Search Results (677)

The increasing penetration of renewable energy into power systems has intensified transient power quality (PQ) disturbances, demanding efficient detection and classification methods to enable timely operational decisions. This paper introduces a hybrid framework combining an Improved Adaptive S-Transform (IAST) with a Random Forest (RF) classifier to address these challenges. The IAST employs a globally adaptive Gaussian window as its kernel function, which automatically adjusts window length and spectral resolution based on real-time frequency characteristics, thereby enhancing time–frequency localization accuracy while reducing algorithmic complexity. To optimize computational efficiency, window parameters are determined through an energy concentration maximization criterion, enabling rapid extraction of discriminative features from diverse PQ disturbances (e.g., voltage sags and transient interruptions). These features are then fed into an RF classifier, which simultaneously mitigates model variance and bias, achieving robust classification. Experimental results show that the proposed IAST–RF method achieves a classification accuracy of 99.73%, demonstrating its potential for real-time PQ monitoring in modern grids with high renewable energy penetration. Full article

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

Cannabis sativa L. is a versatile plant with significant medicinal, industrial, and recreational applications. Its therapeutic potential is attributed to cannabinoids like THC and CBD, whose production is influenced by environmental factors, such as radiation, temperature, and humidity. Radiation, for instance, is essential for photosynthetic processes, acting as both a primary energy source and a regulator of plant growth and development. This review covers key factors affecting C. sativa cultivation, including photoperiod, light spectrum, cultivation methods, environmental controls, and plant growth regulators. It highlights how these elements influence flowering, biomass, and cannabinoid production across different growing systems, offering insights for optimizing both medicinal and industrial cannabis cultivation. Studies indicate that photoperiod sensitivity varies among cultivars, with some achieving optimal flowering and cannabinoid production under extended light periods rather than the traditional 12/12 h cycle. Light spectrum adjustments, especially red, far-red, and blue wavelengths, significantly impact photosynthesis, plant morphology, and secondary metabolite accumulation. Advances in LED technology allow precise spectral control, enhancing energy efficiency and cannabinoid profiles compared to conventional lighting. The photoperiod plays a vital role in the cultivation of C. sativa spp., directly impacting the plant’s developmental cycle, biomass production, and the concentration of cannabinoids and terpenes. The response to photoperiod varies among different cannabis cultivars, as demonstrated in studies comparing cultivars of diverse genetic origins. On the other hand, indoor or in vitro cultivation may serve as an excellent alternative for plant breeding programs in C. sativa, given the substantial inter-cultivar variability that hinders the fixation of desirable traits. Full article

This work analyzes the performance of a photoreactor built with UV-LED technology. For this task, a UV-LED wavelength of 365 nm was used as an irradiation source, and it was electrically and spectrally characterized to ensure correct operation. To evaluate the functionality, the photoreactor was tested on the degradation of Rhodamine B (Rh B), a dye commonly used in the textile industry. The experiment was conducted under optimal conditions, using a concentration of 17 ppm of Rh B and 100 mg of zinc oxide (ZnO) as a photocatalyst in a glass reactor. The mixture was continuously stirred for 120 min, achieving 99.42% efficiency. The results showed that the UV-LED photoreactor performs well in activating ZnO for the removal of Rh B from the solution, highlighting its potential for treating textile industry wastewater. The use of LEDs offers advantages such as energy efficiency and lower environmental impact compared to traditional UV lamps. ZnO, known for its reactivity under UV light, acted as a stable photocatalyst, ensuring complete degradation of the dye without producing harmful by-products. This method provides an efficient approach to dye removal in wastewater treatment, promoting cleaner and more sustainable industrial practices. Full article

Light-emitting diodes (LEDs) in agricultural systems mainly contribute their capacity to create a precise and constant light spectral environment. However, the potential of LED in crop production was underestimated. LEDs serve not only as efficient artificial light sources for plant growth, but are also a good tool for enhancing biomass production with limited energy consumption. This article reviewed innovative applications of LED in facility agriculture, e.g., plant factory, and greenhouse. Compared to conventional application of LED, innovative lighting strategies such as intermittent lighting, night break, continuous lighting, alternate lighting, dynamic lighting, and end-of-day (EOD) far-red provided by LED light can elevate the production efficiency effectively. However, the scientific explanation of the above lighting strategies remains to be clearly revealed, providing theoretical support for the further optimization of conducting parameters. This review summarizes the physiological effects of different lighting strategies on crop cultivation and illustrates their future application in facility agriculture, aiming to provide novel methods for elevating the energy utilization efficiency and lowering the cost in facility agriculture using artificial light. Full article

The advance of 5G technology marks a significant evolution in wireless communications, characterized by ultra-high data rates, low latency, and massive connectivity across varied areas. A fundamental enabler of these capabilities is represented by beamforming, an advanced signal processing technique that focuses radio energy to a specific user equipment (UE), thereby enhancing signal quality—crucial for maximizing spectral efficiency. The work presents a classification of beamforming techniques, categorized according to the implementation within 5G New Radio (NR) architectures. Furthermore, the paper investigates beam management (BM) procedures, which are essential Layer 1 and Layer 2 mechanisms responsible for the dynamic configuration, monitoring, and maintenance of optimal beam pair links between gNodeBs and UEs. The article emphasizes the spectral spectrogram of Synchronization Signal Blocks (SSBs) generated under various deployment scenarios, illustrating how parameters such as subcarrier spacing (SCS), frequency band, and the number of SSBs influence the spectral occupancy and synchronization performance. These insights provide a technical foundation for optimizing initial access and beam tracking in high-frequency 5G deployments, particularly within Frequency Range (FR2). Additionally, the versatility of 5G’s time-frequency structure is demonstrated by the spectrogram analysis of SSBs in a variety of deployment scenarios. These results provide insight into how different configurations affect the synchronization signals’ temporal and spectral occupancy, which directly affects initial access, cell identification, and energy efficiency. Full article

Atmospheric climate science lacks the capacity to integrate thermodynamics with the gravitational potential of air in a classical quantum theory. To what extent can we identify Carnot’s ideal heat engine cycle in reversible isothermal and isentropic phases between dual temperatures partitioning heat flow with coupled work processes in the atmosphere? Using statistical action mechanics to describe Carnot’s cycle, the maximum rate of work possible can be integrated for the working gases as equal to variations in the absolute Gibbs energy, estimated as sustaining field quanta consistent with Carnot’s definition of heat as caloric. His treatise of 1824 even gave equations expressing work potential as a function of differences in temperature and the logarithm of the change in density and volume. Second, Carnot’s mechanical principle of cooling caused by gas dilation or warming by compression can be applied to tropospheric heat–work cycles in anticyclones and cyclones. Third, the virial theorem of Lagrange and Clausius based on least action predicts a more accurate temperature gradient with altitude near 6.5–6.9 °C per km, requiring that the Gibbs rotational quantum energies of gas molecules exchange reversibly with gravitational potential. This predicts a diminished role for the radiative transfer of energy from the atmosphere to the surface, in contrast to the Trenberth global radiative budget of ≈330 watts per square metre as downwelling radiation. The spectral absorptivity of greenhouse gas for surface radiation into the troposphere enables thermal recycling, sustaining air masses in Lagrangian action. This obviates the current paradigm of cooling with altitude by adiabatic expansion. The virial-action theorem must also control non-reversible heat–work Carnot cycles, with turbulent friction raising the surface temperature. Dissipative surface warming raises the surface pressure by heating, sustaining the weight of the atmosphere to varying altitudes according to latitude and seasonal angles of insolation. New predictions for experimental testing are now emerging from this virial-action hypothesis for climate, linking vortical energy potential with convective and turbulent exchanges of work and heat, proposed as the efficient cause setting the thermal temperature of surface materials. Full article

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. Full article

Energy generation from renewable sources has increased exponentially worldwide, particularly wind energy, which is converted into electricity through wind turbines. The growing demand for renewable energy has driven the development of horizontal-axis wind turbines with larger dimensions, as the energy captured is proportional to the area swept by the rotor blades. In this context, the dynamic loads typically observed in wind turbine towers include vibrations caused by rotating blades at the top of the tower, wind pressure, and earthquakes (less common). In offshore wind farms, wind turbine towers are also subjected to dynamic loads from waves and ocean currents. Vortex-induced vibration can be an undesirable phenomenon, as it may lead to significant adverse effects on wind turbine structures. This study presents a two-dimensional transient model for a rigid body anchored by a torsional spring subjected to a constant velocity flow. We applied a coupling of the Fourier pseudospectral method (FPM) and immersed boundary method (IBM), referred to in this study as IMERSPEC, for a two-dimensional, incompressible, and isothermal flow with constant properties—the FPM to solve the Navier–Stokes equations, and IBM to represent the geometries. Computational simulations, solved at an aspect ratio of $\varphi =4.0$, were analyzed, considering Reynolds numbers ranging from $Re=150$ to Re = 1000 when the cylinder is stationary, and $Re=250$ when the cylinder is in motion. In addition to evaluating vortex shedding and Strouhal number, the study focuses on the characterization of space–time symmetry during the galloping response. The results show a spatial symmetry breaking in the flow patterns, while the oscillatory motion of the rigid body preserves temporal symmetry. The numerical accuracy suggested that the IMERSPEC methodology can effectively solve complex problems. Moreover, the proposed IMERSPEC approach demonstrates notable advantages over conventional techniques, particularly in terms of spectral accuracy, low numerical diffusion, and ease of implementation for moving boundaries. These features make the model especially efficient and suitable for capturing intricate fluid–structure interactions, offering a promising tool for analyzing wind turbine dynamics and other similar systems. Full article

Cell-free massive multiple-input multiple-output (CF M-MIMO) is one of the most promising technologies for future wireless communication such as 5G and beyond fifth-generation (B5G) networks. It is a type of network technology that uses a massive number of distributed antennas to serve a large number of users at the same time. It has the ability to provide high spectral efficiency (SE) as well as improved coverage and interference management, compared to traditional cellular networks. However, estimating the channel with high-performance, low-cost computational methods is still a problem. Different algorithms have been developed to address these challenges in channel estimation. One of the high-performance channel estimators is a phase-aware minimum mean square error (MMSE) estimator. This channel estimator has high computational complexity. To address the shortcomings of the existing estimator, this paper proposed an efficient phase-aware element-wise minimum mean square error (PA-EW-MMSE) channel estimator with QR decomposition and a precoding matrix at the user side. The closed form uplink (UL) SE with the phase MMSE and proposed estimators are evaluated using MMSE combining. The energy efficiency and area throughput are also calculated from the SE. The simulation results show that the proposed estimator achieved the best SE, EE, and area throughput performance with a substantial reduction in the complexity of the computation. Full article

In Network-on-Chip (NoC) research, the task mapping problem has attracted considerable attention as a core issue influencing system performance. As an NP-hard problem, it remains challenging, and existing algorithms exhibit limitations in both mapping quality and computational efficiency. To address this, a method named ET (Enhanced Coati Optimization Algorithm) is proposed, which leverages the nature-inspired Coati Optimization Algorithm (COA) for task mapping. An incremental hill-climbing strategy is integrated to improve local search capabilities, and a dynamic mechanism for adjusting the exploration–exploitation ratio is designed to better balance global and local searches. Additionally, an initial mapping strategy based on spectral clustering is introduced, which utilizes inter-task communication strength to cluster tasks, thereby improving the quality of the initial population. To evaluate the effectiveness of the proposed algorithm, the performance of the ET algorithm is compared and analyzed against various existing algorithms in terms of communication cost, energy consumption, and latency, using both real benchmark task maps and randomly generated task maps. Experimental results demonstrate that the ET algorithm consistently outperforms the compared algorithms across all performance metrics, thereby confirming its superiority in addressing the NoC task mapping problem. Full article

The hydraulic turbine serves as the cornerstone of hydropower generation systems, with the sealing system’s performance critically influencing energy conversion efficiency and operational cost-effectiveness. The sealing ring is a pivotal component, which mitigates leakage and energy loss by regulating flow within the narrow gap between itself and the frame. This study investigates the intricate flow dynamics within the gap between the sealing ring and the upper frame of a super-large-scale Francis turbine, with a specific focus on the rotating wall’s impact on the flow field. Employing theoretical modeling and three-dimensional transient computational fluid dynamics (CFD) simulations grounded in real turbine design parameters, the research reveals that the rotating wall significantly alters shear flow and vortex formation within the gap. Tangential velocity exhibits a nonlinear profile, accompanied by heightened turbulence intensity near the wall. The short flow channel height markedly shapes flow evolution, driving the axial velocity profile away from a conventional parabolic pattern. Further analysis of rotation-induced vortices and flow instabilities, supported by turbulence kinetic energy monitoring and spectral analysis, reveals the periodic nature of vortex shedding and pressure fluctuations. These findings elucidate the internal flow mechanisms of the sealing ring, offering a theoretical framework for analyzing flow in microscale gaps. Moreover, the resulting flow field data establishes a robust foundation for future studies on upper crown gap flow stability and sealing ring dynamics. Full article

The colour evolution of malvidin-3-O-glucoside (Mv-3-O-glc) elicited by caffeic acid (CAF), (+)-catechin (CA), or syringic acid (SI) was spectrophotometrically monitored in model wine solution, modulating the malvidin-to-polyphenol molar ratio (1:1 to 1:20) and the pH (2.8–3.8). The spectral features provided the thermodynamic parameters Gibbs free energy (ΔG⁰) and equilibrium constant (K_eq), showing that the copigmentation extent is maximized at pH 3.6 and a higher molar ratio (1:20), and that copigments have different efficiency. In a long-term evolution (12 months), transient complexes evolved into different colour characteristics. Spectrophotometry and colorimetry (chroma C*, hue H*, and lightness L*) revealed the formation of stable pigments with peculiar orange-reddish colour when CAF was present; however, in the case of CA, an accentuated yellow tone was observed. SI showed minimum impact in the long-term evolution of Mv-3-O-glc. This study expands knowledge on oenological copigmentation, further exploring its potential implication in the colour of aged red wines. Full article

Electromagnetic wave (EMW) absorption materials are crucial for a wide range of applications, yet most existing materials suffer from complex fabrication and narrow absorption bands, particularly under harsh environmental conditions. In this study, we introduce a broadband metamaterial absorber based on Ti₃C₂O₂ MXene, a novel two-dimensional material that uniquely combines high electrical and metallic conductivity with hydrophilicity, biocompatibility, and an extensive surface area. Through advanced finite-difference time-domain (FDTD) simulations, the proposed absorber achieves over 95% absorption from 0.3 µm to 18 µm. Additionally, other MXene variants, including Ti₃C₂F₂ and Ti₃C₂(OH)₂, demonstrate robust absorption above 85%. This absorber not only outperforms previously reported structures in terms of efficiency and spectral coverage but also opens avenues for integration into applications such as infrared sensing, energy harvesting, wearable electronics, and Internet of Things (IoT) systems. Full article

This work presents a high-performance novel photodetector based on two-dimensional boron nitride (BN) nanosheets functionalized with gold nanoparticles (Au NPs), offering ultra-broadband photoresponse from 0.25 to 5.9 μm. Operating in both photovoltaic and photoconductive modes, the device features rapid response times (<0.5 ms), high responsivity (up to 1015 mA/W at 250 nm and 2.5 V bias), and thermal stability up to 100 °C. The synthesis process involved CO₂ laser exfoliation of hexagonal boron nitride, followed by gold NP deposition via RF sputtering and thermal annealing. Structural and compositional analyses confirmed the formation of a three-dimensional network of atomically thin BN nanosheets decorated with uniformly distributed gold nanoparticles. This architecture facilitates plasmon-enhanced absorption and efficient charge separation via heterojunction interfaces, significantly boosting photocurrent generation across the deep ultraviolet (DUV), visible, near-infrared (NIR), and mid-infrared (MIR) spectral regions. First-principles calculations support the observed broadband response, confirming bandgap narrowing induced by defects in h-BN and functionalization by gold nanoparticles. The device’s self-driven operation, wide spectral response, and durability under elevated temperatures underscore its strong potential for next-generation broadband, self-powered, and wearable sensing and optoelectronic applications. Full article