Search Results (6,406)

The paper aims to explore the relationship between emotions and reflexivity, with reference to the constructs of critical reflexivity and emotional granularity. These two constructs and their operationalization constitute the theoretical–methodological background of an empirical exploratory research study conducted on a sample of adult workers aged between 18 and 55, who were subjected to a diarist-style reflective writing course. The overall aim of the course was to ascertain whether, how and to what extent reflective practices of the narrative type can influence and modulate the stress response, both from the point of view of the participants’ assumption of awareness and from the point of view of the adoption of new behaviors. The central question that the present article proposes to discuss is related to the exploration of what the basic requirements/skills are on which the development of critical reflexivity is built over time, with particular attention to the role played by emotional competencies. This aspect represents one of the most relevant gaps in current research on critical reflexivity, which is severely limited by a general tendency towards the hyper-cognization of the models of analysis adopted in much of the research devoted to reflexivity, as well as by the little space given to the investigation of the emotional dynamics at play in its onset processes. The study carried out represents an initial exploration of this aspect, testing two main hypotheses: (a) the possibility of identifying and describing a preliminary threshold to the manifest development of critical reflexivity, prior to the development of process reflexivity; (b) the possibility that crossing this threshold may be facilitated by the acquisition of a good level of emotional competence, measurable through the emotional granularity construct. In the light of the quali-quantitative analyses carried out on the diaristic corpus, the hypotheses put forward have all been confirmed, consolidating the line of research aimed at investigating the role played by emotional competence in the development of critical reflexivity, in interaction and combination with the increasingly complex structuring of the cognitive processes underlying reflexivity. Full article

Constrained by the low grade and poor floatability of the run-of-mine ore, the beneficiation of porphyry-type copper–molybdenum sulfide ores generates large quantities of molybdenum tailings, leading to significant environmental risks and resource losses and necessitating urgent recovery and reutilization. In this study, a representative sample of molybdenum tailings with a Mo grade of 0.354% was investigated to analyze its process mineralogy. The results show that molybdenite predominantly exists as fine, flaky particles intimately intergrown with quartz, pyrite, and aluminosilicate minerals, exhibiting an extremely low degree of liberation and an overall ultrafine particle size. Laboratory flotation tests show that the flotation kinetics conform to a first-order model; however, a considerable amount of molybdenum remains in the tailings, indicating that the mineralization process needs to be intensified. Through structural optimization and confined-space design, a vortex-based mineralization reactor was developed. Computational fluid dynamics simulations demonstrate that the mineralizer can generate flow fields with high turbulence intensity and dissipation rates and can induce high-energy, small-scale micro-vortices. On this basis, a semi-industrial rougher flotation system was established by coupling the developed mineralizer with a flotation column. Under optimized operating conditions, namely a feed pressure of 0.06 MPa and an impeller frequency of 20 Hz, single-stage treatment of the tailings produced molybdenum concentrates with a grade of 1.90% and a recovery of 81.29%, while the Mo grade of the tailings was reduced to 0.08%. The results are markedly superior to those obtained using a conventional laboratory flotation cell, demonstrating a substantial enhancement in mineralization efficiency and molybdenum recovery. The proposed approach, therefore, provides a practical reference for the flotation recovery of molybdenum tailings as well as other micro-fine, low-grade metal tailings. Full article

In this research, Fe-P scaffolds were successfully fabricated by the powder metallurgy method for the first time, using NaCl as the space holder for bone tissue engineering applications, with apparent porosities of approximately 70%. The Fe₃P powder was successfully synthesized by the mechanochemical method under an argon atmosphere using an initial mixture of Fe and P powders. The XRD patterns show that Fe₃P was obtained after sintering the milled powders at 1000 °C. Fe, Fe₃P, and Fe-50 wt% Fe₃P composite scaffolds and bulk pellets were prepared by sintering the milled powder at 1000 °C. Furthermore, the mechanical properties (compression strength) and bioactivity of the Fe-P scaffolds were determined. According to the compression test results, the composite scaffold showed higher compressive strength, lower fracture strain, and higher elastic modulus than the Fe and Fe₃P scaffolds, indicating that adding Fe₃P to Fe improves the mechanical properties. Moreover, among the scaffolds prepared by sintering at 1000 °C, the Fe scaffold exhibited the highest corrosion rate compared to the Fe₃P and composite samples, while the corrosion resistance of the composite sample was 3 times higher than that of the Fe sample. The ICP analysis showed that the amount of Fe released from the bulk pellets during soaking in PBS solution after four weeks was 3220 μg/dL, 4003 μg/dL, and 4774 μg/dL for the composite, Fe₃P, and Fe samples, respectively. The composite sample showed the highest cell viability, while the Fe sample had the lowest. The compressive strength (12.62 MPa) and fracture strain (5.98%) of the porous sintered composite scaffold at 1000 °C were within the range of trabecular bone, while the compressive strength of the composite sample was 17 times higher than that of the Fe sample. Furthermore, the MTS test showed that all the samples had good viability, while the composite sample had the best cell viability. The scaffolds were not cytotoxic. It can be concluded that the mechanical and biological properties of the composite sample were superior to those of the Fe and Fe₃P samples and that it may be a promising candidate for bone tissue engineering applications, especially for trabecular bone replacement. Full article

In the context of global efforts to promote energy conservation and emission reduction, geopolitical conflicts have intensified the challenges of mitigating marine climate change, posing increasingly severe economic and climatic pressures on the shipping industry worldwide. Research on multi-objective route optimization is of great significance for addressing climate challenges and enhancing economic efficiencies. This field focuses on constructing multi-objective optimization models that aim to reduce voyage time, fuel consumption, navigational risks, and carbon emissions and solving them using various algorithms. However, determining the optimal route and sailing speed under complex and variable meteorological conditions remains a significant challenge owing to the presence of numerous unevenly distributed feasible solutions within a vast solution space, making it difficult for traditional intelligent algorithms to effectively explore this space. To address this issue, this study proposes a hybrid algorithm named NRRT by integrating the Rapidly exploring Random Tree (RRT) algorithm with the Non-dominated Sorting Genetic Algorithm III (NSGA-III). By improving the sampling logic of the RRT algorithm and combining the vessel’s voluntary speed loss with the sampling step size, the algorithm efficiently explored the feasible route set, enhancing the quality and diversity of the solutions. Subsequently, the NSGA-III algorithm treats sailing speed and heading as direct decision variables to perform multi-objective optimization on the explored routes and generate Pareto-optimal solutions. The optimization results demonstrate that the proposed method excels at generating route plans that effectively reduce costs, minimize emissions, and mitigate risks compared with the 3D Dijkstra algorithm and the improved NSGA-III algorithm. Full article

School buildings are important in terms of energy performance, and their energy demand varies significantly across different climates. Early design decisions strongly influence this demand; however, building energy simulations are computationally intensive and limit rapid evaluation of alternative design options at scale. This study proposes a machine learning-based surrogate modeling framework to support early design energy assessment of school buildings across Türkiye’s six TS 825 climatic regions. A comprehensive design space is defined by varying key parameters, including building shape, orientation, window-to-wall ratio, shading, glazing systems, and insulation alternatives. Representative design configurations are generated using stratified random sampling, and then simulated in EnergyPlus, resulting in a dataset of 30,000 samples. Random Forest, Support Vector Regression, and Multilayer Perceptron models are developed within a multi-output regression framework to predict annual heating and cooling energy demand across climatic regions. The models achieve high predictive accuracy and consistent generalization, with test R² values exceeding 0.93, while exhibiting performance differences among the evaluated algorithms. Feature importance analysis identifies window-to-wall ratio and glazing-related parameters as the most influential early design variables. Overall, the results demonstrate that machine learning-based surrogate models can substantially reduce computational effort while providing reliable, climate-responsive support for early design decision-making. Full article

Hydrogen embrittlement poses a critical threat to the durability of metallic components in emerging hydrogen energy infrastructure. Reliable fatigue life assessment in hydrogen-rich environments is, however, severely constrained by the high cost and low throughput of high-pressure testing, resulting in characteristically sparse experimental datasets. Conventional empirical fatigue models struggle to capture hydrogen–mechanical coupling effects, while purely data-driven approaches often suffer from severe overfitting under data-scarce conditions. To address this challenge, this study develops a physics-enhanced learning framework that integrates established fracture mechanics principles with machine learning. Using high-strength GS80A steel as a case study, two complementary strategies are introduced. First, a physically augmented input strategy reformulates raw experimental variables into dimensionless physical descriptors derived from the Basquin and Goodman relations, thereby reducing the complexity of the learning space. Second, a physics-regularized ensemble strategy combines deterministic physical predictions with neural network outputs through a dual-pathway inference scheme, ensuring physically admissible behavior during extrapolation. An automated hyperparameter selection module is further employed to establish a robust data-driven baseline. Comparative evaluation against optimized multi-layer perceptron and support vector regression models demonstrates that the proposed framework significantly improves predictive robustness in small-sample regimes. Specifically, the coefficient of determination ($R^2$) exceeds 0.975, with the root mean square error (RMSE) reduced by approximately 70% compared to the pure data-driven baseline. By systematically embedding mechanistic priors into the learning process, the proposed approach provides a reliable and interpretable tool for fatigue assessment of metallic components operating in hydrogen environments. Full article

Traditional non-negative matrix factorization (NMF) faces challenges when dealing with complex data, primarily characterized by sensitivity to noise, neglect of data geometric structure, and inability to effectively utilize supervised information. To address these limitations, this paper proposes a class-driven robust non-negative matrix factorization with dual-hypergraph regularization (CRNMFDH) framework. The core contributions of this framework include the following: Firstly, the design of a novel dual-hypergraph regularization term that symmetrically captures and preserves the higher-order geometric structures of both the sample space and feature space, establishing a mutually reinforcing topological relationship between them. Secondly, an introduction of a class-driven mechanism to effectively integrate label information into the decomposition process, significantly enhancing the discriminative capability of the low-dimensional representations. Finally, the employment of a loss function based on correntropy to replace the traditional Euclidean distance, thereby enhancing the model’s robustness against noise and outliers. Extensive experiments across nine datasets demonstrate that CRNMFDH significantly outperforms existing state-of-the-art algorithms in multiple clustering evaluation metrics and noise robustness, providing an effective new solution for complex data clustering tasks. Full article

Subspace decomposition is a widely adopted approach for mitigating clutter interference in complex sea clutter scenarios. Based on the subspace decomposition principle, this paper expands the method to manifold space and presents a set of matrix information geometry (MIG) detectors with manifold subspace projection (MSP) for handling the target detection problem in nonhomogeneous sea clutter. According to the general method, the sample data are modeled as Hermitian positive-definite (HPD) matrices, and the clutter covariance matrix is estimated as the geometric mean of secondary HPD matrices. Through subspace projection, we map the HPD matrices onto a submanifold to enhance target–clutter separability. Three MSP-MIG detectors are put forward in line with different geometric measures. The experimental results show that our MSP-MIG detectors perform better than both traditional detectors and their non-MSP counterparts in nonhomogeneous sea clutter environment. Full article

The widespread connectivity of the Industrial Internet of Things (IIoT) improves the efficiency and functionality of connected devices. However, it also raises serious concerns about cybersecurity threats. Implementing an effective intrusion detection system (IDS) for IIoT is challenging due to heterogeneous data, high feature dimensionality, class imbalance, and the risk of data leakage during evaluation. This paper presents a leakage-safe hybrid intrusion detection framework that combines text-based and numerical network flow features in an IIoT environment. Each network flow is converted into a short text description and encoded using a frozen Large Language Model (LLM) called the Bidirectional Encoder Representations from Transformers (BERT) model to obtain fixed semantic embeddings, while numerical traffic features are standardized in parallel. To improve class separation, class prototypes are computed in Principal Component Analysis (PCA) space, and cosine similarity scores for these prototypes are added to the feature set. Class imbalance is handled only in the training data using the Synthetic Minority Over-sampling Technique (SMOTE). A Random Forest (RF) is used to select the top features, followed by a Histogram-based Gradient Boosting (HGB) classifier for final prediction. The proposed framework is evaluated on the Edge-IIoTset and ToN_IoT datasets and achieves promising results. Empirically, the framework attains 98.19% accuracy on Edge-IIoTset and 99.15% accuracy on ToN_IoT, indicating robust, leakage-safe performance. Full article

To address coverage gaps in high-latitude space weather monitoring caused by constraints in energy, bandwidth, and labeled samples, this study presents a systematic solution deployed in Hailar, China. We constructed a Cloud–Edge–Terminal system featuring wind–solar hybrid energy and RK3588-based edge computing, achieving six months of stable ionospheric–geomagnetic observation under −40 °C. Furthermore, we propose a Dual-Adversarial Recurrent Autoencoder (DA-RAE) for anomaly detection. Utilizing a single-source domain strategy, the model learns physical manifolds from quiet-day data, enabling zero-shot anomaly perception in the unsupervised target domain. Field tests in March 2025 demonstrated superior generalized anomaly detection capabilities, successfully identifying both transient space weather events and environmental equipment faults (baseline drifts). This work validates the value of edge intelligence for autonomous operations in extreme environments, providing a reproducible paradigm for global ground-based networks. Full article

The early diagnosis of depression is often impeded by the subjectivity inherent in traditional clinical assessments. To advance objective screening, this study proposes a lightweight neural network framework designed to discriminate between pathological depressive states and non-pathological transient negative emotions using EEG signals. Diverging from conventional methods that rely on single-domain features, we construct a comprehensive multi-domain feature space via Wavelet Packet Decomposition. Specifically, the framework integrates frequency (α/β power spectral density ratio), spatial (normalized α-asymmetry), and non-linear (Sample Entropy) attributes to capture the heterogeneous neurophysiological dynamics of depression. To effectively synthesize these diverse features, a multi-head additive attention mechanism is introduced. This mechanism empowers the model to adaptively recalibrate feature weights, thereby prioritizing the most discriminative patterns associated with the disorder. Experimental validation on the DEAP (negative emotion) and HUSM (major depressive disorder) datasets demonstrates that the proposed method achieves a classification accuracy of 92.2% and an F1-score of 93%. Comparative results indicate that our model significantly outperforms baseline SVM and standard deep learning approaches. Furthermore, the architecture exhibits high computational efficiency and rapid convergence, highlighting its potential as a deployable engine for real-time mental health monitoring in clinical scenarios. Full article

Connected and Autonomous Vehicles (CAVs) are exposed to increasingly sophisticated cyber threats hidden within high-dimensional, heterogeneous network traffic. A critical bottleneck in existing Intrusion Detection Systems (IDS) is the feature heterogeneity gap: discrete protocol signatures (e.g., flags, services) and continuous traffic statistics (e.g., flow duration, packet rates) reside in disjoint latent spaces. Traditional deep learning approaches typically rely on naive feature concatenation, which fails to capture the intricate, non-linear semantic dependencies between these modalities, leading to suboptimal performance on long-tail, minority attack classes. This paper proposes HCA-IDS, a novel framework centered on Semantics-Aware Cross-Modal Alignment. Unlike heavy-weight models, HCA-IDS adopts a streamlined Multi-Layer Perceptron (MLP) backbone optimized for edge deployment. We introduce a dedicated Multi-Head Cross-Attention mechanism that explicitly utilizes static “Pattern” features to dynamically query and re-weight relevant dynamic “State” behaviors. This architecture forces the model to learn a unified semantic manifold where protocol anomalies are automatically aligned with their corresponding statistical footprints. Empirical assessments on the NSL-KDD and CICIDS2018 datasets, validated through rigorous 5-Fold Cross-Validation, substantiate the robustness of this approach. The model achieves a Macro-F1 score of over 94% on 7 consolidated attack categories, exhibiting exceptional sensitivity to minority attacks (e.g., Web Attacks and Infiltration). Crucially, HCA-IDS is ultra-lightweight, with a model size of approximately 1.00 MB and an inference latency of 0.0037 ms per sample. These results confirm that explicit semantic alignment combined with a lightweight architecture is key to robust, real-time intrusion detection in resource-constrained CAVs. Full article

Skin cancer is a widespread and fatal disease in which early and accurate detection is an important aspect for effective treatment. The issues that arise when performing automated analysis of dermatoscopic images include artifacts such as hair, low contrast, and irregular edges of lesions that interfere with segmentation and classification. This study proposes an automated image preprocessing pipeline designed to remove artifacts while saving lesion texture and boundary. The method combines various computer vision methods and processes to produce a hairless dermatoscopic image of the sample, and lesion segmentation is subsequently performed using the HSV color space and binary masking. The effectiveness of the proposed preprocessing approach is evaluated using five state-of-the-art models: VGG16, ResNet50, InceptionV3, EfficientNet-B4, and DenseNet121. Full article

In this study, waste polypropylene (PP) and magnetite (Fe₃O₄) mineral-reinforced cement-based pyramidal composite structures were designed, manufactured, and experimentally characterized to reduce electromagnetic interference (EMI) problems in the 3.3–4.9 GHz frequency band for 5G communication systems. Unlike traditional planar concrete surfaces, the aim was to minimize surface reflections and obtain an absorption-dominant shielding mechanism by providing gradient impedance matching through the pyramidal geometry. Although the use of carbon-based nanomaterials is common in the current literature, their high cost and corrosion risks limit their large-scale applications. This study involves the evaluation of waste polypropylene disposal and self-enriching magnetite mineral together. Theoretical analyses were supported by the Lichtenecker Logarithmic Mixing Rule and the Maxwell–Garnett model, and seven different mixing scenarios (S1–S7) were measured using the free-space method with a Libre vector network analyzer. Experimental results showed that the pure concrete sample exhibited predominantly reflective behaviour, with shielding performance improving significantly as the filler ratio increased. The S4 sample, containing 15% PP and 10% magnetite, offered broadband and balanced absorption performance, while the S7 sample, containing 25% PP and 25% magnetite, provided the highest shielding effectiveness with reflection below −10 dB across the entire band and transmission loss reaching −65 dB. Full article

For businesses to optimize management decisions in the digital transformation, a process inherently characterized by symmetry between feedback collection and strategic adjustment, it is essential to automatically extract fine-grained opinions from large volumes of unstructured evaluations. However, traditional evaluation management techniques often fail to reflect this symmetrical balance between user perception and organizational response, primarily due to their inefficiency in processing unstructured textual data. Moreover, existing aspect–opinion mining algorithms exhibit limited practical generalization performance due to poor robustness against noise and semantic variations in real-world reviews. To address these gaps, this paper proposes MixContrast, an aspect–opinion mining method based on mix contrastive learning, which integrates mixed sample construction with data augmentation to generate continuous semantic transition samples. By symmetrically aligning positive and negative samples through a contrastive learning mechanism, MixContrast enhances representation learning and improves model generalization. Experiments conducted on cosmetics and multi-domain e-commerce review datasets demonstrate that MixContrast significantly outperforms several strong baseline models in both aspect and opinion extraction tasks. Theoretical analysis shows that MixContrast enhances robustness by ensuring Lipschitz continuity and enabling gradient decomposition in the representation space. Based on MixContrast predictions, we conduct a correlation analysis among aspects, opinions, and sentiment tendencies, delivering real-time quantitative support for marketing strategy formulation, product optimization, and service enhancement. Beyond advancing aspect–opinion mining technology, this work enables data-driven, symmetrical integration of technical insights with managerial decision-making, holding significant theoretical and practical value for digitally transforming enterprises. Full article