Search Results (41)

Fiber-reinforced composites contain microscale features such as variations in local fiber volume fraction, fiber clusters, and resin-rich regions, which may impact mechanical properties. Microscale models need to be large enough to capture these features while maintaining high fidelity to capture the localized fiber-to-fiber interactions. This makes it difficult to efficiently model regions with equivalent fiber morphologies to as-manufactured scans and to perform large statistical studies to examine how these features drive mechanical performance. This study uses a novel microstructure generator and an efficient micromechanical model along with a characterization method that measures the geometry of these features to simulate a wide range of microstructures for strength and stiffness. After understanding how the mechanical properties are affected by morphology through correlation matrices, equivalent microstructures were generated to regions of an as-manufactured composite. The generation of microstructures based on different morphological descriptors allows for an understanding of which features are valuable when modeling these materials. In comparing microstructures with different equivalent descriptors to the case with all six descriptors, it was found that only using local fiber volume fraction median resulted in over predictions of strength and stiffness. Once two descriptors or more were introduced, such as local fiber volume fraction median and inter-quartile range, there was no significant difference in strength and stiffness. This suggests that at least two descriptors should be considered when generating equivalent microstructures for mechanical properties. Full article

Electrocaloric materials, which exhibit adiabatic temperature change under an applied electric field, are promising for solid-state cooling technologies. In this study, the electrocaloric response of lead-free Ba_xCa_1−xZr_yTi_1−yO₃ (BCZT) ceramics was modeled to investigate the effects of composition, processing, and measurement conditions on performance. A high-accuracy XGBoost regression model (R² = 0.99, MAE = 0.02 °C) was developed using a dataset of 2188 literature-derived data points to predict and design the electrocaloric response of BCZT ceramics. The feature space incorporated compositional ratios, processing parameters, measurement settings, and atomic-level Magpie descriptors, along with Curie temperature to account for phase-transition behavior. Feature importance analysis revealed that electric field, measurement temperature, and proximity to the Curie point are the most critical factors influencing ΔT_EC. Bayesian optimization was applied to navigate the design space and identify performance maxima under unconstrained and realistic constraints, offering valuable insights into the nonlinear interactions governing electrocaloric performance. Under room temperature and moderate-field conditions (24 °C, 40 kV/cm), the optimized ΔT_EC achieved a value of 1.03 °C for Ba_0.85Ca_0.15Zr_0.40Ti_0.60, to be processed at 1090 °C for 3 h during calcination, 1300 °C for 2 h during sintering. By integrating experimental insight with machine learning and optimization, this study offers a refined, interpretable framework for accelerating the design of high-performance electrocaloric ceramics while reducing the experimental workload. Full article

Hepatitis C virus (HCV) infection remains a significant global health burden, driven by the emergence of drug-resistant strains and the limited efficacy of current antiviral therapies. A promising strategy for therapeutic intervention involves targeting the NS3 protease, a viral enzyme essential for replication. In this study, we present the first computational model specifically designed to identify NS3 protease inhibitory peptides (NS3IPs). Using amino acid composition (AAC) and K-spaced amino acid pair composition (CKSAAP) features, we developed machine learning classifiers based on support vector machine (SVM) and random forest (RF), achieving accuracies of 98.85% and 97.83%, respectively, validated through 5-fold cross-validation and independent testing. To support the accessibility of the strategy, we implemented a web-based tool, iDNS3IP, which enables real-time prediction of NS3IPs. In addition, we performed feature space analyses using PCA, t-SNE, and LDA based on AAindex descriptors. The resulting visualizations showed a distinguishable clustering between NS3IPs and non-inhibitory peptides, suggesting that inhibitory activity may correlate with characteristic physicochemical patterns. This study provides a reliable and interpretable platform to assist in the discovery of therapeutic peptides and supports continued research into peptide-based antiviral strategies for drug-resistant HCV. To enhance its flexibility, the iDNS3IP web tool also incorporates a BLAST-based similarity search function, enabling users to evaluate inhibitory candidates from both predictive and homology-based perspectives. Full article

This study explores how contemporary text-to-image models interpret and generate Classical and Baroque styles under Wölfflin’s framework—two categories that are atemporal and transversal across media. Our goal is to see whether generative AI can replicate the nuanced stylistic cues that art historians attribute to them. We prompted two popular models (DALL•E and Midjourney) using explicit style labels (e.g., “baroque” and “classical”) as well as more implicit cues (e.g., “dynamic”, “static”, or reworked Wölfflin descriptors). We then collected expert ratings and conducted broader qualitative reviews to assess how each output aligned with Wölfflin’s characteristics. Our findings suggest that the term “baroque” usually evokes features recognizable in typically historical Baroque artworks, while “classical” often yields less distinct results, particularly when a specified genre (portrait, still life) imposes a centered, closed-form composition. Removing explicit style labels may produce highly abstract images, revealing that Wölfflin’s descriptors alone may be insufficient to convey Classical or Baroque styles efficiently. Interestingly, the term “dynamic” gives rather chaotic images, yet this chaos is somehow ordered, centered, and has an almost Classical feel. Altogether, these observations highlight the complexity of bridging canonical stylistic frameworks and contemporary AI training biases, underscoring the need to update or refine Wölfflin’s atemporal categories to accommodate how generative models—and modern visual culture—reinterpret Classical and Baroque. Full article

The influence of machine learning (ML) on scientific domains continues to grow, and the number of publications at the intersection of ML, CO₂ capture, and material science is growing rapidly. Approaches for building ML models vary in both objectives and the methods through which materials are represented (i.e., featurised). Featurisation based on descriptors, being a crucial step in building ML models, is the focus of this review. Metal organic frameworks, ionic liquids, and other materials are discussed in this paper with a focus on the descriptors used in the representation of CO₂-capturing materials. It is shown that operating conditions must be included in ML models in which multiple temperatures and/or pressures are used. Material descriptors can be used to differentiate the CO₂ capture candidates through descriptors falling under the broad categories of charge and orbital, thermodynamic, structural, and chemical composition-based descriptors. Depending on the application, dataset, and ML model used, these descriptors carry varying degrees of importance in the predictions made. Design strategies can then be derived based on a selection of important features. Overall, this review predicts that ML will play an even greater role in future innovations in CO₂ capture. Full article

The sea bottom acts as a key natural archive where the memory of long-term timescale environmental changes is recorded. This study discusses some ecological and chemical features of fjord sediments that were explored during the AREX cruise carried out in the Svalbard archipelago in the summer of 2021. The activity rates of the enzymes leucine aminopeptidase (LAP), beta-glucosidase (GLU), and alkaline phosphatase (AP) and community-level physiological profiles (CLPPs) were studied with the aim of determining the functional diversity of the benthic microbial community, while bacterial isolates were screened for their susceptibility to antibiotics in order to explore the role of these extreme environments as potential reservoirs of antibiotic resistance. Enzyme activity rates were obtained using fluorogenic substrates, and CLPPs were obtained using Biolog Ecoplates; antibiotic susceptibility assays were performed through the standard disk diffusion method. Spatial trends observed in the functional profiles of the microbial community suggested variability in the microbial community’s composition, presumably related to the patchy distribution of organic substrates. Complex carbon sources, carbohydrates, and amino acids were the organic polymers preferentially metabolized by the microbial community. Multi-resistance to enrofloxacin and tetracycline was detected in all of the examined samples, stressing the role of sediments as a potential reservoir of chemical wastes ascribable to antibiotic residuals. This study provides new insights on the health status of fjord sediments of West Spitsbergen, applying a dual ecological and biochemical approach. Microbial communities in the fjord sediments showed globally a good functional diversity, suggesting their versatility to rapidly react to changing conditions. The lack of significant diversification among the three studied areas suggests that microbial variables alone cannot be suitable descriptors of sediment health, and that additional measures (i.e., physical–chemical characteristics) should be taken to better define environmental status. Full article

Breast cancer is the first cancer incidence and the second cancer mortality in women. Therefore, for the life and health of breast cancer patients, the research and development of breast cancer drugs should be accelerated. In drug development, the search for compounds with good bioactivity, pharmacokinetics, and safety, including Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), has always been a time-consuming and labor-intensive process. In this paper, the relationship between the molecular descriptor and ADMET properties of compounds is studied. Aiming at the problem of composite ADMET attribute classification, a Stacking Algorithm based on Graph Convolutional Network (SA-GCN) was proposed. Firstly, feature selection was performed in the data of molecular descriptors. Then the SA-GCN is developed by integrating the advantages of ten classical classification algorithms. Finally, various performance indicators were used to conduct comparative experiments. Experiments show that the SA-GCN is superior to other classifiers in the classification performance of ADMET, and the classification accuracy is 97.6391%, 98.1450%, 94.4351%, 96.4587%, and 97.9764% compared to other classifiers. Therefore, this method can be well applied to the classification of ADMET properties of compounds and then could provide some help to screen out compounds with good biological activities. Full article

Craters are regarded as significant navigation landmarks during the descent and landing process in small body exploration missions for their universality. Recognizing and matching craters is a crucial prerequisite for visual and LIDAR-based navigation tasks. Compared to traditional algorithms, deep learning-based crater detection algorithms can achieve a higher recognition rate. However, matching crater detection results under various image transformations still poses challenges. To address the problem, a composite feature-matching algorithm that combines geometric descriptors and region descriptors (extracting normalized region pixel gradient features as feature vectors) is proposed. First, the geometric configuration map is constructed based on the crater detection results. Then, geometric descriptors and region descriptors are established within each feature primitive of the map. Subsequently, taking the salience of geometric features into consideration, composite feature descriptors with scale, rotation, and illumination invariance are generated through fusion geometric and region descriptors. Finally, descriptor matching is accomplished by computing the relative distances between descriptors and adhering to the nearest neighbor principle. Experimental results show that the composite feature descriptor proposed in this paper has better matching performance than only using shape descriptors or region descriptors, and can achieve a more than 90% correct matching rate, which can provide technical support for the small body visual navigation task. Full article

SiCp/Al composites offer the advantages of lightweight construction, high strength, and corrosion resistance, rendering them extensively applicable across various domains such as aerospace and precision instrumentation. Nonetheless, the interfacial reaction between SiC and Al under high temperatures leads to degradation in material properties. In this study, the interface segregation energy and interface binding energy subsequent to the inclusion of alloying elements were computed through a first-principle methodology, serving as a dataset for machine learning. Feature descriptors for machine learning undergo refinement via feature engineering. Leveraging the theory of machine-learning-accelerated first-principle computation, six machine learning models—RBF, SVM, BPNN, ENS, ANN, and RF—were developed to train the dataset, with the ANN model selected based on R² and MSE metrics. Through this model, the accelerated computation of interface segregation energy and interface binding energy was achieved for 89 elements. The results indicate that elements including B, Si, Fe, Co, Ni, Cu, Zn, Ga, and Ge exhibit dual functionality, inhibiting interfacial reactions while bolstering interfacial binding. Furthermore, the atomic-scale mechanism elucidates the interfacial modulation of these elements. This investigation furnishes a theoretical framework for the compositional design of SiCp/Al composites. Full article

A quantitative evaluation of the musical timbre and its variations is important for the analysis of audio recordings and computer-aided music composition. Using the FFT acoustic descriptors and their representation in an abstract timbral space, variations in a sample of monophonic sounds of chordophones (violin, cello) and aerophones (trumpet, transverse flute, and clarinet) sounds are analyzed. It is concluded that the FFT acoustic descriptors allow us to distinguish the timbral variations in the musical dynamics, including crescendo and vibrato. Furthermore, using the Random Forest algorithm, it is shown that the FFT-Acoustic provides a statistically significant classification to distinguish musical instruments, families of instruments, and dynamics. We observed an improvement in the FFT-Acoustic descriptors when classifying pitch compared to some timbral features of Librosa. Full article

Particle morphology is an important feature of sediments that reflects their transport history and depositional environment. In this study, we used dynamic image analysis (DIA) to measure the size and shape of beach sediments on the east and west coasts of Hainan Island in South China Sea. DIA is a fast and accurate method that can capture and analyze a large number of sediment particles in real-time. We extracted morphological descriptors of each particle, such as equivalent diameter, sphericity, aspect ratio and symmetry, and their distributions based on volume and number. We performed multivariate analysis on the particle morphological data, including alpha diversity, statistical analysis and fingerprint techniques. We found that the Shannon index, calculated by the number distribution of sediment particle morphology, can effectively discriminate between the two sites, reflecting different sediment sources, transport processes and depositional conditions. We also established a composite fingerprint based on seven morphological parameters and diversity indices, which can accurately distinguish between aeolian and hydraulic sedimentary environments. Our study demonstrates the potential application of DIA in identifying sedimentary environments and establishing sediment fingerprints. This can help us understand the sediment transport processes and depositional mechanisms in coastal areas. Full article

The composition of amino acids forming the active site of a CYP enzyme is impactful in its substrate selectivity. For CYP2E1, the role of PHE residues in the formation of effective binding orientations for its aromatic substrates remains unclear. In this study, molecular docking and molecular dynamics analysis were performed to reflect the interactions between PHEs in the active site of human CYP2E1 and various aromatic compounds known as its substrates. The results indicated that the orientation of 1-methylpyrene (1-MP) in the active site was highly determined by the presence of PHEs, PHE478 contributing to the binding free energy most significantly. Moreover, by building a random forest model the relationship between each of 19 molecular descriptors of polychlorinated biphenyl (PCB) compounds (from molecular docking, quantum mechanics, and physicochemical properties) and their human CYP2E1-dependent mutagenicityas established mostly in our lab, was investigated. The presence of PHEs did not appear to significantly modify the electronic or structural feature of each bound ligand (PCB), instead, the flexibility of the conformation of PHEs contributed substantially to the effective binding energy and orientation. It is supposed that PHE residues adjust their own conformation to permit a suitablly shaped cavity for holding the ligand and forming its orientation as favorable for a biochemical reaction. This study has provided some insights into the role of PHEs in guiding the interactive adaptation of the active site of human CYP2E1 for the binding and metabolism of aromatic substrates. Full article

In recent years, Brassicaceae have piqued the interest of researchers due to their extremely rich chemical composition, particularly the abundance of antioxidants and anti-inflammatory compounds, as well as because of their antimutagenic and potential anticarcinogenic activity. Vegetables in this family can be found practically everywhere on the planet. In Italy, numerous varieties of Brassicaceae, as well as a diverse pool of local variants, are regularly cultivated. These landraces, which have a variety of peculiar features, have recently sparked increased interest, and the need to safeguard them to preserve genetic biodiversity has become a relevant topic. In the present study, eight distinct Brassicaceae folk varieties were studied using non-destructive tools (Multivariate Image analysis and agro-morphological descriptors). Eventually, the data were handled using explorative analysis (EA) and Soft Independent Modeling by Class Analogy (SIMCA). EA pointed out similarities/dissimilarities among the diverse investigated populations. SIMCA led to high sensitivity (>70%) in prediction (on the external test set) for seven (over eight) investigated classes. Although the investigated plants belong to different landraces, they bear strong similarities. This is mainly linked to the ability of Brassicaceae to hybridize. Despite this, the combination of colorgrams and SIMCA allowed for classifying samples with excellent accuracy. Full article

The present study focused on the molecular, morphological, and nutritional characterisation of a globe artichoke landrace at risk of genetic erosion still cultivated in the municipality of Orte (Lazio Region, Central Italy) and therefore named “Carciofo Ortano”. Molecular analysis based on SSR and ISSR markers was carried out on 73 genotypes selected at random from 20 smallholdings located in the Orte countryside and 17 accessions of landraces/clones belonging to the main varietal types cultivated in Italy. The results confirmed that “Carciofo Ortano” belongs to the “Romanesco” varietal typology and revealed the presence within the landrace of two distinct genetic populations named Orte 1 and Orte 2. Despite the high level of within-population genetic variation detected, the two populations were genetically differentiated from each other and from the landraces/clones of the main varietal types cultivated in Italy. Morphological and nutritional characterisation was performed on representative genotypes for each of the two populations of the “Carciofo Ortano” and the four landraces/clones included in the varietal platform of the PGI “CARCIOFO ROMANESCO DEL LAZIO” used as reference genotypes (“Campagnano”, “Castellammare”, “C3”, and “Grato 1”). Principal component analysis showed that, of the 43 morphological descriptors considered, 12, including plant height, head shape index, head yield, and earliness, allowed a clear grouping of genotypes, distinguishing Orte 1 and Orte 2 populations from the reference genotypes. Regarding the nutritional composition of heads, particular attention should be devoted to the Orte 2 genotypes for their high dietary fibre, inulin, flavonoid, and phenol content, a feature that could be highly appreciated by the market. Full article

Many composite manufacturing processes employ the consolidation of pre-impregnated preforms. However, in order to obtain adequate performance of the formed part, intimate contact and molecular diffusion across the different composites’ preform layers must be ensured. The latter takes place as soon as the intimate contact occurs and the temperature remains high enough during the molecular reptation characteristic time. The former, in turn, depends on the applied compression force, the temperature and the composite rheology, which, during the processing, induce the flow of asperities, promoting the intimate contact. Thus, the initial roughness and its evolution during the process, become critical factors in the composite consolidation. Processing optimization and control are needed for an adequate model, enabling it to infer the consolidation degree from the material and process features. The parameters associated with the process are easily identifiable and measurable (e.g., temperature, compression force, process time, ⋯). The ones concerning the materials are also accessible; however, describing the surface roughness remains an issue. Usual statistical descriptors are too poor and, moreover, they are too far from the involved physics. The present paper focuses on the use of advanced descriptors out-performing usual statistical descriptors, in particular those based on the use of homology persistence (at the heart of the so-called topological data analysis—TDA), and their connection with fractional Brownian surfaces. The latter constitutes a performance surface generator able to represent the surface evolution all along the consolidation process, as the present paper emphasizes. Full article