Search Results (26,355)

Aiming for convenience and the low cost of goods transfer between towns, this paper proposes a trade hub location and allocation method based on a novel artificial eagle-inspired optimization algorithm. Firstly, the trade hub location and allocation model is established, taking the total cost consisting of construction and transportation costs as the objective function. Then, to solve the nonlinear model, a novel artificial eagle optimization algorithm (AEOA) is proposed by simulating the collective migration behaviors of artificial eagles when facing a severe living environment. Three main strategies are designed to help the algorithm effectively explore the decision space: the situational awareness and analysis stage, the free exploration stage, and the flight formation integration stage. In the first stage, artificial eagles are endowed with intelligent thinking, thus generating new positions closer to the optimum by perceiving the current situation and updating their positions. In the free exploration stage, artificial eagles update their positions by drawing on the current optimal position, ensuring more suitable habitats can be found. Meanwhile, inspired by the consciousness of teamwork, a formation flying method based on distance information is introduced in the last stage to improve stability and success rate. Test results from the CEC2022 suite indicate that the AEOA can obtain better solutions for 11 functions out of all 12 functions compared with 8 other popular algorithms. Faster convergence speed and stronger stability of the AEOA are also proved by quantitative analysis. Finally, the trade hub location and allocation method is proposed by combining the optimization model and the AEOA. By solving two typical simulated cases, this method can select suitable hubs with lower construction costs and achieve reasonable allocation between hubs and the rest of the towns to reduce transportation costs. Thus, it is used to solve the trade hub location and allocation problem of Henan province in China to help the government make sound decisions. Full article

Offshore wind turbine structures (OWTs) commonly use monopile foundations for support, and long-term exposure to wind–wave cyclic loads may induce changes in foundation stiffness. Variations in foundation stiffness can significantly alter the inherent vibration characteristics of OWTs, potentially leading to amplified vibrations or resonant conditions. In this study, a numerical model considering soil–pile interaction was developed on the FLAC3D platform to analyze the natural frequency of OWTs under long-term cyclic loading. The study first validated the numerical model’s effectiveness through comparison with measured data; a degradation stiffness model (DSM) was then embedded to assess how prolonged cyclic loading affects the degradation of foundation stiffness. A series of parametric studies were conducted in medium-dense and dense sand layers to investigate natural frequency alterations induced by prolonged cyclic loading. Finally, a simplified method for evaluating long-term natural frequency changes was established, and a 3.6 MW offshore wind turbine case was used to reveal the evolution characteristics of its natural frequency under long-term cyclic loads. The data reveal that the natural frequency of the structure undergoes a downward tendency as cyclic loading and frequency increase. To ensure long-term safe operation, the designed natural frequency should preferably shift toward 3P (where P is the blade rotation frequency). Full article

In recent years, domain adaptation (DA) has been extensively applied to predicting the remaining useful life (RUL) of bearings across conditions. Although traditional DA-based methods have achieved accurate predictions, most methods fail to extract multi-scale degradation information, focus only on global-scale DA, and ignore the importance of temporal weights. These limitations hinder further improvements in prediction accuracy. This paper proposes a novel model, called the health indicator-weighted subdomain alignment network (HIWSAN), which first learns feature representations at multiple scales, then constructs health indicators as temporal weights, and finally performs subdomain-level alignment. Two case studies based on the XJTU-SY and PRONOSTIA datasets were conducted, covering ablation, comparison, and generalization experiments to evaluate the proposed HIWSAN. Experimental results show that HIWSAN achieves an average MAE of 0.0989 and an average RMSE of 0.1189 across two datasets, representing reductions of 21.07% and 25.13%, respectively, compared to existing state-of-the-art methods. Full article

In the last two decades, there has been a considerable surge in the development of artificial intelligence. Imaging is most frequently employed for the diagnostic evaluation of patients, as it is regarded as one of the most precise methods for identifying the presence of a disease. However, a study indicates that approximately 800,000 individuals in the USA die or incur permanent disability because of misdiagnosis. The present study is based on the use of computer-aided diagnosis of colorectal cancer. The objective of this study is to develop a practical, low-cost, AI-based decision-support tool that integrates clinical test data (blood/stool) and, if needed, colonoscopy images to help reduce misdiagnosis and improve early detection of colorectal cancer for clinicians. Convolutional neural networks (CNNs) and artificial neural networks (ANNs) are utilized in conjunction with a graphical user interface (GUI), which caters to individuals lacking programming expertise. The performance of the artificial neural network (ANN) is measured using the mean squared error (MSE) metric, and the obtained performance is 7.38. For CNN, two distinct cases are under consideration: one with two outputs and one with three outputs. The precision of the models is 97.2% for RGB and 96.7% for grayscale, respectively, in the first instance, and 83% for RGB and 82% for grayscale in the second instance. However, using a pretrained network yielded superior performance with 99.5% for 2-output models and 93% for 3-output models. The GUI is composed of two panels, with the best ANN model and the best CNN model being utilized in each. The primary function of the tool is to assist medical personnel in reducing the time required to make decisions and the probability of misdiagnosis. Full article

The great megalithic sites reveal an extended use of their monuments. In Late Prehistory, in Protohistory, and even in historical times, dolmens remained visible references on the landscape and were central for navigating it. The megaliths of Menga, Viera, and Romeral provide quality data to confirm their continued relevance. Our aim here is to understand whether menhirs also played that role, using the area of Tierras de Antequera, which is connected to the sea, as a case study. With that goal in mind, a research project has been initiated through intensive archaeological field surveying, combined with the collection of testimonies from oral tradition and other archaeological tools such as GIS, geophysical prospection, photogrammetry and RTI, for the detection of engravings and paintings on some of the located landmarks. We present in this paper the first geological analyses in the megalithic territory of Antequera to determine the raw material of the menhirs that are studied and the geological outcrops from which they come. Full article

The non-traditional companion animal (NTCA) sector, particularly involving avian species, has significantly expanded in Italy, raising concerns over the spread of infectious diseases. These animals can harbor various pathogens and act as reservoirs, posing risks to native wildlife through legal or illegal trade, escapes, or intentional releases. However, the epidemiology of avian pathogens in NTCAs remains poorly understood and is typically investigated only in symptomatic individuals. In the present study, cloacal and choanal cleft swabs were collected from 319 ornamental and raptor birds across 19 families, pooled and tested for beak and feather disease virus (BFDV), avian polyomavirus (APV-1), psittacid herpesvirus 1 (PsHV-1), and avian metapneumovirus (aMPV). BFDV and APV-1 were detected in 13.79% and 2.19% of birds, respectively, with five co-infections. No cases of PsHV-1 or aMPV were found. Both viruses showed a higher prevalence than in previous Italian and most of international studies, with several non-psittacine species, including birds of prey, testing positive—some for the first time. Mixed-species settings and participation in public exhibitions were proven as significant infection risk factors. The study highlights the growing relevance of BFDV and APV-1 in non-commercial birds and recommends improved biosecurity and preventive screening to reduce disease spread and safeguard animal health. Full article

In this study, we explore the paired disjunctive domination number—a recently introduced parameter by Henning et al.—within the broader framework of graph and network sensitivity and vulnerability analysis. Building on this concept, we introduce and investigate the paired disjunctive bondage number (PDBN), which measures the minimum number of edge deletions required to increase the paired disjunctive domination number of a graph or its corresponding network model. We begin by computing this new bondage number for several well-known network classes. The focus then shifts to specific families of trees, where we first determine their paired disjunctive domination numbers in detail. Using these values, we calculate the corresponding bondage numbers for various structurally symmetric, hierarchical, and compound tree structures, including double star, comet, double comet, $E_p^t$, and binomial trees, all of which model different types of infrastructural networks. Finally, we present an algorithm for computing PDBN, accompanied by a complexity analysis, and illustrate the practical relevance of the parameter through a case study applying it to a real-life network problem. Our results offer foundational insights into the behavior of this new domination parameter and its bondage variant, contributing to the growing literature on graph vulnerability and suggesting potential applications in the design of resilient and failure-aware networks. Full article

This article examines the seismic assessment and strengthening of a traditional load-bearing masonry structure subjected to strong motion data, with particular emphasis on the effects of soil–structure interaction (SSI). The case study is the Archaeological Museum of Lemnos (AML)—a three-storey building with a composite load-bearing system of timber-framed stone masonry. Over time, the structure has undergone irreversible modifications, primarily involving reinforced concrete (RC) interventions. The building’s seismic performance was evaluated using two finite element models developed in the SAP2000 software (v. 25.3.00). The first model simulates the original structure, strengthened by grout injections, while the second represents the current condition of the structural system following RC additions. Soil–structure interaction was also investigated, given that the local soil is classified as Category D according to Eurocode 8 (EC8). Each model was analyzed under two different support conditions: fixed-base and SSI-inclusive. A suite of appropriate accelerograms was applied to both models, in compliance with Eurocode 8 using the SeismoMatch software, and linear time-history analyses were conducted. The results underscore the significant impact of SSI on the increase of peak tensile stress and interstorey drift ratios (IDRs), and highlight the influence of different strengthening techniques on the seismic response of historic load-bearing masonry structures. Full article

In this study, we address the question of whether a waveguide with absorbing sidewalls can be considered pseudo free space and if the free-space transfer function is valid in such a medium. We test this hypothesis by applying a phase retrieval algorithm based on the free-space transfer function. First, optical measurements are carried out to measure the optical properties of a stack of thin films and select the parameters of simulations. Next, the propagation of light in a waveguide was simulated in COMSOL, and the phase of a wave was retrieved in MATLAB. Analysis was performed both for free-space conditions, and for a waveguide with absorbing sidewalls. The cross-correlation between the distributions of intensity under both conditions was about 0.40. The RMS error of the wave retrieved under free-space conditions was 0.378 rad, while that in the case of absorbing sidewalls was 0.323 rad, indicating successful retrieval. The successfully recovered phase of the input wave suggests that a waveguide with absorbing sidewalls can be approximated as pseudo free space and the free-space transfer function may be valid. These results may be used in future studies on how to shorten the phase retrieval of two-dimensional objects. Full article

Background: Antiphospholipid syndrome (APS) is one of the highest risk factors for obstetric complications. This article contains a case report of a patient with obstetric APS who experienced fetal loss during their first pregnancy and experienced a successful second pregnancy upon treatment with acetylsalicylic acid (ASA), low-molecular-weight heparin (LMWH), and hydroxychloroquine (HCQ). We compare placental pathology in these two pregnancies and discuss the impact of antiphospholipid antibodies and clinical management on pregnancy outcomes. We also propose methods to monitor obstetric antiphospholipid syndrome (OAPS) patients during pregnancy. Methods: A 26-year-old woman presented with a history of stillbirth at 25 weeks of pregnancy due to placental insufficiency. Before pregnancy, she experienced symptoms suggestive of autoimmune disease (thrombocytopenia, recurrent mouth aphthous ulcers, and Raynaud’s phenomenon) but had no diagnosis. Placental dysfunction correlated with the high ratio of sFlt-1/PIGF (soluble fms-like tyrosine kinase 1 and the placental growth factors index). Laboratory tests revealed the presence of antinuclear antibodies (ANAs) and triple positivity for antiphospholipid antibodies (aPLs). Results: Following the initiation of treatment for OAPS and regular monitoring consistent with current guidelines, the patient conceived and successfully delivered a healthy child. Conclusions: Adequate therapy and close monitoring during pregnancy, including clinical observation, placental biomarkers and regular ultrasonography, may help to reduce the risks and increase chances for optimal pregnancy outcomes. Additionally, pathological examination and clinical collaboration are essential components in future pregnancy counseling and should be a part of multidisciplinary management. Full article

Background: Staphylococcus aureus is a significant pathogen causing both local and systemic infections in children, with deep tissue involvement leading to severe complications. This study aimed to assess clinical manifestations and identify risk factors for deep tissue involvement in pediatric S. aureus infections. Methods: All children between 1 month and 18 years who had S. aureus growth in blood, pus, or joint fluid culture were included. Results: A total of 61 patients (median age 55 months) were included, with 22.9% having deep tissue infections. Osteoarticular infections, pyomyositis, and pulmonary involvement were common. Deep-seated infections were significantly associated with community-acquired infections and positive hemocultures after 72 hours (p < 0.01). Laboratory results showed significantly higher levels of C-reactive protein, sedimentation rate, D-dimer, and fibrinogen in the group with deep-seated infections (p = 0.02, p = 0.018, p = 0.01, and p = 0.015, respectively). The decision tree model showed that the first indicator of deep-seated infection was a D-dimer level above 1.15 mg/L, followed by a fibrinogen level above 334 mg/dL. Conclusions: Deep-seated S. aureus infections are more frequently associated with community-acquired cases, persistent hemoculture positivity, and methicillin-susceptible Staphylococcus aureus (MSSA) strains. Additionally, elevated D-dimer and fibrinogen levels may serve as valuable markers for identifying deep-seated infections in pediatric patients. Full article

Sickle cell disease (SCD) is a prevalent genetic disorder caused by a mutation in the beta-globin gene. Hyperhemolysis (HS) is a severe complication involving the rapid destruction of both transfused and endogenous red blood cells, commonly found in SCD. This literature review explores the clinical presentation, diagnosis, pathogenesis, and management of HS in SCD. HS can manifest acutely or in a delayed manner, complicating diagnosis due to overlapping symptoms and varying reticulocyte responses. Immunohematological assessments often reveal delayed positivity in direct antiglobulin tests and antibody screens. HS typically presents severe anemia, jaundice, hemoglobinuria, and hemodynamic instability. Diagnostic markers include elevated bilirubin and lactate dehydrogenase levels alongside a reduced reticulocyte count. The management of HS is primarily empirical, with no clinical trials to support standardized treatment protocols. First-line treatments involve steroids and intravenous immunoglobulins (IVIG), which modulate immune responses and mitigate hemolysis. Refractory cases may require additional agents such as rituximab, eculizumab, tocilizumab, and, in some instances, plasma exchange or erythropoietin-stimulating agents. Novel therapeutic approaches, including bortezomib and Hemopure, have shown promise but require further investigation. Current management strategies are empirical, underscoring the need for robust clinical trials to establish effective treatment protocols that ultimately improve outcomes for SCD patients experiencing HS. Full article

Biographies of Max Wilson and Gunnar Jungner were published in 2017 and 2020. An in-depth appreciation of the Wilson and Jungner principles, and the publication they were presented in, ‘Principles and Practice of Screening for Disease’, published as nr. 34 in the Public Health Paper-series of the World Health Organisation (W.H.O), called PHP-34 hereafter, was not published as yet. Here an analysis is given of PHP-34 and the ten screening principles, focusing on three subjects. First, by careful analysis of PHP-34, the literature published in the peer reviewed scientific literature, and other sources, the historical background and origin of the ten principles is determined. Second, the precise composition of PHP-34 is described, as parts of the monograph were derived from other seminal works published between roughly 1950 and 1965. Third, it is determined what the contributions of both authors of the monograph were. Results together are discussed in relation to the time PHP-34 was conceptualized and the importance of PHP-34 and the ten principles in the current era. Results show that in the 15 years preceding the publication of PHP-34, many principles of screening were published by authors in the United States of America, a selection of which ended up in PHP-34. Secondly, about 33% of the 145 pages of PHP-34 are drawn from other publications and studies on screening. Thirdly, the case can be made that the actual writing of PHP-34 was done (almost) entirely by Wilson. Regardless, Wilson and Jungner to this day should be applauded for their work. It is a testimony to the value of PHP-34 that we are still reflecting upon, discussing and seeking to intelligently apply the screening principles almost 60 years after their original publication. Full article

This paper provides a comparative study of unsupervised Deep Learning (DL) methods for anomaly detection in Flight Data Monitoring (FDM). The paper applies Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Convolutional Neural Network (CNN), classic Transformer architecture, and LSTM combined with a self-attention mechanism to real-world flight data and compares the results to the current state-of-the-art flight data analysis techniques applied in the industry. The paper finds that LSTM, when integrated with a self-attention mechanism, offers notable benefits over other deep learning methods as it effectively handles lengthy time series like those present in flight data, establishes a generalized model applicable across various airports and facilitates the detection of trends across the entire fleet. The results were validated by industrial experts. The paper additionally investigates a range of methods for feeding flight data (lengthy time series) to a neural network. The innovation of this paper involves utilizing Transformer architecture and LSTM with self-attention mechanism for the first time in the realm of aviation data, exploring the optimal method for inputting flight data into a model and evaluating all deep learning techniques for anomaly detection against the ground truth determined by human experts. The paper puts forth a compelling case for shifting from the existing method, which relies on examining events through threshold exceedances, to a deep learning-based approach that offers a more proactive style of data analysis. This not only enhances the generalization of the FDM process but also has the potential to improve air transport safety and optimize aviation operations. Full article

The weak grid strength in regions with large-scale renewable energy integration has emerged as a universal challenge, limiting the further expansion of renewable energy development. Currently, the short-circuit ratio (SCR) is widely used to quantify the relative strength between AC systems and renewable energy. To address this issue, this study first analyzes and compares how different reactive power compensation methods enhance the SCR. It then proposes calculation frameworks for both the SCR and critical short-circuit ratio (CSCR) in renewable energy grid-connected systems integrated with reactive power compensation. Furthermore, based on these formulations, a quantitative evaluation methodology for voltage support strength is developed to systematically assess the improvement effects of various compensation approaches on grid strength. Finally, case studies verify that reactive power compensation provided by synchronous condensers effectively strengthens grid strength and facilitates the safe expansion of the renewable energy integration scale. Full article