Search Results (6,585)

Due to their extraordinary qualities, including fire resistance, excellent crashworthiness, low thermal conductivity, permeability, non-toxicity, and reduced density, cellular materials have found extensive use in various engineering applications. This study uses a finite element analysis (FEA) to model the dynamic compressive behaviour of agglomerated cork to ascertain how its material density and stress relaxation behaviour are related. Adding the Mullins effect into the constitutive modelling of impact tests, its rebound phase and subsequent second impact were further examined and simulated. Quasi-static and dynamic compression tests were used to evaluate the mechanical properties of three distinct agglomerated cork composite samples to feed the numerical model. According to the results, agglomerated cork has a significant capacity for elastic rebound, especially under dynamic strain rates, with minimal permanent deformation. For instance, the minimum value of its bounce-back energy is 11.8% of the initial kinetic energy, and its maximum permanent plastic deformation is less than 10%. The material’s model simulation adequately depicts the agglomerated cork’s response to initial and follow-up impacts by accurately reproducing the material’s dynamic compressive behaviour. In terms of innovation, this work stands out since it tackles the rebounding phenomena, which was not previously investigated in this group’s prior publication, either numerically or experimentally. Thus, this group has expanded the research on cork materials’ attributes. Full article

In recent years, great interest has been focused on the development of highly reproducible 3D in vitro models that are able to mimic the physiological architecture and functionality of native tissues. To date, a wide range of techniques have been proposed to recreate an intestinal barrier in vitro, including synthetic scaffolds and hydrogels, as well as complex on-a-chip systems and organoids. Here, we describe a novel protocol for the generation of an artificial intestine based on the creation of decellularized bio-scaffolds and their repopulation with intestinal stromal and epithelial cells. Organs collected at the local slaughterhouse are subjected to a decellularization protocol that includes a freezing/thawing step, followed by sequential incubation in 1% SDS for 12 h, 1% Triton X-100 for 12 h, and 2% deoxycholate for 12 h. At the end of the procedure, the generated bio-scaffolds are repopulated with intestinal fibroblasts and then with epithelial cells. The protocol described here represents a promising and novel strategy to generate an in vitro bioengineered intestine platform able to mimic some of the complex functions of the intestinal barrier, thus constituting a promising 3D strategy for nutritional, pharmaceutical, and toxicological studies. Full article

Time-series forecasting represents of one of the most challenging and widely studied research areas in both academic and industrial communities. Despite the recent advancements in deep learning, the prediction of future time-series values remains a considerable endeavor due to the complexity and dynamic nature of time-series data. In this work, a new prediction model is proposed, named C-KAN, for multi-step forecasting, which is based on integrating convolutional layers with Kolmogorov–Arnold network architecture. The proposed model’s advantages are (i) the utilization of convolutional layers for learning the behavior and internal representation of time-series input data; (ii) activation at the edges of the Kolmogorov–Arnold network for potentially altering training dynamics; and (iii) modular non-linearity for allowing the differentiated treatment of features and potentially more precise control over inputs’ influence on outputs. Furthermore, the proposed model is trained using the DILATE loss function, which ensures that it is able to effectively deal with the dynamics and high volatility of non-stationary time-series data. The numerical experiments and statistical analysis were conducted on five challenging non-stationary time-series datasets, and provide strong evidence that C-KAN constitutes an efficient and accurate model, well suited for time-series forecasting tasks. Full article

The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogenic factors is crucial for effective wildfire modeling and management. Despite significant advancements in wildfire modeling using machine learning (ML) methods, their limited explainability remains a barrier to utilizing them for in-depth wildfire analysis. This paper employs Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models along with the MODIS global fire atlas dataset (2004–2020) to study the influence of meteorological, topographic, vegetation, and human factors on wildfire occurrences in the mountainous region of Southwest China. It also utilizes Shapley Additive exPlanations (SHAP) values, a method within explainable artificial intelligence (XAI), to demonstrate the influence of key controlling factors on the frequency of fire occurrences. The results indicate that wildfires in this region are primarily influenced by meteorological conditions, particularly sunshine duration, relative humidity (seasonal and daily), seasonal precipitation, and daily land surface temperature. Among local variables, altitude, proximity to roads, railways, residential areas, and population density are significant factors. All models demonstrate strong predictive capabilities with AUC values over 0.8 and prediction accuracies ranging from 76.0% to 95.0%. XGBoost outperforms LR and RF in predictive accuracy across all factor groups (climatic, local, and combinations thereof). The inclusion of topographic factors and human activities enhances model optimization to some extent. SHAP results reveal critical features that significantly influence wildfire occurrences, and the thresholds of positive or negative changes, highlighting that relative humidity, rain-free days, and land use land cover changes (LULC) are primary contributors to frequent wildfires in this region. Based on regional differences in wildfire drivers, a wildfire-risk zoning map for the mountainous region of Southwest China is created. Areas identified as high risk are predominantly located in the Northwestern and Southern parts of the study area, particularly in Yanyuan and Miyi, while areas assessed as low risk are mainly distributed in the Northeastern region. Full article

Concrete structures such as bridge decks and road pavements are subjected to repetitive loading and are susceptible to fatigue failure. A simplified stress–strain analysis method that can simulate concrete behavior with a sound physical basis, acceptable prediction precision, and reasonable computation cost is urgently needed to address the critical issue of high-cycle fatigue in structural engineering. An equivalent fatigue constitutive model at discrete loading cycles incorporated into the concrete damaged plasticity model (CDPM) in Abaqus is proposed based on fatigue damage evolution. A damage variable is constructed from maximum fatigue strains, and fatigue damage evolution is described by a general equation whose parameters’ physical meaning and value range are identified. With the descending branch of the monotonic stress–strain curve as the envelope of fatigue residual strength and fatigue damage evolution equation as shape function, fatigue residual strength, residual stiffness, and residual strain are calculated. The equivalent fatigue constitutive model is validated through comparison with experimental data, where satisfactory simulation results were obtained for axial compression and flexural tension fatigue. The model’s novelty lies in integrating the fatigue damage evolution equation with CDPM, explicitly explaining performance degradation caused by fatigue damage. The proposed model could accommodate various forms of concrete constitution and fatigue stress states and has a broad application prospect for fatigue analysis of concrete structures. Full article

The paper is devoted to the modeling of nonlinear viscoelastic materials. The constitutive equations are considered in differential form via relations between strain, stress, and their derivatives in the Lagrangian description. The thermodynamic consistency is established by using the Clausius–Duhem inequality through a procedure that involves two uncommon features. Firstly, the entropy production is regarded as a positive-valued constitutive function per se. This view implies that the inequality is in fact an equation. Secondly, this statement of the second law is investigated by using an algebraic representation formula, thus arriving at quite general results for rate terms that are usually overlooked in thermodynamic analyses. Starting from strain-rate or stress-rate equations, the corresponding finite equations are derived. It then emerges that a greater generality of the constitutive equations of the classical models, such as those of Boltzmann and Maxwell, are obtained as special cases. Full article

Agricultural phosphorus (P) loss constitutes a significant factor in agricultural non-point source pollution (ANSP). Due to the widespread occurrence and complexity of ANSP, emphasis on risk prevention and control is preferable to retroactive treatment, to reduce costs. Effective risk identification is an issue that needs to be addressed urgently. Henan Province, a typical intensive agricultural region in China, was used as a case study to develop a straightforward and precise model for assessing the risk of P loss. Total phosphorus (TP) emission intensity at the county level in Henan Province was estimated based on planting, livestock and poultry breeding, and rural domestic activities. Subsequently, influential factors were selected to determine the extent of P loss in rivers. Finally, the model was validated using water quality data. The results indicate that (1) TP emission and rainfall are the primary contributors to the risk of P loss, whereas vegetation coverage has negligible effects. (2) The primary sources of TP emission, in descending order of magnitude, are livestock and poultry breeding, rural domestic activities, and planting. Livestock and poultry breeding represents the largest proportion at approximately 50%. (3) High-risk areas for P loss are concentrated in the plains of the central, eastern, and northern Henan Province, while low-risk areas are mainly located in the western mountainous and hilly regions. (4) The model exhibits high accuracy with a determination coefficient (R²) of 0.81 when compared to surface water quality monitoring data. This study provides a new framework for assessing the risk of P loss in intensive agricultural settings. Full article

Salmonellosis in dairy cattle represents an increasing problem for both animal and public health. Nevertheless, in Italy, there is no control plan in place on dairy farms. The aim of this study was to describe a Salmonella Bredeney outbreak that occurred on a dairy farm and the measures that were adopted to control the outbreak. Management consisted in identifying the spread of infection and assessing the environmental contamination of Salmonella spp. and the associated risk factors. After the farm visit, laboratory investigations showed that 48% of rectal swabs collected from calves and 33% of environmental samples were positive for S. Bredeney, and a poor biosecurity level was detected. The farmer and practitioner were provided with a health management plan to control the spread of Salmonella spp., followed by a monitoring period and a follow-up visit in which all samples resulted negative. The results demonstrated the efficacy of indirect prophylaxis measures in reducing the circulation of Salmonella spp., leading to the extinction of the outbreak. Collaboration with farmers, practitioners, and public health veterinarians and the introduction of measures reported in the health management plan constitute a possible model for the management of Salmonella spp. outbreaks in dairy herds, even in complex farm situations. Full article

Air pollution in megacities worldwide has been a severe public health and environmental problem; it contributes to climate change and threatens life. Among all services, the transport sector accounts for most of these pollutants. However, despite the strategies implemented to reduce these pollutants, mitigate their effects, and promote prosperity and sustainability, emission reduction targets remain unmet, causing the average global temperatures to keep increasing. In this study, the air pollution in the Mexico City Metropolitan Area (MCMA) is estimated through the design of an environmental simulation model using system dynamics, which constitutes a possibility for authorities to foresee the evolution of air quality in MCMA by assessing the emissions from the transport sector from a holistic perspective, based on the region DESTEP analysis factors. Simulation results estimate a more significant reduction than predicted by the local government’s current forecast; this emission reduction would be up to 106% lower for PM₁₀, 176% for PM_2.5, 34% for NOx, and 17% for VOC. The conclusion demonstrated that one of the main factors with the most significant impact on the control and reduction of emissions is the use and promotion of public transportation, along with the improvement of its road infrastructure. Full article

The combination of different energy vectors like electrical energy, hydrogen, methane, and water is a crucial aspect to deal with in integrated local energy communities (ILECs). The ILEC stands for a set of active energy users that maximise benefits and minimise costs using optimisation procedures in producing and sharing energy. In particular, the proper management of different energy vectors is fundamental for achieving the best operating conditions of ILECs in terms of both energy and economic perspectives. To this end, different solutions have been developed, including advanced control and monitoring systems, distributed energy resources, and storage. Energy management planning software plays a pivotal role in developing ILECs in terms of performance evaluation and optimisation within a multi-carrier concept. In this paper, the state-of-the-art of ILECs is further enhanced by providing important details on the critical aspects related to the overall value chain for constituting an ILEC (e.g., conceptualisation, connecting technologies, barriers/limitations, control, and monitoring systems, and modelling tools for planning phases). By providing a clear understanding of the technical solutions and energy planning software, this paper can support the energy system transition towards cleaner systems by identifying the most suitable solutions and fostering the advancement of ILECs. Full article

Artificial intelligence (AI) requires the provision of learnable data to successfully deliver requisite prediction power. In this article, it is demonstrable that standard physico-chemical parameters, while useful, are insufficient for the development of powerful antimicrobial prediction algorithms. Initial models that focussed solely on the values extractable from the knowledge on electrotopological, structural and constitutional descriptors did not meet the acceptance criteria for classifying antimicrobial activity. In contrast, efforts to conceptually define the diametric opposite of an antimicrobial compound helped to advance the predicted category as a learnable trait. Remarkably, the inclusion of ligand–receptor interactions using the ability of the molecules to stimulate transmembrane TAS2Rs receptor helped to increase the ability to distinguish the antimicrobial molecules from the inactive ones, confirming the hypothesis of a predictor–predicted synergy behind bitterness psychophysics and antimicrobial activity. Therefore, in a single bio–endogenic psychophysical vector representation, this manuscript helps demonstrate the contribution to parametrization and the identification of relevant chemical manifolds for molecular design and (re-)engineering. This novel approach to the development of AI models accelerated molecular design and facilitated the selection of newer, more powerful antimicrobial agents. This is especially valuable in an age where antimicrobial resistance could be ruinous to modern health systems. Full article

Rosemary is a versatile Mediterranean shrub valued for its culinary and medicinal uses, also finding applications as a food additive (E-392). This study explores the potential of rosemary for large-scale cultivation as well as the valorization of its distillation residue, which constitutes more than 95% of the total biomass. Rich in bioactive compounds, this solid waste represents a valuable opportunity to develop renewable plant-based products. This study monitored the agronomic adaptations of cultivated clones of rosemary and evaluated the essential oil and phenolic content. This study also evaluated the biological potential of the ethanolic extracts from the distilled residue as an antifungal, antioxidant, chelator, and biostimulant in model tests. Interestingly, the extracts showed substantial phenolic content, exhibiting strong antifungal activity, antioxidant capacity, and efficient metal chelation. Furthermore, all extracts also demonstrated promising biostimulant effects on rooting. Among the clones evaluated, Pina de Ebro stood out especially for its balanced adaptability, high essential oil yield, and outstanding phenolic content, along with uniform biological capacities among individual plants and plots. Therefore, this study highlights the potential of utilizing the entire rosemary plant, enhancing the overall profitability of the crop and meeting the growing demand for eco-friendly and renewable resources in the market. Full article

Background/Objectives: The gonadotropins luteinizing hormone (LH) and follicle-stimulating hormone (FSH) and their receptors are major regulators of reproduction in mammals and are absent in insects. We previously established transgenic Drosophila lines expressing a constitutively active human LH receptor variant (LHR^D578Y) and the wild-type receptor (LHR^wt; inactive in the absence of an agonist). That study showed that ubiquitously expression of LHR^D578Y—but not of LHR^wt—resulted in pupal lethality, and targeted expression in midline cells resulted in thorax/bristles defects. To further study the Drosophila model for an in vivo drug screening platform, we investigated here whether expressing LHR^D578Y in the fly gonads alters reproduction, as shown in a transgenic mice model. Methods: The receptor was expressed in somatic cells of the gonads using the tissue-specific traffic jam-Gal4 driver. Western blot analysis confirmed receptor expression in the ovaries. Results: A fecundity assay indicated that the ectopic expression of LHR^D578Y resulted in a decrease in egg laying compared to control flies carrying, but not expressing the transgene (~40% decrease in two independent fly lines, p < 0.001). No significant reduction in the number of laid eggs was seen in flies expressing the LHR^WT (<10% decrease compared to non-driven flies, p > 0.05). The decreased egg laying demonstrates a phenotype of the active receptor in the fly gonads, the prime target organs of the gonadotropins in mammals. We suggest that this versatile Drosophila model can be used for the pharmacological search for gonadotropin modulators. Conclusions: This is expected to provide: (a) new mimetic drug candidates (receptor-agonists/signaling-activators) for assisted reproduction treatment, (b) blockers for potential fertility regulation, and (c) leads relevant for the purpose of managing extra gonadotropic reported activities. Full article

In the simulation of foundation pit excavation, the traditional element birth–death method commonly used tends to encounter issues such as uncoordinated deformation and changes in the constitutive model, affecting the accuracy of the prediction results. To address these issues, this study proposes the use of element tracking. By duplicating elements for temporary supports or structures requiring changes in material properties and appropriately activating or deactivating them at the right moments, the simulation of the foundation pit excavation process can be achieved more precisely. Using the construction process of the Tangxi Passenger Transport Station’s comprehensive transportation hub foundation pit as an example, this study applied the proposed simulation method and compared the results with actual measurements, demonstrating its effectiveness. This research offers a more accurate approach for simulating foundation pit excavation and provides a reference for similar numerical simulation problems. Full article

The presentation of a constitutive model could help researchers to predict the mechanical behavior of a material, which also contributes to the further generalization of the material. This paper is to explore the tensile constitutive model of engineered cementitious composites (ECCs) reinforced by high-strength steel wire mesh based on experiments and numerical simulations. DIANA was used to simulate the tensile process of the specimens, and experiments were carried out to validate the numerical model. The effect of the ECCs’ tensile strength, reinforcement ratio and specimen size were considered during the specimen design process. The results showed that most of the errors of the simulated values compared to the experimental results were within 5%, which proved that the numerical model was quite accurate. The proposed constitutive model revealed the different roles played by ECCs and high-strength steel wires at different stress stages, and the calculation results were in high agreement with the simulation results, indicating the effectiveness of the constitutive model. The study in this paper could provide an important reference for the popularization and application of ECCs reinforced by high-strength steel wire mesh. Full article