bluuebunny/crossref_metadata_2025 huggingface dataset:
Offshore Submerged Aquaculture Flow-Net Interaction Simulation: A Numerical Approach for the Hydrodynamic Characteristics of Nets Produced from Different Materials
Authors: Wang Zhiyuan , He Wei , Li Weiqiang , Chen Hongxing , Zhang Feng , Qin Hongling
Published: January 2025

The mechanical and hydrodynamic characteristics of single-piece nets are key to the design and optimization of offshore aquaculture net cages. A numerical approach for offshore submerged aquaculture net materials based on the Morison equations and finite element is proposed, simulating the hydrodynamic characteristics of single-piece nets under varying parameters such as wire diameter, mesh size, and flow velocity, and simulating the impact of marine organism attachment on nets by modifying the drag coefficient. The simulation results of nets made from materials such as Copper–Zinc Alloy (Cu-Zn), Zinc–Aluminum Alloy (Zn-Al), Semi-Rigid Polyethylene Terephthalate (PET), and Ultra-High Molecular Weight Polyethylene (UHMWPE) are compared, which provides a theoretical basis for optimizing design parameters and selecting materials for nets based on force conditions and hydrodynamic characteristics. The simulation results indicate that the current force on the net is positively correlated with flow velocity; the maximum displacement of the net is also positively correlated with the flow rate. Compared to other materials, the Cu-Zn net is subjected to the greatest water flow force, while the UHMWPE net experiences the greatest displacement; the larger the diameter of the netting twine, the greater the current force on the net; the mesh size is inversely related to the current force on the net. With increasing drag coefficient, both the maximum displacement of the net and the current force experiences increase, and UHMWPE material nets are more sensitive to increases in the drag coefficient, which indicates a greater impact from the attachment of marine organisms. The density and elastic modulus of the netting material affect the rate of increase in force on the net. The research results can provide a basis for further research on material selection and design of deep-sea aquaculture nets.
The Time-Domain Design Stress Method for Fatigue Analysis of the Reactor Pressure Vessel in Floating Nuclear Power Plants
Authors: Yuan Jialong , Ma Fuxuan , Zhang Meng , Shen Kai , Tang Jinfeng
Published: January 2025

Nuclear power technology has rapidly advanced with the growing global demand for clean energy. As one of the core components of nuclear power plants (NPPs), the design and lifespan evaluation of reactor pressure vessels (RPVs) are critically important. However, while fatigue analysis methods for RPVs in land-based NPPs are relatively well established, the application of these methods to floating nuclear power plants (FNPPs) faces great challenges. Existing analysis methods are difficult to directly apply, and no widely accepted fatigue analysis approach currently exists for this context due to the complex working conditions in marine environments. A time-domain design stress (TDDS) method is developed in this study for the fatigue analysis of RPVs in FNPPs. This method systematically analyzes the impacts of wave loads, internal pressure, and thermal effects on the fatigue life of RPVs by simplifying the wave environment into a time-domain model of roll and pitch motions and adopting the regular wave superposition techniques. This method further adjusts the initial phases of regular waves considering the uncertainty of various load combinations, and superimposes the stress components caused by regular waves with different initial phases, thermal loads, and pressure loads. Subsequently, stress history curves are analyzed using the rainflow counting method, and combined with the damage accumulation theory, the upper and lower limits of fatigue damage are obtained. The results demonstrate that compared to traditional methods in time-domain analysis, the proposed TDDS method provides greater accuracy in evaluating the fatigue life of RPVs in FNPPs, with the average error in fatigue damage values being only 0.033%. Furthermore, the TDDS method reduces analysis time by approximately 70%, which significantly improves computational performance. These findings underscore the reliability and effectiveness of this method in practical applications.
A Damage Combination Method for Fatigue Analysis of Pressure Equipment in Floating Nuclear Power Plants
Authors: Shen Kai , Ma Fuxuan , Yuan Jialong , Zhang Meng
Published: January 2025

The operation of floating nuclear power plants is subject to a number of environmental factors in addition to the typical working temperature and pressure loads. These include marine environmental loads, which can cause fatigue damage and therefore must be taken into account. The fatigue analysis of marine structures frequently employs frequency domain methods, whereas the fatigue analysis of pressure equipment predominantly utilizes time-domain methods. At present, there is no comprehensive and accessible approach for conducting a fatigue analysis of pressure equipment in floating nuclear power plants. In light of the aforementioned considerations, this paper puts forth a novel approach to evaluating fatigue damage based on the principle of damage combination. This article presents a finite element model of pressure equipment and a methodology for calculating the transfer function of such equipment under wave loads. The frequency domain method is employed to calculate the fatigue damage caused by wave loads, with consideration given to both the working temperature and pressure load. The stress time history curve of pressure-bearing equipment is then calculated using the time-domain method. Subsequently, the fatigue damage caused by thermal pressure loads is obtained through a combination of the rainflow counting method and cumulative damage theory, with verification conducted using time-domain calculations. In comparison to alternative damage combination methodologies, the novel approach offers more precise and straightforward damage calculations, with promising potential for integration into engineering design.
Neural Network for Underwater Fish Image Segmentation Using an Enhanced Feature Pyramid Convolutional Architecture
Authors: Yang Guang , Yang Junyi , Fan Wenyao , Yang Donghe
Published: January 2025

Underwater fish image segmentation is a crucial technique in marine fish monitoring. However, typical underwater fish images often suffer from issues such as color distortion, low contrast, and blurriness, primarily due to the complex and dynamic nature of the marine environment. To enhance the accuracy of underwater fish image segmentation, this paper introduces an innovative neural network model that combines the attention mechanism with a feature pyramid module. After the backbone network processes the input image through convolution, the data pass through the enhanced feature pyramid module, where it is iteratively processed by multiple weighted branches. Unlike conventional methods, the multi-scale feature extraction module that we designed not only improves the extraction of high-level semantic features but also optimizes the distribution of low-level shape feature weights through the synergistic interactions of the branches, all while preserving the inherent properties of the image. This novel architecture significantly boosts segmentation accuracy, offering a new solution for fish image segmentation tasks. To further enhance the model’s robustness, the Mix-up and CutMix data augmentation techniques were employed. The model was validated using the Fish4Knowledge dataset, and the experimental results demonstrate that the model achieves a Mean Intersection over Union (MIoU) of 95.1%, with improvements of 1.3%, 1.5%, and 1.7% in the MIoU, Mean Pixel Accuracy (PA), and F1 score, respectively, compared to traditional segmentation methods. Additionally, a real fish image dataset captured in deep-sea environments was constructed to verify the practical applicability of the proposed algorithm.
A Novel Approach to Road Safety: Detecting Illegal Overtaking Using Smartphone Cameras and Deep Learning for Vehicle Auditing
Authors: Marcomini Karem Daiane , Brito Vitória de Carvalho , Balestra Gregori da Cruz , Tosetto Vitor , Duarte Luiz Carlos , Donadon Antonio Roberto
Published: January 2025

Overtaking relies heavily on the driver’s attention and cognitive state, and illegal overtaking can lead to accidents, severe injuries, or fatalities. To enhance highway safety, we propose a method for accurately detecting illegal overtaking on continuous road lanes. We used dashboard-mounted smartphone cameras and geolocation data to filter the analysis areas. We used the state-of-the-art deep learning model You Only Look Once version 8 (YOLOv8) to detect yellow road lanes. When these lanes suggest potential illegal overtaking, we apply the YOLO for Panoptic driving Perception version 2 (YOLOPv2) model, followed by post-processing. We confirm overtaking events by checking for overlaps between detections from both models. We store confirmed instances and evaluate the information temporally rather than just from individual frames. We then analyze the entire video to identify violations and extract the moments of occurrence. We tested the algorithm on real-world traffic data under various weather and lighting conditions. Our method demonstrates reliability and consistency in identifying illegal overtaking. We achieved 16 TP and only 1 FP over 56 videos totaling 41 h, 9 min, and 24 s, with precision, recall, and F1-score values of 1.000, 0.941, and 0.970, respectively. Consequently, our innovative and practical solution, utilizing simple cameras and advanced computer vision models, can significantly enhance highway safety and support vehicle auditing systems.
Urban Micro-Segregation: Taking Segregation Analysis at the Micro Level
Authors: Maloutas Thomas , Lin Sainan , Logan John R.
Published: January 2025

Urban micro-segregation refers to segregation at the small scale, below the scale of the neighborhood [...]
Exploring Crop Production Strategies to Mitigate Greenhouse Gas Emissions Based on Scenario Analysis
Authors: Gu Zhuoyuan , Xue Jing , Han Hongfang , Wang Chao
Published: January 2025

In the context of global climate change and carbon neutrality goals, agriculture has emerged as a major source of greenhouse gas (GHG) emissions, and faces the critical challenge of reducing emissions while ensuring food security. However, existing research has rarely focused on dynamic simulation and scenario-based analysis of optimised agricultural layouts and their impact on GHG emissions. Taking the three northeastern provinces (Heilongjiang, Jilin, and Liaoning) of China as the study area, this study quantifies GHG emissions from major grain crops and employs time-series analysis and machine learning methods to conduct a scenario analysis, including three scenarios (Business as Usual, Sustainable Optimisation, and Ecological Priority). Specific policy implications are proposed for optimising agricultural layouts and mitigating GHG emissions. The results indicate that GHG emissions in Northeast China primarily stem from methane emissions in rice cultivation and nitrous oxide emissions from fertiliser use. A scenario analysis reveals that the “Sustainable Optimisation” scenario reduces GHG emissions by 22.0% through optimised planting layouts while maintaining stable crop production. The “Ecological Priority” scenario further enhances emission reductions to 25.2% by increasing the share of low-emission crops, such as corn, and reducing high-emission crops, such as rice. The study provides a practical reference for promoting the low carbonisation of agriculture, and demonstrates that optimising planting layouts and production structures can simultaneously achieve food security and climate change mitigation.
Measurement of Urban–Rural Integration Development Level and Diagnosis of Obstacle Factors: Evidence from the Beijing–Tianjin–Hebei Urban Agglomeration, China
Authors: Wu Qiuyi , Chang Wei , Song Mengfei , Zhu Honghui
Published: January 2025

Advancing urban–rural integration (URI) is pivotal to addressing the current urban–rural development imbalance in China. The urban agglomeration, as a crucial engine propelling China’s modernization, holds significant importance in accelerating this integration process. Comprehensive quantitative analysis of URI development within the Beijing–Tianjin–Hebei (BTH) urban agglomeration is often lacking in existing research. This study constructs an indicator system for evaluating the level of integration using data from 14 cities in the region from 2010 to 2022, focusing on economic, social, and ecological perspectives. Utilizing the Coupling Coordination Model and the Obstacle Degree Model, this study analyzes the level and evolutionary trends of URI development within the BTH urban agglomeration. The main conclusions are as follows: (1) The level of URI in the BTH urban agglomeration exhibits an overall upward trend, increasing from 0.377 in 2010 to 0.543 in 2022. The economic, social, and ecological subsystems all demonstrate positive integration trends. (2) The spatial evolution of the integration level reveals a distinct core–periphery structure. Beijing and Tianjin, as the core areas, continuously foster the collaborative development of surrounding cities through radiation and spillover effects. The core of URI has shifted gradually from the central–east to the central–north, indicating an upward movement of the core area, as revealed by trend surface analysis. Although Shijiazhuang, a central city in the South BTH urban agglomeration, has seen rapid improvement in integration levels, its role in driving development is less significant than that of Beijing and Tianjin. (3) The URI subsystems in the 14 cities of the BTH urban agglomeration show improved coordination. The coordination development between Beijing and Tianjin has yielded significant results, emerging as a key driver in promoting the coordinated development of urban agglomerations. Most regions in the urban agglomeration exhibit mild imbalance or coordination, with the central and northern areas scoring higher in coordination. (4) The obstacles analysis indicates that the economic integration and social integration systems are the primary obstacles to enhancing the ecological integration of urban agglomerations. Urban–rural transportation, investment levels, and economic development are key obstacles for the BTH urban agglomeration integrated development. This study offers key insights for strategic planning in the BTH urban agglomeration region.
Identifying Indicators Contributing to the Social Vulnerability Index via a Scoping Review
Authors: Kim Kangmin , Kang Jeon-Young , Hwang Chulsue
Published: January 2025

Social vulnerability plays a vital role in understanding how various societal characteristics influence communities during extreme events. This study aimed to systematically identify key indicators and methodological approaches used in Social Vulnerability Index (SVI) research by utilizing the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The study specifically addresses gaps in indicator selection and emphasizes incorporating land-related and diverse variables for improved applications across various contexts. Social variables essential for SVI development were collected, and their applications across studies were analyzed. A total of 30,443 articles were identified from multiple databases, with 72 meeting the inclusion criteria after rigorous evaluation. Key aspects such as methodologies, weighting schemes, and primary variables used in SVI computation were outlined. Principal component analysis emerged as the most commonly employed method, though diverse approaches have gained traction in recent years. Significant variability was observed in the variables across studies, with demographic indicators being the most frequently utilized. The identified variables were categorized into 21 domains, comprising 61 indicators. While the findings of this study focus on improving the understanding of SVI development and its diverse applications, they also hold potential for informing sustainable land management and disaster resilience strategies, particularly in tailoring interventions to region-specific vulnerabilities.
The Predictive Processing of Number Information in L1 and L2 Arabic Speakers
Authors: Alzahrani Alaa
Published: January 2025

Prior research has shown that people can predict the syntactic features of an upcoming word during sentence comprehension. However, evidence for morphosyntactic predictive processing has been limited to gender or case marking in a small subset of Indo-European languages. In the current study, we implemented the eye-tracking visual world paradigm to investigate whether L1 (n = 18) and L2 (n = 40) Arabic speakers could extract number information from singular-marked verbs to anticipate the next noun. In a between-subject design, L1 and L2 speakers heard the singular verb in the simple past form (Exp 1) and the progressive past form (Exp 2). The effect of L2 proficiency (measured using a C-test and a receptive vocabulary test) on number prediction was also examined. L1 Arabic speakers showed earlier and stronger number prediction effects regardless of verb aspect. In contrast, L2 speakers exhibited delayed (Exp 1) or limited (Exp 2) prediction, suggesting a mediating role for verb aspect. Increased L2 proficiency did not influence anticipatory eye-movements during the verb region, and only emerged as significant during the noun region. These results confirm and extend earlier research on L1 and L2 number predictive processing.
Improved Inhibitors Targeting the Thymidylate Kinase of Multidrug-Resistant Mycobacterium tuberculosis with Favorable Pharmacokinetics
Authors: Konate Souleymane , Allangba Koffi N’Guessan Placide Gabin , Fofana Issouf , N’Guessan Raymond Kre , Megnassan Eugene , Miertus Stanislav , Frecer Vladimir
Published: January 2025

This study aims to design improved inhibitors targeting the thymidylate kinase (TMK) of Mycobacterium tuberculosis (Mtb), the causative agent of infectious disease tuberculosis that is associated with high morbidity and mortality in developing countries. TMK is an essential enzyme for the synthesis of bacterial DNA. We have performed computer-aided molecular design of MtbTMK inhibitors by modification of the reference crystal structures of the lead micromolar inhibitor TKI1 1-(1-((4-(3-Chlorophenoxy)quinolin-2-yl)methyl)piperidin-4-yl)-5-methylpyrimidine-2,4(1H,3H)-dione bound to TMK of Mtb strain H37Rv (PDB entries: 5NRN and 5NR7) using the computational approach MM-PBSA. A QSAR model was prepared for a training set of 31 MtbTMK inhibitors with published inhibitory potencies (IC50exp) and showed a significant correlation between the calculated relative Gibbs free energies of the MtbTMK–TKIx complex formation and the observed potencies. This model was able to explain approximately 95% of the variation in the in vitro inhibition data and validated our molecular model of MtbTMK inhibition for the subsequent design of new TKI analogs. Furthermore, we have confirmed the predictive capacity of this complexation QSAR model by generating a 3D QSAR PH4 pharmacophore-based model. A satisfactory correlation was also obtained for the validation PH4 model of MtbTMK inhibition (R2 = 0.84). We have extended the hydrophobic m-chloro-phenoxyquinolin-2-yl group of TKI1 that can occupy the entry into the thymidine binding cleft of MtbTMK by alternative larger hydrophobic groups. Analysis of residue interactions at the enzyme binding site made it possible to select suitable building blocks to be used in the preparation of a virtual combinatorial library of 28,900 analogs of TKI1. Structural information derived from the complexation model and the PH4 pharmacophore guided the in silico screening of the library of analogs and led to the identification of new potential MtbTMK inhibitors that were predicted to be effective in the low nanomolar concentration range. The QSAR complexation model predicted an inhibitory concentration IC50pre of 9.5 nM for the best new virtual inhibitor candidate TKI 13_1, which represents a significant improvement in estimated inhibitory potency compared to TKI1. Finally, the stability of the MtbTMK–inhibitor complexes and the flexibility of the active conformation of the inhibitors were assessed by molecular dynamics for five top-ranking analogs. This computational study resulted in the discovery of new MtbTMK inhibitors with predicted enhanced inhibitory potencies, which also showed favorable predicted pharmacokinetic profiles.
The Role of the Claustrum in Parkinson’s Disease and Vascular Parkinsonism: A Matter of Network?
Authors: Zedde Marialuisa , Quatrale Rocco , Cossu Gianni , Sette Massimo Del , Pascarella Rosario
Published: January 2025

Background: The mechanisms underlying extrapyramidal disorders and their anatomical substrate have been extensively investigated. Recently, the role of the claustrum in Parkinson’s disease and other neurodegenerative conditions has been better detailed. The main aim of this review was to summarize the supporting evidence for the role of the claustrum in degenerative and vascular parkinsonism. Methods: The anatomy, biology, vascular supply, and connections of the claustrum in humans were identified and described, providing the substrate for the vascular involvement of the claustrum in large- and small-vessel disease. The vascular supply of the claustrum includes up to three different sources from a single artery, the middle cerebral artery, and it is known as territory with an intermediate hemodynamic risk. The connections of the claustrum make it a sensory integrator and a relevant point in several networks, from consciousness to movement planning. Conclusions: The claustrum is still an incompletely explained structure. However, recent description of its multiple connections indicate that it is involved in several diseases, including Parkinson’s disease. The evidence underlying its potential role in vascular parkinsonism is still scarce, but it might be a field warranting future investigations.
Effects of Repetitive Transcranial Magnetic Stimulation Applied over the Primary Motor Cortex on the Offset Analgesia Phenomenon
Authors: Antoniazzi Elisa , Cavigioli Camilla , Tang Vanessa , Zoccola Clara , Todisco Massimiliano , Tassorelli Cristina , Cosentino Giuseppe
Published: January 2025

In this study, we investigate the effects of high-frequency repetitive transcranial magnetic stimulation (rTMS) applied over the left upper limb primary motor cortex (M1) on the offset analgesia (OA) phenomenon, a measure of endogenous pain modulation. In particular, we aim to determine whether rTMS influences OA differently in the forearm region, corresponding to the stimulated cortical area, compared to the trigeminal region. Twenty-two healthy volunteers underwent three experimental sessions: a baseline session without stimulation, an active rTMS session, and a sham rTMS session. Quantitative sensory testing (QST) paradigms, including warm and cold detection thresholds, heat pain threshold corresponding to a visual analogue scale (VAS) score of approximately 50–60 out of 100 (Pain50–60), and constant and offset trials, were assessed in both the forearm and trigeminal regions. The results revealed that active rTMS significantly enhanced the OA phenomenon in the forearm during the late phase, while no significant effects were observed in the trigeminal region. These findings suggest that rTMS may modulate central pain mechanisms in a body region-specific manner, potentially linked to the somatotopic organization of M1. This study points to possible mechanisms of action of rTMS for pain relief, highlighting the importance of region-specific effects in chronic pain treatment. Further research is needed to investigate the underlying mechanisms and clinical applicability of rTMS in patients with chronic pain conditions, especially when OA is compromised.
Efficacy of Recombinant Human Bone Morphogenetic Protein-2 in Alveolar Cleft Treatment for Children: Systematic Review and Meta-Analysis
Authors: Alawami Ebtihal Ali A. , Alomari Fouad , Aloqaybi Sarah A. , Aloweiny Qusay , Alswayed Lina Khalid , Alshafai Narjes W. , Alhelal Rawan , Alfuraydi Moayad M. , Samandar Abdulaziz Fahad , Alsaeed Renad Abdulaziz Saleh , Aldulaijan Danah
Published: January 2025

Background: Alveolar bone reconstruction with recombinant protein has several advantages, including less surgical timing, and reduced infection. This systematic review aims to assess the efficacy of recombinant human bone morphogenetic protein-2 (rhBMP-2) as a treatment modality for children with cleft lip and palate compared to the conventional iliac crest bone grafting approach. Methods: For current systematic review and meta-analysis, five electronic databases, namely, MEDLINE/PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), ClinicalTrials.gov, Web of Science, and ScienceDirect, were searched. The primary outcome measured in this review was bone volume and height after alveolar bone reconstruction surgery. The Risk of Bias Tool 2 assessed the risk of bias for randomized control trials and the Risk of Bias tool for non-randomized trials of interventions for non-randomized studies. By evaluating pooled meta-analysis, the mean difference was calculated. GRADE uncertainty of evidence was performed to assess the certainty of the results. Results: Of 230 identified studies, 6 randomized and 2 non-randomized studies were included in the current review. The average bone volume was higher among the rhBMP-2 group at 61.11% ± 24.6% than the iliac crest group at 59.12% ± 18.59%. The calculated mean bone height was higher in the iliac crest group at 78.65% ± 14.38% than in the rhBMP-2 group at 67.5% ± 5.45%. The risk of bias reported in the studies was low to moderate. The result of the meta-analysis supported using rhBMP-2 in alveolar bone reconstruction; however, no significant association was found (mean difference: −1.24; confidence interval: −4.14 to 1.67). Conclusions: The calculated meta-analysis reported no significant difference, and the quality of evidence measured was also moderate. Hence, more clinical trials are required to support using rhBMP-2 as an alternative to traditional techniques for treating cleft lip and palate.
The Seasonality of Retinal Detachment: Peaks, Troughs, and Global Trends
Authors: Tsiropoulos Georgios N. , Amaxilati Efstratia , Tranou Marianna , Papadopoulou Eleni P. , Vagiakis Iordanis , Topouzis Fotis , Panos Georgios D.
Published: January 2025

Purpose: To examine the seasonality of retinal detachment (RD) and explore global patterns and contributing factors through a narrative review. Methods: Studies investigating seasonal trends in RD incidence across diverse regions were analysed for peak seasons, meteorological influences, and behavioural factors. Results: RD seasonality varies by region. Northern climates (e.g., Finland) report summer peaks linked to prolonged daylight, while warmer climates (e.g., Kuwait) show winter peaks associated with outdoor activity. Some studies found correlations with temperature, light, and atmospheric pressure, while others reported no seasonal variation. Conclusions: RD seasonality reflects a complex interplay of environmental and behavioural factors. Future research should focus on standardised methodologies to clarify these relationships and inform preventative strategies.
Features of the Defect Structure of the Compositionally Homogeneous Crystal LiNbO3:Er3+(3.1 wt%) and the Gradient Crystal LiNbO3:Er3+ and Their Manifestation in the IR Transmission Spectra in the Region of Stretching Vibrations of Hydrogen Atoms of OH−-Groups
Authors: Sidorov Nikolay , Pyatyshev Alexander , Stroganova Elena , Galutskiy Valeriy , Bushunov Andrey , Tarabrin Mikhail
Published: January 2025

Based on the analysis of the IR transmission spectra in the region of stretching vibrations of hydrogen atoms of OH−-groups, it was established that the oxygen-octahedral MeO6 clusters (Me-Li+, Nb5+, vacant octahedron V, impurity ion) of the structure of the compositionally homogeneous crystal LiNbO3:Er3+(3.1 wt%) and the gradient crystal LiNbO3:Er3+(congruent composition by the main components, Er gradient of 0.55 at%/cm) have a shape close to the regular one. In this case, the value of R = [Li]/[Nb] ≈ 1, and in the structure of both crystals, there are practically no point defects in NbLi responsible for the photorefraction effect. By using the IR transmission spectra and Klauer’s method, it was found that the volume concentration of OH−-groups in the gradient crystal LiNbO3:Er3+ is almost an order of magnitude lower than in the compositionally homogeneous LiNbO3:Er3+(3.1 wt%) crystal. This fact explains the lower hydrogen conductivity of the gradient crystal LiNbO3:Er3+ and the lower photorefraction effect compared to the compositionally homogeneous LiNbO3:Er3+(3.1 wt%) crystal. The results obtained are important for the development of materials for active nonlinear laser media and for the conversion of laser radiation.
Inference and Optimal Design on Partially Accelerated Life Tests for the Power Half-Logistic Distribution Under Adaptive Type II Progressive Censoring
Authors: Haj Ahmad Hanan , El-Awady Mahmoud M.
Published: January 2025

This study explores accelerated life tests to examine the durability of highly reliable products. These tests involve applying higher stress levels, such as increased temperature, voltage, or pressure, that cause early failures. The power half-logistic (PHL) distribution is utilized due to its flexibility in modeling the probability density and hazard rate functions, effectively representing various data patterns commonly encountered in practical applications. The step stress partially accelerated life testing model is analyzed under an adaptive type II progressive censoring scheme, with samples drawn from the PHL distribution. The maximum likelihood method estimates model parameters and calculates asymptotic confidence intervals. Bayesian estimates are also obtained using Lindley’s approximation and the Markov Chain Monte Carlo (MCMC) method under different loss functions. Additionally, D- and A-optimality criteria are applied to determine the optimal stress-changing time. Simulation studies are conducted to evaluate the performance of the estimation methods and the optimality criteria. Finally, real-world datasets are analyzed to demonstrate the practical usefulness of the proposed model.
Enhancing Zero-Shot Learning Through Kernelized Visual Prototypes and Similarity Learning
Authors: Cheng Kanglong , Fang Bowen
Published: January 2025

Zero-shot learning (ZSL) holds significant promise for scaling image classification to previously unseen classes by leveraging previously acquired knowledge. However, conventional ZSL methods face challenges such as domain-shift and hubness problems. To address these issues, we propose a novel kernelized similarity learning approach that reduces intraclass similarity while increasing interclass similarity. Specifically, we utilize kernelized ridge regression to learn visual prototypes for unseen classes in the semantic vectors. Furthermore, we introduce kernel polarization and autoencoder structures into the similarity function to enhance discriminative ability and mitigate the hubness and domain-shift problems. Extensive experiments on five benchmark datasets demonstrate that our method outperforms state-of-the-art ZSL and generalized zero-shot learning (GZSL) methods, highlighting its effectiveness in improving classification performance for unseen classes.
An Improved Marriage in Honey-Bee Optimization Algorithm for Minimizing Earliness/Tardiness Penalties in Single-Machine Scheduling with a Restrictive Common Due Date
Authors: Palominos Pedro , Mazo Mauricio , Fuertes Guillermo , Alfaro Miguel
Published: January 2025

This study evaluates the efficiency of a swarm intelligence algorithm called marriage in honey-bee optimization (MBO) in solving the single-machine weighted earliness/tardiness problem, a type of NP-hard combinatorial optimization problem. The goal is to find the optimal sequence for completing a set of tasks on a single machine, minimizing the total penalty incurred for tasks being completed too early or too late compared to their deadlines. To achieve this goal, the study adapts the MBO metaheuristic by introducing modifications to optimize the objective function and produce high-quality solutions within reasonable execution times. The novelty of this work lies in the application of MBO to the single-machine weighted earliness/tardiness problem, an approach previously unexplored in this context. MBO was evaluated using the test problem set from Biskup and Feldmann. It achieved an average improvement of 1.03% across 280 problems, surpassing upper bounds in 141 cases (50.35%) and matching or exceeding them in 193 cases (68.93%). In the most constrained problems (h = 0.2 and h = 0.4), the method achieved an average improvement of 3.77%, while for h = 0.6 and h = 0.8, the average error was 1.72%. Compared to other metaheuristics, MBO demonstrated competitiveness, with a maximum error of 1.12%. Overall, MBO exhibited strong competitiveness, delivering significant improvements and high efficiency in the problems studied.
Analytical and Computational Investigations of Stochastic Functional Integral Equations: Solution Existence and Euler–Karhunen–Loève Simulation
Authors: Kazemi Manochehr , Yaghoobnia AliReza , Moghaddam Behrouz Parsa , Galhano Alexandra
Published: January 2025

This paper presents a comprehensive investigation into the solution existence of stochastic functional integral equations within real separable Banach spaces, emphasizing the establishment of sufficient conditions. Leveraging advanced mathematical tools including probability measures of noncompactness and Petryshyn’s fixed-point theorem adapted for stochastic processes, a robust analytical framework is developed. Additionally, this paper introduces the Euler–Karhunen–Loève method, which utilizes the Karhunen–Loève expansion to represent stochastic processes, particularly suited for handling continuous-time processes with an infinite number of random variables. By conducting thorough analysis and computational simulations, which also involve implementing the Euler–Karhunen–Loève method, this paper effectively highlights the practical relevance of the proposed methodology. Two specific instances, namely, the Delay Cox–Ingersoll–Ross process and modified Black–Scholes with proportional delay model, are utilized as illustrative examples to underscore the effectiveness of this approach in tackling real-world challenges encountered in the realms of finance and stochastic dynamics.
Robust Fine-Grained Learning for Cloth-Changing Person Re-Identification
Authors: Yin Qingze , Ding Guodong , Zhang Tongpo , Gong Yumei
Published: January 2025

Cloth-changing Person Re-Identification (CC-ReID) poses a significant challenge in tracking pedestrians across cameras while accounting for changes in clothing appearance. Despite recent progress in CC-ReID, existing methods predominantly focus on learning the unique biological features of pedestrians, often overlooking constraints that promote the learning of cloth-agnostic features. Addressing this limitation, we propose a Robust Fine-grained Learning Network (RFLNet) to effectively learn robust cloth-agnostic features by leveraging fine-grained semantic constraints. Specifically, we introduce a four-body-part attention module to enhance the learning of detailed pedestrian semantic features. To further strengthen the model’s robustness to clothing variations, we employ a random erasing algorithm, encouraging the network to concentrate on cloth-irrelevant attributes. Additionally, we design a fine-grained semantic loss to guide the model in learning identity-related, detailed semantic features, thereby improving its focus on cloth-agnostic regions. Comprehensive experiments on widely used CC-ReID benchmarks demonstrate the effectiveness of RFLNet. Our method achieves state-of-the-art performance, including a 0.7% increase in mAP on PRCC and a 1.6% improvement in rank-1 accuracy on DeepChange.
High-Frequency and Ultra-High-Frequency Ultrasound in Dermatologic Diseases and Aesthetic Medicine
Authors: Argalia Giulio , Reginelli Alfonso , Molinelli Elisa , Russo Anna , Michelucci Alessandra , Sechi Andrea , Marzano Angelo Valerio , Desyatnikova Stella , Fogante Marco , Patanè Vittorio , Granieri Giammarco , Tagliati Corrado , Rizzetto Giulio , De Simoni Edoardo , Matteucci Marco , Candelora Matteo , Lanza Cecilia , Ventura Claudio , Carboni Nicola , Esposito Roberto , Esposito Stefano , Paolinelli Massimiliano , Esposto Elisabetta , Lanni Giuseppe , Lucidi Pressanti Gabriella , Giorgi Chiara , Principi Fabiola , Rebonato Alberto , Malinowska Sylwia Patrycja , Mlosek Robert Krzysztof , Giuseppetti Gian Marco , Dini Valentina , Romanelli Marco , Offidani Annamaria , Cappabianca Salvatore , Wortsman Ximena , Simonetti Oriana
Published: January 2025

Dermatologic ultrasonography applications are rapidly growing in all skin fields. Thanks to very high spatial resolution, high-frequency and ultra-high-frequency ultrasound can evaluate smaller structures, allowing us to improve diagnosis accuracy and disease activity. Moreover, they can guide treatment, such as drug injection, and assess therapy efficacy and complications. In this narrative review, we evaluated high-frequency ultrasound and ultra-high-frequency ultrasound in infections, inflammatory dermatoses, metabolic and genetic disorders, specific cutaneous structure skin disorders, vascular and external-agent-associated disorders, neoplastic diseases, and aesthetics.
Effect of Plyometric Exercises of Lower Limb on Strength, Postural Control, and Risk of Falling in Stroke Patients
Authors: Abd Elsabour Ahmed K. , Zakaria Hoda M. , Fahmy Ebtesam M. , Khalil Azza Sayed Abdelrehim , Alwhaibi Reem M. , Ragab Walaa M. , Taha Shreen I.
Published: January 2025

Background and Objective: Stroke, a major contributor to long-term disability worldwide, often results in significant impairments in motor function. These impairments can include weakness, impaired balance, and decreased coordination, which can have a significant influence on one’s quality of life and independence. Finding an effective protocol for rehabilitation to improve these points will decrease the impact of stroke and its coast of rehabilitation. Materials and Methods: This study was conducted to assess the effect of lower limb plyometric exercises on strength, postural control, and risk of falling in stroke patients. Materials and Methods: This study involved 40 chronic left stroke patients randomly divided into two equal groups. The experimental group participated in a 12-week supervised plyometric training program, while the control group received conventional physical therapy program. Lower limb muscle strength was measured using a handheld dynamometer, and balance and fall risk were assessed via the Biodex Balance System (BBS). These measurements were conducted before and after the intervention period to evaluate treatment effects. Results: The results of this study demonstrated significant improvements in muscle strength and balance parameters among stroke patients who underwent plyometric exercise compared to those receiving a conventional program. The plyometric group exhibited significantly greater increases in knee extension strength (p < 0.05), hip abduction strength (p < 0.05), ankle dorsiflexion strength (p < 0.05), and ankle eversion strength (p < 0.05). Furthermore, the plyometric group showed significant improvements in overall stability (p < 0.05), mediolateral stability (p < 0.05), and anteroposterior stability (p < 0.05), as measured by the Biodex Balance System (BBS). Conclusions: The results of this study suggest that plyometric exercise may be an effective intervention for decreased risk of falling and enhancing muscle strength and balance during recovery from stroke.
The Development and Validation of a Nomogram for Predicting Sepsis Risk in Diabetic Patients with Urinary Tract Infection
Authors: Tan Hua-Qiao , Duan Xiang-Jie , Qu Wan , Shu Mi , Zhong Guang-Yao , Liang Li-Hong , Bin Dong-Mei , Chen Yu-Ming
Published: January 2025

Background and Objectives: Urinary tract infection (UTI) is a common comorbidity in diabetic patients, making up one of the causes of sepsis. This study aims to develop a nomogram to predict the risk probability of sepsis in diabetic patients with UTI (DPUTIs). Materials and Methods: This is a retrospective observational study. Clinical data for DPUTIs were extracted from the Medical Information Mart for Intensive Care IV database. Eligible DPUTIs were randomly divided into training and validation cohorts in a 7:3 ratio. Independent prognostic factors for sepsis risk were determined using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. A corresponding nomogram based on these factors was constructed to predict sepsis occurrence in DPUTIs. The discrimination of the nomogram was assessed by multiple indicators, including the area under the receiver operating characteristic curve (AUC), net reclassification improvement index (NRI), and integrated discrimination improvement (IDI). In addition, a calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Results: A total of 1990 DPUTIs were included. Nine independent prognostic factors were identified as predictive factors for sepsis risk in DPUTIs. The prognostic factors included urine red blood cell classification (urine RBC cat), urine white blood cell classification (urine WBC cat), blood glucose, age, temperature, white blood cells (WBCs), sequential organ failure assessment (SOFA) score, lymphocytes, and hematocrit. The AUC, NRI, and IDI of the nomogram indicated robust discrimination. The calibration curve and Hosmer–Lemeshow test showed good calibration of the nomogram. The DCA curve demonstrated a better clinical utility of the nomogram. Conclusions: The nomogram established in this study helps clinicians predict the probability of sepsis in DPUTIs, providing evidence for optimizing the management of related risk factors.
Microstructure Evolution of Extruded TiAl Alloy During Vacuum Isothermal Superplastic Forging Process
Authors: Li Jintao , Wang Xiaopeng , Gong Minyu , Guo Zhenyu , Kong Fantao
Published: January 2025

Vacuum isothermal forging is an ideal method for preparing high-performance TiAl alloy forgings, as it is carried out under the conditions of a uniform temperature field and oxygen isolation. The mechanical properties of TiAl alloys strongly depend on their microstructure, so it is important to study their microstructure evolution during the forging process to improve their properties. In this study, TiAl alloy forgings with different deformations were produced from the extruded billets by vacuum isothermal superplastic forging under lower temperatures and extremely low strain rate conditions. The results indicate that the streamlined structure in the extruded alloy was destroyed during the forging process. As the deformation increased, the dynamic recrystallization was more fully carried out, leading to a substantial decrease in remnant lamellar colonies and a significant increase in the γ phase, and the microstructure was transformed from nearly lamellar (NL) to near gamma (NG) structure. The proportion of high-angle grain boundaries (HAGB) increased with increasing deformation, while the grain size reduced from 20 μm to 4.6 μm. In addition, the streamlined features and textures exhibited a weakening trend with increasing deformation, leading to a decrease in the ultimate strength from 891 MPa to 722 MPa. To maintain the streamlined characteristics and retain strengthening effects, the forging deformation should not exceed 56.7%.
Electrochemical and Optical Carbon Dots and Glassy Carbon Biosensors: A Review on Their Development and Applications in Early Cancer Detection
Authors: López Juana G. , Muñoz Mariana , Arias Valentina , García Valentina , Calvo Paulo C. , Ondo-Méndez Alejandro O. , Rodríguez-Burbano Diana C. , Fonthal Faruk
Published: January 2025

Cancer remains one of the leading causes of mortality worldwide, making early detection a critical factor in improving patient outcomes and survival rates. Developing advanced biosensors is essential for achieving early detection and accurate cancer diagnosis. This review offers a comprehensive overview of the development and application of carbon dots (CDs) and glassy carbon (GC) biosensors for early cancer detection. It covers the synthesis of CDs and GC, electrode fabrication methods, and electrochemical and optical transduction principles. This review explores various biosensors, including enzymatic and non-enzymatic, and discusses key biomarkers relevant to cancer detection. It also examines characterization techniques for electrochemical and optical biosensors, such as electrochemical impedance spectroscopy, cyclic voltammetry, UV–VIS, and confocal microscopy. The findings highlight the advancements in biosensor performance, emphasizing improvements in sensitivity, selectivity, and stability, as well as underscoring the potential of integrating different transduction methods and characterization approaches to enhance early cancer detection.
Advancing Microscale Electromagnetic Simulations for Liquid Crystal Terahertz Phase Shifters: A Diagnostic Framework for Higher-Order Mode Analysis in Closed-Source Simulators
Authors: Li Haorong , Li Jinfeng
Published: January 2025

This work addresses a critical challenge in microscale computational electromagnetics for liquid crystal-based reconfigurable components: the inadequate capability of current software to accurately identify and simulate higher-order modes (HoMs) in complex electromagnetic structures. Specifically, commercial simulators often fail to capture modes such as Transverse Electric (TE11) beyond the fundamental transverse electromagnetic (TEM) mode in coaxial liquid crystal phase shifters operating in the terahertz (THz) regime, leading to inaccurate performance predictions and suboptimal designs for telecommunication engineering applications. To address this limitation, we propose a novel diagnostic methodology incorporating three lossless assumptions to enhance the identification and analysis of pseudo-HoMs in full-wave simulators. Our approach theoretically eliminates losses associated with metallic conductivity, dielectric dissipation, and reflection effects, enabling precise assessment of frequency-dependent HoM power propagation alongside the primary TEM mode. We validate the methodology by applying it to a coaxially filled liquid crystal variable phase shifter device structure, underscoring its effectiveness in advancing the design and characterization of THz devices. This work provides valuable insights for researchers and engineers utilizing closed-source commercial simulators in micro- and nano-electromagnetic device development. The findings are particularly relevant for microscale engineering applications, including millimeter-wave (mmW), sub-mmW, and THz systems, with potential impacts on next-generation communication technologies.