bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multiple Objectives Escaping Bird Search Optimization and Its application in Stock Market Prediction Based on Transformer Model
Authors: Wei Dedai , Wang Zimo , Qiu Minyu , Yu Juntao , Yu Jiaquan , Jin Yurun , Sha Xinye , OuYang Kaichen 
Published: October 2024

Abstract

Stock market prediction is a popular topic in both academia and industry due to its potential to offer significant financial returns, while transformer model is a cutting-edge tool in this task. However, how to fine-tune the hyperparameters in a reasonable timeframe without excessive computational resources remains a challenge. This paper purposes an innovative algorithm which extends the Escaping Bird Search optimization (EBS) into the area of multi-objective optimization, namely MOEBS, for efficient and effective hyperparameter fine-tuning of Transformer to predict stock prices. Initially, we validate MOEBS by conducting benchmark testing built upon widely used problem sets like ZDT, DTLZ, WFG, etc., together with numerical experiments based on evaluation of 4 recognized metrics, GD, Spacing, IGD, and HV. Next, we apply MOEBS to optimal hyperparameter searching for Transformer, such as Learning rate, Number of Heads and L2 regularization coefficient. The fine-tuned Transformer models by MOEBS and other competing algorithms are then applied to training and predicting on Google's historical stock price data set from 2016 to 2021. Comparative experiments demonstrate the extraordinary excellence of MOEBS-Transformer in terms of RMSE, RPD, and R2 metrics, matching and surpassing the performance of the state-of-the-art competitors. As a novel and powerful stock price prediction model, MOEBS-Transformer has potential to become a reliable and accurate predictor for stock prices. The fine-tuned Transformer models by MOEBS and other competing algorithms are then applied to training and predicting on Google's historical stock price data set from 2016 to 2021. The Google stock data set used in this research can be accessed publicly through Kaggle at the following link: https://www.kaggle.com/datasets/varpit94/google-stock-data.
Design and Implementation of a Facility Intelligent Carbon Dioxide Incubator Control System Based on Improved POA Optimized Fuzzy PID for the Incubation of Termitomyces mushrooms
Authors: Luo Qiangke , Zhao Zeneng , Zhang Huanbo , Lin Guan , Gao Juntao , Liu Yong , Kong Lingxue 
Published: October 2024

Abstract

To address the issue of accurately controlling the carbon dioxide environment required for the cultivation of Termitomyces mushrooms in traditional carbon dioxide incubators, and improve the cultivation environment of Termitomyces mushrooms, this study designed an intelligent carbon dioxide incubator control system suitable for the laboratory cultivation of Termitomyces mushrooms. The system was based on an improved POA to optimize fuzzy PID control, regulating the opening of the solenoid valve to precisely control the carbon dioxide concentration in the incubator. Simulation and comparison tests of the improved POA-based optimized fuzzy PID control, PID control, and fuzzy PID control were carried out by MATLAB/Simulink software, which indicated that the system’s overshoot was reduced by 11.7%, the adjustment time was shortened by 71.1 s, and the steady-state error was reduced by 0.1%. The reductions in overshoot, adjustment time, and steady-state error showed that POA-optimized fuzzy PID control was superior to fuzzy PID control in terms of accuracy, response, and stability, with better robustness. In addition, the growth of the Termitomyces eurrhizus MLY19 strain in the incubator environment was compared to that of a traditional carbon dioxide incubator, and we found that the diameters of the cultured Termitomyces mushroom MLY19 cell colonies in the improved incubator were 45.3% and 73.8% larger at 7 and 14 days, respectively. Therefore, the carbon dioxide control system of the Termitomyces mushroom incubator offered a high practical application value.
Comparative Analysis of DeepLabv3+ for Urban Waterbody Mapping: Enhancing Policy-Making and Sustainable Urban Development
Authors: Rajeswari S. , Rathika P. 
Published: October 2024

Abstract

Waterbody extraction plays a crucial role in various applications such as environmental monitoring, urban planning, and disaster management. Over the past decade, numerous convolutional neural networks (CNNs) have been developed specifically for the accurate segmentation of waterbodies. However segmenting tiny branch-like structures of waterbodies observed in images remains challenging. DeepLabV3 + is indeed one of the top segmentation models excelling in the task of segmenting tiny waterbody structure. However, its computational demands are a major drawback. Therefore, this paper investigates the performance of deepLabV3 + using various backbone networks such as EfficientNet, MobileNet, ResNet50, DenseNet121, and YOLOv8. Among the selected backbone networks, EfficientNet achieves excellent accuracy with relatively efficient computation time because of its compound scaling approach. It surpasses DenseNet by 1.2%, outperforms ResNet50 by 1.62%, achieves 1.86% higher accuracy than MobileNet, and significantly exceeds YOLOv8 by 3.71%. Experimental results demonstrate that deepLabV3 + using EfficientNet stands out as the most effective segmentation model, achieving the highest Structural Similarity Index (SSIM) value of 0.963 and lowest mean absolute distance (MAD) value of 0.891, particularly for customized data segmentation of water bodies with tiny branch-like patterns.
A Qualitative Exploration on Understanding Progression Patterns and Nursing Implications of Chronic Kidney Disease
Authors: Saguban Reynita , AlAbd Asmaa Mohamed  Ali , Rondilla Evalynn , Buta Joyce , Marzouk Salwa Ali , Maestrado Richard , Aljarboa Bader Emad , Mosto Romeo Jr , Alshammari Mubarak 
Published: October 2024

Abstract

Introduction: Despite the alarming increase in the prevalence of Chronic Kidney Disease (CKD) worldwide owing to high-risk factors, little information is available on patients who experience consistent CKD progression. The purpose of this qualitative study was to explore the diverse courses of chronic kidney disease and understand illness perspectives from patients, fostering customized nursing approaches.
Methods: This qualitative study employed purposive and snowball sampling techniques to recruit 15 patients with CKD (stages 1 and 2) from the outpatient department of King Khalid Hospital. Data were collected through semi-structured interviews that lasted between 45 and 60 minutes, each with the participants’ consent for audio recording. Data were collected between March and April 2024. Thematic analysis was employed to analyze the transcriptions of the interviews.
Results: Four themes, including five subthemes, emerged from the verbatim accounts obtained from all 15 participants involved in this research. These are: (1) slow creep (The Range of Experiences in Early Stage CKD), (2) Balancing Act of Hope and Uncertainty (navigating a new reality), (3) turning point (contributory factors), and (4) CKD progression patterns (challenges managing CKD; Hopes, Fears, and Living with Uncertainty).
Conclusion: “slow creep,” which includes various experiences during initial diagnosis; “Balancing Act of Hope and Uncertainty” where emotional contradictions were raised, “Turning point marking an essential period in the progression of the illness; and lastly, “CKD Progression Patterns” which shows how difficult it is for someone to manage uncertainty through one’s emotions. This understanding offers a framework for understanding early stage CKD, but more studies need to be conducted before any conclusive statements can be made about them.
Learning Risk Preferences Through Social Interaction: An Active Inference Approach
Authors: Tehrani-Safa Amir Hossein , Ghaderi Reza , Sarabi-Jamab Atiye 
Published: October 2024

Abstract

Social learning involves the ability to make assumptions about a person's attitudes and preferences on the basis of their observed behaviors. These characteristics are then used to make predictions and influence how one interacts with individuals in various social contexts. In this research, we used an active inference framework to explore how humans can assess another person's risk preferences. The active inference learner uses a probabilistic generative model to model others' decision making process and then employs a Variational Bayesian method based on the free-energy principle to invert the generative model. On a trial-by-trial basis, the observer updates the posterior estimate of the other's hidden characteristic via social prediction error, a mismatch between what we predicted and what they chose. The model begins with a high learning rate and gradually reduces it as more trials occur. This reflects that humans learn from examples sequentially, exploring and using the information as they progress. As more data are accumulated from someone who follows a consistent decision-making process, the observer's faith in his own judgments should grow, and he should be less affected by each new observation. Understanding others' risk preference is a complex example of social reasoning. We employed a sequential scenario where an observer watched the other person's choices between a high-risk gamble and a guaranteed small reward. We discovered that the inferential uncertainty that the learner has in trying to estimate the agent's risk attitude, is largely based on the prediction uncertainty accumulated by the observer when attempting to guess the agent's choices.
A New Differential Gene Expression Based Simulated Annealing for Solving Gene Selection Problem: A Case Study on Eosinophilic Esophagitis and Few Other Gastro-Intestinal Diseases
Authors: Sinha Koushiki , Chakraborty Sanchari , Bardhan Arohit , Saha Riju , Chakraborty Srijan , Biswas Surama 
Published: October 2024

Abstract

Identifying the set of genes collectively responsible for causing a disease from differential gene expression data is called gene selection problem. Though many complex methodologies have been applied to solve gene selection, formulated as an optimization problem, this study introduces a new simple, efficient and biologically plausible solution procedure where the collective power of the targeted gene set to discriminate between diseased and normal gene expression profiles was focused. It uses Simulated Annealing to solve the underlying optimization problem and termed here as Differential Gene Expression Based Simulated Annealing (DGESA). The Ranked Variance (RV) method has been applied to prioritize genes to form reference set to compare with the outcome of DGESA. In a case study on EoE and other gastrointestinal diseases, RV identified the top 40 high-variance genes, overlapping with disease-causing genes from DGESA. DGESA identified 40 gene pathways each for EoE, Crohn's Disease (CD), and Ulcerative Colitis (UC), with 10 genes for EoE, 8 for CD, and 7 for UC confirmed in literature. For EoE, confirmed genes include KRT79, CRISP2, IL36G, SPRR2B, SPRR2D, and SPRR2E. For CD, validated genes are NPDC1, SLC2A4RG, LGALS8, CDKN1A, XAF1, and CYBA. For UC, confirmed genes include TRAF3, BAG6, CCDC80, CDC42SE2, and HSPA9. RV and DGESA effectively elucidate molecular signatures in gastrointestinal diseases. Validating genes like SPRR2B, SPRR2D, SPRR2E, and STAT6 for EoE demonstrates DGESA's efficacy, highlighting potential targets for future research.
The impact of beachface morphology on habitat suitability and breeding success of least terns and snowy plovers
Authors: de Alegría-Arzaburu Amaia Ruiz , Garcia-Walther Julian , Serrato Liliana Ortiz , Vega Jonathan Vargas , Senner Nathan R 
Published: October 2024

Abstract

Beach-nesting birds rely on predictable and stable beach conditions for successful breeding, yet the complex impacts of climatic events, wave action, and variation in beach morphology on nest site selection remain underexplored. This study examines how variations in beach morphology influence nest site selection and breeding success in two conservation-priority species —the least tern (Sterna antillarum) and the snowy plover (Anarhynchus nivosus)—at Barra del Estero Beach, Baja California, Mexico. This beach, formed in the mid-to-late 1990s, likely due to alongshore sediment transport from neighboring beaches during energetic El Niño events, reveals that nest abundance correlates strongly with supratidal beach morphology. Both species exhibited significantly higher survival rates when nesting on berms compared to other areas. They presented highest nest densities on stable profiles featuring prominent berms extending approximately 40 m horizontally from the dune toe at elevations exceeding 2.8 m, 0.5 m above the highest spring high-tide level. While berms erode in winter due to high-energy waves, they serve as secure nesting platforms in summer, protecting nests from flooding. Thus, the ability of the beach to rebuild wide berms after the winter is crucial for nesting success. As sea-level rise and extreme weather events increasingly alter beach morphology, species with greater flexibility in nest site selection may more successfully identify higher-quality nesting sites. This research highlights the urgent need for comprehensive solutions to mitigate the growing risk of nest flooding in beach-nesting birds.
Designing selective fruit fly lures by combining odors that stimulate antennal and palpal neurons
Authors: Biasazin Tibebe Dejene , Miano Raphael Njurai , Cheseto Xavier , Ndlela Shepard , Mohamed Samira A. , Dekker Teun 
Published: October 2024

Abstract

The oriental fruit fly Bactrocera dorsalis, is a highly destructive and invasive pest that threatens global horticulture, causing economic losses through fruit damage and quarantine regulations. While attract-and-kill baits are widely used to suppress fly populations, these baits also impact many non-target species. To achieve ecological sustainability, our study aimed to develop a method that selectively targets female fruit flies. Using gas chromatography coupled electro-antennal or palpal detection (GC-EAD / GC-EPD), we identified compounds that elicited responses in the maxillary palp and antenna of three different fruit flies including B. dorsalis, Ceratitis capitata and Zeugodacus cucurbitae. As previous studies indicated the significance of compounds whose detection was shared among fruit fly species, we composed blends of such ‘shared compounds’. To test the idea that compounds active on the palps and antennae are complementary and synergize attraction, we thus formulated three blends, a palpal, an antennal and a combined blend. Compounds were formulated in a wax emulsion for slow release and tested on B. dorsalis fruit flies in six-choice and large room laboratory assays, as well as field trials. A blend of both antenna and maxillary palp active compounds attracted more B. dorsalis than blends based on either antennae or palps alone, and exhibited a female-bias in captures. Furthermore, the blend was highly selective, capturing very few non-target organisms compared to torula yeast, a lure considered to be selective. Such lures are important in advancing targeted pest control strategies, ultimately contributing to more effective management of the devastating B. dorsalis population, and safeguarding global horticulture productivity.
Prevalence of Protozoan in HIV/AIDS Patients with Gastrointestinal Symptoms in Zinder National Hospital, Niger Republic
Authors: Lamine MAHAMAN MOUSTAPHA , Mahamadou DOUTCHI , Mahamadou MOUSSA AMADOU , Hassane BOUREIMA , Ousmane ABDOULAYE , FILS SOULEYMANE ADOUM , Yacouba ABDOURAHAMANE , Adehossi ERIC 
Published: October 2024

Abstract

Background
Intestinal parasitic infections constitute one of the stubborn public health issues, particularly in low- and middle-income countries. This study aimed to investigate the prevalence and diversity of intestinal parasites among patients in a medical center in Zinder, Niger Republic.
Methods
A prospective study of 93 patients and a retrospective study of 131 patients were conducted. Stool samples were examined for the presence of parasites using standard microscopic techniques.
Results
In this prospective study, 83.7% of stool samples were tested positive for parasites, with the cystic form of Entamoeba histolytica (25.8%) and Cryptosporidium spp. (30.1%) being the most prevalent. Four patients had vegetative forms of protozoa, including two with Giardia intestinalis (2.1%) and two with E. histolytica (2.1%), and one patient was positive for Strongyloides stercoralis. In the retrospective study, 46.9% of stool samples were positive for parasites, with the E. histolytica and Pentatrichomonas hominis being the most prevalent 26.1% and 7.1%, respectively. Trophozoite of E. histolytica and G. intestinalis were present respectively in 3.2% and 2.8% of positive samples as well. Oocysts of two protozoans were identified. Cryptosporidium spp., is characterized by 28 oocysts, constituted 30.1% of the observed entities. Simultaneously, Cystoisospora belli, represented by 12 oocysts, accounted for 12.9% of the total.
Conclusion
Our findings reveal a high prevalence of intestinal protozoan parasites in HIV/AIDS patients in Niger. This points at the need for effective interventions to prevent and treat these infections. In addition, the results of this study provide important preliminary data that could inform public health policy and decision makers in the region.
How much does electric vehicle adoption contribute to the transportation sector’s GHG emissions reduction?
Authors: Tan Xiaomei , Rose Tyler 
Published: October 2024

Abstract

This study examines the impact of electric vehicle (EV) adoption on greenhouse gas (GHG) emissions in California's transportation sector from 2000-2021. Using data from the California Air Resources Board's GHG Emissions Inventory, the analysis focuses on three primary factors affecting transportation emissions: EV population, fuel economy of light-duty vehicles, and gasoline sales. Through correlation and regression analyses, the study finds that gasoline sales have the most substantial impact on emissions. Improvements in fuel economy and increases in EV population both contribute to reducing emissions, with fuel economy showing a stronger impact than EV adoption. Specifically, for every 1 MPG increase in fuel economy, emissions decrease by approximately 1.7644 MMTCO2e, while for every 1,000 increase in EV population, emissions decrease by about 0.0416 MMTCO2e. The findings suggest that while promoting EV adoption is important, it should be complemented with policies that enhance fuel economy and reduce gasoline consumption to achieve more significant and immediate reductions in transportation emissions. The study concludes that a multifaceted approach addressing fuel economy, gasoline consumption, and EV adoption is needed to substantially reduce transportation emissions and achieve California's long-term climate goals.
Aerobic exercise attenuates high-fat diet-induced glycometabolism abnormalities in skeletal muscle of rats via EGR-1/PTP1B signaling pathway
Authors: Zhang Liangzhi , Liu Xiaojie , Hu Jing , Quan Helong , Lee Sang Ki , Korivi Mallikarjuna , Wang Lifeng , Li Ting , Li Wei 
Published: October 2024

Abstract

Objective
Defects in glycogen anabolism led to the development of insulin resistance (IR). Aerobic exercise ameliorates IR by augmenting insulin signaling, yet, the precise mechanism behind this improvement remains unclear. This study explored whether 6 weeks aerobic exercise enhances glycogen anabolism and insulin sensitivity via EGR-1/PTP1B signaling pathway in skeletal muscle.
Methods
Sprague-Dawley rats fed a high-fat diet (HFD) performed treadmill exercise, and were examined for an oral glucose tolerance test and IR. PAS staining and anthrone colorimetry were used to assess the skeletal muscle glycogen content. RT-qPCR, western blot and immunofluorescence were used to detect EGR-1/PTP1B pathway and associated signaling molecules.
Results
Results showed that exercise reduced blood glucose levels and HOMA-IR, increased muscle glycogen content, inhibited EGR-1, PTP1B, and GSK-3β mRNA and protein levels, and promoted IRS-1, AKT, and GLUT4 protein and mRNA expression.
Conclusion
In summary, aerobic exercise facilitates skeletal muscle glycogen anabolism and improves insulin sensitivity in HFD rats through the EGR-1/PTP1B pathway, with significant implications for preventing IR.
MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data
Authors: Arturi Katarzyna , Harris Eliza Jane , Gasser Lilian , Escher Beate I , Bosshard Robin , Hollender Juliane 
Published: October 2024

Abstract

MLinvitroTox is an automated Python pipeline developed for high-throughput hazard-driven prioritization of toxicologically relevant features detected in complex environmental samples through nontarget high-resolution mass spectrometry (NTS HRMS/MS). MLinvitroTox is a machine learning (ML) framework comprising 490 independent XGBoost classifiers trained on molecular fingerprints from chemical structures and target-specific endpoints from the ToxCast/Tox21 invitroDBv4.1 database. For each analyzed feature, MLinvitroTox generates a 490-bit bioactivity fingerprint used as a basis for prioritization, focusing the time-consuming molecular identification efforts on features most likely to cause adverse effects. The practical advantages of MLinvitroTox are demonstrated for groundwater HRMS data. Among the 874 features for which molecular fingerprints were derived from spectra, including 630 nontargets, 185 spectral matches, and 59 targets, around 4\% of the feature/endpoint relationship pairs were predicted to be active. Cross-checking the predictions for targets and spectral matches with invitroDB data confirmed the bioactivity of 120 active and 6,791 nonactive pairs while mislabelling of 88 active and 56 non-active relationships. By applying the provided scores and metrics, the number of potentially toxic features was further reduced by at least one order of magnitude. This refinement makes the analytical confirmation of the toxicologically most relevant features feasible, offering significant benefits for cost-efficient chemical risk assessment.
Scientific Contribution: In contrast to the classical ML-based approaches for toxicity prediction, MLinvitroTox predicts bioactivity for HRMS features based on MS2 fragmentation spectra rather than the chemical structures from the identified features. While the original proof of concept study was accompanied by the release of a MLinvitroTox v1 KNIME workflow, in this study, we release a Python MLinvitroTox v2 package, which, in addition to automation, expands functionality to include predicting toxicity from structures, cleaning up and generating chemical fingerprints, customizing models, and retraining on custom data. Furthermore, as a result of improvements in bioactivity data processing, realized in the concurrently released pytcpl Python package for the custom processing of invitroDBv4.1 input data used for training MLinvitroTox, the current release introduces enhancements in model accuracy, coverage of biological mechanistic targets, and overall interpretability.