Search Results (1,706)

Background/Objectives: Wound infections caused by multidrug-resistant Gram-negative bacteria, such as Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii, pose a major clinical challenge. This study evaluated the interactions between gamma-polyglutamic acid (γ-PGA), cefiderocol, and silver nanoparticles (AgNPs) within multilayer wound dressing configurations. The primary goal was to clarify the dual role of γ-PGA as a healing promoter and a potential protector of bacterial cells against antimicrobial agents. Methods: Multilayer dressing models were assembled in 96-well plates to simulate vertical stratification of antimicrobial layers4. Bacterial viability was assessed through relative OD₆₀₀ measurements following incubation with varying concentrations and spatial arrangements of cefiderocol, AgNPs, and γ-PGA. Data were analyzed using generalized linear modeling (GLM) with a gamma distribution and random forest regression to determine the relative importance of each factor in modulating bacterial survival. Results: γ-PGA concentration emerged as the dominant factor influencing bacterial viability, accounting for nearly 100% of variable importance in random forest analysis. Despite high antimicrobial pressure from cefiderocol and AgNPs, bacterial viability stabilized at approximately 40% in the presence of γ-PGA. The vertical positioning of γ-PGA significantly impacted survival; direct physical contact between the polymer and bacteria, particularly at high concentrations, enhanced bacterial persistence in P. aeruginosa and E. coli. Cefiderocol showed strain-specific potency, while AgNPs provided consistent growth inhibition. Conclusions: γ-PGA plays a paradoxical role in wound care by providing moisture retention while simultaneously acting as a cytoprotective agent that reduces antimicrobial efficacy, likely by facilitating biofilm formation. These findings underscore the necessity of optimizing the spatial layering and concentration of biopolymers in advanced dressings. Strategic design is crucial to balance regenerative benefits with maximal antimicrobial control to improve clinical outcomes in chronic wound management. Full article

Although probiotics are widely used in the food industry due to their health-promoting effects, their application is often limited by low stability and poor viability under processing, storage, and gastrointestinal conditions. Encapsulation has emerged as a promising strategy to address these issues, offering enhanced protection and controlled release of probiotic strains. This review summarizes recent advances in encapsulation techniques relevant to food applications, including spray drying, freeze drying, coacervation, and liposome formation, as well as novel approaches such as multilayer nanocoatings and dual-core systems. The use of natural biopolymers such as alginate, chitosan, and pectin, along with food-grade synthetic materials, has greatly improved the stability of probiotics in complex food matrices. Furthermore, emerging technologies such as cell-mediated coatings offer improved resistance to gastric acid and oxygen, enhancing probiotic survival through the gastrointestinal tract. These advances contribute to the development of functional foods with better health benefits. However, challenges remain regarding scalability, strain-specific encapsulation efficiency, and regulatory approval. Future research should focus on optimizing food-grade materials, exploring synergistic effects with bioactive compounds, and ensuring consistent performance across food systems. Full article

Background: Solid microneedles (MNs) are effective transdermal delivery devices but are commonly fabricated from metallic or non-biodegradable materials, raising concerns related to sustainability, waste management, and processing constraints. This study aimed to evaluate the suitability of the biodegradable biopolyester poly(3-hydroxybutyrate-co-3-hydroxyvalerate-co-3-hydroxyhexanoate) (PHBHVHHx) as a structuring material for solvent-free fabrication of solid MN arrays and to assess their mechanical performance, insertion capability, and drug delivery potential. Methods: PHBHVHHx MN arrays were fabricated by solvent-free micromolding at 200 °C. The resulting MNs were morphologically characterized by scanning electron microscopy. Mechanical properties were assessed by axial compression testing, and insertion performance was evaluated using a multilayer Parafilm skin simulant model. Diclofenac sodium was used as a model drug and applied via surface coating using a FucoPol-based formulation. In vitro drug release was assessed in phosphate-buffered saline under sink conditions and quantified by UV–Vis spectroscopy. Results: PHBHVHHx MN arrays consisted of sharp, well-defined conical needles (681 ± 45 µm length; 330 µm base diameter) with micro-textured surfaces. The MNs withstood compressive forces up to 0.25 ± 0.03 N/needle and achieved insertion depths of approximately 396 µm in the Parafilm model. Drug-coated MNs retained adequate mechanical integrity and exhibited a rapid release profile, with approximately 73% of diclofenac sodium released within 10 min. Conclusions: The results demonstrate that PHBHVHHx is a suitable biodegradable thermoplastic for the fabrication of solid MN arrays via a solvent-free process. PHBHVHHx MNs combine adequate mechanical performance, reliable insertion capability, and compatibility with coated drug delivery, supporting their potential as sustainable alternatives to conventional solid MN systems. Full article

To achieve precise and safe laser acupuncture treatment, a computational model of the skin acupoint was constructed utilizing COMSOL Multiphysics (Version 6.1). This model incorporates its multilayer anatomical structure: the epidermis, papillary dermis, reticular dermis, hypodermis, and muscle layer. A coupled multiphysics approach integrating geometric optics, radiation beams, and bioheat transfer was employed to investigate the effects of light source parameters and cooling layers on the photothermal response and thermal damage of acupoints. Under optimized parameters (808 nm, 3 mm beam waist, 50 mW) with a 0.5 mm glycerol layer, 600 s irradiation achieved a stable dermal temperature (40.86–42.04 °C) and a negligible epidermal thermal damage factor (0.0063), significantly below the subclinical injury threshold of 0.15; under identical parameters, the dermal temperature for the Gaussian periodic pulsed source was maintained between 38.85 and 40.35 °C, with a corresponding epidermal thermal damage factor of merely 0.0010. The model exhibited good robustness, tolerating variations of ±5% in laser power and ±40% in glycerol layer thickness. The resultant temperature deviations in the epidermis and dermis were well within the safe range, and the thermal damage factor remained below the injury threshold. This work serves as a guideline for selecting laser acupuncture parameters according to acupoint depth. Full article

Salinization represents a significant threat to freshwater resources worldwide, compromising water quality and security. In the Vieira de Leiria–Marinha Grande aquifer, salinization mechanisms are a complex interaction between seawater intrusion and evaporite dissolution. Near the coast, groundwater is mainly influenced by seawater, evidenced by Na-Cl hydrochemical facies, high electrical conductivity, and Na⁺/Cl⁻, Cl⁻/Br⁻ and SO₄^2−/Cl⁻ molar ratios consistent with marine signatures. In areas affected by diapiric dissolution, besides elevated electrical conductivity, groundwater is enriched in SO₄²⁻ and Ca²⁺ and in minor elements like K⁺, Li⁺, B³⁺, Ba²⁺ and Sr²⁺, and high SO₄²⁻/Cl⁻ and Ca²⁺/HCO₃⁻ molar ratios, indicative of gypsum/anhydrite dissolution. The relationship between δ¹⁸O and electrical conductivity further supports the identification of distinct salinity sources. This study integrates hydrogeochemical tracers to investigate hydrochemical evolution in the aquifer with increasing residence time and influence of water–rock interaction, as well as the accurate characterization of salinization mechanisms in multilayer aquifers. A comprehensive understanding of these processes is essential for identifying vulnerable zones and developing effective management strategies to ensure the protection and sustainable use of groundwater resources. Full article

This article proposes single-layer neural network algorithms for solving second-order ordinary differential equations, based on the principles of functional connection. According to this principle, the hidden layer of the neural network is replaced by a functional expansion unit to improve input patterns using orthogonal Chebyshev, Legendre, and Laguerre polynomials. The polynomial neural network algorithms were implemented in the Python programming language using the PyCharm environment. The performance of the polynomial neural network algorithms was tested by solving initial-boundary value problems for the nonlinear Lane–Emden equation. The solution results are compared with the exact solution of the problems under consideration, as well as with the solution obtained using a multilayer perceptron. It is shown that polynomial neural networks can perform more efficiently than multilayer neural networks. Furthermore, a neural network based on Laguerre polynomials can, in some cases, perform more accurately and faster than neural networks based on Legendre and Chebyshev polynomials. The issues of overtraining of polynomial neural networks and scenarios for overcoming it are also considered. Full article

A comfortable, reliable, safe and environmentally friendly high-speed train journey that saves time and offers an unforgettable experience for passengers is not a dream. Passengers can enjoy panoramic views, delicious cuisine and use their mobile devices without restrictions. High-speed trains, powered by environmentally friendly methods, are a sustainable form of transport, reducing harmful emissions. Integrating intelligent control and management into railway networks has the capacity to increase efficiency and improve reliability and safety, as well as reduce development and maintenance costs. Future intelligent railway network architectures are expected to focus on integrated, multi-layered systems that deeply embed artificial intelligence (AI), the Internet of Things (IoT) and advanced communication technologies (5G/6G) to ensure intelligent operation, improved reliability and increased safety. Distributed intelligent control in railways refers to an advanced approach in which decision-making capabilities are distributed across network components (trains, stations, track sections, control centers) rather than being concentrated in a single central location. The recent advances in AI in railways are associated with numerous scientific papers that enable intelligent traffic management, automatic train control, and predictive maintenance, with each of the proposed intelligent solutions being evaluated in terms of key performance indicators such as latency, reliability, and accuracy. This study focuses on how different intelligent solutions in railways can be implemented in network components based on the requirements for real-time control, near-real-time control, and non-real-time operation. The analysis of related works is focused on the proposed intelligent railway frameworks and architectures. The description of typical use cases for implementing intelligent control aims to summarize latency requirements and the possible distribution of control logic between network components, taking into account time constraints. The considered use case of automatic train protection aims to evaluate the added latency of communication. The requirements for the nodes that host and execute the control logic are identified. Full article

Ellipsometric mapping measurements and Bayesian evaluation were performed with a non-collimated, imaging ellipsometer using an LCD monitor as a light source. In such a configuration, the polarization state of the illumination and the local angle of incidence vary spatially and spectrally, rendering conventional spectroscopic ellipsometry inversion methods hardly applicable. To address these limitations, a multilayer optical forward model is augmented with instrument-specific correction parameters describing the polarization state of the monitor and the angle-of-incidence map. These parameters are determined through a Bayesian calibration procedure using well-characterized Si-SiO₂ reference wafers. The resulting posterior distribution is explored by global optimization based on simulated annealing, yielding a maximum a posteriori estimate, followed by marginalization to quantify uncertainties and parameter correlations. The calibrated correction parameters are subsequently incorporated as informative priors in the Bayesian analysis of unknown samples, including polycrystalline–silicon layers deposited on Si-SiO₂ substrates and additional Si-SiO₂ wafers outside the calibration set. The approach allows consistent propagation of calibration uncertainties into the inferred layer parameters and provides credible intervals and correlation information that cannot be obtained from conventional least-squares methods. The results demonstrate that, despite the broadband nature of the RGB measurement and the limited number of analyzer orientations, reliable layer thicknesses can be obtained with quantified uncertainties for a wide range of technologically relevant samples. The proposed Bayesian framework enables a transparent interpretation of the measurement accuracy and limitations, providing a robust basis for large-area ellipsometric mapping of multilayer structures. Full article

In this study, using global liner shipping schedules, UNCTAD’s Port Liner Shipping Connectivity Index and Liner Shipping Bilateral Connectivity Index, together with bilateral trade-value data for 2022–2024, we construct a multilayer weighted port-to-port network that explicitly embeds port-level cargo-handling and service organisation capabilities, as well as demand-side routing pressure, into node and edge weights. Building on this network, we apply CONCOR-based structural-equivalence analysis to delineate functionally homogeneous port clusters, and adopt a structural role identification framework that combines multi-indicator connectivity metrics with Rank-Sum Ratio–entropy weighting and Probit-based binning to classify ports into high-efficiency core, bridge-control, and free-form bridge roles, thereby tracing the reconfiguration of cluster-level functional structures before and after the Red Sea crisis. Empirically, the clustering identifies four persistent communities—the Intertropical Maritime Hub Corridor (IMHC), Pacific Rim Mega-Port Agglomeration (PRMPA), Southern Commodity Export Gateway (SCEG), and Euro-Asian Intermodal Chokepoints (EAIC)—and reveals a marked spatial and functional reorganisation between 2022 and 2024. IMHC expands from 96 to 113 ports and SCEG from 33 to 56, whereas EAIC contracts from 27 to 10 nodes as gateway functions are reallocated across clusters, and the combined share of bridge-control and free-form bridge ports increases from 9.6% to 15.5% of all nodes, demonstrating a thicker functional backbone under rerouting pressures. Spatially, IMHC extends from a Mediterranean-centred configuration into tropical, trans-equatorial routes; PRMPA consolidates its role as the densest trans-Pacific belt; SCEG evolves from a commodity-based export gateway into a cross-regional Southern Hemisphere hub; and EAIC reorients from an Atlantic-dominated structure towards Eurasian corridors and emerging bypass routes. Functionally, Singapore, Rotterdam, and Shanghai remain dominant high-efficiency cores, while several Mediterranean and Red Sea ports (e.g., Jeddah, Alexandria) lose centrality as East and Southeast Asian nodes gain prominence; bridge-control functions are increasingly taken up by European and East Asian hubs (e.g., Antwerp, Hamburg, Busan, Kobe), acting as secondary transshipment buffers; and free-form bridge ports such as Manila, Haiphong, and Genoa strengthen their roles as elastic connectors that enhance intra-cluster cohesion and provide redundancy for inter-cluster rerouting. Overall, these patterns show that resilience under the Red Sea crisis is expressed through the cluster-level rebalancing of core–control–bridge roles, suggesting that port managers should prioritise parallel gateways, short-sea and coastal buffers, and sea–land intermodality within clusters when designing capacity expansion, hinterland access, and rerouting strategies. Full article

The Internet of Things (IoT) has transformed global connectivity by linking people, smart devices, and data. However, as the number of connected devices continues to grow, ensuring secure data transmission and communication has become increasingly challenging. IoT security threats arise at the device level due to limited computing resources, mobility, and the large diversity of devices, as well as at the network level, where the use of varied protocols by different vendors introduces further vulnerabilities. Physical Unclonable Functions (PUFs) provide a lightweight, hardware-based security primitive that exploits inherent device-specific variations to ensure uniqueness, unpredictability, and enhanced protection of data and user privacy. Additionally, modeling attacks against PUF architectures is challenging due to the random and unpredictable physical variations inherent in their design, making it nearly impossible for attackers to accurately replicate their unique responses. This study collected approximately 80,000 Challenge Response Pairs (CRPs) from a Ring Oscillator (RO) PUF design to evaluate its resilience against modeling attacks. The predictive performance of five machine learning algorithms, i.e., Support Vector Machines, Logistic Regression, Artificial Neural Networks with a Multilayer Perceptron, K-Nearest Neighbors, and Gradient Boosting, was analyzed, and the results showed an average accuracy of approximately 60%, demonstrating the strong resistance of the RO PUF to these attacks. The NIST statistical test suite was applied to the CRP data of the RO PUF to evaluate its randomness quality. The p-values from the 15 statistical tests confirm that the CRP data exhibit true randomness, with most values exceeding the 0.01 threshold and supporting the null hypothesis of randomness. Full article

Brown adipose tissue (BAT) and beige adipocytes play a crucial role in adaptive thermogenesis, primarily via uncoupling protein 1 (UCP1)-driven heat production. Once considered physiologically irrelevant in adults, BAT is now recognized as an active tissue that contributes to energy expenditure and metabolic homeostasis and represents a potential therapeutic target for obesity and metabolic disorders. This review provides an integrated overview of the molecular regulation of thermogenic adipocytes, emphasizing both canonical UCP1-dependent as well as non-canonical UCP1-independent mechanisms of heat generation. Key transcriptional and epigenetic regulators are discussed in the context of mitochondrial biogenesis, substrate utilization, and thermogenic gene programs. Major upstream signaling routes are further summarized, encompassing classical β-adrenergic pathways, as well as alternative regulatory nodes including AMP-activated protein kinase (AMPK) and mechanistic target of rapamycin (mTOR) together with diverse nutrient- and hormone-responsive cues that converge to activate brown and beige adipocytes. Finally, the cross-talk among neuronal, endocrine, immune, and gut microbiota-derived signals is highlighted as a key determinant of thermogenic adipocyte function. Together, these multilayered regulatory inputs provide a comprehensive framework for understanding how thermogenic adipose tissue integrates environmental, metabolic, and microbial cues to regulate systemic energy balance—knowledge that is essential for developing targeted therapies to combat obesity and metabolic diseases. Full article

The present work uses numerical methods to explore the impact of spatial orientation on the behavior of molten pool and thermal responses during the laser–Cold Metal Transfer (CMT) hybrid additive manufacturing of metallic cladding layers. Based on the traditional double-ellipsoidal heat source model, an adaptive CMT arc heat source model was developed and optimized using experimentally calibrated parameters to accurately represent the coupled energy distribution of the laser and CMT arc. The improved model was employed to simulate temperature and velocity fields under horizontal, transverse, vertical-up, and vertical-down orientations. The results revealed that variations in gravity direction had a limited effect on the overall molten pool morphology due to the dominant role of vapor recoil pressure, while significantly influencing the local convection patterns and temperature gradients. The simulations further demonstrated the formation of keyholes, dual-vortex flow structures, and Marangoni-driven circulation within the molten pool, as well as the redistribution of molten metal under different orientations. In multi-layer deposition simulations, optimized heat input effectively mitigated excessive thermal stresses, ensured uniform interlayer bonding, and maintained high forming accuracy. This work establishes a comprehensive numerical framework for analyzing orientation-dependent heat and mass transfer mechanisms and provides a solid foundation for the adaptive control and optimization of laser–CMT hybrid additive manufacturing processes. Full article

Biomedical antennas are essential components in modern healthcare systems, supporting wireless communication, physiological monitoring, diagnostic imaging, and therapeutic energy delivery. Their performance is strongly influenced by proximity to the human body, creating challenges such as impedance detuning, signal absorption, and size constraints that motivate new materials and fabrication approaches. This work reviews recent advances enabling next-generation wearable and implantable antennas, with emphasis on printed electronics, additive manufacturing, flexible hybrid integration, and metamaterial design. Methods discussed include 3D printing and inkjet, aerosol jet, and screen printing for fabricating conductive traces on textiles, elastomers, and biodegradable substrates, as well as multilayer Flexible Hybrid Electronics that co-integrate sensing, power management, and RF components into thin, body-conforming assemblies. Key results highlight how metamaterial and metasurface concepts provide artificial control over dispersion, radiation, and near-field interactions, enabling antenna miniaturization, enhanced gain and focusing, and improved isolation from lossy biological tissue. These approaches reduce SAR, stabilize impedance under deformation, and support more efficient communication and energy transfer. The review concludes that the convergence of novel materials, engineered electromagnetic structures, and AI-assisted optimization is enabling biomedical antennas that are compact, stretchable, personalized, and highly adaptive, supporting future developments in unobtrusive monitoring, wireless implants, point-of-care diagnostics, and continuous clinical interfacing. Full article

At present, conventional cold storage facilities in China are poorly suited to on-farm storage demands for agricultural produce, mainly due to their large spatial requirements, complex and labor-intensive installation procedures, limited portability, and insufficient coverage in rural areas. These limitations significantly contribute to post-harvest losses of perishable crops such as cherry tomatoes. To address this challenge, the present study proposes a compact and temporary cold storage system—gas-inflated membrane cold storage (GIMCS)—which exploits the inherent safety, cost-effectiveness, ease of deployment, and adaptability of inflatable membrane structures. A series of mechanical performance tests, including tensile strength, pressure resistance, and burst tests, were conducted on PA/PE (Polyamide/Polyethylene) composite membranes. The optimal configuration was identified as a membrane thickness of 70 μm, a gas column width of 2 cm, and a PA/PE composition ratio of 35%/65%. Thermal performance evaluations further revealed that filling the inflatable structure with 100% CO₂ yielded the most effective insulation. Through structural optimization, a cotton-filled gas-inflated membrane cold storage system (CF-GIMCS) incorporating a dual insulation strategy—combining intra-membrane and extra-membrane insulation—was developed. This multilayer configuration significantly reduced conductive and convective heat transfer, resulting in enhanced thermal performance. A comparative evaluation between GIMCS and a conventional cold storage system of equivalent capacity was conducted over a 15-day storage period, considering construction cost, temperature uniformity, and fruit preservation quality. The results showed that the construction cost of GIMCS was only 38% of that of conventional cold storage. The internal temperature distribution of GIMCS was highly uniform, with a maximum horizontal temperature difference of 1.4 °C, demonstrating thermal stability comparable to conventional systems. No statistically significant differences were observed between the two systems in key post-harvest quality indicators, including weight loss and respiration rate. Notably, GIMCS exhibited superior performance in maintaining fruit firmness, with a hardness of 1.30 kg·cm⁻² compared to 1.26 kg·cm⁻² in conventional storage, indicating a potential advantage in shelf-life extension. Overall, these findings demonstrate that GIMCS represents an affordable, technically robust, and portable cold storage solution capable of delivering preservation performance comparable to—or exceeding—that of conventional cold storage. Its modularity, mobility, and ease of relocation make it particularly well suited to the operational and economic constraints of smallholder farming systems, offering a practical and scalable pathway for improving on-farm cold chain infrastructure. Full article

This article presents the results of the first stage of a four-phase research program aimed at the comprehensive evaluation and enhancement in the functional properties of polymeric packaging films intended for active food packaging systems through their modification with detonative nanodiamonds (DND). Stage I involved the characterization of ten commercial single- and multi-layer films without the addition of DND, differing in structure, base material, thickness, and intended application. The scope of analyses included the assessment of biological and physicochemical properties relevant to food contact, such as surface wettability (contact angle), thermal stability (TGA, DSC), antimicrobial and antiviral activity (using E. coli and M. luteus models), as well as the quality of thermal seals examined by SEM. Biological activity was assessed in accordance with ISO 22196:2011. The results revealed significant differences among the tested samples in terms of microbiological resistance, surface properties, and thermal stability. Films with printed layers exhibited the highest antimicrobial activity, whereas some polypropylene samples showed no activity at all or even supported microbial survival. Cross-sectional analysis of welds indicated that the quality of thermal seals is strongly dependent on the surface properties of the base material. The obtained results provide a reference point for subsequent research stages, in which DND-modified films will be analyzed regarding their effects on mechanical, barrier, and biological properties. Preliminary trials with nanodiamonds confirmed their high application potential and the possibility of producing films with increased hydrophilicity or hydrophobicity and durability, which are crucial for the development of modern active food packaging systems. Full article