Search Results (180)

The pea aphid, Acyrthosiphon pisum, possesses horizontally acquired fungal carotenoid biosynthesis genes, enabling de novo production of carotenoids. Although carotenoids are known to contribute to photo-protection and coloration, their potential role in energy metabolism and population fitness under thermal stress is still unclear. This study investigated the interactive effects of temperature and light intensity on energy homeostasis and life-history traits in A. pisum. Using controlled environmental regimes, we demonstrate that light intensity significantly influenced the ATP content, development, and reproductive output of A. pisum at 12 °C, but not at 22 °C. Under cold stress (12 °C), high light intensity (5000 lux) increased ATP content by 240%, shortened the pre-reproductive period by 46%, extended reproductive duration by 62%, and enhanced the net reproductive rate (R₀) and intrinsic rate of increase (rₘ) compared to low light intensity (200 lux). These effects were abolished at the optimal temperature (22 °C), indicating a temperature-gated, light-dependent mechanism. Demographic analyses revealed that carotenoid-associated solar energy harvesting significantly improves fitness under cold conditions, likely compensating for metabolic depression. Our findings reveal a novel ecological adaptation in aphids, where horizontally transferred genes may enable light-driven energy supplementation during thermal stress. This study provides new insights into the physiological mechanisms underlying insect resilience to climate variability and highlights the importance of light as a key environmental factor in shaping life-history strategies in temperate agroecosystems. Full article

Objective: This study aimed to evaluate the hepatic volume, iodine concentration, and extracellular volume (ECV) of each hepatic segment in cirrhotic patients using three-dimensional (3D) volumetric iodine mapping of the liver segment derived from contrast-enhanced dual-energy CT (DECT) superimposed on extracted color-coded CT liver segments in comparison with non-cirrhotic patients. Methods: The study population consisted of 66 patients, 34 with cirrhosis and 32 without cirrhosis. Using 3D volumetric iodine mapping of the liver segment derived from contrast-enhanced DECT superimposed on extracted color-coded CT liver segments, the volume and iodine concentration of each hepatic segment in the portal venous phase (PVP) and equilibrium phase (EP), the difference in iodine concentration between PVP and EP (ICPVP-liver—ICEP-liver), and ECV fractions were compared between cirrhotic and non-cirrhotic groups. Results: The iodine concentration was not significantly different in all hepatic segments between the cirrhotic and non-cirrhotic groups. Conversely, the difference in iodine concentration between PVP and EP (ICPVP-liver—ICEP-liver) was significantly smaller in the cirrhosis group than in the non-cirrhosis group for all hepatic segments (p < 0.001). The ECV fraction of the left medial segment was significantly higher in the cirrhosis group than in the non-cirrhotic group ([26.4 ± 7.6] vs. [23.1 ± 5.1]; p < 0.05). Conclusions: The decreased difference in iodine concentration between PVP and EP calculated from 3D volumetric iodine mapping of the liver segment using DECT may be a clinically useful indicator for evaluating patients with compensated cirrhosis, suggesting a combined effect of a reduced portal venous flow and increased interstitial space associated with fibrosis. Full article

This paper proposes IFEM-YOLOv13, a high-precision underwater target detection method designed to address challenges such as image degradation, low contrast, and small target obscurity caused by light attenuation, scattering, and biofouling. Its core innovation is an end-to-end degradation-aware system featuring: (1) an Intelligent Feature Enhancement Module (IFEM) that employs learnable sharpening and pixel-level filtering for adaptive optical compensation, incorporating principles of symmetry in its multi-branch enhancement to balance color and structural recovery; (2) a degradation-aware Focal Loss incorporating dynamic gradient remapping and class balancing to mitigate sample imbalance through symmetry-preserving optimization; and (3) a cross-layer feature association mechanism for multi-scale contextual modeling that respects the inherent scale symmetry of natural objects. Evaluated on the J-EDI dataset, IFEM-YOLOv13 achieves 98.6% mAP@0.5 and 82.1% mAP@0.5:0.95, outperforming the baseline YOLOv13 by 0.7% and 3.0%, respectively. With only 2.5 M parameters and operating at 217 FPS, it surpasses methods including Faster R-CNN, YOLO variants, and RE-DETR. These results demonstrate its robust real-time detection capability for diverse underwater targets such as plastic debris, biofouled objects, and artificial structures, while effectively handling the symmetry-breaking distortions introduced by the underwater environment. Full article

Automotive interior lighting has progressed from basic functional illumination to sophisticated aesthetic systems emphasizing chromatic stability under thermal variations. This study enhances an RGB temperature compensation algorithm for LEDs, extending it to an RGBW solution. While several approaches for LED temperature compensation have been proposed in the literature, none have addressed a complete RGBW solution where the white channel is derived and actively adjusted on thermal variations. This research aims to fill this gap by extending an RGB algorithm to RGBW and validating it under realistic automotive conditions. While the proposed compensation strategies are general and may be applied to other LED systems, the automotive interior lighting domain has been selected as a representative case study because it combines stringent chromatic stability requirements ($\Delta u^{\prime }{v}^{\prime }\le 0.01$) and high industrial relevance. Leveraging Infineon’s LITIX™ LED drivers, experimental results show that the algorithm maintains chromatic stability with deviations below $\Delta u^{\prime }{v}^{\prime }=0.00562$ in RGB mode and $\Delta u^{\prime }{v}^{\prime }=0.0067$ in RGBW mode across the tested temperature range. The addition of the white channel improves the color rendering index (CRI) by up to 58.9 points (from 19.7 to 78.6) while preserving color quality. Compared to previous works limited to RGB systems, our approach provides the first practical RGBW compensation algorithm experimentally validated under realistic automotive conditions. Full article

This study proposes an image processing framework based on Bionic principles to optimize 3D visual perception in virtual reality (VR) systems. By simulating the physiological mechanisms of the human visual system, the framework significantly enhances depth perception and visual fidelity in VR content. The research focuses on three core algorithms: Gabor texture feature extraction algorithm based on directional selectivity of neurons in the V1 region of the visual cortex, which enhances edge detection capability through fourth-order Gaussian kernel; improved Retinex model based on adaptive mechanism of retinal illumination, achieving brightness balance under complex illumination through horizontal–vertical dual-channel decomposition; the RGB adaptive adjustment algorithm, based on the three color response characteristics of cone cells, integrates color temperature compensation with depth cue optimization, enhances color naturalness and stereoscopic depth. Build a modular processing system on the Unity platform, integrate the above algorithms to form a collaborative optimization process, and ensure per-frame processing time meets VR real-time constraints. The experiment uses RMSE, AbsRel, and SSIM metrics, combined with subjective evaluation to verify the effectiveness of the algorithm. The results show that compared with traditional methods (SSAO, SSR, SH), our algorithm demonstrates significant advantages in simple scenes and marginal superiority in composite metrics for complex scenes. Collaborative processing of three algorithms can significantly improve depth map noise and enhance the user’s subjective experience. The research results provide a solution that combines biological rationality and engineering practicality for visual optimization in fields such as implantable metaverse, VR healthcare, and education. Full article

Urban form and surface properties significantly influence city liveability. Material choices in urban infrastructure affect heat absorption and reflectivity, contributing to the urban heat island (UHI) effect and residents’ thermal comfort. Among UHI mitigation strategies, urban parks play a key role by modifying the microclimate through albedo and evapotranspiration. Their effectiveness depends on their composition, such as tree cover, herbaceous layers, and paved surfaces. The selection of tree species affects the radiation dynamics via foliage color, leaf persistence, and plant morphology. Despite their ecological potential, park designs often prioritize aesthetics and cost over environmental performance. This study proposes a novel approach using CO₂ compensation as a decision-making criterion for surface allocation. By applying the radiative forcing concept, surface albedo variations were converted into CO₂-equivalent emissions to allow for a cross-comparison with different ecosystem services. This method, applied to four parks in two Italian cities, employed reference data, drone surveys, and satellite imagery processed through the Greenpix software v1.0.6. The results showed that adjusting the surface albedo can significantly reduce CO₂ emissions. While dark-foliage trees may underperform compared to certain paved surfaces, light-foliage trees and lawns increase the reflectivity. Including evapotranspiration, the CO₂ compensation benefits rose by over fifty times, supporting the expansion of vegetated surfaces in urban parks for climate resilience. Full article

The use of microwaves (MWs) has been proposed as an energy-efficient method for reducing checking. Along with understanding moisture distribution, it is essential to consider structural characteristics to explain how MWs reduce checking. The influence of MWs on these characteristics depends on the food matrix’s dielectric and viscoelastic properties, which vary significantly between fresh and pre-baked dough. This study investigates the effects of MW treatment applied before (MW-O) or after conventional oven baking (O-MW) on low-fat biscuits that are prone to checking. Color (CIELab), thickness, moisture content and distribution, checking rate, texture, sensory properties, energy consumption and baking time were analyzed. The findings suggest that MWs reduce checking rate by eliminating internal moisture differences, while also changing structural properties, as evidenced by increased thickness and hardness. MW-O eliminated checking (control samples showed 100%) but negatively affected color, texture (increased hardness and breaking work), and sensory quality. The O-MW checking rate (3.41%) was slightly higher than in MW-O, probably due to the resulting different structural properties (less thickness, less hardness and breaking work). O-MW biscuits were the most preferred by consumers (54.76% ranked them first), with color and texture close to the control samples. MW-O reduced total energy consumption by 16.39% and baking time by 25.00%. For producers, these improvements could compensate for the lower biscuit quality. O-MW did not affect energy consumption but reduced baking time by 14.38%. The productivity improvement, along with the reduction in checking and the satisfactory sensory quality, indicates that O-MW could be beneficial for the bakery sector. Full article

To address the concerns of contrast deterioration, detail loss, and color distortion in images produced under haze conditions in scenarios such as intelligent driving and remote sensing detection, an algorithm for image defogging that combines Retinex theory and the dark channel prior is proposed in this paper. The method involves building a two-stage optimization framework: in the first stage, global contrast enhancement is achieved by Retinex preprocessing, which effectively improves the detail information regarding the dark area and the accuracy of the transmittance map and atmospheric light intensity estimation; in the second stage, an a priori compensation model for the dark channel is constructed, and a depth-map-guided transmittance correction mechanism is introduced to obtain a refined transmittance map. At the same time, the atmospheric light intensity is accurately calculated by the Otsu algorithm and edge constraints, which effectively suppresses the halo artifacts and color deviation of the sky region in the dark channel a priori defogging algorithm. The experiments based on self-collected data and public datasets show that the algorithm in this paper presents better detail preservation ability (the visible edge ratio is minimally improved by 0.1305) and color reproduction (the saturated pixel ratio is reduced to about 0) in the subjective evaluation, and the average gradient ratio of the objective indexes reaches a maximum value of 3.8009, which is improved by 36–56% compared with the classical DCP and Tarel algorithms. The method provides a robust image defogging solution for computer vision systems under complex meteorological conditions. Full article

Accurate and rapid aquatic organism recognition is a core technology for fisheries automation and aquatic organism statistical research. However, due to absorption and scattering effects, images of aquatic organisms often suffer from poor contrast and color distortion. Additionally, the clustering behavior of aquatic organisms often leads to occlusion, further complicating the identification task. This study proposes a lightweight object detection model, Mobile-YOLO, for the recognition of four representative aquatic organisms, namely holothurian, echinus, scallop, and starfish. Our model first utilizes the Mobile-Nano backbone network we proposed, which enhances feature perception while maintaining a lightweight design. Then, we propose a lightweight detection head, LDtect, which achieves a balance between lightweight structure and high accuracy. Additionally, we introduce Dysample (dynamic sampling) and HWD (Haar wavelet downsampling) modules, aiming to optimize the feature fusion structure and achieve lightweight goals by improving the processes of upsampling and downsampling. These modules also help compensate for the accuracy loss caused by the lightweight design of LDtect. Compared to the baseline model, our model reduces Params (parameters) by 32.2%, FLOPs (floating point operations) by 28.4%, and weights (model storage size) by 30.8%, while improving FPS (frames per second) by 95.2%. The improvement in mAP (mean average precision) can also lead to better accuracy in practical applications, such as marine species monitoring, conservation efforts, and biodiversity assessment. Furthermore, the model’s accuracy is enhanced, with the mAP increased by 1.6%, demonstrating the advanced nature of our approach. Compared with YOLO (You Only Look Once) series (YOLOv5-12), SSD (Single Shot MultiBox Detector), EfficientDet (Efficient Detection), RetinaNet, and RT-DETR (Real-Time Detection Transformer), our model achieves leading comprehensive performance in terms of both accuracy and lightweight design. The results indicate that our research provides technological support for precise and rapid aquatic organism recognition. Full article

The Italian market for non-alcoholic beer is very small, with a volume per capita of around 0.7 L. However, there are interesting prospects for future growth for reasons ranging from strict traffic code rules on the quantity of alcohol ingested to simple curiosity. This research aimed to investigate the willingness of Italian consumers/potential consumers to pay for non-alcoholic beer. To accomplish this, a questionnaire was administered using the Google Forms application. Three hundred and ninety-two people participated in this survey voluntarily and without monetary compensation. A probit regression model was used to estimate the impact of certain sociodemographic characteristics (number of inhabitants of the place of residence, region of residence, age group, gender, education level, employment situation, and annual net income), participants’ consumption habits with respect to alcoholic beer, and participants’ knowledge of and preference for non-alcoholic beers with respect to willingness to pay for non-alcoholic beers. The prices respondents were willing to pay ranged from EUR 1.51 to 2.00 for a 33 cL glass bottle. Only two factors significantly affected (p < 0.1) non-alcoholic beer WTP, namely, “Age” and “Non-alcoholic beer color”. WTP decreased as the age of the respondents increased and was higher for the darker beer. Full article

Enhancing low-light remote sensing images is crucial for preserving the accuracy and reliability of downstream analyses in a wide range of applications. Although numerous enhancement algorithms have been developed, many fail to effectively address the challenges posed by non-uniform illumination in low-light scenes. These images often exhibit significant brightness inconsistencies, leading to two primary problems: insufficient enhancement in darker regions and over-enhancement in brighter areas, frequently accompanied by color distortion and visual artifacts. These issues largely stem from the limitations of existing methods, which insufficiently account for non-uniform atmospheric attenuation and local brightness variations in reflectance estimation. To overcome these challenges, we propose a robust enhancement method based on non-uniform illumination compensation and the Atmospheric Scattering Model (ASM). Unlike conventional approaches, our method utilizes ASM to initialize reflectance estimation by adaptively adjusting atmospheric light and transmittance. A weighted graph is then employed to effectively handle local brightness variation. Additionally, a regularization term is introduced to suppress noise, refine reflectance estimation, and maintain balanced brightness enhancement. Extensive experiments on multiple benchmark remote sensing datasets demonstrate that our approach outperforms state-of-the-art methods, delivering superior enhancement performance and visual quality, even under complex non-uniform low-light conditions. Full article

Accurate estimation of hazelnut yield is crucial for optimizing resource management and harvest planning. Although the number of female flowers on a flowering plant is a reliable indicator of annual production, counting them remains difficult because of their extremely small size and inconspicuous shape and color. Currently, manual flower counting is the only available method, but it is time-consuming and prone to errors. In this study, a novel vision-based method for automatic flower counting specifically designed for hazelnut plants (Corylus avellana) exploiting a commercial high-resolution imaging system and an image-tiling strategy to enhance small-object detection is proposed. The method is designed to be fast and scalable, requiring less than 8 s per plant for processing, in contrast to 30–60 min typically required for manual counting by human operators. A dataset of 2000 labeled frames was used to train and evaluate multiple female hazelnut flower detection models. To improve the detection of small, low-contrast flowers, a modified YOLO11x architecture was introduced by adding a P2 layer, improving the preservation of fine-grained spatial information and resulting in a precision of 0.98 and a Mean Average Precision (mAP@50-95) of 0.89. The proposed method has been validated on images collected from hazelnut groves and compared with manual counting by four experienced operators in the field, demonstrating its ability to detect small, low-contrast flowers despite occlusions and varying lighting conditions. A regression-based bias correction was applied to compensate for systematic counting deviations, further improving accuracy and reducing the mean absolute percentage error to 27.44%, a value comparable to the variability observed in manual counting. The results indicate that the system can provide a scalable and efficient alternative to traditional female flower manual counting methods, offering an automated solution tailored to the unique challenges of hazelnut yield estimation. Full article

The properties of light propagation underwater typically cause color distortion and reduced contrast in underwater images. In addition, complex underwater lighting conditions can result in issues such as non-uniform illumination, spotting, and noise. To address these challenges, we propose an innovative underwater-image enhancement (UIE) approach based on maximum information-channel compensation and edge-preserving filtering techniques. Specifically, we first develop a channel information transmission strategy grounded in maximum information preservation principles, utilizing the maximum information channel to improve the color fidelity of the input image. Next, we locally enhance the color-corrected image using guided filtering and generate a series of globally contrast-enhanced images by applying gamma transformations with varying parameter values. In the final stage, the enhanced image sequence is decomposed into low-frequency (LF) and high-frequency (HF) components via side-window filtering. For the HF component, a weight map is constructed by calculating the difference between the current exposedness and the optimum exposure. For the LF component, we derive a comprehensive feature map by integrating the brightness map, saturation map, and saliency map, thereby accurately assessing the quality of degraded regions in a manner that aligns with the symmetry principle inherent in human vision. Ultimately, we combine the LF and HF components through a weighted summation process, resulting in a high-quality underwater image. Experimental results demonstrate that our method effectively achieves both color restoration and contrast enhancement, outperforming several State-of-the-Art UIE techniques across multiple datasets. Full article

Image dehazing is a critical task in image restoration, aiming to retrieve clear images from hazy scenes. This process is vital for various applications, including machine recognition, security monitoring, and aerial photography. Current dehazing algorithms often encounter challenges in multi-scale feature extraction, detail preservation, effective haze removal, and maintaining color fidelity. To address these limitations, this paper introduces a novel Parallel Image-Dehazing Network (PID-Net). PID-Net uniquely combines a Convolutional Neural Network (CNN) for precise local feature extraction and a Vision Transformer (ViT) to capture global contextual information, overcoming the shortcomings of methods relying solely on either local or global features. A multi-scale CNN branch effectively extracts diverse local details through varying receptive fields, thereby enhancing the restoration of fine textures and details. To optimize the ViT component, a lightweight attention mechanism with CNN compensation is integrated, maintaining performance while minimizing the parameter count. Furthermore, a Redundant Feature Filtering Module is incorporated to filter out noise and haze-related artifacts, promoting the learning of subtle details. Our extensive experiments on public datasets demonstrated PID-Net’s significant superiority over state-of-the-art dehazing algorithms in both quantitative metrics and visual quality. Full article

To rectify significant heading calculation errors in polarized light navigation for unmanned aerial vehicles (UAVs) under tilted states, this paper proposes a method for compensating horizontal attitude angles based on horizon detection. First, a defogging enhancement algorithm that integrates Retinex theory with dark channel prior is adopted to improve image quality in low-illumination and hazy environments. Second, a dynamic threshold segmentation method in the HSV color space (Hue, Saturation, and Value) is proposed for robust horizon region extraction, combined with an improved adaptive bilateral filtering Canny operator for edge detection, aimed at balancing detail preservation and noise suppression. Then, the progressive probabilistic Hough transform is used to efficiently extract parameters of the horizon line. The calculated horizontal attitude angles are utilized to convert the body frame to the navigation frame, achieving compensation for polarization orientation errors. Onboard experiments demonstrate that the horizontal attitude angle estimation error remains within 0.3°, and the heading accuracy after compensation is improved by approximately 77.4% relative to uncompensated heading accuracy, thereby validating the effectiveness of the proposed algorithm. Full article