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We have achieved a successful synthesis of a label-free, flexible, portable, and colour-indicating biosensor using cholesteric nanostructure liquid crystals (LCs), commonly referred to as cholesteric LCs (CLCs), on paper fibres. This innovative approach holds significant promise for rapid screening in precision medicine and other applications. We incorporated optically active nematic LCs in various configurations within CLCs, allowing them to rotate along different axes. When exposed to light, these LCs reflected light at specific wavelengths. By employing polarised optical microscopy and a charged couple device (CCD), we conducted titration experiments with bovine serum albumin (BSA) on the paper substrate and quantified protein concentrations based on the observed colour shifts. Utilising internal Python pixel analysis software, we evaluated the red, green, and blue colour components, which served as biomarkers for different BSA concentrations. Our results demonstrated a linear correlation between BSA concentration and colour variations. This CLC-based paper-substrate biosensor exhibits the potential to efficiently screen a large number of samples, thereby enhancing the diagnostic accuracy in clinical settings. Combining the colour-indicating and quantifiable attributes of CLCs with the affordability and portability of paper substrates, we have developed a versatile biosensor. Its ease of use, rapid response, and high sensitivity make it a promising tool for future disease detection endeavours. [ABSTRACT FROM AUTHOR]

To attain an improved knowledge about ink–paper coating adhesion during offset printing, we have developed a laboratory procedure to measure adhesion failure between ink and coated paper during ink setting. This paper describes the technique and a special case where two ground calcium carbonate (GCC)-based coatings have been printed, one normal coating containing the delivered amount of dispersing agent already present in a typical GCC slurry and another coating made from the same GCC slurry but with excess additional free dispersant in the form of sodium polyacrylate. The areas where the ink–paper coating adhesion had failed at the pull-off points after tack testing were characterized by determining white spots in the retained print using image analysis. The white spots represent areas where ink has detached from the coating after contact with a rubber coated cylinder. This area is referred to as ink–paper coating adhesion failure area (IPCAFA). A minimum in IPCAFA at the point of maximum in tack force was found for both coatings where the print was applied onto dry coating. When the coatings, however, were pre-damped, IPCAFA increased on the normal coating over a short time period, independent of the dampening level. For the coating with excess dispersant, on the other hand, the higher dampening level strongly increased the IPCAFA. The strong impact of excess dispersing agent in this work may be explained by a number of related effects, including increased polarity of the coating surface, an associated moisture layer and an effective reduction of fine pores due to water-swelling polyacrylate that blocks these pores. [ABSTRACT FROM AUTHOR]

The present paper proposes an automated approach to estimate the aerosol reflectance at the Advanced Very High Resolution Radiometer (AVHRR) red channel. The aerosol dominant pixels were separated through two orthogonal transforms. The aerosol reflectance ratio at these pixels was estimated through regression. The results are validated with in situ measurements. The retrieved water-leaving reflectance matched the modelled values with a relative error below 45%. The smallest error values were at the stations with the closest sampling time to image acquisition. However, a weak correlation of 16% was found between water-leaving reflectance and aerosol signals. This suggested that these errors could be attributed to the spatial and temporal variability between the two sampling methods (ship measurement and pixel reflectance). [ABSTRACT FROM AUTHOR]

Video processing is the most emergent area of image processing in Research and Development. This paper, we present some advanced technology interrelated to video and image processing, which will provide a super-resolution high-quality most prominent visible light video for better human visualization and background motions or gesticulations of the human brain. There are two approaches for increasing the current resolution level of an image. The first one is the improvement of spatial resolution by reducing the size of pixels through the techniques of sensor manufacturing by about 40 $ {\mathcal{M}}{\textrm{m}^2} $ M m 2 for a 0.35 $ {\mathcal{M}}\textrm{m} $ M m CMOS process and the second approach is the enhancement of chip size that also enhances the capacitance. In video processing, we mainly focus on object images, i.e. any form of signal processing in which the input is an image and the output is a photograph or a frame of video. So, it will be better to converge over techniques of digital image processing. In the proposed algorithm (MHI Simulink model), edge extraction is performed to find the precious area of MRI and CT-Scan images. Using FPGA, edge detection methods have been implemented over scanned images to get better accuracy. The accuracy of the proposed method has been compared with Prewitt and Sobel's edge detection techniques. The proposed method has given better accuracy than Prewitt and Sobel methods. Finally, this paper shows future directions for researchers to enhance the characteristics of digital image and video processing. [ABSTRACT FROM AUTHOR]

The feasibility of the application of image/signal processing for measuring, marking, matching, and sorting vast quantities of data derived from materials typically found in artworks is presented through four case studies. Different patterns produced by canvas weave structures, surface textures of historic photographic papers, chain line intervals in Rembrandt's printing papers, and watermark variations have been subjected to different modes of computational analysis. The art-historical implications that result from computer-generated algorithms – including dating, attribution, authenticity, and workshop practices – can be considered as "computational connoisseurship." The case studies discussed point to future areas for research. Finally, because of the need for statistically meaningful datasets of images, a practical means of recording internal paper structure is introduced. [ABSTRACT FROM AUTHOR]

This smartphone colorimetry investigated RGB (red, green and blue), HSV (hue, saturation and value of brightness) and CMYK (cyan, magenta, yellow and key) color parameters for characterizing bloodstains. Blood samples, separately deposited on cotton cloths and tissue papers, were analyzed by the 'Colorimeter X' application on an iPhone XR after 1, 5, 7, 9 and 11 days. The reductions in RGB and V values recorded by the smartphone application are consistent with darker bloodstain over time. Bloodstain colors are sensitive to the substrate and person-to-person variation, but a similar correlation with time could be established by a linear least squares fitting. With the highest coefficient of determination averaged from four samples, the V value is the most effective parameter for describing bloodstain ageing. For other color spaces, the R and K almost match the V value in measuring the bloodstains. Because these three values may be susceptible to the deviation in the measurement, the B value is suggested as an additional parameter in the bloodstain characterization. This small-scale study demonstrates the potential of colorimetric parameters in an on-site analysis by a cost-effective smartphone. [ABSTRACT FROM AUTHOR]

Accidents and Osteoporosis are two prime reasons of femur bone fractures. Osteoporosis causes weakening of bone in elder patients. Proper treatment during surgery will help patients for early rehabilitation. This paper portrays a review of concerned literature identified with Biomedical Image analysis and modelling of femur bone fractures for an operative planning. Paper intended to review literature for Biomedical image preprocessing to identify the proper region of Interest (ROI) by using an appropriate image modality, building up a 3D model of femur bone structure using image database with software, analysis of created 3D model for applying implant and its parametric analysis for evaluation of virtual surgery and operative planning to surgeon depending on assessment report produced utilising earlier advances. Such sort of work will keep away from mismatches of fracture which may prompt genuine implanted complications for patients including secondary fractures, poor anatomical alignment and bone debilitating too. Paper will summarise conclusion of concern literature to guide new researcher to start with [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) has all-weather and all-day observation capabilities and a certain ability to penetrate the surface; therefore, it has unique advantages in many aspects, which other remote sensing methods cannot achieve. However, owing to imaging principles, the interpretation of SAR images is complicated. Therefore, converting SAR images into optical remote sensing images is one method of SAR image interpretation. Therefore, this paper proposes an improved conditional generative adversarial network (cGAN) that includes a generator based on the encoder-decoder structure of the swin transformer feature extraction module. The generator uses a spatial pyramid structure and multi-scale depth feature extraction structure to extract the SAR image features at different scales. A multi-scale discriminator was used to discriminate images on different scales. The introduction of feature matching loss makes the discriminator's feedback to the generator richer and uses perceptual loss based on VGGNet to evaluate the image quality in depth. A series of experiments verified the effectiveness of the method in this study and proved the potential of the transformer structure in the field of SAR image translation. The method presented in this paper has obvious advantages and can translate SAR images into optical remote sensing images. [ABSTRACT FROM AUTHOR]

Directional mobility and controlled adhesion of water drops were obtained on superhydrophobic paper surfaces by generating high surface energy ink patterns using commercial desktop printing. By changing the curvature/shapes of the ink patterns, geometrical constraints were imposed on the movement of the three-phase liquid/solid/vapor contact line. With proper design of pattern shapes, the critical sliding angle of water drops was manipulated with respect to different directions. Adhesion tunability of water drops on superhydrophobic paper was established by design of chemical heterogeneity. Printing of 'checker' ink patterns with appropriate area fraction and feature dimensions allowed variation in the critical sliding angle of water drops. The manipulation of directional mobility and adhesion of water drops on superhydrophobic paper surfaces allows the design of novel components such as 2D flow paths, gates/diodes, junctions and drop size filters for emerging 2D paper-based microfluidics technology for biomedical diagnostic applications. [ABSTRACT FROM AUTHOR]

Morphological parameters of substrate surfaces determine the adhesion of coatings and pressure-sensitive adhesives. This paper is concerned with the assessment of fractal parameters by means of cross-section images and contact stylus instrument measurements of blast-cleaned substrates. Mild steel samples were prepared with different abrasive materials. Images were taken from a total of 12 surface configurations with an optical microscope. From each of the cross-section, a 2D profile of the steel surface was derived using the ImageJ image manipulation software. The fractal dimensions of these profiles were estimated by means of the box-counting method and then compared fractal values for 2D profiles taken from contact stylus measurements. Design of Experiment (DoE) was applied to statistically analyze the relationships. The factors considered included abrasive type, surface preparation grade, surface roughness, and most importantly for this paper, the surface profile evaluation method. The profile evaluation methods included cross-section analysis and contact stylus instrument measurements. Regardless of the method used to determine the surface profile, identical relative trends resulted for both measurement variants, with normalised values approximately twice as high for the cross-section profiles compared to the ones from contact stylus data. [ABSTRACT FROM AUTHOR]

Image processing fields, such as separating data from healthcare images, can benefit greatly from using deep learning algorithms. This can be extremely beneficial. For the treatment of someone with a brain tumour, discovering the tumour as soon as possible is critical. The patient's life expectancy would increase if the tumour was discovered sooner rather than later. Medical images typically lack contrast because of noise or because there aren't enough diffusive boundaries in the image. This is because of noise. In order to better understand tumours in medical image segmentation, MRI was used to diagnose the tumour, but it also played a beneficial role in clinical image analysis. Why did an MRI need to be done? This paper will examine how bad a brain tumour is using the hybrid deep learning algorithm, which is what this paper is about. Algorithms help us get the correct answer. Based on the direction of the images taken by MR, there will be a lot of information about how to divide up the pictures. In this way, three separate networks are trained to improve their segmentation results. [ABSTRACT FROM AUTHOR]

Linting is the process where small particles are removed from the surface of newsprint during offset printing. The buildup of these particles on the offset printing blanket is one of the major quality issues in offset printing. In this study, we apply Weibull statistics to particle size distribution of lint collected after both sheet-fed, printing either 1500 or 3000 copies, and web-fed printing trials, printing 23 000 copies. Weibull statistics has previously been applied successfully to the distribution of strength of paper samples. The lint data were obtained by washing lint from the blanket, filtering, imaging using a microscope and then analysing the images to measure the particles. To analyse the data we used the lint particle size as the independent variable. The results showed that particle size distribution of lint collected after printing 1500 or 3000 sheet-fed copies could be very well fitted with a Weibull distribution, with R2 values 0.98 or higher. The Weibull distribution power term ranged from 1.52 to 1.26. The Weibull distribution power term gives the shape of the distribution of bonding strength of lint particles to the sheet surface as well as the response of the surface to a change in the force applied by the ink to remove particles as lint. The fits were much poorer for the lint collected after the web-fed trials, with Weibull power term ranging from 1.17 to 0.47. The differences between the sheet and web-fed trials were due to the preferential movement of smaller lint particles off the blanket during the much longer runs of the web-fed trials. [ABSTRACT FROM AUTHOR]

This paper aims to explore the narrative expression skill of design students through manipulating image structure. Moreover, narrative expression is the skill of interpreting design with narrative. The study is a classroom experiment conducted naturally, and the experimental data is obtained and analysed by quantitative analysis. We found differences in students' narrative tendencies and richness in overall narrative expression. The teaching and training of design narrative can effectively help students familiarise themselves with the design process and personal skill integration and encourage students to pay attention to the design of elements and content. This will improve students' design competencies under skills different from technology. The paper concludes that students must improve and integrate design competencies through practical methods under other narrative tendencies. Furthermore, the lecturer can explore and adjust design teaching through design narrative training to strengthen or balance students' design performance competency. [ABSTRACT FROM AUTHOR]

Declassified images from the Keyhole (KH)-9 HEXAGON mapping camera system (MCS) offer fine-scale details of urban regions. However, these images have seldom been utilized in urban research due to challenges in labelling (collecting training samples), having only a single panchromatic band and classification. To tackle these limitations, this paper focuses on developing a multi-stage reconstructed historical fine-scale urban landscape (RHFUL) pipeline for KH-9 HEXAGON MCS. The proposed pipeline first integrates internalized parameters, hierarchical object-based image analysis properties and class variability to synthesize new features, abbreviated to IHC. Second, the pipeline uses a weak semi-automated supervised labelling (WSSL) approach to acquire training samples. Finally, the training samples and generated features are subjected to the SegNet deep learning architecture. The performance of each step was assessed against corresponding state-of-the-art benchmark approaches for each of synthesizing features, labelling and classification. In the proposed RHFUL pipeline, the proposed IHC provided the most salient information for urban classification, WSSL labelled urban features more accurately, and the SegNet architecture classified more accurately the urban features relative to the benchmarks. Considering the potential advantages, but also limitations of KH-9 HEXAGON MCS images, further research should be undertaken, particularly drawing on the current advances in pattern recognition techniques for contemporary digital satellite sensors. [ABSTRACT FROM AUTHOR]

Classification with a few samples of training set has been a long-standing issue in the field of polarimetric synthetic aperture radar (PolSAR) image analysis and processing. In fact, one of the most important challenges of the PolSAR image classification task is the number of labelled samples. In essence, training network by utilizing just few numbers of training samples cannot be led to the reliable result since the neural network is so sensitive to the number of training samples. In addition, in the real PolSAR image classification task, the huge number of training samples is not accessible. On the other hand, classification performance using just small number of labelled samples is not an accurate result. So, in this paper, aiming at the small number of training samples of the PolSAR image classification task, a novel ensemble self-supervised feature-learning (ESSFL) model is designed. The designed ESSFL can automatically extract PolSAR features conducive to PolSAR image classification with a small number of training samples. In addition, it can significantly decrease the dependence of neural network algorithms on large labelled samples of training set. First, to utilize the spatial–polarimetric features of PolSAR data perfectly, the EfficientNet-B0 is presented and utilized as the main section of the deep learning (DL) model to extract DL features of PolSAR data. Then, using an optimization function that constrains the cross-correlation matrix of various distortions of each sample to the identity matrix, the designed deep learning model can obtain the effective features of homogeneous samples gathering together and heterogeneous samples separating from each other in a self-supervised manner. Moreover, two ensemble learning (EL) models, feature-level and view-level ensemble, are proposed to increase the feature extraction capability and classification result by jointly using spatial features at different scales and polarimetric information at different bands. Finally, the stack of the obtained features and the main polarimetric information of PolSAR data can be input into classifier for the classification of PolSAR data. In this paper we use two different classifiers, the first one is the Random Forest (RF) classifier and the second one is the Support Vector Machine (SVM) classifier. The advantages of SVM include that it can be used to avoid the difficulties of using linear functions in the high-dimensional feature space, and the optimization problem is transformed into dual convex quadratic programs that are so usable for an actual image classification task. In addition, among all the available classification methods, random forests provide one of the highest accuracies. The random forest technique can also handle big data with numerous variables running into thousands. In fact, it can automatically balance data sets when a class is more infrequent than other classes in the data that is so important for a real PolSAR image classification task. Experimental results on three well-known PolSAR data sets illustrate that the designed ESSFL can extract more discriminant features using the designed deep learning model. In the end, the experiments prove that the designed ESSFL has a significant classification performance compared with different deep learning models in the case of small number of training samples, and also it can achieve a better result in the case of large number of training samples by comparison with the most of the deep learning models. [ABSTRACT FROM AUTHOR]

Linting is one of the biggest concerns for the newspaper printing industry. In the offset printing process, lint particles from the paper are removed from the paper surface under the force of the tacky ink, adhere to the printing blanket and disrupt the transfer of ink, causing deterioration in image quality and affecting the pressroom productivity. This paper deals with the bond strength of the lint particles released from the paper substrate and printing blanket surface. The strength distribution of lint particles was characterised in order to study the dynamics of linting. The dynamics was modelled using a system with two first-order rate equations. Weibull statistics were used to describe the particle size distribution of the lint collected after the sheet-fed printing trials. In this investigation, the lint samples were collected by washing the lint from the printing blanket, filtering and performing image analysis to measure the particles. In order to analyse the data, the lint particle size was set as an independent variable. The number of small particles removed is as expected much higher than the number of larger particles. When the ink tack is increased and thus also the force working on the lint particles, then the relative increase in large particles is higher than the number of small particles. The removal rate of particles from the printing blanket is independent of particle size indicating that both the adhesion force between parti-cles and blanket and the removal force due to ink tack is proportional to the particle area. [ABSTRACT FROM AUTHOR]

In the last few decades, many studies have been performed with the main hope of utilizing imaging methods so as to detect static (bubble size and shape, color, texture) and dynamic (velocity and stability) features of froth that involve crucial information about the process state in order to assess and monitor the performance of flotation process. Although several types of flotation automated control system problems are being successfully solved using the various techniques of features extraction from froth images, there are still a number of unresolved subjects and obstacles. Hence, a suitable review of these methods is needed. After reviewing the technical aspects of froth images structural features in part 1 of this two-part review paper, this work provides an overview of several different approaches for the sake of analyzing and classifying froth image texture and dynamic features. Finally, we conclude our review by linking concentrate grade and recovery with the froth image features described in this two-part paper. In the close future, it is expected with ever-growing application needs and research progress, image analysis systems are becoming even more effective solutions for monitoring and controlling flotation performance. This review provides a platform for future initiatives and potential developments in this area. [ABSTRACT FROM AUTHOR]

We present a bibliometric analysis of the evolving field of digital image forensics (DIF) from 2014 to early 2020. The study analyses and discusses the results obtained from the highly indexed Scopus database for various subject areas, renowned authors, funding agencies, and affiliations using a mind map approach. We adapt the PRISMA methodology formerly applied for systematic reviews to refine our search results. The results of this study indicate an increasing awareness of DIF in countries like China, India, and Italy along with an increase in publications since 2018, specifically, with the application of deep learning techniques for DIF. [ABSTRACT FROM AUTHOR]

The Anatolian was at one time a single plate that is currently being turned into many small plates. The earliest separation stage occurred in the southeastern Anatolian from the Palaeocene to middle Eocene times. This study for the first time confirmed that the Anatolian plate was broken into a group of small tectonic plates with an area less than 1000 km2. We have called them nanoplates. We have detected formation of (9) nano plates in the East Anatolian region with (6) nanoplates in the West Anatolian region and single nanoplate in the central Anatolian region. The key geological feature that we targeted here to find these nano plate boundaries is the fault network. A network of deep fractures along the nano plates boundaries. This paper determined these boundaries by integrating and analysing huge remote sensing geophysical data. This paper ued for the first time the weights of filters in inception convolutional neural network (CNN) in Anaconda python Tensor Flow and Keras platform. Also used latest special scientific programming libraries of Python language like SciPy, Arcpy, NumPy and Pandas for data preparation. In addition, this study used ArcGis10.8 from ESRI foundation for GIS analysis beside PCI GEOMATICA 2020 for image analysis. [ABSTRACT FROM AUTHOR]

This paper focuses on the visual representation of "the urban people" by the Spanish leftwing populist party Unidas Podemos (UP) during the campaign for regional elections in Madrid in 2021. The political environment was characterised by increasing polarisation and the hyper-leadership of two candidates, right-wing Isabel Díaz Ayuso and UP's national leader Pablo Iglesias. In this context, UP employed a diverse range of images and audiovisual material with a specific focus on the urban dimension. This paper explores how the populist logic and societal split--the people vs. the elite--deployed by UP are visually represented and connected with the urban space. Drawing on the central role of images in politics, this paper contributes to the emerging scholarship on the visual and spatial dimensions of populism by (a) exploring the connections between populist imaginary, space, and the visual; (b) advancing an empirical analysis of the image of "the people" in a left-wing political party; and (c) connecting the imaginary of populism to its geo-graphical dimension, stressing both the urban and class divide. [ABSTRACT FROM AUTHOR]

This paper critically interrogates Elon Musk's celebrity image through an analysis of Musk's appearance in the podcast The Joe Rogan's Experience. It shows that the billionaire's public persona is a constitutive part of his corporate strategy, and introduces the concept of 'celebrity management' to theorise how the affordances of celebrity can be mobilised to create conditions for a company to operate. Combining celebrity studies with critical political economy, Musk's stardom is situated in the context of financialised capitalism, addressing the systemic contradictions contained (and artificially solved) in his image, while also demonstrating that it is tactically deployed to further financialised logics of his companies. [ABSTRACT FROM AUTHOR]

This paper is devoted to second-order optimality conditions of constrained vector optimization problems with continuously Fréchet differentiable data. Different from conventional approaches, we employ a new one, image space analysis, to establish suitable regularity conditions and then present new second-order necessary optimality conditions that depend on one single KKT multiplier for all critical directions. Moreover, we propose one more regularity condition for the multiobjective case and compare it with the previous one. Besides, we compare the main regularity conditions with the existing ones in the literature for the scalar case. [ABSTRACT FROM AUTHOR]

This article aims to present a Mixed Integrative Heuristic Approach (MIHA) for image studies based on textual content (media news) analysis. MIHA was developed during an exploratory study on the image of China and the Chinese in Zambia and Angola. The analyzed database was relatively small (2477 articles), and the material was very heterogeneous, because of the wide scope of topics covered by the texts and the fact that we analyzed content from both English- and Portuguese-language media. To obtain the best possible results, we decided to employ the mixed methods approach for a bottom-up created dictionary, and to design a set of sentiment indexes based on both automatic and manual coding. This paper focuses on the two most important steps of the method development: dictionary building and sentiment indexes design. [ABSTRACT FROM AUTHOR]

Cohesive soil was often submitted to the shrinkage when it is dried. Constrained shrinkage and heterogeneity of the soil in terms of density or porosity and water content conduct to cracks of the fine soil. The unsaturated permeability as a function of suction changes significantly with cracks number and opening. The paper aims to study the flow exchange flow in the unsaturated cracked clay flow, by measurement of the unsaturated permeability using tensiometer and hygrometer sensors in cracked specimens under drying and controlled in an environmental chamber. The instantaneous profile method was used to interpret these tests. Cracks were characterised by Image Analysis which conducted to define some known quantitative parameters as the Cracks Intensity Factor (CIF). Aiming at reproducing the experimental results, the paper presents a model which leads to the prediction of the suction field and the flow rate exchange. This model integrates analytical equations of the unsaturated permeability and water retention property of the cracked soil and also the shrinkage behaviour. A good agreement between numerical and experimental results is highlighted in terms of flow exchange rate. [ABSTRACT FROM AUTHOR]

In 2002, Israel started the construction of a 708-kilometer wall to control the Palestinian territories. The Wall offered a massive canvas, which the Palestinians used to develop graffiti and murals. The artwork and graffiti are location-specific, ephemeral, transitory, and unstable. This paper aims to document, archive, and analyze discourse and themes emerging from the artwork that developed on the Wall between 2018 and 2022. The analysis of these images has revealed that, while the purpose of building the Wall was to curb and stifle their resistance, Palestinians turned it into a platform where they were able to express their commitment to national resistance, freedom, justice, love, poetry, business, and even personalization of their own stories. The Wall has also attracted international solidarity and sparked a new protest movement called "struggle tourism", focusing on the Palestinian presence in the region's physical, moral, and political landscape. [ABSTRACT FROM AUTHOR]

Deep learning (DL)-based approaches are notable for their ability to establish feature associations without relying on physical constraints, unlike traditional strategies that are complex and dependent on expert experience. However, three main challenges hinder the versatility of semantic segmentation models. First, the targets in these images are dense and exist at varying spatial scales, which imposes higher demands on the model for accurate segmentation across scales. Second, the segmentation of small targets in the images is often overlooked, leading to a compromise between fine segmentation and model efficiency. Lastly, the data-intensive nature of remote sensing images and the resource-intensive operations of large-scale networks impose significant communication and computation burdens on edge devices, which may not have sufficient resources to handle them effectively. To address these challenges, this paper proposes a lightweight semantic segmentation method for remote sensing images to achieve high-precision segmentation for multi-scale targets while maintaining low computational complexity. The main components include: (1) embedding the inverted residual block structure to minimize the number of model parameters and computational costs; (2) introducing the parallel irregular space pyramid pooling module to efficiently aggregate multi-scale contextual information for fine-grained recognition of small targets; and (3) embedding transfer learning into the encoder-decoder structure to speed up the convergence rate and improve multi-scale feature fusion capability, thereby reducing semantic information loss. The proposed lightweight method has been extensively tested on real-world high-resolution remote sensing datasets. It achieved PA, MPA, MIoU, and FWIoU scores of 87.90%, 75.76%, 66.29%, and 78.81% on the Vaihingen dataset; 87.03%, 85.31%, 74.85%, and 77.54% on the Potsdam dataset; and 95.37%, 83.33%, 75.70%, and 91.31% on the Aeroscapes dataset. Compared to other popular semantic segmentation models, the proposed method achieved the highest values in all four evaluation indicators, demonstrating its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

The Remote Sensing Image Change Captioning (RSICC) has recently emerged in the field of remote sensing image interpretation; it aims to automatically predict natural language captions of significant semantic changes in bi-temporal remote sensing images. Recent studies of RSICC have improved the accuracy of change captions of bi-temporal remote sensing images to a large extent. Nevertheless, there still remain challenges in multi-scale perception of ground objects and feature enhancement of bi-temporal remote sensing images. To address these challenges and further improve the accuracy of RSICC, a novel deep learning–based end-to-end scale-wised feature enhancement network (SFEN) is proposed in this paper. SFEN integrates four efficient blocks: 1) the siamese backbone network (SBN) to extract initial features of bi-temporal remote sensing images, 2) the siamese receptive field fusion (SRFF) block to explicitly capture multi-scale semantic information of ground objects in bi-temporal feature maps, 3) the siamese global feature enhancement (SGFE) block to adaptively enhance key information and filtering redundant features of bi-temporal feature maps in both channel and spatial dimensions, 4) the change caption decoder (CCD) to map bi-temporal feature maps into natural language. The SFEN aims to precisely capture significant semantic information of ground objects in bi-temporal remote sensing images and predict accurate change captions. Experimental results on LEVIR-CC dataset demonstrate our SFEN outperforms recent state-of-the-art (SOTA) approach in RSICC by 5.2% on CIDEr-D and achieves a new SOTA. [ABSTRACT FROM AUTHOR]

Folklore reflects the concept of fear, with which genres such as incantations and prayers are directly associated. These texts establish adialogue with danger, either with commands or supplications, and they indicate what kind of dangers are imminent. By combining psychoanalytical and folklore theories, the paper offers the analysis of the conceptual image of fear and of the defensive function of prayers and incantations within the material of Slovenian incantation and prayer collections: what kind of threats one wants to chase away by incantations or/and magical signs and objects; and what kind of form and use contain the preventive incantations themselves. [ABSTRACT FROM AUTHOR]

Automatic pancreas detection and cropping with high precision from medical images is an important yet challenging problem for medical image analysis and Computer-Aided Diagnosis (CAD). Factors relating to the limited availability of image data and segmentation methodology hinder this task. High variability in the location of the pancreas,which occupies a very small area of the pancreatic Computed Tomography (CT) scans,and the anatomy of organs also add to the list of issues. These challenges necessitate an urgent need for the development of localization and auto-cropping methods of the region of interest (ROI). This paper presents the results obtained by the implementation of Region-based Convolutional Neural Network (RCNN)-Crop inspired by the Region Proposal Network (RPN) and Feature Pyramid Network (FPN) to localize the pancreas by building bounding boxes and auto-crop the ROI obtained from various other organs in the pancreatic CT scans and has a Mean Average Precision (mAP) of 28.10% for the dataset provided. [ABSTRACT FROM AUTHOR]

AbstractResearch on geospatial foundation models (GFMs) has become a trending topic in geospatial artificial intelligence (AI) research due to their potential for achieving high generalizability and domain adaptability, reducing model training costs for individual researchers. Unlike large language models, such as ChatGPT, constructing visual foundation models for image analysis, particularly in remote sensing, encountered significant challenges such as formulating diverse vision tasks into a general problem framework. This paper evaluates the recently released NASA-IBM GFM Prithvi for its predictive performance on high-level image analysis tasks across multiple benchmark datasets. Prithvi was selected because it is one of the first open-source GFMs trained on time-series of high-resolution remote sensing imagery. A series of experiments were designed to assess Prithvi’s performance as compared to other pre-trained task-specific AI models in geospatial image analysis. New strategies, including band adaptation, multi-scale feature generation, and fine-tuning techniques, are introduced and integrated into an image analysis pipeline to enhance Prithvi’s domain adaptation capability and improve model performance. In-depth analyses reveal Prithvi’s strengths and weaknesses, offering insights for both improving Prithvi and developing future visual foundation models for geospatial tasks. [ABSTRACT FROM AUTHOR]

In this paper, a digital bio-dosimeter (DBD) with a square window size of 7 × 7 pixels is proposed for the quantitative detection of the deformed biological cells irradiated by X-rays with different energies and γ-rays from 60Co. The basic idea of the DBD is to compute the energy of the bio-dosimeter window size by summing up the entire gray levels. Peripheral blood films (PBFs) from male albino rats irradiated by ionizing radiations are prepared and photographed using an Automatic Image Contour Analysis system with high magnification to produce a 2D image. The surf function in MATLAB is used to convert the 2D image to a 3D shaded surface to resample the date more finely. The proposed DBD shows that the computed energies on the boundaries of the normal RBCs are nearly the same, while the computed energies on the boundaries of the deformed cells are significantly larger. [ABSTRACT FROM AUTHOR]

Assessing ecosystem health on a large scale is crucial for a wide range of management and regulatory decisions. Technologies such as hyperspectral imaging allow noninvasive and rapid estimation of key attributes based on observed reflectance. However, these images are high-dimensional and real-world applications require models based on fewer wavelengths. This paper proposes a new wavelength selection and feature extraction method for hyperspectral image analysis based on genetic programming to automatically select key wavelength regions and informative image features. A dataset of hyperspectral images of sediment in the field was collected and paired with ground-truth measurements of the sediment porosity and organic matter content. Two new program structures were proposed to construct feature extraction trees from either the mean reflectance spectra (spectra-based) or full hyperspectral images (image-based). SVR models were constructed to predict attributes based on the extracted features. Various regression models were used to predict the porosity and organic matter content. Full-wavelength models were constructed to reliably predict the organic matter content. The proposed spectra-based genetic programming solutions show competitive results compared to common wavelength selection methods, such as SPA, CARS, and RC. Finally, the best-evolved solution was applied to predict sediment organic matter content across all collected images. [ABSTRACT FROM AUTHOR]

AbstractGeogrid-reinforced soil (GRS) is a widely employed compound material in the construction of retaining walls. This paper presents a set of reduced-scale model tests to study the performance of GRS walls with dry backfill under monotonic footing loading, varying parameters such as wall inclination angle and the number of reinforcement layers. Lateral deflections of the retaining wall, bearing capacity of the footing and the failure mode of the soil were analysed. The results indicated that augmenting the reinforcement layers will lessen wall lateral deflections, and increasing the angle of inclination could significantly enhance ultimate bearing capacity of the footing. A linear relationship was found between the overall lateral deflections of the retaining wall and the footing settlement. The failure of geogrid could lead to a breakdown in the connection between the geogrid and the soil, consequently causing local failure. The failure modes of the GRS wall under strip footing was composed of shear failure in the reinforcement layer and spiral failure in the weak layer. At last, the logarithmic spiral analysis method was modified slightly to calculate the ultimate bearing capacity. The analytical solution and the outcomes derived from experimental models exhibit a high level of concordance. [ABSTRACT FROM AUTHOR]

The development of innovative applications of imaging technologies for monitoring change in cultural heritage objects is central to the CHANGE-ITN project. Within this framework, the authors' ongoing work targets the harnessing of hyperspectral imaging (HSI) for the documentation of cleaning treatments on the monumental University of Oslo Aula unvarnished oil paintings on canvas (1909–1916) by Edvard Munch (1863–1944). This is applicable to unvarnished paint surfaces in general. Particularly, this paper evaluates HSI techniques for the investigation of cleaning tests carried out in 2008, which targeted the removal of visible, ingrained particulate matter from Munch's unvarnished oil paints and exposed grounds on Kjemi (1914–1916). An exploratory in situ HSI campaign using visible to near-infrared (VNIR) and short-wave infrared (SWIR) cameras was undertaken to capture the soiled or previously cleaned areas on the painting's surface, with comparisons to analogous mock-ups. Principal component analysis (PCA) was used to extract information about possible trends with respect to the soiling. Results indicate that in both VNIR and SWIR ranges, the intensity of reflectance can be used to discriminate statistically between soiled and unsoiled/cleaned areas, whereas in SWIR, combination and first overtone bands of oil peaks can also serve as markers. [ABSTRACT FROM AUTHOR]

Early detection and individualized treatment of patients with liver disease is the key to survival. Radiomics can extract high-throughput quantitative features by multimode imaging, which has good application prospects for the diagnosis, staging and prognosis of benign and malignant liver diseases. Therefore, this paper summarizes the current research status in the field of liver disease, in order to help these patients achieve personalized and precision medical care. This paper uses several keywords on the PubMed database to search the references, and reviews the workflow of traditional radiomics, as well as the characteristics and influencing factors of different imaging modes. At the same time, the references on the application of imaging in different benign and malignant liver diseases were also summarized. For patients with liver disease, the traditional imaging evaluation can only provide limited information. Radiomics exploits the characteristics of high-throughput and high-dimensional extraction, enabling liver imaging capabilities far beyond the scope of traditional visual image analysis. Recent studies have demonstrated the prospect of this technology in personalized diagnosis and treatment decision in various fields of the liver. However, further clinical validation is needed in its application and practice. [ABSTRACT FROM AUTHOR]

Simple plastic face shields have numerous practical advantages over regular surgical masks. In light of the spreading COVID-19 pandemic, the potential of face shields as a substitution for surgical masks was investigated. In order to determine the efficacy of the protective equipment we used a cough simulator. The protective equipment considered was placed on a manikin head that simulated human breathing. Concentration and size distribution of small particles that reached the manikin respiration pathways during the few tens of seconds following the cough event were monitored. Additionally, water sensitive papers were taped on the tested protective equipment and the manikin face. In the case of frontal exposure, for droplet diameter larger than 3 μm, the shield efficiency in blocking cough droplets was found to be comparable to that of regular surgical masks, with enhanced protection for portions of the face that the mask does not cover. Additionally, for finer particles, down to 0.3 µm diameter, a shield blocked about 10 times more fine particles than the surgical mask. When exposure from the side was considered, the performance of the shield was found to depend dramatically on its geometry. While a narrow shield allowed more droplets and aerosol to penetrate in comparison to a mask under the same configuration, a slightly wider shield significantly improved the performance. The source control potential of shields was also investigated. A shield, and alternatively, a surgical mask, were placed on the cough simulator, while the breathing simulator, situated 60 cm away in the jet direction, remained totally exposed. In both cases, no droplets or particles were found in the vicinity of the breathing simulator. Conducted experiments were limited to short time periods after expiratory events, and do not include longer time ranges associated with exposure to suspended aerosol. Thus, additional evidence regarding the risk posed by floating aerosol is needed to establish practical conclusions regarding actual transmittance reduction potential of face shields and surgical face masks. [ABSTRACT FROM AUTHOR]

In this work, we propose a DnCNN-based method to enhance the contrast of images. Fundus images are widely used in clinical practice for detecting retinal dark spots. These spots trouble ophthalmologists to distinguish and decipher diseases for diabetic retinopathy. This work aims is to describe a fundus picture noise removal with enhancement technique. Our simulation results demonstrate that our DnCNN-based method outperforms other contrast enhancement methods. This paper presents a denoising Convolutional Neutral Network (DnCNN)-based technique to enhance images for diabetic retinopathy. The contrast of images can be adaptively improved. Denoising convolutional neural networks (DnCNNs) show progress in image denoising architecture, learning algorithm, and regularization method. DnCNN removes the latent clean image in the hidden layers implicitly. Our proposed (DnCNN) method, compared with the various histogram (Equalization (HE), Adaptive Histogram Equalization (ADHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Exposure based Sub-Image Histogram Equalization (ESIHE)) pre-processes fundus images for identifying diabetic retinopathy. The fundus images were collected from the DRIVE database. The Denoising Convolutional Neural Network strategy is analyzed with the execution parameter measurements of PSNR and SSIM. According to the simulation results, the DnCNN method has a higher visual quality than the previous method. In addition, the proposed method's average PSNR values 25.4303 and 11 dB are higher than those of the existing methods. However, the DnCNN method has a slightly ∼0.2 lower SSIM value of 0.3584 than the previous methods. The decrease in SSIM value indicates a loss of detailed information. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for encrypting images using a Sudoku as its encryption key. This algorithm uses both symmetric and asymmetric key cryptography. It works with any type of data, Sudoku size, and keyspace. The image undergoes the process of modified thresholding, using a pseudo-random number generated from a Sudoku as the threshold. This image is then padded with zeros or the average pixel values to ensure the dimensions are multiples of the Sudoku's size and the image rows are shuffled randomly. For each iteration, the image rows are shuffled, followed by the columns, and finally, the image is rotated clockwise by 90 degrees. The resultant image is highly encrypted and resilient to brute-forcing methods. The algorithm requires roughly 25 milliseconds per iteration for a colored square image of dimensions 512 × 512 and has an NPCR value of 99.60% and a UACI value of 35.65%. The gargantuan keyspace offered by the Sudoku keys ensures obedience of Kirchoff's principle and Shannon's maxim. [ABSTRACT FROM AUTHOR]

This paper concentrates on robust duality relations for uncertain cone-constrained vector optimization problems in more general nonconvex settings. First, different from the existing results, a new class of generalized Lagrange functions of the considered problem is introduced by combining the image space analysis method and scalarization technique. Then, the Lagrange robust vector dual problem is formulated. Subsequently, the results of robust weak duality, strong duality and converse duality are given respectively, which characterize vector dual relations between the primal worst and dual best problems. Simultaneously, some examples are given to illustrate our results. [ABSTRACT FROM AUTHOR]

In this paper, we investigate robust necessary optimality for scalar uncertain optimization problems under the strictly robust counterpart, where the uncertainties are included in the objective and the constraints. Based on generalized differentiable assumptions, some robust necessary conditions via a separation scheme are derived. Besides, by means of strong separation functions, several characterizations of robust necessary optimality can be established. Finally, an example is given to show the effectiveness of the results derived in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the preliminary study on the impact of filamentous particles (FP) in the septic tank effluent (STE) on filter cake (FC) development was presented. The number, length and diameter (30 p./cm3, 451 and 121 μm, respectively, on average) of FPs were measured using microscope image analysis of STE samples condensed using a vacuum evaporation set. Results of this study showed, that 0.73% of volatile suspended solids (VSSs) mass from the STE occurs in the form of FPs. No correlation between FP total mass and VSS was found. An experiment with a layer of FPs simulated by ground toilet paper was conducted and showed the impact of this layer (4.89 mg/cm2) on wastewater hydraulic conductivity – for an FC with FPs (FC–FP), hydraulic conductivity was seven times lower than for the FC without the FP layer, and on outflow quality (lower concentration of organic matter expressed as chemical oxygen demand (COD) in effluent from the FC–FP filter than in the effluent from the FC filter: 618 and 732 gO2/m3, respectively). Despite a relatively small amount of FPs in STE solids (as volume fraction), they play an important role in FC development due to their relatively high length and low degradability. Probably relatively small pores of the FC containing FPs (FC–FP) caused a small particle blocking and a decrease in permeability. [ABSTRACT FROM PUBLISHER]

Kudzu plants have become increasingly invasive in the US, yet are not highly utilized. Intending to produce eco-friendly fibers, this research used fresh kudzu vines to produce fibers by four different processes: boiling, retting, enzymatic and mild chemical processes. Two types of fibers resulted from each process – soft and woody. Unlike more popularly used natural bast fibers such as linen, there is no standard description of the surface structure of kudzu fibers established, this report provides a broad microscopic analysis and images for kudzu fibers. Resultant kudzu fibers, with an average length of 20–300 cm, exhibited a breaking load of 4–20 kg/tex and a breaking elongation of 22.67–37.94%. Results suggest that soft kudzu fibers are promising for apparel and use in fiber-reinforced composites while woody fiber as basketry or paper. [ABSTRACT FROM AUTHOR]

This paper will provide a case study to show how the psychoanalyst can use the intuitive archetypal image from a client in the analytic process. The animal image occurred in the analysis has been amplified beyond personal, cultural, historical associations as a ¨fox¨ in modern China. The fox image represents an archetype of masculine energy emerged from female agency as the animal instinct to break through tradition and the limitations of culture and gender. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to investigate Lagrange saddle points and calmness properties for a nonconvex multiobjective optimization problem by virtue of image regularity conditions. Following along with the image space analysis, the quasi-interior, quasi-relative interior and linear image regularity conditions are presented. Simultaneously, some equivalent characterizations to Fritz John and Karush/Kuhn-Tucker saddle points are established respectively by virtue of the proposed image regularity conditions. Moreover, some calmness properties are also obtained by means of local conic image regularity conditions in the image space of the multiobjective optimization problem. [ABSTRACT FROM AUTHOR]

The fusion of hyperspectral and multispectral images presents a challenge as it involves blending a low-resolution hyperspectral image (HSI) with a corresponding multispectral image (MSI) to produce a high-resolution hyperspectral image (HRI). A number of existing techniques have limitations; for instance, matrix decomposition-based approaches fail to retain adequate spatial and spectral image information during fusion, while tensor decomposition-based processes have high computational overhead. In this paper, we propose a novel method for fusing hyperspectral and multispectral images. Our method leverages the strong correlation among the spectral bands of hyperspectral images and employs the SVD technique to extract spectral feature subspaces. This approach results in a more compact and representative feature space for fusion. Secondly, the proposed method utilizes Elastic Net regularization in combination with ${L_1}$ L 1 and ${L_2}$ L 2 regularization for effective feature selection of highly covariate features. Weighted group sparse regularization is employed to enhance the fusion effect, enabling better representation of the image's structure and features. The algorithm is subsequently evaluated on multiple datasets to confirm its effectiveness. The results of the experiment suggest that the suggested algorithm greatly enhances the spatial resolution and visual clarity of HSI images while preserving the spectral characteristics when compared to conventional methods for fusion of hyperspectral images. Additionally, the constraint for regulation can competently reduce any noise or artefacts, thereby boosting image discrimination. To summarize, the utilization of a sparse tensor-based hyperspectral image fusion algorithm with subspace learning provides an efficacious approach for processing hyperspectral imagery. This method is capable of improving spatial resolution, extracting advantageous features from hyperspectral imagery, and ultimately supports the process of remote sensing image analysis and application. [ABSTRACT FROM AUTHOR]

The photo-elicitation method can provide rich insights into home-making – the process whereby residents use, modify and personalize domestic space. However, previous studies have prioritized the words of participants, gathered in follow-up interviews, over photographs themselves. This paper presents an alternative methodological approach. Specifically, we approach photographs as primary sources, with a focus on their composition. First, we demonstrate how formal elements of photographs can be identified, and their meanings analysed. We show that domestic photographs speak to people's relationships with their homes, and identify empirical insights into scale, light and absences. Second, we combine data from follow-up interviews with formal analysis of images to confirm these insights, and generate additional findings – e.g., regarding the negotiation of domestic architecture and the meanings of possessions. We conclude that photo-elicitation studies of home-making benefit from both visual and narrative insights into participants' homes, and that both types of data merit serious analysis. [ABSTRACT FROM AUTHOR]

Understanding wildfire spread in Canada is critical to promoting forest health and protecting human life and infrastructure. Quantifying fire spread from noisy images, where change-point boundaries separate regions of fire, is critical to accurately estimating fire spread rates. The challenge lies in denoising the fire images and accurately identifying highly non-linear fire lines without smoothing over boundaries. In this paper, we develop an iterative smoothing algorithm for change-point data that utilizes oversmoothed estimates of the underlying data generating process to inform re-smoothing. We demonstrate its effectiveness on simulated one- and two-dimensional change-point data, and robustness to response outliers. Then, we apply the methodology to fire spread images from laboratory micro-fire experiments and show that the regions fuel, burning and burnt-out are smoothed while boundaries are preserved. [ABSTRACT FROM AUTHOR]

Similarity of point patterns is critical to geographic information retrieval. Many methods depend on measuring the similarity between point patterns within the spatial database. However, previous researches mainly focus on point density which is only one aspect of point patterns. A point distribution can be complex by its numerous alignments of point groups, which usually imply different geographical meanings in reality. In this paper, we propose a new method that uses image analysis techniques to comprehensively consider the characteristics of a point pattern. Specifically, given a set of point datasets falling in the same region, our method first generates the point intensity surfaces to transform the original vector space to raster space; then, the method constructs a matrix to describe all the pattern-related information. Finally, the point pattern similarity is calculated by decomposing this matrix into the lower-order representation and the factorized basis features. Due to the use of matrix decomposition, the proposed method has the merits that it can eliminate noises from the original data and assess the similarity of two patterns with emphasis on their major features. As a case study, our method is effective in discovering regularity from the taxi pick-up/drop-off point datasets. [ABSTRACT FROM AUTHOR]

In nanotechnology, the size and shape control of nanoparticles is crucial as their properties are highly dependent on their morphology. This obliges researchers to work on obtaining uniform size and shape distribution in synthesized particles so that their electrical, optical, and magnetic properties remain uniform within the same batch. This brings the problem of how to quantify the shape and size of nanoparticles in the most accurate way. The most common way to determine size distribution is using UV-vis spectrometry; however, this method disregards the existence of agglomerated particles and may sometimes give an incorrect measurement. Therefore a technique that can obtain this data directly from the transmission electron microscopy (TEM) images of particles would be more reliable as it would capture data for every single particle in the batch. In a classical statistical sense, our interest is the shape and size distribution of nanoparticles. In this paper, we quantify the size and shape of nanoparticles using the statistical techniques applied on TEM images of particles: Functional Data and Shape Analysis. By using this shape theory we obtain the geodesic distances not only between the particles but also among the frames. We further cluster the nanoparticles in terms of their similarities. We want to emphasize the importance of the deformation and how it is done of one nanoparticle onto another. [ABSTRACT FROM AUTHOR]

The rapid processing, analysis, and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms (RS-CCPs) have recently become a new trend. The existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation, which ignores remote sensing data characteristics such as large image size, large-scale change, multiple data channels, and geographic knowledge embedding, thus impairing computational efficiency and accuracy. We construct a LuoJiaAI platform composed of a standard large-scale sample database (LuoJiaSET) and a dedicated deep learning framework (LuoJiaNET) to achieve state-of-the-art performance on five typical remote sensing interpretation tasks, including scene classification, object detection, land-use classification, change detection, and multi-view 3D reconstruction. The details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial application. In addition, LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium (OGC) standards for better developing and sharing Earth Artificial Intelligence (AI) algorithms and products on benchmark datasets. LuoJiaAI narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism, showing great potential in high-precision remote sensing mapping applications. [ABSTRACT FROM AUTHOR]