bluuebunny/crossref_metadata_2025_split huggingface dataset:
Human-Like Named Entity Recognition with Large Language Models in Unstructured Text-based Electronic Healthcare Records: An Evaluation Study
Authors: Akbasli Izzet Turkalp , Birbilen Ahmet Ziya , Teksam Ozlem 
Published: March 2024

Abstract
Background
The integration of big data and artificial intelligence (AI) in healthcare, particularly through the analysis of electronic health records (EHR), presents significant opportunities for improving diagnostic accuracy and patient outcomes. However, the challenge of processing and accurately labeling vast amounts of unstructured data remains a critical bottleneck, necessitating efficient and reliable solutions. This study investigates the ability of domain specific, fine-tuned large language models (LLMs) to classify unstructured EHR texts with typographical errors through named entity recognition tasks, aiming to improve the efficiency and reliability of supervised learning AI models in healthcare.
Methods
Clinical notes from pediatric emergency room admissions at Hacettepe University İhsan Doğramacı Children's Hospital from 2018 to 2023 were analyzed. The data were preprocessed with open source Python libraries and categorized using a pretrained GPT-3 model, "text-davinci-003," before and after fine-tuning with domain-specific data on respiratory tract infections (RTI). The model's predictions were compared against ground truth labels established by pediatric specialists.
Results
Out of 24,229 patient records classified as "Others ()", 18,879 were identified without typographical errors and confirmed for RTI through filtering methods. The fine-tuned model achieved a 99.96% accuracy, significantly outperforming the pretrained model's 78.54% accuracy in identifying RTI cases among the remaining records. The fine-tuned model demonstrated superior performance metrics across all evaluated aspects compared to the pretrained model.
Conclusions
Fine-tuned LLMs can categorize unstructured EHR data with high accuracy, closely approximating the performance of domain experts. This approach significantly reduces the time and costs associated with manual data labeling, demonstrating the potential to streamline the processing of large-scale healthcare data for AI applications.
Evolution of the chemical composition of fogwater collected at four sites in the Alsace region between 2015 and 2021
Authors: Khoury Dani , Millet Maurice , Jabali Yasmine , Weissenberger Thomas , Delhomme Olivier 
Published: March 2024

Abstract

Four sites representing urban (Strasbourg), suburban (Geispolsheim and Cronenbourg), and rural (Erstein) in the Alsace region, north east of France are sampled to investigate the evolution of the chemical composition of fog water between 2015 and 2021. For this aim, forty-two fog samples are collected using a Caltech Active Strand Cloud Collector (CASCC). The samples are analyzed for their inorganic species (metals and ions) and their physico-chemical properties (pH, dissolved organic carbon (DOC), liquid water content (LWC), and conductivity (K)). Temporal and spatial evolutions have been also investigated between the four sampling sites. Our results are then compared to previous studies performed in 1990’s at Strasbourg. Since 1999, there is a lack of fog knowledge in Strasbourg metropolitan and complete absence of fog studies. That’s why it is interesting to resume fogwater collection in order to re-build a solid background regarding air quality in France, particularly in Alsace, and assess the effectiveness of the rules and regulations that have been implemented over time.
Research on the Dynamic Flexible Support Machining Method for Propeller Blades
Authors: Li Songmo , Wang Rui , Ge Yuhao , Guo Xiangyu , Liu Mengmeng , Zhu Xiaohu 
Published: March 2024

Abstract
At present, most of the propeller machining adopts single-sided machining, and its machining vibration and deformation seriously affect the machining accuracy. To reduce the machining vibration, a dynamic flexible support machining method is proposed, i.e., while the tool is machining, the multi-point flexible support device supports the blade and counteracts the milling force to suppress the vibration and deformation. Due to the complex shape of the blade and the special structure of the support device, the blade is divided into different areas, and a support motion trajectory combining symmetric and asymmetric motions is planned, and then a set of post-processing systems is introduced. After obtaining the tool position points, the support points are solved cyclically by the ergodic method. Subsequently, the support points are interpolated, and the vectors are smoothed to obtain smooth and continuous support trajectories. Finally, the machining parameters are calculated, and the machining data applicable to the XYZ-3RPS hybrid machine are integrated. The feasibility of the proposed support trajectory and post-processing algorithm was ultimately demonstrated through practical machining experiments.
Awareness and use of improved livestock feed technologies among agro-pastoral households in the West African Sahel
Authors: Umutoni clarisse , AYANTUNDE Augustine 
Published: March 2024

Abstract

Seasonal scarcity of quality feed continues to be a bottleneck for agro-pastoralists and is the most important limitation to livestock production in agro-pastoral systems in the West African Sahel. In this regard, diverse promising technologies to increase the production and availability of quality feed have been introduced. Despite the introduction of various feed technologies in the region, the adoption rate by smallholder farmers is very low. The objective of this study was to identify factors affecting the likelihood of using improved livestock feed technologies among agro-pastoral households in two regions of Niger. A logit model was employed using data collected from randomly selected 218 agro-pastoral households between February and April 2023. The study revealed low awareness and use of improved livestock feed technologies among agro-pastoral households. The findings suggested the positive impact of education level, membership of farmer groups and ownership of small ruminant on enhancing the probability of using improved livestock feed technologies. Households’ geographical area was one of the determinant factors in using improved livestock feed technologies. The use of improved livestock feed technologies was also influenced by family size.The study provides guidance to inform strategies by the decision-makers to enhance the utilization of improved livestock feed technologies among agro-pastoral households in the West African Sahel. Suggested strategies may in turn alleviate the current problem of low productivity of livestock, contribute to improving household food security and nutrition among agro-pastoral communities.
Dengue vector specie’s niche distribution modeling over India using the machine learning-based MaxEnt model
Authors: Prasad Peri Hari 
Published: March 2024

Abstract

NTD (Neglected Tropical diseases), such as dengue, will severely impact public health. So, proper measures and preventive steps should be taken to mitigate dengue outbreaks. This is accomplished by predicting dengue hotspots using SDM models. A well-known Maxent (Maximum Entropy) model was employed to forecast the future spread of vectors based on environmental data, including bio-climatic variables. Accuracy evaluation was performed using AUC values. Historical data on the presence of Aedes aegypti and Aedes albopictus were gathered from GBIF.org (1981–2004), along with corresponding climatic data from (https://chelsa-climate.org/). Features were selected through correlation analysis and AUC optimization, and the model was fitted accordingly. Predictions about future distribution were made under three climatic scenarios, namely SSP126, SSP370, and SSP585, derived from CMIP-6 data. There is a significant aegypti vector distribution over India. Meanwhile, albopictus distribution is less severe compared to the aegypti vector. The vector expansion is visible in all three climatic scenarios, especially in northeastern regions such as West Bengal, partial IGP regions like Madhya Pradesh, and all union territories. The model fitted with utmost accuracy in both training and testing. The aegypti accuracy for training and testing are 0.8081 and 0.7658, and similarly for albopictus, 0.8252 and 0.8056. This analysis will give public health experts a vision for planning mitigation strategies. This was only a preliminary analysis based on environmental modeling rather than mechanistic modeling, which may give more insights. However, climate change will profoundly impact VBD (Vector-Borne Diseases).
Empowering Emerging Adults with Type 1 Diabetes: Crafting a Financial and Health Insurance Toolkit through Community-Based Participatory Action Research
Authors: Blanchette Julia E. , Lewis Claudia B. , Shannon Chantel S. , Kanchibhatla Anuhya , Rieke Jorden , Roche Mary Jane , Johnson Dove-Anna , Williams Dionne , Webb Shay , Diaz Crystal N. , Lundgrin Erika L. , Allen Nancy A. , Litchman Michelle L. , Hatipoglu Betul 
Published: March 2024

Abstract

Background: Emerging adults aged 18-30 years face challenges during life transitions, with an added burden of navigating the health care system and additional costs associated with diabetes. This stress is compounded by overall low levels of health insurance literacy in this population, as people may not know about available financial and health care resources to minimize suboptimal diabetes outcomes. This study aimed to tailor a financial and health insurance toolkit to emerging adults with type 1 diabetes, including racially, ethnically diverse, and Medicaid-insured individuals, through community-based participatory action research.
Methods: An academic research team and citizen scientists from a national organization held six online community advisory board (CAB) content-creation meetings to understand how to tailor a financial and health insurance toolkit. The CAB comprised six racially and insurance-diverse emerging adults with type 1 diabetes and four content experts (clinical, financial, and insurance). A deductive thematic qualitative analysis was utilized. Three researchers independently coded the data and then convened to reach group consensus. Two CAB members performed member-checking.
Results: The following key themes emerged to tailor the toolkit: ensuring that content covers empowerment and self-advocacy, including genuine stories and multimedia visuals for aesthetics, addressing clinician bias, acknowledging racial and ethnic disparities in care, incorporating cultural representation, and demystifying Medicaid stigma.
Conclusions: By successfully partnering with the CAB and a community organization through a community-based participatory action research approach, we will develop a financial and health insurance Toolkit tailored to the needs of racially and ethnically diverse and Medicaid-insured emerging adults with type 1 diabetes.
Machine learning reveals the interplay between muscle fatty acids and the gut microbiota: implications for high-quality pork
Authors: Pan Ningfeng , Shi Pengjun , Zhu Qian , Wang Chenyu , Ouyang Qing , Huang Xingguo , Hung Ifen , Liu Chunxue , Xu Kang , Li Yuying , Yang Guan , Yin Yulong , Yin Jie 
Published: March 2024

Abstract

Background
High-quality pork is rich in functional fatty acids such as α-linolenic (ALA), γ-linolenic acid (GLA), docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA), which are thought to prolong lifespan. Multiple studies have confirmed the role of the gut microbiota in muscle fatty acid deposition in pigs, but the inherent complexity of the microbiota remains poorly understood.
Results
Using a machine learning-based data mining approach, we analyzed a dataset containing meat quality, muscle fatty acids, and gut microbiota from 291 pigs to identify potential predictors of total fatty acid (TFA) and unsaturated fatty acids (UFA) content for high-quality pork with better meat color or marbling score. Microbial α diversity exhibited positive predictive power for muscle polyunsaturated fatty acids (PUFAs), especially for GLA, DHA, and EPA. A core set of two microbial phyla (Actinobacteria and Spirochaetes) and 11 genera extracted from the pig gut microbiome correlated with the level of muscle fatty acids. In addition to the fatty acid degradation pathway, Escherichia was negatively associated with the levels of most fatty acids, including oleic acid, ALA, GLA, and EPA. Positive correlations of GLA, DHA, and EPA depositions in the muscle were shared in two microbial genera (Bradyrhizobium and Lachnoclostridium), which also negatively correlated with the fatty acid degradation pathway.
Conclusions
Overall, we revealed an intricate network of correlations between muscle fatty acids and the gut microbiota, suggesting that there are multiple routes to produce high-quality healthy pork that is rich in functional fatty acids.
Determination of the Temporal-Spatial Distribution Patterns of Ancient Heritage Sites in China and their Influencing Factors via GIS
Authors: Li Xiaojia 
Published: March 2024

Abstract

Exploring the temporal and spatial distribution as well as the influencing factors of ancient archaeological sites can illuminate the developmental trajectory of human civilization, aiding in the comprehension of the geographical environment, resource distribution, climatic conditions,and other aspects of historical periods.In particular, exploring the temporal and spatial distribution is highly important for the management and protection of these sites. Using ArcGIS 10.8 software, we analysed the distribution of 1,194 historical ancient sites among the key national cultural heritage sites. The research findings are as follows: (1) The distribution of ancient sites demonstrates an agglomerative spatial pattern. The nearest neighbour index (R<1) for sites from various historical periods indicates an agglomerative spatial distribution of ancient sites across different historical periods, with the clustering degree being relatively poor for sites from the Wei-Jin, Northern and Southern Dynasties and in the Ming and Qing periods. The regions with a concentration of ancient sites are the middle and lower reaches of the Yangtze River and Yellow River basins. Furthermore, there is a higher abundance of ancient sites in the southeastern region than in the northwestern region. (2) The distribution of ancient sites in different historical periods also exhibits an imbalance, with an overall downwards trend in the number of ancient sites in China. Notably, more ancient sites were found for the prehistoric and pre-Qin periods. The temporal trend of ancient sites during historical periods follows a trajectory from northeast to northwest, northeast to southwest, etc. (3) The temporal and spatial distributions of ancient sites are influenced by multiple factors, including social development, the natural environment, geographical elements, and socioeconomic and political factors. Finally, based on an understanding of the spatiotemporal distribution pattern of ancient sites and the factors that influence this pattern, recommendations for conservation and development can be proposed and supported by modern technological methods, with the aim of offering insights for the protection and sustainable development of heritage sites.
Enhancing Qualitative Microwave Imaging Indicators by Autoencoder Based Deep Learning Algorithm
Authors: BAYAT İbrahim Halil , DOĞU Semih , AKINCI Mehmet Nuri , CROCCO Lorenzo , AKDUMAN İbrahim 
Published: March 2024

Abstract
Enhancing microwave inverse imaging methods has garnered significant attention in the field of deep learning and microwave imaging, owing to their immense potential in tackling non-linear problems. Microwave imaging techniques commonly encounter the adverse effects of noise in measurements, leading to suboptimal results. In this research, we propose a novel deep neural network-based model that effectively mitigates the noise effects on qualitative microwave indicators, resulting in high-quality reconstructions. We consider various configurations of dielectric objects in free space to address the microwave imaging problem. The scattered field is computed using the two-dimensional method of moments, employing an antenna array comprising 36 infinitely long line sources. Subsequently, the linear sampling method is utilized to generate multi-frequency qualitative indicators, which are then employed as input to a deep learning network along with corresponding binary ground-truth images. The proposed deep learning model comprises 11 layers, including convolutional and deconvolutional layers, without any dropout or pooling layers to prevent information loss. The results of the network are compared with Jaccard index to see how similar they are. The proposed method achieves higher accuracy and Jaccard score, demonstrating the efficacy of the proposed deep learning approach. Notably, the simplicity of the model construction contributes to its appeal, as it operates in conjunction with the results obtained from the linear sampling method. The presented results underscore the remarkable capabilities of the proposed deep learning method in enhancing microwave inverse imaging.
Damage Detection of wire rope Broken wire Damage Based on deep Semi- supervised Learning
Authors: zhang Juwei , Ye Qiang , Chen Quankun 
Published: March 2024

Abstract
Aiming at the problems such as the existence of a large amount of background information for wire rope image, the lack of efficient and accurate damage area extraction methods. A novel wire rope image preprocessing method is proposed, which can automatically adjust the pose of wire rope, remove the background information of wire rope image, and extract the damage area of wire rope. In response to the problem that deep learning based wire rope damage detection methods require a large amount of labeled data, a convolutional neural network model WR-CNN adapted to the steel wire rope dataset is designed, And combine WR-CNN with the semi-supervised learning example provided by Temporal Ensembling to form WR-TE-CNN. Aiming at the problem that the existing detection methods of broken wire damage do not consider the difference information between different types of damage and the similar information between the same type of damage, the distance loss function is introduced into the loss function of WR-TE-CNN and the characteristic quantity of calculating distance loss function is improved ( Before the improvement it was called WR-TE-DLCNN, after the improvement it was called WR-TE-IDLCNN ). The experimental results show that When the proportion of labeled data is 0.5, the wire breakage recognition rate of WR-TE-IDLCNN exceeds WR-CNN and is 99.04%. WR-TE-IDLCNN can further utilize the information between different types of damage and between the same types on the basis of WR-TE-DLCNN.
Nursing Students' and Faculty Perspectives: Challenges and Insights into Digital Classes during the Pandemic
Authors: K Puvaneswari. , Sriniva Umavalli , Christhumary. Preetha Carolin , Sakthivel Rajathi , Kanagaraj Anbueswari , Jacob Jessy , Yesuwilson Albin Joseph 
Published: March 2024

Abstract
Introduction
The pedagogical transition from traditional to distance learning is a challenge for nursing education during the pandemic in India, since the faculty and students were not exposed to the virtual classes
Methods
A qualitative research examined faculty and students perspectives of the challenges and insights into digital classes in the during the pandemic. Convenient sampling was employed to gather qualitative data from the diploma, undergraduate, or postgraduate programs students who had experienced digital classes during the pandemic. Six focus group interviews were done with the total of 32 nursing students, and 14 faculty. After obtaining consent each session lasting between 1.5 to 2 hours. The data were analyzed using thematic analysis, following Braun and Clarke's (2012) six-phase analytical process.
Results
The major themes created through students' perspectives were 1)Closing the Educational Divide: Bridging the Gap with E-Technology, 2.Exposed to Different Approaches of teaching -learning, 3. Challenges & Technological Glitches in the digital Classroom, 4.Expression of Dissatisfaction, 5.Struggling with Self-Motivation and 6.Environmental influences. The major themes created through faculty perspectives were 1.Escalating Workload: Navigating Increased Demands, 2.Enhanced Technology Proficiency: Gaining Improved Access, 3.Discipline Concerns, 4.Improved methods of teaching, 5.Lack of Clinical learning and 6.Need for Special Platform. Along with the major themes sub-themes were also reported.
Conclusion
This study concluded that digital classes were very convenient and beneficial during pandemic. Both the group of respondents stressed the challenges which need to be focused to fill the gap in future. Keywords- Digital classes, Pandemic, Nursing students, Nursing faculty, Challenges, Insights
Scope of recruitment for insider immigrant community gatekeepers: A case example of recruiting Ghanaian immigrants in the United States for a research study
Authors: Kodwo-Nyameazea Yale 
Published: March 2024

Abstract
This study draws on the author’s fieldwork experience to explore the scope of participant recruitment for insider immigrant community gatekeepers. The scope of recruitment includes being a member of the immigrant community, soliciting for participation from community members, volunteering, and being self-motivated to recruit participants. In their role, insider immigrant community gatekeepers not only decided who received the research information and who eventually participated in the research activities but also provided support to the researchers that they had granted community access. Insider immigrant community gatekeepers use a combination of personal qualities and a range of skills to recruit participants. They drew on their credibility and trustworthiness to build and maintain rapport and relationships that elicited community members’ participation in research. They used their good communication and storytelling skills to attract and persuade potential participants to complete research activities. Insider immigrant community gatekeepers have patience and self-motivation to persist and overcome community members’ rejection of recruitment overtures, organize activities that facilitate participation in research, and provide information that helps researchers make informed decisions about recruitment.