Search Results (5,703)

This study presents a bibliometric and thematic analysis of economic convergence analysis from 1982 to 2025, based on a corpus of 2924 Scopus-indexed articles. Using VOSviewer and the bibliometrix R package, this research maps the field’s intellectual structure, identifying five main thematic clusters: (1) formal statistical models, (2) institutional-contextual approaches, (3) theoretical–statistical foundations, (4) nonlinear historical dynamics, and (5) normative and policy assessments. These reflect a shift from descriptive to explanatory and prescriptive frameworks, with growing integration of sustainability, spatial analysis, and institutional factors. The most productive journals include Journal of Econometrics (121 articles), Applied Economics (117), and Journal of Cleaner Production (81), while seminal contributions by Quah, Im et al., and Levin et al. anchor the co-citation network. International collaboration is significant, with 25.99% of publications involving cross-country co-authorship, particularly in European and North American networks. The field has grown at a compound annual rate of 14.4%, accelerating after 2000 and peaking in 2022–2024, indicating sustained academic interest. These findings highlight the maturation of convergence analysis as a multidisciplinary domain. Practically, this study underscores the value of composite indicators and spatial econometric models for monitoring regional, environmental, and technological convergence—offering policymakers tools for inclusive growth, climate resilience, and innovation strategies. Moreover, the emergence of clusters around sustainability and digital transformation reveals fertile ground for future research at the intersection of transitions in energy, digital, and institutional domains and sustainable development (a broader sense of structural change). Full article

Gradient structure provides an effective approach to improve the combination of high strength and toughness compared to a uniform one. The gradient interfaces or boundaries in gradient-structured metallic glass composites play a crucial role in influencing mechanical properties. Our findings indicate the gradient microstructure significantly induces temporal and spatial stress localization, which can modulate the generation and propagation of shear bands. The synergistic gradient effects generated by heterogeneous grain sizes and interface characteristics can enhance both the strength (yield stress and peak stress) and the toughness of gradient network-structured metallic glass composites as the grain size gradient and the boundary width increase. Our work demonstrates the appropriate gradient of grain size, and the boundary structure should potentially lead to enhanced work hardening. Full article

We synthesized the current evidence from the literature and conducted a 2 × 3 factorial experiment to quantify the impact of thermocycling on the Vickers microhardness (HV) of dental CAD/CAM materials: VITA ENAMIC (VE, polymer-infiltrated ceramic network) and CERASMART (CS, nanofilled resin-matrix). Sixty polished specimens (n = 10 per Material × Cycles cell; 12 × 2 × 2 mm) were thermocycled at 5–55 °C (0, 10,000, 20,000 cycles; 30 s dwell, ≈10 s transfer) and tested as HV0.3/10 (300 gf, 10 s; five indentations/specimen with standard spacing). Assumptions regarding the model residuals were met (Shapiro–Wilk W ≈ 0.98, p ≈ 0.36; Levene F(5,54) ≈ 1.12, p ≈ 0.36), so a two-way ANOVA (Type II) with Tukey’s HSD post hoc (α = 0.05) was applied. VE maintained consistently higher HV than CS at all cycle levels and showed a smaller drop from baseline: VE (mean ± SD): 200.2 ± 10.8 (0), 192.4 ± 13.9 (10,000), and 196.7 ± 9.3 (20,000); CS: 60.8 ± 6.1 (0), 53.4 ± 4.7 (10,000), and 62.1 ± 3.8 (20,000). ANOVA revealed significant main effects from the material (η²p = 0.972) and cycles (η²p = 0.316), plus a Material × Cycles interaction (η²p = 0.201). Results: Thermocycling produced material-dependent changes in microhardness. Relative to baseline, VE varied by −3.9% (10,000) and −1.7% (20,000), while CS varied by −12.2% (10,000) and +2.1% (20,000); from 10,000→20,000 cycles, microhardness recovered by +2.2% (VE) and +16.3% (CS). Pairwise comparisons were consistent with these trends (CS decreased at 10,000 vs. 0 and recovered at 20,000; VE only showed a modest change). Conclusions: Thermocycling effects were material-dependent, with smaller losses and better retention in VE (PICN) than in CS. These results align with the literature (resin-matrix/hybrids are more sensitive to thermal aging; polished finishes mitigate losses). While HV is only one facet of performance, the superior retention observed in PICN under thermal challenge suggests the improved preservation of superficial integrity; standardized reporting of aging parameters and integration with wear, fatigue, and adhesion outcomes are recommended to inform indications and longevity. Full article

Fibre-reinforced composites are facing new challenges in the context particular in sustainability and recyclability. Vitrimers could be useful as new matrices to support the increase in sustainability. Due to their high strength, which is comparable to that of thermosets often used in composites, and their covalent adaptive networks, which make them reshapeable for scaled-up manufacturing and recycling purposes, they are very useful. Orthogonal cutting is used for precise reshaping and functional integration into carbon fibre reinforced plastics. Vitrimers could improve processing results at the cutting edge as well as surface quality thanks to their self-healing properties compared to brittle matrices, as well as enabling the recycling of formed chips and scrap. This study showcases the manufacturing of a carbon fibre-reinforced vitrimer using 4-aminophenyl disulfide as a hardener, with vacuum-assisted resin infusion. The temperature of chip formation and the cutting parameters are then shown for different fibre orientations, cutting widths and speeds. The observed cutting forces are lower (less than 140 N) and more irregular for fibre orientations 45°/135°, increasing with cutting depth, and fluctuating periodically during machining. Despite varying cutting speeds, the forces remain relatively constant in range between 85 N and 175 N for 0°/90° fibre orientation and 50 N and 120 N for 45°/135° fibre orientation, with no significant tool wear observed and lower-damage depth and overhanging fibres observed for 0°/90° fibre orientation. Damage observation of the cutting tool shows promising results, with lower abrasion observed compared to thermoset matrices. Microscopic images of the broached surface also show good quality, which could be improved by self-healing of the matrix at higher temperatures. Temperature measurements of chip formation using a high-speed camera show a high temperature gradient as cutting speeds increase, but the temperature only ever exceeds 180 °C at cutting speeds of 150 m/min, ensuring reprocessability since this is below the degradation temperature. Therefore, orthogonal cutting of vitrimers can impact sustainable composite processing. Full article

This work takes up the task of the determination of the rate of pointwise and uniform convergences to the unit operator of the “normalized cusp neural network operators”. The cusp is a compact support activation function, which is the composition of two general activation functions having as domain the whole real line. These convergences are given via the modulus of continuity of the engaged function or its derivative in the form of Jackson type inequalities. The composition of activation functions aims to more flexible and powerful neural networks, introducing for the first time the reduction in infinite domains to the one domain of compact support. Full article

This paper presents a comprehensive study on automated defect detection in complex structures using phased array ultrasonic testing data, focusing on both traditional signal processing and advanced deep learning methods. As a non-AI baseline, the well-known signal-to-noise ratio algorithm was improved by introducing automatic depth gate calculation using derivative analysis and eliminated the need for manual parameter tuning. Even though this method demonstrates robust flaw indication, it faces difficulties for automatic defect detection in highly noisy data or in cases with large pore zones. Considering this, multiple DL architectures—including fully connected networks, convolutional neural networks, and a novel Convolutional Attention Temporal Transformer for Sequences—are developed and trained on diverse datasets comprising simulated CIVA data and real-world data files from welded and composite specimens. Experimental results show that while the FCN architecture is limited in its ability to model dependencies, the CNN achieves a strong performance with a test accuracy of 94.9%, effectively capturing local features from PAUT signals. The CATT-S model, which integrates a convolutional feature extractor with a self-attention mechanism, consistently outperforms the other baselines by effectively modeling both fine-grained signal morphology and long-range inter-beam dependencies. Achieving a remarkable accuracy of 99.4% and a strong F1-score of 0.905 on experimental data, this integrated approach demonstrates significant practical potential for improving the reliability and efficiency of NDT in complex, heterogeneous materials. Full article

Background and Objectives: Sarcopenia is a progressive loss of skeletal muscle mass and function in elderly adults, posing a significant risk of frailty, falls, and morbidity. The current study designs and evaluates SarcoNet, a novel artificial neural network (ANN)-based classification framework developed in order to classify Sarcopenic from non-Sarcopenic subjects using a comprehensive real-time dataset. Methods: This pilot study involved 30 subjects, who were divided into Sarcopenic and non-Sarcopenic groups based on physician assessment. The collected dataset consists of thirty-one clinical parameters like skeletal muscle mass, which is collected using various equipment such as Body Composition Analyser, along with ten kinetic features which are derived from video-based gait analysis of joint angles obtained during walking on three terrain types such as slope, steps, and parallel path. The performance of the designed ANN-based SarcoNet was benchmarked against the traditional machine learning classifiers utilised including Support Vector Machine (SVM), k-Nearest Neighbours (k-NN), and Random Forest (RF), as well as hard and soft voting ensemble classifiers. Results: SarcoNet achieved the highest overall classification accuracy of about 94%, with a specificity and precision of about 100%, an F1-score of about 92.4%, and an AUC of 0.94, outperforming all other models. The incorporation of lower-limb joint kinetics such as knee flexion, extension, ankle plantarflexion and dorsiflexion significantly enhanced predictive capability of the model and thus reflecting the functional deterioration characteristic of muscles in Sarcopenia. Conclusions: SarcoNet provides a promising AI-driven solution in Sarcopenia diagnosis, especially in low-resource healthcare settings. Future work will focus on improving the dataset, validating the model across diverse populations, and incorporating explainable AI to improve clinical adoption. Full article

Electronic-waste (e-waste) management is a key challenge in engineering smart cities due to its rapid accumulation, complex composition, sparse data availability, and significant environmental and economic impacts. This study employs a bespoke machine learning infrastructure on an Indian e-waste supply chain network (SCN) focusing on the three pillars of sustainability—environmental, economic, and social. The economic resilience of the SCN is investigated against external perturbations, like market fluctuations or policy changes, by analyzing six stochastically perturbed modules, generated from the optimal point of the original dataset using Monte Carlo Simulation (MCS). In the process, MCS is demonstrated as a powerful technique to deal with sparse statistics in SCN modeling. The perturbed model is then analyzed to uncover “hidden” non-linear relationships between key variables and their sensitivity in dictating economic arbitrage. Two complementary ensemble-based approaches have been used—Feedforward Neural Network (FNN) model and Random Forest (RF) model. While FNN excels in regressing the model performance against the industry-specified target, RF is better in dealing with feature engineering and dimensional reduction, thus identifying the most influential variables. Our results demonstrate that the FNN model is a superior predictor of arbitrage conditions compared to the RF model. The tangible deliverable is a data-driven toolkit for smart engineering solutions to ensure sustainable e-waste management. Full article

A novel bio-gelatin fiber-reinforced composite (BFRC) was first developed by incorporating industrial bone glue/gelatin as the matrix, magnesium oxide (MgO) as an additive, and natural or synthetic fibers as reinforcement. Systematic tests evaluated mechanical, impact, and thermal performance, alongside microstructural mechanisms. Results showed that polyethylene (PE) fiber-reinforced composites achieved a tensile strength of 3.40 MPa and tensile strain of 10.77%, with notable improvements in compressive and flexural strength. PE-based composites also showed excellent impact energy absorption, while bamboo fiber-reinforced composites exhibited higher thermal conductivity. Microstructural analysis revealed that coordination between Mg²⁺ ions and amino acids in gelatin formed a stable cross-linked network, densifying the matrix and improving structural integrity. A multi-criteria evaluation using the TOPSIS model identified the BC-PE formulation as the most balanced system, combining strength, toughness, and thermal regulation. These findings demonstrate that ionic coordination and fiber reinforcement can overcome inherent weaknesses of gelatin matrices, offering a sustainable pathway for building insulation and cushioning packaging applications. Full article

Gelatin is a collagen-derived biopolymer widely used in food, pharmaceutical and biomedical applications due to its biocompatibility and gelling ability. However, gelatin hydrogels suffer from unstable mechanical strength, limited thermal resistance and susceptibility to microbial contamination. The main aim of the present study is to investigate the influence of gelatin cryostructuring followed by photo-induced menadione sodium bisulfite (MSB) chemical crosslinking on the structural and functional characteristics of mammalian and fish gelatin hydrogels. The integration of scanning electron microscopy, dielectric spectroscopy and rheological experiments provides a comprehensive view of the of molecular, morphological and mechanical properties of gelatin hydrogels under photo-induced chemical crosslinking. The SEM results revealed that crosslinked hydrogels are characterized by enlarged pores compared to non-crosslinked systems. For mammalian gelatin, multiple pores with thin partitions are formed, giving a dense and stable polymer network. For fish gelatin, large oval pores with thickened partitions are formed, preserving a less stable ordered architecture. Rheological data show strong reinforcement of the elastic and thermal stability of mammalian gelatin. The crosslinked mammalian system maintains the gel state at higher temperatures. Fish gelatin exhibits reduced elasticity retention even after crosslinking because of a different amino acid composition. Dielectric results show that crosslinking increases the portion of bound water in hydrogels considerably, but for fish gelatin, bound water is more mobile, which may explain weaker mechanical properties. Full article

In the context of low carbon, achieving accurate forecasting of electrical energy is critical for power management with the continuous development of power systems. For the sake of improving the performance of load forecasting, an improved MNet-Atten electric vehicle charging load forecasting based on composite decomposition and the evolutionary predator–prey and strategy model is proposed. In this light, through the data decomposition theory, each subsequence is processed using complementary ensemble empirical mode decomposition and filters out high-frequency white noise by using singular value decomposition based on matrix operation, which improves the anti-interference ability and computational efficiency of the model. In the model construction stage, the MNet-Atten prediction model is developed and constructed. The convolution module is used to mine the local dependencies of the sequences, and the long term and short-term features of the data are extracted through the loop and loop skip modules to improve the predictability of the data itself. Furthermore, the evolutionary predator and prey strategy is used to iteratively optimize the learning rate of the MNet-Atten for improving the forecasting performance and convergence speed of the model. The autoregressive module is used to enhance the ability of the neural network to identify linear features and improve the prediction performance of the model. Increasing temporal attention to give more weight to important features for global and local linkage capture. Additionally, the electric vehicle charging load data in a certain region, as an example, is verified, and the average value of 30 running times of the combined model proposed is 117.3231 s, and the correlation coefficient PCC of the CEEMD-SVD-EPPS-MNet-Atten model is closer to 1. Furthermore, the CEEMD-SVD-EPPS-MNet-Atten model has the lowest MAPE, RMSE, and PCC. The results show that the model in this paper can better extract the characteristics of the data, improve the modeling efficiency, and have a high data prediction accuracy. Full article

Cordyceps militaris (C. militaris) is a medicinal fungus renowned for its diverse therapeutic properties, largely attributed to bioactive compounds such as cordycepin, polysaccharides, adenosine, D-mannitol, carotenoids, and ergosterol. However, the production and composition of these metabolites are highly influenced by cultivation conditions, highlighting the need for systematic optimization strategies. This review synthesizes current findings on how nutritional factors—including carbon and nitrogen sources, their ratios, and trace elements—and environmental parameters such as oxygen availability, pH, temperature, and light regulate C. militaris metabolite biosynthesis. The impacts of solid-state fermentation (using grains, insects, and agro-industrial residues) and liquid state fermentation (submerged and surface cultures) are compared, with attention to their roles in mycelial growth, fruiting body formation, and secondary metabolite production. Special emphasis is placed on mixed grain–insect substrates and light regulation, which have emerged as promising methods to enhance cordycepin accumulation. Beyond summarizing advances, this review also identifies key knowledge gaps that must be addressed: (i) the incomplete understanding of metabolite regulatory networks, (ii) the absence of standardized cultivation protocols, and (iii) unresolved challenges in scale-up, including oxygen transfer, foam control, and downstream processing. We propose that future research should integrate multi-omics approaches with bioprocess engineering to overcome these limitations. Collectively, this review highlights both current progress and remaining challenges, providing a roadmap for advancing the sustainable, scalable, and application-driven production of bioactive compounds from C. militaris. Full article

The paper presents a comparative analysis of various machine learning algorithms for the classification of epoxy composites reinforced with basalt fiber and modified with inorganic fillers. The classification is based on key thermophysical characteristics, in particular, the mass fraction of the filler, temperature, and thermal conductivity coefficient. A dataset of 16,056 interpolated samples was used to train and evaluate more than a dozen models. Among the tested algorithms, the MLP neural network model showed the highest accuracy of 99.7% and balanced classification metrics F1-measure and G-Mean. Ensemble methods, including XGBoost, CatBoost, ExtraTrees, and HistGradientBoosting, also showed high classification accuracy. To interpret the results of the MLP model, SHAP analysis was applied, which confirmed the predominant influence of the mass fraction of the filler on decision-making for all classes. The results of the study confirm the high effectiveness of machine learning methods for recognizing filler type in composite materials, as well as the potential of interpretable AI in materials science tasks. Full article

Low-Earth-Orbit Satellite Internet (LEO SI), with its capability for seamless global coverage, is a key solution for connecting IoT devices in areas beyond terrestrial network reach, playing a vital role in building a future ubiquitous IoT system. Inspired by the IEEE 802.15.4 Improved Adaptive Backoff Algorithm (I-ABA), this paper proposes an Attack-Aware Adaptive Backoff Indicator (AA-BI) mechanism to enhance the security and robustness of the two-step random access process in LEO SI. The mechanism constructs a composite threat intensity indicator that incorporates collision probability, Denial-of-Service (DoS) attack strength, and replay attack intensity. This quantified threat level is smoothly mapped to a dynamic backoff window to achieve adaptive backoff adjustment. Simulation results demonstrate that, with 200 pieces of user equipment (UE), the AA-BI mechanism significantly improves the access success rate (ASR) and jamming resistance rate (JRR) under various attack scenarios compared to the I-ABA and Binary Exponential Backoff (BEB) algorithms. Notably, under high-attack conditions, AA-BI improves ASR by up to 25.1% and 56.6% over I-ABA and BEB, respectively. Moreover, under high-load conditions with 800 users, AA-BI still maintains superior performance, achieving an ASR of 0.42 and a JRR of 0.68, thereby effectively ensuring the access performance and reliability of satellite Internet in malicious environments. Full article

This study determined the efficacy of in-feed supplementation of a medium-chain fatty acid, caprylic acid (CA), on the cecal colonization of multidrug-resistant (MDR) Salmonella Heidelberg (SH) and its effect on the cecal microbiome of commercial broilers. A total of 24, 4-week-old commercial Ross 708 chickens were randomly allocated to two replicates of four treatment groups in eight BSL2 isolators (3 birds/isolator): Negative control (NC), Positive Control (PC), Antibiotic group (AB), and caprylic acid (CA) groups. The birds received a Salmonella-free standard corn–soy-based diet, with the broilers in the AB receiving 50 g/ton bacitracin methylene disalicylate, and the CA group receiving caprylic acid (1% w/w), in feed from days 1 to 35. All birds, except those in the NC group, were challenged with ~3.7 log₁₀ CFU of MDR SH/5 mL by crop gavage on day 29. Cecal samples were collected 7 days after the challenge for SH recovery by direct plating and enrichment, as well as for DNA extraction for 16S rRNA gene amplicon sequencing. Compared to the PC group, a 3.6 log₁₀ CFU/g reduction in SH was observed in the CA group (p < 0.05). Although no significant effect of CA on cecal microbial composition was observed, a significant difference in taxonomic α- and β-diversities was observed in the AB. CA also resulted in significant differences in hub taxa compared to PC in the network association analysis, indicating a potential role for microbiome modulation in its mechanism of action. Full article