Search Results (1,401)

Fetal movement monitoring (FMM) is crucial for assessing fetal well-being, traditionally relying on clinical assessments or maternal perception, each with inherent limitations. This study presents a novel lightweight deep learning framework for real-time FMM on edge devices. Data were collected from 120 participants using a wearable device equipped with an inertial measurement unit, which captured both accelerometer and gyroscope data, coupled with a rigorous two-stage labeling protocol integrating maternal perception and ultrasound validation. We addressed class imbalance using virtual-rotation-based augmentation and adaptive clustering-based undersampling. The data were transformed into spectrograms using the Short-Time Fourier Transform, serving as input for deep learning models. To ensure model efficiency suitable for resource-constrained microcontrollers, we employed knowledge distillation, transferring knowledge from larger, high-performing teacher models to compact student architectures. Post-training integer quantization further optimized the models, reducing the memory footprint by 74.8%. The final optimized model achieved a sensitivity (SEN) of 90.05%, a precision (PRE) of 87.29%, and an F1-score (F1) of 88.64%. Practical energy assessments showed continuous operation capability for approximately 25 h on a single battery charge. Our approach offers a practical framework adaptable to other medical monitoring tasks on edge devices, paving the way for improved prenatal care, especially in resource-limited settings. Full article

β-Hydroxy-β-methylbutyrate (HMB), a natural metabolite derived from the essential amino acid leucine, is primarily recognised for its anabolic and anti-catabolic effects on skeletal muscle tissue. Recent studies indicate that HMB may also play a role in influencing the structural organisation of extracellular matrix (ECM) components, particularly collagen, which is crucial for maintaining the mechanical integrity of connective tissues. In this investigation, bovine type I collagen was polymerised in the presence of two concentrations of HMB (0.025 M and 0.25 M) to explore its potential function as a molecular modulator of fibrillogenesis. The morphology of the resulting collagen fibres and their molecular architecture were examined using atomic force microscopy (AFM) and Fourier-transform infrared (FTIR) spectroscopy. The findings demonstrated that lower levels of HMB facilitated the formation of more regular and well-organised fibrillar structures, exhibiting increased D-band periodicity and enhanced stabilisation of the native collagen triple helix, as indicated by Amide I and III band profiles. Conversely, higher concentrations of HMB led to significant disruption of fibril morphology and alterations in secondary structure, suggesting that HMB interferes with the self-assembly of collagen monomers. These structural changes are consistent with a non-covalent influence on interchain interactions and fibril organisation, to which hydrogen bonding and short-range electrostatics may contribute. Collectively, the results highlight the potential of HMB as a small-molecule regulator for soft-tissue matrix engineering, extending its consideration beyond metabolic supplementation towards controllable, materials-oriented modulation of ECM structure. Full article

With the rapid evolution of artificial intelligence technologies in power systems, data-driven time-series forecasting has become instrumental in enhancing the stability and reliability of power systems, allowing operators to anticipate demand fluctuations and optimize energy distribution. Despite the notable progress made by current methods, they are still hindered by two major limitations: most existing models are relatively small in architecture, failing to fully leverage the potential of large-scale models, and they are based on fixed nonlinear mapping functions that cannot adequately capture complex patterns, leading to information loss. To this end, an LLM-Empowered Kolmogorov–Arnold frequency learning (LKFL) is proposed for time series forecasting in power systems, which consists of LLM-based prompt representation learning, KAN-based frequency representation learning, and entropy-oriented cross-modal fusion. Specifically, LKFL first transforms multivariable time-series data into text prompts and leverages a pre-trained LLM to extract semantic-rich prompt representations. It then applies Fast Fourier Transform to convert the time-series data into the frequency domain and employs Kolmogorov–Arnold networks (KAN) to capture multi-scale periodic structures and complex frequency characteristics. Finally, LKFL integrates the prompt and frequency representations through an entropy-oriented cross-modal fusion strategy, which minimizes the semantic gap between different modalities and ensures full integration of complementary information. This comprehensive approach enables LKFL to achieve superior forecasting performance in power systems. Extensive evaluations on five benchmarks verify that LKFL sets a new standard for time-series forecasting in power systems compared with baseline methods. Full article

Due to the increasing demand for accurate and robust GNSS positioning for location-based services (LBS) in urban regions, the impacts prevalent in metropolitan areas, like multipath reflections and various interferences, have become persistent challenges. Consequently, developing effective strategies to address these sophisticated influences has become both a primary research focus and a shared priority. In this paper, the authors explore an approach to identify and mitigate the drawbacks arising from multipath effects in urban positioning. Unlike conventional ways for building complex models, an adaptive data-driven methodology is proposed to identify the fingerprints of a multipath in GNSS observations. This approach utilizes the Fourier transform (FT) to examine code multipath and other error sources in terms of frequency, as represented by the power spectrum. Wavelet decomposition and signal spectrum methods are subsequently applied to seek traces of code multipath in multilayer decompositions. Based on the exhibited multipath features, the impacts of multipath in GNSS observations are detected and mitigated in the reconstructed observations. The proposed method is validated for both static and dynamic positioning scenarios, demonstrating seamless integration with existing positioning models. The feasibility has been verified through a series of experiments and tests under urban environments using navigation terminals and smartphones. Full article

Optical transmission gratings with quasi-sinusoidal surface-relief profiles were inscribed in IOG and Pyrex glasses and in Bi₁₂GeO₂₀, Er: LiNbO_3, and Er: Fe: LiNbO₃ crystals by microbeams of carbon, nitrogen, and oxygen ions at ion energies of 5, 6, and 10.5 MeV. Grating constants were 4, 8, and 16 μm. Amplitudes of the surface-relief gratings were in the 10–2000 nm range. The diffraction efficiency of the gratings was measured at a wavelength of 640 nm. Maximum diffraction efficiencies were close to the theoretical maximum of 33% for thin gratings. Grating profiles were measured by optical microscopic profilometry. Measurement of the diffraction efficiencies at higher orders and Fourier analysis of the grating profiles revealed the dependence of the residual nonlinearity of the grating profiles on the implanted ion fluence. The ion microbeam-written gratings can be used as light coupling elements in integrated optics for sensors and telecommunication. Full article

Accurate prediction of soil–structure interface shear strength (τ_max) is critical for reliable geotechnical design. This study combines experimental testing with interpretable machine learning to overcome the limitations of traditional empirical models and black-box approaches. Ninety large-displacement ring shear tests were performed on five sands and three interface materials (steel, PVC, and stone) under normal stresses of 25–100 kPa. The results showed that particle morphology, quantified by the regularity index (RI), and surface roughness (R_t) are dominant factors. Irregular grains and rougher interfaces mobilised higher τ_max through enhanced interlocking, while smoother particles reduced this benefit. Harder surfaces resisted asperity crushing and maintained higher shear strength, whereas softer materials such as PVC showed localised deformation and lower resistance. These experimental findings formed the basis for a hybrid symbolic regression framework integrating Genetic Programming (GP) with Shapley Additive Explanations (SHAP), Fourier feature augmentation, and physics-informed constraints. Compared with multiple linear regression and other hybrid GP variants, the Physics-Informed Neural Fourier GP (PIN-FGP) model achieved the best performance (R² = 0.9866, RMSE = 2.0 kPa). The outcome is a set of five interpretable and physics-consistent formulas linking measurable soil and interface properties to τ_max. The study provides both new experimental insights and transparent predictive tools, supporting safer and more defensible geotechnical design and analysis. Full article

This study presents the development of an advanced three-dimensional (3D) bioprinted skin scaffold integrating sodium alginate (SA), gelatin (Gel), human skin-derived decellularized extracellular matrix (dECM), and microbiota-derived postbiotics. To ensure a biocompatible and functional ECM source, human skin samples collected during elective aesthetic surgical procedures were utilized. Following enzymatic treatment, the dermal layer was carefully separated from the epidermis and subjected to four different decellularization protocols. Among them, Protocol IV emerged as the most suitable, achieving significant DNA removal while maintaining the structural and biochemical integrity of the ECM, as confirmed by Fourier-transform infrared spectroscopy. Building on this optimized dECM-4, microbiota-derived postbiotics from Limosilactobacillus reuteri EIR/Spx-2 were incorporated to further enhance the scaffold’s bioactivity. Hybrid scaffolds were then fabricated using 7% Gel, 2% SA, 1% dECM-4, and 40 mg/mL postbiotics in five-layered grid structures via 3D bioprinting technology. Although this composition resulted in reduced mechanical strength, it exhibited improved hydrophilicity and biodegradability. Moreover, antimicrobial assays demonstrated inhibition zones of 16 mm and 13 mm against methicillin-resistant Staphylococcus aureus (MRSA, ATCC 43300) and Pseudomonas aeruginosa (ATCC 27853), respectively. Importantly, biocompatibility was confirmed through in vitro studies using human keratinocyte (HaCaT) cells, which adhered, proliferated, and maintained normal morphology over a 7-day culture period. Taken together, these findings suggest that the engineered hybrid scaffold provides both regenerative support and antimicrobial protection, making it a strong candidate for clinical applications, particularly in the management of chronic wounds. Full article

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications. Full article

Wind turbines face significant operational challenges due to their complex electromechanical systems, exposure to harsh environmental conditions, and high maintenance costs. Reliable structural health monitoring and condition monitoring are therefore essential for early fault detection, minimizing downtime, and optimizing maintenance strategies. Traditional approaches typically rely on either Trend Monitoring (TM) or Change Point Detection (CPD). TM methods track the long-term behaviour of process parameters, using statistical analysis or machine learning (ML) to identify abnormal patterns that may indicate emerging faults. In contrast, CPD techniques focus on detecting abrupt changes in time-series data, identifying shifts in mean, variance, or distribution, and providing accurate fault onset detection. While each approach has strengths, they also face limitations: TM effectively identifies fault type but lacks precision in timing, while CPD excels at locating fault occurrence but lacks detailed fault classification. This review critically examines the integration of TM and CPD methods for wind turbine diagnostics, highlighting their complementary strengths and weaknesses through an analysis of widely used TM techniques (e.g., Fast Fourier Transform, Wavelet Transform, Hilbert–Huang Transform, Empirical Mode Decomposition) and CPD methods (e.g., Bayesian Online Change Point Detection, Kullback–Leibler Divergence, Cumulative Sum). By combining both approaches, diagnostic accuracy can be enhanced, leveraging TM’s detailed fault characterization with CPD’s precise fault timing. The effectiveness of this synthesis is demonstrated in a case study on wind turbine blade fault diagnosis. Results shows that TM–CPD integration enhances early detection through coupling vibration and frequency trend analysis with robust statistical validation of fault onset. Full article

This study reports on green-synthesized zinc oxide nanoparticles (ZnONPs), focusing on their physicochemical characterization, photocatalytic properties, and agricultural applications. Dynamic light scattering (DLS) analysis revealed a mean hydrodynamic diameter of 337.3 nm and a polydispersity index (PDI) of 0.400, indicating moderate polydispersity and nanoparticle aggregation, typical of biologically synthesized systems. High-resolution transmission electron microscopy (HR-TEM) showed predominantly spherical particles with an average diameter of ~28 nm, exhibiting slight agglomeration. Energy-dispersive X-ray spectroscopy (EDX) confirmed the elemental composition of zinc and oxygen, while X-ray diffraction (XRD) analysis identified a hexagonal wurtzite crystal structure with a dominant (002) plane and an average crystallite size of ~29 nm. Photoluminescence (PL) spectroscopy displayed a distinct near-band-edge emission at ~462 nm and a broad blue–green emission band (430–600 nm) with relatively low intensity. The ultraviolet–visible spectroscopy (UV–Vis) absorption spectrum of the synthesized ZnONPs exhibited a strong absorption peak at 372 nm, and the optical band gap was calculated as 2.67 eV using the Tauc method. Fourier-transform infrared spectroscopy (FTIR) analysis revealed both similarities and distinct differences to the pigeon extract, confirming the successful formation of nanoparticles. A prominent absorption band observed at 455 cm⁻¹ was assigned to Zn–O stretching vibrations. X-ray photoelectron spectroscopy (XPS) analysis showed that raw pigeon droppings contained no Zn signals, while their extract provided organic biomolecules for reduction and stabilization, and it confirmed Zn²⁺ species and Zn–O bonding in the synthesized ZnONPs. Photocatalytic degradation assays demonstrated the efficient removal of pollutants from sewage water, leading to significant reductions in total dissolved solids (TDS), chemical oxygen demand (COD), and total suspended solids (TSS). These results are consistent with reported values for ZnO-based photocatalytic systems, which achieve biochemical oxygen demand (BOD) levels below 2 mg/L and COD values around 11.8 mg/L. Subsequent reuse of treated water for irrigation yielded promising agronomic outcomes. Wheat and barley seeds exhibited 100% germination rates with ZnO NP-treated water, which were markedly higher than those obtained using chlorine-treated effluent (65–68%) and even the control (89–91%). After 21 days, root and shoot lengths under ZnO NP irrigation exceeded those of the control group by 30–50%, indicating enhanced seedling vigor. These findings demonstrate that biosynthesized ZnONPs represent a sustainable and multifunctional solution for wastewater remediation and agricultural enhancement, positioning them as a promising candidate for integration into green technologies that support sustainable urban development. Full article

Aqueous zinc-ion batteries (AZIBs) have attracted significant attention for large-scale energy storage owing to their high safety, low cost, and environmental friendliness. However, issues such as dendrite growth, hydrogen evolution, and corrosion at the zinc anode severely limit their cycling stability. In this study, a “synergistic solvation shell–interfacial adsorption regulation” strategy is proposed, employing potassium gluconate (KG) and dimethyl sulfoxide (DMSO) as composite additives to achieve highly reversible zinc anodes. DMSO integrates into the Zn²⁺ solvation shell, weakening Zn²⁺-H₂O interactions and suppressing the activity of free water, while gluconate anions preferentially adsorb onto the zinc anode surface, inducing the formation of a robust solid electrolyte interphase (SEI) enriched in Zn(OH)₂ and ZnCO₃. Nuclear magnetic resonance(NMR), Raman, and Fourier transform infrared spectroscopy(FTIR) analyses confirm the reconstruction of the solvation structure and reduction in water activity, and X-ray photoelectron spectroscopy(XPS) verifies the formation of the SEI layer. Benefiting from this strategy, Zn||Zn symmetric cells exhibit stable cycling for over 1800 h at 1 mA cm⁻² and 1 mAh cm⁻², and Zn||Cu cells achieve an average coulombic efficiency of 96.39%, along with pronounced suppression of the hydrogen evolution reaction. This work provides a new paradigm for the design of low-cost and high-performance electrolyte additives. Full article

Liposomes, nanoscale vesicles with distinct structural and functional properties, are widely utilized in drug delivery due to their biocompatibility and ability to encapsulate diverse therapeutic agents. Effective sterilization is essential to ensure the safety and efficacy of liposomal formulations in biomedical applications, yet its impact on liposome integrity and functionality remains inadequately studied. This work systematically evaluates the effects of three sterilization methods: autoclaving, UV radiation, and filtration—on liposomes composed of dipalmitoylphosphatidylcholine (DPPC) and distearoylphosphatidylcholine (DSPC), two phospholipids differing in lipid chain length. Sterilization altered liposome properties in a lipid chain length-dependent manner, affecting particle size, zeta potential, and phospholipid content. Filtration caused significant hydrocarbon loss, confirmed by Fourier-transform infrared spectroscopy (FTIR) and Raman spectroscopy, and led to a higher reduction in phospholipid content in DPPC liposomes compared to DSPC liposomes. Biological evaluations showed that autoclaved and UV-irradiated DPPC liposomes exhibited higher cytotoxic and lower stability than their DSPC counterparts. While autoclaving and UV irradiation resulted in minimal chemical alterations, both methods significantly influenced biological properties. Filtration, although less disruptive to biocompatibility, also reduced key liposomal integrity and efficacy. This study underscores the critical importance of post-sterilization evaluation to optimize liposomal formulations for clinical and biomedical use. Full article

This study explores the formulation of chitosan microparticles through ionic gelation and presents detailed physicochemical characterization, release studies, and the utility and potential uses for drug delivery. Three formulations were prepared under rate-controlled conditions (stirring at 800 rpm and pH maintained at 4.6) with and without stabilizers to examine the effects of formulation parameters on particle morphology and structural stability. To determine different structural and chemical characteristics, Attenuated Total Reflectance Fourier-Transform Infrared spectroscopy (ATR–FTIR), Scanning Electron Microscopy (SEM), and dynamic light scattering (DLS) were utilized, which confirmed that the particles formed and assessed size distribution and structural integrity. Atomic force microscopy (AFM) was used to quantify surface roughness and potential nanomechanical differences that may derive from the use of different modifiers. Coformulation of bovine serum albumin (BSA) permitted assessment of encapsulation efficiency and drug release capacity. Based on in vitro release evidence, the protein released at a different rate, and the dispersion of formulations under physiological conditions (PBS, pH 7.4, 37 °C) confirmed the differences in stability between formulations. The tunable physical characteristics, mild fabrication conditions, and controlled drug release demonstrated that the chitosan particles could have useful relevance as a substrate for localized drug delivery and as a bioactive scaffold for tissue regenerative purposes. Full article

In the marine environment, plastic fragments are constantly engaged in a complex degradation process under exposure to various physical and chemical factors, one of which is ultraviolet (UV) radiation. These processes result in the formation of smaller micro- and nano-sized plastic particles, which are highly bioavailable to marine organisms. To clarify the toxicological effects of the exposure of degraded plastic on the marine organisms, the model used in this study was the Pacific mussel Mytilus trossulus and polymethylmethacrylate (PMMA), which is commonly found in marine debris. Using molecular and biochemical markers (DNA damage, lysosomal membrane stability, integral antiradical activity (IAA) of biological samples, and malondialdehyde (MDA) as a product of lipid peroxidation), the toxicity of pristine PMMA and photoaged (PMMA-UV) particles was assessed. Using Fourier transform infrared spectroscopy, the characteristics of the macromolecular changes in the chemical structure of PMMA-UV were obtained, with an oxidation index of 6.83 ± 0.46, compared to the pristine PMMA of 5.15 ± 0.54. Using a laser analyzer, the sizes of PMMA particles were determined, and it was found that after UV irradiation, the ratio of size groups changed—the proportion of particles with sizes of 500–1000 μm decreased, and the number of particles with sizes of 50–125 μm increased twofold. Analysis of mussel cell viability showed that after exposure to both types of PMMA microparticles, there was a decrease in the ability to retain neutral red dye in lysosomes: PMMA and PMMA-UV had a similar effect on hemocytes, reducing dye retention in cells to 55.2 ± 3.24% and 61.1 ± 1.99%, respectively. In gill and digestive gland cells, PMMA-UV particles reduced the stability of lysosomal membranes to a greater extent than PMMA. After PMMA and PMMA-UV particle exposure, the levels of DNA damage were as follows: in hemocytes, 10.1 ± 1.4% and 12.7 ± 0.8%, respectively; in gills, 7.8 ± 1.1% and 14.4 ± 2.9%, respectively; and in the digestive gland, 19.0 ± 1.3% and 21.9 ± 2.8%, respectively, according to the control values 3.6 ± 1.3%, 4.6 ± 1.1%, 5.1 ± 1.5%, respectively. According to the results of biochemical markers, the reaction of mussels to the presence of PMMA and PMMA-UV particles in the environment was tissue-specific: in the cells of the digestive gland, the level of IAA increased by 2 and 1.3 times compared to the control group of mussels (76.22 ± 6.77 nmol trolox/g wet weight and 52.43 ± 2.36 nmol trolox/g wet, respectively), while in the gill cells, the non-significant increase in antiradical activity was noted. An increase in MDA content was also observed in gill cells (255.8 ± 9.12 nmol MDA/g wet weight and 263.46 ± 9.45 nmol MDA/g wet weight, respectively) compared with the control group. This study showed that UV irradiation of PMMA microparticles increases their bioavailability and toxicity to M. trossulus. Full article

A Bayesian approach for constructing ARMA probability density estimators is proposed. Such estimators are ratios of trigonometric polynomials and have a number of advantages over Fourier series estimators, including parsimony and greater efficiency under common conditions. The Bayesian approach is carried out via MCMC, the output of which can be used to obtain probability intervals for unknown parameters and the underlying density. Finite sample efficiency and methods for choosing the estimator’s smoothing parameter are considered in a simulation study, and the ideas are illustrated with data on a wine attribute. Full article