Search Results (2,264)

Cell-penetrating peptides offer a promising strategy for intracellular delivery; however, non-specific uptake and off-target cytotoxicity limit their clinical utility. To address these limitations, a cold atmospheric plasma-responsive delivery platform was developed in which the membrane activity of a peptide was transiently suppressed upon complexation with a DNA-based nanostructure. Upon localized plasma exposure, DNA masking was disrupted, restoring the biological functions of the peptides. Transmission electron microscopy revealed that the synthesized DNA nanoflower structures were approximately 150–250 nm in size. Structural and functional analyses confirmed that the system remained inert under physiological conditions and was rapidly activated by plasma treatment. Fluorescence recovery, cellular uptake assays, and cytotoxicity measurements demonstrated that the peptide activity could be precisely controlled in both monolayer and three-dimensional spheroid models. This externally activatable nanomaterial-based system enables the spatial and temporal regulation of peptide function without requiring biochemical triggers or permanent chemical modifications. This platform provides a modular strategy for the development of potential peptide therapeutics that require precise control of activation in complex biological environments. Full article

Understanding the early-stage physical interactions between polymeric membranes and supersaturated salt solutions is crucial for advancing membrane-assisted crystallization (MCr) processes. In this study, we employed molecular dynamics (MD) simulations to investigate the short-term morphological response of an isotactic polypropylene (PP) membrane in contact with LiF solutions at different concentrations (5.8 M and 8.9 M) and temperatures (300–353 K), across multiple time points (0, 150, and 300 ns). These data were used as input for computational fluid dynamics (CFD) analysis to evaluate structural descriptors of the membrane, including tortuosity, connectivity, void fraction, anisotropy, and deviatoric anisotropy, under varying thermodynamic conditions. The results show subtle but consistent rearrangements of polymer chains upon exposure to the hypersaline environment, with a marked reduction in anisotropy and connectivity, indicating a more compact and isotropic local structure. Surface charge density analyses further suggest a temperature- and concentration-dependent modulation of chain mobility and terminal group orientation at the membrane–solution interface. Despite localized rearrangements, the membrane consistently maintains a net negative surface charge. This electrostatic feature may influence ion–membrane interactions during the crystallization process. While these non-reactive, short-timescale simulations do not capture long-term degradation or fouling mechanisms, they provide mechanistic insight into the initial physical response of PP membranes under MCr-relevant conditions. This study lays a computational foundation for future investigations bridging atomistic modeling and membrane performance in real-world applications. Full article

Inter-turn short-circuit (ITSC) faults in permanent magnet synchronous machines (PMSMs) present a significant reliability challenge in electric vehicle (EV) drivetrains, particularly under non-stationary operating conditions characterized by inverter-driven transients, variable loads, and magnetic saturation. Existing diagnostic approaches, including motor current signature analysis (MCSA) and wavelet-based methods, are primarily designed for steady-state conditions and rely on manual feature selection, limiting their applicability in real-time embedded systems. Furthermore, the lack of publicly available, high-fidelity datasets capturing the transient dynamics and nonlinear flux-linkage behaviors of PMSMs under fault conditions poses an additional barrier to developing data-driven diagnostic solutions. To address these challenges, this study introduces a simulation framework that generates a comprehensive dataset using finite element method (FEM) models, incorporating magnetic saturation effects and inverter-driven transients across diverse EV operating scenarios. Time-frequency features extracted via Discrete Wavelet Transform (DWT) from stator current signals are used to train a Transformer model for automated ITSC fault detection. The Transformer model, leveraging self-attention mechanisms, captures both local transient patterns and long-range dependencies within the time-frequency feature space. This architecture operates without sequential processing, in contrast to recurrent models such as LSTM or RNN models, enabling efficient inference with a relatively low parameter count, which is advantageous for embedded applications. The proposed model achieves 97% validation accuracy on simulated data, demonstrating its potential for real-time PMSM fault detection. Additionally, the provided dataset and methodology contribute to the facilitation of reproducible research in ITSC diagnostics under realistic EV operating conditions. Full article

This paper focuses on McKean-Vlasov stochastic differential equations under local Lipschitz conditions. We first introduce the stochastic interacting particle system and prove the propagation of chaos. Then we establish a truncated stochastic theta scheme to approximate the interacting particle system and obtain the strong convergence of the continuous-time truncated stochastic theta scheme to the non-interacting particle system. Furthermore, we study the asymptotical mean square stability of the interacting particle system and the truncated stochastic theta method. Finally, we give one numerical example to verify our theoretical results. Full article

Periodontitis is a prevalent chronic inflammatory disease with complex etiopathogenesis involving microbial dysbiosis, host immune response, environmental factors, and genetic susceptibility. Among the cytokines implicated in periodontal immunoregulation, interleukin-35 (IL-35) has emerged as a novel anti-inflammatory mediator with potential diagnostic and therapeutic relevance. This narrative review evaluates the role of IL-35 in periodontal disease by exploring its local and systemic expression, response to non-surgical periodontal therapy (NSPT), and association with clinical disease severity. Additionally, current evidence regarding IL-35 gene polymorphisms and their potential contribution to individual susceptibility and disease progression, as well as their relevance in related systemic conditions, is assessed. A comprehensive review and synthesis of recent clinical and experimental studies were conducted, focusing on IL-35 levels in saliva, serum, and gingival crevicular fluid (GCF) among patients with healthy periodontium, gingivitis, and various stages of periodontitis, both before and after NSPT. Emphasis was placed on longitudinal studies evaluating IL-35 dynamics in correlation with periodontal parameters, as well as genetic association studies investigating IL-12A and EBI3 gene polymorphisms. IL-35 levels were generally found to be higher in healthy individuals and reduced in periodontitis patients, indicating a possible protective role in maintaining periodontal homeostasis. Following NSPT, IL-35 levels significantly increased, corresponding with clinical improvement and reduced inflammatory burden. Genetic studies revealed variable associations between IL-35 polymorphisms and susceptibility to periodontitis and related systemic conditions, although further research is needed for validation. IL-35 appears to function as a modulator of immune resolution in periodontal disease, with potential utility as a non-invasive biomarker for disease activity and therapeutic response. Its upregulation during periodontal healing supports its role in promoting tissue stabilization. The integration of cytokine profiling and genetic screening may enhance personalized risk assessment and targeted interventions in periodontal care. Full article

The Pennes bioheat equation is the most widely used model for describing heat transfer in living tissue during thermal exposure. It is derived from the classical Fourier law of heat conduction and assumes energy exchange between blood vessels and surrounding tissues. The literature presents various numerical methods for solving the bioheat equation, with exact solutions developed for different boundary conditions and geometries. However, analytical models based on this framework are rarely reported. This study aims to develop an analytical three-dimensional model using MATHEMATICA software, with subsequent mathematical validation performed through COMSOL simulations, to characterize heat transfer in biological tissues induced by laser irradiation under various therapeutic conditions. The objective is to refine the conventional bioheat equation by introducing three key improvements: (a) incorporating a non-Fourier framework for the Pennes equation, thereby accounting for the relaxation time in thermal response; (b) integrating Dirac functions and the telegraph equation into the bioheat model to simulate localized point heating of diseased tissue; and (c) deriving a closed-form analytical solution for the Pennes equation in both its classical (Fourier-based) and improved (non-Fourier-based) formulations. This paper investigates the nuanced relationship between the relaxation time parameter in the telegraph equation and the thermal relaxation time employed in the bioheat transfer equation. Considering all these aspects, the optimal thermal relaxation time determined for these simulations was 1.16 s, while the investigated thermal exposure time ranged from 0.01 s to 120 s. This study introduces a generalized version of the model, providing a more realistic representation of heat exchange between biological tissue and blood flow by accounting for non-uniform temperature distribution. It is important to note that a reasonable agreement was observed between the two modeling approaches: analytical (MATHEMATICA) and numerical (COMSOL) simulations. As a result, this research paves the way for advancements in laser-based medical treatments and thermal therapies, ultimately contributing to more optimized therapeutic outcomes. Full article

This study evaluated the activity concentrations of radionuclides in honey, bee pollen, bee bread, and propolis from multiple regions in Poland (Europe) to assess the levels of radiological contamination and their implications for public health. Furthermore, the work considers the use of bee products as bioindicators of the state of environmental contamination with radionuclides. The apiaries from which the samples were collected were selected in eight provinces in Poland, and are also complemented by reference data from soil contamination monitoring. Radionuclide measurements included both natural (e.g., ⁴⁰K, ²²⁶Ra) and anthropogenic isotopes (e.g., ¹³⁷Cs). The results show that although the overall activity concentrations were generally low, certain locations exhibited elevated levels of ¹³⁷Cs in bee products, likely reflecting historical deposition in soils. Propolis was best correlated with ¹³⁷Cs deposited in soil compared to the other products studied. The patterns observed substantiate the hypothesis that bee products, predominantly propolis, accurately reflect local radiological conditions, thereby providing a practical and non-intrusive approach to monitoring radionuclide contamination and informing risk management strategies. An assessment of potential health risks indicates that the effective dose is safe and ranges from 0.02 to 10.3 µSv per year, depending on the type of product and consumption. Full article

The rapid expansion of deep-sea sensing networks underscores the critical need for accurate underwater positioning of observation base stations. However, achieving precise acoustic localization, particularly at depths exceeding 4 km, remains a significant challenge due to systematic ranging errors, clock drift, and inaccuracies in sound speed modeling. This study proposes and validates a three-tier calibration framework consisting of a Dynamic Single-Difference (DSD) solver, a geometrically optimized reference buoy selection algorithm, and a Virtual Beacon (VB) depth inversion method based on sound speed profiles. Through simulations under varying noise conditions, the DSD method effectively mitigates common ranging and clock errors. The geometric reference optimization algorithm enhances the selection of optimal buoy layouts and reference points. At a depth of 4 km, the VB method improves vertical positioning accuracy by 15% compared to the DSD method alone, and nearly doubles vertical accuracy compared to traditional non-differential approaches. This research demonstrates that deep-sea underwater target calibration can be achieved without high-precision time synchronization and in the presence of fixed ranging errors. The proposed framework has the potential to lower technological barriers for large-scale deep-sea network deployments and provides a robust foundation for autonomous deep-sea exploration. Full article

Sustainability in the non-alcoholic beverage industry requires effective metrics to assess environmental, social, and economic performance. However, the lack of standardised indicators for small and medium-sized enterprises (SMEs) hinders the implementation of sustainable strategies. This study aims to select a set of sustainability indicators for small and medium-sized non-alcoholic beverage industries in Brazil. Seventy-four indicators were identified based on the Global Reporting Initiative (GRI) guidelines, which were subsequently evaluated and refined by industry experts for prioritisation. Statistical analysis led to the selection of 31 final indicators, distributed across environmental (10), social (12), and economic (9) dimensions. In the environmental dimension, priority indicators include water management, energy efficiency, carbon emissions, and waste recycling. The social dimension highlights working conditions, occupational safety, gender equity, and impacts on local communities. In the economic dimension, key indicators relate to supply chain efficiency, technological innovation, financial transparency, and anti-corruption practices. The results provide a robust framework to guide managers in adopting sustainable practices and support policymakers in improving the environmental, social, and economic performance of small and medium-sized non-alcoholic beverage industries. Full article

To address the limitations of low detection efficiency and poor spatial resolution of traditional cable insulation diagnosis methods, a novel cable insulation diagnosis method based on impedance spectroscopy has been proposed. An impedance spectroscopy analysis model of the frequency response of high-voltage single-core cables under different aging conditions has been established. The initial classification of insulation condition is achieved based on the impedance phase deviation between the test cable and the reference cable. Under localized aging conditions, the impedance phase spectroscopy is more than twice as sensitive to dielectric changes as the amplitude spectroscopy. Leveraging this advantage, a multi-parameter diagnostic framework is developed that integrates key spectral features such as the first phase angle zero-crossing frequency, initial phase, and resonance peak amplitude. The proposed method enables quantitative estimation of aging severity, spatial extent, and location. This technique offers a non-invasive, high-resolution solution for advanced cable health diagnostics and provides a foundation for practical deployment of power system asset management. Full article

The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challenging due to limitations in memory, computational power, and energy. Traditional methods like cloud-based inference and model compression often incur bandwidth, privacy, and accuracy trade-offs. This paper introduces a novel Decentralized Distributed Sequential Neural Network (DDSNN) designed for low-power MCUs in Tiny Machine Learning (TinyML) applications. Unlike the existing methods that rely on centralized cluster-based approaches, DDSNN partitions a pre-trained LeNet across multiple MCUs, enabling fully decentralized inference in wireless sensor networks (WSNs). We validate DDSNN in a real-world predictive maintenance scenario, where vibration data from an industrial pump is analyzed in real-time. The experimental results demonstrate that DDSNN achieves 99.01% accuracy, explicitly maintaining the accuracy of the non-distributed baseline model and reducing inference latency by approximately 50%, highlighting its significant enhancement over traditional, non-distributed approaches, demonstrating its practical feasibility under realistic operating conditions. Full article

Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the improved bidirectional A* algorithm to generate collision-free paths from the starting point to the charging area, dynamically adjusting the heuristic function by combining node–target distance and search iterations to optimize bidirectional search weights, pruning expanded nodes via a greedy strategy and smoothing paths into cubic Bézier curves for practical vehicle motion. For precise localization of charging areas and piles, the YOLOv11n model is enhanced with a CAFMFusion mechanism to bridge semantic gaps between shallow and deep features, enabling effective local–global feature fusion and improving detection accuracy. Experimental evaluations in long corridors and complex indoor environments showed that the improved bidirectional A* algorithm outperforms the traditional improved A* algorithm in all metrics, particularly in that it reduces computation time significantly while maintaining robustness in symmetric/non-symmetric and dynamic/non-dynamic scenarios. The optimized YOLOv11n model achieves state-of-the-art precision (P) and mAP@0.5 compared to YOLOv5, YOLOv8n, and the baseline model, with a minor 0.9% recall (R) deficit compared to YOLOv5 but more balanced overall performance and superior capability for small-object detection. By fusing the two improved modules, the proposed system successfully realizes autonomous charging navigation, providing an efficient solution for energy management in intelligent vehicles in real-world environments. Full article

Federated learning (FL) enables multiple participants to collaboratively train models while efficiently mitigating the issue of data silos. However, large-scale heterogeneous data distributions result in inconsistent client objectives and catastrophic forgetting, leading to model bias and slow convergence. To address the challenges under non-independent and identically distributed (non-IID) data, we propose DPAO-PFL, a Dynamic Parameter-Aware Optimization framework that leverages continual learning principles to improve Personalized Federated Learning under non-IID conditions. We decomposed the parameters into two components: local personalized parameters tailored to client characteristics, and global shared parameters that capture the accumulated marginal effects of parameter updates over historical rounds. Specifically, we leverage the Fisher information matrix to estimate parameter importance online, integrate the path sensitivity scores within a time-series sliding window to construct a dynamic regularization term, and adaptively adjust the constraint strength to mitigate the conflict overall tasks. We evaluate the effectiveness of DPAO-PFL through extensive experiments on several benchmarks under IID and non-IID data distributions. Comprehensive experimental results indicate that DPAO-PFL outperforms baselines with improvements from $5.41\%$ to $30.42\%$ in average classification accuracy. By decoupling model parameters and incorporating an adaptive regularization mechanism, DPAO-PFL effectively balances generalization and personalization. Furthermore, DPAO-PFL exhibits superior performance in convergence and collaborative optimization compared to state-of-the-art FL methods. Full article

Corrosion-related failures have emerged as a critical driver of premature support bolt failures in underground mines, emphasizing the urgency of understanding the phenomenon with respect to enhancing safety in underground environments. This study investigated key factors influencing bolt degradation through extensive experimental evaluation of cable bolts in simulated underground bolt environments. Multi-stranded cable specimens were exposed to saturated clay, coal, mine water, and grout/cement environments. Water samples were collected weekly from critical packing sections and analyzed for pH, electrical conductivity, and dissolved oxygen. The mineralogy and atmospheric conditions were identified as principal corrosion factors, and clay-rich and coal matrices accelerated corrosion, linked to high ion mobility and oxygen diffusion. Secondary factors correlated context-dependently: pH was negatively associated with corrosion in mineral-packed environments, while conductivity was correlated with non-mineral matrices. Notably, multi-stranded cables exhibited higher localized galvanic corrosion in inter-strand zones, highlighting design vulnerabilities. This work provides pioneering evidence that geological conditions are primary drivers for corrosion-related failures, offering actionable guidance for corrosion mitigation strategies in mining infrastructure. Full article

Urban residential design influences local microclimates and human thermal comfort. This study combines empirical microclimate data with remotely sensed data on tree canopy cover, housing lot size, surface permeability, and roof colour to examine thermal differences between three newly built and three established residential suburbs in Western Sydney, Australia. Established areas featured larger housing lots and mature street trees, while newly developed suburbs had smaller lots and limited vegetation cover. Microclimate data were collected during summer 2021 under both heatwave and non-heatwave conditions in full sun, measuring air temperature, relative humidity, wind speed, and wet-bulb globe temperature (WBGT) as an index of heat stress. Daily maximum air temperatures reached 42.7 °C in new suburbs, compared to 39.3 °C in established ones (p < 0.001). WBGT levels during heatwaves were in the “extreme caution” category in new suburbs, while remaining in the “caution” range in established ones. These findings highlight the benefits of larger green spaces, permeable surfaces, and lighter roof colours in the context of urban heat exposure. Maintaining mature trees and avoiding dark roofs can significantly reduce summer heat and improve outdoor thermal comfort across a range of conditions. Results of this work can inform bottom-up approaches to climate-responsive urban design where informed homeowners can influence development outcomes. Full article