Search Results (178)

The spatiotemporal heterogeneity of hydrothermal conditions during the spring sowing period profoundly shapes cropping layouts and sowing strategies. Using NASA’s GLDAS remote sensing reanalysis, we developed a continuous agricultural climate risk index that integrates three remotely driven indicators—spring sowing window days (SWDs) derived from a “continuous suitable-day” logic, the hydrothermal coordination degree (D value), and a comprehensive suitability index (SSH_SI)—thus advancing risk assessment from single metrics to a multidimensional framework. Methodologically, dominant periodic structures of spring sowing hydrothermal risk were extracted via a combination of wavelet power spectra and the global wavelet spectrum (GWS), while spatial trend-surface fitting and three-dimensional directional analysis captured spatial non-stationarity. The index’s spatial migration trajectories and centroid-evolution paths were then quantified. Results reveal pronounced gradients along the Great Wall Belt: SWD displays a “central-high, terminal-low” pattern, with sowing windows restricted to only 3–6 days in northeastern Inner Mongolia and western Liaoning but extending to 11–13 days in the central plains of Inner Mongolia and Shanxi; SSH_SI and D values form an overall “south-west high, north-east low” pattern, indicating more favorable hydrothermal coordination in southwestern areas. Temporally, although SWD and SSH_SI show no significant downward trend, their interannual variability has increased, signaling rising instability, whereas the D value declines markedly in most regions, reflecting intensified hydrothermal imbalance. The integrated risk index identifies high-risk hotspots in eastern Inner Mongolia and northern North China, and low-risk zones in western provinces such as Gansu and Ningxia. Centroid-shift analysis further uncovers a dynamic regional adjustment in optimal sowing patterns, offering scientific evidence for addressing spring sowing climate risks. These findings provide a theoretical foundation and decision support for optimizing regional cropping structures, issuing climate risk warnings, and precisely regulating spring sowing schedules. Full article

In this article, we investigate the fractional soliton solutions as well as the chaotic analysis of the fourth-order nonlinear Ablowitz–Kaup–Newell–Segur wave equation. This model is considered an intriguing high-order nonlinear partial differential equation that integrates additional spatial and dispersive effects to extend the concepts to more intricate wave dynamics, relevant in engineering and science for understanding complex phenomena. To examine the solitary wave solutions of the proposed model, we employ sophisticated analytical techniques, including the generalized projective Riccati equation method, the new improved generalized exponential rational function method, and the modified F-expansion method, along with mathematical simulations, to obtain a deeper insight into wave propagation. To explore desirable soliton solutions, the nonlinear partial differential equation is converted into its respective ordinary differential equations by wave transforms utilizing $\beta$-fractional derivatives. Further, the solutions in the forms of bright, dark, singular, combined, and complex solitons are secured. Various physical parameter values and arrangements are employed to investigate the soliton solutions of the system. Variations in parameter values result in specific behaviors of the solutions, which we illustrate via various types of visualizations. Additionally, a key aspect of this research involves analyzing the chaotic behavior of the governing model. A perturbed version of the system is derived and then analyzed using chaos detection techniques such as power spectrum analysis, Poincaré return maps, and basin attractor visualization. The study of nonlinear dynamics reveals the system’s sensitivity to initial conditions and its dependence on time-decay effects. This indicates that the system exhibits chaotic behavior under perturbations, where even minor variations in the starting conditions can lead to drastically different outcomes as time progresses. Such behavior underscores the complexity and unpredictability inherent in the system, highlighting the importance of understanding its chaotic dynamics. This study evaluates the effectiveness of currently employed methodologies and elucidates the specific behaviors of the system’s nonlinear dynamics, thus providing new insights into the field of high-dimensional nonlinear scientific wave phenomena. The results demonstrate the effectiveness and versatility of the approach used to address complex nonlinear partial differential equations. Full article

Understanding spatial variations in land surface temperature (LST) is critical for analyzing urban climate dynamics, especially within the framework of two-dimensional (2D) turbulence theory. This study assesses the spatial sampling behavior of urban thermal fields across eight metropolitan areas, encompassing diverse morphologies, surface materials, and Köppen–Geiger climate zones. We analyzed thermal infrared (TIR) imagery from two remote sensing platforms—MODIS (1 km) and Landsat (30 m)—to evaluate resolution-dependent turbulence indicators such as spectral slopes and breakpoints. Power spectral analysis revealed systematic divergences across spatial scales. Landsat exhibited more negative breakpoint values, indicating a greater ability to capture fine-scale thermal heterogeneity tied to vegetation, buildings, and surface cover. MODIS, in contrast, emphasized broader thermal gradients, suitable for regional-scale assessments. Seasonal differences reinforced the turbulence framework: summer spectra displayed steeper, more variable slopes, reflecting increased thermal activity and surface–atmosphere decoupling. Despite occasional agreement between sensors, spectral metrics remain inherently resolution-dependent. MODIS is better suited for macro-scale thermal structures, while Landsat provides detailed insights into intra-urban processes. Our findings confirm that 2D turbulence indicators are not fully scale-invariant and vary with sensor resolution, season, and urban form. This multi-sensor comparison offers a framework for interpreting LST data in support of climate adaptation, urban design, and remote sensing integration. Full article

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field ¹H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H₂O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by ¹H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the ¹H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. ¹H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the ¹H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The ¹H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article

Optimizing artificial lighting in controlled-environment agriculture is crucial for enhancing crop productivity and resource efficiency. This study presents a spectral–spatial co-optimization strategy for LED lighting tailored to the physiological needs of Italian lettuce (Lactuca sativa L. var. italica). A miniature plant factory system was developed with dimensions of 400 mm × 400 mm × 500 mm (L × W × H). Seven customized spectral treatments were created using 2835-packaged LEDs, incorporating various combinations of blue and violet LED chips with precisely controlled concentrations of red phosphor. The spectral configurations were aligned with the measured absorption peaks of Italian lettuce (450–470 nm and 640–670 nm), achieving a spectral mixing uniformity exceeding 99%, while the spatial light intensity uniformity surpassed 90%. To address spatial light heterogeneity, a particle swarm optimization (PSO) algorithm was employed to determine the optimal LED arrangement, which increased the photosynthetic photon flux density (PPFD) uniformity from 83% to 93%. The system operates with a fixture-level power consumption of only 75 W. Experimental evaluations across seven treatment groups demonstrated that the E-spectrum group—comprising two violet chips, one blue chip, and 0.21 g of red phosphor—achieved the highest agronomic performance. Compared to the A-spectrum group (three blue chips and 0.19 g of red phosphor), the E-spectrum group resulted in a 25% increase in fresh weight (90.0 g vs. 72.0 g), a 30% reduction in SPAD value (indicative of improved light-use efficiency), and compared with Group A, Group E exhibited significant improvements in plant morphological parameters, including a 7.05% increase in plant height (15.63 cm vs. 14.60 cm), a 25.64% increase in leaf width (6.37 cm vs. 5.07 cm), and a 6.35% increase in leaf length (10.22 cm vs. 9.61 cm). Furthermore, energy consumption was reduced from 9.2 kWh (Group A) to 7.3 kWh (Group E). These results demonstrate that integrating spectral customization with algorithmically optimized spatial distribution is an effective and scalable approach for enhancing both crop yield and energy efficiency in vertical farming systems. Full article

Currently, infrared object detection is utilized in a broad spectrum of fields, including military applications, security, and aerospace. Nonetheless, the limited computational power of edge devices presents a considerable challenge in achieving an optimal balance between accuracy and computational efficiency in infrared object detection. In order to enhance the accuracy of infrared target detection and strengthen the implementation of robust models on edge platforms for rapid real-time inference, this paper presents LKD-YOLOv8, an innovative infrared object detection method that integrates YOLOv8 architecture with masked generative distillation (MGD), further augmented by the lightweight convolution design and attention mechanism for improved feature adaptability. Linear deformable convolution (LDConv) strengthens spatial feature extraction by dynamically adjusting kernel offsets, while coordinate attention (CA) refines feature alignment through channel-wise interaction. We employ a large-scale model (YOLOv8s) as the teacher to imparts knowledge and supervise the training of a compact student model (YOLOv8n). Experiments show that LKD-YOLOv8 achieves a 1.18% mAP@0.5:0.95 improvement over baseline methods while reducing the parameter size by 7.9%. Our approach effectively balances accuracy and efficiency, rendering it applicable for resource-constrained edge devices in infrared scenarios. Full article

The unprecedented expansion of wireless networks has resulted in spectrum sharing between numerous connected devices, demanding advanced interference management and higher energy consumption, which exacerbates the carbon footprint. Near-field communication emerges as a promising solution to these challenges as it enables precise signal focusing which reduces power consumption by providing higher spatial multiplexing gains. This review explores how near-field (NF) multiple-input multiple-output (MIMO) beamforming can enhance energy efficiency by optimizing beamfocusing and minimizing unnecessary energy expenditure. We discuss the latest advancements in near-field beamforming, emphasizing energy-efficient strategies and sustainable practices. Recognizing which practical channel models are better suited for near-field communication, we delve into the integration of Electromagnetic Information Theory (EIT) as a joint model for realistic applications. We also discuss the channel models for near-field beamforming, incorporating EIT to provide a comprehensive overview of current methodologies. We further analyze the strengths and limitations of existing channel models and discuss the state-of-the-art models which address existing gaps. We also explore opportunities for the practical deployment of energy-efficient near-field beamforming systems. By summarizing future research directions, this review aims to advance the understanding and application of sustainable energy practices in near-field communication technologies. Full article

An event is an occurrence that takes place at a specific time and location that can be either weather-related (snowfall), social (crime), natural (earthquake), political (political unrest), or medical (pandemic) in nature. These events do not belong to the “normal” or “usual” spectrum and result in a change in a given situation; thus, their prediction would be very beneficial, both in terms of timely response to them and for their prevention, for example, the prevention of traffic accidents. However, this is currently challenging for researchers, who are called upon to manage and analyze a huge volume of data in order to design applications for predicting events using artificial intelligence and high computing power. Although significant progress has been made in this area, the heterogeneity in the input data that a forecasting application needs to process—in terms of their nature (spatial, temporal, and semantic)—and the corresponding complex dependencies between them constitute the greatest challenge for researchers. For this reason, the initial forecasting applications process data for specific situations, in terms of number and characteristics, while, at the same time, having the possibility to respond to different situations, e.g., an application that predicts a pandemic can also predict a central phenomenon, simply by using different data types. In this work, we present the forecasting applications that have been designed to date. We also present a model for predicting traffic accidents using categorical logic, creating a Knowledge Base using the Resolution algorithm as a proof of concept. We study and analyze all possible scenarios that arise under different conditions. Finally, we implement the traffic accident prediction model using the Prolog language with the corresponding Queries in JPL. Full article

The increased use of smartphones in daily life challenges researchers regarding the quality of light emitted by screens. This study aims to analyze displays’ qualitative and quantitative light parameters from various smartphone models available on the market over the last decade. Advanced photometric and colorimetric measurements using complex instrumentation were performed. It covered the color gamut, channel linearity response, refresh rate, flickering, spatial radiation distribution, luminance, uniformity, and static contrast. The analysis showed that, despite advances in smartphone display technology, differences in visible radiation parameters between older and newer models are surprisingly marginal. However, improvements were observed in newer models in terms of viewing angles and compliance with the sRGB standard. Tested built-in blue light reduction filters were ineffective. It only slightly reduces light between 380 nm and 480 nm. In contrast, much higher decreases in this spectral range were achieved for dedicated applications. However, it lowered radiant power density across the visible spectrum, significantly decreasing the displays’ correlated color temperature. Enabling the power-saving mode caused the deterioration of parameters such as refresh rate, but the flicker depth remained constant. Static contrast for most tested devices was also at the same level. The findings confirm the need for further studies on display technology development that supports user well-being while minimizing its harmful effects. Full article

The development of a universal method for real-time gas leak localisation imaging is crucial for preventing substantial financial losses and hazardous incidents. To achieve this objective, this study integrates array signal processing and electronic techniques to construct an ultrasonic sensor array for gas leak detection and localisation. A digital microelectromechanical system microphone array is used to capture spatial ultrasonic information. By processing the array signals using beamforming algorithms, an acoustic spatial power spectrum is obtained, which facilitates the estimation of the locations of potential gas leak sources. In the pre-processing of beamforming, the Hilbert transform is employed instead of the fast Fourier transform to save computational resources. Subsequently, the spatial power spectrum is fused with visible-light images to generate acoustic localisation images, which enables the visualisation of gas leak sources. Experimental validation demonstrates that the system detects minor and multiple gas leaks in real time, meeting the sensitivity and accuracy requirements of embedded industrial applications. These findings contribute to the development of practical, cost-effective, and scalable gas leak detection systems for industrial and environmental safety applications. Full article

The present study focuses on the development of a diagnostic system for measuring radiated power and core soft X-ray intensity emissions with the goal of detecting a broad spectrum of photon energies emitted from the central plasma region of the DEMO tokamak. The principal objective of the diagnostic apparatus is to deliver a comprehensive characterization of the radiation emitted by the plasma, with a particular focus on estimating the radiated power from the core region. This measurement is essential for determining and monitoring the power crossing the separatrix, which is a critical parameter controlling overall plasma performance. Since diagnostics rely on line-integrated measurements, the application of tomographic reconstruction techniques is necessary to extract spatially resolved information on core plasma radiation. This contribution presents the development of numerical algorithms addressing the problem of radiation tomography reconstruction. A robust and computationally efficient method is proposed for reconstructing the spatial distribution of plasma radiated power, with a view toward enabling real-time applications. The reconstruction methodology is based on a linear model formulated using a set of predefined basis functions, which define the radiation distribution within a specified plasma cross-section. In the initial stages of emissivity reconstruction in tokamak plasmas, it is typically assumed that the radiation distribution is dependent on magnetic flux surfaces. As a baseline approach, the plasma radiative properties are considered invariant along these surfaces and can thus be represented as one-dimensional profiles parameterized by the poloidal magnetic flux. Within this framework, the reconstruction method employs an approximation model utilizing three sets of basis functions: (i) polynomial splines, as well as Gaussian functions with (ii) sigma parameters and (iii) position parameters. The performance of the proposed method was evaluated using two synthetic radiated power emission phantoms, developed for the DEMO plasma scenario. The results indicate that the method is effective under the specified conditions. Full article

Challenges are brought to high impedance fault (HIF) line selection in traditional distribution networks by the fault signals with short windows and weak characteristics provided by new energy power sources. A new method driven by the symmetry of current traveling wave spectrum is proposed in this paper. Frequency-domain features are extracted by using Pisarenko spectral decomposition, and the differences in amplitude, frequency, and polarity between faulted and healthy feeders are analyzed. A similarity matrix is constructed with the help of Manhattan distance, and an improved density-based spatial clustering of application with noise (DBSCAN) clustering is adopted to achieve intelligent fault line selection. Experimental results show that compared with the steady state component method and the transient component method, the accuracy of this method is increased to 97.5%, with an improvement of more than 12.5%. Quantitative thresholds are replaced by qualitative spectrum differences, and this method is not affected by weak signals, thus solving the problem of threshold setting caused by the access of new energy. The accuracy of this method under different fault types, phases, and resistances is verified by simulation, ensuring easy engineering implementation. Full article

Optimizing light conditions in controlled-environment agriculture is critical for enhancing crop yield and energy efficiency, particularly in high-value crops like strawberries, where precise spectral tuning can significantly influence both vegetative growth and fruit production. In this study, a windmill-style vertical farming system was developed to facilitate efficient strawberry cultivation under low-light conditions. A custom LED lighting fixture, measuring 3 m in length, was suspended 30 cm above the canopy to uniformly illuminate a planting zone of 3.0 m × 0.3 m. The lighting system, which combines red (655–665 nm) and full-spectrum white LEDs, was optimized using a particle swarm optimization (PSO) algorithm to enhance spatial light distribution. The uniformity of photosynthetic photon flux density (PPFD) improved from 71% to 85%, and the standard deviation decreased from 75 to 15. Under a 16 h optimized lighting regime, strawberry plants exhibited a 55% increase in height compared to the non-supplemented control group (Group D), a 40% increase in leaf width, and a 36% increase in fruit weight (69.76 g per plant) relative to the 12 h supplemental lighting group (Group A). The system operates at a fixture-level power consumption of just 160 W, with its spectral output aligned with the absorption characteristics of strawberry foliage and fruit. These results demonstrate that an algorithm-driven lighting layout can significantly enhance both vegetative and reproductive performance in vertical strawberry farming while maintaining high energy efficiency. Full article

In light of the soaring growth of pumped hydro energy storage (PHES) plants in China in recent years, there is an urgent need for a comprehensive understanding of their developmental trajectory and the identification of their multidimensional impacts. This paper reviews the development of PHES in China and highlights its various impacts. Despite the relatively late start of PHES development in China, the country has recently ranked first worldwide with an aggregated installed capacity of 50.94 GW in operation in 2023. These plants are primarily distributed in North China, East China, and South China, contributing to the safe and stable operation of regional power grids. Furthermore, over 300 plants are under construction or in the planning stage across the whole country, aiming to support large-scale renewable energy development and facilitate a sustainable energy transition. However, it is important to recognize that such extensive PHES development requires significant land resources, which can lead to disturbances in local ecosystems and affect nearby residents. Additionally, environmental emissions may arise from a life-cycle perspective. Finally, several countermeasures are proposed to enhance sustainable PHES development in China. They include strengthening the rational planning of new plants to optimize their spatial distribution, refining the engineering design of new plants, and exploring avenues for sharing the benefits of PHES development with a broad spectrum of local residents. Full article

Urban heat islands (UHIs) constitute one of the most conspicuous anthropogenic impacts on local climates, characterized by elevated land surface temperatures in urban areas compared to surrounding rural regions. This study represents a novel and comprehensive effort to characterize the spectral signature of SUHI through the lens of the two-dimensional (2D) turbulence theory, with a particular focus on identifying energy cascade regimes and their climatic modulation. The theory of two-dimensional (2D) turbulence, first described by Kraichnan and Batchelor, predicts two distinct energy cascade regimes: an inverse energy cascade at larger scales (low wavenumbers) and a direct enstrophy cascade at smaller scales (high wavenumbers). These cascades can be detected and characterized through spatial power spectra analysis, offering a scale-dependent understanding of the SUHI phenomenon. Despite the theoretical appeal, empirical validation of the 2D turbulence hypothesis in urban thermal landscapes remains scarce. This study aims to fill this gap by analyzing the spatial power spectra of land surface temperatures across 14 cities representing diverse climatic zones, capturing varied urban morphologies, structures, and materials. We analyzed multi-decadal LST datasets to compute spatial power spectra across summer and winter seasons, identifying spectral breakpoints that separate large-scale energy retention from small-scale dissipative processes. The findings reveal systematic deviations from classical turbulence scaling laws, with spectral slopes before the breakpoint ranging from ~K^−1.6 to ~K^−2.7 in winter and ~K^−1.5 to ~K^−2.4 in summer, while post-breakpoint slopes steepened significantly to ~K^−3.5 to ~K^−4.6 in winter and ~K^−3.3 to ~K^−4.3 in summer. These deviations suggest that urban heat turbulence is modulated by anisotropic surface heterogeneities, mesoscale instabilities, and seasonally dependent energy dissipation mechanisms. Notably, desert and Mediterranean climates exhibited the most pronounced small-scale dissipation, whereas oceanic and humid subtropical cities showed more gradual spectral transitions, likely due to differences in moisture availability and convective mixing. These results underscore the necessity of incorporating turbulence theory into urban climate models to better capture the scale-dependent nature of urban heat exchange. The observed spectral breakpoints offer a diagnostic tool for identifying critical scales at which urban heat mitigation strategies—such as green infrastructure, optimized urban ventilation, and reflective materials—can be most effective. Furthermore, our findings highlight the importance of regional climatic context in shaping urban spectral energy distributions, necessitating climate-specific urban design interventions. By advancing our understanding of urban thermal turbulence, this research contributes to the broader discourse on sustainable urban development and resilience in a warming world. Full article