bluuebunny/crossref_metadata_2025 huggingface dataset:
Framework for Analysis of Queueing Systems with Correlated Arrival Processes and Simultaneous Service of a Restricted Number of Customers in Scenarios with an Infinite Buffer and Retrials
Authors: Dudin Alexander , Dudin Sergei , Melikov Agassi , Dudina Olga
Published: November 2024

In this paper, we create a framework for the uniform algorithmic analysis of queueing systems with the Markov arrival process and the simultaneous service of a restricted number of customers, described by a multidimensional Markov chain. This chain behaves as the finite-state quasi-death process between successive service-beginning epochs, with jumps occurring at these epochs. Such a description of the service process generalizes many known mechanisms of restricted resource sharing and is well suited for describing various future mechanisms. Scenarios involving customers who cannot enter service upon arrival, access via waiting in an infinite buffer, and access via retrials are considered. We compare the generators of the multidimensional Markov chains describing the operation of queueing systems with a buffer and with retrials and show that the sufficient conditions for the ergodicity of these systems coincide. The computation of the stationary distributions of these chains is briefly discussed. The results can be used for performance evaluation and capacity planning of various queueing models with the Markov arrival process and a variety of different service mechanisms that provide simultaneous service to many customers.
NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking
Authors: Kabashkin Igor
Published: November 2024

The paper presents a novel framework for implementing decentralized algorithms based on non-fungible tokens (NFTs) for digital twin management in aviation, with a focus on component lifecycle tracking. The proposed approach uses NFTs to create unique, immutable digital representations of physical aviation components capturing real-time records of a component’s entire lifecycle, from manufacture to retirement. This paper outlines detailed workflows for key processes, including part tracking, maintenance records, certification and compliance, supply chain management, flight logs, ownership and leasing, technical documentation, and quality assurance. This paper introduces a class of algorithms designed to manage the complex relationships between physical components, their digital twins, and associated NFTs. A unified model is presented to demonstrate how NFTs are created and updated across various stages of a component’s lifecycle, ensuring data integrity, regulatory compliance, and operational efficiency. This paper also discusses the architecture of the proposed system, exploring the relationships between data sources, digital twins, blockchain, NFTs, and other critical components. It further examines the main challenges of the NFT-based approach and outlines future research directions.
Nonlinear Dynamic Process Monitoring Based on Discriminative Denoising Autoencoder and Canonical Variate Analysis
Authors: Liang Jun , Liu Daoguang , Zhan Yinxiao , Fan Jiayu
Published: November 2024

Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing these dynamic and nonlinear characteristics is essential for effective process monitoring. However, many existing methods for process monitoring and fault diagnosis are insufficient in handling these challenges. In this article, we present a novel process monitoring approach, CVA-DisDAE, which integrates an improved Denoising Autoencoder (DAE) with Canonical Variate Analysis (CVA) to address the challenges posed by dynamic behaviors and nonlinear relationships in industrial processes. First, CVA is employed to reduce data dimensionality and minimize information redundancy by maximizing correlations between past and future observations, thereby effectively capturing process dynamics. Following this, we introduce a discriminative DAE model (DisDAE) designed to serve as a semi-supervised denoising autoencoder for precise feature extraction. This is achieved by incorporating both between-class separability and within-class variability into the traditional DAE framework. The key distinction between the proposed DisDAE and the conventional DAE lies in the integration of a linear discriminant analysis (LDA) penalty into the DAE’s loss function, resulting in extracted features that are more conducive to fault classification. Finally, we validate the effectiveness of the proposed semi-supervised dynamic process monitoring approach through its application to the Tennessee Eastman benchmark process, demonstrating its superior performance.
Application of a Multi-Spectral UAV Imagery in Germplasm Characterization: Prediction of Forage Biomass and Growth Patterns of Cicer Milkvetch (Astragalus cicer L.) Populations
Authors: MacTaggart David , Shirtliffe Steve , Beattie Aaron , Lardner Herbert A. , Biligetu Bill
Published: November 2024

Unmanned aerial vehicles (UAV)-based multi-spectral imaging could reduce the intensive labour required in phenotyping germplasm in crop breeding. The objectives of this study were to examine if UAV-based imaging could differentiate cicer milkvetch (Astragalus cicer L.) germplasm and identify UAV-based vegetation indices with correlations to its dry matter yield (DMY). A spaced nursery from 27 cicer milkvetch populations was established near Saskatoon, SK, Canada, in 2019. From 1 June to 15 October in 2020 and 2021, phenotypic traits including maximum stem length, leaf number per stem, rhizome spread rate, and stem density, along with two UAV-measured traits, green area and canopy volume, were measured bi-weekly. Forage DMY was determined in late June and mid-October of each year. In this study, normalized difference vegetation index (NDVI) green area and NDVI canopy volume data differentiated the three selected populations. NDVI green area had the highest correlation with forage DMY among the traits (June harvest: r = 0.91, October harvest: r = 0.77). Among measured phenotypic traits, maximum stem length had the highest correlation with forage DMY (June harvest: r = 0.74, October harvest: r = 0.83), which was significantly correlated to NDVI green area. The results indicated potential use of UAV-phenotyping in single plant evaluation in plant breeding.
Prokaryotic Expression of Coat Protein Gene of Grapevine Berry Inner Necrosis Virus and Preparation of Its Polyclonal Antibody
Authors: Deng Xiaolong , Wang Zhilei , Wang Nian , Zhao Haiting , Qin Lang , Jiang Runzhou , Yuan Meng , Chen Xijun , He Zhen
Published: November 2024

Grapevine berry inner necrosis virus (GINV) and grapevine Pinot gris virus (GPGV) are prevalent viral diseases affecting viticulture, posing significant threats in grape-producing regions of China. Previous studies have emphasized the harmful effects of grape viruses on the grape industry all over the world. However, few reports have focused specifically on GINV. In wild grapevines, GINV infection frequently leads to grapevine fanleaf degeneration disease (GFDD). GINV often co-occurs with other grape viruses, exacerbating its harmful effects on the grapevine industry in China. In this study, we collected grapevine samples from Taizhou city, Jiangsu Province, where GINV infection was confirmed. Based on the GINV coat protein (CP) gene, we developed a high-throughput and high-sensitivity direct antigen-coated ELISA and Dot blot assay for field diagnosis of GINV CP in grape samples. The CP gene was cloned from GINV-infected grape samples, and the GINV CP was expressed using the pET30(a) vector. Specific polyclonal antiserum CPGINV was generated by immunizing rabbits with the purified protein, and its sensitivity was determined to be satisfactory. Leveraging the high accuracy and sensitivity of the CPGINV antiserum, we developed a rapid, precise, and scalable diagnostic method for GINV in the grapevine industry. The established ELISA and Dot blot assays successfully detected GINV-infected grapevine samples. The occurrence of GINV is relatively common in China, which poses a risk of transmission and threatens the healthy development of the grape industry. Therefore, this study prepared CPGINV antiserum and established an efficient, rapid, sensitive, accurate, and high-throughput diagnostic method, providing a foundational approach for the prevention and control of vitis viral diseases.
A Retrospective Report of Carprofen Administration as Post-Operative Analgesia Reveals Negative Effects of Recommended Doses
Authors: Jäckel Zoë , Adžemović Ahmed , Kloos Benedikt , Hardung Stefanie , Sanchez-Brandelik Rita , Coulon Philippe , Diester Ilka
Published: November 2024

Effective pain management in laboratory animals is crucial for both animal welfare and the reliability of scientific research. We retrospectively examined the effects of carprofen as post-operative analgesia in Sprague Dawley rats following stereotactic surgery. Our data indicate that administering carprofen twice daily (5 mg/kg), as currently recommended by Die Gesellschaft für Versuchstierkunde/Society for Laboratory Animal Science (GV-SOLAS), led to adverse effects such as reduced food and water intake, disrupted fecal excretion, and abdominal bloating consistent with peritonitis. Continued administration exacerbated these symptoms, with post-mortem findings of intestinal obstructions and ulcers. However, when the frequency was reduced to once daily, such adverse symptoms were not observed. These results are based on incidental data collected from various neuroscientific experiments, resulting in small and uneven sample groups across various experimental cohorts. The inherent imbalances in these groups present challenges for statistical interpretation. While the findings suggest that less frequent carprofen use may reduce adverse effects, the surgical interventions and concurrent use of other drugs in these experiments likely exacerbated these outcomes. Further investigation into the interactions between carprofen, surgical stress, and other perioperative factors is needed to refine analgesia protocols in laboratory animals. Despite these limitations, these observations contribute to understanding analgesia protocols and may assist in improving animal welfare practices.
Enteric Methane Emission in Livestock Sector: Bibliometric Research from 1986 to 2024 with Text Mining and Topic Analysis Approach by Machine Learning Algorithms
Authors: Evangelista Chiara , Milanesi Marco , Pietrucci Daniele , Chillemi Giovanni , Bernabucci Umberto
Published: November 2024

Methane (CH4) from livestock, particularly enteric CH4 emission (EME), is one contributor to greenhouse gas emissions and climate change. This review analyzed 1294 scientific abstracts on EME in ruminants from 1986 to May 2024, using Scopus® data. Descriptive statistics, text mining, and topic analysis were performed. Publications on EME have risen significantly since 2005, with the Journal of Dairy Science being the most frequent publisher. Most studies (82.1%) were original research, with Northern Hemisphere countries leading in publication numbers. The most frequent terms were “milk”, “cow”, and “diet”, while key research topics included greenhouse gas emissions from livestock, diet composition, and prediction models. Despite progress, some areas like CH4 emission from animals need further investigation.
Spatiotemporal Ultrastructural, Histological, and Morphometric Changes in the Buccal Cavity of Grass Carp (Ctenopharyngodon idella) During Fingerling, Yearling, and Adult Stages
Authors: Abdellatif Ahmed M. , Ateya Ahmed I. , Hasan Khadiga A. , Alghamdi Mansour A. , Madkour Fatma A.
Published: November 2024

Grass carp (Ctenopharyngodon idella) is a Cyprinid fish of aquacultural and research importance. The buccal cavity represents the gateway of the digestive tract. The present study investigated the adaptational changes involving various components of the buccal cavity of fingerling (three months posthatching, mph), yearling (12 mph), and adult (48 mph) C. idella using scanning electron microscopy, histology, and morphometry. The upper and lower lips appeared uninterrupted at the three studied stages and were limited caudally by the upper and lower jaws. The jaw epithelium was the thickest part of the buccal epithelium; ultrastructurally, it appeared smooth with ridge-like borders in fingerlings that transformed into microgrooves in yearlings. In adult C. idella, the jaw mucosa was organized into dome-shaped masses separated from each other by tight furrows. Each mass was formed from several keratinocytes with corrugated surfaces that featured numerous microdepressions. Except for the jaws, taste buds, mainly of type I, and acidic and neutral goblet cells were observed throughout the mucosa of the buccal cavity, and their densities were highest along the mucosal folds of the palate and oral floor, suggesting a principal role for these parts in both gustation and food lubrication. This study is the first to report age-associated spatiotemporal changes in the buccal cavity of grass carp and will serve as a fundamental reference during the interpretation of various types of oral pathologies in teleost.
The Use of Ceftazidime–Avibactam in a Pediatric Intensive Care Unit—An Observational Prospective Study
Authors: García Romero Raquel , Fresán-Ruiz Elena , Guitart Carmina , Bobillo-Perez Sara , Jordan Iolanda
Published: November 2024

Background/objectives: Infections caused by carbapenem-resistant Enterobacterales (CRE) are progressively increasing in Pediatric Intensive Care Units (PICUs). Its treatment is challenging due to the lack of pediatric trials. CRE infections are associated with significantly poor outcomes, but ceftazidime–avibactam (CAZ-AVI) has been reported to be successful in their treatment. This study aimed to describe the use and outcome of CAZ-AVI in a PICU. Results: Ten patients were included, with 12 episodes of clinical suspicion or confirmed multidrug-resistant (MDR) bacterial infections treated with CAZ-AVI for surgical prophylaxis, suspicion of sepsis, pneumonia, and surgical wound infection. Of these patients, 80% received empirical treatment because of previous MDR bacterial colonization, and 60% were administrated combination therapy with aztreonam for Metallo-β-Lactamases (MBL)strains. No bacteria were resistant to CAZ-AVI. The average duration of the treatment was 3 days when cultures turned negative and 7 days when MDR bacteria were isolated. Methods: This was an observational prospective study of children treated with CAZ-AVI in the PICU of a tertiary hospital in 2022. Epidemiological, clinical, microbiological, and outcome data were collected. Conclusions: The most frequent use of CAZ-AVI in our PICU was the short-term empirical treatment for patients with previous MDR bacterial colonization and clinical suspicion of bacteremia or sepsis. Furthermore, the combination of CAZ-AVI plus aztreonam could be more effective for CRE infections, especially type Ambler class B as MBL strains.
Dairy Chain Safety in the Context of Antibiotic Residues—Current Status of Confirmatory Liquid Chromatography Methods: A Review
Authors: Dluhošová Sandra , Bartáková Klára , Vorlová Lenka , Navrátilová Pavlína , Hanuš Oto , Samková Eva
Published: November 2024

With ever-developing analytical capabilities, the protection of the dairy chain from contamination by residues of veterinary drugs is improving. Legislative requirements are an inherent part of this process. Of antibiotics in dairy farming, representatives of the beta-lactams, tetracyclines, sulfonamides, (fluoro)quinolones, aminoglycosides, or polypeptide antibiotics are the most widely used. Due to the typically low levels of antibiotic residues in milk, mass spectrometry is the most commonly used detection technique. However, the interference of the sample matrix is one of its main limiting factors, and therefore, it needs to be eliminated. In the first step, the lipid fraction is removed and proteins are precipitated, followed by solid-phase or liquid–liquid extraction. The current trends include the reduction in the consumption of organic solvents (to reduce occupational hazards and burden to the environment) and automation, eliminating the influence of human error and optimizing the workflow. These trends lead to the development of new microextraction and automated techniques as well as the use of new sorbents and/or (green) solvents of natural origin. To capture the latest developments in the field and the relatively recent aforementioned trends, this review focuses on papers investigating antimicrobial residues in milk that were published between 2015 and 2024, providing an overview of emerging techniques applicable to antibiotic residue detection in milk samples.
The Use of Cefiderocol in Gram-Negative Bacterial Infections at International Medical Center, Jeddah, Saudi Arabia
Authors: Kaki Reham , Taj Amjad , Bagaaifar Sultan
Published: November 2024

Background/Objectives: The necessity for ground-breaking treatments for Gram-negative infections is evident. The World Health Organization, the Infectious Diseases Society of America, and the European Commission have highlighted the critical insufficiency of efficient antibiotics, urging pharmaceutical businesses to manufacture new antibiotics. Therefore, developing new antibiotics with broad efficacy against Gram-negative pathogens is essential. Thus, this research aimed to evaluate the safety and effectiveness of cefiderocol in treating multidrug-resistant Gram-negative bacterial infections at the International Medical Center (IMC), Jeddah, Saudi Arabia. Methods: A retrospective analysis was conducted on patients treated from January 2021 to February 2023. Thirteen case groups treated with cefiderocol were compared to twenty control groups treated with other antibiotics. Results: The results indicated no statistically significant differences in ICU stay, comorbidity indices, or mortality rates between the two groups. Cefiderocol showed high clinical and microbiological cure rates, despite the severity of the patients’ conditions. Carbapenem-resistant Klebsiella pneumoniae and difficult-to-treat resistance Pseudomonas aeruginosa were the most prevalent pathogens in the case and control group, respectively. Two patients treated with cefiderocol developed Clostridioides difficile infection, emphasizing the need for close monitoring of potential adverse effects. Conclusions: The results of this study support cefiderocol as a viable alternative for managing serious infections instigated by multidrug-resistant Gram-negative bacteria.
Mutations Selectively Evolving Peroxidase Activity Among Alternative Catalytic Functions of Human Glutathione Transferase P1-1
Authors: Ismail Aram , Mannervik Bengt
Published: November 2024

Glutathione transferases are detoxication enzymes with broad catalytic diversity, and small alterations to the protein’s primary structure can have considerable effects on the enzyme’s substrate selectivity profile. We demonstrate that two point mutations in glutathione transferase P1-1 suffice to generate 20-fold enhanced non-selenium-dependent peroxidase activity indicating a facile evolutionary trajectory. Designed mutant libraries of the enzyme were screened for catalytic activities with alternative substrates representing four divergent chemistries. The chemical reactions comprised aromatic substitution, Michael addition, thiocarbamoylation, and hydroperoxide reduction. Two mutants, R1 (Y109H) and an R1-based mutant V2 (Q40M-E41Q-A46S-Y109H-V200L), were discovered with 16.3- and 30-foldincreased peroxidase activity with cumene hydroperoxide (CuOOH) compared to the wildtype enzyme, respectively. The basis of the improved peroxidase activity of the mutant V2 was elucidated by constructing double-point mutants. The mutants V501 (Q40M-Y109H) and V503 (E41Q-Y109H) were found to have 20- and 21-fold improvements in peroxidase activity relative to the wildtype enzyme, respectively. The steady-state kinetic profiles of mutants R1 and V2 in the reduction of CuOOH were compared to the wildtype parameters. The kcat values for R1 and V2 were 34- and 57-fold higher, respectively, than that of the wildtype enzyme, whereas the mutant Km values were increased approximately 3-fold. A 10-fold increased catalytic efficiency (kcat/Km) in CuOOH reduction is accomplished by the Tyr109His point mutation in R1. The 23-fold increase of the efficiency obtained in V2 was caused by adding further mutations primarily enhancing kcat. In all mutants with elevated peroxidase activity, His109 played a pivotal role.
Development and Optimization of a Redox Enzyme-Based Fluorescence Biosensor for the Identification of MsrB1 Inhibitors
Authors: Shim Hyun Bo , Lee Hyunjeong , Cho Hwa Yeon , Jo Young Ho , Tarrago Lionel , Kim Hyunggee , Gladyshev Vadim N. , Lee Byung Cheon
Published: November 2024

MsrB1 is a thiol-dependent enzyme that reduces protein methionine-R-sulfoxide and regulates inflammatory response in macrophages. Therefore, MsrB1 could be a promising therapeutic target for the control of inflammation. To identify MsrB1 inhibitors, we construct a redox protein-based fluorescence biosensor composed of MsrB1, a circularly permutated fluorescent protein, and the thioredoxin1 in a single polypeptide chain. This protein-based biosensor, named RIYsense, efficiently measures protein methionine sulfoxide reduction by ratiometric fluorescence increase. We used it for high-throughput screening of potential MsrB1 inhibitors among 6868 compounds. A total of 192 compounds were selected based on their ability to reduce relative fluorescence intensity by more than 50% compared to the control. Then, we used molecular docking simulations of the compound on MsrB1, affinity assays, and MsrB1 activity measurement to identify compounds with reliable and strong inhibitory effects. Two compounds were selected as MsrB1 inhibitors: 4-[5-(4-ethylphenyl)-3-(4-hydroxyphenyl)-3,4-dihydropyrazol-2-yl]benzenesulfonamide and 6-chloro-10-(4-ethylphenyl)pyrimido[4,5-b]quinoline-2,4-dione. They are heterocyclic, polyaromatic compounds with a substituted phenyl moiety interacting with the MsrB1 active site, as revealed by docking simulation. These compounds were found to decrease the expression of anti-inflammatory cytokines such as IL-10 and IL-1rn, leading to auricular skin swelling and increased thickness in an ear edema model, effectively mimicking the effects observed in MsrB1 knockout mice. In summary, using a novel redox protein-based fluorescence biosensor, we identified potential MsrB1 inhibitors that can regulate the inflammatory response, particularly by influencing the expression of anti-inflammatory cytokines. These compounds are promising tools for understanding MsrB1’s role during inflammation and eventually controlling inflammation in therapeutic approaches.
Investigation of Algerian Crataegus monogyna Jacq Phenolic Compounds (Using LC-ESI-MS/MS Analysis, Antioxidant Activity, and Enzyme Inhibition) and Their Potential Implications for Food and Nutraceutical Applications
Authors: Goudjil Sabrina , Boussekine Samira , Goudjil Sarra , Goudjil Hanane , Yilmaz Mustafa Abdullah , Ola Mohammad Shamsul , Ali Ahmad , Cakir Oguz
Published: November 2024

Investigations into the phenolic constituents of the butanolic fraction of Crataegus monogyna were optimized using LC-ESI-MS/MS analysis, identifying and quantifying at least 23 fingerprint phytochemical compounds. The major phenolic compounds were epicatechin (99.916 ± 2.208 mg/g), isoquercetrin (53.31 ± 1.172 mg/g), chlorogenic acid (47.457 ± 1.010 mg/g), quinic acid (37.819 ± 1.406 mg/g), rutin (29.98 ± 0.740 mg/g), hesperidin (5.296 ± 0.177 mg/g, detected for the first time in the C. monogyna species), astragalin (1.774 ± 0.020 mg/g), and nicotiflorin (1.482 ± 0.016 mg/g). The antioxidant properties of the lyophilized butanolic fraction were evaluated using DPPH, GOR, ABTS, CUPRAC, and reducing power assays, all of which demonstrated that there was strong activity. Additionally, the neuroprotective effect was evaluated in vitro, showing a potent inhibitory effect on acetylcholinesterase (AChE) with an IC50 of 43.65 ± 2.10 µg/mL. The antidiabetic effect was investigated through α-amylase inhibition (IC50 = 91.19 ± 0.10 µg/mL), showing high inhibitory activity. In addition, the butanolic extract exhibited significant urease inhibition with an IC50 of 26.36 ± 0.05 µg/mL. These results suggest that Algerian C. monogyna has potential as a therapeutic agent for managing diabetes complications and as a natural source of AChE inhibitors, making it a promising subject for the treatment of urease-related conditions. Its high concentrations of natural antioxidants, such as epicatechin, isoquercetrin, chlorogenic acid, quinic acid, rutin, hesperidin, and astragalin, make it suitable for integration into medicine, pharmaceuticals, cosmetics, and the food sector.
Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods
Authors: Kızıldağ Mehmet , Abut Fatih , Akay Mehmet Fatih
Published: November 2024

The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply–demand equilibrium and serving as a key indicator of market conditions. Accurate SMP forecasting is essential for ensuring market stability and economic efficiency. This study addresses the challenges of SMP prediction in Turkey by proposing a comprehensive forecasting framework that integrates machine learning, deep learning, and statistical models. Advanced feature selection techniques, such as Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), are employed to refine model inputs. The framework incorporates time series methods like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional LSTM (ConvLSTM) to capture complex temporal patterns, alongside models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM) for modeling non-linear relationships. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) across regular weekdays, weekends, and public holidays. XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating exceptional accuracy and robustness. Among all of the models, XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating superior accuracy and robustness. The results highlight the inadequacy of traditional models like ARIMA and SARIMA in capturing non-linear and highly volatile patterns, reinforcing the necessity of using advanced techniques for effective SMP forecasting. Overall, this study presents a novel and comprehensive approach tailored for complex electricity markets, significantly enhancing predictive reliability by incorporating economic indicators and sophisticated feature selection methods.
Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
Authors: Lamrabet Mehdi , Giot Rudi , Almeida Jorge , Mendes Mateus
Published: November 2024

The downtown area of the city of Coimbra, Portugal, is at low altitude and has historically suffered floods that have caused serious economic losses. The present research proposes a mobile augmented reality (MAR) application aimed at visualising the effect of possible scenarios of flooding in an area of higher risk in the city. A realistic 3D model of the city was created, using data extracted with BLosm and processed through Blender, followed by its integration into Unity with Vuforia for AR visualisation. The methodology encompasses the extraction and simplification of 3D models, mapping real-world coordinates in Unity, analysing several datasets, obtaining a model through regression and implementing a workflow to manage interactions between various Unity objects. The MAR application enables users to visualise potential flood impacts on buildings, utilising colour-coded indicators to represent different levels of water contact. The system’s efficacy was evaluated by simulating various use-case scenarios, demonstrating the application’s capability to provide real-time, interactive flood risk assessments. The results underline the potential of integrating AR and machine learning for enhancing urban flood management and prevention.
The Impact of Climate Change and Urbanization on Compound Flood Risks in Coastal Areas: A Comprehensive Review of Methods
Authors: Ruan Xuejing , Sun Hai , Shou Wenchi , Wang Jun
Published: November 2024

Many cities worldwide are increasingly threatened by compound floods resulting from the interaction of multiple flood drivers. Simultaneously, rapid urbanization in coastal areas, which increases the proportion of impervious surfaces, has made the mechanisms and simulation methods of compound flood disasters more complex. This study employs a comprehensive literature review to analyze 64 articles on compound flood risk under climate change from the Web of Science Core Collection from 2014 to 2024. The review identifies methods for quantifying the impact of climate change factors such as sea level rise, storm surges, and extreme rainfall, as well as urbanization factors like land subsidence, impervious surfaces, and drainage systems on compound floods. Four commonly used quantitative methods for studying compound floods are discussed: statistical models, numerical models, machine learning models, and coupled models. Due to the complex structure and high computational demand of three-dimensional joint probability statistical models, along with the increasing number of flood drivers complicating the grid interfaces and frameworks for coupling different numerical models, most current research focuses on the superposition of two disaster-causing factors. The joint impact of three or more climate change-driving factors on compound flood disasters is emerging as a significant future research trend. Furthermore, urbanization factors are often overlooked in compound flood studies and should be considered when establishing models. Future research should focus on exploring coupled numerical models, statistical models, and machine learning models to better simulate, predict, and understand the mechanisms, evolution processes, and disaster ranges of compound floods under climate change.
Evaluating Uncertainty Quantification in Medical Image Segmentation: A Multi-Dataset, Multi-Algorithm Study
Authors: Jalal Nyaz , Śliwińska Małgorzata , Wojciechowski Wadim , Kucybała Iwona , Rozynek Miłosz , Krupa Kamil , Matusik Patrycja , Jarczewski Jarosław , Tabor Zbisław
Published: November 2024

Deep learning is revolutionizing various scientific fields, with medical applications at the forefront. One key focus is automating image segmentation, a process crucial in many clinical services. However, medical images are often ambiguous and challenging even for experts. To address this, reliable models need to quantify their uncertainty, allowing physicians to understand the model’s confidence in its segmentation. This paper explores how the performance and uncertainty of a model are influenced by the number of annotations per input sample. We examine the effects of both single and multiple manual annotations on various deep learning architectures. To tackle this question, we employ three widely recognized deep learning architectures and evaluate them across four publicly available datasets. Furthermore, we explore the effects of dropout rates on Monte Carlo models by examining uncertainty models with dropout rates of 20%, 40%, 60%, and 80%. Subsequently, we evaluate the models using various measurement metrics. The findings reveal that the influence of multiple annotations varies significantly depending on the datasets. Additionally, we observe that the dropout rate has minimal or no impact on the model’s performance unless there is a substantial loss of training data, primarily evident in the 80% dropout rate scenario.
Land Subsidence Predictions Based on a Multi-Component Temporal Convolutional Gated Recurrent Unit Model in Kunming City
Authors: Chen Tao , Ning Di , Liu Yuhang
Published: November 2024

Land subsidence (LS) is a geological hazard driven by both natural conditions and human activities. Traditional LS time-series prediction models often struggle to accurately capture nonlinear data characteristics, leading to suboptimal predictions. To address this issue, this paper introduces a multi-component temporal convolutional gate recurrent unit (MC-TCGRU) model, which integrates a fully adaptive noise-ensemble empirical-mode decomposition algorithm with a deep neural network to account for the complexity of time-series data. The model was validated using typical InSAR subsidence data from Kunming, analyzing the impact of each component on the prediction performance. A comparative analysis with the TCGRU model and models based on seasonal-trend decomposition using LOESS (STL) and empirical-mode decomposition (EMD) revealed that the MC-TCGRU model significantly enhanced the prediction accuracy by reducing the complexity of the original data. The model achieved R² values of 0.90, 0.93, 0.51, 0.93, and 0.96 across five points, outperforming the compared models.
Accelerometer-Based Pavement Classification for Vehicle Dynamics Analysis Using Neural Networks
Authors: Surblys Vytenis , Kozłowski Edward , Matijošius Jonas , Gołda Paweł , Laskowska Agnieszka , Kilikevičius Artūras
Published: November 2024

This research examines the influence of various pavement types on vehicle dynamics, specifically concentrating on vertical acceleration and its implications for unsprung mass, including the wheels and suspension system. The objective of this project was to categorize pavement types with accelerometer data, enabling a deeper comprehension of the impact of road surface conditions on vehicle stability, comfort, and mechanical stress. Two categorization methods were utilized: a neural network and a multinomial logistic regression model. Accelerometer data were gathered while a car navigated diverse terrain types, such as grates, potholes, and cobblestones. The neural network model exhibited exceptional performance, with 100% accuracy in categorizing all surface types, while the multinomial logistic regression model reached 97.14% accuracy. The neural network demonstrated exceptional efficacy in differentiating intricate surface types such as potholes and grates, surpassing the logistic regression model which had difficulties with these surfaces. These results underscore the neural network’s effectiveness in the real-time categorization of road surfaces, enhancing the comprehension of vehicle dynamics influenced by pavement conditions. Future studies must tackle the difficulty of identifying analogous surfaces by enhancing methodologies or integrating more data attributes for greater precision.
Energy Storage in Carbon Fiber-Based Batteries: Trends and Future Perspectives
Authors: del Bosque Antonio , Vergara Diego , Lampropoulos Georgios , Fernández-Arias Pablo
Published: November 2024

Carbon fiber-based batteries, integrating energy storage with structural functionality, are emerging as a key innovation in the transition toward energy sustainability. Offering significant potential for lighter and more efficient designs, these advanced battery systems are increasingly gaining ground. Through a bibliometric analysis of scientific literature, the study identifies three primary research areas: (i) the development of anodes for lithium-ion batteries, tackling challenges such as dendrite formation and performance degradation; (ii) the creation of new carbon fiber-based cathodes with coatings of LiFePO4, LiCoO2, or other nanoparticles, alongside efforts to develop cobalt-free alternatives; and (iii) the advancement of solid electrolytes that achieve a balance between ionic conductivity and mechanical strength. These advancements position carbon fiber-based batteries as promising solutions for seamless integration into various structural applications. The analysis of publication trends, citation patterns, and collaboration networks provides critical insights into the ongoing technological developments, current research challenges, and emerging trends in this field. Moreover, the study highlights potential research directions, underscoring the importance of continuous innovation to fully realize the potential of carbon fiber-based energy storage technologies.
The Mathematical Model Based on the Parameters of Simulation Results Predicts the Fire Extinguishing Resource Demand of Naval Fires
Authors: Guo Jingjing , Zhang Ying , Zhao Bo , Wu Shusheng
Published: November 2024

In recent years, due to the diversity of fire scenes in ports and ships, the problem of fire command is complicated. In the case of power failure, the original command platform and fire extinguishing system will not be able to be used normally, or the fire extinguishing resources carried by the ship when it is on fire cannot be used. In the traditional firefighting ability research, there is no clear measure of firefighting ability, resulting in inaccurate calculation results. By corresponding to the quantity and composition of resources for the study of fire extinguishing capability, the combination of the fire dynamics simulation software PyroSim2020 and a calculation model that converts its resources into the total amount and flow of fire extinguishing agent can be provided. Based on the analytic hierarchy process (AHP), a firefighting demand grade calculation and judgment model was established, which included five factors: fire scale, combustion material characteristics, foam extinguishing agent performance, firefighting object characteristics, and external factors. It concluded that the demand assignment values of each index element were 2, 5, 6, 9, and 10, and further proposed the grade judgment criteria for the calculation results of comprehensive fire demand. Through the quantitative simulation of firefighting demand based on the fire scenario and calculation model test of the consumption prediction of cooperative firefighting equipment, it can also provide a strategic reference for related cooperative fire rescue.
Transformative Transparent Hybrid Deep Learning Framework for Accurate Cataract Detection
Authors: Olaniyan Julius , Olaniyan Deborah , Obagbuwa Ibidun Christiana , Esiefarienrhe Bukohwo Michael , Odighi Matthew
Published: November 2024

This paper presents a transformative explainable convolutional neural network (CNN) framework for cataract detection, utilizing a hybrid deep learning model combining Siamese networks with VGG16. By leveraging a learning rate scheduler and Grad-CAM (Gradient-weighted Class Activation Mapping) for explainability, the proposed model not only achieves high accuracy in identifying cataract-infected images but also provides interpretable visual explanations of its predictions. Performance evaluation metrics such as accuracy, precision, recall, and F1 score demonstrate the model’s robustness, with a perfect accuracy of 100%. Grad-CAM visualizations highlight the key image regions—primarily around the iris and pupil—that contribute most to the model’s decision-making, making the system more transparent for clinical use. Additionally, novel statistical analysis methods, including saliency map evaluation metrics like AUC (Area Under the Curve) and the Pointing Game, were employed to quantify the quality of the model’s explanations. These metrics enhance the interpretability of the model and support its practical applicability in medical image analysis. This approach advances the integration of deep learning with explainable AI, offering a robust, accurate, and interpretable solution for cataract detection with the potential for broader adoption in ocular disease diagnosis and medical decision support systems.
Image Dehazing Enhancement Strategy Based on Polarization Detection of Space Targets
Authors: Miao Shuzhuo , Li Zhengwei , Zhang Han , Li Hongwen
Published: November 2024

In view of the fact that the technology of polarization detection performs better at identifying targets through clouds and fog, the recognition ability of the space target detection system under haze conditions will be improved by applying the technology. However, due to the low ambient brightness and limited target radiation information during space target detection, the polarization information of space target is seriously lost, and the advantages of polarization detection technology in identifying targets through clouds and fog cannot be effectively exerted under the condition of haze detection. In order to solve the above problem, a dehazing enhancement strategy specifically applied to polarization images of space targets is proposed. Firstly, a hybrid multi-channel interpolation method based on regional correlation analysis is proposed to improve the calculation accuracy of polarization information during preprocessing. Secondly, an image processing method based on full polarization information inversion is proposed to obtain the degree of polarization of the image after inversion and the intensity of the image after dehazing. Finally, the image fusion method based on discrete cosine transform is used to obtain the dehazing polarization fusion enhancement image. The effectiveness of the proposed image processing strategy is verified by carrying out simulated and real space target detection experiments. Compared with other methods, by using the proposed image processing strategy, the quality of the polarization images of space targets obtained under the haze condition is significantly improved. Our research results have important practical implications for promoting the wide application of polarization detection technology in the field of space target detection.
Efficient I/O Performance-Focused Scheduling in High-Performance Computing
Authors: Kim Soeun , Kim Sunggon , Kim Hwajung
Published: November 2024

High-performance computing (HPC) systems are becoming increasingly important as contemporary exascale applications with demand extensive computational and data processing capability. To optimize these systems, efficient scheduling of HPC applications is important. In particular, because I/O is a shared resource among applications and is becoming more important due to the emergence of big data, it is possible to improve performance by considering the architecture of HPC systems and scheduling jobs based on I/O resource requirements. In this paper, we propose a scheduling scheme that prioritizes HPC applications based on their I/O requirements. To accomplish this, our scheme analyzes the IOPS of scheduled applications by examining their execution history. Then, it schedules the applications at pre-configured intervals based on their expected IOPS to maximize the available IOPS across the entire system. Compared to the existing first-come first-served (FCFS) algorithm, experimental results using real-world HPC log data show that our scheme reduces total execution time by 305 h and decreases costs by USD 53 when scheduling 10,000 jobs utilizing public cloud resources.
Reconstruction of Typhoon-Induced Ocean Thermal Structures Using Deep Learning and Multi-Source Satellite Data with News Impact Analysis
Authors: Zhao Yang , Gao Ziming , Fan Ruimin , Yu Fangjie , Zhang Xinglong , Tang Junwu , Chen Ge
Published: November 2024

Reconstructing the three-dimensional thermal structure of the ocean under typhoon conditions presents significant challenges due to the scarcity of observational data, particularly in subsurface regions, and the limitations of existing observation methods in terms of spatial and temporal resolution. Accurate reconstruction of these structures is crucial for understanding the energy exchange between the ocean and typhoons, as this exchange directly influences typhoon intensity and trajectory. To address these challenges, this study introduces a fully connected transformer network (FCT), which integrates fully connected layers with a transformer model. The FCT model leverages the attention mechanisms inherent in the transformer architecture to effectively extract and integrate multi-scale ocean dynamical features. Using data from Typhoon Lekima in 2019, this study reconstructs ocean thermal structures at various depths and achieves an RMSE of 1.03 °C and an MAE of 0.83 °C when validated against Argo data. Furthermore, the model’s robustness was demonstrated through five-fold cross-validation, with the validation loss exhibiting minor fluctuations across folds but remaining stable overall, with an average validation loss of 0.986 °C, indicating the model’s generalizability. Sensitivity analysis also revealed the model’s resilience to variations in key input variables, showing minimal impact on output even with perturbations of up to 10% in input data. In addition, the study incorporates content analysis of typhoon-related news reports from 2011 to 2020, revealing a predominance of political topics, which underscores the central role of government in disaster response, with economic and ecological topics following. This integrated approach not only enhances our understanding of the interactions between ocean thermal structures and typhoon dynamics but also provides valuable insights into the societal impacts of typhoons, as reflected in media coverage, contributing to improved disaster management strategies.
Hybrid Renewable Systems for Small Energy Communities: What Is the Best Solution?
Authors: Coelho João S. T. , Pérez-Sánchez Modesto , Coronado-Hernández Oscar E. , Sánchez-Romero Francisco-Javier , McNabola Aonghus , Ramos Helena M.
Published: November 2024

This research developed smart integrated hybrid renewable systems for small energy communities and applied them to a real system to achieve energy self-sufficiency and promote sustainable decentralized energy generation. It compares stand-alone (SA) and grid-connected (GC) configurations using a developed optimized mathematical model and data-driven optimization, with economic analysis of various renewable combinations (PV, Wind, PHS, BESS, and Grid) to search for the optimal solution. Four cases were developed: two stand-alone (SA1: PV + Wind + PHS, SA2: PV + Wind + PHS + BESS) and two grid-connected (GC1: PV + PHS + Grid, GC2: Wind + PHS + Grid). GC2 shows the most economical with stable cash flow (−€123.2 annually), low CO2 costs (€367.2), and 91.7% of grid independence, requiring 125 kW of installed power. While GC options had lower initial investments (between €157k to €205k), the SA configurations provided lower levelized costs of energy (LCOE) ranging from €0.039 to €0.044/kWh. The integration of pumped hydropower storage enhances energy independence, supporting peak loads for up to two days with a storage capacity of 2.17 MWh.