Search Results (5,572)

The development of sustainable materials for additive manufacturing (AM) has positioned starch as a compelling alternative to conventional thermoplastics. However, the successful implementation of starch in 3D printing (3DP) relies on precise control of its supramolecular organization, rheological behavior, and processing conditions. This article analyzes starch-based hydrogels for extrusion-driven AM, establishing explicit links between molecular architecture, gelatinization, and viscoelastic performance. The influence of key rheological parameters on extrudability and shape fidelity is examined in parallel with critical processing conditions. Chemical and physical modification routes of starch are compared in terms of their structural impact and printability enhancement. The integration of additives such as polysaccharides, proteins, and inorganic salts is discussed as a strategy to overcome intrinsic mechanical limitation. Emerging non-food application in drug delivery, tissue engineering, or conductive/intelligent hydrogels demonstrates the expanding technological relevance of starch-based formulation. Remaining challenges include predictive, rheology-based design, and the development of multifunctional 4D-printing capabilities. Progress in this field is crucial for positioning starch as a robust platform for sustainable AM, but, despite this progress, a systematic and quantitative framework linking starch molecular architecture and rheological behavior to printing-process parameters and outcomes is still lacking, thus constituting the central gap addressed in this review. Full article

Recent advances in non-thermal and hybrid extraction technologies—such as pulsed electric field (PEF), ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), and enzymatic treatments—have been widely reviewed in the context of general food processing. However, the current literature lacks a critical evaluation of how these modalities reshape the molecular fingerprints, authenticity markers, and cultivar-specific phenolic baselines of olive products, particularly within the Greek production landscape. Existing studies primarily emphasize extraction yield, operational parameters, or sustainability aspects, leaving important gaps regarding molecular consequences, including shifts in secoiridoids, lignans, pigments, oxidation markers, and the composition of by-products such as olive mill wastewater and pomace. This commentary addresses these gaps by integrating emerging extraction technologies with Greece’s omics-enabled analytical capacity (FoodOmicsGR_RI), highlighting how processing innovations may influence authenticity claims, phenolic integrity, and circular economy valorization routes. We discuss mechanistic pathways, molecular-level effects, and technology-specific limitations and propose a structured framework for developing national databases of processing-induced molecular markers. By linking technological mechanisms with cultivar-dependent molecular responses, this commentary aims to support coordinated Greek research efforts toward robust authenticity assurance, sustainable processing, and high-value valorization of olive by-products under evolving climatic and industrial pressures. Full article

In the thermal insulation winding process of solid rocket motors, the roller-tape contact pressure is a critical factor determining bonding quality. However, accurately predicting this pressure is challenging due to the complex three-dimensional contact involving a thin, nearly incompressible rubber tape and an elliptical concave press roller. This paper proposes a theoretical model extending the classical elastic foundation model by incorporating correction strategies to account for material incompressibility and geometric confinement. A finite element (FE) model was developed to simulate the contact and verified against Hertz theory. Two key parameters of the theoretical model were calibrated using the FE results and justified through a parametric study on Poisson’s ratio and a theoretical analysis of the contact half-width. The theoretical predictions of deformation, contact pressure distribution, and pressing force agree well with the FE results under different applied displacements and mandrel radii without parameter recalibration, demonstrating the model’s generality. Experimental validation employing hybrid inverse analysis confirms the model’s global accuracy, yielding <11% relative error between the predicted and measured pressing forces. This study establishes a theoretical foundation for pressure control in the winding process and provides insights into contact problems for thin elastic layers with high Poisson’s ratios (≥0.45). Full article

Polymer-based three-dimensional (3D) printing has evolved from a prototyping approach toward a manufacturing strategy with emerging clinical relevance for individualized dosage forms, local drug depots, microneedle systems, microfluidic cartridges, biosensor housings and integrated theranostic platforms. Its value arises from the simultaneous control of polymer chemistry, device architecture and process history: infill, porosity, shell thickness, crosslink density, swelling, degradation and surface chemistry can be used as design variables rather than incidental manufacturing outcomes. This review critically synthesizes recent progress in polymer-enabled additive manufacturing for drug delivery and diagnostic applications, with emphasis on thermoplastic and biodegradable polymers, hydrogels, photopolymers, elastomers, conductive composites, stimuli-responsive networks and bioinks. Fused deposition modelling, hot-melt extrusion, semi-solid extrusion, vat photopolymerization, two-photon polymerization, selective laser sintering, binder jetting and inkjet/aerosol jet approaches are compared in relation to drug stability, diagnostic compatibility, feature resolution, scalability and regulatory risk. Particular attention is given to geometry-controlled release, multi-drug printlets, microneedles, implants, scaffold-based local therapy, microfluidic diagnostics, electrochemical biosensors and wearable or closed-loop systems. Translation is discussed through quality-by-design, critical material attributes, critical process parameters, process analytical technology, extractables/leachables, sterilization, point-of-care manufacturing, data integrity and clinical evidence requirements. Future advances should connect polymer–process–property relationships with clinically meaningful use cases, verified quality attributes and realistic regulatory pathways. Full article

Roof-contact backfilling is a critical determinant of stope stability in cut-and-fill mining, and the rheological properties of backfill slurry decisively influence the quality of roof contact. To investigate the flow characteristics of unclassified tailings backfill slurry and their effect on rheological parameters, this study uses the Daye Iron Mine as its engineering case. It adopts a combined laboratory and numerical simulation approach. The physicochemical characteristics of the unclassified tailings and the rheological behavior of the slurry were systematically characterized using particle-size analysis, density measurements, spreadability tests, and rheometer measurements. Subsequently, a numerical model of the L-type flow tester was developed in COMSOL Multiphysics (6.4) to simulate the flow process at varying concentrations. Based on the simulation results, a Gaussian process regression (GPR)-based inversion model for rheological parameters was proposed, and the predictive performance of different kernel functions was compared and evaluated. Finally, the existing backfilling scheme at the Daye Iron Mine was optimized based on the obtained rheological characteristics to improve the roof-contact rate. The results indicate that the unclassified tailings from the Daye Iron Mine have a median particle size of 12.1 μm and a density of 2855 kg·m⁻³, with CaO, Al₂O₃, and MgO as the primary active components. Under the same cement-to-tailings ratio, slurry flowability decreases markedly with increasing concentration. The rheological curves exhibit three stages, with the third conforming to the Bingham model; both yield stress and viscosity increase exponentially with concentration. Evaluation of the inversion results demonstrates that the GPR model with the Rational Quadratic (RQ) kernel achieves optimal performance. The recommended slurry concentration for the Daye Iron Mine is determined to be in the range of 69–71%, and the recommended spacing between filling pipelines is 13.34–18 m. This study reveals the flow evolution patterns of unclassified tailings backfill slurry, demonstrates the potential of the GPR-based inversion approach, and optimizes the roof-contact backfilling scheme, offering a scientific reference for flow characterization and backfill optimization in analogous mining operations. Full article

Timely and accurate defect classification is critical for ensuring product quality and safety in industrial inspection scenarios. The widespread deployment of Internet of Things (IoT) devices equipped with sensing, computing, and communication capabilities has promoted the development of AI-enabled industrial applications. However, conventional AI approaches typically rely on centralized data collection and processing, which become impractical in real-world IoT environments due to growing privacy concerns and constrained device resources. To address these challenges, this paper proposes a communication-efficient adaptive federated learning algorithm for heterogeneous defect classification tasks. The proposed approach jointly accelerates the training process through three mechanisms: (i) adaptive local updates that balance communication and computation overheads; (ii) parameter compression that trades off communication cost against model accuracy; (iii) joint bandwidth and computation-power allocation that optimizes per-round communication and computation time across participating devices. We further analyze the joint effects of these three mechanisms and provide a convergence analysis. Extensive simulations show that the proposed method achieves competitive classification accuracy while reducing single-round training time by up to 70%. Full article

Accurate remaining useful life (RUL) prediction of lithium-ion batteries is critical for reliable operation of the system. The performance evolution of lithium-ion batteries shows long-range dependence and capacity regeneration. However, existing performance evolution models fail to properly consider the joint effect of long-range dependence and capacity regeneration, and consequently have a deficiency in mechanism interpretability, which may limit the prediction accuracy of RUL. To address this gap, this paper separately characterizes the degradation and regeneration processes of the discharge capacity, and proposes a novel discharge capacity evolution model incorporating fractional Brownian motion and the Poisson capacity regeneration process. For the estimation problem of the model parameters caused by the Poisson regeneration, we approximately transform the proposed model to one with independent increments according to the weak convergence theorem, and then develop a maximum likelihood estimation method. From a limit perspective and using the theory of total probability, we derive the distribution of RUL, and provide the point estimation of RUL. Finally, we utilize the data set of lithium-ion batteries produced by NASA to verify the effectiveness of the proposed method, and the mean absolute errors of the proposed method are declined by at least 24% compared to the existing methods. Full article

The increasing demand for sustainable and environmentally synthesis routes has driven significant interest in biogenic precursors for nanomaterial fabrication. Among these, egg-derived materials—including eggshell, eggshell membrane (ESM), egg white, and egg yolk—have emerged as versatile, low-cost, and multifunctional resources for green nanotechnology. This review provides a comprehensive and critical analysis of the physicochemical properties and functional roles of egg-derived components in nanomaterial synthesis. A comparative evaluation of egg-derived and conventional synthesis methods is presented, highlighting the trade-off between environmental sustainability and control over physicochemical parameters. Egg-derived approaches offer reduced toxicity, lower energy consumption, and intrinsic functionalization, but remain limited by compositional variability, reduced reproducibility, and challenges in process scalability. Furthermore, an application-oriented framework is proposed for selecting appropriate egg-derived precursors based on material type, targeted functionality, and processing constraints. The review also identifies key limitations, including mechanistic uncertainties, organic residue formation, and regulatory considerations, and outlines future research directions focused on process standardization, in situ characterization, and hybrid synthesis strategies. Full article

Rolling bearings are critical components in rotating machinery, and their failures may cause unexpected downtime and safety risks. However, conventional deep diagnostic models are often difficult to deploy on resource-constrained edge devices because of their high computational cost and memory consumption. This paper proposes a lightweight time–frequency attention network (LTFANet) for multi-fault diagnosis of rolling bearings on an edge platform. The proposed model directly processes one-dimensional vibration signals and employs multi-scale depthwise separable convolutions to capture impact and periodic fault features with low computational complexity. A lightweight frequency branch is introduced to enhance fault-frequency representation, while an efficient channel attention module adaptively emphasizes fault-sensitive features. Moreover, a severity-aware multi-task extension is introduced to jointly identify the fault location and degradation level. To further improve edge inference efficiency, knowledge distillation, structured pruning, and TensorRT-based acceleration are integrated into the deployment pipeline. Experiments on CWRU-10 and Paderborn achieve 97.20% and 90.25% accuracy, respectively, while LTFANet contains only 0.020 M parameters and requires 0.610 M FLOPs. Knowledge distillation increases the CWRU-10 accuracy to 98.50%, and the severity-aware extension achieves 95.18% severity accuracy. On the NVIDIA Jetson Nano, the pruned TensorRT FP16 implementation achieves an average inference latency of 0.520 ms and a throughput of 1923.08 samples/s. The framework provides an effective solution for real-time and low-cost bearing condition monitoring at the edge. Full article

The knee is a mechanically demanding synovial organ in which joint loading, tissue deformation, cellular mechanotransduction and matrix turnover are coupled. This narrative review critically links those scales and asks where the evidence is sufficiently mature for mechanistic or clinical inference. PubMed/MEDLINE and Europe PMC were searched from database inception to 20 July 2026 using structured terms for knee biomechanics, cartilage and osteochondral mechanobiology, finite element modelling, mechanosensitive channels, osteoarthritis, machine learning and digital twins. Landmark studies were selected for foundational models, while recent studies were prioritised for causal experiments, validation and translation. Instrumented implants show that common activities generate tibiofemoral forces of several times body weight, but tissue-level exposure also depends on muscle co-contraction, geometry and material properties. Biphasic and fibril-reinforced models explain how those loads become stress, strain, fluid pressure and osmotic signals. At the cell scale, TRPV4 and PIEZO1/2 participate in overlapping, context-dependent calcium signalling rather than a universal protective–pathological binary; most causal evidence remains preclinical. Osteoarthritis is therefore framed as a mechanically amplified feedback process involving cartilage, bone, synovium and systemic modifiers. Computational degeneration models and machine-learning surrogates are increasingly informative, although prospective validation, parameter identifiability and uncertainty propagation remain limiting. The review’s added value is an explicit transmission-and-validation framework that connects whole-joint observables to molecular responses while labelling the evidence source and translational readiness at every step. Full article

High-precision tunnel breakthroughs depend critically on the north-seeking accuracy of maglev gyroscopes. However, external disturbances during underground construction often introduce abrupt jumps into rotor current signals, significantly reducing the orientation reliability. Existing signal-processing methods either require manually defined segmentation windows or apply identical denoising strategies to both stationary and disturbed signal intervals, resulting in limited adaptability and suboptimal denoising performance. To overcome these limitations, this study proposes an improved rotor current denoising algorithm based on the MAF-ARIMA framework by incorporating the Bayesian ensemble algorithm for abrupt change, seasonality, and trend (BEAST) and an optimized wavelet transform (OWT). First, the BEAST is employed to automatically detect the structural change point of the rotor current signal, enabling the adaptive segmentation of stationary and jump intervals without manual intervention. Subsequently, empirical mode decomposition is performed, and the OWT applies different denoising parameters to the dominant components of the stationary and jump segments according to their distinct fluctuation characteristics. Finally, moving-average smoothing is adopted to preserve the signal continuity at the segmentation boundary, while the autoregressive integrated moving average (ARIMA) model reconstructs the missing trend component of the jump interval to obtain the complete denoised signal. Comparative experiments using 12 field-collected rotor current datasets demonstrated that the proposed method reduced the standard deviation of the denoised signal by 70.96% and the absolute azimuth error by 50.36% compared with the raw signal, outperforming the optimized Hilbert–Huang transform, HSA-KS, and the original MAF-ARIMA algorithm. By introducing adaptive change-point detection and segment-specific denoising into the existing MAF-ARIMA framework, the proposed method significantly improves the adaptability and denoising performance of maglev gyro rotor current processing under complex tunnel construction environments while preserving the signal continuity and reconstruction accuracy. Full article

Background: The clinical translation of cell-based therapies for knee articular cartilage repair is fundamentally restricted by the severe biological unpredictability of autologous cell sources, inherent manufacturing bottlenecks, and the rapid phenotypic dedifferentiation of cells expanded in conventional 2D monolayers. To overcome these translational hurdles, this study engineered a scaffold-free, 3D formulation of highly characterized allogeneic FE002-Cart chondroprogenitor spheroids. Methods: We systematically investigated the specific microenvironmental cues and Good Manufacturing Practice (GMP) formulation parameters required to direct functional chondrogenesis. The structural and biochemical performance of this allogeneic formulation was benchmarked against multiple primary adult autologous chondrocyte types. Finally, we evaluated the phenotypic resilience of the microtissues in simulated osteoarthritic (OA) environments and investigated both short-term liquid storage and advanced terminal preservation strategies to establish off-the-shelf logistical viability. Results: Precise microenvironmental regulation proved to be a critical biological prerequisite. The synergistic combination of physiological hypoxia (2% O₂) and stringent glucocorticoid limitation (10 nM dexamethasone) induced robust glycosaminoglycan (GAG) deposition and a > 200-fold upregulation of ACAN and COL2, while suppressing the terminal hypertrophic drift observed in adult chondrocytes. Benchmarking revealed that the allogeneic FE002-Cart formulation substantially mitigates the profound morphological and biochemical unpredictability inherent to adult autologous cell sources. Furthermore, the scaffold-free spheroid geometry yielded a 10-fold increase in GAG production per cell compared to traditional matrix-seeded (MACI) platforms. Transitioning to a GMP-compatible manufacturing process revealed extreme cellular sensitivities; excipients within standard pharmaceutical-grade dexamethasone severely aborted chondrogenic differentiation, emphasizing the necessity of rigorous raw-material qualification. Functionally, the 3D architecture acted as a protective physical shield, sustaining high cellular viability when subjected to severe inflammatory stress and 100% OA patient synovial fluid. Logistically, the viable spheroids maintained matrix integrity and inter-spheroid fusion potential for up to 7 days at ambient temperature in transport medium. Finally, advanced spheroid preservation via lyophilization and high-dose gamma irradiation eliminated biological viability but successfully transitioned the microtissues into highly organized, terminally irradiated matrices capable of heterologous in vitro structural merging. Conclusions: These findings define the critical biological thresholds for manufacturing, demonstrate the enhanced in vitro biosynthetic efficiency of 3D allogeneic microtissues compared to specific autologous and matrix-dependent baselines, and establish a highly practical, off-the-shelf logistical framework for the regenerative treatment of large knee chondral defects. Full article

The use of manufactured sand (MS) as a substitute for natural sand or quartz sand in the production of ultra-high-performance manufactured sand concrete (UHPMC) represents a critical approach to alleviating the shortage of high-quality aggregates and promoting low-carbon development. However, after steel fibers and steel rebars are introduced into this material system, the synergistic working mechanism and damage evolution characteristics of the resulting ultra-high-performance manufactured sand-reinforced concrete (UHPMRC) beams under flexural loading remain largely unexplored. Acoustic emission (AE) technology, owing to its high sensitivity to the initiation and propagation of microcracks, enables real-time dynamic monitoring of UHPMRC beams throughout the entire process from the elastic stage to fracture failure, thereby providing an effective means to reveal the internal performance degradation law. Accordingly, this study conducted simultaneous AE monitoring on small-scale reinforced beams under four-point bending and investigated the effects of MS replacement ratios (0%, 50%, 100%) and steel fiber contents (1.0%, 1.5%, 2.0%). Results show that UHPMRC beams with 100% MS replacement and 1.5% steel fiber content achieve optimal performance. Compared to 0% MS specimens, those with 100% MS exhibit superior early stiffness, ductility, and flexural capacity due to the combined effects of steel fibers and MS. Beams with 2% steel fiber content experienced fiber clustering, reducing bridging capability and promoting earlier cracking relative to those with 1.5% fibers. AE energy parameters accurately identified cracking and characterized crack propagation in UHPMRC beams. Increasing MS content raised the proportion of shear cracks while reducing tensile cracks. The highest shear signal proportion occurred at 1.0% steel fiber content. These findings provide a valuable reference for the design of sustainable high-performance reinforced-concrete structures using manufactured sand. Full article

The morphology and pore structure of ZSM-48 zeolite are critical parameters determining the catalytic performance of bifunctional catalysts in the hydroisomerization of long-chain n-paraffins. This study investigates the effects of the Na₂O/SiO₂ molar ratio (0.02–0.12) in the synthesis gel and hydrothermal treatment duration (48–72 h) on the crystallization kinetics, phase purity, and physicochemical properties of ZSM-48. Low alkalinity (Na₂O/SiO₂ = 0.04–0.06) and shorter synthesis times (48 h) promote the formation of small aggregates composed of short needle-like crystals with enhanced intercrystalline mesoporosity. Conversely, increasing the alkalinity and crystallization duration accelerates crystal growth, resulting in dense pseudo-spherical aggregates (up to 4–7 μm in size) with restricted external surface area and increased diffusion limitations. Catalytic testing of Pt/ZSM-48 (0.5 wt.% Pt) in n-hexadecane hydroisomerization demonstrates that crystal morphology, size, and porosity significantly influence process selectivity. The catalyst based on nanosized ZSM-48 (Pt/Z48-06-2) effectively mitigates diffusion resistance, yielding a maximum isomer yield of 73% at 82% selectivity. In contrast, larger, densely packed aggregates with high but poorly accessible acidity intensify secondary hydrocracking reactions, reducing a maximum isomer yield to 46%. These results highlight the ability to tune the catalytic properties of ZSM-48 through careful control over gel alkalinity and crystallization kinetics. Full article

Semantic segmentation is a critical task in computer vision aiming to identify and classify individual pixels in an image, with numerous applications, for example, in autonomous driving and medical image analysis. However, semantic segmentation can be highly challenging, particularly due to the need for large amounts of annotated data. Annotating images is a time-consuming and costly process, often requiring expert knowledge and significant effort; moreover, saving the annotated images could dramatically increase the storage space. In this paper, we propose a novel approach for semantic segmentation, requiring only rough information about the proportions of individual semantic classes, hereafter referred to as semantic proportions (SPs), rather than the necessity of ground-truth segmentation maps. This greatly simplifies the data annotation process and thus will significantly reduce the annotation time, cost and storage space, opening up new possibilities for semantic segmentation tasks where obtaining the full ground-truth segmentation maps may not be feasible or practical. Our proposed method of utilising semantic proportions can (i) further be utilised as a booster in the presence of ground-truth segmentation maps to gain performance without extra data and model complexity, and (ii) also be seen as a parameter-free plug-and-play module, which can be attached to existing deep neural networks designed for semantic segmentation. Extensive experimental results demonstrate the good performance of our method compared to benchmark methods that rely on ground-truth segmentation maps. Utilising semantic proportions suggested in this work offers a promising direction for future semantic segmentation research. Full article