Search Results (138)

Speech Emotion Recognition (SER) plays a vital role in supporting applications such as healthcare, human–computer interaction, and security. However, many existing approaches still face challenges in achieving robust generalization and maintaining high recall, particularly for emotions related to stress and anxiety. This study proposes a dual-stream hybrid model that combines prosodic features with spatio-temporal representations derived from the Multitaper Mel-Frequency Spectrogram (MTMFS) and the Constant-Q Transform Spectrogram (CQTS). Prosodic cues, including pitch, intensity, jitter, shimmer, HNR, pause rate, and speech rate, were processed using dense layers, while MTMFS and CQTS features were encoded with CNN and BiGRU. A Multi-Head Attention mechanism was then applied to adaptively fuse the two feature streams, allowing the model to focus on the most relevant emotional cues. Evaluations conducted on the RAVDESS dataset with subject-independent 5-fold cross-validation demonstrated an accuracy of 97.64% and a macro F1-score of 0.9745. These results confirm that combining prosodic and advanced spectrogram features with attention-based fusion improves precision, recall, and overall robustness, offering a promising framework for more reliable SER systems. Full article

Dielectric crystals with switchable electric polarizations represent the key functional materials utilized for a broad range of practical applications. They allow for academically intriguing platforms, where the use of a strong external electric field can potentially unveil hidden crystal phases. Proton–π-electron correlated bistable systems turn out to be promising for exploring such electrically induced crystal polymorphisms, mainly because strong π-electronic polarization can be sensitively switched depending on mobile hydrogen locations. Pseudo-symmetry and hydrogen disorder are utilized as clues for the data mining of the Cambridge Structural Database in the search for molecular candidates with novel switchable dielectrics. The polarization hysteresis, electrostriction, and second harmonic generation of the candidates were experimentally evaluated, together with the re-inspection of crystal structure. This feature article highlights the rich variation and competition of some candidate polarization configurations and switching modes in close relation to high and efficient electrical energy storage/discharge, large electrostriction effects, polarization rotations, and multistage switching phenomena. The experimental findings are well-reproduced by the computational optimization of crystal structure and the simulation of the switchable polarization, piezoelectric coefficients, and relative stability for each of the real or hypothetical hydrogen-ordered crystal phases. Effective prediction and strategic design are thereby guaranteed by systematically understanding the appropriate integration of experimental, computational, and data sciences. Full article

Accurately predicting glass density is crucial for designing novel materials. This study aims to develop a robust predictive model for the density of oxide glasses and, more importantly, to investigate how physically informed feature engineering can create accurate and interpretable models that reveal underlying physical principles. Using a dataset of 76,593 oxide glasses from the SciGlass database, three machine learning (ML) models (ElasticNet, XGBoost, MLP) were trained and evaluated. Four distinct feature sets were constructed with increasing physical complexity, ranging from simple elemental composition to the advanced Magpie descriptors. The best model was further analyzed for interpretability using feature importance and SHapley Additive exPlanations (SHAP) analysis. A clear hierarchical improvement in predictive accuracy was observed with increasing feature sophistication across all models. The XGBoost model combined with the Magpie feature set provided the best performance, achieving a coefficient of determination (R²) of 0.97. Interpretability analysis revealed that the model’s predictions were overwhelmingly driven by physical attributes, with mean atomic weight being the most influential predictor. The model learns to approximate the fundamental density equation using mean atomic weight as a proxy for molar mass and electronic structure features to estimate molar volume. This demonstrates that a data-driven approach can function as a scientifically valid and interpretable tool, accelerating the discovery of new materials. Full article

The purpose of this paper is to use an informatics-based analysis to develop a rational design approach to the accelerated screening of nano-composite materials. Using existing nano-composite data, we develop a quantitative structure–activity relationship (QSAR) as a function of polymer matrix chemistry and nano-additive volume, with the property predicted being electrical conductivity. The development of a QSAR for the electrical conductivity of nano-composites presents challenges in representing the polymer matrix chemistry and backbone structure, the additive content, and the interactions between the components while capturing the non-linearity of electrical conductivity with changing nano-additive volume. An important aspect of this work is designing chemistries with small training data sizes, as the uncertainty in modeling is high, and potentially the representated physics may be minimal. In this work, we explore two important components of this aspect. First, an assessment via Uniform Manifold Approximation and Projection (UMAP) is used to assess the variability provided by new data points and how much information is contributed by data, which is significantly more important than the actual data size (i.e., how much new information is provided by each data point?). The second component involves assessing multiple training/testing splits to ensure that any results are not due to a specific case but rather that the results are statistically meaningful. This work will accelerate the rational design of polymer nano-composites by fully considering the large array of possible variables while providing a high-speed screening of polymer chemistries. Full article

Driven by industry–education integration and digital technology, higher vocational education textbooks are transitioning from traditional formats to an integrated “loose-leaf + digital” model. Combining the flexibility of loose-leaf textbooks with digital technology, these new materials enable real-time updates and align closely with industry practices. We explored the era connotations of integrated textbooks and proposed a development process based on cognitive psychology, interdisciplinary integration, and synergy theory. Continuous optimization through robust evaluation systems and digital platforms is required to provide modernized and informatized vocational education. Full article

This study presents a robust machine learning framework based on Gaussian process regression (GPR) to predict the tensile strength of polymer nanocomposites reinforced with various nanofillers and processed under diverse techniques. A comprehensive dataset comprising 25 polymer matrices, 22 surface functionalization methods, and 24 processing routes was constructed from the literature. GPR, coupled with Monte Carlo sampling across 2000 randomized iterations, was employed to capture nonlinear dependencies and uncertainty propagation within the dataset. The model achieved a mean coefficient of determination (R²) of 0.96, RMSE of 12.14 MPa, MAE of 7.56 MPa, and MAPE of 31.73% over 2000 Monte Carlo iterations, outperforming conventional models such as support vector machine (SVM), regression tree (RT), and artificial neural network (ANN). Sensitivity analysis revealed the dominant influence of Carbon Nanotubes (CNT) weight fraction, matrix tensile strength, and surface modification methods on predictive accuracy. The findings demonstrate the efficacy of the proposed GPR framework for accurate, reliable prediction of composite mechanical properties under data-scarce conditions, supporting informed material design and optimization. Full article

Polymers have a wide range of applications in materials science, chemistry, and biomedical domains. Conventional design methods for polymers are mostly event-oriented, directed by intuition, experience, and abstract insights. Nevertheless, they have been effectively utilized to determine several essential materials; these techniques are facing important challenges owing to the great requirement of original materials and the huge design area of organic polymers and molecules. Enhanced and inverse materials design is the best solution to these challenges. With developments in high-performing calculations, artificial intelligence (AI) (particularly Deep learning (DL) and Machine learning (ML))-aided materials design is developing as a promising tool to show development in various domains of materials science and engineering. Several ML and DL methods are established to perform well for polymer classification and detection presently. In this paper, we design and develop a Simplified Molecular Input Line Entry System Based Polymer Property Detection and Classification Using Pareto Optimization Algorithm (SMILES-PPDCPOA) model. This study presents a novel deep learning framework tailored for polymer property classification using SMILES input. By integrating a one-dimensional convolutional neural network (1DCNN) with a gated recurrent unit (GRU) and optimizing the model via Pareto Optimization, the SMILES-PPDCPOA model demonstrates superior classification accuracy and generalization. Unlike existing methods, our model is designed to capture both local substructures and long-range chemical dependencies, offering a scalable and domain-specific solution for polymer informatics. Furthermore, the proposed SMILES-PPDCPOA model executes a one-dimensional convolutional neural network and gated recurrent unit (1DCNN-GRU) technique for the classification process. Finally, the Pareto optimization algorithm (POA) adjusts the hyperparameter values of the 1DCNN-GRU algorithm optimally and results in greater classification performance. Results on a benchmark dataset show that SMILES-PPDCPOA achieves an average classification accuracy of 98.66% (70% Training, 30% Testing) across eight polymer property classes, with high precision and recall metrics. Additionally, it demonstrates superior computational efficiency, completing tasks in 4.97 s, outperforming other established methods such as GCN-LR and ECFP-NN. The experimental validation highlights the potential of SMILES-PPDCPOA in polymer property classification, making it a promising approach for materials science and engineering. The simulation result highlighted the improvement of the SMILES-PPDCPOA system when compared to other existing techniques. Full article

Preserving cultural heritage can be efficiently supported by structured and semantic representation of historical artifacts. Bookbinding, a critical aspect of book history, provides valuable insights into past craftsmanship, material use, and conservation practices. However, existing bibliographic records often lack the depth needed to analyze bookbinding techniques, provenance, and preservation status. This paper presents a proof-of-concept system that explores how Large Language Models (LLMs) can support knowledge graph engineering within the context of 19th-century Greek bookbinding (1830–1900), and as a result, generate a domain-specific ontology and a knowledge graph. Our ontology encapsulates materials, binding techniques, artistic styles, and conservation history, integrating metadata standards like MARC and Dublin Core to ensure interoperability with existing library and archival systems. To validate its effectiveness, we construct a Neo4j knowledge graph, based on the generated ontology and utilize Cypher Queries—including LLM-generated queries—to extract insights about bookbinding practices and trends. This study also explores how semantic reasoning over the knowledge graph can identify historical binding patterns, assess book conservation needs, and infer relationships between bookbinding workshops. Unlike previous bibliographic ontologies, our approach provides a comprehensive, semantically rich representation of bookbinding history, methods and techniques, supporting scholars, conservators, and cultural heritage institutions. By demonstrating how LLMs can assist in ontology/KG creation and query generation, we introduce and evaluate a semi-automated pipeline as a methodological demonstration for studying historical bookbinding, contributing to digital humanities, book conservation, and cultural informatics. Finally, the proposed approach can be used in other domains, thus, being generally applicable in knowledge engineering. Full article

The rational design of photoanode materials is pivotal for advancing photoelectrochemical (PEC) water splitting toward sustainable hydrogen production. This review highlights recent progress in the machine learning (ML)-assisted development of nanostructured metal oxide photoanodes, focusing on bridging materials discovery and device-level performance optimization. We first delineate the fundamental physicochemical criteria for efficient photoanodes, including suitable band alignment, visible-light absorption, charge carrier mobility, and electrochemical stability. Conventional strategies such as nanostructuring, elemental doping, and surface/interface engineering are critically evaluated. We then discuss the integration of ML techniques—ranging from high-throughput density functional theory (DFT)-based screening to experimental data-driven modeling—for accelerating the identification of promising oxides (e.g., BiVO₄, Fe₂O₃, WO₃) and optimizing key parameters such as dopant selection, morphology, and catalyst interfaces. Particular attention is given to surrogate modeling, Bayesian optimization, convolutional neural networks, and explainable AI approaches that enable closed-loop synthesis-experiment-ML frameworks. ML-assisted performance prediction and tandem device design are also addressed. Finally, current challenges in data standardization, model generalizability, and experimental validation are outlined, and future perspectives are proposed for integrating ML with automated platforms and physics-informed modeling to facilitate scalable PEC material development for clean energy applications. Full article

Adrenal cortex carcinoma (ACC) in children is a rare tumor that is probably of multifactorial origin and is mainly associated with genetic and environmental alterations. In the south and part of the southeast of Brazil, as well as in the Paraguayan region bordering the Brazilian State of Paraná, ACC prevalence is higher than in any other country, which is associated with the high prevalence of the TP53 p.R337H variant in Paraná (0.30%), Santa Catarina (0.249%), cities around Campinas-SP (0.21%), and the Paraguayan cities on the border with Paraná (0.05%). Recent research suggests that the co-segregation of XAF1-E134* and TP53-R337H mutations leads to a more aggressive cancer phenotype than TP53-R337H alone. Breast cancer may be mildly influenced by co-segregation with XAF1 p.E134*, and this variant can also confer risk for sarcoma. Objectives: The objectives of this study were to (1) estimate the prevalence of the germline TP53 p.R337H and XAF1 p.E134* variants in Paraguay (excluding cities on the border with Paraná State, Brazil) and (2) estimate whether the ethnic origin of TP53 p.R337H carriers in Paraguay is similar to that of ethnic groups in Paraná (possible Portuguese/Spanish origin). Materials and methods: DNA tests for the identification of TP53 p.R337H were carried out from 2016 to 2019 at the Bio-Materials Laboratory of Facultad Politecnica, UNA, and at the Research Center in Biotechnology and Informatics (CEBIOTEC), Asunción, Paraguay. Polymerase chain reaction followed by restriction enzyme digestion (PCR-RFLP) was used to identify TP53 p.R337H, and real-time PCR (RT-PCR) was employed for XAF1 p.E134*. Peripheral blood samples from 40,000 Paraguayan newborns (NBs) were used for the TP53 p.R337H tests. The XAF1 p.E134* tests (RT-PCR) were performed on samples from 2000 Paraguayan newborns at the Pelé Pequeno Principe Research Institute, Curitiba, PR, Brazil. Results: The TP53 p.R337H variant was not found in any of the 14 Paraguayan departments investigated. A total of 12 of the 2000 Paraguayan NBs were positive for one XAF1 p.E134* allele. Conclusions: The hypothesis of Spanish immigrants carrying p.R337H to Paraguay was disproved. TP53 p.R337H neonatal testing in Paraguay is not recommended, except when there are families with Brazilian ancestry presenting cancer cases. Additional epidemiological studies are required to determine the likelihood of the identified prevalence of the XAF1 p.E134* allele (1/153) in NBs from Paraguay without TP53 p.R337H to present cancer risk. This study complements the first national initiative for the DNA screening of newborns aimed at mapping the TP53 p.R337H and XAF1 p.E134* variants in Paraguay (based on the regions of residence of the newborns). Full article

The application of ceramic particle-reinforced metal matrix composites (CPRMMCs) in the nuclear power sector is primarily dependent on their mechanical and thermal properties. A comprehensive understanding of the structure–property (SP) linkages between microstructures and macroscopic properties is critical for optimizing material properties. However, traditional studies on SP linkages generally rely on experimental methods, theoretical analysis, and numerical simulations, which are often associated with high time and economic costs. To address this challenge, this study proposes a novel method based on Materials Informatics (MI), combining the finite element method (FEM), graph Fourier transform, principal component analysis (PCA), and machine learning models to establish the SP linkages between the microstructure and thermodynamic properties of CPRMMCs. Specifically, FEM is used to model the microstructures of CPRMMCs with varying particle volume fractions and sizes, and their elastic modulus, thermal conductivity, and coefficient of thermal expansion are computed. Next, the statistical features of the microstructure are captured using graph Fourier transform based on two-point spatial correlations, and PCA is applied to reduce dimensionality and extract key features. Finally, a polynomial kernel support vector regression (Poly-SVR) model optimized by Bayesian methods is employed to establish the nonlinear relationship between the microstructure and thermodynamic properties. The results show that this method can effectively predict FEM results using only 5–6 microstructure features, with the R² values exceeding 0.91 for the prediction of thermodynamic properties. This study provides a promising approach for accelerating the innovation and design optimization of CPRMMCs. Full article

With the rapid advancement of artificial intelligence and machine learning technologies, the demand for enhanced device computing capabilities has significantly increased. Neuromorphic computing, an emerging computational paradigm inspired by the human brain, has garnered growing attention as a promising research frontier. Inspired by the human brain’s functionality, this technology mimics the behavior of neurons and synapses to enable efficient, low-power computing. Unlike conventional digital systems, this approach offers a potentially superior alternative. This article delves into the application of nanowire materials (and devices) in neuromorphic computing simulations: First, it introduces the synthesis and preparation methods of nanowire materials. Then, it analyzes in detail the key role of nanowire devices in constructing artificial neural networks, especially their advantages in simulating the functions of neurons and synapses. Compared with traditional silicon-based material devices, it focuses on how nanowire devices can achieve higher connection density and lower energy consumption, thereby enabling new types of neuromorphic computing. Finally, it looks forward to the application potential of nanowire devices in the field of future neuromorphic computing, expecting them to become a key force in promoting the development of intelligent computing, with extensive application prospects in the fields of informatics and medicine. Full article

Proteins and their surrounding hydration water engage in a dynamic interplay that is critical for maintaining structural stability and functional integrity. However, the intricate coupling between protein dynamics and the structural order of hydration water remains poorly understood. Here, we employ all-atom molecular dynamics simulations to investigate this relationship across four representative proteins. Our results reveal that protein residues with greater flexibility or solvent exposure are surrounded by more disordered hydration water, akin to bulk water, whereas rigid and buried non-polar residues are associated with structurally ordered hydration shells. Due to their strong hydrogen bonding and electrostatic interactions, charged residues exhibit the most disordered hydration water, while non-polar residues are associated with the structurally most ordered hydration water. We further uncovered a positive correlation between the relaxation dynamics of protein residues and their hydration water: slower (faster) protein relaxation is coupled with slower (faster) relaxation of the structural order of hydration water. Notably, this coupling weakens with increasing residue flexibility or solvent exposure, with non-polar residues displaying the strongest coupling, and charged residues the weakest. To further uncover their coupling mechanism, we elucidate residue-specific coupled fluctuations between protein residues and hydration water by generating scatter plots. These findings provide a comprehensive understanding of the mechanisms underlying protein–water interactions, offering valuable insights into the role of hydration water in protein stability, dynamics, and function. Full article

This paper focuses on algorithms and technologies for unmanned aerial vehicles (UAVs) networking across different fields of applications. Given the limitations of UAVs in both computations and communications, UAVs usually need algorithms for either low latency or energy efficiency. In addition, coverage problems should be considered to improve UAV deployment in many monitoring or sensing applications. Hence, this work firstly addresses common applications of UAV groups or swarms. Communication routing protocols are then reviewed, as they can make UAVs capable of supporting these applications. Furthermore, control algorithms are examined to ensure UAVs operate in optimal positions for specific purposes. AI-based approaches are considered to enhance UAV performance. We provide either the latest work or evaluations of existing results that can suggest suitable solutions for specific practical applications. This work can be considered as a comprehensive survey for both general and specific problems associated with UAVs in monitoring and sensing fields. Full article

This review is focused on FC-BENTEN, an advanced synchrotron X-ray experimental database developed at SPring-8 with support from Japan’s New Energy and Industrial Technology Development Organization (NEDO). Designed to advance polymer electrolyte fuel cells (PEFCs) research, FC-BENTEN addresses challenges in improving efficiency, durability, and cost-effectiveness through data-driven approaches informed by materials informatics (MI). Through standardization of protocols for sample preparation, data acquisition, analysis, and formatting, the database ensures high-quality, reproducible data essential for reliable scientific outcomes. FC-BENTEN streamlines metadata creation using automated processes and template-based tools, enhancing data management, accessibility, and interoperability. Security measures include two-factor authentication, safeguarding sensitive information and maintaining controlled user access. Planned integration with MI platforms will broaden data cross-referencing capabilities, facilitate PEFC applications expansion, and guide future research. This review discusses FC-BENTEN’s architectural framework, metadata standardization efforts, and role in advancing PEFC research through a high-throughput experimental workflow. It illustrates how data-driven methods and standardized practices contribute to innovation, underscoring databases’ potential to accelerate next-generation PEFC technologies development. Full article