bluuebunny/crossref_metadata_2025 huggingface dataset:
Solutionsof Fuzzy Goursat Problems with Generalized Hukuhara (gH)-Differentiability Concept
Authors: Jamal Noor , Sarwar Muhammad , Abodayeh Kamaleldin , Hleili Manel , Chasreechai Saowaluck , Sitthiwirattham Thanin
Published: September 2024

In this manuscript, we will discuss the solutions of Goursat problems with fuzzy boundary conditions involving gH-differentiability. The solutions to these problems face two main challenges. The first challenge is to deal with the two types of fuzzy gH-differentiability: (i)-differentiability and (ii)-differentiability. The sign of coefficients in Goursat problems and gH-differentiability produces sixteen possible cases. The existing literature does not afford a solution method that addresses all the possible cases of this problem. The second challenge is the mixed derivative term in Goursat problems with fuzzy boundary conditions. Therefore, we propose to discuss the solutions of fuzzy Goursat problems with gH-differentiability. We will discuss the solutions of fuzzy Goursat problems in series form with natural transform and Adomian decompositions. To demonstrate the usability of the established solution methods, we will provide some numerical examples.
Deep Learning-Based Fine-Tuning Approach of Coarse Registration for Ear–Nose–Throat (ENT) Surgical Navigation Systems
Authors: Lee Dongjun , Choi Ahnryul , Mun Joung Hwan
Published: September 2024

Accurate registration between medical images and patient anatomy is crucial for surgical navigation systems in minimally invasive surgeries. This study introduces a novel deep learning-based refinement step to enhance the accuracy of surface registration without disrupting established workflows. The proposed method integrates a machine learning model between conventional coarse registration and ICP fine registration. A deep-learning model was trained using simulated anatomical landmarks with introduced localization errors. The model architecture features global feature-based learning, an iterative prediction structure, and independent processing of rotational and translational components. Validation with silicon-masked head phantoms and CT imaging compared the proposed method to both conventional registration and a recent deep-learning approach. The results demonstrated significant improvements in target registration error (TRE) across different facial regions and depths. The average TRE for the proposed method (1.58 ± 0.52 mm) was significantly lower than that of the conventional (2.37 ± 1.14 mm) and previous deep-learning (2.29 ± 0.95 mm) approaches (p < 0.01). The method showed a consistent performance across various facial regions and enhanced registration accuracy for deeper areas. This advancement could significantly enhance precision and safety in minimally invasive surgical procedures.
AI-Powered Telemedicine for Automatic Scoring of Neuromuscular Examinations
Authors: Lesport Quentin , Palmie Davis , Öztosun Gülşen , Kaminski Henry J. , Garbey Marc
Published: September 2024

Telemedicine is now being used more frequently to evaluate patients with myasthenia gravis (MG). Assessing this condition involves clinical outcome measures, such as the standardized MG-ADL scale or the more complex MG-CE score obtained during clinical exams. However, human subjectivity limits the reliability of these examinations. We propose a set of AI-powered digital tools to improve scoring efficiency and quality using computer vision, deep learning, and natural language processing. This paper focuses on automating a standard telemedicine video by segmenting it into clips corresponding to the MG-CE assessment. This AI-powered solution offers a quantitative assessment of neurological deficits, improving upon subjective evaluations prone to examiner variability. It has the potential to enhance efficiency, patient participation in MG clinical trials, and broader applicability to various neurological diseases.
Elevated Cellular Uptake of Succinimide- and Glucose-Modified Liposomes for Blood–Brain Barrier Transfer and Glioblastoma Therapy
Authors: Lubitz Larissa J. , Haffner Moritz P. , Rieger Harden , Leneweit Gero
Published: September 2024

The uptake of four liposomal formulations was tested with the murine endothelial cell line bEnd.3 and the human glioblastoma cell line U-87 MG. All formulations were composed of DPPC, cholesterol, 5 mol% of mPEG (2000 Da, conjugated to DSPE), and the dye DiD. Three of the formulations had an additional PEG chain (nominally 5000 Da, conjugated to DSPE) with either succinimide (NHS), glucose (PEG-bound at C-6), or 4-aminophenyl β-D-glucopyranoside (bound at C-1) as ligands at the distal end. Measuring the uptake kinetics at 1 h and 3 h for liposomal incubation concentrations of 100 µM, 500 µM, and 1000 µM, we calculated the liposomal uptake saturation S and the saturation half-time t1/2. We show that only succinimide has an elevated uptake in bEnd.3 cells, which makes it a very promising and so far largely unexplored candidate for BBB transfer and brain cancer therapies. Half-times are uniform at low concentrations but diversify for high concentrations for bEnd.3 cells. Contrary, U-87 MG cells show almost identical saturations for all three ligands, making a uniform uptake mechanism likely. Only mPEG liposomes stay at 60% of the saturation for ligand-coated liposomes. Half-times are diverse at low concentrations but unify at high concentrations for U-87 MG cells.
COVID-19 Inflammatory Syndrome: Lessons from TNFRI and CRP about the Risk of Death in Severe Disease
Authors: Farnesi-de-Assunção Thaís Soares , Oliveira-Scussel Ana Carolina de Morais , Rodrigues Wellington Francisco , Matos Beatriz Sodré , da Silva Djalma Alexandre Alves , de Andrade e Silva Leonardo Eurípedes , Mundim Fabiano Vilela , Helmo Fernanda Rodrigues , Bernardes e Borges Anna Victória , Desidério Chamberttan Souza , Trevisan Rafael Obata , Obata Malu Mateus Santos , Barbosa Laís Milagres , Lemes Marcela Rezende , Costa-Madeira Juliana Cristina , Barbosa Rafaela Miranda , Cunha Andrezza Cristina Cancian Hortolani , Pereira Loren Queli , Tanaka Sarah Cristina Sato Vaz , de Vito Fernanda Bernadelli , Monteiro Ivan Borges , Ferreira Yulsef Moura , Machado Guilherme Henrique , Moraes-Souza Hélio , Rodrigues Denise Bertulucci Rocha , de Oliveira Carlo José Freire , da Silva Marcos Vinicius , Júnior Virmondes Rodrigues
Published: September 2024

Background/Objectives: Cytokine storm in severe COVID-19 is responsible for irreversible tissue damage and death. Soluble mediators from the TNF superfamily, their correlation with clinical outcome, and the use of TNF receptors as a potent predictor for clinical outcome were evaluated. Methods: Severe COVID-19 patients had the levels of soluble mediators from the TNF superfamily quantified and categorized according to the clinical outcome (death versus survival). Statistical modeling was performed to predict clinical outcomes. Results: COVID-19 patients have elevated serum levels from the TNF superfamily. Regardless of sex and age, the sTNFRI levels were observed to be significantly higher in deceased patients from the first weeks following the onset of symptoms. We analyzed hematological parameters and inflammatory markers, and there was a difference between the groups for the following factors: erythrocytes, hemoglobin, hematocrit, leukocytes, neutrophils, band cells, lymphocytes, monocytes, CRP, IL-8, IFN-γ, IL-10, IL-6, IL-4, IL-2, leptin MIF sCD40L, and sTNFRI (p < 0.05). A post hoc analysis showed an inferential capacity over 70% for some hematological markers, CRP, and inflammatory mediators in deceased patients. sTNFRI was strongly associated with death, and the sTNFRI/sTNFRII ratio differed between outcomes (p < 0.001; power above 90%), highlighting the impact of these proteins on clinical results. The final logistic model, including sTNFRI/sTNFRII and CRP, indicated high sensitivity, specificity, accuracy, and an eight-fold higher odds ratio for an unfavorable outcome. Conclusions: The joint use of the sTNFRI/sTNFRII ratio with CRP proves to be a promising tool to assist in the clinical management of patients hospitalized for COVID-19.
Analysis of Brain Age Gap across Subject Cohorts and Prediction Model Architectures
Authors: Dular Lara , Špiclin Žiga ,   for the Alzheimer’s Disease Neuroimaging Initiative,   the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing
Published: September 2024

Background: Brain age prediction from brain MRI scans and the resulting brain age gap (BAG)—the difference between predicted brain age and chronological age—is a general biomarker for a variety of neurological, psychiatric, and other diseases or disorders. Methods: This study examined the differences in BAG values derived from T1-weighted scans using five state-of-the-art deep learning model architectures previously used in the brain age literature: 2D/3D VGG, RelationNet, ResNet, and SFCN. The models were evaluated on healthy controls and cohorts with sleep apnea, diabetes, multiple sclerosis, Parkinson’s disease, mild cognitive impairment, and Alzheimer’s disease, employing rigorous statistical analysis, including repeated model training and linear mixed-effects models. Results: All five models consistently identified a statistically significant positive BAG for diabetes (ranging from 0.79 years with RelationNet to 2.13 years with SFCN), multiple sclerosis (2.67 years with 3D VGG to 4.24 years with 2D VGG), mild cognitive impairment (2.13 years with 2D VGG to 2.59 years with 3D VGG), and Alzheimer’s dementia (5.54 years with ResNet to 6.48 years with SFCN). For Parkinson’s disease, a statistically significant BAG increase was observed in all models except ResNet (1.30 years with 2D VGG to 2.59 years with 3D VGG). For sleep apnea, a statistically significant BAG increase was only detected with the SFCN model (1.59 years). Additionally, we observed a trend of decreasing BAG with increasing chronological age, which was more pronounced in diseased cohorts, particularly those with the largest BAG, such as multiple sclerosis (−0.34 to −0.2), mild cognitive impairment (−0.37 to −0.26), and Alzheimer’s dementia (−0.66 to −0.47), compared to healthy controls (−0.18 to −0.1). Conclusions: Consistent with previous research, Alzheimer’s dementia and multiple sclerosis exhibited the largest BAG across all models, with SFCN predicting the highest BAG overall. The negative BAG trend suggests a complex interplay of survival bias, disease progression, adaptation, and therapy that influences brain age prediction across the age spectrum.
Seismic Performance of Recycled-Aggregate-Concrete-Based Shear Walls with Concealed Bracing
Authors: Liu Yibin , Cao Wanlin , Yang Zhaoyuan , Bian Jinliang , Bao Xin
Published: September 2024

Relatively few studies have been conducted on the seismic performance of recycled aggregate concrete (RAC) shear walls with concealed bracing. To promote the development of high-performance green building structures and the application of RAC in structural components, the seismic performance of RAC shear walls under different influencing factors was tested, and low-cycle reversed loading tests were performed on ten RAC shear walls with different shear-to-span ratios. The test parameters included the recycled coarse aggregate (RCA) replacement ratio, the recycled fine aggregate (RFA) replacement ratio, the axial compression ratio, the shear span ratio and whether to set up the concealed bracing. The influence of the above variables on the seismic performance was then assessed. The results revealed that the bearing capacity, ductility, stiffness and energy dissipation capacity of the RAC shear walls decreased in line with an increase in the replacement ratio of the RFA. However, the bearing capacity, energy consumption and stiffness of the RAC shear walls decreased within 10% and the ductility decreased within 15%. The RAC shear walls were able to meet the seismic requirements of the building structure after reasonable design and use. As the axial compression ratio increased, the bearing capacity of the RAC shear walls improved, but their elastic–plastic deformation capacity was reduced. Setting the concealed bracing significantly improved the seismic performance of the RAC shear walls, such that they achieved a seismic performance close to that of the natural aggregate concrete (NAC) shear wall. After setting up the concealed bracing, the load carrying capacity of the RAC shear walls increased by up to 15%, the ductility increased by up to 20% and the energy consumption capacity increased by up to 50%. A mechanical calculation model of the RAC shear wall was then established by considering the effect of recycled aggregate, the calculated results of which were a good match with the test results.
Active Child, Accomplished Youth: Middle Childhood Active Leisure Fuels Academic Success by Emerging Adulthood
Authors: Kosak Laurie-Anne , Harandian Kianoush , Bacon Simon L. , Fitzpatrick Caroline , Correale Luca , Pagani Linda S.
Published: September 2024

Background/Objectives: Physical activity is an important protective factor throughout life. However, little research has observed the associations between the practice of physical activity and academic success longitudinally, and none have done so with a pan-Canadian sample. This article aims to examine the prospective associations between active leisure in middle childhood and academic achievement in emerging adulthood, for both boys and girls, beyond several family factors. Methods: Participants are 2775 children from the National Longitudinal Study on Children and Youth (NLSCY) aged between 12 and 20 years. Active leisure was self-reported by children at age 12 years regarding their weekly organized sport, artistic sport, and unstructured physical activity participation outside of school hours. Academic success was measured by self-reported school average at age 18 years and the obtention of high school diploma at age 20 years. Results: Girls who engaged in more organized or artistic sports at age 12 years had better academic results at age 18 years (respectively β = −0.082, p < 0.01; β = −0.228, p < 0.001). Both boys and girls who partook in more organized sports at age 12 years were more likely to graduate from high school by age 20 years (respectively β = −0.146, p < 0.001; β = −0.071, p < 0.05). However, girls who engaged in more unstructured physical activity at age 12 years had lower academic achievement at age 18 years (β = 0.077, p < 0.001). Conclusions: Policy makers should aim to reduce the many barriers to an active lifestyle in childhood. Parents should be encouraged to lead their children to go play outside with friends to allow them to fully reap the benefits of an active lifestyle from a young age.
Insights into Hospitalized Children with Urinary Tract Infections: Epidemiology and Antimicrobial Resistance Patterns in Israel—A Single Center Study
Authors: Zaitoon Hussein , Garkaby Jenny , Nassrallah Basheer , Sharkansky Livnat , Shnaider Morya , Chistyakov Irina , Genizi Jacob , Nathan Keren
Published: September 2024

Background: The escalating resistance of uropathogens in pediatric febrile urinary tract infection (F-UTI) is a global concern. This study examined changing trends in F-UTI epidemiology and resistance patterns among Israeli pediatric inpatients over a decade. Methods: Demographic, clinical, and laboratory data for children between 3 months and 18 years old with febrile UTI from 2010 to 2021 were retrieved from electronic medical records. Results: A total of 761 cases of F-UTI were identified (702 females, mean age 43 months). Escherichia coli was the most common pathogen (85.9%), followed by Pseudomonas aeruginosa (3.5%) and Klebsiella pneumoniae (3.4%). Compared with the non-complicated UTI group, the complicated UTI group had significantly higher rates of Pseudomonas aeruginosa (5.3% vs. 1.0%, p = 0.002) and Klebsiella pneumoniae (4.6% vs. 1.6%, p = 0.03). Antibiotic resistance analysis revealed significant differences between the groups: resistance to cephalexin was higher in the complicated UTI group (19.3%) compared with the non-complicated UTI group (13.4%, p = 0.03). Notably, relatively low resistance rates were observed for ceftriaxone (4.4%) and gentamicin (6.0%). Over time, a significant decreasing trend in resistance to ampicillin was observed (slope = −0.0193, p = 0.011). No significant trends were found for trimethoprim–sulfamethoxazole, cephalexin, amoxicillin–clavulanic acid, ceftriaxone, and cefuroxime. Conclusions: Significant differences in pathogen distribution and resistance patterns between complicated UTI and non-complicated UTI groups highlight the need for continuous resistance monitoring and adherence to local guidelines. For the treatment of severe community F-UTI, ceftriaxone could be a reasonable option for first-onset F-UTI. Further studies are needed to implement antibiotic stewardship and optimize usage.
Hyaluronic Acid in Nanopharmaceuticals: An Overview
Authors: Matalqah Sina , Lafi Zainab , Asha Sara Yousef
Published: September 2024

Hyaluronic acid (HA) is a naturally occurring, long, unbranched polysaccharide that plays a critical role in maintaining skin structure and hydration. Its unique properties make it a valuable component in the field of nanopharmaceuticals. The combination of HA into nanopharmaceuticals enhances its ability to interact with various therapeutic agents, improving the delivery and efficacy of drugs. HA-based nanoparticles, including solid lipid nanoparticles, and polymeric nanogels, offer controlled release, enhanced stability, and targeted delivery of therapeutic agents. These innovations significantly improve therapeutic outcomes and reduce side effects, making HA an essential tool in modern medicine. In general, HA-modified liposomes enhance drug encapsulation and targeting, while HA-modified solid lipid nanoparticles (SLNs) provide a solid lipid core for drug encapsulation, offering controlled release and stability. This article provides an overview of the potential applications and recent advancements of HA in nanopharmaceuticals, emphasizing its significant impact on the evolving field of targeted drug delivery and advanced therapeutic strategies. By delving into the unique properties of HA and its compatibility with various therapeutic agents, this review underscores the promising potential of HA in revolutionizing nanopharmaceuticals.
Intranasal Insulin Eases Autism in Rats via GDF-15 and Anti-Inflammatory Pathways
Authors: Arda Duygu Burcu , Tunç Kerem Can , Bozkurt Mehmet Fatih , Bora Ejder Saylav , Çiğel Ayşe , Erbaş Oytun
Published: September 2024

In rat models, it is well-documented that chronic administration of propionic acid (PPA) leads to autism-like behaviors. Although the intranasal (IN) insulin approach is predominantly recognized for its effects on food restriction, it has also been shown to enhance cognitive memory by influencing various proteins, modulating anti-inflammatory pathways in the brain, and reducing signaling molecules such as interleukins. This study seeks to explore the potential therapeutic benefits of IN insulin in a rat model of autism induced by PPA. Thirty male Wistar albino rats were categorized into three cohorts: the control group, the PPA-induced autism (250 mg/kg/day intraperitoneal PPA dosage for five days) group, treated with saline via IN, and the PPA-induced autism group, treated with 25 U/kg/day (250 µL/kg/day) insulin via IN. All treatments were administered for 15 days. After behavioral testing, all animals were euthanized, and brain tissue and blood samples were collected for histopathological and biochemical assessments. Following insulin administration, a substantial reduction in autism symptoms was observed in all three social behavior tests conducted on the rats. Moreover, insulin exhibited noteworthy capabilities in decreasing brain MDA, IL-2, IL-17, and TNF-α levels within autism models. Additionally, there is a notable elevation in the brain nerve growth factor level (p < 0.05) and GDF-15 (p < 0.05). The assessment of cell counts within the hippocampal region and cerebellum revealed that insulin displayed effects in decreasing glial cells and inducing a significant augmentation in cell types such as the Purkinje and Pyramidal cells. The administration of insulin via IN exhibits alleviating effects on autism-like behavioral, biochemical, and histopathological alterations induced by PPA in rats. Insulin-dependent protective effects show anti-inflammatory, anti-oxidative, and neuroprotective roles of insulin admitted nasally.
Predicting the Conservation Behaviour of Cat Owners: Involvement, Attitudes and Approach-Avoidance Conflict
Authors: Kaine Geoff , Wright Vic , Turk Zachary
Published: September 2024

Cats, including companion cats, inflict extensive harm on wildlife. Using a national survey of 2000 households in New Zealand, we investigated the influence of involvement (an indicator of motivation) on the willingness of cat owners to keep their cats indoors at night. We found that respondents’ intentions to protect wildlife, and the frequency with which respondents with cats kept them indoors at night, was influenced by their involvement with cat welfare and their involvement with protecting wildlife, in addition to their attitudes and subjective norms. We also found that keeping cats indoors at night could be characterised as involving approach-avoidance conflict. Our findings have implications for efforts to increase the adoption by cat owners of keeping cats inside at night regarding the attentiveness of cat owners to promotional activities. Our findings suggest that such activities will not be particularly effective in the absence of cat-friendly, inexpensive, practical, and easily maintained devices that enable cats to be kept inside. Importantly, when the adoption of keeping cats inside at night is appropriately characterised as approach-avoidance conflict, our results suggest that promotional activities seeking to persuade cat owners that pet cats cause much greater harm to wildlife than they might believe are most likely to have a limited and likely temporary effect and may even be counterproductive.
The Botany, Phytochemistry and the Effects of the Juglans regia on Healthy and Diseased Skin
Authors: Adamovic Miljan , Adamovic Ana , Andjic Marijana , Dimitrijevic Jovana , Zdravkovic Nebojsa , Kostic Olivera , Pecarski Danijela , Pecarski Teodora , Obradovic Dusica , Tomovic Marina
Published: September 2024

This review aims to provide a detailed overview of the botanical, phytochemical, and dermatological properties of Juglans regia (J. regia). The entire tree contains a wealth of chemical compounds, including phenols, tannins, alkaloids, saponins, reducing sugars, and amino acids, which contribute to its significant nutritional and pharmacological value. Extracts and oils from all parts of J. regia have been studied for their effects on various skin conditions, demonstrating antioxidant, antimicrobial, anti-inflammatory, UV-protective, and chemoprotective properties. Additionally, these substances have shown potential in promoting wound healing, anti-aging, skin hydration, anti-tyrosinase activity, and hair dyeing. These benefits have been evaluated in various in vitro and in vivo studies. The therapeutic potential of J. regia suggests that its components could be integrated into treatment protocols and skincare routines. However, to optimize effectiveness and safety, future research should focus on in vivo studies in human subjects to determine the ideal concentrations and formulations of J. regia active compounds for specific skin conditions.
Hovering of Bi-Directional Motor Driven Flapping Wing Micro Aerial Vehicle Based on Deep Reinforcement Learning
Authors: Hu Haitian , Zhang Zhiyuan , Wang Zhaoguo , Wang Xuan
Published: September 2024

Inspired by hummingbirds and certain insects, flapping wing micro aerial vehicles (FWMAVs) exhibit potential energy efficiency and maneuverability advantages. Among them, the bi-directional motor-driven tailless FWMAV with simple structure prevails in research, but it requires active pose control for hovering. In this paper, we employ deep reinforcement learning to train a low-level hovering strategy that directly maps the drone’s state to motor voltage outputs. To our knowledge, other FWMAVs in both reality and simulations still rely on classical proportional-derivative controllers for pose control. Our learning-based approach enhances strategy robustness through domain randomization, eliminating the need for manually fine-tuning gain parameters. The effectiveness of the strategy is validated in a high-fidelity simulation environment, showing that for an FWMAV with a wingspan of approximately 200 mm, the center of mass is maintained within a 20 mm radius during hovering. Furthermore, the strategy is utilized to demonstrate point-to-point flight, trajectory tracking, and controlled flight of multiple drones.
A Novel Method for a Pursuit–Evasion Game Based on Fuzzy Q-Learning and Model-Predictive Control
Authors: Hu Penglin , Zhao Chunhui , Pan Quan
Published: September 2024

This paper explores a pursuit–evasion game (PEG) based on quadrotors by combining fuzzy Q-learning (FQL) and model-predictive control (MPC) algorithms. Initially, the FQL algorithm is employed to perceive, make decisions, and predict the trajectory of the evader. Based on the position and velocity information of both players in the game, the pursuer quadrotor determines its action strategy using the FQL algorithm. Subsequently, a state feedback controller is designed using the MPC algorithm, with reference inputs derived from the FQL algorithm. Within each MPC cycle, the FQL algorithm dynamically provides reference inputs to the MPC, thereby enhancing its robust control and dynamic optimization for the quadrotor. Finally, simulation results verify the effectiveness of the proposed algorithm.
Intelligent Identification Method of Low Voltage AC Series Arc Fault Based on Using Residual Model and Rime Optimization Algorithm
Authors: He Xiao , Kawaguchi Takahiro , Hashimoto Seiji
Published: September 2024

Aiming at the problem of accurate AC series arc fault detection, this paper proposes a low voltage AC series arc fault intelligent detection model based on deep learning. According to the GB/T 31143—2014 standard, an experimental platform was established. This system comprises a lower computer (slave computer) and an upper computer (master computer). It facilitates the acquisition of experimental data and the detection of arc faults during the data acquisition process. Based on a one-dimensional Convolutional Neural Network (CNN), Residual model (Res) and RIME optimization algorithm (RIME) are introduced to optimize the CNN. The current signals collected using high-frequency current, low-frequency coupled current, and high-frequency coupled current are used to construct an arc fault feature set for training of the necessary detection model. The experimental results indicate that the RIME optimization algorithm delivers the best performance when optimizing a one-dimensional CNN detection model with an introduced Res. This model achieves a detection accuracy of 99.42% ± 0.13% and a kappa coefficient of 95.69% ± 0.96%. For collection methods, high-frequency coupled current signals are identified as the optimal choice for detecting low-voltage AC series arc faults. Regarding feature selection, random forest-based feature importance ranking proves to be the most effective method.
Feasibility of Carbon Dioxide as Cushion Gas in Depleted Gas Reservoirs: An Experiment Study on CO2–CH4 Dispersion during Flow Alternation
Authors: Yang Jinhui , Ju Binshan , Yang An , Cui Zixian , Wang Meng , Tian Yapeng , Qi Hengchen
Published: September 2024

This study investigates the feasibility of utilizing carbon dioxide (CO2) as a cushion gas in depleted reservoirs for enhanced gas storage efficiency and carbon sequestration against the backdrop of rising natural gas stable supply demand and climate change concerns. Simulations of gas storage reservoir scenarios require accurate dispersion parameters at flow alternation conditions to quantify the size of the miscible displacement front. Several experimental studies using core-flooding equipment aimed at measuring related parameters have been reported over the last decade but did not take flow alternation into consideration. We simulated directionally variable displacements to mimic the cyclic injection and extraction processes in gas storage, focusing on the dispersion characteristics of CO2 and methane (CH4) during flow alternation. Key findings were observed using Nuclear Magnetic Resonance (NMR) imaging, which provided real-time data on the spatial distribution and temporal changes of CH4 signals in rock cores. The results revealed that dispersion, influenced predominantly by dispersion coefficients rather than molecular diffusion, was significantly higher during alternating flow compared to concurrent displacement. Additionally, CO2 exhibited a greater dispersion effect when displacing CH4 than the reverse. This enhanced mixing efficiency during flow alternation supports the potential of CO2 as a cost-effective and efficient cushion gas, offering both improved storage performance and the added benefit of CO2 sequestration. These findings contribute valuable insights for the numerical simulation and operational adaptation of CO2 in gas storage reservoirs, emphasizing the importance of understanding fluid interactions under varying flow conditions to optimize storage efficiency and environmental benefits.
Renewable Energy Generation Efficiency of Asian Economies: An Application of Dynamic Data Envelopment Analysis
Authors: Hu Jin-Li , Huang Yu-Shih , You Chian-Yi
Published: September 2024

Due to the continuous growth of global energy demand and the urgent pursuit of sustainable development goals, renewable energy development has become a vital strategy to deal with energy challenges and environmental issues. Renewable energy generation efficiency (REGE) around the world has begun to be examined, and ambitious goals with a sense of mission within a predetermined timeline have been set. The goal of this paper is to use the dynamic slacks-based measure (DSBM) data envelopment analysis (DEA) method to obtain the REGE for 44 Asian economies from 2010 to 2021. This paper also uses Tobit regression analysis to explore the factors that may affect the REGE. The empirical results indicate that the REGE in 17 economies reached the efficiency target during this period. When classified by income level, differences in average REGE are observed among high-income, upper-middle-income, lower-middle-income, and low-income economies. Additionally, differences in average REGE exist between tropical and temperate economies when classified by geographic latitude. Furthermore, through the Tobit regression model, we determine that information digitalization, financial openness, technological innovation ability, and renewable energy device capacity share all have significant positive effects on REGE, but life quality and democracy degree have significant negative impacts on REGE. Moreover, it has been found that the REGE scores of Asian economies exhibit a status similar to the middle-income trap. The outcome of the research provides Asian governments and those middle-income economies with ways to enhance REGE. Due to data limitations, this study cannot estimate the convergent solution based on the data of the research sample, and a new advanced Panel Tobit model is required.
Reliable Energy Optimization Strategy for Fuel Cell Hybrid Electric Vehicles Considering Fuel Cell and Battery Health
Authors: Ji Cong , Kamal Elkhatib , Ghorbani Reza
Published: September 2024

To enhance the fuel efficiency of fuel cell hybrid electric vehicles (FCHEVs), we propose a hierarchical energy management strategy (HEMS) to efficiently allocate power to a hybrid system comprising a fuel cell and a battery. Firstly, the upper-layer supervisor employs a fuzzy fault-tolerant control and prediction strategy for the battery and fuel cell management system, ensuring vehicle stability and maintaining a healthy state of charge for both the battery and fuel cell, even during faults. Secondly, in the lower layer, dynamic programming and Pontryagin’s minimum principle are utilized to distribute the necessary power between the fuel cell system and the battery. This layer also incorporates an optimized proportional-integral controller for precise tracking of vehicle subsystem set-points. Finally, we compare the economic and dynamic performance of the vehicle using HEMS with other strategies, such as the equivalent consumption minimization strategy and fuzzy logic control strategy. Simulation results demonstrate that HEMS reduces hydrogen consumption and enhances overall vehicle energy efficiency across all operating conditions, indicating superior economic performance. Additionally, the dynamic performance of the vehicle shows significant improvement.
The Development of Bi-LSTM Based on Fault Diagnosis Scheme in MVDC System
Authors: Lim Jae-Sung , Cho Haesong , Kwon Dohoon , Hong Junho
Published: September 2024

Diagnosing faults is crucial for ensuring the safety and reliability of medium-voltage direct current (MVDC) systems. In this study, we propose a bidirectional long short-term memory (Bi-LSTM)-based fault diagnosis scheme for the accurate classification of faults occurring in MVDC systems. First, to ensure stability in case a fault occurs, we modeled an MVDC system that included a resistor-based fault current limiter (R-FCL) and a direct current circuit breaker (DCCB). A discrete wavelet transform (DWT) extracted the transient voltages and currents measured using DC lines and AC grids in the frequency–time domain. Based on the digital signal normalized by the DWT, using the measurement data, the Bi-LSTM algorithm was used to classify and learn the types and locations of faults, such as DC line (PTP, P-PTG, and N-PTG) and internal inverter faults. The effectiveness of the proposed fault diagnosis scheme was validated through comparative analysis within the four-terminal MVDC system, demonstrating superior accuracy and a faster diagnosis time compared to those of the existing schemes that utilize other AI algorithms, such as the CNN and LSTM. According to the test results, the proposed fault diagnosis scheme detects MVDC faults and shows a high recognition accuracy of 97.7%. Additionally, when applying the Bi-LSTM-based fault diagnosis scheme, it was confirmed that not only the training diagnosis time (TraDT) but also the average diagnosis time (AvgDT) were 0.03 ms and 0.05 ms faster than LSTM and CNN, respectively. The results validate the superior fault clarification and fast diagnosis performance of the proposed scheme over those of the other methods.
Application of Ultrasonography in Stratifying Malignancy Risk for Indeterminate Thyroid Nodules as per TBSRTC 2023
Authors: Guerreiro Sofia , Mourão Mariana , Loureiro Isabel , Eusébio Rosário , Canberk Sule , Pinto Marques Hugo
Published: September 2024

Introduction: Thyroid nodules are extremely common and require complex management to prevent unnecessary surgical intervention and ensure that no malignant disease is overlooked. Several diagnostic tools and scoring systems are available to evaluate the risk of malignancy (ROM). The goal is to assess variables that can aid and support the clinical recommendations suggested by the updated Bethesda System for Reporting Thyroid Cytopathology (TBSRTC-2023), such as the ultrasonographic features of thyroid nodules, particularly for the indeterminate categories III (atypia of undetermined significance) and IV (follicular neoplasm). Methods: We retrospectively analysed the correlation of the demographic and ultrasonographic characteristics of thyroid nodules with the cytopathological and histopathological diagnoses of TBSRTC categories III (atypia of undetermined significance), IV (follicular neoplasm), V (suspicious for malignancy), and VI (malignant) in patients who underwent surgery in a single Portuguese centre over a 10-year period. Results: In total, 360 nodules were evaluated in 341 patients, and 57% were histopathologically malignant or borderline. The majority were included in the TBSRTC indeterminate categories III and IV, with ROMs of 44% and 43%, respectively. The ultrasonographic characteristics associated with a higher TBSRTC category and a greater ROM value were hypoechogenicity, the presence of microcalcifications, irregular margins, and the presence of cervical adenopathy. When correlating with a malignant histology, only adenopathy and the presence of microcalcifications were observed to be statistically significant. Discussion: The indeterminate categories of the TBSRTC have been the most challenging ones to manage. The new TBSRTC (2023) guidelines, as well as the ultrasonographic characteristics of a patient’s nodule, can be helpful in assessing the ROM and deciding on an appropriate course of treatment. Other resources, such as molecular tests, are also playing a more important role in the clinical decision process and may become crucial in the future. Conclusions: The worrisome ultrasound features that this study found to statistically correlate with a malignant histology were the presence of microcalcifications and adenopathy. The clinical management of thyroid nodules requires a careful analysis of clinical history and an evaluation of demographic details, personal and family history, ultrasonographic features, and the results of cytopathology, thyroid function, and molecular/genetic tests.
Improved Lift for Thick Flatback Airfoils in the Inboard Blades of Large Wind Turbines
Authors: Pucci Micol , Zanforlin Stefania
Published: September 2024

Thick airfoils are often used in the inboard sections of blades in commercial wind turbines. The main reason for this is to give the blade greater structural strength, but it is well known that thick airfoils degrade aerodynamic performance by stalling at relatively small angles of attack. The adoption of flatback airfoils instead of sharp trailing edges allows high lift coefficient to be maintained in thick airfoils. In this paper, we propose a novel airfoil design based on a passive flap to further improve the lift coefficient. This new design was tested by numerical simulation on several airfoils with different maximum thickness and different TE thickness. The improved design for flatback airfoils yields a higher lift coefficient, while the drag behaviour is strictly related to the baseline airfoil shape: some airfoils show a decrease in drag at certain angles of attack, while others exhibit a drag increase. In conclusion, the practical implications of the flap’s utilisation on a state-of-the-art blade designed for a 5 MW wind turbine are analysed. The findings demonstrate that, due to the enhanced lift coefficient, it is feasible to shorten the chord while maintaining the power output, thereby reducing material costs.
Unraveling the Complexities of Beef Marination: Effect of Marinating Time, Marination Treatments, and Breed
Authors: Ardicli Sena , Ardicli Ozge , Ustuner Hakan
Published: September 2024

The present study focused on evaluating the effects of beef marination on quality traits and consumer acceptability. In this context, m. longissimus thoracis et lumborum and m. semimembranosus samples (n = 192) were obtained from Aberdeen Angus, Hereford, Charolais, and Limousine bulls and were marinated with milk (pasteurized, 100%), garlic and olive oil (2.35 g/500 mL), and lemon (citrus) juice (31% orange juice, 31% lemon juice, 38% distilled water) for 12, 24, and 72 h. Marinade components were selected based on traditional culinary practices and their scientifically proven effects on meat quality. Beef samples on day 0 and non-marinated samples were used as control groups. Beef color, water holding capacity, pH, cooking loss, and Warner–Bratzler shear force were measured three times for each sample. A taste panel assessment was performed to determine the sensory characteristics. Statistical analysis was performed using general linear model (GLM) procedures followed by Tukey’s post-hoc comparison. Results revealed that marination time, as well as its two- and three-way interactions, significantly influenced beef quality parameters. These results indicate that the cattle breed is an important factor in evaluating the effectiveness of beef marination applications. The olive oil−garlic marinade was the most preferred by the panel across both types of muscle, as indicated by sensory evaluation results. The findings will not only enrich the scientific literature but also have practical implications for the beef industry.
Development of Cracked Egg Detection Device Using Electric Discharge Phenomenon
Authors: Joe Sung Yong , So Jun Hwi , Oh Seung Eel , Jun Soojin , Lee Seung Hyun
Published: September 2024

Eggs are a highly nutritious food; however, those are also fragile and susceptible to cracks, which can lead to bacterial contamination and economic losses. Traditional methods for detecting cracks, particularly in processed eggs, often fall short due to changes in the eggs’ physical properties during processing. This study was aimed at developing a novel device for detecting egg cracks using electric discharge phenomena. The system was designed to apply a high-voltage electric field to the eggs, where sparks were generated at crack locations due to the differences in electrical conductivity between the insulative eggshell and the more conductive inner membrane exposed by the cracks. The detection apparatus consisted of a custom-built high-voltage power supply, flexible electrode pins, and a rotation mechanism to ensure a complete 360-degree inspection of each egg. Numerical simulations were performed to analyze the distribution of the electric field and charge density, confirming the method’s validity. The results demonstrated that this system could efficiently detect cracks in both raw and processed eggs, overcoming the limitations of existing detection technologies. The proposed method offers high precision, reliability, and the potential for broader application in the inspection of various poultry products, representing a significant advancement in food safety and quality control.
A Reflective Spectroscopy Proof-of-Concept Study of Urea for Supporting Investigations of Human Waste in Multiple Forensic Contexts
Authors: McClelland Lilly , Belak Ethan , Curtis Juliana , Krekeler Ethan , Sanders April , Krekeler Mark P. S.
Published: September 2024

Human urine and its detection are of interest in forensic studies in numerous contexts. Both crystalline urea and 1.0 M solutions of urea, as synthetic analog endmember components of human urine, were investigated as a proof-of-concept study to determine if detailed lab spectroscopy would be viable. Urea was reliably detected on Ottawa sand at concentrations of approximately 3.2% in dried experiments. Urea was detectable after 1 week of solution evaporation under lab conditions, at 9.65 wt.% 1 M solution. This investigation successfully establishes urea as a material of interest for reflective spectroscopy and hyperspectral remote sensing/image spectroscopy on a wide range of spatial scales, from specific centimeter-scale areas in a crime scene to searching large outdoor regions > 1 km2. In addition, this investigation is relevant to improving the monitoring of human trafficking, status and condition of refugee camps, and monitoring sewage.
Dynamic Event-Triggered Prescribed-Time Consensus Tracking of Nonlinear Time-Delay Multiagent Systems by Output Feedback
Authors: Yoo Sung Jin , Park Bong Seok
Published: September 2024

Event-triggering mechanisms reported in the existing prescribed-time (PT) control do not adequately capture the dynamic nature of network environments, and are not applied to distributed consensus tracking problems with unknown time delays. Therefore, designing a dynamic event-triggering mechanism is crucial to ensuring PT stability, even in the presence of unknown time delays. This article focuses on developing a dynamic event-triggering mechanism to achieve adaptive practical PT output-feedback consensus tracking for nonlinear uncertain multiagent systems with unknown time delays. Firstly, a delay-independent PT state observer using a time-varying gain function is designed to estimate the immeasurable states. Following this, a novel distributed delay-independent PT consensus tracking scheme is constructed, incorporating a dynamic event-triggered mechanism through the command-filtered backstepping approach. In this design, dynamic variables based on a time-varying gain function are developed to support the event-triggering mechanism, ensuring practical stability within the prescribed settling time. Consequently, the proposed output-feedback control protocol can achieve practical PT stability, meaning that consensus tracking errors are constrained to a neighborhood around zero within a pre-specified time, regardless of the initial states of the agents or design parameters, while also avoiding the Zeno phenomenon. Finally, the effectiveness of the proposed strategy is validated through an illustrative example, which includes a comparative analysis.
Forming Ni-Fe and Co-Fe Bimetallic Structures on SrTiO3-Based SOFC Anode Candidates
Authors: Kujawska Kinga , Koliński Wojciech , Bochentyn Beata
Published: September 2024

The aim of this work was to verify the possibility of forming Ni-Fe and Co-Fe alloys via topotactic ion exchange exsolution in Fe-infiltrated (La,Sr,Ce)0.9(Ni,Ti)O3-δ or (La,Sr,Ce)0.9(Co,Ti)O3-δ ceramics. For this purpose, samples were synthesized using the Pechini method and then infiltrated with an iron nitrate solution. The reduction process in dry H2 forced the topotactic ion exchange exsolution, leading to the formation of additional round-shape structures on the surfaces of grains. EDS scans and XRD analysis confirmed the formation of bimetallic alloys, which suggests that these materials have great potential for further use as anode materials for Solid Oxide Fuel Cells (SOFCs).
Dynamic Approach to Update Utility and Choice by Emerging Technologies to Reduce Risk in Urban Road Transportation Systems
Authors: Russo Francesco , Comi Antonio , Chilà Giovanna
Published: September 2024

International research attention on evacuation issues has increased significantly following the human and natural disasters at the turn of the century, such as 9/11, Hurricane Katrina, Cyclones Idai and Kenneth, the Black Saturday forest fires and tsunamis in Japan. The main problem concerning when a disaster can occur involves studying the risk reduction. Risk, following all the theoretical and experimental studies, is determined by the product of three components: occurrence, vulnerability and exposure. Vulnerability can be improved over time through major infrastructure actions, but absolute security cannot be achieved. When the event will occur with certainty, only exposure remains to reduce the risk to people before the effect hits them. Exposure can be improved, under fixed conditions of occurrence and vulnerability, by improving evacuation. The main problem in terms of evacuating the population from an area is the available transport system, which must be used to its fullest. So, if the system is well managed, the evacuation improves (shorter times), meaning the exposure is reduced, and therefore, the risk is reduced. A key factor in the analysis of transport systems under emergency conditions is the behavior of the user, and therefore, the study of demand. This work identifies the main research lines that are useful for studying demand under exposure-related risk conditions. The classification of demand models that simulate evacuation conditions in relation to the effect on the transportation system is summarized. The contribution proposes a model for updating choice in relation to emergency conditions and utility. The contribution of emerging ICTs to actualization is formally introduced into the models. Intelligent technologies make it possible to improve user decisions, reducing exposure and therefore risk. The proposed model moves within the two approaches of the literature: it is an inter-period dynamic model with the probability expressed within the discrete choice theory; furthermore, it is a sequential dynamic model with the probability dependent on the previous choices. The contribution presents an example of application of the model, developing a transition matrix considering the case of choice updating under two extreme conditions.