Search Results (1,416)

This study investigates steady-state heat transfer in a three-layer cylindrical system with angular non-uniformity of the temperature field. For the considered geometry, a mathematical model of heat conduction is formulated in cylindrical coordinates with piecewise constant thermophysical properties and continuity conditions at the interfaces between layers. The direct problem is solved analytically using a Fourier series expansion of the temperature field with respect to the angular coordinate. Based on experimental temperature measurements obtained for various configurations of soil layers, an inverse problem is formulated and solved to reconstruct the thermal conductivities of the individual layers and the heat transfer coefficient at the external boundary. To stabilize the solution, a regularized least-squares approach is employed. The convergence of the recovered parameters with respect to the harmonic number is analyzed, and the averaged reconstructed values are compared with the exact parameters used in the direct problem. The obtained results demonstrate the stability and accuracy of the proposed method, confirming its applicability to the identification of thermophysical parameters in multilayer soil systems based on experimental data. Full article

A series of Mg-Al LDH-based photocatalysts were synthesized via a one-pot steam-assisted method, including pure Mg-Al LDH (MA), Zn-In ion-exchange-modified Mg-Al LDH (MAZ), BiOCl-loaded pristine Mg-Al LDH (MAB), and Zn-In-modified Mg-Al LDH co-loaded with TiO₂ and BiOCl (MA/Zn-In/TiO₂/BiOCl, MAZB). The one-pot synthesis facilitated the in situ intercalation and uniform loading of BiOCl/TiO₂/Zn-In, while Zn²⁺/In³⁺ modified the MA layers via ion exchange, leading to an expansion of the interlayer spacing. The innovation of this work is reflected in two aspects: first, all raw materials are added via a one-pot strategy to achieve in situ preparation of modified hydrotalcite; second, this synthetic route features simple post-treatment without complicated washing, pressure filtration, and other tedious operations. The samples were characterized by X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and N₂ adsorption–desorption isotherms. The bismuth chloride oxide/TiO₂/LDHs exhibited a layered structure, with the active components uniformly distributed between the layers and on the MA surface. Under simulated sunlight irradiation, MAZB achieved 97.5% degradation of 20 mg/L MB within 120 min, with an apparent rate constant of 0.0297 min⁻¹, which is 7.2 times, 2.4 times, and 2.9 times that of MA, MAZ, and MAB, respectively. The degradation rate of MAZB still remained at 89.5% after five cycles, demonstrating excellent stability and reusability. Compared with traditional hydrothermal methods, this steam-assisted system features mild reaction conditions (180 °C, atmospheric pressure), sodium-free raw materials, no washing requirement, and zero waste discharge, showing prominent green advantages. Full article

This study investigates the ecological footprint (EF) convergence dynamics of the “Big Ten Emerging Economies” (BTEs) over the period 1967–2024. Employing the Maximum Overlap Discrete Wavelet Transform (MODWT) in conjunction with the Fourier KPSS (FKPSS) stationarity test, the analysis decomposes the EF series into short-, medium-, and long-term frequency components, allowing the stochastic convergence hypothesis to be examined separately across multiple time horizons. The empirical results reveal that convergence is largely absent in the original series, with stochastic convergence detected only for India, Indonesia, and Türkiye at the aggregate level. Once the series are decomposed, convergence becomes considerably more visible. In the short run, convergence is supported for Argentina, Indonesia, Mexico, Poland, and Türkiye. The medium run emerges as the most robust convergence horizon, with all ten economies exhibiting stochastic convergence—a result that becomes visible only after accounting for nonlinear structural breaks through the Fourier framework. In the long run, convergence is supported for Argentina, Brazil, China, Korea, Poland, and South Africa, while India, Indonesia, Mexico, and Türkiye exhibit persistent divergence. No single country maintains convergence consistently across all time horizons, underscoring the heterogeneous and frequency-dependent nature of EF dynamics in major emerging economies. The robustness analysis based on the Fourier ADF and standard ADF tests supports the primary findings. These results contribute to the EF convergence literature by demonstrating that environmental convergence is a multi-layered and frequency-dependent phenomenon, and offer empirical insights relevant to the design of long-run sustainability policies for emerging economies. Full article

Biologically related time-series data characterize the dynamic evolution of biological systems, including genetic inheritance, disease diagnosis, and the biological microenvironment. However, accurate prediction of these data remains challenging due to their pronounced time-varying, non-stationary, and noisy characteristics. Existing approaches often fail to capture latent shifts of biologically related time series, limiting both predictive performance and time-varying pattern recognition capability. Thus, in this study, we first propose a time-varying neural network (TVNN) model that combines frequency-domain information with Koopman theory. TVNN-model Koopman transition matrices are used to model global dynamics and local time-varying behaviors for pattern extraction. Secondly, a time-varying pattern recognition large language model (TVPRLLM) is introduced to recognize and classify the extracted time-varying patterns, enabling the identification of potential pattern categories. Thirdly, we have developed a biology-related time-series predictive platform that can offer visualization, data analysis, and predictive services. Experimental results demonstrate that the TVNN model outperforms existing mainstream methods in predicting biology-related time-varying time series, and that it achieves competitive forecasting performance, though its behavior depends strongly on the design of the frequency-domain decomposition. Additional robustness analyses reveal that the choice of Fourier masking strategy can materially affect both RMSE and long-horizon stability. We further show that Koopman-derived time-varying representations are highly discriminative for dynamic state recognition. Full article

Rainfall prediction is essential for the enhanced understanding of several issues related to water resources and agriculture, such as flood and drought alerts and flood management. Neural network models are frequently used due to their capability of effectively handling large datasets and addressing the non-stationarity of rainfall data series, resulting in better accuracy and affordable solutions. However, further study is necessary to comprehend the dynamic nature and extreme events of rainfall. Therefore, we implemented a novel wavelet Fourier-enhanced network (W-FENet) that included a Fourier enhancement module (FEMEX) and an improved U-Net mechanism to strengthen the predictive accuracy of daily rainfall. The adopted U-Net structure facilitated efficient multiscale feature extraction and preservation of temporal rainfall information through encoder–decoder connections and residual learning. The results of the developed models for one-day-ahead rainfall prediction were evaluated against two traditional neural network models, i.e., artificial neural networks and long short-term memory networks. Mongla, being a coastal station and having a highly non-linear rainfall pattern, operated by the Bangladesh Meteorological Department, was selected as the study area. Four preprocessing techniques were incorporated to enhance the robustness of the models: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), and successive variational mode decomposition (SVMD). The SVMD-enhanced W-FENet model (abbreviated as W5) demonstrated significant improvements over existing literature with RMSE = 2.226 mm, MAE = 1.131 mm, PCC = 0.988, NSE = 0.974, and WI = 0.993 at the testing phase. Full article

This study introduces the dynamic characteristics of stepped functionally graded (FG) cylindrical shells under general boundary conditions using the Legendre–Ritz method. The calculated model is established based on the first-order shear deformation theory and the domain decomposition method, and the artificial spring is introduced to simulate the boundary conditions and ensure segment continuity. The Legendre polynomials and the Fourier series are used to form the admissible displacement function. The Rayleigh–Ritz method is employed to determine the free and forced vibration characteristics of stepped FG cylindrical shells. Results are presented for various boundary conditions, material parameters and geometric dimensions, and comparisons with published studies are performed. The method demonstrates good accuracy, providing a basis for analyzing the vibration behavior of stepped FG cylindrical shells. Full article

In this study, zinc-modified biochar (ZBC) was prepared from rose willow, and NiCoMn-LDHs@ZBC composites were synthesized using a hydrothermal method. The composites were characterized by X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area analysis, scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). The adsorption mechanism of As(V) from aqueous solution onto NiCoMn-LDHs@ZBC was investigated through a series of arsenic adsorption experiments. The effects of various experimental parameters (including adsorbent composition and ratio, adsorbent dosage, solution pH, contact time, temperature, and coexisting ions) on the adsorption capacity were evaluated. Additionally, adsorption model fitting and kinetic analysis were conducted. The results indicate that the adsorption process follows the pseudo-second-order kinetic model (linear correlation coefficient R² = 0.99), while the isothermal adsorption process adheres to the Langmuir model, with a maximum adsorption capacity of 159.780 mg/g. The adsorption process is primarily dominated by chemisorption and involves three pathways: first, electrostatic attraction between the material surface and arsenic-containing ions; second, ion exchange between arsenic-containing ions and interlayer carbonate ions; and third, coordination reactions between the surface hydroxyl groups (-OH) of NiCoMn-LDHs@ZBC and As, forming As-O-M inner-sphere complexes as adsorption proceeds. Furthermore, the NiCoMn-LDHs@ZBC composite exhibits relatively stable reusability, demonstrating significant potential for the treatment of arsenic pollution in water bodies. Full article

The removal of sulfur compounds such as ethyl mercaptan from natural gas remains a critical challenge due to their detrimental effects on downstream processes, catalyst poisoning, and environmental emissions. In this study, a series of halloysite-supported transition metal oxide catalysts was synthesized and evaluated for the removal of sulfur compounds from natural gas at 25 °C, 200 psi, and 36 mL/min, using 0.5 g of the catalyst. The nanotubular structure and dual surface chemistry of halloysite promote enhanced metal dispersion and improved mass transfer. Single-metal (manganese, copper, zinc, and nickel) catalysts were developed and tested, after which a multi-metal oxide (base) catalyst comprising a composite of the single metals (Zn-Cu-Mn-Ni) was developed as a base catalyst to combine adsorption-active and redox-active functionalities, and its performance was further enhanced by the addition of palladium as promoter. A combination of analytical techniques, including X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier transform infra-red spectroscopy (FTIR), Brunauer–Emmett–Teller (BET) analysis, scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS), provided evidence that highly dispersed metal oxide phases were formed and the halloysite structure was preserved. XPS data showed the presence of oxidation states of metals that were active (Zn²⁺, Cu²⁺, Ni²⁺, Mn³⁺/Mn⁴⁺ and Pd²⁺), an indication of a redox-active surface for sulfur interaction. Results from the breakthrough experiments showed that the base catalyst significantly improved sulfur removal compared to single-metal catalysts, while the Pd-promoted catalyst exhibited the highest performance, with a breakthrough time of 630 min. Palladium was incorporated at low loading as a promoter, enhancing adsorption performance while maintaining a favorable balance between efficiency and material cost. This enhancement is attributed to synergistic interactions between adsorption-active sites and redox-active species, as well as improved electron transfer facilitated by palladium. The results demonstrate that rational design of multi-metal oxide catalysts supported on naturally occurring halloysite provides an effective and scalable approach for sulfur removal from natural gas, with strong potential for industrial applications. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

The control of nonlinear, open-loop unstable dynamics is a prevalent engineering challenge, often benchmarked through magnetic levitation (Maglev) systems. While continuous-time adaptive neural networks are commonly used to reject disturbances, their direct digital implementation often induces closed-loop instability due to unaccounted sampling effects. To address this, this paper proposes a discrete-time Fourier Series Neural Network (FSNN) control architecture for nonlinear Single-Input Single-Output (SISO) systems that can be transformed into the Brunovsky canonical form. The parameter adaptation laws are synthesized strictly in the discrete-time domain using Lyapunov stability theory. This approach yields an explicit upper bound for the digital sampling period, ensuring a proper implementation. Furthermore, it guarantees the Uniform Ultimate Boundedness (UUB) of the tracking error in the presence of bounded unmodeled dynamics and periodic disturbances. Numerical simulations of Maglev dynamics validate the theoretical bounds, demonstrating that the FSNN controller achieves rapid learning and generates a smooth control effort. Ultimately, by eliminating the instability risks of continuous-time approximations, this methodology bridges the gap between theoretical design and digital implementation, providing a practical framework for the robust control of electromagnetic actuators and other nonlinear industrial processes. Full article

An endogenous extension of the classical logistic equation, in which the carrying capacity $\kappa \left(t\right)$ varies in a periodic manner, is developed within this study. Existing theoretical results characterize the behavior of models of this nature. Nevertheless, to the best of our knowledge, the extant literature does not seem to include contributions with a comparable pragmatic computational purpose. By leveraging the Bernoulli structure of the model alongside a Fourier representation of the periodic forcing, closed-form expressions for the associated periodic solution are derived, and a unified Fourier-series framework is constructed to reconstruct trajectories under general periodic inputs. This approach refines and extends earlier findings on periodic logistic dynamics, resulting in an explicit characterization of the oscillatory regime induced by $\kappa \left(t\right)$, which influences the system’s nonlinear feedback and long-term behavior. Full article

This paper proposes an optimized variable-frequency phase-shift modulation strategy based on frequency-domain analysis to address the issues of large reactive circulating current and low transmission efficiency in dual-active-bridge (DAB) converters under voltage mismatch conditions. First, a unified frequency-domain analytical model for extended phase-shift (EPS) modulation is established using Fourier series, which avoids the complexity introduced by mode division in traditional time-domain analysis. The Karush–Kuhn–Tucker (KKT) conditions are then utilized to analytically derive the optimal phase-shift angles that minimize the RMS current over the entire power range. Based on this, a control method is proposed to suppress the reactive circulating current by adjusting the switching frequency. Experimental results demonstrate that the proposed strategy significantly reduces the RMS current and reactive circulating current, thereby improving efficiency across a wide voltage gain and full load range, compared to traditional single phase-shift and extended phase-shift strategies. Full article

This work is devoted to the application of complex-valued neural networks based on the multilayer neural network with multi-valued neurons (MLMVN) for short-term electrical load forecasting in smart grid energy systems. Accurate forecasting is a critical component of energy management systems, as it directly impacts the efficiency of control and optimization strategies in increasingly distributed and stochastic environments. The proposed approach leverages the intrinsic properties of complex numbers to model periodicity and nonlinear relationships typical of load time series. A compact feedforward architecture with two hidden layers is adopted and combined with multiple preprocessing strategies, including unit circle encoding, Fourier transform representations, and hybrid feature mappings incorporating temporal information such as the day of the week. The performance of the proposed models is evaluated on real-world prosumer data and compared against two benchmarks: a seasonal persistence model and a Long Short-Term Memory network. Results show that MLMVN-based approaches achieve comparable or improved performance in terms of RMSE and error reduction capability, despite their lower architectural complexity. Fourier-based preprocessing methods demonstrate strong effectiveness in capturing underlying temporal patterns. These findings suggest that complex-valued representations provide a promising alternative to traditional deep learning approaches, offering a favorable balance between accuracy, interpretability, and computational efficiency in Smart Grid forecasting applications. Full article

Hydrogenation of CO₂ to ethanol is regarded as a promising approach for the resource utilization of CO₂. Ethanol can be synthesized via the acetate pathway over cobalt-based catalysts, in which the regulation of Co⁰-Co^δ+ is crucial to increasing the space–time yield of ethanol. In this research, a series of Co-W-C catalysts was prepared via the reduction-carburization method and their catalytic performance was investigated for synthesizing ethanol from CO₂ hydrogenation. During the preparation of Co-W-C catalysts, the Co, Co₆W₆C and WC phases were generated by employing a precursor containing Co, W and citric acid. This process drove the formation of Co^δ+ species and the consequent generation of Co⁰-Co^δ+ active pairs. Under the cooperation of Co⁰-Co^δ+ and WC, ethanol was obtained with high selectivity and space–time yield from the CO₂ hydrogenation under the promotion of DMF. Over the Co-W-C-1 catalyst prepared by a Co/W molar ratio of 1:1 in the precursor, an ethanol space–time yield of 17.1 mmol·g⁻¹·h⁻¹ with an ethanol selectivity of 99.6% among organic products was obtained. Furthermore, key intermediate species formed during the reaction were identified by in situ Diffuse Reflectance Infrared Fourier Transform Spectroscopy, and a possible reaction pathway was also proposed. Full article

Ammonia decomposition represents a promising route for CO₂-free hydrogen production; however, the development of efficient and stable catalysts remains a critical challenge. In this work, a series of Al-based mixed-oxide catalysts (AlM, where M = Ni, Co, Ce) were synthesized via co-precipitation and systematically investigated to elucidate the relationship between physicochemical properties and catalytic performance in ammonia decomposition. Comprehensive characterization by X-ray diffraction (XRD), N₂ physisorption (BET), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDX), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), and pyridine-adsorbed Fourier transform infrared spectroscopy (FTIR-Py) revealed significant variations in surface area, morphology, dispersion, and acidity as a function of the incorporated metal. Among the investigated catalysts, the AlNi system exhibited superior activity, achieving the highest ammonia conversion over the studied temperature range. This enhanced performance is attributed to its high specific surface area, homogeneous mesoporous structure, and a balanced distribution of Lewis/Brønsted acid sites, which promote effective ammonia adsorption, activation and decomposition. Kinetic analysis further confirmed the favorable reaction pathway on AlNi, as evidenced by its lower apparent activation energy and higher pre-exponential factor compared to the other materials. The results demonstrate a clear correlation between surface acidity, textural properties, and catalytic performance, highlighting the pivotal role of AlM interactions in governing ammonia decomposition. These findings provide valuable insights for the rational design of efficient catalysts for hydrogen production from ammonia. Full article