a DW gap at 151 K, with an energy scale of 66 meV as determined by the Rothwarf-Taylor model. With increasing pressure, this phonon bottleneck effect is gradually suppressed and completely disappears around 26 GPa. Remarkably, at pressures above 29.4 GPa, we observe the emergence of a new DW order with a transition temperature of approximately 130 K. Our study provides the first experimental evidence of the evolution of the DW gap under high pressure, offering critical insights into the correlation between DW order and superconductivity in La3Ni2O7. These findings highlight the complex electronic phase transitions in this material and underscore the role of high pressure in tuning its superconducting and DW properties.
Low-Grade Waste Heat Recovery and Utilisation: A Bibliometric Analysis and Literature Review
Authors: Nyakuma Bemgba B. 
Published: June 2024

Abstract

This study examines the research landscape on the recovery and utilisation of low-grade waste heat (RU/LGWH) through bibliometric analysis and literature review based on publications indexed in Scopus from 2008 to 2022. During this period, a total of 135 publications and 4,759 citations were recorded in RU/LGWH research. Publications trends showed that high publication rates and scientific interest in the topic were largely occasioned by efforts to achieve humanity’s quest for cleaner sources of energy and net zero emissions. The most prolific researchers, affiliations and nations are Sheng Yang, Central South University and China, respectively. The observed dominance of Chinese-based researchers and institutions is ascribed to research funding available from funding organisations/programmes such as the National Natural Science Foundation, among others, that account for 6 out of the top 10 funders of RU/LGWH research. High rates of collaboration between countries (RC = 69.23%) compared to authors (RC = 54.05%) were observed during social network analysis. Hotspots analysis revealed that RU/LGWH research has been majorly focused on three core areas: Energy Recovery and Management (ERM), Economics and Efficiency Assessment (EEA), and Exergy and Performance Analysis (EPA). As such, the various stakeholders have devoted significant resources to exploring and exploiting the technological feasibility, economic viability, and thermodynamic performance of LGWH. Despite the enormous progress recorded, selected grey areas still exist, and we opine the prospects of the field greatly lie in finding solutions to these research gaps.
Automatic ovarian follicle detection using object detection models
Authors: Hassan Maya Haj , Reiter Eric , Razzaq Misbah 
Published: June 2024

Abstract

Ovaries are of paramount importance in reproduction as they produce female gametes through a complex developmental process known as folliculogenesis. In the prospect of better understanding the mechanisms of folliculogenesis and of developing novel pharmacological approaches to control it, it is important to accurately and quantitatively assess the later stages of ovarian folliculogenesis (i.e. the formation of antral follicles and corpus lutea). Manual counting from histological sections is commonly employed to determine the number of these follicular structures, however it is a laborious and error prone task. In this work, we show the benefits of deep learning models for counting antral follicles and corpus lutea in ovarian histology sections. Here, we use various backbone architectures to build two one-stage object detection models, i.e. YOLO and RetinaNet. We employ transfer learning, early stopping, and data augmentation approaches to improve the generalizability of the object detectors. Furthermore, we use sampling strategy to mitigate the foreground-foreground class imbalance and focal loss to reduce the imbalance between the foreground-background classes. Our models were trained and validated using a dataset containing only 1000 images. With RetinaNet, we achieved a mean average precision of 83\% whereas with YOLO of 75% on the testing dataset. Our results demonstrate that deep learning methods are useful to speed up the follicle counting process and improve accuracy by correcting manual counting errors.
Fatores interferentes na reposição de vitamina D e cálcio em idosos: Inquérito domiciliar
Authors: Claro Maísa de Lima , Moura Paloma Lima , Ibiapina Aline Raquel de Sousa , Dos Santos Luís Eduardo Soares , Costa e Silva Danilla Michelle , Formiga Laura Maria Feitosa 
Published: June 2024

O presente trabalho teve por objetivo analisar a frequência e fatores associados a reposição de vitamina D e cálcio em idosos, residentes da zona urbana da cidade de Picos/Piauí. Consistiu de um estudo observacional do tipo transversal, cujos dados advém do Inquérito de Saúde de Base Populacional. Participou 143 idosos, residentes da zona urbana. Os dados coletados foram reposição de vitamina D e cálcio, sociodemográficos, percepção de saúde e estilo de vida, realizando análises estatísticas inferenciais. Os dados apontaram que mais da metade dos idosos realizavam exposição solar, porém não faziam reposição de vitamina D e cálcio. Foi observado que o sexo, escolaridade, estado de saúde e participação de atividades sociais são os principais fatores associados à reposição. Sugere-se que participar de grupos socias e ser do sexo feminino é fator de proteção para a saúde. Achados relevantes destacam a baixa reposição de vitamina D e cálcio entre a maioria dos idosos estudados, e que diversos fatores podem contribuir para esta condição.