Search Results (7,773)

Visual cues are critical for orientation and migration in riverine fishes, providing potential mechanisms for behavioral guidance. This study investigated how light spectrum and intensity interact to modulate phototactic responses in two schizothoracine fishes from the upper Yalong River. Results showed Schizopygopsis malacanthus preferred blue light and avoided red light, with preferences shifting with flow intensity; Schizothorax kozlovi favored green light and avoided light-red light, with minimal flow impact. We propose that engineers build fishway entrances or ideal habitat attractors that prioritize low-intensity blue light (10 lx) and medium-intensity green light (50 lx), supplemented by medium-intensity blue light (50 lx). This study provides scientific evidence and application value for restoring fish habitats, fish passages, and fisheries. Full article

Nighttime light (NTL), a crucial indicator of human activity intensity, has not been systematically analyzed for its interactive mechanisms with air pollution and climate change. This study first investigates the spatiotemporal evolution of China’s total nighttime light (TNTL) and average nighttime light (ANTL), alongside key indicators of meteorological parameters and air pollution, at the grid scale from 2000 to 2023. We then employ prefecture-level city data and a geographically and temporally weighted regression (GTWR) model to quantify the spatiotemporally heterogeneous associations of temperature (TMP), precipitation (PRE), fine particulate matter (PM_2.5), ozone (O₃), land use (LUL), topography, and socioeconomic factors with NTL. The results indicate that (1) China’s NTL exhibits a significant overall upward trend, with areas of increase or significant increase comprising 92.04% of the total study area. TNTL growth demonstrates regional heterogeneity, expanding by a factor of 4.91 in East China and 2.65 in Northeast China; (2) meteorological and air pollution indicators display spatiotemporal non-stationarity, with the synergistic effect between O₃ and PRE being the strongest; (3) among NTL drivers, LUL contributes most significantly (0.44), followed by TMP (0.14) > PM_2.5 (−0.33 × 10⁻¹) > O₃ (0.17 × 10⁻¹) > PRE (−0.33 × 10⁻⁶); (4) TMP and PRE may primarily influence NTL by altering ecological conditions and nighttime activity patterns. TMP shows a strong positive correlation with NTL in the junction zone of South, East, and Central China, whereas PRE predominantly exerts a negative influence; (5) air pollution exhibits distinct spatiotemporal effects: high PM_2.5 and O₃ generally correspond to lower NTL, though positive correlations persist in some areas due to industrial structures, highlighting the need for integrated policies that balance air quality management with sustainable urban planning; (6) the 2013 “Air Pollution Prevention and Control Action Plan” significantly strengthened the negative correlation between PM_2.5 and NTL in North China. However, O₃ concentrations increased by 28.9% after 2017, underscoring the challenge of coordinating VOC and NOx controls for long-term atmospheric sustainability. Full article

Land use practices are a key driver of soil nitrogen (N) dynamics, yet their influence on N accumulation within distinct soil organic matter (SOM) fractions remains insufficiently understood. This study aimed to elucidate the responses of N accrual in different SOM fractions to contrasting land uses. To achieve this purpose, soil samples were collected from seven representative land uses: forest, pasture, corn plantation, sugarcane plantation, cassava plantation, orchard, and abandoned land. Subsequently, soil samples were fractionated into free particulate SOM (fSOM), occluded light SOM (oSOM), weakly bound form SOM (wSOM), and strongly bound form SOM (sSOM) fractions, and N contents were quantified for each fraction. The results showed pronounced land use effects on both the magnitude and distribution of N among SOM fractions. The forest land use consistently promoted greater N accumulation in fSOM (0.15 g N kg⁻¹ soil), oSOM (0.14 g N kg⁻¹ soil), and wSOM fractions (0.29 g N kg⁻¹ soil), reflecting high organic inputs and low disturbance intensity. The pasture land use exhibited the highest N accumulation in the sSOM fraction (1.01 g N kg⁻¹ soil), indicating enhanced stabilization of N through strong organo-mineral associations. Intensively managed croplands and abandoned land generally displayed lower N storage across SOM fractions. Overall, these findings highlight the critical role of land use in regulating N partitioning and stabilization within SOM fractions and underscore the importance of low-disturbance, perennial vegetation systems for improving long-term soil N retention. Full article

Atmospheric laser beam propagation is typically perturbed by the dual influences of aerosol particle systems and atmospheric turbulence. This joint perturbation induces intensity fluctuations in the transmitted optical field, which significantly degrades the performance of laser-based systems. This study integrates and improves upon existing simulation algorithms, establishing a coupled model that combines the Monte Carlo method and multi-phase screens. The model accurately characterizes optical field evolution and reveals that the impacts of scattering and turbulence on the scintillation index (SI) are not simply additive: turbulence perturbation enhances intensity fluctuations, leading to an increase in SI; however, as the energy proportion of scattered light rises, its statistical stationarity begins to dominate the optical field characteristics, stabilizing SI. Based on radiative transfer and Mie scattering theories, an analytical formula for single-scattering SI is derived, enabling direct calculation from fundamental parameters. Furthermore, a composite SI expression is established using the scattered-to-transmitted light intensity ratio. To address model deviations along the dimensions of visibility and turbulence strength, a sinusoidal compensation model and a logarithmic compensation model are proposed, respectively. Validation results verify the complementary and competitive mechanisms of scattering and turbulence in modulating intensity fluctuations. This research provides efficient theoretical tools and practical references for simulating and optimizing laser transmission in complex atmospheric environments. Full article

Geiger-mode avalanche photodiode (GM-APD) array light detection and ranging (LiDAR) has significant advantages in low-light scenes due to its single-photon-level detection sensitivity. However, it is susceptible to noise, which leads to a decrease in target localization accuracy. Traditional methods rely on long-term accumulation to distinguish signal photons from noise photons, making it difficult to achieve efficient processing, especially in scenarios with sparse echo photons and low signal-to-noise ratio (SNR), where performance is limited. To quickly and accurately obtain three-dimensional (3D) information of the target under such extreme conditions, this paper proposes a method for target detection and temporal window depth estimation based on intensity information guidance. First, noise suppression is performed on the intensity image according to its statistical characteristics, and an outlier detection mechanism based on neighborhood sparsity is introduced to remove outliers, thereby completing the target detection. Next, by exploiting the spatial continuity and reflectivity similarity of the target, local fusion of photon data within the target neighborhood is performed to construct highly consistent “superpixels”. Finally, according to the distribution difference between signal photons and noise photons on the time axis, temporal window screening is applied to the superpixels to extract depth information, and empty pixels are filled using a convex segmentation method to achieve depth estimation of the target. The experimental results demonstrate that under conditions of low photon counts and strong noise, the proposed method significantly outperforms traditional and existing methods in target recovery and depth estimation by effectively integrating target intensity information. Furthermore, this method achieves faster reconstruction speed, enabling high-precision and high-efficiency 3D target reconstruction. Full article

Background: Wild jujube (Ziziphus jujuba var. spinosa) exhibits remarkable tolerance to saline-alkali stress, yet its molecular mechanisms remain poorly understood. 3-ketoacyl-CoA synthase (KCS) is a key enzyme involved in the biosynthesis of very-long-chain fatty acids (VLCFAs), which constitute pivotal precursors for membrane lipids involved in stress adaptation. Methods: Through genome-wide analysis and molecular biology techniques, 20 ZjKCS genes were identified. Results: The ZjKCS genes were grouped into nine subfamilies, exhibiting highly conserved gene structures, motifs, and functional domains within each subfamily. Two pairs of collinear gene pairs were identified, with the ZjKCS12-ZjKCS18 pair retaining core conserved functions despite intense purifying selection. ZjKCS genes are rich in cis-acting elements associated with light transduction, phytohormone responses, and abiotic stress adaptation. Tissue-specific expression patterns of ZjKCS under light, ABA (abscisic acid), and MeJA (methyl jasmonate) treatments were analyzed by quantitative real-time PCR (qRT-PCR). Under saline-alkali stress, ZjKCS genes were significantly upregulated, with most showing strong sustained induction during later treatment stages. Conclusions: These findings indicate that the ZjKCS family participates in saline-alkali stress and abiotic stress adaptation, potentially by enhancing VLCFA synthesis to reinforce and remodel membrane lipid structure. This study provides a foundation for elucidating lipid-mediated stress resistance mechanisms in stress-tolerant fruit trees. Full article

Light availability is a key environmental factor influencing plant functional traits and ecological strategies. To investigate how natural populations of Iris pumila respond to contrasting irradiance, we conducted an in situ reciprocal transplant experiment using clonal genotypes from two natural populations, each originating from an open dune and a shaded forest habitat. Leaves collected from each of the replanted and transplanted genotypes were analyzed for structural (specific leaf area—SLA, leaf dry matter content—LDMC), physiological (specific leaf water content—SLWC, photosynthetic pigments) and biochemical (peroxidase—POD, glutathione reductase—GR, phenolics and anthocyanins) traits. Shade-grown individuals developed thinner leaves with higher SLA and chlorophyll content, enhancing light-harvesting efficiency, whereas sun-exposed plants exhibited greater LDMC, increased POD and GR activities and higher anthocyanin levels—traits consistent with enhanced photoprotection under high irradiance. All genotypes exhibited pronounced plasticity to light intensity, with habitat exerting a stronger influence on trait expression than population origin. To evaluate oxidative balance, we proposed the ODAC index (Oxidative Damage to Antioxidant Capacity), which integrates lipid peroxidation with antioxidant capacity. ODAC values revealed consistent population-level differences, with higher values in Dune genotypes across habitats, indicating a constitutively elevated oxidative load relative to antioxidant protection and suggesting differentiation in redox regulation between populations. Overall, leaf trait variation in I. pumila appears to be primarily driven by plastic responses to light conditions, while differentiation in oxidative physiology contributes to functional divergence between populations. Full article

In urban driving cycles, battery electric vehicles are subject to frequent start–stop operations, which lead to substantial braking energy losses. Although fuzzy control (FC) strategies are commonly employed for regenerative braking, their performance is often constrained by subjectively defined membership functions and rules. To address this limitation, this paper proposes an improved FC strategy that is optimized using the particle swarm optimization (PSO) algorithm. Focusing on a front-wheel-drive BEV, a three-input single-output fuzzy controller is developed in accordance with ECE regulations, where braking intensity, battery state of charge (SOC), and vehicle speed serve as inputs, and the motor braking force ratio serves as the output. A co-simulation platform based on AVL-Cruise 2019 and Matlab/Simulink 2017a is established to evaluate the strategy under the New European Driving Cycle (NEDC) and the Worldwide Light Vehicles Test Cycle (WLTC). Additionally, hardware-in-the-loop (HIL) tests are conducted to validate the practical feasibility and accuracy of the optimized strategy. The results demonstrate that the PSO-optimized FC strategy achieves a performance in real-world controllers that is comparable to that observed in a simulation, confirming its real-time applicability. Specifically, under the NEDC, the optimized strategy reduces battery SOC from 0.90 to 0.8795, representing improvements of 0.2515% and 0.4670% over the unoptimized FC strategy and the ideal distribution strategy, respectively. The regenerative braking efficiency is enhanced by 2.45% and 10.48%. Under the WLTC, the final SOC with the optimized strategy is 0.8488, reflecting gains of 0.5202% and 0.8380% over the two reference strategies, while regenerative braking efficiency improves by 2.32% and 8.95%. These findings indicate that the proposed strategy offers a safe and effective solution for improving the regenerative braking performance in electric vehicles. Full article

Light spectral composition plays a central role in regulating plant growth, morphology, nutrient uptake, and pigment biosynthesis, particularly in controlled-environment agriculture. This study investigated the effects of targeted LED spectral modulation, focusing on green light deprivation and different red-to-blue (R:B) ratios at constant photon flux density, on morphological traits, mineral composition, and photosynthetic pigments in Salvia officinalis L. and Cannabis sativa L. grown under controlled conditions. Plants were cultivated under three LED treatments providing equal light intensity but differing in spectral composition. Morphological parameters, mineral nutrients, inorganic anions, and photosynthetic pigments were assessed at harvest. Total biomass production was not significantly affected by the light treatments in either species; however, clear species-specific responses were observed. In S. officinalis, higher R:B ratios promoted stem elongation without affecting leaf number or fresh weight, whereas in C. sativa, the higher R:B ratio significantly increased leaf number. Green light deprivation and red–blue enrichment generally enhanced mineral accumulation and nitrogen content, although the magnitude and direction of these effects varied between species. Photosynthetic pigment responses were more pronounced in hemp, with increased chlorophylls and carotenoids under green light deprivation, while salvia showed a selective increase in carotenoids under higher R:B ratios. Overall, these findings emphasize the importance of species-specific LED spectral optimization to improve physiological performance and nutritional quality in indoor cultivation of medicinal plants. Full article

High-order Poincaré sphere (HOPS) beams have attracted tremendous interest due to their complex polarization and phase characteristics. However, manipulating the second harmonics generation (SHG) of HOPS beams is still challenging. Here, we developed a vector-coupled wave model to predict petal-shaped intensity patterns and reveal a linear correlation between petal number and topological order (n = 2 → 4). Moreover, we experimentally investigated the multidimensional regulation of SHG in HOPS beams through tailored phase-matching strategies. By employing three distinct configurations—(i) type-I phase matching, (ii) type-II phase matching, and (iii) orthogonally arranged BBO crystals based on Type-I phase matching—we establish a comprehensive framework for controlling the spatial and polarization properties of SHG in n = 2 HOPS beams. These results advance the manipulation of structured light in nonlinear optics, providing insights for optimizing applications in optical communication and polarization imaging. Full article

This paper experimentally analyzes how stepwise platform vibration (Baseline-S3, approximately 0.3–0.6 mm amplitude) alters the statistical structure of distance measurement errors in a dual-channel LIght Detection And Ranging (LiDAR) (0° and −3°) at a fixed horizontal distance of 1.5 m. The mean error remained at the ${10}^{-5}$ m level across all vibration stages, indicating negligible systematic bias. However, distribution-based metrics showed substantial amplification. The interquartile range (IQR) increased by approximately threefold from Baseline to S3, while the total error range expanded by roughly 4–11 times. The outlier ratio increased by about 1.5–2 times under high-vibration conditions. Both variance and root mean square error (RMSE) exhibited nonlinear growth with increasing vibration intensity. Two-way analysis of variance (ANOVA) revealed no statistically significant differences at the mean level ($p>0.05$), whereas variability-based indicators consistently demonstrated dispersion amplification. These findings indicate that LiDAR degradation under vibration is governed primarily by stochastic dispersion expansion and extreme-value behavior rather than systematic bias shift. Full article

Field trials were conducted to define several parameters associated with adding LEDs to monitoring traps for codling moth (CM), Cydia pomonella (L.), using both a sex pheromone lure (PH1X) and a non-pheromone lure (CM4K). Traps with LEDs emitting at a peak of 395 nm with 1000–2000 mW/m² were the most effective. Lights with greater intensities caught similar numbers of CMs and significantly more non-targets. Adding the UV-A lights did not increase moth catches early in the season with either the PH1X or CM4K lures. However, UV-A LEDs, when used with these two lures, significantly increased total moth catches 7- and 3-fold in July and August, respectively. The addition of the UV-A LEDs allowed CM4K-baited traps to perform significantly better in previously limiting situations, such as in weedy orchards, and in pear relative to apple. Distance from the light source is a key factor affecting light energy. Irradiance dropped >90% at 15 cm, which is the distance from the lure to the entrance of a standard delta trap. A smaller trap (7.5 cm radius) had a 4-fold greater irradiance at its entrance and caught greater numbers of non-targets but not CMs than delta traps without LEDs. Full article

Chiral bound states in the continuum (BIC) metasurfaces have emerged as a promising platform for enhancing light–matter interactions, which have potential applications in advanced photonic and quantum information devices. However, simultaneously achieving near-perfect circular dichroism and highly efficient nonlinear conversion with highly symmetric structures in metasurfaces remains an open challenge. In this work, we design a $C_4$-symmetric chiral metasurface composed of eight elliptical silicon nanorods on a ${\mathrm{SiO}}_2$ substrate, where monocrystalline silicon is used as the nonlinear optical material. By combining simulations and nonlinear time-domain coupled-mode theory (TCMT), we discovered that both the optimal chirality and the nonlinear conversion efficiency can be attained simultaneously due to the critical coupling between the metasurface mode and the quasi-BIC mode. Meanwhile, a near-perfect circular dichroism (CD = 0.99) and a high nonlinear conversion efficiency of $7\times {10}^{-5}$ under a radiation intensity of $5{\mathrm{kW}/\mathrm{cm}}^2$ are numerically achieved due to the robustness of bound states in the continuum. This work offers a promising route toward high-performance chiral nonlinear photonic components, which is of great importance for the development of ultra-compact optical devices such as circular polarization detectors, chiral sensors, and nonlinear photonic chips for integrated optical and quantum information systems. Our research not only contributes to the fundamental understanding of chiral metasurfaces but also provides a practical approach for achieving high-efficiency nonlinear optical devices. Full article

Pomegranate (Punica granatum L.) is a major fruit crop in tropical and subtropical regions, but changing climatic conditions—especially rising temperatures and intense solar radiation—are increasing physiological disorders. Sunburn, a key heat- and light-induced disorder, causes peel discoloration and tissue damage. This results in significant yield loss and reduced fruit quality. The objective of this study was to characterize sunburn-induced anatomical changes in the widely grown, highly sensitive Hicaznar cultivar in Türkiye, and to identify the optimal phenological stage for the application of sunburn-preventive practices. For this purpose, pomegranate fruit peels were fixed in FAA (Formalin–Acetic Acid–Alcohol) solution, embedded in paraffin blocks, and sectioned at a thickness of 5–7 µm. The sections were stained using the hematoxylin–eosin method and examined under a light microscope. The images captured with a digital camera wereanalyzed and revealed that sunburn damage in the pomegranate peel first appears in the cuticle layer, followed by disruption and fragmentation of the cutaneous and epidermal layers beneath it, and ultimately leads to damage of the parenchyma cells. Furthermore, Light microscopy showed that before visible discoloration, cells near the epidermis undergo phenolic accumulation, cell-wall thickening, and lignification, which are early indicators of sunburn. These microscopic changes provide early diagnostic features for detecting sunburn damage before external symptoms manifest. The study concluded that anatomical changes begin before the visible symptoms of sunburn appear on the fruit, and the most appropriate timing for applying preventive measures against sunburn has been identified. Light microscopy showed that before visible discoloration, cells near the epidermis undergo phenolic accumulation, cell-wall thickening, and lignification, which are early indicators of sunburn. Full article

Against the background of intensifying global climate change, extreme precipitation events have become increasingly frequent. Improving the accuracy of short-term precipitation nowcasting is therefore essential for disaster prevention and mitigation. Traditional numerical weather prediction (NWP) approaches are constrained by computational latency and errors arising from physical parameterizations, making it difficult to satisfy real-time forecasting requirements at high spatiotemporal resolution. Using the SEVIR dataset, this study conducts a systematic comparison of two Transformer-based deep learning models—Earthformer and LLMDiff—for short-term extreme precipitation nowcasting. Model performance is evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and Success Ratio (SUCR). Results indicate that, for 0–30 min lead times, Earthformer more efficiently captures both local and long-range spatiotemporal dependencies via its Cuboid Attention mechanism and shows a slight advantage for low-intensity precipitation. As the lead time extends to 60 min, LLMDiff demonstrates stronger longer-horizon skill due to its diffusion-based probabilistic modeling and a frozen large language model (LLM) module, which enhance the representation of uncertainty and longer-term evolution of precipitation systems. However, LLMDiff tends to produce a higher false-alarm rate. Overall, Earthformer is better suited for rapid early warning of light precipitation, whereas LLMDiff is more appropriate for high-accuracy nowcasting of heavy precipitation, offering useful insights for intelligent forecasting of extreme weather. Full article