bluuebunny/crossref_metadata_2025_split huggingface dataset:
AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach
Authors: Rao G. Madhukar , Ramesh Dharavath , Sharma Vandana , Sinha Anurag , Hassan Md. Mehedi , Gandomi Amir H. 
Published: April 2024

AbstractHeart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.
Cross-domain information fusion and personalized recommendation in artificial intelligence recommendation system based on mathematical matrix decomposition
Authors: Meng Xiaoyan 
Published: April 2024

AbstractGiven the challenges of inter-domain information fusion and data sparsity in collaborative filtering algorithms, this paper proposes a cross-domain information fusion matrix decomposition algorithm to enhance the accuracy of personalized recommendations in artificial intelligence recommendation systems. The study begins by collecting Douban movie rating data and social network information. To ensure data integrity, Levenshtein distance detection is employed to remove duplicate scores, while natural language processing technology is utilized to extract keywords and topic information from social texts. Additionally, graph convolutional networks are utilized to convert user relationships into feature vectors, and a unique thermal coding method is used to convert discrete user and movie information into binary matrices. To prevent overfitting, the Ridge regularization method is introduced to gradually optimize potential feature vectors. Weighted average and feature connection techniques are then applied to integrate features from different fields. Moreover, the paper combines the item-based collaborative filtering algorithm with merged user characteristics to generate personalized recommendation lists.In the experimental stage, the paper conducts cross-domain information fusion optimization on four mainstream mathematical matrix decomposition algorithms: alternating least squares method, non-negative matrix decomposition, singular value decomposition, and latent factor model (LFM). It compares these algorithms with the non-fused approach. The results indicate a significant improvement in score accuracy, with mean absolute error and root mean squared error reduced by 12.8% and 13.2% respectively across the four algorithms. Additionally, when k = 10, the average F1 score reaches 0.97, and the ranking accuracy coverage of the LFM algorithm increases by 54.2%. Overall, the mathematical matrix decomposition algorithm combined with cross-domain information fusion demonstrates clear advantages in accuracy, prediction performance, recommendation diversity, and ranking quality, and improves the accuracy and diversity of the recommendation system. By effectively addressing collaborative filtering challenges through the integration of diverse techniques, it significantly surpasses traditional models in recommendation accuracy and variety.
Amino acid-specific isotopes reveal changing five-dimensional niche segregation in Pacific seabirds over 50 years
Authors: van Oordt Francis , Cuba Antonio , Choy Emily S. , Elliott John E. , Elliott Kyle H. 
Published: April 2024

AbstractHutchison’s niche theory suggests that coexisting competing species occupy non-overlapping hypervolumes, which are theoretical spaces encompassing more than three dimensions, within an n-dimensional space. The analysis of multiple stable isotopes can be used to test these ideas where each isotope can be considered a dimension of niche space. These hypervolumes may change over time in response to variation in behaviour or habitat, within or among species, consequently changing the niche space itself. Here, we use isotopic values of carbon and nitrogen of ten amino acids, as well as sulphur isotopic values, to produce multi-isotope models to examine niche segregation among an assemblage of five coexisting seabird species (ancient murrelet Synthliboramphus antiquus, double-crested cormorant Phalacrocorax auritus, Leach’s storm-petrel Oceanodrama leucorhoa, rhinoceros auklet Cerorhinca monocerata, pelagic cormorant Phalacrocorax pelagicus) that inhabit coastal British Columbia. When only one or two isotope dimensions were considered, the five species overlapped considerably, but segregation increased in more dimensions, but often in complex ways. Thus, each of the five species occupied their own isotopic hypervolume (niche), but that became apparent only when factoring the increased information from sulphur and amino acid specific isotope values, rather than just relying on proxies of δ15N and δ13C alone. For cormorants, there was reduction of niche size for both species consistent with a decline in their dominant prey, Pacific herring Clupea pallasii, from 1970 to 2006. Consistent with niche theory, cormorant species showed segregation across time, with the double-crested demonstrating a marked change in diet in response to prey shifts in a higher dimensional space. In brief, incorporating multiple isotopes (sulfur, PC1 of δ15N [baselines], PC2 of δ15N [trophic position], PC1 and PC2 of δ13C) metrics allowed us to infer changes and differences in food web topology that were not apparent from classic carbon–nitrogen biplots.
A framework for design optimization across multiple concepts
Authors: Kenny Angus , Ray Tapabrata , Singh Hemant 
Published: April 2024

AbstractIn engineering design, there often exist multiple conceptual solutions to a given problem. Concept design and selection is the first phase of the design process that is estimated to affect up to 70% of the life cycle cost of a product. Currently, optimization methods are rarely used in this phase, since standard optimization methods inherently assume a fixed (given) concept; and undertaking a full-fledged optimization for each possible concept is untenable. In this paper, we aim to address this gap by developing a framework that searches for optimum solutions efficiently across multiple concepts, where each concept may be defined using a different number, or type, of variables (continuous, binary, discrete, categorical etc.). The proposed approach makes progressive data-driven decisions regarding which concept(s) and corresponding solution(s) should be evaluated over the course of search, so as to minimize the computational budget spent on less promising concepts, as well as ensuring that the search does not prematurely converge to a non-optimal concept. This is achieved through the use of a tree-structured Parzen estimator (TPE) based sampler in addition to Gaussian process (GP), and random forest (RF) regressors. Aside from extending the use of GP and RF to search across multiple concepts, this study highlights the previously unexplored benefits of TPE for design optimization. The performance of the approach is demonstrated using diverse case studies, including design of a cantilever beam, coronary stents, and lattice structures using a limited computational budget. We believe this contribution fills an important gap and capitalizes on the developments in the machine learning domain to support designers involved in concept-based design.
Deep learning hybridization for improved malware detection in smart Internet of Things
Authors: Almazroi Abdulwahab Ali , Ayub Nasir 
Published: April 2024

AbstractThe rapid expansion of AI-enabled Internet of Things (IoT) devices presents significant security challenges, impacting both privacy and organizational resources. The dynamic increase in big data generated by IoT devices poses a persistent problem, particularly in making decisions based on the continuously growing data. To address this challenge in a dynamic environment, this study introduces a specialized BERT-based Feed Forward Neural Network Framework (BEFNet) designed for IoT scenarios. In this evaluation, a novel framework with distinct modules is employed for a thorough analysis of 8 datasets, each representing a different type of malware. BEFSONet is optimized using the Spotted Hyena Optimizer (SO), highlighting its adaptability to diverse shapes of malware data. Thorough exploratory analyses and comparative evaluations underscore BEFSONet’s exceptional performance metrics, achieving 97.99% accuracy, 97.96 Matthews Correlation Coefficient, 97% F1-Score, 98.37% Area under the ROC Curve(AUC-ROC), and 95.89 Cohen’s Kappa. This research positions BEFSONet as a robust defense mechanism in the era of IoT security, offering an effective solution to evolving challenges in dynamic decision-making environments.
Hepatic extracellular ATP/adenosine dynamics in zebrafish models of alcoholic and metabolic steatotic liver disease
Authors: Tokumaru Tomoko , Apolinario Magdeline E. Carrasco , Shimizu Nobuyuki , Umeda Ryohei , Honda Koichi , Shikano Kenshiro , Teranishi Hitoshi , Hikida Takatoshi , Hanada Toshikatsu , Ohta Keisuke , Li Yulong , Murakami Kazunari , Hanada Reiko 
Published: April 2024

AbstractSteatotic liver disease (SLD) is a burgeoning health problem predominantly associated with excessive alcohol consumption, which causes alcohol-related liver disease (ALD), and high caloric intake, which results in metabolic dysfunction-associated SLD (MASLD). The pathogenesis of ALD and MASLD, which can progress from steatohepatitis to more severe conditions such as liver fibrosis, cirrhosis, and hepatocellular carcinoma, is complicated by several factors. Recently, extracellular ATP and adenosine (Ado), as damage-associated molecular patterns, were reported to promote inflammation and liver fibrosis, contributing to SLD pathogenesis. Here, we explored the in vivo dynamics of hepatic extracellular ATP and Ado during the progression of steatohepatitis using a genetically encoded GPCR-activation-based sensor (GRAB) in zebrafish models. We established hepatocyte-specific GRABATP and GRABAdo in zebrafish and investigated the changes in in vivo hepatic extracellular ATP and Ado levels under ALD or MASLD conditions. Disease-specific changes in hepatocyte extracellular ATP and Ado levels were observed, clearly indicating a correlation between hepatocyte extracellular ATP/Ado dynamics and disease progression. Furthermore, clodronate, a vesicular nucleotide transporter inhibitor, alleviated the MASLD phenotype by reducing the hepatic extracellular ATP and Ado content. These findings provide deep insights into extracellular ATP/Ado dynamics in disease progression, suggesting therapeutic potential for ALD and MASLD.
Mosaic crack mapping of footings by convolutional neural networks
Authors: Buatik Apichat , Thansirichaisree Phromphat , Kalpiyapun Phisutwat , Khademi Navid , Pasityothin Ittipon , Poovarodom Nakhorn 
Published: April 2024

AbstractCracks are the primary indicator informing the structural health of concrete structures. Frequent inspection is essential for maintenance, and automatic crack inspection offers a significant advantage, given its efficiency and accuracy. Previously, image-based crack detection systems have been utilized for individual images, yet these systems are not effective for large inspection areas. This paper thereby proposes an image-based crack detection system using a Deep Convolution Neural Network (DCNN) to identify cracks in mosaic images composed from UAV photos of concrete footings. UAV images are transformed into 3D footing models, from which the composite images are created. The CNN model is trained on 224 $$\times$$
×
224 pixel patches, and training samples are augmented by various image transformation techniques. The proposed method is applied to localize cracks on composite images through the sliding window technique. The proposed VGG16 CNN detection system, with 95% detection accuracy, indicates superior performance to feature-based detection systems.
Performance and robustness analysis of V-Tiger PID controller for automatic voltage regulator
Authors: Gopi Pasala , Reddy S. Venkateswarlu , Bajaj Mohit , Zaitsev Ievgen , Prokop Lukas 
Published: April 2024

AbstractThis paper presents a comprehensive study on the implementation and analysis of PID controllers in an automated voltage regulator (AVR) system. A novel tuning technique, Virtual Time response-based iterative gain evaluation and re-design (V-Tiger), is introduced to iteratively adjust PID gains for optimal control performance. The study begins with the development of a mathematical model for the AVR system and initialization of PID gains using the Pessen Integral Rule. Virtual time-response analysis is then conducted to evaluate system performance, followed by iterative gain adjustments using Particle Swarm Optimization (PSO) within the V-Tiger framework. MATLAB simulations are employed to implement various controllers, including the V-Tiger PID controller, and their performance is compared in terms of transient response, stability, and control signal generation. Robustness analysis is conducted to assess the system's stability under uncertainties, and worst-case gain analysis is performed to quantify robustness. The transient response of the AVR with the proposed PID controller is compared with other heuristic controllers such as the Flower Pollination Algorithm, Teaching–Learning-based Optimization, Pessen Integral Rule, and Zeigler-Nichols methods. By measuring the peak closed-loop gain of the AVR with the controller and adding uncertainty to the AVR's field exciter and amplifier, the robustness of proposed controller is determined. Plotting the performance degradation curves yields robust stability margins and the accompanying maximum uncertainty that the AVR can withstand without compromising its stability or performance. Based on the degradation curves, robust stability margin of the V-Tiger PID controller is estimated at 3.5. The worst-case peak gains are also estimated using the performance degradation curves. Future research directions include exploring novel optimization techniques for further enhancing control performance in various industrial applications.
Population-wide modelling reveals prospects of marker-assisted selection for parasitic mite resistance in honey bees
Authors: Lefebre Regis , Broeckx Bart J. G. , De Smet Lina , Peelman Luc , de Graaf Dirk C. 
Published: April 2024

AbstractIn 2019, a joint eight-variant model was published in which eight single nucleotide polymorphisms (SNPs) in seven Apis mellifera genes were associated with Varroa destructor drone brood resistance (DBR, i.e. mite non-reproduction in drone brood). As this model was derived from only one Darwinian Black Bee Box colony, it could not directly be applied on a population-overarching scale in the northern part of Belgium (Flanders), where beekeepers prefer the carnica subspecies. To determine whether these eight SNPs remained associated with the DBR trait on a Flemish colony-broad scope, we performed population-wide modelling through sampling of various A. mellifera carnica colonies, DBR scoring of Varroa-infested drone brood and variant genotyping. Novel eight-variant modelling was performed and the classification performance of the eight SNPs was evaluated. Besides, we built a reduced three-variant model retaining only three genetic variants and found that this model classified 76% of the phenotyped drones correctly. To examine the spread of beneficial alleles and predict the DBR probability distribution in Flanders, we determined the allelic frequencies of the three variants in 292 A. mellifera carnica queens. As such, this research reveals prospects of marker-assisted selection for Varroa drone brood resistance in honeybees.
Experimental and data-driven analysis for predicting nanofluid performance in improving foam stability and reducing mobility at critical micelle concentration
Authors: Issakhov Miras , Khanjani Maral , Muratkhozhina Adiya , Pourafshary Peyman , Aidarova Saule , Sharipova Altynay 
Published: April 2024

AbstractApplication of surfactant-based foam flooding is an effective approach to reduce mobility and control early breakthrough. Despite the proper performance of surfactant-based foams in decreasing the channeling of the flooded gas and water, high pressure, high temperature, and high salinity of the reservoirs put some limitations on the foam flooding efficiency. Nanoparticles are used to improve the quality of the foams, enhance stability, and transcend the limitations. Although there are many benefits of using nanoparticles in foam flooding, their performance at surfactant critical micelle concentration (CMC) is not fully investigated and the optimum nanoparticle concentration is not specified. In this study, an experimental investigation using nanosilica with surfactants at CMC to improve the stability (half-life) and mobility reduction factor (MRF) has been conducted. Furthermore, data from the literature were collected and analyzed to evaluate the change in MRF and stability for a nanofluid-based foam at CMC. Both experimental results and literature data showed that application of nanofluid-based foam is a successful approach to develop a more stable foam with lower mobility. Nanoparticle (NP) concentration is the dominant parameter at different salinities and temperatures that affects foam flow through porous media. The range of 0.2–0.4 wt% is the optimum nanoparticle concentration to develop a strong foam with acceptable performance in controlling mobility.
Positive association between sodium-to-chloride ratio and in-hospital mortality of acute heart failure
Authors: Wei Dongmei , Chen Shaojun , Xiao Di , Chen Rongtao , Meng Yuanting 
Published: April 2024

AbstractPrevious studies have suggested that levels of sodium and chloride in the blood may be indicative of the prognosis of different medical conditions. Nevertheless, the assessment of the prognostic significance of the sodium-to-chloride (Na/Cl) ratio in relation to in-hospital mortality among individuals suffering from acute heart failure (AHF) remains unexplored. In this study, the participants were selected from the Medical Information Mart for Intensive Care IV database and divided into three groups based on the Na/Cl ratio level upon admission. The primary results were the mortality rate within the hospital. Cox regression, Kaplan–Meier curves, receiver operator characteristic (ROC) curve analysis and subgroup analyses were utilized to investigate the correlation between the admission Na/Cl ratio and outcomes in critically ill patients with AHF. A total of 7844 patients who met the selection criteria were included in this study. After adjusting for confounders, the multivariable Cox regression analysis revealed that the baseline Na/Cl ratio significantly elevated the risk of in-hospital mortality among critically ill patients with AHF (HR = 1.34, 95% CI 1.21–1.49). Furthermore, when the Na/Cl ratio was converted into a categorical factor and the initial tertile was taken as a point of comparison, the hazard ratios (HRs) and 95% confidence intervals (CIs) for the second and third tertiles were 1.27 (1.05–1.54) and 1.53 (1.27–1.84), respectively. Additionally, a P value indicating a significant trend of < 0.001 was observed. ROC curve analysis showed that Na/Cl ratio had a more sensitive prognostic value in predicting in-hospital mortality of AHF than the sodium or chloride level alone (0.564 vs. 0.505, 0.544). Subgroup examinations indicated that the association between the Na/Cl ratio upon admission and the mortality rate of critically ill patients with AHF remained consistent in the subgroups of hyponatremia and hypochlorhydria (P for interaction > 0.05). The linear relationship between the Na/Cl ratio and in-hospital mortality in AHF patients indicates a positive association.
Genome-wide CRISPR screens identify PKMYT1 as a therapeutic target in pancreatic ductal adenocarcinoma
Authors: Wang Simin , Xiong Yangjie , Luo Yuxiang , Shen Yanying , Zhang Fengrui , Lan Haoqi , Pang Yuzhi , Wang Xiaofang , Li Xiaoqi , Zheng Xufen , Lu Xiaojing , Liu Xiaoxiao , Cheng Yumei , Wu Tanwen , Dong Yue , Lu Yuan , Cui Jiujie , Jia Xiaona , Yang Sheng , Wang Liwei , Wang Yuexiang 
Published: April 2024

AbstractPancreatic ductal adenocarcinoma (PDAC) is a devastating disease with an overall 5-year survival rate of <12% due to the lack of effective treatments. Novel treatment strategies are urgently needed. Here, PKMYT1 is identified through genome-wide CRISPR screens as a non-mutant, genetic vulnerability of PDAC. Higher PKMYT1 expression levels indicate poor prognosis in PDAC patients. PKMYT1 ablation inhibits tumor growth and proliferation in vitro and in vivo by regulating cell cycle progression and inducing apoptosis. Moreover, pharmacological inhibition of PKMYT1 shows efficacy in multiple PDAC cell models and effectively induces tumor regression without overt toxicity in PDAC cell line-derived xenograft and in more clinically relevant patient-derived xenograft models. Mechanistically, in addition to its canonical function of phosphorylating CDK1, PKMYT1 functions as an oncogene to promote PDAC tumorigenesis by regulating PLK1 expression and phosphorylation. Finally, TP53 function and PRKDC activation are shown to modulate the sensitivity to PKMYT1 inhibition. These results define PKMYT1 dependency in PDAC and identify potential therapeutic strategies for clinical translation.