Search Results (7,766)

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

Railway traffic monitoring requires robust detection technologies capable of operating reliably under real-world vibration and environmental conditions. In this work, we present the design and validation of an optical vibration sensor based on a Single-mode–Multimode–Single-mode (SMS) fiber structure for Light Rail Vehicle (LRV) detection. The sensing mechanism relies on multimodal interference in the multimode fiber (MMF), where rail-induced vibrations modify the guided mode distribution and, consequently, the transmitted optical intensity. The optical signal is converted to voltage and processed through an embedded acquisition system. Additionally, we conducted tests with freight trains and maintenance trains in order to evaluate the applicability of the sensor in other types of trains besides the LRV. We conducted laboratory experiments to assess mechanical stability, sensibility, and packaging strategies, followed by supervised field tests on an operational LRV line. The recorded time-domain signal exhibited clear modulation during train passage, and first-derivative and sliding-window variance analyses were applied to reliably identify vibration events, even in the presence of slow baseline drift. In addition, frequency-domain analysis was performed by applying the Fast Fourier Transform (FFT) to the measured signal, enabling the identification of characteristic low-frequency spectral components induced by train passage. A quantitative sensitivity assessment was further carried out by correlating the integrated spectral energy (0–12 Hz) with vehicle weight, yielding a linear response with a sensitivity of 0.0017 a.u./t and coefficient of determination $R^2=0.933$. The proposed solution demonstrated stable operation using commercially available low-cost components, confirming the feasibility of SMS-based optical sensing for railway monitoring. These results indicate strong potential for future deployment in traffic safety systems and distributed sensing networks. Full article

Severe foliage occlusion and dynamically changing lighting conditions in complex orchard environments pose significant challenges for visual perception systems in automated apple harvesting, including low detection accuracy, poor robustness, and insufficient real-time performance. To address these issues, this study proposes an improved lightweight detection network based on YOLOv11, named YOLO-WBL, along with a precise yield estimation algorithm based on 3D point clouds, termed CLV. The YOLO-WBL network is optimized in three aspects: (1) A C3K2_WT module integrating wavelet transform is introduced into the backbone network to enhance multi-scale feature extraction capability; (2) A weighted bidirectional feature pyramid network (BiFPN) is adopted in the neck network to improve the efficiency of multi-scale feature fusion; (3) A lightweight shared convolution separated batch normalization detection head (Detect-SCGN) is designed to significantly reduce the parameter count while maintaining accuracy. Based on this detection model, the CLV algorithm deeply integrates depth camera point cloud information through 3D coordinate mapping, irregular point cloud reconstruction, and convex hull volume calculation to achieve accurate estimation of individual fruit volume and total yield. Experimental results demonstrate that: (1) The YOLO-WBL model achieves a precision of 93.8%, recall of 79.3%, and mean average precision (mAP@0.5) of 87.2% on the apple test set; (2) The model size is only 3.72 MB, a reduction of 28.87% compared to the baseline model; (3) When deployed on an NVIDIA Jetson Xavier NX edge device, its inference speed reaches 8.7 FPS, meeting real-time requirements; (4) In scenarios with an occlusion rate below 40%, the mean absolute percentage error (MAPE) of yield estimation can be controlled within 8%. Experimental validation was conducted using apple images selected from the dataset under varying lighting intensities and fruit occlusion conditions. The results demonstrate that the CLV algorithm significantly outperforms traditional average-weight-based estimation methods. This study provides an efficient, accurate, and deployable visual solution for intelligent apple harvesting and yield estimation in complex orchard environments, offering practical reference value for advancing smart orchard production. Full article

The market value of fragrant rice is largely defined by the presence and intensity of its aroma, which is primarily attributed to volatile compound 2-acetyl-1-pyrroline (2-AP). The biosynthesis of 2-AP is chiefly governed by recessive alleles of the badh2 gene. Nevertheless, 2-AP accumulation is also profoundly shaped by environmental factors and agronomic management. Field practices—such as balanced nitrogen and potassium fertilization, supplementation with trace elements, and application of plant growth regulators like methyl jasmonate—promote 2-AP synthesis by increasing precursor availability and enhancing the activity of key enzymes. Additionally, tillage systems, alternate wetting and drying irrigation, optimal planting density, and harvest timing significantly affect aroma quality. Abiotic stresses, including moderate drought, salinity, optimal temperatures around 25 °C, and low light during grain filling, can also stimulate 2-AP accumulation, often through shifts in proline metabolism and activation of stress-responsive pathways involving GABA and methylglyoxal. Despite the promise of these strategies, several challenges persist, such as the common trade-off between yield and aroma intensity, complex genotype-by-environment interactions, and incomplete elucidation of the molecular mechanisms involved. Moving forward, integrating multi-omics analyses with smart agriculture technologies will be essential to unravel the regulatory networks underlying aroma formation and to advance the breeding of high-yielding fragrant rice varieties with stable aroma traits under changing climate scenarios. Full article

The assessment of landscape ecological risk and the exploration of its driving factors is a critical approach to alleviating the conflict between the growing demand of human activities and ecological environment conservation, and the Five Lakes Area in Central Yunnan serves as a typical representative of landscape ecological risk issues in plateau lake regions. Therefore, this study, based on the land use transfer change characteristics of the Five Lakes Area in Central Yunnan across four periods (1995–2024), employed the landscape pattern index method to calculate the spatiotemporal variation characteristics of the landscape ecological risk index; additionally, 10 driving factors (including natural and socio-economic factors) were selected, and the XGBoost-SHAP model and Random Forest model were applied to explore the driving factors, with the results showing that: (1) In terms of land use transfer, farmland, forest, and Grass land were transferred among each other, the inflow of Construction land increased, and Grass land had the largest outflow area; (2) regarding landscape ecological risk, the landscape pattern was unstable, the loss degree increased, and the moderate and moderately high-risk areas expanded; and (3) for driving factors, the dominance shifted from natural factors to socio-economic factors; among these, Precipitation, NDVI (Normalized Difference Vegetation Index), Land use intensity, and Night-time light index were significant influencing factors. Based on the above results, a zoning management and control strategy for landscape ecological risk was proposed, aiming to provide a scientific reference for policy formulation to reduce risks and alleviate human–land conflicts. Full article

Background/Objectives: Research examining the emotional and psychological challenges experienced by couples undergoing in vitro fertilisation (IVF) remains limited. Existing evidence suggests that women undergoing IVF often report elevated levels of depression, anxiety, and emotional distress, whereas men may experience feelings of anger, inadequacy, and self-doubt, especially following unsuccessful treatment cycles. Successful IVF outcomes are commonly associated with intense joy, relief, and fulfilment as couples realise their aspiration to become parents. In light of the limited qualitative research conducted in Greece to date, in the present study, we aimed to explore the lived experiences of couples undergoing IVF treatment, with particular attention to emotional, relational, and systemic dimensions. Methods: A qualitative research design was employed. Semi-structured, in-depth interviews were conducted with six heterosexual couples (aged 18–49 years) residing in Athens and Karditsa, Greece, all of whom had undergone IVF treatment. Interviews were audio-recorded, transcribed verbatim, and analysed using Interpretative Phenomenological Analysis. Results: Our analysis revealed five interrelated superordinate themes with associated subordinate themes: (1) making sense of infertility and IVF, (2) negotiating relationships under the strain of IVF, (3) IVF as an emotionally demanding journey, (4) navigating institutional and systemic barriers, and (5) projecting the future through IVF experience. Lived experiences of infertile couples undergoing IVF treatment highlighted a range of emotions, social pressure, and attitudes towards IVF and related policies. Conclusions: In Greece, where demographic decline has been widely discussed in policy debates, IVF has gained societal and policy attention. For many participants, IVF represented a hopeful pathway towards achieving parenthood despite the emotional and practical challenges involved. Full article

Metasurfaces offer an unprecedented avenue to facilitate light-matter interactions. However, traditional design methodologies rely on computationally intensive trial-and-error processes. Moreover, existing deep learning (DL) schemes are predominantly hindered by their massive data requirements and limited exploration of freeform design spaces. To overcome these challenges, a multi-model-driven generative-evolutionary strategy (GES) is proposed, for the on-demand inverse design of bespoke Terahertz (THz) metasurface sensors. By leveraging a Conditional Diffusion Generator (CDG) and an Attention-Enhanced Residual Network (ARN), this framework enables the exploration of an expansive design space encompassing $2^{100}$ possible configurations. The GES effectively overcomes the data bottleneck by selectively generating high-potential data in stages. Full-wave simulations confirm that the inversely designed metasurfaces exhibit high-contrast resonance peaks and exceptional sensitivity across low, mid, and high THz bands. This work provides a versatile paradigm for the efficient design of high-performance functional metamaterials, significantly accelerating the advancement of application-specific THz sensing. Full article

This work presents the real-world deployment of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for intelligent energy control in resource-constrained IoT devices. The proposed system employs a first-order Takagi–Sugeno fuzzy model with three Gaussian membership functions per input: ambient temperature, light intensity, and battery voltage. The model was trained offline using augmented environmental datasets and subsequently translated into optimized embedded C code for execution on an ESP32 microcontroller. The controller dynamically adjusts the node’s deep sleep duration according to environmental conditions, enabling adaptive behavior based solely on local environmental conditions without requiring external connectivity. A 10-day field deployment compared the ANFIS controller with conventional fixed and rule-based strategies. Results show that the ANFIS-based strategy reduced energy consumption by 31.1% relative to the fixed approach while maintaining accurate adaptation to environmental conditions (RMSE = 9.6 s). The inference process required less than 2.5 ms and used under 30 KB of RAM, confirming the feasibility of real-time fuzzy inference on resource-constrained embedded platforms. Full article