bluuebunny/crossref_metadata_2025 huggingface dataset:
Categorizing urban avoiders, utilizers, and dwellers for identifying bird conservation priorities in a Northern Andean city
Authors: Garizábal-Carmona Jaime A. , Betancur Jefry S. , Montoya-Arango Sergio , Franco-Espinosa Laura , Mancera-Rodríguez N. Javier
Published: August 2024

Introduction

Categorizing species according to their frequencies across urbanization levels and identifying some species traits that explain this variation could be a valuable tool for focusing conservation efforts, particularly in biodiversity hotspots with high endemism rates. This study proposes a semi-quantitative and multi-scale protocol to categorize bird species as urban avoiders, utilizers, and dwellers, based on their frequencies at different urbanization levels. Additionally, it evaluates the relationships of these categories with altitudinal ranges, trophic guilds, and foraging strata.

Methods

We performed bird counts in 124 points located within urban areas, and in 15 points located in non-urban areas of a Colombian Northern Andean city (Medellín and surroundings). Each urban point was assigned to urbanization levels based on 200, 500, and 1,000 m buffers categorized as high (67–100% of built cover), moderate (34–66% of built cover), or low (0–33% of built cover).

Results

We categorized 103 bird species: 49 as urban avoiders, 31 as urban utilizers, and 23 as urban dwellers. The two recorded Colombian endemic species and seven near-endemics were categorized as urban avoiders, with only one near-endemic species categorized as an urban utilizer (the other three were data deficient). Furthermore, most bird species with exclusive Andean distribution were categorized as urban avoiders (78.57%). Urban avoiders had narrower altitudinal ranges (1,969 ± 524 m) than utilizers (2,287 ± 592m) and dwellers (2,569 ± 654m), and they had the largest proportion of frugivorous and frugivorous-insectivorous species, while urban dwellers had a greater proportion of omnivorous species. Overall, bird species with exclusive Andean distribution and narrow altitudinal ranges are the most threatened by urban sprawl, irrespective of their trophic guild or foraging strata.

Discussion

This study emphasizes the importance of protecting native forest remnants in urban surroundings for conserving native Andean bird species, as urban green spaces in high-density cities may not sufficiently support their long-term survival and reproduction. Also, it highlights the need to identify conservation priorities based on local biodiversity patterns, taking into account that species-specific urban tolerance depends on particular landscape dynamics and species regional pools.
Impact of environmental conditions on English Channel long-finned squid (Loligo spp.) recruitment strength and spatial location
Authors: Marcout Anna , Foucher Eric , Pierce Graham J. , Robin Jean-Paul
Published: August 2024

The English Channel has the highest long-finned squid landings in the Northeast Atlantic, making squid one of the most valuable resources exploited by demersal fisheries operating in this area. This resource consists of two short-lived long-finned squid species: Loligo forbesii and L. vulgaris, which have a similar appearance (they are not distinguished by fishers) but differ in the timing of their life cycle: in L. forbesii, the recruitment peak occurs in July while in L. vulgaris recruitment peak occurs in November. The abundance and distribution of cephalopod species, such as Loligo spp., depends on favourable environmental conditions to support growth, reproduction and successful recruitment. This study investigated the role of several environmental variables (bottom temperature, salinity, current velocity, phosphate and chlorophyll concentrations) on recruitment biomass (in July for L. forbesii and November for L. vulgaris), as based on environmental data for pre-recruitment period from the Copernicus Marine Service and commercial catches of French bottom trawlers during the recruitment period over the years 2000 to 2021. To account for non-linear relationship between environmental descriptors and the biological response, General Additive Models (GAM) were fitted to the data. Separate models were obtained to forecast L. vulgaris and L. forbesii biomass indices during their respective recruitment periods. These models explain a high percentage of variation in biomass indices (65.8% for L. forbesii and 56.7% for L. vulgaris) and may be suitable to forecast the abundance (in terms of biomass) and spatial distribution of the resource. Such forecasts are desirable tools to guide fishery managers. Since these models can be fitted shortly before the start of the fishing season, their routine implementation would take place in real-time fishery management (as promoted by fishery scientists dealing with short-lived species).
An Efficient AdaBoost Algorithm for Enhancing Skin Cancer Detection and Classification
Authors: Gamil Seham , Zeng Feng , Alrifaey Moath , Asim Muhammad , Ahmad Naveed
Published: August 2024

Skin cancer is a prevalent and perilous form of cancer and presents significant diagnostic challenges due to its high costs, dependence on medical experts, and time-consuming procedures. The existing diagnostic process is inefficient and expensive, requiring extensive medical expertise and time. To tackle these issues, researchers have explored the application of artificial intelligence (AI) tools, particularly machine learning techniques such as shallow and deep learning, to enhance the diagnostic process for skin cancer. These tools employ computer algorithms and deep neural networks to identify and categorize skin cancer. However, accurately distinguishing between skin cancer and benign tumors remains challenging, necessitating the extraction of pertinent features from image data for classification. This study addresses these challenges by employing Principal Component Analysis (PCA), a dimensionality-reduction approach, to extract relevant features from skin images. Additionally, accurately classifying skin images into malignant and benign categories presents another obstacle. To improve accuracy, the AdaBoost algorithm is utilized, which amalgamates weak classification models into a robust classifier with high accuracy. This research introduces a novel approach to skin cancer diagnosis by integrating Principal Component Analysis (PCA), AdaBoost, and EfficientNet B0, leveraging artificial intelligence (AI) tools. The novelty lies in the combination of these techniques to develop a robust and accurate system for skin cancer classification. The advantage of this approach is its ability to significantly reduce costs, minimize reliance on medical experts, and expedite the diagnostic process. The developed model achieved an accuracy of 93.00% using the DermIS dataset and demonstrated excellent precision, recall, and F1-score values, confirming its ability to correctly classify skin lesions as malignant or benign. Additionally, the model achieved an accuracy of 91.00% using the ISIC dataset, which is widely recognized for its comprehensive collection of annotated dermoscopic images, providing a robust foundation for training and validation. These advancements have the potential to significantly enhance the efficiency and accuracy of skin cancer diagnosis and classification. Ultimately, the integration of AI tools and techniques in skin cancer diagnosis can lead to cost reduction and improved patient outcomes, benefiting both patients and healthcare providers.
High-Resolution CAD-Based Shape Parametrisation of a U-Bend Channel
Authors: Jesudasan Rejish , Müeller Jens-Dominik
Published: August 2024

The parametrisation of the geometry in shape optimisation has an important influence on the quality of the optimum and the rate of convergence of the optimiser. Refinement studies for the parametrisation are not shown in the literature, as most methods use non-orthogonal parametrisations, which cause issues with convergence when the parametrisation is refined. The NURBS-based parametrisation with complex constraints (NSPCC) is the only CAD-based parametrisation method that guarantees orthogonal shape modes by constructing an optimal basis. We conduct a parametrisation refinement study for the benchmark turbomachinery cooling bend (U-bend) geometry, an intially symmetric geometry. Using an adjoint RANS solver, we optimise for mininmal total pressure drop. The results show significant effects of the control net density on the final shape, with the finest control net producing an asymmetric optimal shape resembling strakes that induces swirl ahead of the bend. These asymmetric modes have not been reported in the literature so far. We also demonstrate that the convergence rate of the optimiser is not significantly affected by the refinement of the parametrisation. The effectiveness of these shape features obtained with single-point optimisation is evaluated for a range of Reynolds numbers. It is shown that total pressure drop reduction is not sensitive to Reynolds number.
Rooting Patterns and Aucubin Content in Plantago lanceolata
Authors: Pol Michal , Potterat Olivier , Tröber Florian , Lewandowska Sylwia , Schmidtke Knut
Published: August 2024

Ribwort plantain (Plantago lanceolata L.) is expected to open up new crop cultivation perspectives in arable farming in order to significantly reduce nitrogen losses through leaching, N2O emissions and to increase resilience under drought conditions. Therefore, this study investigated the properties of the roots as an alternative to grasses for forage production under drought conditions. Ten genotypes of ribwort plantain were compared under field conditions in northern Switzerland, focusing on the characteristics of the root system and the aucubin content in the leaves and roots. Aucubin, known for inhibiting the nitrification process in the soil, varied according to genotype and season. All tested genotypes showed higher aucubin content in leaves than in roots, which may indicate the greater importance of leaves in reducing soil nitrification. Dry matter yield and for the first time root characteristics, such as root length density (RLD) and specific root length (SRL), were evaluated at two different soil depths, and at different distances from the plants. The results showed that ribwort is well adapted to acquire water and nutrients in terms of rooting in topsoil. In addition, a second field experiment conducted in eastern Germany (Saxony) confirmed the plant’s ability to root deeply after one year of cultivation, reaching depths of up to even 1.88 m. The obtained results indicate the high capacity of ribwort plantain to take up water and nutrients also from deeper soil layers. To reduce N2O emissions through the cultivation of ribwort plantain, the choice of genotype seems to be of great importance due to varying aucubin contents.
Exploiting the Yield Potential of Spring Barley in Poland: The Roles of Crop Rotation, Cultivar, and Plant Protection
Authors: Kostrzewska Marta K. , Jastrzębska Magdalena
Published: August 2024

The need to better exploit crop yield potential through agronomic practices is driven by the necessity to feed a growing population in a changing climate. In this regard, cereals, including barley, represent strategic crops. Barley grain yield in Poland is comparable to the European average. Under good habitat and agronomic conditions, it can exceed the average. The effects of cropping system (continuous cropping, CC; crop rotation, CR), cultivar (Radek, Skald), plant protection level (control treatment, CT; herbicide, H; herbicide and fungicide, HF), and the interactions among these factors on spring barley yield were evaluated in northeast Poland in 2017–2022. Growing spring barley in the CR system promoted higher yields compared to the CC system due to increased spike density and 1000-grain weight (TGW). Skald, with a higher TGW, yielded more than Radek. H treatment increased barley yield by improving the spike density and TGW, while fungicide inclusion (HF) contributed to further yield increase by improving the TGW. The interaction of CR and HF practices contributed to the greatest realization of the yield potential of both cultivars. The study confirmed the strong contribution of inter-annual weather variability in modifying the effects of cropping system, cultivar, and plant protection strategy on barley yield.
Seasonal Photosynthetic and Water Relation Responses of Three Cool Temperate Garden Shrubs to Drought Stress
Authors: Li Jiaying , Lu Shaowei , Li Shaoning , Li Bin , Hou Liwei , Zhao Na , Xu Xiaotian
Published: August 2024

The globally increasing frequency of extreme drought events exacerbates the contradiction between the supply of water and the demand for high-quality urban greening. However, the mechanism of the response of urban shrubs to drought stress remains unclear. In this study, three typical urban shrubs (Euonymus japonicus, golden vicary [Ligustrum × vicaryi], and Japanese purple barberry [Berberis thunbergii var. atropurpurea]) that are used for greening in northern China were exposed to three levels of water (full irrigation, natural rain-fed, and extreme drought) in different periods of the growing season (April to May, June to July, and August to September) to investigate the responses of leaf water potential and photosynthetic parameters. The main results were as follows: (1) all the leaf water potentials (Ψ) and photosynthetic parameters (Pn) showed a typical linear relationship along the water gradient in the middle of the growing season. Extreme drought decreased the photosynthetic rates by 1.26~11.03 μmol·m−2·s−1 compared with the irrigated groups. However, the responses were less pronounced in the early and late growing seasons. (2) Different shrubs responded with different intensities and mechanisms. B. thunbergii var. atropurpurea showed clear anisohydric behavior throughout the whole growing season, while L. × vicaryi and E. japonicus showed stronger isohydric behavior during the early and late growing seasons. These findings are important to improve the sustainability of maintenance of ornamental plants from the scope of the efficient utilization of urban water resources.
Evaluation and Development of Analytical Procedures to Assess Buffering Capacity of Carbonate Ruminant Feed Buffers
Authors: Quille Patrick , Higgins Tommy , Neville Enda W. , Regan Katy , O’Connell Shane
Published: August 2024

The inclusion of rumen buffers in ruminant feeds has gained widespread adoption for the prevention of rumen acidosis, thereby avoiding the negative production and health consequences of low rumen pH and resulting in improved feed efficiency. Benchmarking and quality controlling the performance of rumen buffer materials is of significant interest to feed mills and end-user producers. The aim of this study was to evaluate, develop and optimise a laboratory protocol to consistently and robustly evaluate rumen buffering materials in order to predict their in vivo efficacy. Three different methods were evaluated for determining the buffering potential of carbonate buffer materials: (a) 2 and 8 h static pH, (b) 8 h fixed HCl acid load addition and (c) 3 h acidotic diet simulation using acetic acid. Buffer material, threshold pH, test duration and interactions between all three variables were significant (p < 0.001) in evaluating the performance of the buffer materials. The acidotic diet simulation was found to provide a different ranking of materials to the 8 h fixed HCl acid load methodology. The results highlight the importance of method selection and test parameters for accurately evaluating the potential efficacy of rumen buffer materials.
Rapid Reversal of Carbapenemase-Producing Pseudomonas aeruginosa Epidemiology from blaVIM- to blaNDM-harbouring Isolates in a Greek Tertiary Care Hospital
Authors: Protonotariou Efthymia , Meletis Georgios , Vlachodimou Nikoletta , Malousi Andigoni , Tychala Areti , Katsanou Charikleia , Daviti Aikaterini , Mantzana Paraskevi , Skoura Lemonia
Published: August 2024

Carbapenemase-producing Pseudomonas aeruginosa strains present a specific geographical distribution regarding the type of carbapenemase-encoding genes that they harbor. For more than twenty years, VIM-type enzymes were the only major carbapenemases that were detected among P. aeruginosa isolates in Greece until the emergence of NDM-1-encoding P. aeruginosa in early 2023. In the present study, we present the rapid reversal of the carbapenemase-producing P. aeruginosa epidemiology from blaVIM- to blaNDM-harbouring isolates that occurred in our hospital since then. Between January 2023 and February 2024, 139 isolates tested positive for carbapenemase production with the NG-Test CARBA 5 immunochromatographic assay. Eight isolates were processed with the Hybrispot antimicrobial resistance direct flow chip molecular assay, and the first NDM-producing isolate was further analyzed through whole genome sequencing and bioinformatics analysis. Multiple resistance genes were detected by molecular techniques in accordance with the extensively drug-resistant phenotype. The isolate that was subjected to whole-genome sequencing belonged to the P. aeruginosa high-risk clone ST308, and the blaNDM was located in the chromosome in accordance with previously reported data. During the study period, NDM-producing isolates were increasingly detected, and only five months after their emergence, they overcame VIM producers. Our results indicate the potential of this new clone to spread rapidly and predominate within healthcare institutions, further restricting the already limited treatment options.
Extraction of Minimal Set of Traffic Features Using Ensemble of Classifiers and Rank Aggregation for Network Intrusion Detection Systems
Authors: Krupski Jacek , Iwanowski Marcin , Graniszewski Waldemar
Published: August 2024

Network traffic classification models, an essential part of intrusion detection systems, need to be as simple as possible due to the high speed of network transmission. One of the fastest approaches is based on decision trees, where the classification process requires a series of tests, resulting in a class assignment. In the network traffic classification process, these tests are performed on extracted traffic features. The classification computational efficiency grows when the number of features and their tests in the decision tree decreases. This paper investigates the relationship between the number of features used to construct the decision-tree-based intrusion detection model and the classification quality. This work deals with a reference dataset that includes IoT/IIoT network traffic. A feature selection process based on the aggregated rank of features computed as the weighted average of rankings obtained using multiple (in this case, six) classifier-based feature selectors is proposed. It results in a ranking of 32 features sorted by importance and usefulness in the classification process. In the outcome of this part of the study, it turns out that acceptable classification results for the smallest number of best features are achieved for the eight most important features at −95.3% accuracy. In the second part of these experiments, the dependence of the classification speed and accuracy on the number of most important features taken from this ranking is analyzed. In this investigation, optimal times are also obtained for eight or fewer number of the most important features, e.g., the trained decision tree needs 0.95 s to classify nearly 7.6 million samples containing eight network traffic features. The conducted experiments prove that a subset of just a few carefully selected features is sufficient to obtain reasonably high classification accuracy and computational efficiency.
Performance Comparison of 500 MW Coal-Fired Thermal Power Plants with Final Feedwater Temperatures
Authors: Jang Yong-Chu , Moon Seung-Jae
Published: August 2024

The final feedwater temperature is the temperature of the feedwater supplied to the boiler in the power plant applied with the Rankine cycle. In thermal power plants adopting a regenerative cycle, it is generally known that the heat transfer of the boiler is reduced, and the efficiency of the power plant is increased. However, the output of the power plant is reduced when the final feedwater temperature is increased. In this study, the net output and efficiency of the power plants depending on the final feedwater temperature under the condition of constant boiler heat transfer rate were analyzed for five cases. The results show that there is a final feedwater temperature at which the net output and the net efficiency are maximized. The additional output of the power plant obtained by increasing the final feedwater temperature has the effect of reducing CO2 emissions. If the final feed water temperature is below 308.3 °C, the net output and the final feed water temperature are proportional for all cases. When the final feedwater temperature increases by 1 °C, the net output increases by 63.02 kW and CO2 emissions are reduced by 60.52 kgCO2/h on average.
Physics-Informed Neural Networks for Cantilever Dynamics and Fluid-Induced Excitation
Authors: Lee Jeongsu , Park Keunhwan , Jung Wonjong
Published: August 2024

Physics-informed neural networks (PINNs) represent a continuous and differentiable mapping function, approximating solution curves for given differential equations. Recent studies have demonstrated the significant potential of PINNs as an alternative or complementary approach to conventional numerical methods. However, their application in structural dynamics, such as cantilever dynamics and fluid-induced excitations, poses challenges. In particular, limited accuracy and robustness in resolving high-order differential equations, including fourth-order differential equations encountered in structural dynamics, are major problems with PINNs. To address these challenges, this study explores optimal strategies for constructing PINNs in the context of cantilever dynamics: (1) performing scaling analysis for the configuration, (2) incorporating the second-order non-linear term of the input variables, and (3) utilizing a neural network architecture that reflects a series solution of decomposed bases. These proposed methods have significantly enhanced the predictive capabilities of PINNs, showing an order-of-magnitude improvement in accuracy compared to standard PINNs in resolving the dynamic oscillation of cantilevers and fluid-induced excitation driven by added mass forces. Furthermore, this study extends to the domain of fluid-induced excitation in cantilever dynamics, representing an extreme case of coupled dynamics in fluid–structure interaction. This research is expected to establish crucial baselines for the further development of PINNs in structural dynamics, with potential applicability to high-order coupled differential equations.
Supervised Machine Learning Models for Mechanical Properties Prediction in Additively Manufactured Composites
Authors: Prada Parra Dario , Ferreira Guilherme Rezende Bessa , Díaz Jorge G. , Gheorghe de Castro Ribeiro Mateus , Braga Arthur Martins Barbosa
Published: August 2024

This paper analyses mechanical property prediction through Machine Learning for continuous fiber-reinforced polymer matrix composites printed using the novel Material Extrusion Additive Manufacturing technique. The composite is formed by a nylon-based matrix and continuous fiber (carbon, Kevlar, or fiberglass). From the literature, the elastic modulus and tensile strength were taken along with printing parameters like fiber content, fiber fill type, matrix lattice, matrix fill density, matrix deposition angle, and fiber deposition angle. Such data were fed to several supervised learning algorithms: Ridge Regression, Bayesian Ridge Regression, Lasso Regression, K-Nearest Neighbor Regression, CatBoost Regression, Decision Tree Regression, Random Forest Regression, and Support Vector Regression. The Machine Learning analysis confirmed that fiber content is the most influential parameter in elasticity (E) and strength (σ). The results show that the K-Nearest Neighbors and CatBoost provided the closest predictions for E and σ compared to the other models, and the tree-based model presented the narrowest error distribution. The computational metrics point to a size versus prediction time tradeoff between these two best predictors, and adopting the prediction time as the most relevant criterion leads to the conclusion that the CatBoost model can be considered, when compared to the others tested, the most appropriate solution to work as a predictor in the task at hand.
Geological Characteristics and Exploration Practices of Multilayer Shale Oil and Gas in the Yanchang Formation, Fuxian–Ganquan Area, Ordos Basin
Authors: Shi Peng , Zhou Wei , Zhang Jianfeng , Yin Jintao , Chen Yiguo
Published: August 2024

The Chang 7, Chang 8, and Chang 9 members of the Triassic Yanchang Formation in the Fuxian–Ganquan area of the Ordos Basin all develop lacustrine shales. However, current geological research and shale oil and gas exploration mainly focus on Chang 7 shale, with little attention given to Chang 8 and Chang 9 shale formations. Based on the experimental data from whole-rock mineral analysis, organic geochemical analysis, field-emission scanning electron microscopy analysis, and hydrocarbon generation simulation experiments, combined with well-logging data, the shale distribution, mineral composition, source rock characteristics, reservoir properties, and oil and gas contents of Chang 7, Chang 8, and Chang 9 shales were comprehensively analyzed. Moreover, the effect of integrated exploration of multilayer shales was evaluated based on a specific example. The results indicate that three sets of shales are extensively developed in the Yanchang Formation in the study area, but their thicknesses and distribution ranges vary greatly, and Chang 7 shale has the largest thickness and distribution range. Their clay mineral contents are relatively high, reaching an average of 46.7%. Also, the types of their organic matter are mainly Type I-II1, with high abundance and an average organic carbon content of 4.7%. Their vitrinite reflectance is between 0.7% and 1.3%, indicating that they are in the oil–gas symbiosis stage. Furthermore, they develop various types of nanoscale pores, such as intergranular pores, intragranular pores, and organic pores, and their porosity has an average value of 2.51% and increases significantly after crude oil is extracted. Oil and gas coexist in these three sets of shales, with an average free hydrocarbon content of 3.9 mg/g and an average gas content of 2.6 m3/t. Finally, in order to explore the integrated exploration and development of multilayer shale oil and gas formations, multilayer staged fracturing tests were carried out on six vertical wells for three sets of shales; the production results show that the gas production rate significantly increased by threefold, with a daily oil production rate of more than 1 ton.
Using Integrated Multimodal Technology: A Way to Personalise Learning in Health Science and Biomedical Engineering Students
Authors: Sáiz-Manzanares María Consuelo , Marticorena-Sánchez Raúl , Escolar-Llamazares María Camino , González-Díez Irene , Martín-Antón Luis Jorge
Published: August 2024

Monitoring the learning process during task solving through different channels will facilitate a better understanding of the learning process. This understanding, in turn, will provide teachers with information that will help them to offer individualised education. In the present study, monitoring was carried out during the execution of a task applied in a self-regulated virtual environment. The data were also analysed using data fusion techniques. The objectives were as follows: (1) to examine whether there were significant differences between students in cognitive load (biomarkers: fixations, saccades, pupil diameter, galvanic skin response—GSR), learning outcomes and perceived student satisfaction with respect to the type of degree (health sciences vs. engineering; and (2) to determine whether there were significant differences in cognitive load metrics, learning outcomes and perceived student satisfaction with respect to task presentation (visual and auditory vs. visual). We worked with a sample of 31 university students (21 health sciences and 10 biomedical engineering). No significant differences were found in the biomarkers (fixations, saccades, pupil diameter and GSR) or in the learning outcomes with respect to the type of degree. Differences were only detected in perceived anxiety regarding the use of virtual laboratories, being higher in biomedical engineering students. Significant differences were detected in the biomarkers of the duration of use of the virtual laboratory and in some learning outcomes related to the execution and presentation of projects with respect to the variable form of the visualisation of the laboratory (visual and auditory vs. visual). Also, in general, the use of tasks presented in self-regulated virtual spaces increased learning outcomes and perceived student satisfaction. Further studies will delve into the detection of different forms of information processing depending on the form of presentation of learning tasks.
Investigation of Flood Hazard Susceptibility Using Various Distance Measures in Technique for Order Preference by Similarity to Ideal Solution
Authors: Akay Hüseyin , Baduna Koçyiğit Müsteyde
Published: August 2024

In the present study, flood hazard susceptibility maps generated using various distance measures in the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) were analyzed. Widely applied distance measures such as Euclidean, Manhattan, Chebyshev, Jaccard, and Soergel were used in TOPSIS to generate flood hazard susceptibility maps of the Gökırmak sub-basin located in the Western Black Sea Region, Türkiye. A frequency ratio (FR) and weight of evidence (WoE) were adapted to hybridize the nine flood conditioning factors considered in this study. The Receiver Operating Characteristic (ROC) analysis and Seed Cell Area Index (SCAI) were used for the validation and testing of the generated flood susceptibility maps by extracting 70% and 30% of the inventory data of the generated flood susceptibility map for validation and testing, respectively. When the Area Under Curve (AUC) and SCAI values were examined, it was found that the Manhattan distance metric hybridized with the FR method gave the best prediction results with AUC values of 0.904 and 0.942 for training and testing, respectively. Furthermore, the natural break method was found to give the best predictions of the flood hazard susceptibility classes. So, the Manhattan distance measure could be preferred to Euclidean for flood susceptibility mapping studies.
Video Quality Modelling—Comparison of the Classical and Machine Learning Techniques
Authors: Klink Janusz , Łuczyński Michał , Brachmański Stefan
Published: August 2024

The classical objective methods of assessing video quality used so far, apart from their advantages, such as low costs, also have disadvantages. The need to eliminate these defects results in the search for better and better solutions. This article proposes a video quality assessment method based on machine learning using a linear regression model. A set of objective quality assessment metrics was used to train the model. The results obtained show that the prediction of video quality based on a machine learning model gives better results than the objective assessment based on individual metrics. The proposed model showed a strong correlation with the subjective user assessments but also a good fit of the regression function to the empirical data. It is an extension and improvement of the efficiency of the classical methods of objective quality assessment that have been used so far. The solution presented here will allow for a more accurate prediction of the video quality perceived by viewers based on an assessment carried out using a much cheaper, objective method.
The Running Performance of Amateur Football Players in Matches with a 1-4-3-3 Formation in Relation to Their Playing Position and the 15-min Time Periods
Authors: Kaldaras Vasilis , Michailidis Yiannis , Gissis Ioannis , Metaxas Thomas I.
Published: August 2024

The purpose of this research is to investigate the differences in specific GPS variables related to the running performance of amateur football players, comparing data over 15 min periods of time throughout the match, as well as examining differences between players of different positions. The participants are adult male amateur football players from the same team. The analysis of the players’ performance was carried out using GPS transmitters over twelve league matches (Amateur Division A) for all starting players (except the goalkeeper) who completed the match without being substituted. The formation was 1-4-3-3, and the players were divided into five categories: Central Defenders (CDs), Full Backs (FBs), Central Midfielders (CMs), Wingers (Ws), and Center Forwards (FWs). Each match was divided into two halves and into periods of fifteen minutes. The analysis concerns the total distance covered within these time periods and in different speed zones. The results showed that CDs covered the shortest distance (p < 0.05), while CMs and Ws covered the longest (p < 0.05) in the most of the 15 min time periods. In both halves, it was observed that players covered greater distances at low intensity in the last 15-min periods. Finally, the total distance covered at low intensity was greater in the last 15-min period of the 2nd half compared to that of the 1st half. As footballers’ loads vary based on their playing position, the data from this study warrant extensive consideration for better training planning.
Identifying Elastic Wave Velocity Distribution with Observation Arrival Time Errors Using Weighted Potential Time in Acoustic Emission Tomography
Authors: Furukawa Mikika , Nakamura Katsuya , Oda Kenichi , Shigemura Satoshi , Kobayashi Yoshikazu
Published: August 2024

Acoustic emission tomography (“AET”) is used to visualize internal structural damage. In this study, we aimed to improve the accuracy of the identified elastic wave velocity distribution when including errors in the observed arrival time, which is used as the observation value in AET. Weights were assigned to the potential excitation times used in location determination and elastic wave velocity distribution according to the magnitude of the potential excitation time and the wave line length. As a result, 100 instances of observation errors were generated for each observation error ratio via truncated normal distribution. The analysis results showed that the normalization error of the elastic wave velocity distribution increased in about 85%, 90%, and 95% of the cases for observation error ratios of 5%, 10%, and 15%, respectively. In conclusion, the weights used for the potential excitation time were effective in identifying elastic wave velocity distribution.
Study of the Application of WRF-FLEXPART in Airborne Balloon Movement Simulation
Authors: Wang Guangjie , Yang Luoqi , Wang Yegui , Cai Qifa , Li Juan , Lan Weiren , Ma Yongjing
Published: August 2024

In the present era, national borders frequently encounter the risk and threat of unidentified airspace objects such as airborne balloons; however, there is a lack of effective methods for predicting their trajectories. To address this gap, we examine the utilization of a Lagrangian diffusion model named FLEXPART for forecasting the movement of airborne balloons. The simulation results show that when taking into account the radius of the airborne balloon, there is a potential increase of up to 1.07 km and 1.45 km in the mean absolute error (MAE) and root mean square error (RMSE), respectively, in the horizontal direction. Similarly, considering the density of the airborne balloon can also result in an increase of 3.46 km (MAE) and 4.71 km (RMSE) horizontally. Therefore, simplifying them into mass points enables FLEXPART to predict their trajectories with higher accuracy. On this basis, we simulate and comparatively analyze the forward and backward trajectories of an airborne balloon observed in China in 2019 using the FLEXPART model. The results demonstrate that within 11 h, FLEXPART predicts the horizontal trajectory of the balloon, with both RMSE and MAE falling within 50 km. Moreover, when it comes to the vertical direction bias within the 11 h prediction, RMSE and MAE are both below 500 m. Furthermore, trajectory bias gradually diminishes as the forecast time increases. The combination of the sensitivity tests and the results of the actual balloon trajectory simulation confirm that the FLEXPART model is capable of predicting the airborne balloon trajectory.
Inhibitory Effect of Dipeptides Containing Acidic Amino Acid Residue on Degranulation of RBL-2H3 Cells
Authors: Nishi Kosuke , Hirakawa Taiki , Izumi Mitsumasa , Kageyama Naoki , Yurue Senri , Ozaki Akari , Toga Yuki , Ishida Momoko , Sugahara Takuya
Published: August 2024

Upon degranulation, basophils and mast cells secrete an array of inflammatory mediators, including histamine, which leads to not only allergic inflammation but also other inflammatory diseases. We previously reported that an aqueous extract from enzyme-treated, dried sardine inhibits the degranulation of RBL-2H3 cells and attenuates the symptoms of Japanese cedar pollinosis in mice. This study evaluated an antiallergic effect of dipeptides containing acidic amino acid residue in an antigen-induced degranulation assay using RBL-2H3 cells. The result showed that acidic amino acid residue-containing dipeptides inhibit the degranulation of RBL-2H3 cells without cytotoxicity. Additionally, L-histidyl-L-glutamic acid (His-Glu), one of the acidic amino acid residue-containing dipeptides tested in this study, inhibited calcium ionophore-induced degranulation. We also found that His-Glu suppressed microtubule reorganization in RBL-2H3 cells after antigen stimulation. His-Glu slightly, but not significantly, suppressed the elevation of cytosolic calcium ion concentration leading to degranulation. Immunoblot analysis revealed that His-Glu significantly suppressed the phosphorylation of phosphoinositide 3-kinase and Akt, but not that of Syk or phospholipase Cγ. Overall results suggest that acidic amino acid residue-containing dipeptides can be used as food ingredients with an antiallergic effect.
Coastal Zone Classification Based on U-Net and Remote Sensing
Authors: Liu Pei , Wang Changhu , Ye Maosong , Han Ruimei
Published: August 2024

The coastal zone is abundant in natural resources but has become increasingly fragile in recent years due to climate change and extensive, improper exploitation. Accurate land use and land cover (LULC) mapping of coastal zones using remotely sensed data is crucial for monitoring environmental changes. Traditional classification methods based on statistical learning require significant spectral differences between ground objects. However, state-of-the-art end-to-end deep learning methods can extract advanced features from remotely sensed data. In this study, we employed ResNet50 as the feature extraction network within the U-Net architecture to achieve accurate classification of coastal areas and assess the model’s performance. Experiments were conducted using Gaofen-2 (GF-2) high-resolution remote sensing data from Shuangyue Bay, a typical coastal area in Guangdong Province. We compared the classification results with those obtained from two popular deep learning models, SegNet and DeepLab v3+, as well as two advanced statistical learning models, Support Vector Machine (SVM) and Random Forest (RF). Additionally, this study further explored the significance of Gray Level Co-occurrence Matrix (GLCM) texture features, Histogram Contrast (HC) features, and Normalized Difference Vegetation Index (NDVI) features in the classification of coastal areas. The research findings indicated that under complex ground conditions, the U-Net model achieved the highest overall accuracy of 86.32% using only spectral channels from GF-2 remotely sensed data. When incorporating multiple features, including spectrum, texture, contrast, and vegetation index, the classification accuracy of the U-Net algorithm significantly improved to 93.65%. The major contributions of this study are twofold: (1) it demonstrates the advantages of deep learning approaches, particularly the U-Net model, for LULC classification in coastal zones using high-resolution remote sensing images, and (2) it analyzes the contributions of spectral and spatial features of GF-2 data for different land cover types through a spectral and spatial combination method.
Exploring the Elemental Interactions of Melamine with Binder–Metal Powder Mixtures: A Pathway to Enhanced Catalytic Debinding and Rheological Control
Authors: Berges Cristina , Hidalgo Javier , Herranz Gemma
Published: August 2024

In advanced powder metallurgy technologies such as metal injection molding (MIM) and material extrusion technology (MEX), the intricate synergy between binder components and metallic powders within feedstocks is predominant, dictating the technological boundaries of metal’s complexity, size, and thickness. This work suggests unique binder constituents (polymers, surfactants and melamine), aiming to boost the debinding efficacy and solid loading while ensuring processability. The interactions and role of melamine by thermal and rheological studies are detailed, spotlighting how, unlike traditional binder systems, the pioneering feedstock introduces beneficial modifications to storage modulus (G′) and loss tangent (tan δ), alongside a lubricating effect on metallic mixtures. This study highlights the potential of melamine to facilitate a more efficient debinding process, with superior formaldehyde management and environmental control. Through this material-centric lens, we offer new pathways to navigate the complexities of MIM and MEX, advancing towards technological enhancement and environmental protection.
Direct Solar Thermal Water-Splitting Using Iron and Iron Oxides at High Temperatures: A Review
Authors: Fuentes Manuel , Pulido Diego , Fuentealba Edward , Soliz Alvaro , Toro Norman , Sagade Atul , Galleguillos Madrid Felipe M.
Published: August 2024

Green hydrogen is poised to play a crucial role in the energy-transition process in developed countries over the coming years, particularly in those countries aiming to achieve net-zero emissions. Consequently, the for green hydrogen is expected to rise significantly. This article explores the fundamental methods of producing hydrogen, focusing on the oxidation reaction within a thermochemical solar cycle for the dissociation of steam. Solar thermochemical cycles have been extensively researched, yet they remain in the development stage as research groups strive to identify optimal materials and conditions to enhance process efficiency, especially at high temperatures. The article analyses theoretical foundations drawn from exhaustive scientific studies related to the oxidation of iron in steam, the relationship with the activation energy of the corrosive process, thermodynamic aspects, and the kinetic model of a heterogeneous reaction. Additionally, it presents various mechanisms of high-temperature oxidation, pH effects, reactors, and materials (including fluidized beds). This scientific review suggests that hydrogen production via a thermochemical cycle is more efficient than production via electrochemical processes (such as electrolysis), provided the limitations of the cycle’s reduction stage can be overcome.
Continuous-Thrust Circular Orbit Phasing Optimization of Deep Space CubeSats
Authors: Quarta Alessandro A.
Published: August 2024

The recent technology advancements in miniaturizing the primary components of spacecraft allow the classic CubeSats to be considered as a valid option in the design of a deep space scientific mission, not just to support a main typical interplanetary spacecraft. In this context, the proposed ESA M-ARGO mission, whose launch is currently planned in 2026, will use the electric thruster installed onboard of a 12U CubeSat to transfer the small satellite from the Sun–Earth second Lagrangian point to the orbit of a small and rapidly spinning asteroid. Starting from the surrogate model of the M-ARGO propulsion system proposed in the recent literature, this paper analyzes a simplified thrust vector model that can be used to study the heliocentric optimal transfer trajectory with a classical indirect approach. This simplified thrust model is a variation of the surrogate one used to complete the preliminary design of the trajectory of the M-ARGO mission, and it allows to calculate, in an analytical form, the typical Euler–Lagrange equations without singularities. The thrust model is then used to study the performance of a M-ARGO-type CubeSat (MTC) in a different scenario (compared to that of the real mission), in which the small satellite moves along a circular heliocentric orbit in the context of a classic phasing maneuver. In this regard, the work discusses a simplified study of the optimal constrained MTC transfer towards one of the two Sun–Earth triangular Lagrangian points. Therefore, the contributions of this paper are essentially two: the first is the simplified thrust model that can be used to analyze the heliocentric trajectory of a MTC; the second is a novel mission application of a CubeSat, equipped with an electric thruster, moving along a circular heliocentric orbit in a phasing maneuver.
Enhanced Detection of Foreign Objects on Molybdenum Conveyor Belt Based on Anchor-Free Image Recognition
Authors: Li Meng , Lu Caiwu , Yan Xuesong , He Runfeng , Zhao Xuyang
Published: August 2024

During the molybdenite mining process, conveyor belts stretching for miles are used to transport ore between the blasting sites, crushing stations, and the concentrator plant. In order to ensure the safety and stability of the industrial production process, this paper introduces a foreign matter detection method based on deep learning for the belt conveyor. Aiming at the problems of insufficient feature extraction capabilities in existing machine vision-based foreign body detection methods and poor detection accuracy due to imbalanced positive and negative samples, an improved foreign body detection method for anchorless frame-type metal mine belt conveyors is proposed. This method introduces atrous convolution in the pooling layer to increase the receptive field of feature extraction and improve the ability of extracting feature details of foreign objects. By optimizing the ratio of positive and negative samples in the training process, the overall loss function value of the algorithm is reduced to ensure the accuracy of foreign body recognition. Finally, the improved model is trained after enhancing and labeling the sample dataset. The experimental results show that the average mean accuracy of foreign body detection (MAP) is 90.9%, better than existing methods. It can be used as an effective new method for detecting foreign objects on molybdenum mine belt conveyors.
Research Prospects for the Optimization of Magneto-Optical Trap Parameters for Cold Atom Interferometers
Authors: Li Dongyi , Qin Fangjun , Xu Rui , Li An
Published: August 2024

This study examines parameter optimization for magneto-optical traps (MOTs) to increase trapping efficiency and improve cold atom interferometer performance. Operational principles of MOTs, control parameters, and performance metrics such as volume, atomic loading time, and resonance frequency are discussed. This research also reviews existing studies on the parameter optimization of MOTs, highlights challenges, and offers suggestions for future research. It proposes enhancing performance metrics, optimization techniques, and operational models to increase precision and practicality in parameter optimization for MOTs in cold atom interferometers.