Infrared and visible light image fusion technology integrates image feature information from two different modalities to generate an image that integrates complementary information from the source images. However, in low-light scenarios, the degradation of lighting in visible light images leads to existing fusion methods being unable to effectively extract texture detail information from the scene. At this time, the target prominence information provided by infrared images alone is far from sufficient. To address this challenge, this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement, named LLE-Fuse. Firstly, the method improves the MobileOne Block by embedding the Sobel operator in the Edge-MobileOne Block to perform feature extraction and downsampling on the source images. Then, intermediate features of different scales are obtained and fused through a cross-modal attention fusion module, followed by image reconstruction using a decoder with upsampling. Next, the CLAHE algorithm is used for image enhancement of infrared and visible light images, and the enhanced loss guides the network model to learn the capability of low-light enhancement. Finally, upon completion of the network training, the method optimizes the Edge-MobileOne Block into the direct connection structure of MobileNetV1 through structural re-parameterization, effectively reducing the computational resource consumption of the model network. Through extensive experimental comparisons, this paper's method demonstrates excellent performance in both visual appeal and evaluation metrics, providing satisfactory fusion results even in low-light scenarios.

With the advent of deep learning, significant progress has been made in low-light image enhancement methods. However, deep learning requires enormous paired training data, which is challenging to capture in real-world scenarios.To address this limitation, this paper presents a novel unsupervised low-light image enhancement method, which first introduces the frequency domain features of images in low-light image enhancement tasks. Our work is inspired by imagining a digital image as a spatially varying metaphoric “field of light”, then subjecting the influence of physical processes such as diffraction and coherent detection back onto the original image space via a frequency-domain to spatial-domain transformation (inverse Fourier transform). However, the mathematical model created by this physical process still requires complex manual tuning of the parameters for different scene conditions to achieve the best adjustment. Therefore, we proposed a dual-branch convolution network to estimate pixel-wise and high-order spatial interactions for dynamic range adjustment of the frequency feature of the given low-light image. Guided by the frequency feature from the “field of light” and parameter estimation networks, our method enables dynamic enhancement of low-light images. Extensive experiments have shown that our method performs well compared to state-of-the-art unsupervised, and its performance approximates the level of the state-of-the-art supervised methods qualitatively and quantitatively. At the same time, the light network structure design allows the proposed method to have an extremely fast inference speed(nearly 150 FPS on an NVIDIA 3090 Ti GPU for an image of size 600×400×3 ). Furthermore, the potential benefits of our method to object detection in the dark are discussed.

Endoscopic medical images can suffer from uneven illumination, low contrast, and lack of texture information due to the use of point directional light sources and the presence of narrow tissue structures, posing diagnostic difficulties for physicians. In this paper, a deep learning-based su-pervised illumination enhancement network is designed for low-light endoscopic images, aiming to improve both global illumination and local details. Initially, a global illumination enhancement module is formulated utilizing a higher-order curve function to improve global illumination. Sec-ondly, a local feature extraction module incorporating dual attention is designed to capture local detailed features. Considering the significance of color fidelity in biomedical scenarios, the designed loss function prioritizes introducing color loss to alleviate image color distortion. Compared with seven state-of-the-art enhancement algorithms on Endo4IE endoscopic datasets, experimental re-sults show that the proposed method can better enhance low-light endoscopic images and avoid image color distortion. It provides an efficient method to enhance images captured by endoscopes which can effectively assist clinical diagnosis and treatment.

Abstract: Kiwifruit, a perennial horticultural crop known for its nutritional value, faces significant challenges due to biotic and abiotic stresses, affecting its yield and quality. Leucine-rich repeat receptor-like proteins (LRR-RLPs), a major group of receptor-like proteins in plants, have diverse functions in plant physiology, including growth, development, signal transduction, and stress responses. Despite their importance, the specific roles of kiwifruit LRR-RLPs in response to biotic and abiotic stresses remain poorly understood. In this study, we investigated the expression patterns of kiwifruit LRR-RLPs under various stress conditions, including pathogen stress, heat stress, waterlogging stress, and copper stress. Through genome and transcriptome analyses, we identified a total of 101, 164, and 105 LRR-RLPs in Actinidia Chinensis 'Hongyang', Actinidia eriantha 'Huate', and Actinidia chinensis 'Red5' kiwifruit, respectively, classified into 9 subgroups based on phylogenetic relationships. Synteny analysis revealed that the expansion of kiwifruit LRR-RLPs was primarily attributed to segmental duplication events. Using RNA-seq data from pathogen-infected kiwifruits, we identified specific LRR-RLP genes potentially involved in different stages of pathogen infection. Additionally, we observed the potential involvement of kiwifruit LRR-RLPs in abiotic stress responses, with upstream transcription factors possibly regulating their expression. Furthermore, protein interaction network analysis unveiled the participation of kiwifruit LRR-RLP genes in the regulatory network of abiotic stress responses. These findings highlight the crucial roles of LRR-RLPs in mediating both biotic and abiotic stress responses in kiwifruit, offering valuable insights for the breeding of stress-resistant kiwifruit varieties.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

In table tennis competitions the rule violation judgment with the greatest controversy is the height of the ball serve. This is because inaccuracy in the ball height judgment, which results in erroneous judgment, is unavoidable. Thus, we designed an automatic image judgment auxiliary system for table tennis ball height during service in this study. We used a high-speed camera to record the ball toss in the table tennis service. The designed algorithm architecture can automatically search for the ball and the position of the hand action under low light source conditions. The algorithm is mainly divided into hue-saturation-value color space processing and morphology processing using Hough transform to search for the circular ball. Experiment result shows that color segmentation can successfully and accurately determine the ball position under low light conditions. The morphology method can find the position of the hand and help determine the moment when the ball leaves the hand during the service ball toss. Finally, the actual size of the target is used to estimate the actual distance unit represented by the image pixel.

A multiscale model that enables quantitative understanding and prediction of the size effect on scattering properties of micro- and nanostructures is crucial for the design of LED surface textures optimized for high light extraction efficiency (LEE). In this paper, a hybrid process for combining full-wave finite-difference time-domain simulation and a ray-tracing technique based on a bidirectional scattering distribution function model is proposed. We apply this method to study the influence of different pattern sizes of a patterned sapphire substrate on GaN-based LED light extraction from the microscale to the nanoscale. The results show that near-wavelength–scaled patterns with strong diffraction are not expected to enhance LEE. By contrast, microscaled patterns with optical diffusion behavior have the highest LEE at a specific aspect ratio, and subwavelength-scaled patterns that have antireflection properties show marked enhancement of LEE for a wide range of aspect ratios.

In recent years, researches on combining wavelet decomposition and convolutional neural network (CNN) together to classify hyperspectral images (HSI) have emerged, and some effective classification models have been proposed and achieved good classification results. However, there are two problems for most of the proposed models. One is the heavy training parameters and the other is that there is no distinction between the classification effectiveness of low frequency and high frequency features after wavelet decomposition. In this paper, a new light-weighted HSI classification model (LLFWCNN) is proposed, which performs multi-layer wavelet decomposition for HSI after dimensionality reduction, and only arranges the low frequency features in a specific stack mode, then classifies them through a well-designed convolutional neural network. Compared with other classification models, the number of parameters is only 2.5% of that of other models. And only the low frequency features after wavelet decomposition are used, while the high frequency features are abandoned. The results showed that compared with the state-of-the-art classification models, LLFWCNN could obtain the same or even better classification results with fewer network parameters and proved that the low frequency features after wavelet decomposition provide LLFWCNN with more favorable information for HSI classification as well.

In this study, we explore the potential of using a straightforward neural network inspired by the retina model to efficiently restore low-light images. The retina model imitates the neurophysiological principles and dynamics of various optical neurons. Our proposed neural network model reduces the computational overhead compared to traditional signal-processing models while achieving results similar to complex deep learning models from a subjective perceptual perspective. By directly simulating retinal neuron functionalities with neural networks, we not only avoid manual parameter optimization but also lay the groundwork for constructing artificial versions of specific neurobiological organizations.

Capsule endoscopy, which uses a wireless camera to take images of the digestive tract, is emerging as an alternative to traditional colonoscopy. The diagnostic values of these images depend on the quality of revealed underlying tissue surfaces. In this paper, we consider the problem of enhancing the visibility of detail and shadowed tissue surfaces for capsule endoscopy images. Using concentric circles at each pixel for random walks combined with stochastic sampling, the proposed method enhances the details of vessel and tissue surfaces. The framework decomposes the image into two detail layers that contain shadowed tissue surfaces and detail features. The target pixel value is recalculated for the smooth layer using similarity of the target pixel to neighboring pixels by weighting against the total gradient variation and intensity differences. In order to evaluate the diagnostic image quality of the proposed method, we used clinical subjective evaluation with a rank order on selected KID image database and compared to state of the art enhancement methods. The result showed that the proposed method provides a better result in terms of diagnostic image quality and objective quality contrast metrics and structural similarity index.

Livestock farming plays a crucial role in agriculture and is important in meeting people's needs for a better life in the new era. However, due to China's "double carbon" target, the development of animal husbandry has been affected by the level of carbon emissions. To study the spatial dis-tribution and dynamic evolution of livestock carbon emissions in Shandong Province, we used panel data from 16 prefecture-level cities in Shandong Province from 2001 to 2022 to determine the spatial differentiation of livestock carbon emissions in Shandong Province and their dynamic evolution by combining spatial and non-parametric estimation methods based on livestock carbon emissions and livestock emission intensity measured by city. The results of the study show that the distribution of carbon emissions from animal husbandry in Shandong Province is in a "dis-persed-aggregated" pattern, and the intensity of carbon emissions shows a gradual downward trend, with a ladder-like distribution characterized by high in the west and low in the east. Through the analysis of the dynamic evolution of nuclear density, we observe that the regional differences of animal husbandry carbon emissions in Shandong Province are expanding, showing the characteristics of polarization. The difference of carbon emissions in the peninsula is gradually narrowing, and the phenomenon of polarization is weakening. The carbon emissions of animal husbandry in the central and western regions of Shandong are polarized.

Rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can obtain images with centimeter or even subcentimeter ground resolution, which provides a new efficient and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we propose a real-time edge reconstruction crack detection network (ERNet) which adopts multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and background. To capture global dependencies across spatial and channel levels, we propose an efficient bilateral decomposed convolutional attention module (BDAM) that combines depth separable convolution and dilated convolution to capture global dependencies across spatial and channel levels. To enhance the accuracy of crack detection, we use a coordinate-based fusion module that integrates spatial, semantic, and edge reconstruction information. In addition, we propose an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. Experimental results demonstrate that our network achieves the best balance between accuracy and inference speed.

Scintillators play a key role in the detection chain of several applications which rely on the use of ionizing radiation, and it is often mandatory to extract and detect the generated scintillation light as efficiently as possible. In positron emission tomography (PET), for example, both energy resolution and coincidence resolving time, two of the key detection parameters, depend strongly on the total amount of light which reaches the photodetector surface as well as its spatial and temporal distribution. Typical inorganic scintillators do however feature a high index of refraction, which impacts light extraction efficiency in a negative way. Furthermore, several applications such as preclinical PET rely on pixelated scintillators with small pitch. In this case, applying reflectors on the crystal pixel surface, as done conventionally, can have a dramatic impact of the packing fraction and thus the overall system sensitivity. This paper presents a study on light extraction techniques, as well as combinations thereof, for two of the most used inorganic scintillators (LYSO and BGO). Novel approaches, employing distributed Bragg reflectors (DBRs), metal coatings, and a modified Photonic Crystal (PhC) structure, are described in detail and compared with commonly used techniques. The nanostructure of the PhC is surrounded by a hybrid organic/inorganic silica sol-gel buffer layer which ensures robustness while maintaining its performance unchanged. We observed in particular a maximum light gain of about 41% on light extraction and 21% on energy resolution for BGO, a scintillator which has gained interest in the recent past due to its prompt Cherenkov component and lower cost.

With the progression of AI, embedding advanced AI technologies into small robotics and mobile devices has become essential, driving research towards lightweighting AI models. Our study enhances the EPINET depth estimation model for light field images, aiming for compactness and faster inference while preserving accuracy. We conducted two-step experiments aimed at enhancing inference efficiency: Initially, by adjusting input streams and convolution layers, we simplified the CNN model, achieving faster inference times at the cost of reduced accuracy. To address this reduction in accuracy, we then applied knowledge distillation, allowing the simplified model to learn from the original model’s more complex patterns. In our quantitative experiments using two error metrics, MSE (Mean Squared Error) and BadPix, we identified optimal knowledge positions and evaluated the required complexity for the student model. As a result, our method improved MSE by 21% and BadPix by 14% compared to training without it. Furthermore, the student model achieved an inference speed 13% faster than the teacher model and surpassed its accuracy by 10% in MSE. Additionally, we demonstrated that repeatedly applying our approach could further enhance both model compactness and accuracy.

The notion of enhancement of the image is to ameliorate the perceptibility of information contained in an image. In the present research, a novel technique for the enhancement of image quality is propounded using fuzzy logic technique with a cuckoo optimization algorithm. Generally, the image is transformed from RGB domain to HSV domain keeping the color information intact within the image. The image has been categorized into three regions: underexposed, overexposed and mixed region on the basis of two threshold values. For the fuzzification of under and overexposed area the degree of membership is defined by the Gaussian membership, while the mixed area is fuzzified by parametric sigmoid function. The key parameters like visual factors and fuzzy contrast provide the quantitative analysis of an image. An objective function is framed which involves entropy and visual factor has been optimized by a new evolutionary cuckoo optimization algorithm. The results procured after simulation by the cuckoo optimization algorithm are compared with Bacterial foraging algorithm and ant colony optimization based image enhancement and this approach is found to be improved.

Neutron computed tomography (nCT) has been established at many major neutron sources worldwide, using high-end equipment requiring major investment and development. Many older and smaller reactors would be capable of doing nCT as well, but cannot afford the investment before feasibility is proven. We have developed a compact low-cost but high-quality detection system using a new cooled CMOS camera that can either be fully integrated into a sophisticated setup, or used with a rudimentary CT control and motion system to quickly evaluate feasibility of neutron CT at a given beam line facility. Exchanging the scintillation screen makes it feasible for X-rays as well, even for visible light (and transparent samples) using a matte screen. The control system uses a hack to combine motion control with existing imaging software so it can be used to test several dozen different cameras without writing specific drivers. Freeware software can do reconstruction and 3D imaging.

This paper develops a detail image signal enhancement that makes images perceived as clearer and more resolved and so is more effective for higher resolution displays. We observe that the local variant signal enhancement makes images more vivid, and the more revealed granular signals harmonically embedded on the local variant signals make images more resolved. Based on this observation, we develop a method that not only emphasizes the local variant signals by scaling up the frequency energy in accordance with human visual perception, but also strengths up the granular signals by embedding the alpha-rooting enhanced frequency components. The proposed energy scaling method emphasizes the detail signals in texture images and rarely boosts noisy signals in plain images. In addition, to avoid the local ringing artifact, the proposed method adjusts the enhancement direction to be parallel to the underlying image signal direction. It was verified through the subjective and objective quality evaluations that the developed method makes images perceived as clearer and highly resolved.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

Abstract: Image aesthetic assessment (IAA) with neural attention has made significant progress due to its effectiveness in object recognition. Current studies have shown that the features learned by convolutional neural networks (CNN) at different learning stages indicate meaningful information. The shallow feature contains the low-level information of images and the deep feature perceives the image semantics and themes. Inspired by this, we propose a visual enhancement network with feature fusion (FF-VEN). It consists of two sub-modules, the visual enhancement module (VE module) and the shallow and deep feature fusion module (SDFF module). The former uses an adaptive filter in the spatial domain to simulate human eyes according to the region of interest (ROI) extracted by neural feedback. The latter not only takes out the shallow feature and the deep feature by transverse connection, but also uses a feature fusion unit (FFU) to fuse the pooled features together with the aim of information contribution maximization. Experiments on standard AVA dataset and Photo.net dataset show the effectiveness of FF-VEN.

Breast cancer is a leading cause of death among women. Conventional screening methods, such as mammography, and ultrasound diagnosis are expensive and have significant limitations. Digital Image Elasto Tomography (DIET) is a new noninvasive breast cancer screening system that has a potential to be a low cost and reliable breast cancer screening tool. It is based on modal analysis of the breast mass, and stereographic 3D image analysis to detect the stiffer abnormal tissues. However, camera sensor noise, especially Gaussian noise is a major source of Optical Flow (OF) error in this approach to tumor detection. This work studies the performance of different conventional filters, including the standard Gaussian filter tool to remove this noise and produce more robust screening results. A radical approach, Multiple Frame Noise Removal (MFNR) is proposed, for use in this type of medical image processing instead of a Gaussian filter or other typical image noise removal tools. Its a multiple frame noise removal method where Probability Density Function (PDF) of noise is extracted from the multiple images by characterizing the same pixel positions in multiple images. The noise becomes deterministic, and hence easily removed. The proposed algorithm was applied to a data set from 10 phantom breast tests with a prototype DIET system, and 10 in-vivo samples from healthy women. Comparisons were made to an optimal Gaussian filter form that is commonly used. Reductions in OF error using these digitally imaged data sets was used to compare performance. Refinement of the images for medical applications requires higher PSNR, which was successfully achieved by using MFNR algorithm. In this study, the algorithm was used to improve the imaging results of a DIET system. The conventional wisdom that states that noise removal and detail preservation are contrasting effects is

In recent years, underwater exploration for deep-sea resource utilization and development has a considerable interest. In an underwater environment, the obtained images and videos undergo several types of quality degradation resulting from light absorption and scattering, low contrast, color deviation, blurred details, and nonuniform illumination. Therefore, the restoration and enhancement of degraded images and videos are critical. Numerous techniques of image processing, pattern recognition and computer vision have been proposed for image restoration and enhancement, but many challenges remain. This survey presents a comparison of the most prominent approaches in underwater image processing and analysis. It also discusses an overview of the underwater environment with a broad classification into enhancement and restoration techniques and introduces the main underwater image degradation reasons in addition to the underwater image model. The existing underwater image analysis techniques, methods, datasets, and evaluation metrics are presented in detail. Furthermore, the existing limitations are analyzed, which are classified into image-related and environment-related categories. In addition, the performance is validated on images from the UIEB dataset for qualitative, quantitative, and computational time assessment. Areas in which underwater images have recently been applied are briefly discussed. Finally, recommendations for future research are provided and the conclusion is presented.

Scene classification plays an important role in the intelligent processing of high-resolution satellite (HRS) remotely sensed image. In HRS image classification, multiple features, e.g. shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS image. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structure across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.

In this paper, we continue the research cycle on the properties of convolutional neural network-based image recognition systems and ways to improve noise immunity and robustness [1]. Currently, a popular research area related to artificial neural networks is adversarial attacks. The effect of adversarial attacks on the image is not highly perceptible to the human eye, also it drastically reduces the neural network accuracy. Image perception by a machine is highly dependent on the propagation of high frequency distortions throughout the network. At the same time, a human efficiently ignores high-frequency distortions, perceiving the shape of objects as a whole. The approach proposed in this paper can improve the image recognition accuracy in the presence of high-frequency distortions, in particular, caused by adversarial attacks. The proposed technique makes it possible to measure up the logic of artificial neural network to that of a human, for whom high-frequency distortions are not decisive in object recognition.

In biology molecules and macromolecules like sugars, proteins, DNA, and RNA are of utter importance. Detecting their presence as well as their conformation is still a challenge in many cases. It is well known that the vibrational states of such molecules lie from the infrared to the TeraHertz range. Spectroscopy can be used to detect such compounds and probe their conformation. Still, terahertz spectroscopy on biosample is a challenge for two main reasons: water absorption; and the small size of the samples. The sample volume is smaller than the cube of the TeraHertz wavelength; the light matter interactions are thus extremely reduced. In this paper, we present the design, fabrication, characterization and the first typical uses of a biophotonic device aiming at increasing light matter interaction to enable terahertz spectroscopy of minute samples on a broad band (0.2–2 THz). We demonstrate time domain spectroscopy experiments on few µl samples showing the validity of our approach.

Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study on the efficacy of Particle Swarm Optimization (PSO) combined with Histogram Equalization (HE) preprocessing for medical image segmentation, focusing on Lung CT-Scan and Chest X-ray datasets. Best Cost values reveal the PSO algorithm’s performance, with HE preprocessing demonstrating significant stabilization and enhanced convergence, particularly in complex Lung CT-Scan images. Evaluation metrics, including Accuracy, Precision, Recall, F-Score, Specificity, Dice, and Jaccard, show substantial improvements with HE preprocessing, emphasizing its impact on segmentation accuracy. Comparative analyses against alternative methods, such as Otsu, Watershed, and K-means, confirm the competitiveness of the PSO-HE approach, especially in Chest X-ray images. The study also underscores the positive influence of preprocessing on image clarity and precision. These findings highlight the promise of the PSO-HE approach in advancing the accuracy and reliability of medical image segmentation, paving the way for further research and method integration to enhance this critical healthcare application.

A fast calculation method for the full parallax high-resolution hologram is proposed based on the elemental light field image (EI) rendering. A 3D object located near the holographic plane is firstly rendered as multiple EIs with a pinhole array. Each EI is interpolated and multiplied by a divergent sphere wave and interfered with a reference wave to form a hogel. Parallel acceleration is used to calculate the high-resolution hologram because calculation of each hogel is independent. A high-resolution hologram with the resolution of 20,0000×20,0000 pixels is calculated only within 8 minutes. Full parallax high-resolution 3D displays are realized by optical reconstructions.

Eelgrass meadows have attracted a lot of attention not only for their ability to maintain marine ecosystems as feeding grounds for marine organisms, but also for their potential to store atmos-pheric and dissolved CO2 as blue carbon. This study comprehensively evaluated the bacterial and chemical composition data obtained from eelgrass sediments of different scales along the coast of Japan to investigate the sediment effect on the acclimatization of eelgrass. Regardless eelgrass habitat, approximately 1% Anaerolineales, Babeliales, Cytophagales, and Phycisphaerales were present in the bottom sediment. Sulfate-reducing bacteria (SRB) were present at 3.69 % in eelgrass sediment compared to 1.70 % in bare sediment. Sulfur-oxidizing bacteria (SOB) were present at 2.81% and 1.10% in the eelgrass and bare sediment, respectively. Bacterial composition analysis and linear discriminant analysis revealed that sulfur-oxidizing bacteria detoxified H2S in the eelgrass meadows and that the larger-scale eelgrass meadows had a higher diversity of sul-fur-oxidizing bacteria. Our result indicated that one of the following systems detoxifies the H2S in eelgrass meadows microbial oxidation by sulfur-oxidizing bacteria, oxidation by O2 perme-ated by the physical diffusion of benthos. This study indicated the issues of maintaining sustain-able conservation of the eelgrass bed.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

Isothermal interrupted hot compression tests of 5754 aluminum were conducted on Gleeble-3500 thermo-mechanical simulator at temperature 350 °C and 450 °C, strain rate 0.1 s-1 and 1s−1. These tests simulated flat rolling to investigate how softening behaviors respond to controlled parameters, such as deformation temperature, strain rate and delay times. This data allowed the parameters for the hot rolling process to be optimized. The delay times during interrupted compression vary between 5s and 60s. The dynamic softening at each pass and metadynamic recrystallization at the intervals of deformation passes were analyzed in detail. 0.2% offset yield strength is applied to calculate the softening fraction undergoing metadynamic recrystallization. A kinetic model was developed to describe metadynamic recrystallization behaviors of the hot deformed 5754 aluminum alloy. Furthermore, the time constant for 50% recrystallization was expressed as functions related to the temperature and the strain rate. The experimental and calculated results were found to be in close agreement, which verified the developed model.

The stepwise hydrogenation of the C=C bond and C=O group of acrolein on Au₃ and Au₅ model systems is investigated using the density functional theory(DFT) PW91 functional. Our results show that the C=C hydrogenation is more favorable than that of C=O bond on Au₃ with the barriers of the rate-determining step being 0.35 and 0.62 eV respectively. On the other hand, the C=O reduction is preferred over the hydrogenation of the C=C bond on Au₅. The corresponding barriers of the rate-determining steps are 0.45 and 0.54 eV, respectively. This demonstrated that the second hydrogenation step controls the reaction on both Au₃ and Au₅ for C=O and C=C hydrogenation and the C=O hydrogenation on Au₅ is preferred over the hydrogenation of the C=C bond, which is helpful to address the reactivity of small size-selected supported gold clusters.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

Automatic target detection of remote sensing images (RSI) plays an important role in military reconnaissance, disaster monitoring, and target rescue. The core task of remote sensing target detection is to judge the target categories and complete precise location. However, the existing target detection algorithms have limited accuracy and weak generalization capability for remote sensing images with complex backgrounds. To achieve accurate detection of different categories targets in remote sensing images, this study presents a novel feature enhancement single shot multibox detector (FESSD) algorithm for remote sensing target detection. The FESSD introduces feature enhancement module and attention mechanism into the convolution neural networks (CNN) model, which can effectively enhance the feature extraction ability and nonlinear relationship between different convolution features. Specifically, the feature enhancement module is used to extract the multi-scale feature information, and enhance the model nonlinear learning ability; the self-learning attention mechanism (SAM) is used to expand the convolution kernel local receptive field, which makes the model extract more valuable features. In addition, the nonlinear relationship between different convolution features is enhanced using the feature pyramid attention mechanism (PAM). The advantage of FESSD over other state-of-the-art target detection methods is validated by experiments on the presented seven-class target detection dataset (SD-RSI) and the public DIOR dataset.

Enhancing underwater images in epicontinental sea is a challenging problem owing to the influence of ocean currents, the refraction, absorption and scattering of light by suspended particles, and the weak illumination intensity. Recently, different methods have relied on the underwater image formation model and deep learning techniques to restore the underwater image, but they tend to degrade underwater image, interference of background clutter and miss boundary details of blue regions. Improved image fusion and enhancement algorithm based on a priori dark channel is proposed in this paper. Image edge features sharpening and dark detail enhancement by homomorphism filtering in CIELab color space is realized. In RGB color space, the multi-scale retinal with color restoration (MSRCR) algorithm is used to improve color deviation and enhance color saturation, and the contrast-limited adaptive histogram equalization (CLAHE) algorithm is used to de fog and enhance image contrast. Finally, according to the dark channel images of the three processing results, the final enhanced image is obtained by linear fusion of multiple images and multiple channels. Experimental results demonstrate the effectiveness and practicality of the proposed method on various datasets.

We describe electron temperature measurements in the SSX MHD wind tunnel using two different methods. First, we estimate T_e along a chord by measuring the ratio of the C__III 97.7 nm to C_IV 155 nm line intensities using a vacuum ultraviolet monochrometer. Second, we record a biasing scan to a double Langmuir probe to obtain a local measurement of T_e. The aim of these studies is to increase the Taylor state lifetime, primarily by increasing the electron temperature. Also, a model is proposed to predict magnetic lifetime of relaxed states and is found of predict the lifetime satisfactorily. Furthermore, we find that proton cooling can be explained by equilibration with the electrons.

In recent years, magnetic fields have emerged as a non-thermophysical treatment with a signifi-cant impact on microbial fermentation processes. Brassica trispora is a microorganism known for its indus-trial-scale production of lycopene and high yield of single cells. This study aimed to in-vestigate the impact of low frequency magnetic fields on lycopene synthesis by Brassica trispora and elucidate the underlying mechanism for enhancing lycopene yield. The results indicated that both the intensity and duration of the magnetic field treatment influenced the cell. Exposing the cell to a 0.5 mT magnetic field for 48 hours on the second day of fermentation resulted in a lyco-pene yield of 1473.16 mg/L, representing a re-markable increase of 216.1% compared to the con-trol group. Transcriptome analysis revealed that al-ternating magnetic field significantly up-regulated genes related to ROS and cell membrane structure, leading to a substantial increase in lycopene production. Scanning electron microscopy revealed that the magnetic field treatment resulted in rough, loose, wrinkled surface morphology of the mycelium, along with a few mi-cropores, thereby altering the cell membrane permeability to some extent. Moreover, there was a significant increase in intracellular ROS content, cell membrane permeability, key enzyme activi-ty involved in lycopene metabolism, and ROS-related enzyme activity. In conclusion, the alter-nating fre-quency magnetic field can activate a self-protective mechanism that enhances lyco-pene synthesis by modulating intracellular ROS content and cell membrane structure. These find-ings not only deepen our understanding of the impact of magnetic fields on microbial growth and metabolism but also provide valuable insights for developing innovative approaches to en-hance carotenoid fermentation.

The SBA-15 loaded with methylene blue was successfully prepared by sample adsorption of methylene blue in SBA-15 and is characterized by an XRD, SEM, FTIR, and BET. The photocatalytic activity for methyl orange (MO) degradation was investigated under visible light irradiation under LEDs. Experimental results demonstrate that the SBA-15-MB exhibited higher photocatalytic activity than SBA-15 under visible light irradiation. The degradation efficiency of MO (50 ppm) by SBA-15-MB could reach 80% within 180 min under 25W LED visible light. The photocatalytic activity of SBA-15 could be attributed to the excitation of methylene blue by visible light, the transfer of electrons from methylene blue to SBA-15, and the production of radicals at the surface of SBA-15.

Employing low tensor rank decompositions in image inpainting has attracted increasing attention. This paper exploits a novel tensor-augmentation schemes to transform an image (a low-order tensor) to a higher-order tensor without changing the total number of pixels. The developed augmentation schemes enhance the low-rankness of an image under three tensor decompositions: matrix SVD, tensor train (TT) decomposition, and tensor singular value decomposition (t-SVD). By exploiting the schemes, we solve the image inpainting problem with three low-rank con-strained models which use the matrix rank, TT rank, and tubal rank as constrained priors re-spectively. The tensor tubal rank and tensor train multi-rank are developed from t-SVD and TT decomposition respectively. We exploit efficient ADMM algorithms for solving the three models. Experimental results demonstrate that our methods are effective for image inpainting and supe-rior to numerous close methods.

This study aims to clarify the influence of photographic environments under different light sources on image-based SPAD value prediction. Radish leaf patches of 1.5 cm diameter were photographed under halogen or LED light. The input variables for the SPAD value prediction using random forests were RGB values, HSL values, and HSV values. Additionally, the light color temperature (LCT) and illuminance (ILL) were used as the input variables. Model performance was assessed using Pearson’s correlation coefficient (COR), Nash-Sutcliffe efficiency (NSE), and root mean squared error (RMSE). SPAD value prediction resulted in high accuracy in a stable light environment; CORRGB+ILL+LCT and CORHSL+ILL+LCT were 0.929 and 0.922, respectively. Image-based SPAD value prediction was effective under halogen light with a similar color temperature at dusk; CORRGB+ILL and CORHSL+ILL were 0.895 and 0.876, respectively. The HSL value under LED could be used to predict the SPAD value with high accuracy; COR, NSE, and RMSE were 0.972, 0.944, and 2.07, respectively. The partial dependence plots of the H value indicate a change from blue to green with increasing SPAD values. Further studies are required to examine this method under outdoor conditions in spatiotemporally dynamic light environments.

Distracted driving is the leading cause of accidents, the cause of these distractions may include drowsiness, using cell phone, smoking a cigarette, etc. The proposed paper aims at monitoring the attentiveness of the driver and alerting the driver when his/her alertness goes below a certain threshold. It uses image processing using OpenCV (Computer Vision) and You Look Only Once (YOLO) algorithm to detect the eyes and object respectively and sounds an alarm when the driver is either drowsy or using his cell phone or smoking a cigarette.

In this work, we'll present the power spectra density, auto-correlation function, dentrended fluctuation analysis and multifractal spectrum are performed to the daily light curves of Blazars monitored by Fermi LAT. Some signs of power law are found.

Prosthetic attack is a problem that must be prevented in the current finger vein recognition application. To solve this problem, a finger vein living detection system was established in this article. The system first captures short-term static finger vein video by uniform near-infrared lighting, segments the veins by Gabor filters with current removing, calculates the multi-Scale spatial-Temporal maps(MSTmap) from the selected vein blocks, and trains the MSTmaps in the proposed Light-ViT network for the liveness detection. The MS maps are used to extract the coarse feature and Light-ViT is used to refine the liveness feature and predict the liveness result. Light-ViT, featuring an enhanced L-ViT backbone as its core, is constructed by interleaving multiple MN blocks and L-ViT blocks. This architecture effectively balances the learning of local image features, controls network parameter complexity, and substantially improves the accuracy of liveness detection. The accuracy of the Light-ViT network is verified to be 99.63% on the self-made living / prosthetic finger vein video dataset. This proposed system can also be directly applied to the finger vein recognition terminal after the model lightweighting.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

Due to an increase in the number of image achieves, Content-Based Image Retrieval (CBIR) has gained attention for research community of computer vision. The image visual contents are represented in a feature space in the form of numerical values that is considered as a feature vector of image. Images belonging to different classes may contain the common visuals and shapes that can result in the closeness of computed feature space of two different images belonging to separate classes. Due to this reason, feature extraction and image representation is selected with appropriate features as it directly affects the performance of image retrieval system. The commonly used visual features are image spatial layout, color, texture and shape. Image feature space is combined to achieve the discriminating ability that is not possible to achieve when the features are used separately. Due to this reason, in this paper, we aim to explore the low-level feature combination that are based on color and shape features. We selected color moments and color histogram to represent color while shape is represented by using invariant moments. We selected this combination, as these features are reported intuitive, compact and robust for image representation. We evaluated the performance of our proposed research by using the Corel, Coil and Ground Truth (GT) image datasets. We evaluated the proposed low-level feature fusion by calculating the precision, recall and time required for feature extraction. The precision, recall and feature extraction values obtained from the proposed low-level feature fusion outperforms the existing research of CBIR.

Obesity is associated with serious comorbidities and economic implications. Bariatric surgery, most commonly Roux-en-Y gastric bypass and sleeve gastrectomy, are effective options for weight loss and improvement of obesity-related comorbidities. With the growing obesity epidemic, there has been a concomitant rise in bariatric surgeries, particularly in sleeve gastrectomy, which has been the most widely performed bariatric surgery since 2013. Gastroesophageal reflux disease (GERD) is highly prevalent in obese individuals, can significantly impact quality of life and may lead to serious complications. Obesity and GERD both improve with weight loss. However, as the incidence of sleeve gastrectomy rises, recent data has revealed a risk of exacerbation of pre-existing GERD or the development of de novo GERD following sleeve gastrectomy. We performed a detailed review of GERD post-sleeve gastrectomy, including its overall incidence, pathophysiology and current treatment paradigms.

This paper describes the first results of an Image Recognition Based Location (IRBL) for mobile application focusing on the procedure to generate a Database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surrounding is needed. In order to achieve this objective a complete 3D survey of two different environment (Bangbae metro station of Seoul and E.T.R.I. building in Daejeon – Republic of Korea) was performed using LiDAR (Light Detection And Ranging) instrument and the obtained scans were processed in order to obtain a spatial model of the environments. From this, two databases of reference images were generated using a specific software realized by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allow to generate synthetically different RGB-D images) centered in the each scan position in the environment. Later, the external parameters (X, Y, Z, ω, φ, κ) and the range information extracted from the DB images retrieved, are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper the survey operations, the approach for generating the RGB-D images and the IRB strategy are reported. Finally the analysis of the results and the validation test are described.

The effects of rare earth Ce on the microstructure and properties of cast pure copper were investigated through thermodynamic calculations, XRD analysis, mechanical testing, metallographic microscopy, and scanning electron microscopy (SEM). The experimental results demonstrate that the reaction between rare earth Ce and oxygen as well as sulfur in copper exhibits a significantly negative Gibbs free energy value, indicating a strong thermodynamic driving force for deoxidation and desulfurization reactions. Ce is capable of removing trace amounts of O and S from copper. Moreover, the maximum solid solubility of Ce in Cu falls within the range of 0.009% to 0.01%. Furthermore, Ce can refine columnar grains while enlarging equiaxed grains in as-cast copper.Upon addition of rare earth Ce, the tensile strength increased by 8.45%, elongation increased by 12.1%, and microhardness rose from 73.5HV to 81.2HV-an increase of 10.5%. Overall, rare earth Ce has been found to enhance both the microstructure and mechanical properties of cast pure copper.

Blazars show rapid and high amplitude variability. In this work , Fermi daily light curve of 130 sources are analyzed, distribution of daily variability are compared with each other by using Kolmogorov-Smirnov (K-S) test. results show: 1)Some pairs of the distributions are similar; 2) In most cases, the distributions are not Gaussian.

Pulsed thermography is a commonly used non-destructive testing method, and is increasingly studied for advanced materials such as carbon fiber-reinforced polymer (CFRP) evaluation. Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, we used a type of matrix decomposition using Robust-PCA via Inexact-ALM in our experiment. We investigate this method as a pre-and post-processing method on thermal data acquired by pulsed thermography. We employed state-of-the-art methods, i.e., PCT, PPT, and PLST, as the main process. The results indicate that pre-processing on thermal data can elevate the defect detectability while post-processing, in some cases, can deteriorate the results.

So that the levels of water stress are not harmful to the development of the crop and affect its productivity, its detection and monitoring are necessary, and it can occur in different ways. One of them is through the Crop Water Stress Index (CWSI). This index quantifies water stress through the normalization of leaf temperature between the maximum and minimum plant temperatures as a function of evaporation conditions. The responses of a low-cost infrared (IR) sensor were crossed with image processing through segmentation by the Excess Green model to develop a water stress detection system using CWSI. A soil/plant temperature map was generated through a point-to-point scan of the IR sensor. And when it overlaid with a segmented image of the experimental area, only points identified as plants had their temperature values maintained. The Non-Water-Stressed Baseline (NWSB) equation was parameterized for the same conditions of the experiment and external environmental. The experimental area was divided into three different treatments, maintained under stable water conditions throughout the experiment and the system was able to identify stably different stress values between treatments. Although the relationship between crop and environment affected the results, this work showed that using an irrigation system based on CWSI is possible.

We report on the design, characterization and validation of a spherical irradiation system for inactivating SARS-CoV-2, based on UV-C 275 nm LEDs. The system is designed to maximize ir-radiation intensity and uniformity, and can be used for irradiating a volume of 18 liters. To this aim: (i) several commercially available LEDs have been acquired and analyzed; (ii) a complete optical study has been carried out in order to optimize the efficacy of the system. (iii) The re-sulting prototype has been characterized optically, and tested for the inactivation of SARS-CoV-2 for different exposure times, doses and surface types. Results indicate that a 99.9% inactivation can be reached after 1 minute of treatment with a dose of 83.1 J/m2.

In this letter, we show an alternative low-cost fabrication method to obtain poly(dimethyl siloxane) (PDMS) microfluidic devices. The proposed method allows the inscription of micron resolution channels on polystyrene (PS) surfaces, used as a mold for the wanted microchip’s production, by applying a high absorption coating film on the PS surface to ablate it with a focused low-power visible laser. The method allows obtaining micro resolution channels at powers between 2 and 10mW and can realize any two-dimensional polymeric devices. The effect of the main processing parameters on the channel’s geometry is presented.

Antisense oligonucleotides (ASOs) are short oligodeoxynucleotides designed to bind to specific regions of target mRNA. ASOs can modulate pre-mRNA splicing, increase levels of functional proteins and decrease levels of toxic proteins. ASOs are being developed for the treatment of several diseases, including motor neuron diseases (MNDs), a group of neurodegenerative disorders characterised by the loss of motor neurons, including spinal muscular atrophy (SMA), amyotrophic lateral sclerosis (ALS) and spinal and bulbar muscular atrophy (SBMA). The biggest success has been the ASO known as nusinersen, the first effective therapy for SMA, able to improve symptoms and slow disease progression. Another success is tofersen, an ASO designed to treat ALS patients with SOD1 gene mutations. Both ASOs have been approved by the FDA and EMA. On the other hand, ASO treatment in ALS patients with the C9orf72 gene mutation did not show any improvement in disease progression. The aim of this review is to provide an up-to-date overview of ASO research in MND, from preclinical studies to clinical trials and, where available, regulatory approval. We highlight the successes and failures, underline the strengths and limitations of the current ASO research, and suggest possible approaches that could lead to more effective treatments.

Synthetic Aperture Radar has been extensively used in numerous fields and can gather a wealth of information about the area of interest. This large-scene data-intensive technology puts a high value on automatic target recognition (ATR) which can free the utilizers and boost the efficiency. Recent advances in artificial intelligence have made it possible to create a deep learning-based SAR ATR that can automatically identify target features from massive input data. In the last 6 years, intensive research has been conducted in this area, however, most papers in the current SAR ATR field used recurrent neural network (RNN) and convolutional neural network (CNN)-varied models to deepen the regime’s understanding of the SAR images. To equip SAR ATR with updated deep learning technology, this paper tries to apply a lightweight vision transformer (LViT)-based model to classify SAR images. The entire structure was verified by an open- accessed SAR data set and recognition results show that the final classification outcomes are robust and more accurate in comparison with referred traditional network structures without even using any convolutional layers.

To improve carbon efficiency for urban logistics service system composed of a third-party logistics service provider (3PLs) and an e-business enterprise, low-carbon operation game between them was studied. Considering low carbon technology investment cost and sales expansion effect of low carbon level, profit functions for both players were constituted. Based on their different bargaining capabilities, totally 5 types of game scenarios were designed. Through analytical solution, Nash Equilibria under varied scenarios were obtained. By analyzing these equilibria, 4 major propositions were given, in which, some key variables and system performance index were compared. Results show that the best system yields could only be achieved under the fully cooperative situation; limited cooperation only for carbon emission reduction would not benefit the system performance improvement; E-business enterprise-leading game’s performance overtook 3PLs-leading ones.

This research introduces a pioneering solution to the challenges posed by gastrointestinal tract (GI) cancer in radiation therapy, focusing on the imperative task of precise organ segmentation for minimizing radiation-induced damage. GI imaging has historically used manual demarcation, which is laborious and uncomfortable for patients. We address this by introducing the ResECA-U-Net deep learning model, a novel combination of the UNet and ResNet34 architectures. Furthermore, we further augment its functionality by incorporating the Efficient Channel Attention (ECA-Net) methodology. By utilizing data from the UW-Madison Carbone Cancer Centre, we carefully investigate several image processing techniques designed to capture critical local characteristics. With its foundation in computer vision concepts, the ResECA-U-Net model is excellent at extracting fine details from GI images. Sophisticated metrics such as intersection over union (IoU) and the dice coefficient are used to evaluate performance. Our study's outcomes demonstrate the effectiveness of the suggested method, yielding an impressive 96.27% Dice coefficient and 91.48% IoU. These results highlight the significant contribution that our strategy has made to the advancement of cancer therapy. Beyond its scientific merits, this work has the potential to significantly enhance cancer patients' quality of life and provide better long-term outcomes. Our work is a significant step towards automating and optimizing the segmentation process, which can potentially change how GI cancer is treated completely.

According to the current European and Italian scenario related to urban re-generation, cultural and landscape heritage, valorisation is being also enhanced by the activation of innovative processes. These involve the development of methodologies and tools that are able to address decision-making processes among low entropy economy, complex values and creative practices. In this perspective, the research aims to investigate the possibilities of developing a Cultural Heritage Low Entropy Enhancement (CHLEE) approach by considering how the complex values of cultural heritage can vary not only through a physical transformation of spaces but also through a program of uses and activities able to produce new values, where the human experience is essential. This type of model modifies the objectives that characterise the valorisation of cultural heritage and landscape, recognising that the fruition is no longer “consumerist” but “experiential”. A crucial role is represented by the heterogeneity of creative practices that contribute to the identificationidentifying and implementation ofimplementing innovative management and governance models. The present paper explores the components of creative regenerative processes, based upon the ex-post evaluation of some Italian experiments, across the PROMETHEE-GAIA multi-criteria method, to understand how creative experiences are building innovation ecosystem thanks to low entropy economy and improve the ex-ante evaluation for new strategies and policies.

The implementation of sentinel lymph node (SLN) biopsy is changing the scenario in the surgical treatment of early-stage cervical cancer and the oncologic safety of replacing bilateral pelvic lymphadenectomy with SLN biopsy is currently under investigation. One of the undisputed values of SLN biopsy is its diagnostic accuracy in detecting low-volume metastases (LVM) via pathologic ultrastaging. In early-stage cervical cancer, the reported incidence of LVM ranges from 4 to 20%. The prognostic impact and the role of adjuvant treatment in patients with LVM is still unclear. Some non-prespecified analyses in prospective studies showed no impact on the oncologic outcomes compared to node-negative disease. However, the heterogeneity of the studies, the differences in the disease stage and the use of adjuvant treatment, and the concomitant pelvic lymphadenectomy (PLND) make any conclusion on this topic hard. Current guidelines suggest considering micrometastasis (MIC) as a node-positive disease, while isolated tumor cells (ITC) as a node-negative disease, with a low level of evidence. This review aims to highlight the unanswered questions about the definition, identification, prognostic and therapeutic role of LVM and underline the present and future challenges we are facing. We hope this review will guide further research, giving robust evidence on LVM and their impact on clinical practice.

Numerical calculations of ultraviolet to near-infrared absorption spectra by cadmium selenide quantum dots (CdSe QDs) doped in anodic aluminum oxide pores were performed using a finite-difference time-domain model. The height, diameter, and periodic spacing of the pores were optimized. Light absorption by the dots was enhanced by increasing the height and decreasing the diameter of the pores. When the height was less than 1 μm, visible light absorption was enhanced as the spacing was reduced from 400 nm to 100 nm. No enhancement was observed for heights greater than 6 μm. Finally, the optical mode coupling of the aluminum oxide and the quantum dots was enhanced by decreasing the pore diameter and periodic spacing, and increasing the height. Laser ablation verified light absorption enhancement by the CdSe QDs. The experiment verified the improvement of the laser-induced damage ability with wavelength of 355-nm after aluminum alloy 6061 coated with functional films, which was fabricated based on numerical calculations.

To clarify whether cryptochrome contributes to stem elongation and flowering promoted by blue lights associated with low phytochrome activity, wild-type Arabidopsis was compared with its cryptochrome-deficient mutants and cryptochrome-overexpressing transgenic plants. Results indicated that the promotion effects were mainly related to low CRY1 activity, despite partial involvement of high-activity CRY2.

Herein, we present the design technique of a resonant rectifier for piezoelectric (PE) energy harvesting. We propose two diode equivalents to reduce the voltage drop in the rectifier operation, a minuscule-drop-diode equivalent (MDDE) and a low-drop-diode equivalent (LDDE). The diode equivalents are embedded in resonant rectifier integrated circuits (ICs), which use symmetric bias-flip to reduce the power wasted for charging and discharging the internal capacitance of a PE transducer. The self-startup function is supported by synchronously generating control pulses for the bias-flip from the PE transducer. Two resonant rectifier ICs, using both MDDE and LDDE, are fabricated in a 0.18 μm CMOS process and their performances are characterized under external and self-power conditions. Under the external-power condition, the rectifier using LDDE delivers an output power POUT of 564 μW and a rectifier output voltage VRECT of 3.36 V with a power conversion efficiency (PCE) of 90.1%. Under self-power conditions, the rectifier using MDDE delivers a POUT of 288 μW and a VRECT of 2.4 V with a corresponding PCE of 74.6%. The result shows that the power extraction capability of the proposed rectifier is 5.9 and 3.0 times higher than that of a conventional full-bridge rectifier.

Surface initiated atom transfer radical polymerization (SI-ATRP) is one of the most versatile technique to modify the surface properties of material. Recent developed metal free SI-ATRP makes such technique more widely applicable. Herein photo-induced metal-free SI-ATRP of methacrylates, such as methyl methacrylate, N-isopropanyl acrylamide, and N,N- dimethylaminoethyl methacrylate, on the surface of SBA-15 was reported to fabricate organic-inorganic hybrid materials. SBA-15 based polymeric composite with adjustable graft ratio was obtained. The structure evolution during the SI-ATRP modification of SBA-15 was monitored and verified by FT-IR, XPS, TGA, BET, and TEM. The obtained polymeric composite showed enhanced adsorption ability for the model compound toluene in aqueous. This procedure provides a low cost, ready availability, and facile modification way to synthesize the polymeric composites without the contamination of metal.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

This article introduces a novel payload structure, which has been experimentally proven to have smaller impact force while the carried internal electronics remains intact. The study describes in details how materials and geometric structures were chosen based on information analysis. It is clarified how possible geometric structures and materials can be chosen to yield optimal efficiency. Finite element method simulations were carried out to investigate the mechanical properties of several geometric structures, which has led to the dodecahedron form. For the frame, Polylactic Acid were chosen as an optimal material. A low air pressure test for parts of the structure, and also the whole structure were carried out with successful results. Drop tests, with force measuring sensors and high speed videos, were carried out for measuring and comparing the maximal impact force of classical payloads and the novel one. Laboratory and real life tests demonstrated that the developed new structure is safer and it is applicable.

There is a growing interest in the endocannabinoid system and the gut microbiota. Both areas have been suggested to play a role in endometriosis, however this is yet to be studied comprehensively. The purpose of this narrative review is to identify the gaps in endometriosis research in the context of these two areas. It underscores the limited knowledge regarding the involvement of the endocannabinoid system and gut microbiota in endometriosis. Further research in these areas is essential to unlock the therapeutic potential of cannabis and enhance the understanding of endometriosis pathogenesis.

Outdoor lighting is becoming increasingly widespread, and residents are suffering from serious light pollution as a result. Residents’ awareness of their rights to protection has gradually increased. However, due to the sometimes-inaccessible nature of residential vertical light incidence intensity data and the high cost of obtaining specific measurements, there is no appropriate hierarchic compensation for residents suffering from different degrees of light pollution. It is therefore important to measure light pollution levels and their damage at the neighborhood scale to provide residents with basic materials for proper protection and to create more politically suitable solutions. This article presents a light pollution assessment method that is easy to perform, is low-cost, and has a short data-processing cycle. This method can be used to monitor residential zone light pollution in other cities. We chose three open areas to test the spatial variation pattern of light intensity. The results are in accordance with spatial interpolation patterns and can be fit, with high precision, using the IDW method. This approach can also be used in 3 dimensions to quantitatively evaluate the distribution of light intensity distribution. We use a mixed-use zone in Beijing known as The Place as our case study area. The vertical illumination at the windows of residential buildings ranges from 2 lux to 23 lux; the illumination in some areas is far higher than the value recommended by CIE. Such severe light pollution can seriously interfere with people's daily lives and has a serious influence on their rest and health. The results of this survey will serve as an important database to assess whether the planning of night-time lighting is scientific and whether it provides residents with a basis for the protection of their rights.

This study addresses the critical challenge of Speech Enhancement (SE) in noisy environments, where the deployment of Deep Neural Networksolutions on microcontrollers is hindered by their extensive computational demands. Focusing on this gap, our research introduces a novel SE method optimized for MCUs, employing a 2-layer GRU model that capitalizes on perceptual speech properties and innovative training methodologies. By incorporating self-reference signals and a dual strategy of compression and recovery based on the Mel scale, we develop an efficient model tailored for low-latency applications. Our GRU-2L-128 model demonstrates a significant reduction in size and computational requirements, achieving a 14.2× decrease in model size and a 409.1× reduction in operations compared to traditional DNN methods like DCCRN, without compromising performance. This advancement offers a promising solution for enhancing speech intelligibility in resource-constrained devices, marking a pivotal step in SE research and application.

The philosophy of testing statistical hypothesis is a natural consequence and functional extension of mathematical analysis of Probability. Along with the concept of recurrence when applied to random sequences and functions, it leads to the analysis of a priori and posterior which implies testing statistical hypothesis. Testing statistical hypothesis also involves algebraic, functional and dimensional considerations, which are found in the works of Laplace. Aspects of mathematical analysis such as universality of solutions, Laws of Large Numbers, Entropy, Information, and various functional dependencies are the main factors explained in the five properties that lead to implication of testing statistical hypothesis. Various interesting examples with modern scientific significance from genetics, astrophysics, and other areas give methodological access to answers of different problems and phenomena which are involved in the logic of testing statistical hypothesis.

Exposure of experimental rodents to controlled cycles of light, food, and temperature is important when investigating alterations in circadian cycles that profoundly influence health and disease. However, applying such stimuli simultaneously is difficult in practice. Our aim was to design, build, test, and open-source describe a simple device that subjects a conventional mouse cage to independent cycles of physiologically relevant environmental variables. The device is based on a box enclosing the rodent cage to modify the light, feeding, and temperature environments. The device provides temperature-controlled air conditioning (heating or cooling) by a Peltier module and includes programmable feeding and illumination. All functions are set by a user-friendly front panel for independent cycle programming. Bench testing with a model simulating the CO2 production of mice in the cage showed: a) suitable air renewal (by measuring actual ambient CO2), b) controlled realistic illumination at the mouse enclosure (measured by a photometer), c) stable temperature control, and d) correct cycling of light, feeding, and temperature. The cost of all the supplies (retail purchased by e-commerce) was < 300 US$). Detailed technical information is open-source provided, allowing for any user to reliably reproduce or modify the device. This approach can considerably facilitate circadian research since using one of the described low-cost devices for any mouse group with a given light-food-temperature paradigm allows for all the experiments to be performed simultaneously, thereby requiring no changes in the light/temperature of a general-use laboratory.

Edge AI accelerators have been emerging as a solution for near customers’ applications in areas such as unmanned aerial vehicles (UAVs), image recognition sensors, wearable devices, robotics, and remote sensing satellites. These applications not only require meeting performance targets but also meeting strict reliability and resilience constraints due to operations in harsh and hostile environments. Numerous research articles have been proposed, but not all of these include full specifications. Most of these tend to compare their architecture with other existing CPUs, GPUs, or other reference research. This implies that the performance results of the articles are not comprehensive. Thus, this work lists the three key features in the specifications such as computation ability, power consumption, and the area size of prior art edge AI accelerators and the CGRA accelerators during the past few years to define and evaluate the low power ultra-small edge AI accelerators. We introduce the actual evaluation results showing the trend in edge AI accelerator design about key performance metrics to guide designers on the actual performance of existing edge AI accelerators’ capability and provide future design directions and trends for other applications with challenging constraints.

Background Augmented reality (AR) in surgery can offer an enhanced view of reality through the superimposition of computer-generated digital images on the real environment. It allows surgeons to integrate image visualisation, improving operative efficiency, surgical outcomes, surgical training and patient education. This review aims to evaluate the current status of augmented reality in surgery, surgical training and potential future applications. Methods We performed a non-systematic review of available literature from January 2005 to August 2021 by searching PubMed, EMBASE and the Cochrane library using a combination of terms “augmented reality”, “virtual reality”, “surgery”, “simulation” and “training”. Articles considered for this review were identified by relevant search criteria including title, keywords, abstract, and full-text. Conclusions AR technologies present an exciting new trend with multiple potential applications in surgery. Intraoperative AR systems have shown promise in specialties involving fine movement of organs during surgical procedures, including Neurosurgery, Ears, Nose and Throat and Orthopaedic Surgery. AR has also exhibited the potential to enhance surgical training and improve knowledge acquisition; it can foster international collaborations via telesurgery and telepresence. In the near future, AR will likely work in symbiosis with surgeons, serving as a complex computer-human coalition which can improve patient outcomes, patient education and surgical training.

In agriculture, soil-borne fungal pathogens, especially Fusarium oxysporum strains, are posing a serious threat to efforts to achieve global food security. In the search for safer agrochemicals, silica nanoparticles (SiO2NPs) have recently been proposed as a new tool to alleviate pathogen damage including Fusarium wilt. Hollow mesoporous silica nanoparticles (HMSNs), a unique class of SiO2NPs, have been widely accepted as a desired pesticide carrier. However, their role in enhancing the disease resistance and the specific mechanism remain unknown. In this study, three sizes of HMSNs (19, 96 and 406 nm as HMSNs–19, HMSNs–96 and HMSNs–406, respectively) were synthesized and characterized to determine their effects on seed germination, seedling growth, and Fusarium oxysporum f. sp. phaseolus (FOP) suppression in cowpea roots by foliar spray using phenotypes, fresh biomass and disease progression as indicators. The results revealed that three HMSNs exhibited no adverse impacts on seed germination and tended to improve plant growth. Also, they exert their FOP suppression with a size- and concentration-dependent manner. HMSNs–406 possessed the best control effect at a concentration of 1000 mg/L showing an upto 40.00% decline in the disease severity. The Si(OH)4 control was also effective on FOP suppression at a lower concentration of 100 mg/L, whereas its higher concentrations exhibited obviously adverse impacts on FOP control, seed germination and plant growth. Moreover, we conformed that HMSNs posed their Fusarium wilt suppression in cowpea plant by activating SA (salicylic acid)-dependent SAR (systemic acquired resistance) responses rather than directly suppressing FOP. A higher level of SA content and elevated expression of its maker genes of PR-1 and PR-5 in HMSNs–406 treated cowpea roots provided substantial evidences of this mode of action. Other resistance-related genes, as well as defense-responsive enzymes, were also involved in the HMSNs-activated SAR pathway. Overall, for the first time, our results extended a new role of HMSNs as a potent elicitor to serve as a versatile alternative for plant disease protection of low cost, highly efficiency and sustainability.

This paper investigates the adaptability of four state-of-the-art Artificial Intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n=856 cancer cases and n=856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish image subsets. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pre-trained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences (P<0.05) in the performances of the four AI models between high-concordance subset and entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with strong concordance of radiologists’ annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models.

Drug resistance in melanoma is a major hindrance in cancer therapy. Growth hormone (GH) plays a pivotal role in contributing to resistance to chemotherapy. Knocking down or blocking the GH receptor has been shown to sensitize the tumor cells to chemotherapy. Extensive studies have demonstrated that exosomes, a subset of extracellular vesicles, play an important role in drug resistance by transferring key factors to sensitize cancer cells to chemotherapy. In this study, we explore how GH modulates exosomal cargoes from melanoma cells and their role in drug resistance. We treated the melanoma cells with GH, doxorubicin, and GHR antagonist, pegvisomant, and analyzed the exosomes released. Additionally, we administered these exosomes to recipient cells. The GH-treated melanoma cells released exosomes with elevated levels of ABC transporters (ABCC1 and ABCB1), N-cadherin and MMP2, enhancing drug resistance and migration in recipient cells. GHR antagonism reduced these exosomal levels, restoring drug sensitivity and attenuating migration. Overall, our findings highlight a novel role of GH in modulating exosomal cargoes that drive chemoresistance and metastasis in melanoma. This understanding provides insights to the mechanisms of GH in melanoma chemoresistance and suggests GHR antagonism as a potential therapy to overcome chemoresistance in melanoma treatment.

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SAR) and Ground Penetrating Radars (GPR) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency Modulated Continuous Wave operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device.

This research proposes a roof-mounted auxiliary power supply (APS) system for 600VDC low-floor light rail vehicle (LRV). The proposed APS system consists of five parallel-connected dc-ac inverter modules (modules 1-5). The inverter modules 1 and 2 are three-phase dc-ac inverters for compressor motors of the cooling system, and the inverter modules 3 and 4 are three-phase dc-ac inverters for air pump motors of the braking system. The inverter module 5 is single-phase dc-ac inverter for 220VAC power supply for onboard electric loads. Simulations and experiments were carried out under variable load torque and output frequency for modules 1 – 4; and under full and no resistive loads for the inverter module 5. The measured total input current and total input power of the proposed APS system under full load condition are 118.76A and 71.25kW. Essentially, the proposed APS system is operationally applicable to the 600VDC low-floor RLV. Besides, the novelty of this research lies in the use of five parallel-connected inverter modules, unlike in the conventional APS systems which require three-phase output transformer or isolated dc-dc converter. Specifically, the proposed APS system requires neither three-phase output transformer nor isolated dc-dc converter.

Retrospective information about the climate system and plant ecophysiology are key inputs in climate and Earth system modelling. Dendrochronology provides such information with large spatiotemporal coverage, and stable carbon isotope (¹³C/¹²C) analysis across tree-ring series is among the most advanced dendrochronological tools. For the past seventy years, this analysis was performed on whole molecules, and, to this day, ¹³C/¹²C variation is attributed to ¹³C discrimination during CO₂ diffusion into leaves and assimilation by rubisco. By contrast, ¹³C discrimination by post-rubisco processes is presumed constant. Here, recently reported results on the first dataset of intramolecular ¹³C discrimination in tree rings were synthesised by variance component analysis. The emerging picture is not consistent with the classical (DR-discrimination-centred) concepts and practices of carbon-isotope dendrochronology. Specifically, leaf and stem post-rubisco discrimination each account for more variation in the data than diffusion-rubisco discrimination, i.e., post-rubisco discrimination is not constant. Furthermore, diffusion-rubisco discrimination is used widely as proxy of leaf intrinsic water-use efficiency (iWUE), a key determinant in the responses of global biogeochemical cycles to climate change. However, since diffusion-rubisco discrimination is a small component of the total data variance, whole-molecule analysis yields confounded iWUE estimates, yet intramolecular analysis will likely offer solutions. Lastly, all currently observed ¹³C/¹²C-climate relationships are attributed to diffusion-rubisco discrimination. However, here, relationships with temperature and radiation derive from leaf-level post-rubisco discrimination while relationships with precipitation derive from stem-level post-rubisco discrimination. That said, advances in mass spectrometry may soon make intramolecular ¹³C/¹²C analysis broadly available taking carbon-isotope dendrochronology to the next level.

End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational complexity, learned image compression methods inherently align with learning-based data processing and analytic pipelines due to their well-suited internal representations. The concept of Video Coding for Machines has garnered significant attention from both academic researchers and industry practitioners. This concept reflects the growing need to integrate data compression with computer vision applications. In light of these developments, we present a comprehensive survey and review of lossy image compression methods. Additionally, we provide a concise overview of two prominent international standards, MPEG Video Coding for Machines and JPEG AI. These standards are designed to bridge the gap between data compression and computer vision, catering to practical industry use cases.

A proposed low-temperature forging method aims to enhance stainless steel bearings by creating a martensitic subsurface layer, significantly boosting bearing fatigue life due to increased surface hardness. This technique induces beneficial residual stresses, particularly in axial bearings, streamlining their construction and improving machine elements. Challenges persist, especially with radial bearings, but simplicity in axial bearing forging promotes compact, resource-efficient facility construction. Future research will focus on applying this technique to axial bearing washers, potentially replicating success in other bearing components. Despite the energy ex-penditure for cooling during forging, the substantial increase in bearing fatigue life offsets this, enhancing overall longevity and reliability of critical machine components. Integration of this forging technique into bearing fabrication appears seamless, offering a promising trade-off be-tween energy use and enhanced performance.

In this paper, a new approach to retrieve semantic images based on shape and geometric features of image in conjunction with multi-class support vector machine is proposed. Zernike moment as shape feature is to verify the invariance of objects for silhouette image. In addition, a set of geometrical features is to explore the objects shape using two features of rectangularity and circularity. Then the extracted features are normalized and employed for multi-class support vector machine either for learning or retrieving processes. The retrieving process relies on three main tasks which namely Query Engine, Matching Module and Ontology Manger, respectively. Query Engine is to build the input text or image query using SPARQL language. The matching module extracts the shape and geometric features of image’s objects and employ them to Ontology Manger which in turn inserts them in ontology knowledge base. Benchmark mammals have been conducted to empirically conclude the outcome of proposed approach. Our experiment on text and image retrieval yields efficient results to problematic phenomena than previously reported.

Total-body PET/CT scanners provide increased sensitivity, enabling the adjustment of imaging parameters by reducing injected activity or shortening acquisition time. This study aimed to evaluate the limitations of reduced [18F]FDG activity doses on image quality, lesion detectabil-ity, and quantification of lesion uptake in the Biograph Vision Quadra, as well as to assess the benefits of the recently introduced ultra-high sensitivity mode in a clinical setting. A number of 26 patients who underwent [18F]FDG-PET/CT (3.0 MBq/kg, 5 min. scan time) were included in this analysis. PET raw data was rebinned for shorter frame durations to simulate 5 min. scans with lower activities in high sensitivity (HS) and ultra-high sensitivity (UHS) modes. Image quality, noise, and lesion detectability (n=82) were assessed using a 5-point Likert scale. The co-efficient of variation (CoV), signal-to-noise ratio (SNR), tumor-to-background ratio (TBR), and standardized uptake values (SUV) including SUVmean, SUVmax, and SUVpeak were evaluated. Sub-jective image ratings were generally superior in UHS compared to HS mode. At 0.5 MBq/kg, le-sion detectability decreased to 95% (HS) and 98% (UHS). SNR was comparable at 1.0 MBq/kg in HS (5.7±0.6) and 0.5 MBq/kg in UHS (5.5±0.5). With lower doses, there were negligible reductions in SUVmean and SUVpeak, whereas SUVmax increased steadily. Reducing [18F]FDG activity to 1.0 MBq/kg (HS/UHS) in a total-body PET/CT provides diagnostic image quality without statistical-ly significant changes in uptake parameters. UHS mode improves image quality, noise, and le-sion detectability compared to HS mode.

The aims of the study are: a) Describe the reactogenicity of WHO-approved two mRNA (Pfizer-BioNTech, Moderna) and two non-RNA vaccines (Oxford-AstraZeneca, Sinovac) among lactating mother and baby pairs; and b) compare and contrast the reactogenicity between mRNA and non-mRNA vaccines. A cross-sectional, self-reported survey was conducted amongst 1784 lactating women who received COVID-19 vaccinations. The most common maternal adverse reaction was a local reaction at the injection site; the largest minority of respondents, 43.7% (780/1784), reported experiencing worse symptoms when receiving the second dose compared to the first dose. There were no major reported adverse effects or behavioural changes in the breastfed infants. Among the respondents who received non-mRNA COVID-19 vaccinations, a majority reported no change in lactation but those who did more commonly reported an increase in milk supply, decrease in milk supply and pain in the breast. The more commonly reported lactation changes (fluctuations in breastmilk supply and pain in the breast) for the non-mRNA vaccines were similar to that of respondents who received mRNA vaccines. Our study, with a large cohort and wide geographical and racial mix, further augments earlier reported findings that COVID-19 vaccines are safe for breastfeeding mothers and her children.

Lead-acid batteries utilised in electrical substations release hydrogen and oxygen when these are charged. These gases could be dangerous and cause a risk of fire if they are not properly ventilated. Therefore, this research seeks to design and implement a network control panel for heating, ventilation, and air-conditioning systems (HVACS). This is done using a specific range of controllers, which have more than thirty loops of proportional, integral and derivative (PID) control to achieve a cost-effective design. It performs the required function of extracting hydrogen, oxygen, and maintaining the desired temperature of the battery storage room within the recommended limits (i.e. 25°C ±1°C tolerance) without compromising quality as set out in the user requirement specification in Appendix-A. The system control panel allows the user to access control parameters such as changing temperature set-points, fan-speed, sensor database amongst others. The hardware is configured to detect extreme hydrogen and oxygen gas content in the battery room and ensure that the HVACS extract the gas content to the outside environment. The results of the system show that the network control panel effectively operates as per the recommended system requirements. Therefore, the effective operation of the HVACS ensures sufficient gas ventilation, thus mitigating the risk of fire in a typical battery storage room. Furthermore, the effective operation of HVACS enhances battery lifespan because of regulated operating temperature, which is conducive to minimise the effect of sulfation in lead acid Batteries (LABs).

A low-cost bio-mass-derived carbon substrate has been employed to synthesize MoS₂@carbon composites through a hydrothermal method. Carbon fibers derived from natural cotton provide a three-dimensional and open framework for the uniform growth of MoS₂ nanosheets, thus constructing hierarchically coaxial architecture. The unique structure could synergistically benefit fast Li-ion and electron transport from the conductive carbon scaffold and porous MoS₂ nanostructures. As a result, the MoS₂@carbon composites, when served as anodes for Li-ion batteries, exhibit a high reversible specific capacity of 820 mAh g^-1, high-rate capability (457 mAh g^-1 at 2 A g^-1), and excellent cycling stability. The superior electrochemical performance makes the MoS₂@carbon composites to be low-cost and promising anode materials for Li-ion batteries.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting resolution for detailed examinations. Currently, prevalent methodologies rely on feature-based approaches for fundus registration. However, certain methods exhibit high feature point density, posing challenges in matching due to point similarity. This study introduces a novel fundus image registration technique integrating U-Net for the extraction of feature points employing FIVES for its training and evaluation, a novel and large dataset for blood vessels segmentation, prioritizing point distribution over abundance. Subsequently, the method employs medial axis transform and pattern detection to obtain feature points characterized by the Fast Retina Keypoint (FREAK) descriptor, facilitating matching for transformation matrix computation. Assessment of the vessel segmentation achieves 0.7559 for Intersection Over Union (IoU), while evaluation on the Fundus Image Registration Dataset (FIRE) demonstrates the method’s comparative performance against existing methods, yielding a registration error of 0.596 for area under the curve, refining similar earlier methods and suggesting promising performance comparable to prior methodologies.

An ultra-low anti-reflection optical coating on both surfaces of a plastic cover slip was studied for use in confocal image measurement. The optical reflectance at a wavelength of 632.8 nm was less than 0.1% when the coated sample was placed in a liquid having a refractive index of 1.34 close to the aqueous solution of the biomaterial. The high- and low-index coating films, Substance-2 (PrTiO3) and silicon dioxide (SiO2), were measured by an ellipsometer to determine their optical refraction indices and extinction coefficients. Theoretically, when the two layer thicknesses are designed using the optical admittance diagram of the cover slip to approach the equivalent index of 1.34, a reflectance of 1.6×10-5% in the liquid could be obtained. Experimentally, the reflectance of the sample deposited on the two faces of the cover slip was 4.223±0.145% as measured in the air; and 0.050±0.002% as measured by a He-Ne laser in the liquid.

Bridges enable communications and transportation of goods nationally and internationally, underpinning economic and social activities, thus they are pylons of our prosperity and mobility. Bridges worldwide are constantly subjected to physical wear, ageing, deterioration, hazards, environmental influences, and increased loading. Loss of performance and functionality of bridge structures would have a crucial impact on overall infrastructural resilience and would cause significant negative economic and social consequences. Monitoring their behaviour for different loading conditions relies on accurate estimations of the stress-strain state of various critical components and remaining capacities. These activities are of high importance for better planning lifespan prolongation, that is the extension of their service life and prevention of unforeseen collapses, in line with sustainability principles of well-informed maintenance. In many cases, access to the structure is limited or even impossible, which causes the need for the deployment of remote and contactless methods. One such innovative technique, which has recently attracted attention in scientific and practical applications is the digital image correlation (DIC). DIC is a contactless approach applicable for long-term measurements, which enables obtaining the full field of strains and deformations. Although the DIC approach has been widely used in world engineering practice for monitoring bridges and has proved to be a reliable and accurate method, there is a lack of systematic integral review on previous practical applications, revealing limitations and perspectives. This is the main motivation and novelty of this study, which will describe selected case studies, in which DIC was used on real full-scale bridge structures and propose improvements for the method.

Background: this interventional pilot study aimed to evaluate the short-term (3 years) efficacy of focal laser ablation (FLA) in treating the index lesion of low-intermediate risk prostate cancer, along with assessing the safety of the procedure (ClinicalTrials.gov ID NCT04045756). Methods: forty patients aged between 46 and 86 with histologically proven organ-confined prostate cancer and low to intermediate progression risk were included. FLA was performed under percutaneous fusion magnetic resonance/ultrasound guidance in a Day Hospital setting under local anesthesia. Patients underwent regular clinical assessments, PSA measurements, post-procedure MRI scans, and biopsies at 36 months or if positive findings were detected earlier. Statistical analyses were conducted to assess trends in PSA levels and cavity dimensions over time. Results: forty patients were initially included, with 15 lost to follow-up. At 36 months, a mean PSA reduction of 60% was observed, and 80% of MRI scans showed no signs of in-field clinically significant residual/recurrent cancer. Biopsies at 36 months revealed no malignant findings in 20 patients. No deterioration in sexual function or urinary symptoms was recorded. Conclusions: FLA appears to be safe, feasible and effective in the index lesion treatment of low-intermediate risk prostate cancer, with a high rate of tumor eradication and preservation of quality of life.

Macrophages undergo different cellular states upon activation that can be hyporesponsive (tolerated) or hyperresponsive (primed or trained) to subsequent stimuli. Epigenetic modifications are known to play a key role in determining these cellular states. However, little is known about the role of signaling pathways that lead to these epigenetic modifications. Here, we examined the effects of various inhibitors targeting key signaling pathways induced by lipopolysaccharide (LPS) on tolerance and priming in the murine macrophages. We found that a prolonged inhibition (>18 h) of the MEK1/2-ERK signaling axis reversed tolerance and primed cells in expressing IL-1β and other inflammatory cytokines such as IL-6, TNFα, and CXCL10. Ectopic expression of catalytically active and inactive MEK1 mutants suppressed and enhanced IL-1β expression, respectively. Primed cells by the MEK1/2 inhibitor U0126 expressed higher levels of gene sets associated with immune responses and cytokine/chemokine production but lower levels of genes with cell cycle progression, chromosome organization, and heterochromatin formation than non-primed cells. Of interest, expression of the histone 3 lysine 9 (H3K9) methyltransferase Suv39h1 and the H3K9 methylation reader Cbx5 was substantially suppressed, whereas the H3K9 demethylase Kdm7a was enhanced, suggesting a role of the MEK1/2-ERK signaling axis in H3K9 demethylation. The H3K9 trimethylation levels in the genomic regions of IL-1β, TNFα, and CXCL10 were decreased by U0126. Also, the H3K9 methyltransferase inhibitor BIX01294 mimicked and overexpression of Cbx5 prevented the U0126 training effects in both RAW264.7 cells and bone marrow-derived macrophages. Collectively, these data suggest that prolonged inhibition of the MEK1/2-ERK signaling axis reverses tolerance and primed macrophages likely through decreasing H3K9 methylation levels.

This study aims to test a model linking destination image, perceived value, tourist satisfaction, and tourist loyalty. Based on a sample of 300 tourists travelling by car from the World Natural Heritage Site of Tianchi, China, a new model of destination image was explored and data were analysed using partial least squares structural equation modelling (PLS-SEM). The results show that perceived value and satisfaction are direct antecedents of destination loyalty. Above all, perceived value and tourist satisfaction mediate the relationship between destination image and loyalty. Finally, this study discusses the theoretical and management implications of the findings in order to boost the tourism industry in the context of car trips.

Air pollution has created a lot of problems in the developed and developing countries. To avoid or reduce these problems, constant monitoring of the air should be ensured. The conventional techniques is costly because it requires a lot of money and time consuming. Biomonitoring has been the alternative method. Moss, lichens and plants are biomonitors available to entrap air pollutants. The aim of this paper is to discuss one of the ways of monitoring air pollution – Moss bag technique. To do this, types, choice, preparation, handling of bags after preparation of moss were discussed. From the literatures consulted, it was discovered that there were differences in the techniques used by the researchers. In all, the use of mosses with emphasis on the employment of moss bag have proved to be a powerful tool in airborne particulate and toxic elements. To conclude, developing countries should focus more on this technique because it will reduce cost of air monitoring.

A large core size and bending resistance are very important properties of mid-infrared energy transfer fibers, but large core sizes usually lead to a deterioration of bending properties. A negative-curvature nested node-free anti-resonant hollow-core fiber (AR-HCF) based on quartz is proposed. It was made by adding a nested layer to a previous AR-HCF design to provide an additional anti-resonance region while keeping the gap between adjacent tubes strictly correlated to the core diameter to produce a node-free structure. These features improve the fiber’s bending resistance while achieving a larger core diameter. The simulation results show that the radial air-glass anti-resonant layer is increased by the introduction of the nested anti-resonant tube, and the weak interference overlap between the fiber core and the cladding mode is reduced, so the fiber core’s limiting loss and the sensitivity to bending are effectively reduced. When the capillary wall thickness t of the fiber is 0.71 μm, the core diameter D is 70 μm, the ratio of the inner diameter of the cladding capillary to the core diameter d/D is 0.62, and the diameter of the nested tube is d0=29 μm, the fiber has a lower limiting loss at the wavelength of 2.79 μm, and the limiting loss is 3.28×10-4 dB/m. At the same time, the optimized structure also has good bending resistance. When the bending radius is 30 mm, the bending loss is only 4.72×10-2 dB/m. To our knowledge, this is the first research to achieve an anti-bending low-loss micro-structure hollow fiber with a bending radius of less than 30 mm in the 2.79 μm band. An anti-bending low-loss anti-resonant hollow-core fiber with this structure constitutes a reliable choice for the light guiding system of a 2.79 μm band erbium laser therapy instrument.

This study evaluated the effect of low-level laser irradiation induces by diode laser on the speed of orthodontic tooth movement of canines submitted to initial retraction. Twenty-four mandibular canines were retracted by using NiTi spring (force of 150 g/side). Thirteen of those were irradiated with a diode laser 980-nm diode laser (Wiser Laser Doctor Smile, Lambda) operating at an 810-nm wavelength (1 W of output power, continuous wave of 66.7 J/cm2) that was equipped with a 0.6-mm optical fiber in continuous-wave mode. The canine retraction was accomplished by using prefabricated coil springs. The right of the mandible was chosen to be irradiated with the laser, whereas the left side was considered the control without laser irradiation. The laser was applied with 0-, 3-, 7-, and 14-day intervals. The amount of canine retraction was measured with a digital electronic caliper while the pain level was prompted by a patient questionnaire. The speed of tooth movement was significantly greater in the laser group than in the control group. The pain intensity was also at a lower level in the laser group. Our findings suggest that diode laser therapy can highly accelerate tooth movement during orthodontic treatment and can also effectively reduce pain level.

Underwater image often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured Blind Super-Resolution Generative Adversarial Network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrate that the proposed method achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Underwater Image Quality Measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

Physics behind collimated highly directional nature of lasers, and factors that keep the seven coloured waves that form white light together during their journey from Sun to Earth, in the face of the natural disruptive forces, is not fully understood. Energy levels were measured, in terms of alterations in induced current and voltage, in beams from a red laser, white LED light and the Sunlight before and during their disruption by diffusers (frosted glass for the lasers) and diffractors (diffraction grating for the white light) using a photovoltaic solar cell panel attached to a digital multimeter. Results show that disruption of the beams results in release of extra energy named as ‘Latent Light Binding’ Energy’. It is hypothesized that the ‘binding’ energy keeps laser waves firmly bound together both end-on and side-on enabling laser beams to travel long distances in collimated manner. Likewise, the 7 coloured waves that constitute white light are kept together, probably side-on, in their journey from the Sun to the Earth. The observation that diffraction of sunbeam is associated with increased power generation provides a new lead to improve harnessing of solar energy, where, currently, the focus is mainly on improving efficiency of photovoltaic cell.

In Indonesia, types of meat are still identified manually due to increasing prices for beef and buf-falo ahead of Idul Fitri. Traders mix beef with pork to thwart the beef scam. Haar wavelet and GLCM, GLCM with angles of 0°, 45°, 90°, and 135°, as well as matrices using contrast, correlation, energy, homogeneity, and entropy, are used in feature extraction. The following are the results of meat image classification testing using k-NN, Haar wavelet, and GLCM: The k-NN algorithm shows superiority in identifying fresh (99%), frozen (99%), and rotten (96%) meat texture images with the most fantastic accuracy results in every situation. Meanwhile, GLCM routinely provides good results, especially regarding the texture image of fresh meat (183.21) and rotten meat (115.79). However, despite delivering less throughput than k-NN and GLCM, Haar wavelets are still helpful, especially when dealing with fresh meat texture images (89,96).

Despite the fact that COVID-19 treatment and management are now considerably regulated, SARS-CoV-2 is still one of the leading causes of death in 2022. The availability of COVID-19 vaccines, FDA-approved antivirals, and monoclonal antibodies in low-income countries still poses an issue to be addressed. Natural products, particularly traditional Chinese medicines (TCMs) and medicinal plant extracts (or their active component) have challenged the dominance of drug repurposing and synthetic compound libraries in COVID-19 therapeutics. Abundant resources and excellent antiviral performance make natural products a relatively cheap and easily available alternative for COVID-19 therapeutics. Here, we deliberately review the anti-SARS-CoV-2 mechanisms of the natural products, their potency (pharmacological profiles), and application strategies for COVID-19 intervention. In light of their advantages, this review is intended to acknowledge the potential of natural products as COVID-19 therapeutic candidates.

Multispectral image (MSI) and hyperspectral image (HSI) fusion (MHIF) aims to address the challenge of acquiring high-resolution (HR) HSI images. This field combines a low-resolution (LR) HSI with an HR-MSI to reconstruct HR-HSI. Existing methods directly utilize transformers to perform feature extraction and fusion. Despite the demonstrated success, there exist two limitations: 1) Employing the entire transformer model for feature extraction and fusion fails to fully harness the transformer’s potential in integrating the spectral of the HSI and spatial information of the MSI. 2) HSI has a strong spectral correlation and exhibits sparsity in the spatial domain. Existing transformer-based models do not optimize this physical property, which makes their methods prone to spectral distortion. To accomplish these issues, this paper introduces a novel framework for MHIF called Sparse Mix-Attention Transformer (SAMformer). Specifically, to fully harness the advantages of the Transformer architecture, we propose a Spectral Mix Attention Block (SMAB), which concatenates the keys and values extracted from LR-HSI and HR-MSI to create a new multi-head attention module. This design facilitates the extraction of detailed long-range information across spatial and spectral dimensions. Besides, to address the spatial sparsity inherent in HSI, we incorporated a sparse mechanism within the core of SMAB called Sparse Spectral Mix Attention Block (SSMAB). In the SSMAB, we compute attention maps from queries and keys and select the K highly correlated values as the sparse attention map. This approach enables us to achieve a sparse representation of spatial information while eliminating spatially disruptive noise. Extensive experiments conducted on three benchmark datasets, namely Cave, Harvard, and Pavia Center, demonstrate the SMAformer method outperforms state-of-the-art methods.

Silicon light funnels are three-dimensional subwavelength structures in the shape of inverted cones with respect to the incoming illumination. Light funnel arrays can serve as an efficient absorbing layers on account of their light trapping capabilities associated with the presence of high density complex Mie modes. Specifically, light funnel arrays exhibit broadband absorption enhancement of the of the solar spectrum. In the current study, we numerically explore the optical coupling between surface light funnel arrays and underlying substrates. We show that the absorption in LF array-substrate complex is higher than the absorption in LF arrays of the same height (~10% increase). This, we suggest, imply that a LF array serves as an efficient surface element that imparts additional momentum components to the impinging illumination, and hence optically excites the substrate by near-field light concentration, excitation of traveling guided modes in the substrate and mode hybridization.