Search Results (23,553)

This paper addresses the critical challenge of selecting suitable pile foundations in port engineering by systematically investigating the axial bearing behavior of large-diameter steel pipe piles and prestressed high-strength concrete (PHC) piles. The study integrates both numerical simulations and field tests within the context of the Yancheng Dafeng Port Security Facilities Project. A self-balanced static load numerical model for PHC piles was developed using Plaxis 3D, enabling the simulation of load-displacement responses, axial force transfer, and side resistance distribution. The accuracy of the model was verified through a comparison with field static load test data. With the verified model parameters, the internal force distribution of steel pipe piles was analysed by modifying material properties and adjusting boundary conditions. A comparative analysis of the two pile types was conducted under identical working conditions. The results reveal that the ultimate bearing capacities of the 1# steel pipe pile and the 2# PHC pile are 6734 kN and 6788 kN, respectively. Despite the PHC pile having a 20% larger diameter, its ultimate bearing capacity is comparable to that of the steel pipe pile, suggesting a more efficient utilisation of material strength in the latter. Further numerical simulations indicate that, under the same working conditions, the ultimate bearing capacity of the steel pipe pile exceeds that of the PHC pile by 18.43%. Additionally, the axial force distribution along the steel pipe pile shaft is more uniform, and side resistance is mobilised more effectively. The reduction in side resistance caused by construction disturbances, combined with the slenderness ratio (L/D = 41.7) of the PHC pile, results in 33.87% of the pile’s total bearing capacity being attributed to tip resistance. The findings of this study provide crucial insights into the selection of optimal pile types for terminal foundations, considering factors such as bearing capacity, environmental conditions, and economic viability. Full article

The aim of this study is to investigate the oscillatory behaviour of a new class of third-order advanced differential equations in their noncanonical form. By employing newly refined transformations, the noncanonical equation is converted into its canonical form. This transformation reduces the number of possible nonoscillatory solution categories from four to two. The present study is based on a thorough and comprehensive review of existing literature and introduces new oscillation criteria by the comparison principle and other analytical techniques. These criteria provide sufficient conditions for the oscillation of solutions without imposing additional restrictive assumptions. The validity and significance of the obtained results are demonstrated through illustrative examples. Full article

Stress in pigs poses significant challenges to animal welfare and productivity in modern pig farming, contributing to increased antimicrobial use and the rise of antimicrobial resistance (AMR). This study involves stress classification in pregnant sows by exploring five deep learning models: ConvNeXt, EfficientNet_V2, MobileNet_V3, RegNet, and Vision Transformer (ViT). These models are used for stress detection from facial images, leveraging an expanded dataset. A facial image dataset of sows was collected at Scotland’s Rural College (SRUC) and the images were categorized into primiparous Low-Stressed (LS) and High-Stress (HS) groups based on expert behavioural assessments and cortisol level analysis. The selected deep learning models were then trained on this enriched dataset and their performance was evaluated using cross-validation on unseen data. The Vision Transformer (ViT) model outperformed the others across the dataset of annotated facial images, achieving an average accuracy of 0.75, an F1 score of 0.78 for high-stress detection, and consistent batch-level performance (up to 0.88 F1 score). These findings highlight the efficacy of transformer-based models for automated stress detection in sows, supporting early intervention strategies to enhance welfare, optimize productivity, and mitigate AMR risks in livestock production. Full article

Although particulate matter (PM) pollution from vehicles’ exhaust has decreased significantly over the years, the contribution from non-exhaust sources (brakes, tyres) has remained at the same levels. In the European Union (EU), Euro 7 regulation introduced PM limits for vehicles’ brake systems. Regenerative braking, i.e., recuperation of the deceleration kinetic and potential energy to the vehicle battery, is one of the strategies to reduce the brake emission levels and improve vehicle efficiency. According to the regulation, the shares of friction and regenerative braking can be determined with actual testing of the vehicle on a chassis dynamometer. In this study we tested the regenerative capabilities of a plug-in hybrid vehicle, both in the laboratory and on the road, under different protocols (including both smooth and aggressive braking) and covering a wide range of driving conditions (urban, rural, motorway) over 10,000 km of driving. Good agreement was obtained between laboratory and on-road tests, with the use of the friction brakes being on average 7% and 5.3%, respectively. However, at the same time it was demonstrated that the friction braking share can vary over a wide range (up to around 30%), depending on the driver’s behaviour. Full article

The sustainable integration of technology into educational practices is pivotal for modern teaching and learning. Grounded in the Technology Acceptance Model (TAM), this study explores the psychological and contextual factors that influence technology acceptance among pre-service and in-service teachers. Employing a nonexperimental, cross-sectional design, data were collected from 347 participants to examine the relationships between perceived usefulness, perceived ease of use, attitude toward use, behavioural intention, and actual system use. Results indicate that pre-service teachers demonstrate stronger openness to technology adoption, driven primarily by attitudinal factors, whereas in-service teachers’ acceptance is more closely linked to perceived utility and usability. This study advances the TAM by integrating a dual serial mediation model and testing the moderating role of professional status, thereby offering a nuanced understanding of sustainable digital engagement across career stages. Our findings underscore the importance of fostering positive perceptions and providing differentiated support throughout teachers’ professional trajectories to achieve long-term, meaningful technology adoption in education. Full article

Effective building operation requires a careful balance between energy conservation and indoor environmental comfort. Although numerous methods have been developed to reduce energy consumption during the operational phase, their objectives and applications vary widely. However, the complexity of building energy management makes it challenging to identify the most suitable methods that simultaneously achieve both comfort and efficiency goals. Existing studies often lack a systematic framework that supports integrated decision-making under comfort constraints. This research aims to address this gap by proposing a decision-making tree for selecting energy conservation methods during building operation with an explicit consideration of indoor environmental comfort. A comprehensive literature review is conducted to identify four main energy-consuming components during building operation: the building envelope, HVAC systems, lighting systems, and plug loads and appliances. Three key comfort indicators—thermal comfort, lighting comfort, and air quality comfort—are defined, and energy conservation methods are categorized into three strategic groups: passive strategies, control optimization strategies, and behavioural intervention strategies. Each method is assessed using a defined set of evaluation criteria. Subsequently, a questionnaire survey is administered for the calibration of the decision tree, incorporating stakeholder preferences and expert judgement. The findings contribute to the advancement of understanding regarding the co-optimization of energy conservation and occupant comfort in building operations. Full article

In this work, the load spectra acting in the vertical direction on the hub carriers and in the horizontal longitudinal direction on the drawbar of a trailed variable chamber round baler were evaluated. To this end, each hub carrier was instrumented with appropriately calibrated strain gauge bridges. Similarly, the baler was equipped with a dynamometric towing pin, instrumented with strain gauge sensors and calibrated in the laboratory, which replaced the original pin connecting the baler and the tractor during the in-field load acquisitions. In both cases, the calibration tests returned the relationship between applied forces and output signals of the strain gauge bridges. Multiple in-field load acquisitions were carried out under typical maneuvers and operating conditions. The synchronous acquisition of a video via an onboard camera and Global Positioning System (GPS) signal allowed to observe the behaviour of the baler in correspondence of particular trends of the vertical and horizontal loads and to point out the most demanding maneuver in view of the fatigue resistance of the baler. Finally, through the application of a rainflow cycle counting algorithm according to ASTM E1049-85, the load spectrum for each maneuver was derived. Full article

Detecting previously unseen anomalies in sensor data is a challenging problem for artificial intelligence when sensor-specific and deployment-specific characteristics of the time series need to be learned from a short calibration period. From the application point of view, this challenge becomes increasingly important because many applications are gravitating towards utilising low-cost sensors for Internet of Things deployments. While these sensors offer cost-effectiveness and customisation, their data quality does not match that of their high-end counterparts. To improve sensor data quality while addressing the challenges of anomaly detection in Internet of Things applications, we present an anomaly detection framework that learns a normality model of sensor data. The framework models the typical behaviour of individual sensors, which is crucial for the reliable detection of sensor data anomalies, especially when dealing with sensors observing significantly different signal characteristics. Our framework learns sensor-specific normality models from a small set of anomaly-free training data while employing an unsupervised feature engineering approach to select statistically significant features. The selected features are subsequently used to train a Local Outlier Factor anomaly detection model, which adaptively determines the boundary separating normal data from anomalies. The proposed anomaly detection framework is evaluated on three real-world public environmental monitoring datasets with heterogeneous sensor readings. The sensor-specific normality models are learned from extremely short calibration periods (as short as the first 3 days or 10% of the total recorded data) and outperform four other state-of-the-art anomaly detection approaches with respect to F1-score (between 5.4% and 9.3% better) and Matthews correlation coefficient (between 4.0% and 7.6% better). Full article

This study investigates the self-assembly and host–guest complexation behaviour of novel resveratrol-based lipophenols (LipoResv)—resveratrol-4′-linoleate (Resv-4′-LA) and resveratrol-4′-docosahexaenoate (Resv-4′-DHA)—with hydroxypropyl-β-cyclodextrins (HP-β-CDs). These amphiphilic molecules display surfactant-like properties, forming micellar aggregates in aqueous media. Fluorescence spectroscopy was used to determine the critical micelle concentration (CMC), revealing that LipoResv exhibit significantly lower CMC values than their free fatty acids, indicating higher hydrophobicity. The formation of inclusion complexes with HP-β-CDs was evaluated based on changes in CMC values and further confirmed by dynamic light scattering (DLS) and molecular modelling analyses. Resv-4′-LA formed 1:1 complexes (Kc = 720 M⁻¹), while Resv-4′-DHA demonstrated a 1:2 stoichiometry with lower affinity constants (K₁ = 17 M⁻¹, K₂ = 0.18 M⁻¹). Environmental parameters (pH, temperature, and ionic strength) significantly modulated CMC and binding constants. Computational docking and molecular dynamics simulations supported the experimental findings by revealing the key structural determinants of the host–guest affinity and micelle stabilization. Ligand efficiency (LE) analysis further aligned with the experimental data, favouring the unmodified fatty acids. These results highlight the versatile encapsulation capacity of HP-β-CDs for bioactive amphiphile molecules and support their potential applications in drug delivery and functional food systems. Full article

The logistics industry faces challenges from evolving consumer expectations, technological advances, sustainability demands, and market disruptions. Logistics collaboration is in theory perceived as one of the most promising solutions to solve these issues, but here are still a lot of challenges that needs to be better understood and addressed. While vertical collaboration among supply chain actors is well advanced, horizontal collaboration among competing service providers remains under-explored. This study developed a novel methodology based on the COM-B behaviour-change framework to better understand the main challenges, opportunities, capabilities and drivers that would motivate competing companies to exploit the potential of horizontal logistics collaboration. A survey was designed and conducted among 71 logistics service providers in Slovenia, chosen for its fragmented market and low willingness to collaborate. Statistical analysis reveals cost reduction (M = 4.21/5) and improved vehicle utilization (M = 4.29/5) as the primary motivators. On the other hand, maintaining company reputation (M = 4.64/5), fair resource sharing (M = 4.20/5), and transparency of logistics processes (M = 4.17/5) all persist as key enabling conditions. These findings underscore the pivotal role of behavioural drivers and suggest strategies that combine economic incentives with targeted trust-building measures. Future research should employ experimental designs in diverse national contexts and integrate vertical–horizontal approaches to validate causal pathways and advance theory. Full article

Economic growth, especially in emerging economies, has altered the composition of international tourism. It is therefore essential to possess the skills necessary to understand the influence of culture on human behaviour, thereby enabling an appropriate response to the traveller. This research aims to develop a tool for identifying openness, flexibility, awareness, and intercultural preparedness. It focuses on the metacognitive and cognitive aspects of cultural intelligence that shape the development of empathy in customer service staff in hotels in Quintana Roo. The variables were validated and incorporated into a quantitative study using multivariate analysis and inferential statistics. A sample of 77 questionnaires was analysed using simple random sampling under a proportional design. Multiple Correspondence Analysis (MCA) was employed as a discriminatory technique to identify the most significant independent variables. These were subsequently entered as regressors into ordinal logistic regression (OLR), along with age and work experience, in order to estimate the probabilities associated with each level of the dependent variable. The results indicated that age had minimal influence on the metacognitive and cognitive variables, whereas years of experience among tourism staff exerted a significant effect. Full article

During the Pleistocene, the successive ice ages prompted the southward expansion of the “Mammoth Steppe” ecosystem, a prevalent habitat that supported species adapted to cold environments such as the mammoth, woolly rhinoceros, and reindeer. Previously, the earliest evidence for such cold-adapted species in the Iberian Peninsula dated back to Marine Isotope Stage 6 (MIS 6, ~191–123 ka). This paper reports the discovery of a reindeer (Rangifer) tooth from Unit GIII of the Galería site at the Atapuerca-Trinchera site complex, dated to MIS 8 (~300–243 ka). This find is significant as it represents not only the oldest evidence of glacial fauna in the Iberian Peninsula but also the southernmost occurrence of reindeer in Europe of this age. The presence of Rangifer at this latitude (42°21′ N) during MIS 8 suggests that the glacial conditions affected the Iberian fauna earlier and with greater intensity than previously understood. Over the subsequent climatic cycles, cold-adapted species spread further south, reaching Madrid (40°20′) during the penultimate glacial period and the province of Granada (37°01′) during the last glacial maximum. The coexistence of human fossils and lithic artefacts within Units GII and GIII at Galería indicates that early humans also inhabited these glacial environments at Atapuerca. This study elaborates on the morphological and archaeological significance of the reindeer fossil, emphasizing its role in understanding the biogeographical patterns of glacial fauna and the adaptability of Middle Pleistocene human populations. Full article

Nutrition plays a fundamental role in promoting health and preventing chronic diseases, with bioactive food components offering a therapeutic potential in biomedical applications. Among these, edible oils are recognised for their functional properties, which contribute to disease prevention and metabolic regulation. The proposed study aims to evaluate the quality of four bioactive oils (olive oil, sunflower oil, tomato seed oil, and pumpkin seed oil) by analysing their thermal behaviour through infrared (IR) imaging. The study designed a customised electronic system to acquire thermographic signals under controlled temperature and humidity conditions. The acquisition system was used to extract thermal data. Analysis of the acquired thermal signals revealed characteristic heat absorption profiles used to infer differences in oil properties related to stability and degradation potential. A hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) units was used to classify and differentiate the oils based on stability, thermal reactivity, and potential health benefits. A signal analysis showed that the AI-based method improves both the accuracy (achieving an F1-score of 93.66%) and the repeatability of quality assessments, providing a non-invasive and intelligent framework for the validation and traceability of nutritional compounds. Full article

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterised by inattention, hyperactivity, and impulsivity, frequently observed in juvenile offenders. This systematic review explores the interplay between ADHD and juvenile delinquency, focusing on behavioural, psychological, and social dimensions. Following the PRISMA guidelines, a systematic literature review was conducted using EBSCO Discovery Service, Science Direct, PubMed, and snowballing techniques. Studies meeting specific inclusion criteria, including juvenile offenders diagnosed with ADHD and comparisons to non-offender or non-ADHD control groups, were analysed. The methodological quality of studies was assessed using the Joanna Briggs Institute appraisal tools. A total of 21 studies were included, highlighting significant associations between ADHD and juvenile delinquency. ADHD symptoms, especially impulsivity and emotional dysregulation, were linked to an earlier onset of offending and higher rates of property crimes. Comorbidities such as conduct disorder, substance use disorder, and depression exacerbated these behaviours. Sociodemographic factors like low education levels and adverse family environments were also critical modifiers. Early intervention and tailored treatment approaches were emphasised to address these challenges. The findings underscore the need for early diagnosis, individualised treatment, and integrative rehabilitation programmes within the juvenile justice system to mitigate long-term risks and promote social inclusion. Full article

Artificial Light at Night (ALAN) is a recent issue of concern for researchers primarily working on the anthropogenic impacts on animal and ecosystem health. Our concern is associated with the ALAN exposure to an aquatic ecosystem by disrupting the natural dark–light cycle, which is essential for maintaining the overall health of the ecosystem and its inhabitants. In this study, we have attempted to understand the adverse consequences of ALAN in inducing neuro-behavioural stress in a freshwater prawn species (aquatic arthropod) Macrobrachium lamarrei by considering grooming behaviour, a well-established indicator of neurological stress in animals. Our results show that continuous ALAN exposure (for seven days) can increase collective grooming activity in Macrobrachium lamarrei over time. In our experiment, we have used two intensities of ALAN (50 and 120 lux). Although the response (in terms collective grooming) to both intensities are apparently different, our fundamental hypothesis is confirmed, where it is evident that prolonged light exposure can induce an elevation in cumulative grooming performances in a freshwater prawn population. Full article