Search Results (1,437)

Millimeter-wave (mm-wave) near-field imaging using frequency-modulated continuous wave (FMCW) radar has emerged as a pivotal technology for high-resolution applications, including security screening, non-destructive testing, and medical diagnostics. This review evaluates the performance and evolution of key imaging algorithms, categorized into spatial-domain and frequency-domain frameworks. We analyze the delay-and-sum (DAS) beamformer for its real-time utility and the back-projection algorithm (BPA) for its baseline phase precision and robust adaptability to irregular scanning trajectories. To address the high computational demands of standard spatial-domain processing, we examine fast alternatives such as the range migration algorithm (RMA). The exact RMA leverages Fourier-domain operations and Stolt coordinate mapping to achieve optimal computational scaling on uniform grids while preserving diffraction-limited spatial resolutions. Concurrently, we evaluate fast spatial-domain approximations, including Fast Back-Projection (Fast-BPA), which introduces localized Taylor-series expansions to linearize near-field range paths within sub-apertures, accelerating voxel reconstruction times at a reduced computational cost. Furthermore, this study explores advanced modifications designed to overcome physical and operational constraints, such as motion-compensated matched filtering (MF) to eliminate the “stop-and-go” assumption in continuous scanning, and sparse multiple-input multiple-output (MIMO) configurations to mitigate aliasing in undersampled environments. Comparative analysis reveals that while spatial-domain methods (DAS/BPA) generally offer higher robustness to non-uniform aperture perturbations, frequency-domain migration pathways (RMA) maximize the computational throughput required for large-volume three-dimensional (3D) reconstructions. The findings demonstrate that achievable resolution is primarily governed by signal bandwidth and aperture synthesis, though practical performance is often limited by calibration errors and computational overhead. Collectively, these advancements validate the potential of mm-wave FMCW systems to achieve sub-millimeter 3D imaging, bridging the gap between theoretical diffraction limits and real-world indoor sensing challenges. Full article

This article presents the results of a study investigating the influence of surface-modified montmorillonite nanoclay and environmental exposure on the mechanical and surface properties of composites based on orthophthalic unsaturated polyester resin. A series of samples containing 0.02 to 0.1 wt.% of nanoclay, as well as a reference sample, were prepared. Following synthesis, the samples were incubated for one week in aqueous solutions with pH values of 4, 7, and 9. Mechanical testing included tensile strength, flexural strength, impact resistance, and surface roughness (Ra). Additionally, fracture surface analysis was performed using scanning electron microscopy (SEM) and digital optical microscopy. The effect of the additive on hardness was also assessed, and Fourier transform infrared (FT-IR) spectroscopy was carried out. The results indicated that nanoclay addition increased the material’s stiffness (Young’s modulus), although higher filler concentrations led to decreased impact strength and flexural performance, likely due to particle agglomeration. Incubation in chemically aggressive environments caused mechanical degradation and significant increases in surface roughness, especially after exposure to neutral and alkaline media. Microscopic observations confirmed the presence of microstructural changes and nanoclay agglomerates in selected samples. The findings confirm that the effectiveness of nanoclay modification depends not only on concentration but also on dispersion quality and the environmental conditions to which the material is exposed. Full article

This study presents an analytical model for the long-term bending behavior of simply supported laminated glass (LG) plates with temperature-dependent viscoelastic interlayers. The glass layers are described based on three-dimensional elasticity theory, and the governing stress and displacement equations are formulated using the state-space method. The polymer interlayer is characterized by the generalized Maxwell model and the Williams–Landel–Ferry equation, while its time-dependent response is described through the Boltzmann convolution principle. By combining double Fourier series expansions with the Laplace-transform technique, analytical solutions for the stresses and displacements of multilayer LG plates are derived. The comparison shows that Kirchhoff–Love plate theory gives results close to the present solution for relatively thin LG plates, whereas the discrepancy becomes increasingly pronounced as the plate thickness increases. The finite element results agree well with those obtained from the proposed model; however, for the representative benchmark case, the present solution is approximately 1.13 × 10³ times faster than the FE simulation, and its memory usage is only about 10.88% of that required by the FE model. Parametric studies further reveal the effects of temperature, interlayer thickness, interlayer material, number of glass layers, and aspect ratio on the stress redistribution and deflection development of LG plates. Full article

Epoxy resins are widely used in advanced engineering applications, including coatings, adhesives, and electronics. Therefore, improving their flame resistance is important for enhancing fire safety and extending their range of applications. A series of flame retardants based on melamine phosphate derivatives, such as melamine phosphate (MP), melamine dibutyl phosphate, and melamine bis(2-ethylhexyl) phosphate, as well as a zinc borate-modified system (ZnB-MP) has been incorporated into commercially available epoxy resin (Epidian® 601). The blends were characterized using Fourier transform infrared spectroscopy (FTIR) to confirm their chemical structure. Thermal behaviour was investigated using differential scanning calorimetry and thermogravimetry coupled with FTIR gas analysis (TG-FTIR). The flammability performance of the epoxy blends was evaluated using pyrolysis combustion flow calorimetry, which allowed parameters such as heat release rate, total heat release, and heat release capacity to be determined. The incorporation of melamine phosphate-based flame retardants was found to significantly reduce the flammability of epoxy blends, leading to substantial decreases in heat release rate, total heat release, and heat release capacity. The most pronounced effect was observed in systems containing higher concentrations of MP and in cooperative ZnB-MP formulations. Full article

High-quality X-ray pulsar observation profiles are vital for investigating both their physical properties and navigation applications. Conventional profile extraction relies on epoch folding, whose performance is constrained by observation duration and bin size, often leading to poor-quality profiles or even failure under extremely low-photon conditions. This paper proposes a novel method that directly extracts high-quality profile frequency spectra merely by statistical analysis of photon sequences followed by the reconstruction of time domain waveforms. Monte Carlo simulations and real observational data demonstrate that the proposed method exhibits higher correlation coefficients and signal-to-noise ratios than those obtained using traditional epoch folding, and also outperforms the Fourier-series-based frequency cutoff method. Moreover, comparable profile quality can be achieved using an order of magnitude fewer photons than required by epoch folding. The lower the photon count, the more significant the improvement, making the method especially suitable for small-area detectors and resource-constrained observation scenarios. Full article

Most traditional photocatalysts exist in powder form and have the disadvantage of being difficult to recycle and causing secondary pollution to the environment after use. To overcome this drawback, this study combined natural biopolymer (silk fibroin (SF)) with a previously developed titanium-based photocatalytic material P/Ag/Ag₂O/Ag₃PO₄/TiO₂ (PAgT) and fabricated a novel SF/PAgT fiber membrane via electrospinning. During the synthesis process, through adjusting the mass concentration of the PAgT dopant (0–0.30 g/mL), a series of photocatalytic fiber membranes were prepared. The morphology and structure of the as-prepared membranes were characterized by various analytical methods, including scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared (FT-IR), contact angle (CA) and thermogravimetric analysis (TGA). The SEM images confirmed that the SF/PAgT composite membrane possessed a protrusive and spindle-shaped structure. FT-IR results verified that the primary structure of SF in all the as-prepared SF/PAgT membranes belonged to the Silk II type. The binding of SF with the PAgT photocatalyst did not disrupt the chemical structure and original properties of SF. Moreover, the XRD and CA measurements indicated that the SF/PAgT-4 fiber membrane exhibited the stronger diffraction peaks of anatase TiO₂ crystal structure and enhanced hydrophilicity. The experimental results clarified that the PAgT photocatalyst was successfully loaded onto the SF fiber membrane by electrospinning. To evaluate the performance of the developed visible-light-driven photocatalytic fiber membranes, Gram-negative Escherichia coli (E. coli) and Gram-positive Staphylococcus aureus (S. aureus) were selected as representative bacteria strains. The results demonstrated that SF/PAgT-4 exhibited the optimal antibacterial activity and can completely inactivate 10⁷ CFU/mL of E. coli and S. aureus within just 30 min and 60 min treatment, respectively, indicating the optimal doping mass concentration of PAgT during the synthesis process was 0.20 g/mL. Furthermore, the scavenger study proved that during the photocatalytic disinfection process by SF/PAgT-4, all three radicals, including ·OH, h⁺ and ·O₂⁻, participated in the current photocatalytic disinfection system. They were capable of attacking the bacterial cells, causing the cell membrane injury, thereby leading to the intracellular component leakage and inducing extensive bacterial inactivation. Hence, by virtue of its excellent recyclability (during five cycles) and thermal stability (below 250 °C), the developed SF/PAgT-4 fiber membrane holds immense potential for highly efficient and sustainable utilization in practical water treatment applications. Full article

This study explores deep eutectic solvents (DESs) as potential alternatives to dimethylformamide (DMF) for extractive desulfurization of gas oil and marine fuel oil. A series of DESs, based on choline chloride (ChCl) and tetrabutylammonium bromide (TBAB), were evaluated using straight-run gas oil (SRGO) and compared to DMF. The influence of an oxidation pre-treatment and water content was also investigated, and the most efficient systems were further applied to marine fuel oil. Firstly, oxidation was found to be essential to enhance sulfur removal by improving the extractability of sulfone compounds. The results show that DES formulation strongly affects desulfurization performance, with TBAB-based DESs achieving efficiencies comparable to DMF (~80% desulfurization rate) and outperforming ChCl-based systems. In ChCl-based DESs, a moderate water content (~10 wt%) improved performance, whereas higher amounts disrupted the hydrogen-bond network, as evidenced by Fourier-transform infrared spectroscopy, leading to a decreased efficiency. When applied to marine fuel oil, similar trends were observed, although lower desulfurization levels were obtained due to the complexity of the feed, and a solvent-to-fuel ratio of 5:1 was required for proper phase separation. Fourier transform mass spectrometry analyses highlighted the persistence of refractory sulfur species, emphasizing the need for tailored solvent design. Overall, DESs represent promising and adaptable alternatives for desulfurization processes. Full article

Solar insecticidal lamps are important physical control devices for green pest management, but faults in their photovoltaic power supply units can reduce trapping efficiency and shorten service life. To improve fault identification under complex agricultural environments, this study proposes a signal-image-level multimodal fusion network (SIL-MMFN) for detecting and classifying photovoltaic panel operating states in solar insecticidal lamps. The method combines time-series measurements with short-time Fourier transform (STFT)-based time–frequency images. A convolutional image branch extracts spatial features from time–frequency representations, whereas a bidirectional GRU branch with attention models temporal dependencies in the original signals. In addition, physics-informed features based on the illumination–current residual and output power are introduced to enhance discriminative fault information. Field data collected from four agricultural deployment nodes were used to classify normal, open-circuit, and mismatch states. Experimental results show that the proposed method achieved an accuracy of 97.5%, precision of 96.7%, recall of 97.8%, and macro-F1 score of 97.3%, outperforming single-modality and representative comparison models. The results indicate that multimodal fusion helps reduce confusion between open-circuit and mismatch faults and provides a potential approach for operating-state monitoring and maintenance of agricultural photovoltaic equipment. In this study, fault diagnosis refers to the detection and classification of photovoltaic panel operating states, including normal, open-circuit, and mismatch conditions. Full article

In this paper, we focus on one-dimensional vertical infiltration, assuming constant diffusivity and a quadratic relationship between hydraulic conductivity and water content. Under these assumptions, Richards’ equation reduces to Burgers’ equation, which we then linearize via the Hopf–Cole transformation. This turns the initial boundary value problem into a diffusion equation on a finite interval with mixed boundary conditions. To solve it, we use the Unified Transform Method (also known as the Fokas method). This approach gives an explicit integral representation of the solution, and when evaluated numerically, the results match classical Fourier series solutions exactly, but with better convergence and stability. Two examples from hydrological applications are examined. Full article

Stock index price series are composed of superimposed multi-frequency components, including long-term trends, cyclical fluctuations, and stochastic noise. Effectively decoupling these heterogeneous components and modeling them separately is key to improving forecasting accuracy. Existing methods under the “decomposition–prediction” paradigm mostly employ fixed-scale decomposition, and the forecasting models are not specifically adapted to the non-stationary and high-noise characteristics of financial data, resulting in limitations in adaptivity and local dynamic capture. This paper proposes a frequency-aware adaptive multi-scale decomposition Transformer hybrid model (FAMS-Transformer). At the decomposition level, the fast Fourier transform is used to dynamically identify dominant cycles, thereby adaptively decoupling trends and fluctuations, overcoming the limitations of fixed-scale decomposition. At the forecasting level, a lightweight depthwise separable convolution is embedded between the self-attention and feedforward network of the Transformer encoder, enhancing the model’s ability to capture local temporal dynamics and achieving collaborative modeling of global dependencies and local information. Comparative experiments with 15 baseline models including LSTM, Transformer, TimesNet, and FreTS on three representative Chinese market indices—Shanghai Composite Index, Shenzhen Component Index, and Small and Medium Enterprises 100 Index—across four prediction horizons from one step to 15 steps demonstrate that FAMS-Transformer achieves the best forecasting accuracy in all scenarios. The coefficient of determination for 15-step prediction remains stably between 0.730 and 0.928. Moreover, the model still performs well on the S & P 500 dataset. Ablation studies and significance tests further validate the effectiveness of each core module and the statistical significance of the performance improvements. Full article

Accurate electricity price forecasting (EPF) is essential for market participants to optimize trading strategies and for power systems to accommodate the increasing penetration of volatile renewable energy sources. However, electricity price series are characterized by strong nonlinearity, high volatility, and significant structural breaks, which pose substantial challenges to conventional forecasting models. Although numerous hybrid deep learning models have been proposed for EPF, most existing approaches either overlook structural breaks or treat them as outliers rather than as signals of regime shifts, often resulting in systematic forecasting degradation when market conditions change abruptly. To address this issue, this study proposes COCAL-TTL, a novel multi-stage structural break-aware forecasting framework that integrates regime-adaptive data partitioning with a functionally differentiated hybrid deep learning architecture. First, a joint detection scheme combining the Iterated Cumulative Sum of Squares (ICSS) algorithm and the Chow test is employed to partition Spanish electricity market data from 2014 to 2023 into distinct regimes. Within each regime, CEEMDAN is applied to extract multi-scale features, which are subsequently reconstructed into trend, periodic, and random components based on an independent sample t-test and Fast Fourier Transform (FFT). The CNN-SE Attention-LSTM (CAL) model, with hyperparameters optimized by the Osprey Optimization Algorithm (OOA), serves as the primary forecasting engine. In addition, a dedicated heterogeneous error correction module, namely TTL, is introduced, in which Temporal Convolutional Network, Transformer, and LSTM are designed to capture local transients, long-range dependencies, and transitional dynamics in the residual series, respectively. Empirical results demonstrate that compared with the Naive benchmark, COCAL-TTL achieves percentage MAPE improvements of 58.48% and 48.97% in low- and high-volatility regimes, respectively. These findings indicate that the proposed structural break-aware framework provides a robust data-driven solution for EPF under heterogeneous market conditions and offers technical support for stable electricity market operation in the context of renewable energy integration. Full article

This work presents a robust preprocessing pipeline for photovoltaic (PV) time series forecasting aimed at improving the quality, consistency, and physical coherence of the input data used in predictive models. The proposed methodology integrates temporal lag correction, Fourier-based temporal enrichment, supervised and unsupervised outlier detection, and feature selection to adapt the preprocessing workflow to different operational conditions and data characteristics. The pipeline is validated using real-world data from two PV plants with different temporal resolutions and operating regimes. The results show that the proposed approach improves dataset coherence and strengthens the relationship between meteorological predictors and PV output, providing a reliable basis for subsequent forecasting tasks. In addition, an online forecasting validation over January 2025 shows that a Random Forest model using preprocessed inputs substantially reduces prediction errors compared with the same model using raw inputs, with MAE reductions of 54.2% for the Test Plant and 25.6% for the Production Plant, and corresponding RMSE reductions of 32.1% and 12.6%. Full article

Background and Objectives: The SARS-CoV-2 pandemic disrupted oral and maxillofacial surgery (OMS) services worldwide because of the high aerosol-generating nature of head-and-neck procedures, restricted access to elective dental care, and systemic reallocation of hospital resources. Continuous longitudinal multi-year data covering both the pandemic and the post-pandemic phases from regional Romanian (and more broadly central and southeastern European) emergency centers remain scarce. We aimed to quantify the impact of the pandemic on OMS activity in a large Romanian regional referral center and to evaluate post-pandemic resilience. Materials and Methods: We conducted a retrospective single-center study of all inpatient admissions to the OMS Clinic of a tertiary emergency hospital in western Romania between 1 January 2018 and 31 December 2024. Three periods were pre-specified: pre-pandemic (2018–2019), pandemic (2020–2022) and post-pandemic (2023–2024). A Newey–West segmented interrupted-time-series (ITS) regression and a negative-binomial monthly count model with Fourier seasonality were fitted; length of hospital stay was further analyzed with a multivariable gamma-log generalized linear model adjusted for age, sex, county, primary ICD-10 chapter and total ICD-10 codes. Variables analyzed included case volume, demographics, primary and secondary ICD-10 diagnoses, length of hospital stay (LOS), case complexity (total ICD-10 codes per admission) and in-hospital mortality. Results: A total of 11,628 inpatient admissions corresponding to 8084 unique patients (56.5% male; mean age 52.2 ± 19.2 years) were analyzed. Compared with the pre-pandemic baseline (mean 2037 admissions/year), annual volume dropped by 45.1% in 2020, 44.0% in 2021 and 32.3% in 2022, with a nadir of −76% during the first state of emergency (April 2020; n = 34 admissions). Recovery was rapid; 2024 exceeded the pre-pandemic baseline by +10.1% on raw counts and by +16.2% on admissions per 100,000 catchment population using year-specific INS denominators. The segmented ITS regression confirmed an immediate level drop of −114.2 admissions/month in March 2020 (95% CI −133.1 to −95.3; p < 0.001) and a positive post-intervention slope of +2.06 admissions/month (95% CI 1.23–2.88; p < 0.001), with observed monthly volume returning to the counterfactual projection by October 2023. The case mix shifted significantly (χ² = 406.9, p < 0.0001); elective benign neoplasm admissions were reduced from 7.2% to 2.0%, while neoplasms of uncertain behavior nearly doubled from 15.7% to 27.5%. Case complexity increased during the pandemic (mean ICD codes 4.08 ± 2.42 vs. 3.44 ± 2.30; p < 0.001); after exclusion of administrative codes (whole Z chapter and U07.x), the difference attenuated to 3.34 vs. 3.17 codes (still p < 0.001 by Kruskal–Wallis), indicating that the largest portion of the unadjusted increase was driven by the new mandatory pre-admission SARS-CoV-2 screening code Z11.5 rather than true clinical complexity. Notably, the clinically interpretable proxy R63.3 (feeding difficulty) independently rose from 41.5% to 53.1%. The crude median LOS did not differ between the pre-pandemic and pandemic periods (3.07 vs. 3.06 d; p = 0.19) and dropped significantly post-pandemic (2.22 d; p < 0.001); however, after multivariable adjustment for case mix, age, sex, county and code count, the LOS was 15.7% shorter during the pandemic (adjusted ratio 0.84, 95% CI 0.82–0.87; p < 0.001) and 22.8% shorter post-pandemic (adjusted ratio 0.77, 95% CI 0.75–0.80; p < 0.001) relative to baseline. Conclusions: The pandemic caused a severe but transient contraction of OMS activity accompanied by increased case complexity and a marked shift away from elective surgery. Inpatient volume returned to and exceeded the pre-pandemic baseline by 2024. These results support the value of standing pandemic-preparedness protocols, sustained access to preventive dental care, and integrated tele-triage pathways for future public-health crises. Full article

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article

This paper presents a new plane stress model for the dynamic analysis of multilayer composite beams with interlayer slip effects. In this model, the cross section of a multilayer composite beam is transformed into an equivalent plane stress cross section. Based on the equilibrium, constitutive and geometric equations of the plane stress problem, state equations are derived in terms of a set of state variables. The state variables are then expanded in Fourier series, and the state equations are solved using the state-space method. The proposed computational model makes it convenient to account for slip at each interface and can represent the entire transition of an interface from fully slipped to fully bonded. Interlayer slip and the corresponding interaction forces are incorporated naturally into the derivation of the governing equations, and the model gives accurate results. A steel–concrete–steel composite beam, a four-layer composite beam and a laminated timber beam are analyzed as examples of multilayer composite beams under both static and dynamic loading. The static analysis results are in good agreement with the literature results, with a maximum error of 0.63% for the maximum mid-span deflection and only 0.143% for the maximum interlayer slip value. Compared with finite element results, the natural frequencies and buckling loads obtained from the dynamic analysis exhibit maximum relative errors of 2.87% and 3.77%, respectively. The relationship between axial force and natural frequency is also presented, which verifies the accuracy and reliability of the proposed model and calculation method. Full article