Pixel Area Research Articles

Alterations in the uterine echotexture, hemodynamics, and histological findings in relation to metabolomic profiles in goats with different ovarian activities (active versus inactive ovaries).

This study investigated, for the first time, the alterations in the uterine echotexture and blood flow in cyclic and acyclic (inactive ovary) goats using ultrasonography. The study aimed also to evaluate the metabolomic changes in the plasma of cyclic and acyclic goats. Furthermore, the histopathological approach was applied to the specimens of the uterus to validate the findings of this study. Based on monitoring the estrous cyclicity, goats were assigned into either a cyclic group or an acyclic one (n = 7, each). Ovarian morphometry and hemodynamics were assessed to confirm group assignment. Full ultrasonographic examinations were performed to assess the uterine echotexture by B-mode ultrasonography and uterine hemodynamics by color Doppler ultrasonography in the cyclic group (at days 10-12) and acyclic group. Additionally, blood samples were withdrawn for measuring hormonal concentrations and for metabolomics analysis. Specimens of the uterus were executed for histopathological evaluation in both groups. Results revealed alterations in the uterine hemodynamics and endometrial echotexture. Goats in the cyclic group attained a significantly higher color pixel area of the endometrium compared to those in the acyclic one (P< 0.001). However, the pixel intensity of the endometrium echotexture was significantly (P< 0.05) lower in the cyclic group than in the smooth inactive ovary one. There were significant (P< 0.05) increases in the concentrations of FSH, LH, and inhibin in the cyclic group compared to their concentrations in the acyclic one. Goats in the acyclic group attained noticeable (P< 0.001) lower concentrations of E2 and P4 than in the cyclic goats. The metabolomic results revealed the existence of several up- and down-regulated metabolites among the studied groups. In this investigation, untargeted metabolomic analysis revealed the existence of 5 up-regulated metabolites (ketoleucine, L-fucose, D-glucurono-6,3-lactone, melatonin, and 5-methoxy tryptamine) and 5 down-regulated ones (p-octopamine, 3-hydroxyisovaleric acid, methylmalonic acid, 4-hydroxyphenylpyruvic acid, and cadaverine) in the cyclic group compared to the acyclic one. The enrichment analysis of the significant metabolites showed top pathways that may be involved in these changes, such as fructose and mannose metabolism, valine. Leucine, and isoleucine biosynthesis, linoleic acid metabolism, arginine biosynthesis, and vitamin B6 metabolism based on the KEGG enriched pathway. Altogether, the histopathological assessment showed noticeable changes in the columnar epithelial lining of the endometrial epithelium, endometrial vascularity, and endometrial glands among the studied groups. In conclusion, this study extrapolated the differences between cyclic goats (during the mid-luteal phase) and acyclic ones in terms of hormonal, hemodynamics, echotexture of the uterus, and circulating metabolomics. These findings are very crucial to fully assess the fertility potential in goats.

Impact of Vertical Scaling on Normal Probability Density Function Plots.

Probability density function (PDF) curves are among the few charts on a Cartesian coordinate system that are commonly presented without y-axes. This design decision may be due to the lack of relevance of vertical scaling in normal PDFs. In fact, as long as two normal PDFs have the same means and standard deviations (SDs), they can be scaled to occupy different amounts of vertical space while still remaining statistically identical. Because unfxed PDF height increases as SD decreases, visualization designers may fnd themselves tempted to vertically shrink low-SD PDFs to avoid occlusion or save white space in their fgures. Although irregular vertical scaling has been explored in bar and line charts, the visualization community has yet to investigate how this visual manipulation may affect reader comparisons of PDFs. In this paper, we present two preregistered experiments (n = 600, n = 401) that systematically demonstrate that vertical scaling can lead to misinterpretations of PDFs. We also test visual interventions to mitigate misinterpretation. In some contexts, we fnd including a y-axis can help reduce this effect. Overall, we fnd that keeping vertical scaling consistent, and therefore maintaining equal pixel areas under PDF curves, results in the highest likelihood of accurate comparisons. Our fndings provide insights into the impact of vertical scaling on PDFs, and reveal the complicated nature of proportional area comparisons.

Application of Improved YOLOv8n-seg in Crayfish Trunk Segmentation

The crayfish industry (Procambarus clarkii) is experiencing rapid growth. However, the processing sector continues to face challenges due to a lack of advanced automation, relying heavily on manual visual inspection to assess crayfish specifications and integrity, which limits efficiency and precision in decision-making. To address the issue of intelligent grading of P. clarkii, this work proposes the GHB-YOLOv8-seg algorithm for segmenting the main trunk of P. clarkii shrimp based on the YOLOv8n-seg model. The original main trunk network is replaced through the coupling of Ghost and HGNetV2, and depth-separable convolution is employed to perform the linear transformation of the features. This results in a reduction in the number of parameters and computational complexity while maintaining high accuracy. The computational complexity is reduced; concurrently, introducing the weighted bidirectional feature pyramid network (BiFPN) enables the model to perform multi-scale feature fusion with greater alacrity, thereby enhancing the model’s performance. Ultimately, the intelligent grading of crayfish specifications was achieved by calculating the pixel area after segmentation and converting it to the actual body weight. The results demonstrated that the number of parameters of the improved YOLOv8n-seg model was reduced by 60.5%, the model size was reduced by 55.4%, and the mAP value was increased from 98.9% to 99.2%. The study indicates that the YOLOv8n-seg algorithm model facilitates precise and lightweight segmentation of the crayfish trunk, which can be integrated into diverse mobile devices.

Digital subpixel algorithm for small pixel photon counting devices

Hybrid pixel detectors are segmented devices widely used for photon detection. They consist of a sensor and readout electronics bonded together. Due to their hybrid structure, sensors of different materials can be used to register a wide range of photon energies. Moreover, the devices working in a single photon counting (SPC) mode allow registering each incoming photon separately, providing noiseless imaging. The spatial resolution of the detectors and photon count rate registered per unit area can be improved by reducing pixel size. However, small-pixel devices suffer from charge sharing. The charge sharing between pixels can be observed if the charge cloud generated in the photon-sensor event spreads due to diffusion and repulsion. Several anti-charge-sharing algorithms exist and some have been successfully implemented inside the ASICs readout. Even though they allow the allocation of the event to the proper pixel and reconstruction of the total photon energy, the detector resolution is limited by the readout channel area which must be large enough to fit the complex mixed-mode functionality. The article presents the simulations of an alternative solution which can improve both spatial resolution and high-count-rate performance. In the authors’ approach, charge sharing is regarded as a positive effect which can be used to estimate the photon interaction position with subpixel resolution. The algorithm is evaluated to improve detection efficiency and required pixel area for implementation in deep submicron technologies.

Estimating canopy cover loss with Landsat dense time series: a Mortality Magnitude Index for the Ecosystem Disturbance and Recovery Tracker (eDaRT)

ABSTRACT Recent drought and insect-induced tree mortality across the western United States has increased the need among land managers and scientists for improved estimation of disturbance timing, type and magnitude, accelerating improvements in high density time series algorithms to map disturbance. We present a novel severity index for tree mortality, called Mortality Magnitude Index (MMI), which expands functionality of a Landsat-processing system, the Ecosystem Disturbance and Recovery Tracker (eDaRT) to estimate tree canopy cover loss as a pixel area proportion. The MMI model utilizes time series of anomaly metrics generated by the eDaRT disturbance detection process, which represent robust normalized statistics of spectral change related to vegetation cover and health. We describe the MMI methodology that integrates the eDaRT metrics over pre- and post-disturbance windows and fits a generalized linear model by beta-regression using training data from the Sierra Nevada, California, including tree mortality that occurred during 2010–2017. The selected model included seven predictor variables (RMSE = 13.0%) and was further validated in southern California for tree mortality events spanning the last two decades. The MMI model exhibited robust temporal and spatial transferability, which have led to subsequent applications of MMI datasets in forest health assessment, wildlife habitat analyses and wildland fire incident support.

Accounting for intensity variation within pixels of Shack-Hartmann wavefront sensors

The accuracy of centroiding algorithms in Shack-Hartmann wavefront sensing is limited by the implicit approximation of uniform pixel illumination. Iterative centroiding algorithms are further limited by the consideration of full pixels to define the image domain for centroiding. Here, we demonstrate two practical and complementary approaches to mitigate both these sources of error. First, we consider partial or ‘fractional’ pixels to maintain centroiding area symmetry around the center of mass. Secondly, we propose methods to perform piece-wise polynomial interpolation to calculate intensity distribution within pixels, which is then used to estimate the centroid within each pixel area. This approach that accounts for intensity non-uniformity across pixels notably reduces centroid errors up to a factor of 5 across lenslet image widths ranging from 1.33 to 3.10 pixels full-width-half-maximum (FWHM). Consequently, wavefront sensing errors decrease from 14 % to 4 %, on average, for FWHM = 1.35 pixels, demonstrating a substantial benefit when the number of pixels per lenslet is minimized to enhance the signal-to-noise ratio or increase frame rate.

Scaling Terrain-Aware Spatial Machine Learning for Flood Mapping on Large Scale Earth Imagery Data

The accurate and prompt mapping of flood-affected regions is important for effective disaster management, including damage assessment and relief efforts. While high-resolution optical imagery from satellites during disasters presents an opportunity for automated flood inundation mapping, existing segmentation models face challenges due to noises like cloud cover and tree canopies. Thanks to the digital elevation model (DEM) data readily available from sources such as United States Geological Survey (USGS), terrain guidance was utilized by recent graphical models such as hidden Markov trees (HMTs) to improve segmentation quality. Unfortunately, these methods either can only handle a small area where water levels at different locations are assumed to be consistent, or require restricted assumptions such as there is only one river channel. This paper presents an algorithm for flood extent mapping on large-scale Earth imagery, applicable to a large geographic area with multiple river channels. Since water level can vary a lot from upstream to downstream, we propose to detect river pixels in order to partition the remaining pixels into localized zones, each with a unique water level. In each zone, water at all locations flow to the same river entry point. Pixels in each zone are organized by an HMT to capture water flow directions guided by elevations. Moreover, a novel regularization scheme is designed to enforce inter-zone consistency by penalizing pixel-pairs of adjacent zones that violate terrain guidance. Efficient parallelization is made possible by coloring the zone adjacency graph to identify zones and zone-pairs that have no dependency and hence can be processed in parallel, and incremental one-pass terrain-guided scanning is conducted wherever applicable to reuse computations. Experiments demonstrate that our solution is more accurate than existing solutions, and can efficiently and accurately map out flooding pixels in a giant area of size 24,805 × 40,129. Despite the imbalanced workloads caused by a few large zonal HMTs dominating the serial computing time, our parallelization approach is effective and manages to achieve up to 14.3 × speedup on a machine with Intel Xeon Gold 6126 CPU @ 2.60GHz (24 cores, 48 threads) using 32 threads.

A Case Study on the Integration of Remote Sensing for Predicting Complicated Forest Fire Spread

Forest fires can occur suddenly and have significant environmental, economic, and social consequences. The timely and accurate evaluation and prediction of their progression, particularly the spread speed in difficult-to-access areas, are essential for emergency management departments to proactively implement prevention strategies and extinguish fires using scientific methods. This paper provides an analysis of models for predicting forest fire spread in China and globally. Incorporating remote sensing (RS) technology and forest fire science as the theoretical foundation, and utilizing the Wang Zhengfei forest fire spread model (1983), which is noted for its broad adaptability in China as the technical framework, this study constructs a forest fire spread model based on remote sensing interpretation. The model improves the existing model by adding elevation an factor and optimizes the method for acquiring certain parameters. By considering regional landforms (ridge lines, valley lines, and slopes) and vegetation coverage, this paper establishes three-dimensional visual interpretation markers for identifying hotspots; the orientation of the hotspots can be identified to simulate the spread of the fire uphill, downhill, in the direction of the wind, left-level slope, and right-level slope. Then, the data of Sentinel-2 and DEM were used to invert the fuel humidity and slope of pixels in the fire line areas. The statistical inversion data from pixels, which replaced fixed-point values in traditional models, were utilized for predicting forest fire spread speed. In this paper, the model was applied to the case of a forest fire in Mianning County, Sichuan Province, China, and verified using high-time-resolution Himawari-8 data, Gaofen-4 data, and historical data. The results demonstrate that the direction and maximum speed of fire spread for the fire lines in Baifen Mountai, Jiaguer Villageand, Muchanggou, Xujiabaozi, and Zhaizigou are uphill, 16.5 m/min; wind direction, 17.32 m/min; wind direction, 1.59 m/min; and wind direction, 5.67 m/min. The differences are mainly due to the locations of the fire lines, moisture content of combustibles, and maximum slopes being different. Across the entire fire line area, the average rate of increase in the area of open flames within one hour was 3.257 hm2/10 min (square hectares per 10 min), closely matching the average increase rate (3.297 hm2/10 min) monitored by the Himawari-8 satellite in 10 min intervals. In contrast, conventional fixed-point fire spread models predicted an average rate of increase of 3.5637 hm2/10 min, which shows a larger discrepancy compared to the Himawari-8 satellite monitoring results. Moreover, when compared to the fire spot monitoring results from the Gaofen-4 satellite taken 54 min after the initial location of the fire line, the predictions from the RS-enabled fire spread model, which integrates remote sensing interpretations, closely matched the actual observed fire boundaries. Although the predictions from the RS-enabled fire spread model and the traditional model both align with historical data in terms of the overall fire development trends, the RS-enabled model exhibits higher reliability and can provide more accurate information for forest fire emergency departments, enabling effective pre-emptive measures and scientific firefighting strategies.

Uncovering true significant trends in global greening

The global greening trend, marked by significant increases in vegetation cover across ecoregions, has attracted widespread attention. However, even robust traditional methods, like the non-parametric Mann-Kendall test, often overlook crucial factors such as serial correlation, spatial autocorrelation, and multiple testing, particularly in spatially gridded data. This oversight can lead to inflated significance of detected spatiotemporal trends. To address these limitations, this research introduces the True Significant Trends (TST) workflow, which enhances the conventional approach by incorporating pre-whitening to control for serial correlation, Theil-Sen (TS) slope for robust trend estimation, the Contextual Mann-Kendall (CMK) test to account for spatial and cross-correlation, and the adaptive False Discovery Rate (FDR) correction. Using AVHRR NDVI data over 42 years (1982–2023), we found that conventional workflow identified up to 50.96% of the Earth's terrestrial land surface as experiencing statistically significant vegetation trends. In contrast, the TST workflow reduced this to 38.16%, effectively filtering out spurious trends and providing a more accurate assessment. Among these significant trends identified using the TST workflow, 76.07% indicated greening, while 23.93% indicated browning. Notably, considering areas (pixels) with NDVI values above 0.15, greening accounted for 85.43% of the significant trends, with browning making up the remaining 14.57%. These findings strongly validate the ongoing global greening of vegetation. They also suggest that incorporating more robust analytical methods, such as the True Significant Trends (TST) approach, could significantly improve the accuracy and reliability of spatiotemporal trend analyses.

Machine vision-based detection of surface defects in cylindrical battery cases

Automotive 21700 series lithium batteries are prone to surface defects during production and transportation, thus affecting their performance, so we propose a full-surface defect detection method for battery cases based on the synthesis of traditional image processing and deep learning to address this problem. First, the mechanism of surface defects on a battery case is analysed, and the types of surface defects are summarized. A suitable platform for image acquisition is designed for the severely reflective surface. Since there is no publicly available defect dataset for cylindrical battery cases, a defect dataset is established, and the dataset is augmented and expanded via the traditional method and the ACGAN model. For the defects on the top of the battery case, a traditional image processing algorithm is used to combine the roundness and the area of the area pixels to make a comprehensive judgement. For the defects on the bottom and side of the battery case, after comparing and analysing a variety of deep learning networks, the YOLOv7 model is selected to address the data characteristics of the large experimental inputs and the small targeted defects. To improve the accuracy of the model to meet the needs of real-time detection in industry, the CA attention mechanism, the DYHEAD dynamic detector head, and the slicing-assisted super inference (SAHI) method are used, with a final map accuracy reaching 98.1 %, which is 2.7 % higher than that of the initial model. Compared with mainstream target detection algorithms, the algorithm in this paper has good detection performance for cylindrical battery case defect detection and can be better applied to real-time detection in industry.

Automatic visual recognition, detection and classification of weeds in cotton fields based on machine vision

Crops and weeds are involved in a continuous competition for equal resources, which may result in a potential decrease in crop yields by up to 31% and an increase in the costs of agricultural inputs by up to 22% of cultivation. Weeds further impact crop production, and their detection is crucial for effective crop management. In this research, we targeted common weeds of cotton field, specifically i) Digitaria sanguinalis (L.) Scop, ii) Amaranthus retroflexus L., iii) Acalypha australis, L., iv) Cephalanoplos segetum, and v) Chenopodium album L. Additionally, image processing techniques such as grayscale conversion, binarization, and Gaussian and morphological filters were also utilized. These methods are based on machine vision and facilitate rapid and straightforward weed detection by segmenting, scrutinizing, and comparing input images. The plant height and area were obtained during cotton planting within 32 days and fitted to develop the growth law concerning planting days for achieving the function of distinguishing cotton from weeds. We conducted recognition experiments by dividing images into four quadrants and categorizing weeds as either inter-row or intra-row. Meanwhile, the inter-row planting information was used to identify weeds, and the leaf pixel area and circularity were used as the identification methods for intra-row weeds, which reduced the algorithm's running time and improved real-time performance. The experimental results indicated that the inter-row weed recognition rate was 89.4%, with an average processing time of 102ms. Whereas in the case of intra-row weeds, the recognition rate was measured at 84.6%, and the overall recognition rate for cotton was 85.0%, with a mean time consumption of 437ms. Furthermore, the present research underscores recent advancements such as machine vision and high-resolution imaging, which have significantly improved the accuracy of automated weed identification in cotton fields while acknowledging ongoing challenges and outlining future opportunities. By Integrating state-of-the-art technology with sustainable agricultural practices, implementing an intelligent system offers a viable approach toward efficient and environmentally friendly weed management in modern agriculture.

U-TPE: A universal approximate thumbnail-preserving encryption method for lossless recovery

Due to the limited local storage space, more and more people are accustomed to uploading images to the cloud, which has aroused concerns about privacy leaks. The traditional solution is to encrypt the images directly. However, in this way, users cannot easily browse the images stored in the cloud. Obviously, the traditional method has lost the visual usability of cloud images. To solve this problem, the Thumbnail-Preserving Encryption (TPE) method is proposed. Although approximate-TPE is more efficient than ideal TPE, it cannot restore the original image without damage and cannot encrypt some images with texture features. Inspired by the above, we propose a universal approximate thumbnail-preserving encryption method with lossless recovery. This method divides the image into equal-sized chunks, each of which is further divided into an embedding area and an adjustment area. The pixels of the embedding area are recorded by prediction. Then, the auxiliary information necessary to restore the image is encrypted and hidden in the embedding area of the encrypted image. Finally, the pixel values of the adjustment area in each block are adjusted so that the average value is close to the original block. Experimental results show that the proposed method can not only restore images losslessly but also process images with different texture features, achieving good generality. On the BOWS2 dataset, all images can be encrypted by adjusting the block size. In addition, it can resist third-party face recognition and comparison, achieving satisfactory results in balancing privacy and visual usability.

Development of a 3D-printed phantom for total skin electron therapy dose assessment.

Total skin electron therapy (TSET) is a complex radiotherapy technique, posing challenges in commissioning and quality assurance (QA), especially due to significant variability in patient body shapes. Previous studies have correlated dose with factors such as obesity index, height, and gender. However, current treatment planning systems cannot simulate TSET plans, necessitating heavy reliance on QA methods using standardized anthropomorphic phantoms and in-vivo dosimetry. Given the relatively few studies on rotational techniques, comprehensive data in commissioning could streamline the process. Developing a full-body phantom would enable a more thorough TSET commissioning process, including testing for position-specific dose distributions and comprehensive measurements across all body surfaces, unlike the typical torso-only phantoms. This was created using digital modeling software, fabricated using 3D-printing FDM technology, and filled with tissue-equivalent gelatine. The phantom was positioned at an SSD of 340cm and irradiated with a standard rotational TSET plan using the 6E HDTSE mode on a Varian TrueBeam linac at gantry angles of±18° from the horizontal. The dose was measured at over 50 points across the surface using Gafchromic EBT3 film. Dose distributions were generally consistent with existing literature values from in-vivo dosimetry, with several position-specific differences identified, including the hands and scalp compared to conventional positions. Hotspots were observed for the mid-dorsum of the foot and nose, with areas under 80% of the dose identified as the soles of the feet, perineum, vertex of the scalp, top of the shoulder, and palm of the hand. Additionally, analysis using an interpolated dose heatmap found that 90% of the pixel area received a dose within 10% of the prescribed dose, indicating good uniformity with the commissioned technique. With high agreement with the current literature, a 3D-printed phantom proves effective for measuring doses in areas typically unmeasurable in TSET commissioning.

A Stepwise Multifactor Regression Analysis of the Interactive Effects of Multiple Climate Factors on the Response of Vegetation Recovery to Drought

In this study, a stepwise multifactor vegetation regression analysis (SMVRA) approach was proposed to investigate the interaction of multiple climate factors on vegetative growth in the study area from 2000 to 2020. It was developed by integrating the stepwise linear regression method, Standardized Precipitation Evapotranspiration Index (SPEI), Normalized Difference Vegetation Index (NDVI), and Pearson correlation coefficient. SMVRA can be used to intuitively understand the interactive effects of multiple correlated factors (e.g., temperature, precipitation, potential evapotranspiration, and the drought index) upon vegetation. The results show that the resilience of vegetation in the BLR basin is influenced by the severity of drought. Annual changes in SPEI over the BLR basin show an increasing trend, with rates of 3.12 × 10−2. Precipitation and NDVI had a strong positive correlation (p &lt; 0.05), found for 34.93% of the total pixels in the study area. In the BLR basin, vegetation growth is inhibited in the 4 years following a drought event. The area near 800 m is most sensitive to drought events. It provides a theoretical basis for future drought response and effective vegetation restoration in the region.

Validation of In-House Imaging System via Code Verification on Petunia Images Collected at Increasing Fertilizer Rates and pHs.

In a production environment, delayed stress recognition can impact yield. Imaging can rapidly and effectively quantify stress symptoms using indexes such as normalized difference vegetation index (NDVI). Commercial systems are effective but cannot be easily customized for specific applications, particularly post-processing. We developed a low-cost customizable imaging system and validated the code to analyze images. Our objective was to verify the image analysis code and custom system could successfully quantify the changes in plant canopy reflectance. 'Supercascade Red', 'Wave© Purple', and 'Carpet Blue' Petunias (Petunia × hybridia) were transplanted individually and subjected to increasing fertilizer treatments and increasing substrate pH in a greenhouse. Treatments for the first trial were the addition of a controlled release fertilizer at six different rates (0, 0.5, 1, 2, 4, and 8 g/pot), and for the second trial, fertilizer solution with four pHs (4, 5.5, 7, and 8.5), with eight replications with one plant each. Plants were imaged twice a week using a commercial imaging system for fertilizer and thrice a week with the custom system for pH. The collected images were analyzed using an in-house program that calculated the indices for each pixel of the plant area. All cultivars showed a significant effect of fertilizer on the projected canopy size and dry weight of the above-substrate biomass and the fertilizer rate treatments (p < 0.01). Plant tissue nitrogen concentration as a function of the applied fertilizer rate showed a significant positive response for all three cultivars (p < 0.001). We verified that the image analysis code successfully quantified the changes in plant canopy reflectance as induced by increasing fertilizer application rate. There was no relationship between the pH and NDVI values for the cultivars tested (p > 0.05). Manganese and phosphorus had no significance with chlorophyll fluorescence for 'Carpet Blue' and 'Wave© Purple' (p > 0.05), though 'Supercascade Red' was found to have significance (p < 0.01). pH did not affect plant canopy size. Chlorophyll fluorescence pixel intensity against the projected canopy size had no significance except in 'Wave© Purple' (p = 0.005). NDVI as a function of the projected canopy size had no statistical significance. We verified the ability of the imaging system with integrated analysis to quantify nutrient deficiency-induced variability in plant canopies by increasing pH levels.

Fresnel Diffraction Model for Laser Dazzling Spots of Complementary Metal Oxide Semiconductor Cameras.

Laser dazzling on complementary metal oxide semiconductor (CMOS) image sensors is an effective method in optoelectronic countermeasures. However, previous research mainly focused on the laser dazzling under far fields, with limited studies on situations that the far-field conditions were not satisfied. In this paper, we established a Fresnel diffraction model of laser dazzling on a CMOS by combining experiments and simulations. We calculated that the laser power density and the area of saturated pixels on the detector exhibit a linear relationship with a slope of 0.64 in a log-log plot. In the experiment, we found that the back side illumination (BSI-CMOS) matched the simulations, with an error margin of 3%, while the front side illumination (FSI-CMOS) slightly mismatched the simulations, with an error margin of 14%. We also found that the full-screen saturation threshold for the BSI-CMOS was 25% higher than the FSI-CMOS. Our work demonstrates the applicability of the Fresnel diffraction model for BSI-CMOS, which provides a valuable reference for studying laser dazzling.

Health Warnings on Instagram Advertisements for Synthetic Nicotine E-Cigarettes and Engagement

Synthetic nicotine is increasingly used in e-cigarette liquids along with flavors to appeal to youths. Regulatory loopholes have allowed tobacco manufacturers to use social media to target youths. To analyze the extent to which synthetic nicotine e-cigarette brands have implemented US Food and Drug Administration (FDA) health warning requirements and to evaluate the association between health warnings and user engagement on Instagram. In this cross-sectional study, posts from 25 brands were analyzed across a 14-month period (August 2021 to October 2022). A content analysis was paired with Warning Label Multi-Layer Image Identification, a computer vision algorithm designed to detect the presence of health warnings and whether the detected health warning complied with FDA guidelines by (1) appearing on the upper portion of the advertisement and (2) occupying at least 20% of the advertisement's area. Data analysis was performed from March to June 2024. Synthetic nicotine e-cigarette advertisement on Instagram. The outcome variables were user engagement (number of likes and comments). Negative binomial regression analyses were used to evaluate the association between the presence and characteristics of health warnings and user engagement. Of a total of 2071 posts, only 263 (13%) complied with both FDA health warning requirements. Among 924 posts with health warnings, 732 (79%) displayed warnings in the upper image portion, and 270 (29%) had a warning covering at least 20% of the pixel area. Posts with warnings received fewer comments than posts without warnings (mean [SD], 1.8 [2.5] vs 5.4 [11.7] comments; adjusted incident rate ratio [aIRR], 0.70; 95% CI, 0.57-0.86; P < .001). For posts containing warnings, a larger percentage of the warning label's pixel area was associated with fewer comments (aIRR, 0.96; 95% CI, 0.93-0.99; P = .003). Flavored posts with health warnings placed in the upper image portion received more likes than posts with warnings in the lower portion (mean [SD], 34.6 [35.2] vs 19.9 [19.2] likes; aIRR, 1.48; 95% CI, 1.07-2.06; P = .02). In this cross-sectional study of synthetic nicotine brand Instagram accounts, 87% of sampled posts did not adhere to FDA health warning requirements in tobacco promotions. Enforcement of FDA compliant health warnings on social media may reduce youth engagement with tobacco marketing.

Зависимость величины сигнала от смещения функции рассеяния точки, вписанной в четыре пиксела матрицы

An algorithm has been developed for calculating changes in the signal level of a matrix pixel when the point scattering function is shifted at an angle of 45º relative to the joining point of 4 matrix pixels. In the initial state of the point spread function, the point is inscribed in four pixels. The spectral sensitivity of a pixel is constant within the pixel area. The distribution of pixel irradiance in the point spread function in the form of uniform irradiance is considered. Method. The calculation is based on the method of dividing the point spread function on each pixel of the matrix into separate areas for which the signal is calculated. The displacement of the point scattering function by Δx along the X axis and by Δy along the Y axis is taken in a form normalized to the radius of the spot. To create a two-dimensional graph of the dependence of the pixel signal on the displacement of the point scattering function along the X, Y axes, we introduce the relative displacement of the point scattering function η. Main results. An algorithm has been developed for calculating changes in the signal level of a matrix pixel when the point scattering function is shifted relative to the matrix pixels for the case of pixel irradiation in the lens scattering circle in the form of uniform irradiance. The dependence of the normalized matrix pixel signal on the relative displacement of the point scattering function η at an angle of 45º has been plotted for the above case of irradiation. Practical significance. Matrix photodetectors are widely used in guidance and tracking systems for space objects and astronomical sensors. For a defocused optical system, uniform distribution is used. When the point scattering function shifts relative to the joining point of the four pixels of the matrix, the matrix signal drops, which, with a low signal-to-noise ratio, can lead to a breakdown in tracking or an increase in the error in measuring the angular coordinates of a space object. The results of these studies can be used to model a target guidance system and determine target coordinates.

Automated analysis of scattering-based light sheet microscopy images of anal squamous intraepithelial lesions.

We developed an algorithm for automatically analyzing scattering-based light sheet microscopy (sLSM) images of anal squamous intraepithelial lesions. We developed a method for automatically segmenting sLSM images for nuclei and calculating seven features: nuclear intensity, intensity slope as a function of depth, nuclear-to-nuclear distance, nuclear-to-cytoplasm ratio, cell density, nuclear area, and proportion of pixels corresponding to nuclei. 187 images from 80 anal biopsies were used for feature analysis and classifier development. The automated nuclear segmentation method provided reliable performance with the precision of 0.97 and recall of 0.91 when compared with the manual segmentation. Among the seven features, six showed statistically significant differences between high-grade squamous intraepithelial lesion (HSIL) and non-HSIL (non-dysplastic or low-grade squamous intraepithelial lesion, LSIL). A classifier using linear support vector machine (SVM) achieved promising performance in diagnosing HSIL versus non-HSIL: sensitivity of 90%, specificity of 70%, and area under the curve (AUC) of 0.89 for per-image diagnosis, and sensitivity of 90%, specificity of 80%, and AUC of 0.92 for per-biopsy diagnosis.

ASSESSING SATELLITE RAINFALL ACCURACY IN DENSE TROPICAL SABAH EAST COAST FOREST, MALAYSIA: A CROSS-VALIDATION OF DOWNSCALING TECHNIQUE

Rainfall is one of pivotal elements in the hydrological cycle that essential for ensuring the water balance in present and in the future. Traditional in-situ observations have been the conventional method for obtaining rainfall data, but their limitations arise from discrete point measurements that fail to represent the entire area. To overcome these limitations, this study utilizes satellite based TRMM data products for rainfall estimation. The research aims to cross-validate the TRMM 3B43-v7 product against corresponding in-situ measured rainfall, focusing on error localization in Sabah's east coast. The derivation of the rainfall rate from TRMM data is adequate with additional data from three manual gauges within the plots. The correlation between TRMM and ground was good (R2 = 0.78, RMSE = 65.65 mm). The Nash and Sutcliffe Error results closed to value 1 indicate that the accuracy of the TRMM data compared to the rain-gauge data in Danum has a good agreement. This is due to the IDW downscaling method for satellite rainfall data using additional data from three manual-gauges within the plots to increase the accuracy of the TRMM data. In summary, the downscaling method proves capable of providing fine spatial resolution and increasing the number of pixels in the study area. Future research endeavours may benefit from incorporating more station data to further improve the interpolation of satellite data and derive more precise results.