Search Results (2,046)

Terpenoids represent a large class of bioactive natural compounds with promising pharmacological properties, including anti-inflammatory, antimicrobial, and anticancer activities. However, their clinical application is often limited by poor aqueous solubility, low membrane permeability, and suboptimal bioavailability. Phytosomal delivery systems have emerged as a promising strategy to enhance the pharmacokinetic performance of plant-derived compounds by forming molecular complexes between bioactive molecules and phospholipids. This review critically examines the structural principles, preparation methods, physicochemical characterization, and biological performance of terpenoid phytosomes. Particular attention is given to the molecular interactions between terpenoids and phospholipids that govern complex formation and vesicular assembly. The review also summarizes current analytical techniques used to confirm phytosome formation and discusses the influence of formulation parameters, including phospholipid composition and molar ratios, on stability and biological activity. In addition, emerging insights from molecular modeling and membrane interaction studies are considered to better understand the mechanisms underlying improved drug delivery. Finally, challenges related to safety assessment, manufacturing scalability, and clinical translation of phytosomal systems are discussed. Overall, terpenoid phytosomes represent a promising nanodelivery platform capable of improving the pharmacokinetic profile and therapeutic potential of terpenoid compounds. Full article

The increasing digitization of manufacturing and the integration of CNC and industrial control systems into the industry 4.0 environment have introduced new cybersecurity risks that directly affect operational reliability. Traditional deterministic risk-assessment methods used for securing ICS—such as SCADA, PLC, and CNC systems—struggle to address uncertainty, dynamic operating conditions, and complex dependencies between technical and organizational factors. To overcome these limitations, this study develops a Bayesian Network (BN) model that captures probabilistic relationships between machine-level configuration parameters, network conditions, and potential security incidents. The model is applied to a CNC machining center (ZPS MCG1000i), where it supports scenario-based prediction of cybersecurity risks and provides interpretable outputs suitable for operator decision-making and human–machine interaction. The results demonstrate that BNs are effective in environments with limited data availability and high uncertainty, offering transparent and quantifiable insights into how specific misconfigurations—such as active remote access or irregular firmware updates—elevate overall system exposure. The proposed approach aligns with current regulatory and standardization requirements, including the NIS2 Directive (EU 2022/2555), ISO/IEC 27001:2022, ISO/IEC 27005:2022, and Regulation (EU) 2024/2847 (Cyber Resilience Act), which define cybersecurity obligations for products with digital elements. The study provides a reproducible and future-oriented methodology for integrating cybersecurity into machinery-safety evaluation in modern industrial environments. Full article

Sustainable xanthan gum (XG) production is increasingly prioritized as global demand rises, and conventional processes face economic and environmental constraints. Traditional manufacturing depends heavily on refined sugars, intensive fermentation control, and solvent-based purification, which elevate production costs and ecological impact. This review highlights recent advancements designed to improve sustainability across the XG value chain, focusing on alternative substrates, optimized fermentation, and greener extraction methods. Agricultural residues, food-processing waste, lignocellulosic biomass, and industrial effluents have emerged as promising low-cost substrates that reduce reliance on refined sugar sources while supporting waste valorization. Pretreatment strategies, such as acid hydrolysis, enzymatic processing, and integrated biological–chemical methods, significantly enhance the accessibility of complex biomass for microbial fermentation. Concurrently, improvements in strain selection, metabolic engineering, and process control have increased XG yield, molecular weight, and rheological performance. Environmentally friendly extraction technologies, including ultrasound-assisted extraction, pulsed electric fields, membrane filtration, and electro-dewatering, further reduce solvent consumption and energy demand in downstream processing. However, challenges persist, including substrate variability, formation of inhibitory compounds, strain instability, and regulatory considerations for waste-derived substrates or genetically modified strains. Future progress will rely on integrating bioprocess intensification, genetic engineering, and techno-economic assessment to build scalable, low-impact, and circular XG production systems. Full article

Metal additive manufacturing (AM) offers unprecedented opportunities to fabricate complex, lightweight metallic components, yet its practical deployment remains fundamentally constrained by defects arising from rapid melting and solidification. Cyclic thermal transients generate cracks, pores, residual stresses, and lack-of-fusion regions, undermining mechanical performance and reliability. Ultrasonic field-assisted laser-based additive manufacturing (UF-LBAM) has emerged as a powerful approach to manipulate melt pool dynamics and suppress defect formation. Nevertheless, the governing physical mechanisms remain poorly understood, particularly under highly non-equilibrium ultrasonic excitation, where acoustic pressure oscillations, melt convection, cavitation, and solidification are intricately coupled across multiple temporal and spatial scales. Here, we provide a systematic review of X-ray based fundamental studies in UF-LBAM and the diverse applications of machine learning (ML), detailing the literature selection criteria and methodology. We highlight advances spanning synchrotron X-ray revealed physical phenomena, ML-driven real-time monitoring and defect prediction, and pathways toward industrial implementation. Critical challenges persist, including fundamental physics gaps, transferability of ML models across alloy systems, and real-time control limitations. We further identify promising directions for the field, such as physics-informed models, multimodal diagnostics, and closed-loop control, which together promise to unlock the full potential of UF-LBAM for high-performance metal component fabrication. Full article

Addressing the thermal management challenges of prismatic aluminum shell lithium battery modules in electric vehicles under high-rate charge–discharge conditions, this study proposes a multi-objective optimization design method for integrated biomimetic liquid cooling plates. By integrating various highly efficient heat transfer structures from nature, including fractal-tree-like networks, leaf vein branching systems, and spider web radial distribution, a novel biomimetic liquid cooling plate topology was constructed. A multi-physics coupled numerical model considering electrochemical heat generation, thermal conduction, convective heat transfer, and thermal stress deformation was established. The NSGA-II algorithm was employed to globally optimize 12 design variables including channel geometric parameters, operating conditions, and structural dimensions, achieving collaborative optimization objectives of maximum temperature minimization, temperature uniformity maximization, pressure drop minimization, and structural lightweighting. The weight coefficients for the four optimization objectives were determined through the Analytic Hierarchy Process (AHP) with verified consistency (CR = 0.02 < 0.10), ensuring rational priority allocation aligned with automotive safety standards. The optimization results demonstrated that compared to the initial design, the optimal solution reduced the maximum temperature under 3C discharge conditions by 9.9% to 34.7 °C, decreased the temperature difference by 31.3% to 3.3 °C, lowered the pressure drop by 24.6% to 2150 Pa, reduced structural mass by 4.0%, and decreased maximum stress by 16.7%. Quantitative comparison with single biomimetic structures under identical boundary conditions showed that the integrated design achieved a 3.3% lower maximum temperature and 25.7% better flow uniformity than the best-performing single structure, demonstrating the synergistic advantages of multi-biomimetic integration. These synergistic performance improvements can be attributed to the hierarchical multi-scale architecture where fractal networks provide macro-scale flow distribution, leaf vein branches ensure meso-scale coverage, and spider web radials achieve micro-scale thermal matching. Long-term cycling tests conducted at 1C/1C rate with 25 ± 1 °C ambient temperature showed that the optimized design maintained a capacity retention rate of 92.3% after 1000 charge–discharge cycles, demonstrating excellent durability. The complex biomimetic channel structure can be fabricated using selective laser melting technology with minimum feature sizes below 0.3 mm, indicating promising manufacturing feasibility. The research findings provide theoretical guidance and technical support for the engineering design of high-performance battery thermal management systems. Full article

Alzheimer’s disease (AD) remains a major unmet medical challenge due to its complex pathology, high interpatient heterogeneity and frequent association with systemic comorbidities. Conventional pharmacotherapy is limited by poor blood–brain barrier permeability, off-target effects and reduced efficacy in polymedicated elderly populations. Smart drug-delivery systems (DDS), particularly nanotechnology-based platforms, have emerged as promising strategies to enhance brain targeting, optimize controlled drug release and mitigate systemic toxicity. This review examines recent advances in intelligent DDS for AD, with a focus on nanocarriers designed to modulate amyloid aggregation, neuroinflammation, oxidative stress and cholinergic dysfunction. Special attention is given to the impact of the most common comorbid conditions on DDS pharmacokinetics, safety and clinical performance. Furthermore, the challenges associated with clinical translation, such as long-term safety, immunogenicity, manufacturing scalability and regulatory harmonization, are critically discussed. In this context, versatile controlled release platforms that integrate rational design, predictive modeling and Quality by Design-driven manufacturing are highlighted as key enablers of translational success. By bridging intelligent formulation design with scalable production and regulatory readiness, advanced controlled release systems offer a pathway toward precision and patient-centered therapies. Such platforms hold significant potential to accelerate the safe integration of smart DDS into Alzheimer’s disease management and broader neurotherapeutic applications. Full article

Achieving high density in complex powder metallurgy components like spur gears is often hindered by friction-induced density gradients and ejection defects. This study investigates a novel elastic die system designed to mitigate these issues through controlled radial deformation. Spur gears were compacted using Ancorsteel 2000 powder under pressures of 400–700 MPa, utilizing a tapered elastic sleeve to apply radial compression. Green and sintered densities were measured, while porosity distribution was quantified via image analysis. Additionally, a 3D finite element simulation using FORGE software was conducted to model the thermo-mechanical behavior and stress distribution during the process. Experimental trials demonstrated that the elastic relaxation of the sleeve enabled free ejection of the compacts without requiring an extraction force. Image analysis confirmed a homogenous porosity distribution across the gear teeth, and higher die pre-stressing strokes were found to correlate with increased sintered density. Finite element modeling accurately predicted critical stress concentrations of 700 MPa at the die–sleeve interface and validated the strain distribution. The results confirm that elastic die technology effectively eliminates ejection friction and improves density uniformity in complex gears, offering a viable solution for reducing tool wear and manufacturing defects in high-precision powder metallurgy. Full article

Pharmaceutical regulation plays a central role in protecting public health by governing clinical trials, market authorization, and post-marketing safety monitoring throughout the medicine life cycle. While substantial literature describes established systems, particularly the United States Food and Drug Administration (FDA), Japan’s Pharmaceuticals and Medical Devices Agency (PMDA), and the European medicines regulatory network coordinated by the European Medicines Agency (EMA) together with national competent authorities, comparative analyses that integrate both mature authorities, emerging regulators and transnational harmonization networks remain limited. This narrative review draws on primary regulator/network documentation and targeted peer-reviewed literature to compare core regulatory functions across jurisdictions, including approval pathways and evidentiary expectations, inspection and good manufacturing practice (GMP) oversight, transparency practices, and pharmacovigilance and risk-management approaches. Across regions, we observe increasing convergence in scientific expectations through initiatives such as the International Council for Harmonisation (ICH) and reliance and work-sharing models, alongside persistent differences in legal mandates, resourcing, timelines, and data requirements. These differences are most consequential for complex products (e.g., advanced therapies) and in crisis settings, where emergency or conditional authorizations amplify the need for strong lifecycle monitoring, real-world evidence governance, and cross-border communication. We conclude by outlining opportunities to strengthen regulatory resilience and equity through fit-for-purpose harmonization, investment in enabling infrastructure, and future work on interoperable data systems, signal detection, and coordinated post-marketing evaluation. Full article

Driven by the evolution of electric drive systems in electric vehicles, aerospace, and industrial machine tools, high-speed operation has become a key direction in motor development. While progress in emerging manufacturing technologies and novel materials has partially alleviated the inherent contradiction between electromagnetic performance and mechanical strength in high-speed rotors, traditional approaches—including geometric optimization of flux bridges and center posts, macroscopic material replacement, and structural reinforcements—tend to make the multi-physics trade-offs increasingly complex. The application of dual-phase soft magnetic laminate presents a promising alternative. By achieving localized regulation of rotor characteristics, this approach effectively decouples electromagnetic performance from mechanical constraints. Although the technical merits have been verified, the existing literature lacks a systematic overview of the fabrication technologies and application status of dual-phase soft magnetic material laminate. Hence, this paper aims to provide a comprehensive review of recent fabrication approaches and development trends, thereby serving as a fundamental reference for researchers aiming to integrate this material into innovative rotor topologies. Full article

Re-entry flights of reusable first or upper stages typically foresee phases in the hypersonic flight regime, characterized by severe aero-thermal loads which could become critical for the most exposed components, like the vehicle forebody or the fin leading edges. These require consequently dedicated thermal protection systems (TPS), whose design generally requires a multi-disciplinary approach. In this framework, a viable solution is the use of high-temperature resistant ceramic matrix composite (CMC) structures, but the implementation of such technology, especially for the manufacturing of complex components and its application in real flight conditions, still presents significant challenges. In this work, the design activities for the CMC-based TPS of the payload forebody of a hypersonic sounding rocket are presented, developed within the framework of the STORT project, whose mission includes in flight demonstration of multiple critical technologies required for sustained flight at Mach numbers above 8, corresponding to a significantly high integral thermal load. Full article

Micro heat exchangers (MHXs) have emerged as a critical technology for advanced thermal management in the food and pharmaceutical industries due to their high surface area-to-volume ratios, compact design, and precise temperature control. This review provides a systematic and integrated analysis of MHX technology, covering their fundamental principles, classification, design methodologies, performance enhancement techniques, and industrial applications. Unlike existing reviews, the present work establishes a unified framework that links microscale heat transfer mechanisms, such as Brownian motion, surface corrugation effects, and non-dimensional parameters, with practical design choices, manufacturing routes, and the process requirements specific to food and pharmaceutical systems. The subsequent sections explore the key performance-influencing factors, including channel geometry, surface enhancement strategies, nanofluid utilization, and governing non-dimensional numbers (e.g., Nusselt, Reynolds, and Knudsen numbers), which are systematically compared across different operating regimes. Recent advances in materials and fabrication techniques, such as laser ablation, lithography, micro-milling, embossing, and additive manufacturing, are analyzed with respect to their scalability, thermal–hydraulic performance, and industrial feasibility. Furthermore, the review highlights the emerging trends in micro heat exchanger (MHX) optimization, including computational fluid dynamics (CFD)-driven design, smart monitoring systems, and energy-efficient integration within processing lines. Finally, the paper also identifies the key challenges and limitations of micro heat exchangers, including pressure drop, fouling, scaling, manufacturing complexity, and cost constraints. These are critically discussed along with future research directions aimed at improving reliability and sustainability. By consolidating the dispersed research outcomes into a coherent, design-oriented perspective, this review offers new insights and practical guidance for researchers, engineers, and industry practitioners seeking to advance the deployment of MHXs in food and pharmaceutical processing. Full article

Knowledge discovery constitutes a vital component in building intelligent CAPP systems, and the effective discovery of process knowledge has become a prominent research focus within intelligent manufacturing. Process decision knowledge is a type of knowledge that reflects the relations between process data items, represented in the form of production rules. However, PDK discovery faces low accuracy challenges from complex high-dimensional manufacturing data and implicit experience-dependent process decisions. This paper proposed a PDK mining framework that combines knowledge constraint and the water wave optimization algorithm. This approach formulated prior knowledge mathematically using an association discriminant matrix and embedded this representation into the knowledge mining model, thus equipping the algorithmic framework with the ability to discover PDK accurately. The WWO is utilized to search within the sample space for combinations of process data items that constitute valid knowledge. In contrast to traditional association rule mining algorithms that lack accuracy and template-based methods that are inherently rigid, the proposed approach provides a robust solution by achieving over 90% correctness in PDK mining. It also serves as a demonstration and offers insights for mining similar rule-based knowledge in other fields. Full article

Achieving natural esthetics has become essential for successful dental restorations and supports the use of modern non-metal materials. However, complexity in esthetic features of natural teeth, determined by both inherent color factors and hierarchical and gradient microstructures, makes recording, determination, and reproduction difficult. This often leads to misunderstanding during manufacturing and dissatisfaction with the final outcome, even when using advanced digital tools. The aim of this study was to investigate a new, easy-to-handle digital tool for determining the color of restorative materials. An industrial-level handheld color identifier, the NCS Colourpin SE, together with the corresponding NCS color system, was tested on three materials: dental resin nanocomposite, self-glazed zirconia (SGZ), and Decore zirconia pellets. The repeatability and impacts of geometrical contributions such as surface roughness and thickness on different colors were measured. The Colourpin SE offered promising repeatability. Decore zirconia showed more than 90% repeatability for most of the colors, independent of thickness. The NCS scanner showed slightly better repeatability than earlier in clinical trials with an intraoral scanner. The shades A3.5 and A3 had lower repeatability, varying from 50 to 90%. It identified effects of material thickness and surface roughness, where the thicker samples were identified with higher blackness levels, and surface roughness seemed to be coupled with a lower blackness level in color identification codes. Small but consistent differences between materials were detected, suggesting that material and manufacturing methods affect the final shade. The NCS Colourpin SE shows potential to be developed into an affordable and easy-to-handle scanner for the identification of a patient’s tooth color, enabling synchronization with digital workflows and improving the match between restoration and the patient’s natural teeth. Nevertheless, further research and development in customized applications for color identification in esthetic dentistry is still required through multidisciplinary collaboration. Full article

Double stator cylindrical linear oscillating generators (DSCLOGs) have been widely used in renewable energy power generation systems due to their higher power density, higher reliability, and low-noise characteristics. However, the detent force of a DSCLOG is an inevitable problem, which causes oscillations in the generator and leads to system instability. Conventionally, auxiliary teeth and skewed pole methods are employed to mitigate detent force, but these approaches often increase the overall machine size and the complexity of the manufacturing process. To solve this issue, an improved DSCLOG with curved teeth (CT-DSCLOG) is proposed to minimize the detent force. First, the structural characteristics and working principle of CT-DSCLOG are introduced. Then, to achieve a rapid and accurate analysis of the magnetic field in the irregular air gap, a 2D magnetic equivalent circuit (MEC) model is established by introducing Schwarz–Christoffel (S-C) mapping. And key structural parameters are identified through variance sensitivity analysis. Subsequently, a multi-objective optimization of the linear generator is performed using the Taguchi method combined with 3D finite element analysis (3D-FEA) to obtain the optimal structural parameters of CT-DSCLOG. Finally, the proposed structure is validated through prototype experiments. The results are provided to validate the effectiveness of the proposed structure. Full article

As Unmanned Aerial Vehicles (UAVs) become integral to critical infrastructure, ranging from precision agriculture to emergency disaster recovery, their security becomes a matter of systemic resilience. This paper provides a comprehensive thematic survey of the security landscape for unmanned devices, bridging the gap between low-level hardware vulnerabilities and high-level mission failures. We propose a multidimensional taxonomy that categorizes challenges into hardware roots of trust, swarm intelligence threats, and domain-specific applications. A primary focus is placed on the Resource–Security Paradox, where the energy cost of heavy cryptographic or AI defenses directly reduces flight endurance, creating a trade-off that adversaries exploit through battery-exhaustion attacks. Beyond standard threats, we analyze emerging risks in additive manufacturing supply chains, the “Sim-to-Real” gap in AI-driven perception, and the legal necessity of Digital Forensic Readiness (DFR) for post-incident attribution. Through a systematic review of defensive frameworks, including lightweight encryption, Mamba-KAN anomaly detection, and blockchain-anchored logging, we evaluate the effectiveness of current solutions against complex adversarial models. Finally, we identify critical research gaps, providing a roadmap for security-by-design in the next generation of critical infrastructure swarms. Full article