bluuebunny/crossref_metadata_2025_split huggingface dataset:
A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions
Authors: Elazab Rasha , Abdelnaby Ahmed T. , Ali A.A. 
Published: February 2024

AbstractThe integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected microgrid, leveraging the principles of Demand-Side Management (DSM). The focus of this work lies in a comprehensive exploration of the implications brought about by the Renewable Generation-Based Dynamic Pricing Demand Response (RGDP-DR) mechanism, particularly in terms of its influence on the optimal microgrid configuration, considering perspectives from end-users and the utility entity. This inquiry is rooted in a holistic assessment that encompasses technical and economic performance benchmarks. The RGDP-induced DR framework adeptly addresses the needs of the consumer base, showcasing notable efficiency and economic feasibility. To address the intricate nonlinear optimization challenge at hand, we employ an evolutionary algorithm named the "Dandelion Algorithm" (DA). A rigorous comparative study is conducted to evaluate the efficacy of four optimization techniques, affirming the supremacy of the proposed DA. Within this discourse, the complexity of microgrid sizing is cast as a dual-objective optimization task. The twin objectives involve minimizing the aggregate annual outlay and reducing emissions. The results of this endeavor unequivocally endorse the superiority of the DA over its counterparts. The DA demonstrates exceptional proficiency in orchestrating the most cost-effective microgrid and consumer invoice, surpassing the performance of alternative optimization methodologies.
Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model
Authors: Lilhore Umesh Kumar , Dalal Surjeet , Varshney Neeraj , Sharma Yogesh Kumar , Rao K. B. V. Brahma , Rao V. V. R. Maheswara , Alroobaea Roobaea , Simaiya Sarita , Margala Martin , Chakrabarti Prasun 
Published: February 2024

AbstractPostpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and ‘speech records’ of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.
Critical scaling of a two-orbital topological model with extended neighboring couplings
Authors: Kartik Y. R. , Kumar Ranjith R. , Sarkar Sujit 
Published: February 2024

AbstractExtended-range models are the interesting systems, which has been widely used to understand the non-local properties of the fermions at quantum scale. We aim to study the interplay between criticality and extended range couplings under various symmetry constraints. Here, we consider a two orbital Bernevig–Hughes–Zhang model in one dimension with longer (finite neighbor) and long-range (infinite neighbor) couplings. We study the behavior of model using scaling laws and universality class for models with Hermitian, parity-time ($$\mathscr{P}\mathscr{T}$$

P
T

) symmetric and broken time-reversal symmetries. We observe the interesting results on multi-criticalities, where the universality class of critical exponent is different than the normal criticalities. Also, the results can be generalized by considering the interplay between criticalities and different symmetry classes of Hamiltonian. Also, with the introduction of extended-range of coupling, there occurs different criticalities, and we provide the analogy to characterize their universality classes. We also show the violation of Lorentz invariance at multi-criticalities and evaluation of short-range limit in long-range models as the highlights of this work.
A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters
Authors: Shaik Nagoor Basha , Jongkittinarukorn Kittiphong , Benjapolakul Watit , Bingi Kishore 
Published: February 2024

AbstractDry gas pipelines can encounter various operational, technical, and environmental issues, such as corrosion, leaks, spills, restrictions, and cyber threats. To address these difficulties, proactive maintenance and management and a new technological strategy are needed to increase safety, reliability, and efficiency. A novel neural network model for forecasting the life of a dry gas pipeline system and detecting the metal loss dimension class that is exposed to a harsh environment is presented in this study to handle the missing data. The proposed strategy blends the strength of deep learning techniques with industry-specific expertise. The main advantage of this study is to predict the pipeline life with a significant advantage of predicting the dimension classification of metal loss simultaneously employing a Bayesian regularization-based neural network framework when there are missing inputs in the datasets. The proposed intelligent model, trained on four pipeline datasets of a dry gas pipeline system, can predict the health condition of pipelines with high accuracy, even if there are missing parameters in the dataset. The proposed model using neural network technology generated satisfactory results in terms of numerical performance, with MSE and R2 values closer to 0 and 1, respectively. A few cases with missing input data are carried out, and the missing data is forecasted for each case. Then, a model is developed to predict the life condition of pipelines with the predicted missing input variables. The findings reveal that the model has the potential for real-world applications in the oil and gas sector for estimating the health condition of pipelines, even if there are missing input parameters. Additionally, multi-model comparative analysis and sensitivity analysis are incorporated, offering an extensive comprehension of multi-model prediction abilities and beneficial insights into the impact of various input variables on model outputs, thereby improving the interpretability and reliability of our results. The proposed framework could help business plans by lowering the chance of severe accidents and environmental harm with better safety and reliability.
Normative body mass-adjusted reference ranges of magnetic resonance imaging signs commonly used in diagnosing idiopathic intracranial hypertension in a healthy standard population
Authors: Kobrow Rike , Gross Stefan , Fleischmann Robert , Baldauf Jörg , Langner Sönke , Strauss Sebastian 
Published: February 2024

AbstractPatients with chronic daily headaches (CDH) are often a diagnostic challenge and frequently undergo neuroimaging. One common underlying cause of CDH is idiopathic intracranial hypertension (IIH). However, certain neuroimaging abnormalities that suggest IIH, such as optic nerve sheath diameters (ONSD), pituitary gland height, and venous sinus diameter, require interpretation due to the absence of established normative values. Notably, intracranial pressure is known to varies with age, sex and weight, further complicating the determination of objectively abnormal findings within a specific patient group. This study aims to assist clinical neuroradiologists in differentiating neuroimaging results in CDH by providing weight-adjusted normative values for imaging characteristics of IIH. In addition to age and BMI we here assessed 1924 population-based T1-weighted MRI datasets of healthy participants for relevant MRI aspects of IIH. Association to BMI was analyzed using linear/logistic regression controlled for age and stratified for sex. ONSD was 4.3 mm [2.8; 5.9]/4.6 mm [3.6; 5.7] and diameter of transverse sinus was 4.67 mm [1.6; 6.5]/4.45 mm [3.0; 7.9]. Height of pituitary gland was 5.1 mm [2.2;8.1]/4.6 mm [1.9;7.1] for female and male respectively. Values generally varied with BMI with regression slopes spanning 0.0001 to 0.05 and were therefor presented as normative values stratified by BMI. Protrusion of ocular papilla, empty sella and transverse sinus occlusion were rare in total. Our data show an association between BMI and commonly used MRI features for diagnosing IIH. We provide categorized normative BMI values for ONSD, pituitary gland height, and transverse sinus diameter. This distinction helps objectively identify potential IIH indicators compared to regular population norms, enhancing diagnostic accuracy for suspected IIH patients. Notably, optic nerve head protrusion, empty sella, and transverse sinus occlusion are rare in healthy individuals, solidifying their importance as imaging markers regardless of BMI.
Role of substrate temperature on the performance of BaTiO3/Si photodetector prepared by pulsed laser deposition
Authors: Abed Nadheer Z. , Ismail Raid A. , Shaker Suaad S. 
Published: February 2024

AbstractIn this study, the pulsed laser deposition (PLD) method was employed to fabricate nanostructured BaTiO3 films on glass and silicon substrates at varying temperatures. The structural analysis confirmed the formation of crystalline nanostructured BaTiO3 with mixed tetragonal and hexagonal phases, and the film deposited at 150 °C has the best crystallinity and largest particle size. The optical energy gap of the BaTiO3 nanostructure decreases from 3.94 to 3.84 eV, with increasing substrate temperature from 60 to 150 °C. Photoluminescence spectra of BaTiO3 films deposited at 25, 60, 100, and 150 °C exhibit emission peaks centered at 450, 512, 474, and 531 nm, respectively. Raman spectra of BaTiO3 films show E (LO), A (TO), E (LO) + TO, and B1 vibration modes. Hall measurements reveal that the mobility of the BaTiO3 film increases with temperature up to 100 °C and then decreases at 150 °C. The current–voltage characteristics of the BaTiO3/p-Si heterojunction, deposited over a temperature range of 25 to 150 °C, were investigated in the dark and under illumination. The heterojunctions exhibit rectifying properties, with the best rectification factor observed for the heterojunction prepared at 100 °C. The values of the ideality factor for the heterojunctions fabricated at 25, 60, 100, and 150 °C were 4.3, 3.8, 2.8, and 5, respectively. The study reveals an improvement in both the figures of merit and the photodetector performance with increased substrate temperature. The responsivity increases from 2.2 to 9.25 A/W as the deposition temperature rises from 25 to 100 °C. The detectivity (D*) and external quantum efficiency (EQE) of the photodetector prepared at the optimum substrate temperature of 100 °C, were found to be 4.62 × 1012 Jones and 114%, respectively, at 500 nm.
A cognitive deep learning approach for medical image processing
Authors: Fakhouri Hussam N. , Alawadi Sadi , Awaysheh Feras M. , Alkhabbas Fahed , Zraqou Jamal 
Published: February 2024

AbstractIn ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of segmentation processes. To overcome these challenges, we introduce the cognitive DL retinal blood vessel segmentation (CoDLRBVS), a novel hybrid model that synergistically combines the deep learning capabilities of the U-Net architecture with a suite of advanced image processing techniques. This model uniquely integrates a preprocessing phase using a matched filter (MF) for feature enhancement and a post-processing phase employing morphological techniques (MT) for refining the segmentation output. Also, the model incorporates multi-scale line detection and scale space methods to enhance its segmentation capabilities. Hence, CoDLRBVS leverages the strengths of these combined approaches within the cognitive computing framework, endowing the system with human-like adaptability and reasoning. This strategic integration enables the model to emphasize blood vessels, accurately segment effectively, and proficiently detect vessels of varying sizes. CoDLRBVS achieves a notable mean accuracy of 96.7%, precision of 96.9%, sensitivity of 99.3%, and specificity of 80.4% across all of the studied datasets, including DRIVE, STARE, HRF, retinal blood vessel and Chase-DB1. CoDLRBVS has been compared with different models, and the resulting metrics surpass the compared models and establish a new benchmark in retinal vessel segmentation. The success of CoDLRBVS underscores its significant potential in advancing medical image processing, particularly in the realm of retinal blood vessel segmentation.
Behavioral analysis of kainate receptor KO mice and the role of GluK3 subunit in anxiety
Authors: Iida Izumi , Konno Kohtarou , Natsume Rie , Abe Manabu , Watanabe Masahiko , Sakimura Kenji , Terunuma Miho 
Published: February 2024

AbstractKainate receptors (KARs) are one of the ionotropic glutamate receptors in the central nervous system (CNS) comprised of five subunits, GluK1-GluK5. There is a growing interest in the association between KARs and psychiatric disorders, and there have been several studies investigating the behavioral phenotypes of KAR deficient mice, however, the difference in the genetic background has been found to affect phenotype in multiple mouse models of human diseases. Here, we examined GluK1-5 single KO mice in a pure C57BL/6N background and identified that GluK3 KO mice specifically express anxiolytic-like behavior with an alteration in dopamine D2 receptor (D2R)-induced anxiety, and reduced D2R expression in the striatum. Biochemical studies in the mouse cortex confirmed that GluK3 subunits do not assemble with GluK4 and GluK5 subunits, that can be activated by lower concentration of agonists. Overall, we found that GluK3-containing KARs function to express anxiety, which may represent promising anti-anxiety medication targets.
Building integrated diffusers’ area ratio optimization
Authors: Elbakheit Abdel Rahman 
Published: February 2024

AbstractThis paper presents an investigation into the effect of area ratio parameter of diffusers on its energy output through power coefficient Cp. This parameter has effect both on diffusers’ energy yield, besides diffuser’s size for architectural integration prospects. A systematic increase in diffusers area ratio is adopted following standardized diffuser profile presented by NACA 1244 aerofoil. A series of area ratios were investigated (i.e., 1.25, 1.5, 1.75, 2, 2.5, 3 and 3.5). Area ratio of 1.5 (i.e., outlet/inlet, 0.75 m/0.50 m) exhibited the highest power coefficient Cp of 4.2, in addition to achieving highest resulting velocity of 25.8 m/s under incident velocity of 16m/s. Considerable wind separation inside inner walls of diffusers occurred from area ratio 1.75 onwards, which impacted resulting velocities. Simulations performed with ANSYS CFD Academic to standalone diffusers. A series of incident velocities employed from 1 to 16 m/s that resulted in velocity increase by 120–156% respectively.
High-dose exposure to butylparaben impairs thyroid ultrastructure and function in rats
Authors: Jiang Qi-Lan , Li Sha , Zeng Yang , Zhang Bo-Tao , Cao Yu , Li Tao , Jiang Jun 
Published: February 2024

AbstractParabens (PBs) are a class of preservatives commonly used in cosmetics and pharmaceuticals. Studies have shown that these compounds may act as endocrine disruptors, affecting thyroxine levels in humans. PBs with longer chain substituents, such as butylparaben (BuP), are less prone to complete biotransformation and are therefore more likely to accumulate in the body. In this study, the effect of high-dose exposure to BuP on thyroid microstructure, ultrastructure, and function was investigated in rats. 50 mg/kg bw per day of BuP was injected subcutaneously into the neck of rats for 4 weeks. Rat thyroid weight, microstructure, and ultrastructure were determined, and the levels of thyroid sodium/iodide symporter (NIS), serum thyroid hormones, and thyroid autoantibodies were measured. The human thyroid cell line was used to study the mechanism of BuP on thyroid epithelial cells. The weight of the thyroid gland of BuP-exposed rats was increased, the structure of the thyroid follicles was irregular and damaged, the mitochondria and rough endoplasmic reticulum were swollen and damaged, and the microvilli at the tip of the epithelium were reduced and disappeared. Serum total T3, total T4, free T3, and free T4 were decreased in BuP-exposed rats, and TSH, peroxidase antibody, and thyroglobulin antibody were increased. In vitro, BuP decreased the level of NIS in thyroid epithelial cells, inhibited proliferation and viability, and induced apoptosis in a dose-dependent manner. This study demonstrated that high-dose exposure to BuP induced structural, ultrastructural, and functional impairment to the thyroid gland of rats, which may be one of the factors leading to hypothyroidism.
A nationwide cohort study on the association between intensive care treatments and mental distress linked psychiatric disorders
Authors: Mossberg Rasmus , Ahlström Björn , Lipcsey Miklos 
Published: February 2024

AbstractGiven the psychic strain patients experience in the intensive care unit (ICU), a potential risk of mental disorders has been suggested. However, the effects of intensive care treatment per se are unknown. We investigated whether the level of intensive care treatments is an independent risk factor for developing long-term mental disorders after intensive care. In a national cohort of adult ICU patients we combined data on diagnoses, treatment, and causes of death. We defined extensive ICU treatment as being treated with invasive ventilation for > 24 h, continuous renal replacement therapy, or both. The primary outcome was incident mental disorder 1 year after ICU admission. Extensive ICU treatment was found to be associated with a decreased risk of developing a mental disorder ≥ 1 year after ICU admission (HR 0.90, 95% CI 0.82–0.99, p = 0.04), and increasing severity of acute illness (HR 1.18, 95% CI 1.06–1.32, p < 0.001) were associated with an increased risk of mental disorders. Because death acted as a competing risk for mental illness, mortality might help explain the apparent protective effect of extensive ICU care.Trial registration Clinical Trials Registry (Identification number NCT05137977). Registered 16 November 2021. As a registry trial the patients were already included at the trial registration i.e. it was retrospectively registered.
Optimizing warfarin dosing for patients with atrial fibrillation using machine learning
Authors: Petch Jeremy , Nelson Walter , Wu Mary , Ghassemi Marzyeh , Benz Alexander , Fatemi Mehdi , Di Shuang , Carnicelli Anthony , Granger Christopher , Giugliano Robert , Hong Hwanhee , Patel Manesh , Wallentin Lars , Eikelboom John , Connolly Stuart J. 
Published: February 2024

AbstractWhile novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke prevention across the world. While effective in reducing the risk of strokes, the complex pharmacodynamics of warfarin make it difficult to use clinically, with many patients experiencing under- and/or over- anticoagulation. In this study we employed a novel implementation of deep reinforcement learning to provide clinical decision support to optimize time in therapeutic International Normalized Ratio (INR) range. We used a novel semi-Markov decision process formulation of the Batch-Constrained deep Q-learning algorithm to develop a reinforcement learning model to dynamically recommend optimal warfarin dosing to achieve INR of 2.0–3.0 for patients with atrial fibrillation. The model was developed using data from 22,502 patients in the warfarin treated groups of the pivotal randomized clinical trials of edoxaban (ENGAGE AF-TIMI 48), apixaban (ARISTOTLE) and rivaroxaban (ROCKET AF). The model was externally validated on data from 5730 warfarin-treated patients in a fourth trial of dabigatran (RE-LY) using multilevel regression models to estimate the relationship between center-level algorithm consistent dosing, time in therapeutic INR range (TTR), and a composite clinical outcome of stroke, systemic embolism or major hemorrhage. External validation showed a positive association between center-level algorithm-consistent dosing and TTR (R2 = 0.56). Each 10% increase in algorithm-consistent dosing at the center level independently predicted a 6.78% improvement in TTR (95% CI 6.29, 7.28; p < 0.001) and a 11% decrease in the composite clinical outcome (HR 0.89; 95% CI 0.81, 1.00; p = 0.015). These results were comparable to those of a rules-based clinical algorithm used for benchmarking, for which each 10% increase in algorithm-consistent dosing independently predicted a 6.10% increase in TTR (95% CI 5.67, 6.54, p < 0.001) and a 10% decrease in the composite outcome (HR 0.90; 95% CI 0.83, 0.98, p = 0.018). Our findings suggest that a deep reinforcement learning algorithm can optimize time in therapeutic range for patients taking warfarin. A digital clinical decision support system to promote algorithm-consistent warfarin dosing could optimize time in therapeutic range and improve clinical outcomes in atrial fibrillation globally.