bluuebunny/crossref_metadata_2025 huggingface dataset:
Sly-miR398 Participates in Heat Stress Tolerance in Tomato by Modulating ROS Accumulation and HSP Response
Authors: Li Baoyu , Wang Peiwen , Zhao Shuaijing , Dong Jiaqi , Mao Shengming , Zhu Xuyongjie , Yuan Tiantian , Qiu Haiying , Cao Long , Xu Yunmin , He Yong , Zhu Zhujun , Yan Guochao
Published: January 2025

Heat stress is one of the most important environmental problems in agriculture, which severely restricts the growth and yield of plants. In plants, microRNA398 (miR398) negatively regulates the activity of superoxide dismutase (SOD) by modulating the expression of its coding genes (CSDs) post-transcriptionally, thereby regulating reactive oxygen species (ROS) homeostasis and stress resistance. In this study, the role of miR398 in heat stress tolerance in tomato was investigated. Under heat stress, the expression of miR398 was upregulated in tomato, while the expression of its target genes (CSD1 and CSD2) and SOD activity was downregulated. Furthermore, by comparing the heat stress response in wild type (WT) and a transgenic line overexpressing MIR398 (miR398-OE), the results showed that overexpression of miR398 promoted tomato growth and the expression of genes encoding heat shock factor (HSF, transcription factor) and heat shock protein (HSP) under heat stress. Meanwhile, downregulated activity of antioxidant enzymes, including SOD, catalase (CAT), peroxidase (POD), and ascorbate peroxidase (APX), and enhanced ROS accumulation was observed in miR398-OE compared with that in WT under heat stress. Further study using dimethylthiourea (DMTU, a ROS scavenger) indicated that the enhanced plant growth and expression of HSFs/HSPs was based on the promoted accumulation of ROS in miR398-OE. Overall, the results of this study revealed that the upregulated expression of miR398 in response to heat stress would modulate the antioxidant system and enhance ROS accumulation, thereby enhancing the expression of HSFs and HSPs and heat stress tolerance in tomato.
Reproductive Performance, Inbreeding, and Genetic Diversity in Montbeliarde Dairy Cattle Obtained by Absorption Crossing
Authors: Cartuche-Macas Luis F. , Guaman Ilvay Oscar J. , Chacón Edilberto , Gutierrez-Reinoso Miguel A. , Garcia-Herreros Manuel
Published: January 2025

Montbeliarde dairy cattle were introduced in Ecuador with the aim of ameliorating the production performance of other cattle breeds. The aim was to analyze the reproductive performance, demographic structure, inbreeding, and genetic diversity of Montbeliarde cattle obtained by absorption crossing. Official pedigree records from the Ecuadorian Montbeliarde Association were used. The total population was divided into seven populations: (i) historical (all individuals), (ii) 1999–2003, (iii) 2004–2008, (iv) 2009–2013, (v) 2014–2018, (vi) 2019–2023 (current), and (vii) reference (individuals with known parents). Demographic structure variables analyzed: pedigree completeness index (PCI), number of equivalents (GEqu), complete (GCom) and maximum (GMax) generations, and generation interval (GI). Inbreeding-derived variables analyzed: inbreeding (F), inbreeding increment (ΔF), average relatedness (AR), co-ancestry (C), non-random mating (α), effective population size (Ne), and genetic conservation index (GCI). Gene origin probability/genetic diversity variables analyzed: number of founders (f), effective number of founders (fe) and ancestors (fa), number of equivalent genomes (fg), fe/fa and fg/fa ratio, and DG losses. The databases were analyzed by ENDOG, POPREP, and CPC software. PCI in the historical population compared to the current population decreased from 72.95 to 65.87% (sire/dam pathway), while the GI decreased from 7.17 to 3.08 years when the historical and current populations were compared. Dam reproductive efficiency increased over time. Moreover, F = ~1%, AR = 2.06%, ΔF = 0.22%, C = 1.03%, α = −0.0077, GCI = 3.12, and Ne = 58 values were obtained. Gene origin probability in the reference population was f = 439, fa = 37, fe = 71.64, fg = 23.43, and fg/fe = 0.33, showing a GD loss due to unequal contribution of founders (0.55%) and bottleneck and genetic drift (4.25%). In conclusion, the Ecuadorian Montbeliarde cattle population displayed a relatively low diversity and high genetic relationship. Inbreeding levels increased while Ne values decreased over time. The introduction of new purebred bloodlines could be important to minimize the inbreeding levels ensuring the long-term conservation of this breed and minimizing GD loss.
A Global Survey on the Perception of Conservationists Regarding Animal Consciousness
Authors: Yakhlef Valentine , Magalhães-Sant’Ana Manuel , Pereira Ana Luísa , Azevedo Alexandre
Published: January 2025

Human perceptions of animal consciousness have evolved over time, influencing how they are treated. This study aimed to assess perceptions of animal consciousness in people working in conservation. An online survey of 87 participants evaluated their perceptions of animals’ capabilities across 10 dimensions of consciousness, including self-consciousness, learning, and reasoning. The sum of the answers to the questions was validated as a “perception index” using a principal component analysis. Next, its variation according to taxonomic, demographic, professional factors, and the duration, type, and frequency of interaction with animals was assessed with generalized linear models and stepwise model selection. Participants’ perceptions varied with taxonomic class and the level of education, with mammals obtaining higher indexes than birds (−0.14; 95% confidence interval [CI] −0.24, −0.03, p < 0.01) or reptiles (−0.41; 95% CI −0.55, −0.27, p < 0.01), and respondents holding a PhD exhibiting higher perception indexes than those with BScs (−0.19; 95% CI −0.32, −0.06, p < 0.01) or lower education (−0.18; 95% CI −0.32, −0.03, p = 0.01). The attribution of consciousness followed a phylogenetic pattern, but several exceptions (e.g., the octopus and raven) were noted on a finer scale supporting a multifactorial influence on the perception of animal consciousness that emerges upon a baseline phylogenetic pattern. Finally, the results suggest that conservationists are influenced by culture and scientific knowledge, as much as their personal experiences, when evaluating animals’ perceptions, highlighting the need for further research on the convergence between perception, belief, and evidence.
From Metagenomes to Functional Expression of Resistance: floR Gene Diversity in Bacteria from Salmon Farms
Authors: Ortiz-Severín Javiera , Hojas Iñaki , Redin Felipe , Serón Ervin , Santana Jorge , Maass Alejandro , Cambiazo Verónica
Published: January 2025

Background. The increase in antibiotic resistance in human-impacted environments, such as coastal waters with aquaculture activity, is related to the widespread use of antibiotics, even at sub-lethal concentrations. In Chile, the world’s second largest producer of salmon, aquaculture is considered the main source of antibiotics in coastal waters. In this work, we aimed to characterize the genetic and phenotypic profiles of antibiotic resistance in bacterial communities from salmon farms. Methods. Bacterial metagenomes from an intensive aquaculture zone in southern Chile were sequenced, and the composition, abundance and sequence of antibiotic resistance genes (ARGs) were analyzed using assembled and raw read data. Total DNA from bacterial communities was used as a template to recover floR gene variants, which were tested by heterologous expression and functional characterization of phenicol resistance. Results. Prediction of ARGs in salmon farm metagenomes using more permissive parameters yielded significantly more results than the default Resistance Gene Identifier (RGI) software. ARGs grouped into drug classes showed similar abundance profiles to global ocean bacteria. The floR gene was the most abundant phenicol-resistance gene with the lowest gene counts, showing a conserved sequence although with variations from the reference floR. These differences were recovered by RGI prediction and, in greater depth, by mapping reads to the floR sequence using SNP base-calling. These variants were analyzed by heterologous expression, revealing the co-existence of high- and low-resistance sequences in the environmental bacteria. Conclusions. This study highlights the importance of combining metagenomic and phenotypic approaches to study the genetic variability in and evolution of antibiotic-resistant bacteria associated with salmon farms.
Follicular Fluid Vanin-1 Levels in Patients Undergoing Ivf: A Preliminary Study
Authors: Várnagy Ákos , Mauchart Péter , Nagy Gábor , Bódis József , Sulyok Endre
Published: January 2025

This preliminary study was designed to determine follicular fluid (FF) vanin-1 levels, to assess their relation to serum vanin-1 and to reveal their potential to predict the outcome of in vitro fertilization (IVF). Eighteen unselected, consecutive women undergoing IVF were included. Serum and pooled FF samples were obtained simultaneously during routine IVF procedures. Vanin-1 levels were measured by using commercially available ELISA kits. As most of the values were below 0.6 ng/mL, the data are given as optical density. It was found that vanin-1 can be detected in FF and that it is not significantly related to its maternal serum levels (p = 0.06). FF vanin-1 levels proved to be higher in non-pregnant as compared to pregnant women (p < 0.04). There are significant positive relationships between the FF to serum vanin-1 ratio and body mass index (BMI, p < 0.02), anti-Müllerian hormone (AMH, p < 0.02) and baseline serum estradiol (p < 0.01). Moreover, the FF/serum vanin-1 ratio tended to increase with cumulative FSH dose, but this increase did not reach statistical significance (p = 0.064). It may be concluded that FF vanin-1 may serve as a biomarker to predict IVF outcome. To confirm this contention, further studies are to be performed.
The Enhanced Growth Performance and Antioxidant Capacity of Juvenile Procambarus clarkii Fed with Microbial Antioxidants
Authors: Cheng Zeyi , Shi Jie , Qian Chen , Li Jinghao , Wu Xugan , Kong Ieong , Li Jiayao
Published: January 2025

Given the economic significance of Procambarus clarkii in freshwater aquaculture and the lack of microbial antioxidants in Procambarus clarkii diet research, this study aimed to investigate the optimal supplementation level and feeding duration of microbial antioxidants in Procambarus clarkii diets. A series of three experiments were conducted to assess the long-term effects of different MA levels on crayfish and evaluate the palatability of the diets by observing feeding behavior and examining the short-term effects of high levels of MA. Our results indicate that long-term feeding using 1.5% MAs markedly increased the activities of antioxidant enzymes (T-AOC, T-SOD, and GSH-PX) and decreased the malondialdehyde (MDA) content in the hepatopancreas and hemolymph, with the crayfish showing significantly higher survival rates due to better antioxidant capacity after 24 h of air exposure stress. Under the condition of long-term feeding, the appropriate level of addition of MAs that can promote the growth of crayfish is 0.62–0.66%. The feeding behavior results indicate that the lower willingness and food intake of the crayfish in the high MA group may be the main reason affecting their growth. Conversely, short-term feeding using MAs alleviated the adverse effects on growth associated with the reduced palatability of the diet. The results indicate that the inclusion of 1.5% MAs in the diet for a period of 21 d optimized crayfish growth, accompanied by an improvement in antioxidant capacity and survival during transportation. This study demonstrates that diets supplemented with microbial antioxidants (MAs) can improve growth performance, antioxidant capacity, and resistance to air exposure stress in Procambarus clarkii. These results provide valuable insights into the potential benefits of MA supplementation in crayfish aquaculture.
Evaluation of Photovoltaic Inverters According to Output Current Distortion in a Steady-State and Maximum Power Point Tracking
Authors: Dimitrijević Marko , Petronijević Milutin , Klimenta Dardan
Published: January 2025

The limits of direct current (DC) injection and output current distortion of grid-connected photovoltaic (PV) inverters are specified in the IEEE 1547-2018 standard. The standard prescribes limits of output current harmonics, but the input voltage and power at which output current distortion is measured are not specified. This manuscript presents the results of DC injection and output current distortion measurements for three commercial single-phase PV inverters, with 3 kVA, 3.3 kVA, and 6 kVA rated power. During the measurements, the inverters are powered by a programmable DC source that emulates the power voltage characteristic of a PV array, providing different input conditions. In addition to steady-state measurements at constant input voltage and power, the change in the output current spectrum over time during the maximum power point tracking (MPPT) is also measured. The results show that the output current distortion depends on the input voltage and power. Moreover, the current distortion of some of the tested inverters exceeds the limits specified by the standard in some cases. The presented results suggest that further research on the dependence of the output current distortion from PV inverters on their input power and voltage is needed.
Multi-Agent Reinforcement Learning-Based Routing and Scheduling Models in Time-Sensitive Networking for Internet of Vehicles Communications Between Transportation Field Cabinets
Authors: Garcia-Cantón Sergi , Ruiz de Mendoza Carlos , Cervelló-Pastor Cristina , Sallent Sebastià
Published: January 2025

Future autonomous vehicles will interact with traffic infrastructure through roadside units (RSUs) directly connected to transportation field cabinets (TFCs). These TFCs must be interconnected to share traffic information, enabling infrastructure-to-infrastructure (I2I) communications that are reliable, synchronous and capable of transmitting vehicle data to the Internet. However, I2I communications present a complex optimization challenge. This study addresses this by proposing the design, implementation, and evaluation of an automated management model for I2I service channels based on multi-agent reinforcement learning (MARL) integrated with deep reinforcement learning (DRL). The proposed models efficiently manage the routing and scheduling of data frames between internet of vehicles (IoV) infrastructure devices through time-sensitive networking (TSN) to ensure real-time synchronous I2I communications. The solution incorporates both a routing model and a scheduling model, evaluated in a simulated shared environment where agents operate within the TSN control plane. Both models are tested for different topologies and background traffic levels. The results demonstrate that the models establish the majority of paths in the scenario, adhering to near-optimal routing and scheduling policies. Recursively, for each individual request to create a service channel, the system establishes online an optimal synchronous path between entities with a limited time budget. In total, 71% of optimal routing paths are established and 97% of optimal schedules are achieved. The approach takes into account the periodic nature of the transmitted data and its robustness through TSN networks, obtaining 99 percent of compliant service requests with flow jitter levels below 100 microseconds for different topologies and different network utility percentages. The proposed solution achieves lower execution delays compared to the iterative ILP approach. Additionally, the solution facilitates the integration of 5G networks for vehicle-to-infrastructure (V2I) communications, which is identified as an area for future exploration.
Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset
Authors: Ormeño-Arriagada Pablo , Márquez Gastón , Araya David , Rimassa Carla , Taramasco Carla
Published: January 2025

Reliably detecting COVID-19 is critical for diagnosis and disease control. However, imbalanced data in medical datasets pose significant challenges for machine learning models, leading to bias and poor generalization. The dataset obtained from the EPIVIGILA system and the Chilean Epidemiological Surveillance Process contains information on over 6,000,000 patients, but, like many current datasets, it suffers from class imbalance. To address this issue, we applied various machine learning algorithms, both with and without sampling methods, and compared them using different classification and diagnostic metrics such as precision, sensitivity, specificity, likelihood ratio positive, and diagnostic odds ratio. Our results showed that applying sampling methods to this dataset improved the metric values and contributed to models with better generalization. Effectively managing imbalanced data is crucial for reliable COVID-19 diagnosis. This study enhances the understanding of how machine learning techniques can improve diagnostic reliability and contribute to better patient outcomes.
Life Cycle Assessment of Proofing Test Production on Printing Surfaces with Use of Carbon Footprint Methodology
Authors: Nogacki Jacek , Buschmann Urban , Krystosiak Krzysztof , Żołek-Tryznowska Zuzanna
Published: January 2025

This study represents a pioneering initiative in the printing industry, especially in Poland, which assessed the environmental impacts and eco-efficiency of proof printing through the life cycle assessment (LCA) methodology. The process of proof printing on a target substrate was compared with the traditional hard proofing process, which requires trial printing in production conditions. The analysis adhered to the ISO 14040 and 14044 standards, assessing greenhouse gas (GHG) emissions, raw material use (e.g., plastics, water), and environmental toxicity. The innovative proofing on the target substrate process exhibits a lower environmental impact, as confirmed by the GHG emissions and plastic and water demand of the process. The GHG emissions were reduced from 2610 kg of CO2e to 68.4 kg of CO2e per functional unit (FU). The water demand for the proofing on the target substrate process was 40 times lower, and the plastic demand was also 40 times lower, decreasing to 20 kg per FU. The toxicity impact of the method based on the proofing system on the target substrate on living organisms is more than six times lower than that of the conventional method. The proof printing on the target substrate process offers an environmentally friendly alternative to the traditional hard proofing process, with lower GHG emissions and a lower environmental impact.
Analysis of the Correlation of Microstructure, Instrumental Texture, and Consumer Acceptance of Shortbread Biscuits with Selected Sweeteners and Fibre
Authors: Marzec Agata , Stępień Alicja , Goclik Agnieszka , Kowalska Hanna , Kowalska Jolanta , Salamon Agnieszka
Published: January 2025

Biscuits are characterized by their popular sweet taste, but they have a poor nutritional profile due to their high sugar and saturated fat content, along with low fibre levels. Their sweetness primarily comes from sucrose, which not only determines the flavour but also performs several technological functions, making it difficult to replace in pastry products. Commercial sweeteners and soluble fibres designed for pastry products are available. Therefore, it is necessary to test the feasibility of using these ingredients in biscuit formulations and assess their impact on biscuit quality. Concurrently, the correlation analysis of dough rheological parameters, structure, and instrumental texture parameters with sensory characteristics will help identify which parameters are strongly correlated and can be used to predict biscuit quality. The purpose of this study was to investigate the dough rheological properties, structure, texture, and sensory characteristics of biscuits in which sucrose was replaced by the commercial sweeteners Tagatesse, maltitol, and erythritol–stevia, with the addition of soluble fibres Nutriose® FB (wheat fibre) and PromOat 35 (oat fibre). At the same time, a correlation analysis was conducted between dough rheological parameters (stickiness, work of adhesion, dough strength) and biscuit quality parameters, such as water activity, water content, colour, texture (pore area, pore shape, pore elongation), and instrumental texture properties (hardness, brittleness, number of acoustic emission (AE) events, AE event energy), with sensory discrimination evaluated through a consumer test. The use of wheat and oat fibres in combination with sucrose resulted in biscuits with lower apparent density, increased porosity, and weaker texture (fracturability, hardness, number of AE events), yet they had better sensory properties compared to biscuits containing sucrose alone. Replacing sucrose with sweeteners combined with fibres led to a deterioration in the sensory quality of the biscuits and a significant change in the dough’s rheological properties. Regardless of the type of sweetener, biscuits with wheat fibre were rated better than those with oat fibre. Of the tested sweeteners, only maltitol combined with wheat fibre resulted in a sensory quality similar to that of sucrose biscuits. Correlation analysis of all measured biscuit quality parameters showed that only the number of AE events had a strong positive correlation with all tested sensory attributes. Porosity was only correlated with sensory crispness, and fracturability was correlated with sweetness, taste, and overall acceptability. Therefore, it appears that the number of AE events recorded at the time of breaking may be a reliable parameter for predicting biscuit quality.
Carbon Monoxide Concentration in the Garage of a Single-Family House—Experiment and One-Dimensional Model of Carbon Monoxide Concentration
Authors: Teleszewski Tomasz Janusz , Gładyszewska-Fiedoruk Katarzyna
Published: January 2025

The paper presents a number of tests of the carbon monoxide concentration in a single-car garage equipped with exhaust ventilation, while the combustion engine of a parked passenger car is operating. The main source of carbon monoxide in the garage is the internal combustion engine of a passenger car. Single-car garages are characterized by a relatively small volume, which causes a rapid accumulation of carbon monoxide inside the garage. The aim of this publication is to present the results of research on carbon monoxide concentration in a single-family building garage with the combustion engine in a passenger car running and at various air exchanges in the garage. The test results showed that the permissible values (WHO, NAAQS) of carbon monoxide concentrations were significantly exceeded, both with the exhaust ventilation switched on and off. The highest carbon monoxide concentration values (2253 ppm) in the garage were observed when the exhaust ventilation was turned off. The study also developed two one-dimensional models of carbon monoxide concentrations in a garage with the combustion engine of a passenger car turned on, with the exhaust ventilation turned on and off. The models developed can be used in ventilation design to estimate the carbon monoxide concentrations in garages, based on the type of car and the number of air changes.
Application of Artificial Intelligence as an Aid for the Correction of the Objective Structured Clinical Examination (OSCE)
Authors: Luordo Davide , Torres Arrese Marta , Tristán Calvo Cristina , Shani Shani Kirti Dayal , Rodríguez Cruz Luis Miguel , García Sánchez Francisco Javier , Lagares Gómez-Abascal Alfonso , Rubio García Rafael , Delgado Jiménez Juan , Pérez Carreras Mercedes , Diez Lobato Ramiro , Granizo Martínez Juan José , Tung-Chen Yale , Villena Garrido Mª Victoria
Published: January 2025

The assessment of clinical competencies is essential in medical training, and the Objective Structured Clinical Examination (OSCE) is an essential tool in this process. There are multiple studies exploring the usefulness of artificial intelligence (AI) in medical education. This study explored the use of the GPT-4 AI model to grade clinical reports written by students during the OSCE at the Teaching Unit of the 12 de Octubre and Infanta Cristina University Hospitals, part of the Faculty of Medicine at the Complutense University of Madrid, comparing its results with those of human graders. Ninety-six (96) students participated, and their reports were evaluated by two experts, an inexperienced grader, and the AI using a checklist designed during the OSCE planning by the teaching team. The results show a significant correlation between the AI and human graders (ICC = 0.77 for single measures and 0.91 for average measures). AI was more stringent, assigning scores on an average of 3.51 points lower (t = −15.358, p < 0.001); its correction was considerably faster, completing the analysis in only 24 min compared to the 2–4 h required by human graders. These results suggest that AI could be a promising tool to enhance efficiency and objectivity in OSCE grading.
Insights from Optimized Non-Landslide Sampling and SHAP Explainability for Landslide Susceptibility Prediction
Authors: Li Mengyuan , Tian Hongling
Published: January 2025

The quality of sampling data critically influences landslide susceptibility prediction accuracy. Current studies commonly use a 1:1 ratio of landslide to non-landslide samples, failing to reflect natural geographical variability. This study develops a region-specific framework by integrating SHAP (SHapley Additive exPlanation) analysis with twelve landslide conditioning factors (LCFs) and three progressive sampling strategies, aiming to create adaptive non-landslide point selection criteria tailored to unique environmental and geological characteristics. The strategies include (1) multi-ratio random sampling (1:1 to 1:200), (2) susceptibility-based sampling adjustments derived from pre-susceptibility analysis, and (3) LCF-based correction using the NDVI threshold identified through SHAP analysis. Results show that LCF-based correction achieved the highest performance, while a 1:5 ratio proved optimal in random sampling, aligning with regional characteristics. This framework demonstrates the importance of region-specific sampling strategies in improving landslide susceptibility prediction.
Does Higher Maturation Make Age-Grouped Swimmers Faster? A Study on Pubertal Female Swimmers
Authors: Sokołowski Kamil , Krężałek Piotr , Wądrzyk Łukasz , Żegleń Magdalena , Strzała Marek
Published: January 2025

Background: The main aim of this study was to identify the differences between subgroups of swimmers based on physiological (peak oxygen uptake—VO2peak), strength (average tethered swimming force—60Fave), stroke kinematics (v100—swimming speed at 100 m front crawl, stroke rate—SR, stroke length—SL), and anthropometrical (i.e.,: biological age—BA, body height—BH, body mass—BM) factors within swimmers at different levels of maturity (BA). Methods: This study involved 39 female swimmers (age: 12.88 ± 0.54 years, BA: 13.98 ± 1.91 years). Cluster analysis (k-cluster) and stepwise multiple regression was performed. Results: Significant correlations were observed between v100 and BA, 60Fave, AS, VO2peak. Stepwise multiple regression indicated 60Fave and VO2peak as the main explanatory variables of v100 (R2 = 0.60, p < 0.0001). Cluster analysis allowed us to distinguish three groups of swimmers, differing in BA (cluster 1: 14.07 ± 0.96 years, cluster 2: 17.05 ± 1.53, cluster 3: 11.94 ± 0.95) and v100, as well as in BH, FFM, AS. Conclusions: There were differences between cluster groups, with early mature swimmers characterized by the highest BH, FFM, AS, 60Fave, and VO2peak. Probably, biologically younger late mature swimmers (cluster 3) are slower than the other 2 groups (cluster 1 and 2) because of being less somatically developed. Based on these study results, coaches should ensure further development of aerobic and anaerobic conditioning among normal mature swimmers with simultaneous focus on improving technique skills among early mature ones.
A Combined High and Low Cycle Fatigue Life Prediction Model for Wind Turbine Blades
Authors: Li Miaomiao , Gao Jianxiong , Zhou Jianxing
Published: January 2025

A novel method is proposed for a combined high and low cycle fatigue (CCF) life prediction model based on Miner’s rule, incorporating load interactions and coupled damage effects to evaluate the fatigue life of wind turbine blades under CCF loading. The method refines the CCF damage curve by modeling the complex damage evolution process under L-H loading and establishes a life prediction model linking low cycle fatigue (LCF) and high cycle fatigue (HCF) damage curves for more accurate predictions. Compared to Miner’s rule, the M-H model, and the T-K model, the proposed approach demonstrates superior prediction accuracy, with results predominantly falling within a life factor of ±1.5. To verify the model’s practical applicability, finite element analysis (FEA) was performed on critical blade sections, reducing the prediction error to 4.3%. This method introduces a novel approach for evaluating the fatigue life of wind turbine blades with improved accuracy over existing methods.
Detection of Pipe Ruptures in Shipboard Firefighting Systems Using Machine Learning and Deep Learning Techniques
Authors: Ferreno-Gonzalez Sara , Diaz-Casas Vicente , Miguez-Gonzalez Marcos , San-Gabino Carlos G.
Published: January 2025

In this paper, the application of machine learning and deep learning algorithms for fault and failure detection in maritime systems is examined, specifically focusing on the detection of pipe ruptures in a ship’s saltwater firefighting (FiFi) system using pressure sensor data. Neural network models were developed to distinguish between normal operational states and anomalies, as well as to accurately locate pipe faults within the ship. Data were collected using real-world tests with FiFi system sensors, capturing both normal operations and simulated pipe ruptures, and were meticulously labeled to facilitate neural network training. Two neural network models were introduced: one for classifying system states (normal or anomalous) based on time-series pressure data, and another for identifying the location of anomalies related to pipe ruptures. Experimental results demonstrated the efficacy of these models in detecting and localizing pipe faults, with performance evaluated using mean squared error (MSE) across different network configurations. The successful implementation of these machine learning and deep learning algorithms highlights their potential for enhancing maritime safety and operational efficiency.
Molybdenite Re–Os and Zircon U–Pb Isotopic Constraints on Gold Mineralization Associated with Fine-Grained Granite in the Xiawolong Deposit, Jiaodong Peninsula, East China
Authors: Wu Mingchao , Wang Zhongliang , Liu Pengyu
Published: January 2025

Molybdenite Re–Os and zircon U–Pb isotopic data are first obtained from the stockwork and disseminated-style gold-bearing ores and the fine-grained granite hosting these ores in the Xiawolong gold mine, respectively, which is located within the Muping–Rushan gold metallogenic belt, eastern Jiaodong Peninsula, so as to illustrate the genesis of gold mineralization and its implication for exploration. Four molybdenite samples yield a well-defined Re–Os isochron age of 118.4 ± 2.5 Ma (2σ), which is identical to the weighted average Re–Os model age of 118 ± 1.7 Ma (2σ). Integration of the new geochronologic data with those reported recently from the other gold mines in the Muping–Rushan gold metallogenic belt suggests that a discrete gold event occurred in Xiawolong ca. 4 m.y. older than that for the other gold mineralization at ca. 114 Ma in eastern Jiaodong. In addition, two fine-grained granite samples, measured using the LA-ICP-MS zircon U–Pb method, produce the first precise ages of 118 ± 2 to 117 ± 2 Ma (2σ), identical to the molybdenite Re–Os ages, within the margin of error and obtained in this study. The fine-grained granite has a similar lithology and emplacement age as those of the medium-grained monzogranite consisting of the marginal facies of the Sanfoshan batholith, and is considered to be the crystallization products of Sanfoshan granitic magma in the late stage. Combined with the previous S-Pb-D-O isotope, fluid inclusion and geological studies, which suggest that the ore-forming fluid of Xiawolong gold mineralization is from magmatic water, and the identification that the magnetite coexists with the gold-bearing pyrite and molybdenite in the gold ores, which indicates a high oxygen fugacity (fO2) of both the magma and resultant hydrothermal fluids, it is logical to infer that the Xiawolong gold deposit is genetically in relation to the Sanfoshan granitic magmatism, which is high in fO2 and rich in Au at the magmatic–hydrothermal transition stage, and the change in fO2 mostly likely makes a significant contribution to the precipitation of Au. This result reveals that the late-stage granitic magma with high fO2, which is crystallized into the fine-grained granite, probably is also rich in Au, except the W–Mo–Cu–Zn–U–Be–Li–Nb–Ta–Sn–Bi-elements. Therefore, based on the extensional tectonic regime for the early Cretaceous Jiaodong gold deposits, we propose that gold exploration in the Jiaodong should not only focus on the fault-hosted Au but also on the fine-grained granite-hosted Au around the apical portions of the late Early Cretaceous small-granitic intrusions with high fO2. This model could also be important for prospecting in other gold ore districts, which have a similar tectonic setting.
A Python-Based Automation Script to Mark Computer-Aided Design Assessments
Authors: Tierney Gregory
Published: January 2025

The integration of automated tools in engineering education has the potential to improve student assessments, ensuring consistency and reducing instructor workload. This study introduces a Python-based automation tool designed to evaluate student Computer-Aided Design (CAD) submissions. The tool utilises software API and Open Cascade library to calculate model parameters. These parameters are compared against expected values from a solution file and marks are assigned based on deviations relative to the solution file. As a use case, seventy-five Solid Edge CAD files were assessed for geometric properties such as volume, surface area, and centre of gravity location to evaluate inter- and intra-marker reliability. The results showed perfect agreement, with a Cohen kappa of 1.0 for both metrics. Furthermore, the automated tool reduced grading time by 89.7% compared to manual evaluation. The potential of automation in improving marking efficiency, consistency, and objectivity in engineering education has been shown, providing a foundation for further integration of software. The python-based automation script is openly available on GitHub.
A (Comprehensive) Review of the Application of Quantitative Structure–Activity Relationship (QSAR) in the Prediction of New Compounds with Anti-Breast Cancer Activity
Authors: Vasilev Boris , Atanasova Mariyana
Published: January 2025

Computational approaches applied in drug discovery have advanced significantly over the past few decades. These techniques are commonly grouped under the term “computer-aided drug design” (CADD) and are now considered one of the key pillars of pharmaceutical discovery pipelines in both academic and industrial settings. In this work, we review Quantitative Structure–Activity Relationships (QSARs), one of the most used ligand-based drug design (LBDD) methods, with a focus on its application in the discovery and development of anti-breast cancer drugs. Critical steps in the QSAR methodology, essential for its correct application—but often overlooked, leading to insignificant or misleading models—are examined. Additionally, current anti-breast cancer treatment strategies were briefly overviewed, along with some targets for future treatments. The review covers QSAR studies from the past five years and includes a discussion of notable works that could serve as models for future applications of this interdisciplinary and complex method and that may help in feature drug design and development.
An Artificial Intelligence Home Monitoring System That Uses CNN and LSTM and Is Based on the Android Studio Development Platform
Authors: Sung Guo-Ming , Kohale Sachin D. , Chiang Te-Hui , Chong Yu-Jie
Published: January 2025

This paper developed an artificial intelligence home environment monitoring system by using the Android Studio development platform. A database was constructed within a server to store sensor data. The proposed system comprises multiple sensors, a message queueing telemetry transport (MQTT) communication protocol, cloud data storage and computation, and end device control. A mobile application was developed using MongoDB software, which is a file-oriented NoSQL database management system developed using C++. This system represents a new database for processing big sensor data. The k-nearest neighbor (KNN) algorithm was used to impute missing data. Node-RED development software was used within the server as a data-receiving, storage, and computing environment that is convenient to manage and maintain. Data on indoor temperature, humidity, and carbon dioxide concentrations are transmitted to a mobile phone application through the MQTT communication protocol for real-time display and monitoring. The system can control a fan or warning light through the mobile application to maintain ambient temperature inside the house and to warn users of emergencies. A long short-term memory (LSTM) model and a convolutional neural network (CNN) model were used to predict indoor temperature, humidity, and carbon dioxide concentrations. Average relative errors in the predicted values of humidity and carbon dioxide concentration were approximately 0.0415% and 0.134%, respectively, for data storage using the KNN algorithm. For indoor temperature prediction, the LSTM model had a mean absolute percentage error of 0.180% and a root-mean-squared error of 0.042 °C. The CNN–LSTM model had a mean absolute percentage error of 1.370% and a root-mean-squared error of 0.117 °C.
Deep Autoencoder Framework for Classifying Damage Mechanisms in Repaired CFRP
Authors: Barile Claudia , Casavola Caterina , Katamba Mpoyi Dany , Pappalettera Giovanni
Published: January 2025

This study investigates the classification of damage modes in adhesively bonded carbon fiber-reinforced plastic (CFRP) composites, a critical factor in advancing lightweight automotive design. Adhesive bonding, replacing traditional riveting, improves structural integrity while reducing weight and CO2 emissions. Mechanical testing on CFRP composites was performed, and acoustic emission (AE) signals were collected to evaluate damage mechanisms. A deep autoencoder (DAE) framework was developed to automate damage characterization by reducing AE signal dimensionality through singular value decomposition (SVD) and classifying features using the k-means algorithm. This approach effectively identified three primary damage modes: matrix cracking, interfacial debonding, and fiber breakage. Traditional AE features, such as entropy and amplitude were also classified and validated using spectral analysis. The DAE-based strategy demonstrated superior capability in real-time damage mode differentiation. Fractographic analysis confirmed crack growth in the adhesive layer, leading to interfacial debonding, fiber-matrix separation, and eventual fiber rupture. These findings highlight the DAE framework’s effectiveness in enhancing damage mode characterization, offering valuable insights for optimizing the structural performance of bonded CFRP composites in automotive applications.
A Microscopic Traffic Model to Investigate the Effect of Connected Autonomous Vehicles at Bottlenecks and the Impact of Cyberattacks
Authors: Ali Faryal , Khan Zawar Hussain , Gulliver Thomas Aaron , Khattak Khurram Shehzad , Altamimi Ahmed B.
Published: January 2025

Bottlenecks reduce both traffic safety and efficiency, resulting in congestion and collisions. The introduction of connected autonomous vehicles (CAVs) has had a significant impact on road networks and can improve traffic efficiency at bottlenecks. This paper proposes a microscopic traffic model to investigate CAV behavior at bottlenecks and examine the effect of cyberattacks. The model is developed using data collected from a roadside sensor node. It is implemented in MATLAB using the Euler scheme to simulate a platoon of vehicles on a circular road of length 1 km. The performance is compared with the intelligent driver (ID) model. The results obtained indicate that the road capacity with the proposed model is 1.4 times higher than with the ID model. Further, the proposed model results in nearly constant speeds with small variations, which is realistic. Conversely, the ID model produces large speed variations that are unrealistic. In addition, the proposed model results in less acceleration and deceleration, which leads to lower vehicle emissions and pollution. The efficiency is better than with the ID model due to CAV communication and coordination, so queues dissipate faster. The traffic flow with the proposed model increases as the density decreases, which is consistent with traffic dynamics. It is also shown that the proposed model can characterize CAV behavior under cyberattacks that cause disruptions in the data. Thus, it can be employed for traffic control and forecasting when bottleneck conditions exist and there is malicious behavior.
A Lightweight Deep Learning Model for Forecasting the Fishing Ground of Purpleback Flying Squid (Sthenoteuthis oualaniensis) in the Northwest Indian Ocean
Authors: Zhang Shengmao , Chen Junlin , Han Haibin , Tang Fenghua , Cui Xuesen , Shi Yongchuang
Published: January 2025

The purpleback flying squid (Sthenoteuthis oualaniensis) is an economically significant cephalopod species in the Northwest Indian Ocean. Predicting its fishing grounds can provide a crucial foundation for fishery management and production. In this research, we collected data from China’s light-purse seine fishery in the Northwest Indian Ocean from 2016 to 2020 to train and validate the AlexNet and VGG11 models. We designed a data partitioning method (DPM) to divide the training set into three scenarios, namely DPM-S1, DPM-S2, and DPM-S3. Firstly, DPM-S1 was employed to select the base model (BM). Subsequently, the optimal BM was lightweighted to obtain the optimal model (OM). The OM, known as the AlexNetMini model, has a model size that is one-third of that of the BM-AlexNet model. Our results also showed the following: (1) the F1-scores for AlexNet and AlexNetMini across the datasets DPM-S1, -S2, and -S3 were 0.6957, 0.7505, and 0.7430 for AlexNet and 0.6992, 0.7495, and 0.7486 for AlexNetMini, suggesting that both models exhibited comparable predictive performance; (2) the optimal dropout values for the AlexNetMini model were 0 and 0.2, and the optimal training set proportion was 0.8; (3) AlexNetMini utilized both DPM-S2 and DPM-S3, yielding comparable outcomes. However, given that the training duration for DPM-S3 was relatively shorter, DPM-S3 was selected as the preferred method for data partitioning. The findings of our study indicated that the lightweight model for the purpleback flying squid fishing ground prediction, specifically AlexNetMini, demonstrated superior performance compared to the original AlexNet model, particularly in terms of efficiency. Our study on the lightweight method for deep learning models provided a reference for enhancing the usability of deep learning in fisheries.
An Experimental Pilot Approach to Evaluate the Infrastructure Accessibility and Level of Service at Metro Station Platforms
Authors: Seriani Sebastian , Aprigliano Vicente , Peña Alvaro , Gonzalez Shirley , Arredondo Bernardo , Bastías Iván , Bustos Emilio , Requesens Jose , Lopez Ariel , Fujiyama Taku
Published: January 2025

Metro stations are essential for daily commuting, but overcrowding due to increased demand can severely impact infrastructure quality and passenger experience. The Level of Service (LOS), a key indicator of congestion, is influenced by factors such as density, flow, and speed, and poor LOS leads to issues like longer boarding times, overcrowded platforms, and reduced accessibility, especially for vulnerable populations. To address these challenges, the study explores innovative solutions to improve platform design infrastructure for better accessibility and LOS, aligning sustainable development goals to create safer, more inclusive transport systems. This study presents two strategies designed to reduce passenger congestion at Francia station on the Valparaíso metro platform infrastructures. The strategies, tested in experimental scenarios, showed minimal differences in boarding and alighting times, with less than a one-second average variation between the two. However, survey results revealed that passengers preferred the strategy “let passengers alight before boarding the train”, as it provided greater comfort and accessibility, reducing the number of passengers per door and improving the LOS from level C to B. Despite the minimal intervention in the experiments, the results suggest potential operational improvements. Future research will focus on measuring passengers’ emotional responses using psychophysiological data to further evaluate the suitability of the proposed strategies.
Comprehensive Probabilistic Analysis and Practical Implications of Rainfall Distribution in Pakistan
Authors: Haseeb Fahad , Ali Shahid , Ahmed Naveed , Alarifi Nassir , Youssef Youssef M.
Published: January 2025

Accurately selecting an appropriate probability distribution model is a critical challenge when predicting extreme rainfall in arid and semi-arid regions, especially in countries with diverse climatic conditions. This study presents a comprehensive methodology for evaluating rainfall probability distributions across Pakistan, and aims to create a probabilistic zoning map that could serve as a valuable resource to inform the development of strategies for efficient water resource management and improved flood resilience in diverse climatic and geographic conditions. Precipitation data from the Pakistan Meteorological Department (PMD) over 42 years were compared with CHIRPS, confirming their accuracy. Nine probability distributions were assessed, with five models—log Pearson type-III (LP3), Weibull (W2), log normal (LN2), Generalized Extreme Value (GEV), and gamma (GAM)—deemed most suitable for the region’s climatic variability. The spatial applicability of these distributions was identified as follows: LP3 (30%), LN2 (30%), W2 (15%), GEV (10%), and GAM (15%). The central and southern regions of Punjab were predominantly characterized by LN2, while GAM was prevalent in the coastal areas of Sindh. Balochistan exhibited a heterogeneous distribution of W2, LP3, and LN2, while the mountainous Gilgit-Baltistan region was exclusively associated with GEV. Khyber Pakhtunkhwa demonstrated a mix of GEV and LP3 distributions. Beyond provincial variations, distinct patterns emerged: GEV dominated high-altitude, cold-temperate areas; LP3 was common in mountainous regions with variable temperature profiles; and W2 was prevalent along the flood-prone Indus River. This study provides a robust framework for region-specific disaster preparedness and contributes to sustainable development initiatives by offering tailored strategies for managing extreme rainfall events across Pakistan’s diverse climatic zones.