bluuebunny/crossref_metadata_2025_split huggingface dataset:
SuBIS: Submodular Subset Selection with Importance Sampling for Data-Efficient Machine Learning
Authors: Trust Paul , Younis Haseeb , Minghim Rosane 
Published: January 2024

Abstract

In machine learning (ML), particularly in fields like natural language processing and computer vision, developing state-of-the-art models faces a significant challenge due to the high computational power needed for training. These models usually require vast datasets and long training periods, resulting in substantial costs and environmental impacts. Even though extremely large-scale models show promising performances without the need for further finetuning through few-shot and zero-shot learning, they still lag behind fine-tuning alternatives by big margins.This research explores training ML models with smaller yet highly representative subsets of datasets, utilizing submodular data selection. We propose a method Submodular Subset Selection with Importance Sampling (SuBIS), a two-stage process that synergizes clustering with importance sampling alongside submodular functions. This approach is designed to enhance dataset diversity while simultaneously reducing computational demands. Our empirical research indicates that training models with as little as \(10%\) carefully selected subsets of the original dataset can achieve performances that are competitively close, within three standard deviations, to those attained using the full training datasets. Moreover, SuBIS demonstrates its efficacy in scaling submodular functions to accommodate extremely large datasets. It substantially reduces the runtime required for these functions on large datasets by nearly a factor of \(10\) without any deterioration in downstream classification performance.
Flood Impacts on Household Livelihoods and Its Mitigation Strategies: Focus of Gelana Woreda, Oromia, Ethiopia
Authors: Deresse Teshome , Baye Simachew 
Published: January 2024

Abstract

This study assessed the impact of floods on household livelihoods and mitigation strategies in Gelana Woreda, Oromia, Ethiopia. The study's goals included determining the contributing cause, calculating the flood's impact, evaluating household mitigation activities in reaction to the threat of flooding, and evaluating how household livelihoods were impacted by the risk of a flood. The results of the study indicate that different flood effect factors cause interpersonal flood variability to increase slightly. Over a ten-year period, seasonal migration (16.6%), disruption of human health (3%), and loss of life (1.5%) were the three most common shocks from flood-induced disasters in the study area; heavy rain/flash flooding (29.7%) and river flooding (49.2%) were the two main causes of these shocks. Based on the GIS-generated flood map, 46.5% of the kebeles chosen for the Gelana district were categorized as having a high risk of flooding; marginally more than 2.3%; and the remaining 22.1%, 19.5%, and 2.3% had extremely high, low, and moderately hazardous levels, respectively, indicating a greater risk of flooding. Whereas Jirme and Bore have areas with extremely low and medium flood threats, the areas surrounding Kersa, Metari, and Shamole Shida are classified as having highly hazardous and moderate hazardous flood danger. However, in the research region, the main obstacles to flood mitigation were informational gaps (15.6%), lack of funding (28.6%), absence of government support (18.6%), and reluctance to join farmer associations (14.1%). Raising farmers' knowledge of agricultural technologies, structural mitigation, environmental restoration, ease of access to credit services, diversification of income streams, building farmers' capacity, promoting multiple strategies as the main flood mitigation strategies, and raising household standards in the study area are therefore important.
Comparison of Deep Learning Algorithms for Site Detection of False Data Injection Attacks in Smart Grids
Authors: Nasir Qassim , Talib Manar Abu , Arshad Muhammad Arbab , Ishak Tracy , Berrim Romaissa , Alsaid Basma , Badway Youssef , Waraga Omnia Abu 
Published: January 2024

Abstract

False Data Injection Attacks (FDIA) pose a significant threat to the stability of smart grids. Traditional Bad Data Detection (BDD) algorithms, deployed to remove low-quality data, can easily be bypassed by these attacks which require minimal knowledge about the parameters of the power bus systems. This makes it essential to develop defence approaches that are generic and scalable to all types of power systems. Deep learning algorithms provide state-of-the-art detection for FDIA while requiring no knowledge about system parameters. However, there are very few works in the literature that evaluate these models for FDIA detection at the level of an individual node in the power system. In this paper, we utilize multi-layer perceptron (MLP), convolutional neural networks (CNN), Long Short-Term Memory (LSTM), attention-based bidirectional LSTM, and hybrid models for the detection of the exact location of the attack node. All the models are evaluated on IEEE-14 and IEEE-118 bus systems in terms of row accuracy (RACC), computational time, and memory space required for training the deep learning model. Each model was further investigated through a manual grid search to determine the optimal architecture of the deep learning model, including the number of layers and neurons in each layer. Finally, the performance of each model was quantified on different variants of the dataset – which varied in their 𝑙2-norm.
Prediction of vaginal birth after previous cesarean delivery using explainable machine learning models
Authors: Thagaard Ida Näslund , Stentoft-Larsen Valdemar , Iglesias Miquel Triana , Demharter Samuel , Krebs Lone 
Published: January 2024

Abstract

Purpose
This study aims to train a novel explainable machine learning method (QLattice) to predict successful vaginal birth after cesarean and compare the performance of these models with other known machine learning- and logistic regression models.
Methods
A Danish cohort study including 11 017 women with a prior cesarean giving birth during year 2004–2016 was used to train and evaluate three machine learning algorithms (LASSO, Random Forest, and QLattice). Grobmans logistic regression model was used as baseline. Two models were developed (antenatal and prelabor).
Results
Overall, 4 897 (44,4%) women had a trial of labor, with 3 441 (70.3%) women delivering vaginally. In the antenatal model predictive variables were epidural (OR = 0.53), breech presentation (OR = 1.70), mothers height (OR = 1.03), pre-pregnancy BMI (OR = 0.95), any vaginal birth (OR = 7.74), and vaginal birth before cesarean (0.24). In the prelabor model induction of labor (OR = 0.59), primary rupture of membranes (OR = 0.52), and infant weight (OR = 0.52) were additional predictors. For the antenatal prediction models, the area under the curve (AUC) for the different methods were QLattice 0.73 (0.70–0.76), LASSO with 62 features 0.75 (0.72–0.78), Random Forest 0.74 (0.71–0.77), and Grobman 0.68 (0.65–0.71). For the Prelabor model, AUC were for QLattice 0.77 (0.74–0.80), LASSO with 67 features 0.77 (0.74–0.80), Random Forest 0.75 (0.72–0.78), and Grobman 0.70 (0.66–0.73).
Conclusions
QLattice performs equal to other machine learning algorithms contributing further by giving explainable insight with fewer variables. Future prospective studies will reveal whether individual decision support tools, based on these models can improve outcomes in women with previous cesarean.
From Efficiency to Excess: Understanding Energy Usage in Green-Certified Homes
Authors: Fan Mingxuan , Agarwal Sumit , Eraral Eduardo , Qin Yu , Zheng Huanhuan 
Published: January 2024

Abstract

Green building certification is critical for reducing carbon emissions in the real estate sector, which accounts for over one-third of the global carbon output. Analysing nationwide account-level water consumption data in Singapore with staggered difference-in-differences regressions, we find an unexpected 3.3% increase in household water consumption for retrofitted residential buildings post-certification. Similar increase is observed for household electricity consumption using block-level data. The increased consumption is unlikely a rebound effect given the lack of household-level efficiency improvements, instead it is attributable to an increase in post-certification house price. The increase in water consumption for household is five-times the reduction for common areas with mandated efficiency improvements. We highlight the importance of enhancing household-level efficiency and implementing stringent certification criteria in offsetting the increase in utility consumption, which is particularly crucial with the rapid proliferation of green buildings around the world.
Construction of an Evaluation Index System for the Emergency Management Capability of Major Infectious Diseases in Urban Communities
Authors: Guan Xiuying , Li Wentao , Cui Nan , Yu Jiacen , An Libin 
Published: January 2024

Abstract
Objective
Taking community as the research perspective, scientifically construct the evaluation index system of emergency management capacity of major infectious diseases in urban communities.
Methods
The research team comprehensively analyzed Introduction to Emergency Management Theory and Practice and the theory of Collaboration, Participation, and Common Interests, policies, and a literature review to form the initial evaluation index system. After that, the final version of the evaluation index system was developed using the modified Delphi and Analytic Hierarchy Process method for the emergency management of the urban community for major infectious diseases.
Results
Twenty-two experts were invited to participate in three rounds of opinion solicitation, and the positive coefficient of experts was 1, the average authority coefficient was 0.88, and the coordination coefficient of the third round was 0.175 (P < 0.001). This system consisted of 4 first-level, 19 second-level, and 54 third-level indexes.
Conclusion
The constructed evaluation index system for the emergency management capacity of major infectious diseases in the urban community setting is novel and scientifically effective, which can provide a reference for future assessment of the cooperation of major infectious diseases emergency management in urban communities.
Pleiotropic effects of PLEC and C1Q on Alzheimer’s disease and cardiovascular traits
Authors: Koskeridis Fotios , Fancy Nurun , Tan Pei Fang , Evangelou Evangelos , Meena Devendra , Elliott Paul , Wang Dennis , Matthews Paul , Dehghan Abbas , Tzoulaki Ioanna 
Published: January 2024

Abstract

Several cardiovascular (CV) traits and diseases co-occur with Alzheimer’s disease (AD). We mapped their shared genetic architecture using multi-trait genome-wide association studies. Subsequent fine-mapping and colocalisation highlighted 19 genetic loci associated with both AD and CV diseases. We prioritised rs11786896, which colocalised with AD, atrial fibrillation (AF) and expression of PLEC in the heart left ventricle, and rs7529220, which colocalised with AD, AF and expression of C1Q family genes. Single-cell RNA-sequencing data, co-expression network and protein-protein interaction analyses provided evidence for different mechanisms of PLEC, which is upregulated in left ventricular endothelium and cardiomyocytes with heart failure (HF) and in brain astrocytes with AD. Similar common mechanisms are implicated for C1Q in heart macrophages with HF and in brain microglia with AD. These findings highlight inflammatory and pleomorphic risk determinants for the co-occurrence of AD and CV diseases and suggest PLEC, C1Q and their interacting proteins as novel therapeutic targets.
Pollen specialist bee species are accurately predicted from visitation, occurrence and phylogenetic data
Authors: Smith Colleen , Bachelder Nick , Russell Avery L. , Morales Vanessa , Mosher Abilene R. , Seltmann Katja C. 
Published: January 2024

Abstract

An animal’s diet breadth is a central aspect of its life history. Yet information about which species have narrow dietary breadths (specialists) and which have comparatively broad dietary breadths (generalists) is missing for many taxa and regions. One possible way to address this gap is to leverage interaction data found on museum specimens and published in the literature. Here, we use bees as our focal taxon to predict dietary specialization and generalization using machine learning models and interaction data, along with a bee phylogeny, and occurrence data for 682 bee species native to the United States. To assess whether our models can transfer to new regions or taxa, we used spatial and phylogenetic blocking in assessing model performance. We found that specialist bees mostly visit their host plants, and that they can be predicted with high accuracy (mean 92% accuracy). Overall model performance was high (mean AUC = 0.84), and our models did a moderate job of predicting generalist bee species, the minority class in our dataset (mean 62% accuracy). Models tested on spatially and phylogenetically blocked data had comparable performance to models tested on randomly blocked data. Our results suggest it is possible to predict specialist bee species in regions and for taxonomic groups where they are unknown but it may be more challenging to predict generalists. Researchers looking to identify pollen specialist and generalist species can generate candidate lists of these species by training models on bees from nearby regions or closely related taxa.
Impact of high body mass index on hepatocellular carcinoma risk in chronic liver disease: population-based prospective cohort study
Authors: Kim Moonho , Jun Baek Gyu , Shin Hwang Sik , Yi Jee-Jeon , Kim Sang Gyune , Yi Sang-Wook 
Published: January 2024

Abstract
We investigated associations between body mass index (BMI) and hepatocellular carcinoma (HCC) in patients with hepatitis B (HBV) and C (HCV) virus infections, alcoholic liver disease (ALD), non-alcoholic fatty liver disease (NAFLD), and liver cirrhosis. We followed 350,608 Korean patients with liver disease from 2003–2006 until December 2018 using national hospital discharge records. Adjusted hazard ratios (HRs) per 5-kg/m2 BMI (≥ 25-kg/m2) increase for HCC risk were calculated using Cox models. HRs (95% confidence intervals) were 1.17 (1.06–1.28), 1.08 (0.87–1.34), 1.34 (1.14–1.58), 1.51 (1.17–1.94), and 1.11 (1.00-1.23) for HBV, HCV, ALD, NAFLD, and liver cirrhosis, respectively. HRs for HBV and liver cirrhosis in women vs men were 1.45 (1.23–1.70) vs 1.06 (0.95–1.19) and 1.27 (1.07–1.50) vs 1.02 (0.90–1.16), respectively. HRs for HBV, HCV, and NAFLD were 1.17 (1.07–1.29), 1.33 (1.03–1.73), and 1.20 (0.87–1.64) in < 65-year-olds and 1.05 (0.70–1.59), 0.74 (0.50–1.10), and 2.40 (1.62–3.54) in ≥ 65-year-olds, respectively. Overweight BMI showed significantly higher HCC risks with HBV, ALD, NAFLD, and liver cirrhosis. Higher BMIs increased HCC risks with HBV, ALD, NAFLD, and liver cirrhosis. Women with HBV and liver cirrhosis had stronger BMI-HCC associations than men. High BMI effect was stronger at older ages with NAFLD and at younger ages with viral hepatitis.
Opinions on Mandatory COVID Vaccinations of United States Healthcare Workers and Educators - In Cross sectional survey Social Media Users are in Favor
Authors: Johnson Calvin , Hector Richard , Pollock Gabriel , Kissen Micheal , Vargas Roberto 
Published: January 2024

Abstract
In the midst of the COVID pandemic, many persons protested to face masks, lock-downs and restrictions on movements. Mandatory vaccinations for healthcare workers also caused protests and some walkouts. As subvariants of COVID-19 will continue to emerge, it is important to assess the opinion of mandatory vaccinations for individuals who come into close contact with the public such as healthcare worker, educators and coaches. Such assessments could be used in policy-making to decrease vaccine hesitancy among these individuals and the general public. Between May 9, 2021 to February 1, 2022 a questionnaire probing the experience with COVID and attitude to COVID getting the COVID vaccine was sent to across 59 social media channels. The data from self-administered questionnaire was analyzed for the response to the question “Should COVID-19 Vaccination be Mandatory?”. The response had three categories coded as (0 = No, 1 = yes – now immediately, 2 = yes – only after full FDA approval) for seven specific subgroups, all healthcare workers, educators and coaches, four age groups as well as pregnant people. We conducted a confirmatory analysis with bivariate tables 1 thru 6 and multinomial logistic regression with predictors sex, age groups, race/ethnicity, marital status, creed, medical practice tables 7 and 8. Associations were assessed at with odds-ratio and 95% confidence intervals. Significant associations excluded 1.0. In multinomial model with 22,198 respondents, women objected more to immediate vaccinations than men, but both were equal on waiting for federal approval. Age category showed more acceptance of immediate vaccinations going from youngest to oldest. Most ethnic minorities were substantially more in favor of vaccinations than Caucasians. The exceptions were Native Americans/Pacific Islander who substantially objected to mandatory vaccinations. All creeds were more in favor of vaccinations than Christians. This was especially so for unbelievers. All healthcare workers regardless of type of practice were more in favor of immediate vaccinations than respondents not in healthcare. This suggests that efforts to promote COVID vaccinations would benefit from addressing the concerns of women, Native Americans and Pacific Islanders to increase participation by persons in these groups.
Future climate projection across Tanzania under CMIP6 with High-Resolution Regional Climate Model
Authors: MAGANG DAWIDO , Ojara Moses , Yunsheng Lou , Philemon Henry 
Published: January 2024

Abstract

Climate change is one of the most pressing challenges faced by developing countries due to lower adaptive capacity, with far- reaching impacts on agriculture. Adaptation is the fundamental option to reduce the impacts. Climate change projection for future with comparison to the past is investigated by focuses on utilization of outputs of high-resolution Regional Climate Models (RCMs) from the Coordinated Regional Climate Downscaling Experiment program (CORDEX-Africa) and Climate Change Knowledge Portal (CCKP) from the world Bank Group. Mid-century (2040–2060) projections of annually, monthly, and seasonal of rainfall, temperature (minimum, maximum) under two emissions scenarios. Mid-century is considered as a critical time for adaptation measures. The Shared Social-economic Pathways (SSPs): SSP2-4.5 and SSP5-8.5 under baseline period of (1991–2020) simulations. The future climate 2040–2060, departure determined with reference period 1995–2014 climate conditions. From the analysis, the result reveal that, across the country historical there’s significantly increase in temperature (minimum, maximum) trend. However, rainfall increased by + 63.2% not significantly. Projected 2040–2060 shows that, throughout the country will experience the increase of the temperature in the anomaly range from 1.4 ºC to 2.8 ºC under SSP5-8.5 emission scenario. Projected rainfall indicates that will be increased in the anomaly range from + 0.6% to + 6.3% and from + 5.2% to + 14.4% under both emissions scenarios.
Impact and Analysis of the “One Visit” System to Optimize the Flow of Medical Services on Patients’ Waiting Time: A Retrospective Study
Authors: Lou Lihua , Yin Shaobo , Xia Mengxuan , Xia Weidong , Wang Ru , Lin Kai , Weng Haoce , Lin Cai , Gao Zhihong 
Published: January 2024

Abstract
Background
Patients’ waiting time exceeded consultation time in outpatient clinics, persisting as a criticized issue. Multiple interventions aimed to reduce patients’ waiting time and enhance satisfaction have been implemented by most Chinese hospitals. However, there is little literature to focus on the full range of services available to patients during their visit.
Methods
We developed and applied an intelligent sensing-driven unattended healthcare system (referred to as “One Visit”). We upgraded hospital facilities and systems to improve the flow of outpatient services, streamline treatment and reduce patients’ waiting time. We compared the waiting time, time of visit, duration of special examination appointment, waiting time for examination of patients, and so on during outpatient clinics in the pilot hospitals before and after the implementation of the “One Visit” system. In addition, we investigated the average length of hospital stay and changes in satisfaction of patients with the outpatient process before and after the implementation of the system.
Results
Post-call waiting time was reduced from 9.23 minutes to 7.83 minutes; time of visit increased from 2.95 minutes to 3.17 minutes; duration of special examination appointment was reduced from an average of 7.94 days to 5.30 days; waiting time for examination was reduced from 48.63 minutes to 45.26 minutes; and the average length of hospital stay was reduced from 9.08 days to 7.27 days. Satisfaction of patients with outpatient services before and after system implementation increased from an average of 78.61% to an average of 80.40%.
Conclusions
With the continuous improvement of the service process, it has been proven that the system effectively guides patients through the entire medical treatment process. The system has increased efficiency in outpatient services and improved satisfaction of patients with the outpatient experience. It is worthy of further promotion.
An Efficient Hybrid Classification Model for Heart Disease Prediction
Authors: Munsif Maaham , Rashid Mehvish , Jabeen Farzana 
Published: January 2024

Abstract

Background: Heart disease prediction is a critical healthcare task for identifying individuals at risk and enabling timely intervention. Accurate prediction models can help reduce morbidity and mortality rates associated with cardiovascular conditions. Various approaches, such as feature selection and hybrid models, have been proposed to improve the effectiveness and accuracy of the prediction of heart disease.
Method: The study employs the Genetic Algorithm-Support Vector
Machine-Convolutional Neural Network (GA-SVM-CNN) approach and evaluates it on three diverse datasets: UCI, Z-Alizadeh Sani, and Cardiovascular Disease Dataset. The genetic algorithm is utilized first to select the most relevant features from the datasets, effectively reducing dimensionality, eliminating irrelevant or redundant features, and choosing the most suitable ones. Subsequently, the hybrid SVM-CNN model is trained using the selected features, harnessing the complementary capabilities of both techniques to enhance prediction accuracy.
Results: The performance of the GA-SVM-CNN approach is assessed using the three benchmark datasets and models. On the UCI dataset, the approach achieves an impressive accuracy of 98%, indicating its effectiveness in accurately predicting heart disease. On the Z-Alizadeh Sani dataset, the approach achieves an accuracy of 97%. On the Cardiovascular Disease Dataset, the approach achieves an accuracy of 86%. These high accuracy rates across different datasets underscore the efficacy of the GA-SVM-CNN approach in heart disease prediction.
Conclusion: The combination of the genetic algorithm’s feature selection and the hybrid SVM-CNN model’s predictive power leads to superior performance in heart disease prediction. By accurately identifying individuals at risk of heart disease, this approach can enable timely interventions and contribute to more effective healthcare interventions.