Image Formation Research Articles

Взаимодействие власти и общества при формировании государственной научно-технической политики РФ в современных условиях: системно-динамический подход

The content of this article is the use of a system-dynamic approach as a tool for studying the interaction be-tween government and society in the formation and development of the state scientific and technological policy of the Russian Federation. Events taking place in the modern world, associated with attempts to isolate and impose sanctions on the part of a number of Western European countries and the American side, including in the scientific and technological sphere, as well as the formation of a negative image of Russia in the interna-tional political arena, have prompted changes and revision of the previously adopted educational system based on the Bologna system of education. At the present stage of development of the political system, we can observe the process of formation of a sovereign and independent state scientific and technological policy. Practical experience, combined with the theoretical knowledge possessed by experts, contributes to the for-mation and implementation of an effective public policy in this area that takes into account the modern realities. The advantages of using a systemic dynamic approach as a research tool are its coverage and consideration of modern realities in the study of the formation of state science and technology policy. The use of a system-dynamic approach allows us to identify a number of problems and features, as well as build ways to solve them, based on the analysis carried out within the framework of the formation of the state scientific and tech-nical policy of the Russian Federation.

Deep learning on photoacoustic tomography to remove image distortion due to inaccurate measurement of the scanning radius.

Photoacoustic tomography (PAT) is a non-invasive, non-ionizing hybrid imaging modality that holds great potential for various biomedical applications and the incorporation with deep learning (DL) methods has experienced notable advancements in recent times. In a typical 2D PAT setup, a single-element ultrasound detector (USD) is used to collect the PA signals by making a 360° full scan of the imaging region. The traditional backprojection (BP) algorithm has been widely used to reconstruct the PAT images from the acquired signals. Accurate determination of the scanning radius (SR) is required for proper image reconstruction. Even a slight deviation from its nominal value can lead to image distortion compromising the quality of the reconstruction. To address this challenge, two approaches have been developed and examined herein. The first framework includes a modified version of dense U-Net (DUNet) architecture. The second procedure involves a DL-based convolutional neural network (CNN) for image classification followed by a DUNet. The first protocol was trained with heterogeneous simulated images generated from three different phantoms to learn the relationship between the reconstructed and the corresponding ground truth (GT) images. In the case of the second scheme, the first stage was trained with the same heterogeneous dataset to classify the image type and the second stage was trained individually with the appropriate images. The performance of these architectures has been tested on both simulated and experimental images. The first method can sustain SR deviation up to approximately 6% for simulated images and 5% for experimental images and can accurately reproduce the GTs. The proposed DL-approach extends the limits further (approximately 7% and 8% for simulated and experimental images, respectively). Our results suggest that classification-based DL method does not need a precise assessment of SR for accurate PAT image formation.

Informational Confrontation of “MoskovskieVedomosti” Journal and European Press During Polish Uprising of 1863

In the article the author examines informational confrontation of “MoskovskieVedomosti” journal with European press during Polish uprising of 1863. In response to the aggressive anti-Russian rhetoric of the Western press, “Moskovskie Vedomosti” vigorously supported the actions of Russian administration in Poland, headed by General M.N. Muravyov. On the basis of the newspaper's publications analysis, the author reveals the main ideological and thematic directions of their statements on the Polish issue: exposes the policy of Western powers that used the situation in Poland to pressure Russia and weaken it; substantiates the the anti-national nature of the uprising, initiated by the Polish nobility and Catholic clergy; shows the formation of the rebels negative image, and exposes the falsifi cations and insinuations of the Western press. The author comes to the conclusion that the publications of “Moscow Vedomosti” during the period of military operations in Poland contributed to the formation of the consolidated public opinion in Russia, and their confrontation with the leading Western European editions can be considered as prototype of modern information wars.

3D printed imaging apertures for MicroPattern gas detector based full field of view X-ray fluorescence spectrometers

Additive Manufacturing, commonly referred as 3D printing, opened a new way to promptly produce cost-effective imaging apertures based on high-density materials, to be used in x-ray imaging systems, such as MicroPattern Gas Detector (MPGD) based Full Field X-ray Fluorescence (FF-XRF) spectrometers. These provide energy and spatially resolved information, allowing the mapping of large area samples. Traditionally, image formation is accomplished with pinhole apertures, devices of easy manufacturing that lead to sub-micrometre position resolution values. However, the sensitivity of the imaging system is severely hampered. To surpass this issue, alternative optical components, such as parallel multi-hole collimators, can be used.In this work, 3D printed multi-hole collimators with different geometries and material compositions are presented and applied to a FF-XRF spectrometer based on the 2D-THCOBRA gas detector. The system's performance parameters with different collimators, namely, relative sensitivity and position resolution, were experimentally determined and compared with the ones obtained with pinhole-based setups. The FF-XRF spectrometer was also simulated, to understand how different collimator geometries affect sensitivity and position resolution.

Gamification design: toward developing image perception scales for generation Z consumers

Purpose Drawing from the Personal Construct Theory, this study aims to analyze the impact of using gamified apps on user behavior by investigating the service-related images and individual preferences of Generation Z (GenZ) consumers, as these emerge from gamified applications in a tourism context. Design/methodology/approach The repertory grid analysis (RGA) elicited the top elements that reflect GenZer perceptions in tourism from empirical studies in the UK and Greece. Generalized procrustes analysis was used to investigate the structure of the data for the creation of representative consensus biplots of the most important conceptual constructs to advance consumer decision-making modeling via gamification. Findings As per different gamified app best-practices considered, the authors extract not only common perceptual elements (e.g. place informative aspects, exploration, lodgings, food/catering) but also different image components (e.g. virtual/interactive, business vs commercial traveling, entertainment, heritage/cultural informative aspects) from comparing UK with Greek GenZers’ responses. These extracted attributes are then presented in two dimensional charts, respectively, toward creating tourist perception scales. Research limitations/implications Notwithstanding the wide availability of gamified apps, research on gamification design in tourism and hospitality is still in the early phase. This study demonstrates the need to identify and optimize the formation of different images among GenZers. It also highlights the advantageous nature of the proposed combination of procrustes analysis with the RGA. Originality/value To the best of the authors’ knowledge, this research is among the first empirical ones toward creating scales for measuring tourist perceptions of GenZers coming from different consumer markets. It responds to scholars’ recent calls for better informing gamification design and improving contemporary consumer experience.

Individual versus sequential: the potential of comic creation in art therapy

ABSTRACT Background: Research suggests that comics are effective at conveying narratives, promoting literacy, empowerment, and as a therapeutic intervention. However, there is a gap in research on the use of comics in art therapy. Aims: This study explored the potential of comics within an art therapy and narrative therapy framework. The primary research question explored the effectiveness of comics compared to single images in evoking narratives from past problems. The secondary research question examined differences in the representation of an event between the two formats. To address these questions, the process of depicting a past problem as a single image was compared to depicting the same problem as a comic. Methods: This study recruited 15 adult participants through convenience sampling. This was a non-clinical population consisting of individuals with no known psychological difficulties or history of psychological disorders. Participants evaluated the formats through a survey and verbal interview. Quantitative and qualitative data were analysed to determine the formats’ effectiveness. Participants’ artwork was analysed for unique characteristics the comic evoked. Results: Data analysis indicated participants responded positively to the comic, which seemed especially effective at facilitating the narrative therapy technique of deconstructing. Artwork analysis indicated the comic evoked increased word inclusion, and unique representations of time and movement. Conclusions: These results support the hypothesis that comics are at least as effective than single images in eliciting narratives from past problems, and evoke unique characteristics. Implications: This study suggests the potential of comics in art therapy, encouraging further research and application.

Multimodal Representations of Lithuanian Brands: The Case of “Džiugas”, “Rūta” and “Pieno žvaigždės”

Figurative representations of brand images have not been thoroughly studied in Lithuanian linguistics. The present research aims to investigate the multimodal metaphors and metonymies explored by the famous Lithuanian corporate brands Džiugas, Rūta and Pieno žvaigždės. The work examines how the multimodal metaphors and metonymies of the brands are cognitively structured from the perspective of illustrations, logos and layout. The interaction of verbal and visual elements in corporate discourse is particularly important when expressed using graphics, logos and layout for the corporate mission statements, descriptions of the brand history and other materials available on their websites. The analysis reveals that the most frequent source domains for brand imaging are PERSON/LIVING ORGANISM, SUCCESS, TRADITIONS and CAUSE FOR EFFECT, PART FOR WHOLE, PRODUCT FOR PRODUCER metonymies, among others. In the case of personification, the brand is defined as an individual who possesses the qualities highly valued in corporate discourse: growth, positivity, adaptability, dynamism and collaboration. These metaphors facilitate a better understanding of the brand and contribute to the formation of a persuasive brand image that resonates with consumers. The analysis of the multimodal manifestations of brand identity in the cases of Džiugas, Rūta and Pieno žvaigždės reveals the intricate interplay of metaphor and metonymy in constructing and conveying meaning across various modes of representation. Each brand’s illustrations, logo and layout incorporate visual elements, typography, and colour palettes that contribute to their unique identities and evoke specific associations.

SORC: an integrated spatial omics resource in cancer.

The interactions between tumor cells and the microenvironment play pivotal roles in the initiation, progression and metastasis of cancer. The advent of spatial transcriptomics data offers an opportunity to unravel the intricate dynamics of cellular states and cell-cell interactions in cancer. Herein, we have developed an integratedspatial omics resource in cancer (SORC, http://bio-bigdata.hrbmu.edu.cn/SORC), which interactively visualizes and analyzes the spatial transcriptomics data in cancer. We manually curated currently available spatial transcriptomics datasets for 17 types of cancer, comprising 722899 spots across 269 slices. Furthermore, we matched reference single-cell RNA sequencingdata in the majority of spatial transcriptomics datasets, involving 334379 cells and 46 distinct cell types. SORC offers five major analytical modules that address the primary requirements of spatial transcriptomics analysis, including slice annotation, identification of spatially variable genes, co-occurrence of immune cells and tumor cells, functional analysis and cell-cell communications. All these spatial transcriptomics data and in-depth analyses have been integrated into easy-to-browse and explore pages, visualized through intuitive tables and various image formats. In summary, SORC serves as a valuable resource for providing an unprecedented spatially resolved cellular map of cancer and identifying specific genes and functional pathways to enhance our understanding of the tumor microenvironment.

Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review.

Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.

The validity of the Czech version of Body Appreciation Scale-2 for adolescents

BackgroundUnderstanding the formation of body image is critical for the prevention and treatment of eating disorders, especially in adolescence, when body image develops significantly. One of the important facets of body image is body appreciation, which consists of positive feelings and attitudes towards the body regardless of its perceived “flaws”. To measure body appreciation, Body Appreciation Scale-2 (Tylka and Wood-Barcalow in Body Image 12:53–67, 2015a), a unidimensional 10-item measure, has been developed and routinely used in body image research. The current study examined the validity (i.e., factor structure, gender and age invariance, associations with other constructs) of the Czech version of Body Appreciation Scale-2 for adolescents.MethodsThe study used two large samples of Czech adolescents, aged 13–18 (N1 = 613, M = 15.5, 52% girls; N2 = 1,530, M = 15.4, 50% girls). The data were collected in August 2021 (N1) and November 2020 (N2) through an online survey. For the data analysis, we used confirmatory factor analysis (CFA), multi-group confirmatory factor analysis (MG-CFA), and Structural Equation Modeling (SEM).ResultsOur findings supported the proposed unidimensional factor structure and the gender (i.e., girls, boys) and age (i.e., 13–15, 16–18) scalar invariance of the Czech version of Body Appreciation Scale-2. The data also showed the expected positive correlations with body satisfaction and self-esteem, and negative correlations with media-ideal internalization, appearance schematicity, and depression. Furthermore, we discovered that body appreciation was more strongly connected to media-ideal internalization and depression for girls than boys.ConclusionsThe present study provided robust evidence that supports the validity of the Czech version of Body Appreciation Scale-2 and its usability for the assessment of body appreciation in Czech adolescents. We also proposed future directions for the research on body appreciation based on the explored gender differences.

Automated Platelet Counter with Detection Using K-Means Clustering

Platelet is a blood cell type that is stored and circulated in the human body. It acts as a blood thickening agent and prevents blood from overflowing whenever bleeding occurs. An excessive or inadequate number of platelets could lead to platelet-related diseases. The current practice of platelet counting involves the manual counting process using a haemocytometer, Wright’s Stain which uses the dyes to facilitate the differentiation of blood cell types, and a tally counter. Yet, this process can be time-consuming, demanding, and exhausting for haematologists, and likely to be prone to errors. Thus, this paper presents a study on automated platelet counter and detection using image processing techniques. The K-Means Clustering was employed to count and detect the presence of platelets in microscopic blood smear images. Several processes were performed prior to the K-means clustering, including image enhancement and YCbCr image formatting. Subsequently, image masking, as well as area thresholding were applied to eliminate every unwanted entity and highlight the visibility of the platelets before the number of platelets could be detected and counted. A comparative experiment was designed in which the K-Means Clustering platelet count and detection were compared with the actual number of platelets reported by haematologists. The platelet counts and detection were categorized into three detection categories which are Less Detection (LD), Accurate Detection (AD), and Over Detection (OD). The proposed study was evaluated to 90 testing platelet images. Out of the 90 testing images, 75 platelet images were perfectly counted and detected which returned 91.67% of accuracy. This signifies that the K-Means Clustering algorithm was discovered to be efficient and dependable for automated platelet counter and detection

Macroscopic temporally and spectrally resolved fluorescence imaging enhanced by laser-wavelength multiplexing.

We present a laser scanning system for macroscopic samples that records fully resolved decay curves in individual pixels, resolves the images in 16 wavelength channels, and records simultaneously at several laser wavelengths. By using confocal detection, the system delivers images that are virtually free of lateral scattering and out-of-focus haze. Image formats can be up to 256 × 256 pixels and up to 1024 time channels. We demonstrate the performance of the system both on model experiments with fluorescent micro-beads and on the tumor model in the living mice.

Organizacijos įvaizdžio vertinimas UAB „Baltic Agro“ vartotojų požiūriu

The article examines the theoretical aspects of the formation of the organization's image. It emphasizes the importance of image in today's business, emphasizing that only by gaining the image of a supportive organization, companies gain a strong advantage in the market. The process of image formation is distinguished, consisting of the internal culture of the organization, employee competencies, product value, manager's behavior, business reputation, social behavior of the organization, visual image and image of the consumers themselves. A questionnaire survey method was selected for the empirical study and a probabilistic method of selecting subjects was applied – random selection. The analysis of the survey results enabled to assess the JSC Baltic Agro company's image based on the distinguished image formation factors and the identified elements of the organization's image. Keywords: image of organization, external image, internal image, elements of the image, factors of image formation.

Motivations, risks, and constraints: An analysis of affective and cognitive images for cannabis tourism in Canada

The aim of this study was to explore Canada's image as a cannabis tourism destination and investigate how tourists' motivations, perceived risks, and travel constraints affect their intentions to visit Canada for leisure cannabis consumption. By examining the image formation process from a pre-travel perspective, this study investigated the impacts of motivation, perceived risks, and travel constraints on the affective and cognitive images that contribute to the formation of a pre-travel image and visiting intention for cannabis tourism.Empirical results indicated that potential cannabis tourists' visiting intentions were influenced more by their affective image towards Canada than their cognitive image. Furthermore, perceived risk fully mediated the relationship between cognitive image and visiting intention. These findings provide valuable insights for local governments, destination marketing groups, and businesses interested in developing the cannabis tourism market.

Analysis of surface roughness in optical coherence elastography using a novel phantom

Compression optical coherence elastography (OCE) has shown promise in detecting diseases, such as breast cancer, based on changes in the elasticity of tissue. While the mechanical models used in OCE assume a flat surface, tissue typically exhibits surface topography that can lead to inaccurate estimation of elasticity in OCE images (elastograms). The coupling of surface topography and heterogeneous mechanical properties in tissue make it challenging to study the relative impact of surface roughness on image formation. To address this, we developed mechanically homogeneous phantoms that accurately replicate the surface topography of human breast tissue. Surface topography of breast tissue specimens was captured in three-dimensional (3-D) optical coherence tomography (OCT) scans. Molds replicating the surface roughness of tissue were generated using 3-D modelling and 3-D printing. The phantoms were then fabricated in the molds using polydimethylsiloxane. OCT was used to validate the accuracy of the phantom surface topography and the phantoms were then used to determine the impact of surface roughness in compression OCE elastograms. Surface roughness phantoms were shown to accurately reflect the surface topography of six breast tissue specimens with a correlation of 0.96 ± 0.03. When imaged with a variant of compression OCE called quantitative micro-elastography, surface roughness resulted in a slight decrease in elasticity accuracy with a mean percentage error of 8.2 % for a flat phantom compared to 11.7 % for the roughness phantom, and a larger decrease in elasticity precision, or sensitivity, with a mean relative standard deviation of 9.4 % for the flat phantom compared to 21.2 %, for the roughness phantom. Our results show that contrast in elastograms moderately correlated with the phantom surface profile (r = 0.59, p < 0.001) and that surface roughness introduces spatially-varying elasticity that should be considered in the analysis of elastograms in compression OCE.

Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps.

microwave imaging (MWI) has emerged as a promising modality for breast cancer screening, offering cost-effective, rapid, safe and comfortable exams. However, the practical application of MWI for tumor detection and localization is hampered by its inherent low resolution and low detection capability. this study aims to generate an accurate tumor probability map directly from the scattering matrix. This direct conversion makes the probability map independent of specific image formation techniques and thus potentially complementary to any image formation technique. An approach based on a convolutional neural network (CNN) is used to convert the scattering matrix into a tumor probability map. The proposed deep learning model is trained using a large realistic numerical dataset of two-dimensional (2D) breast slices. The performance of the model is assessed through visual inspection and quantitative measures to assess the predictive quality at various levels of detail. the results demonstrate a remarkably high accuracy (0.9995) in classifying profiles as healthy or diseased, and exhibit the model's ability to accurately locate the core of a single tumor (within 0.9 cm for most cases). overall, this research demonstrates that an approach based on neural networks (NN) for direct conversion from scattering matrices to tumor probability maps holds promise in advancing state-of-the-art tumor detection algorithms in the MWI domain.

Resolution-enhanced x-ray ghost imaging with polycapillary optics

Ghost imaging (GI) enables compressive and lens-free image formation using a single-pixel detector and structured illumination, thus providing a promising and cost-effective approach to dose-reduced x-ray imaging. At this stage, a major bottleneck in almost all x-ray GI schemes is that the minimum resolution is strictly subject to the unit size of structured illumination. To overcome this widespread and inflexible resolution limitation, we introduced polycapillary optics into x-ray GI, in which polycapillary x-ray optics scaled down the input x-ray shadow pattern intactly and experimentally enabled a ∼3× resolution improvement. In general, polycapillary x-ray optics could be flexibly integrated in various x-ray GI applications with a demand of resolution larger than the diameter of the individual capillary composing of polycapillary optics, to achieve free resolution gain.

Exotic image formation in strong gravitational lensing by clusters of galaxies – IV. Elliptical NFW lenses and hyperbolic umbilics

ABSTRACT A source lying near a hyperbolic umbilic (HU) singularity leads to a ring-like image formation, constituting four images with high magnification factors and lying in a small region of the lens plane. Since (based on our earlier work) the observed number of HU image formations in cluster lenses is expected to increase in the future, it is timely to investigate them in more detail. Like fold and cusp singularities, HU also satisfies the magnification relation, i.e. the signed magnification sum of the four images equals zero. This work presents a detailed study of the HU magnification relation (Rhu) considering the elliptical Navarro–Frenk–White (eNFW) lens profile suitable for cluster scale dark matter haloes. Our results show that for an isolated eNFW lens, Rhu is more sensitive to ellipticity than its mass or concentration parameter. An ellipticity greater than 0.3 results in Rhu lying close to zero with a small scatter around it. A substructure near the HU image formation causes the average Rhu value to deviate from zero and increases the scatter, with the amount of deviation depending on the image type near which the substructure lies. However, a population of substructures in the lens plane (equivalent to the galaxies inside the cluster) does not significantly shift the average Rhu value from zero but increases the scatter around it. We find that Rhu ≃ 0 for HU image formation in the Abell 1703 cluster. Repeating this test in other clusters with HU formations can be a useful indicator of substructure in cluster haloes.

Classification of deep learning convolutional neural network feature extraction for student graduation prediction

&lt;span&gt;One indicator of a university’s educational quality is the proportion of enrolled students who actually graduate within four years. This proportion is typically fewer than the number of students that enroll in a given year. A low graduation rate can have a negative impact on both the university’s reputation and its accreditation because it indicates that fewer students are completing their degrees. Student activity, economic, and other issues all play a role in why some students are unable to complete their degrees on time. As a result, stakeholders need a model that can predict whether or not students will graduate on time as a means of evaluating and giving a basis for policy actions. This research proposes a model for converting textual data into an image format using a deep learning convolutional neural network (CNN), and then classifying the extracted features using a variety of machine learning classification algorithms like the decision tree, random forest, Naive Bayes, support vector machine (SVM), and k-nearest neighbor (K-NN). The classification model trained on feature extraction data had a 96.1% accuracy rate, while the classification model trained on the original data achieved a 71.2% accuracy rate.&lt;/span&gt;

Detection of intended and unintended emissions from Starlink satellites in the SKA-Low frequency range, at the SKA-Low site, with an SKA-Low station analogue

Context. Intended and unintended radio emissions from satellites can interfere with sensitive radio telescopes in the frequency ranges of key experiments in astrophysics and cosmology. We detect strong intended and unintended electromagnetic radiation from Starlink satellites at the site of the future SKA-Low facility in Western Australia, using an SKA-Low prototype station known as the Engineering Development Array version 2 (EDA2). Aims. We aim to show that Starlink satellites are easily detectable utilising a configuration of low frequency radio antennas representative of an SKA-Low ‘station’ and that our results complement similar findings with the LOFAR telescope. Methods. Utilising the EDA2 at frequencies of 137.5 MHz and 159.4 MHz, we detected trains of Starlink satellites on March 17 and 18, 2023, and November 16 and 17, 2021, respectively, via the formation of all-sky images with a frequency resolution of 0.926 MHz and a time resolution of 2 s. Time differencing techniques were utilised to isolate and characterise the transmissions from Starlink and other satellites. Results. We observed Starlink satellites reaching intensities of 106 Jy beam−1, with the detected transmissions exhibiting a range of behaviours, from periodic bursts to steady transmission. The results are notable because they demonstrate that Starlink satellites are detected in the SKA-Low frequency range, transmitting both intentionally and unintentionally. Follow-up work and discussion are needed to identify the cause of this unintentional radiation as it has the potential to interfere with SKA-Low science. It is likely that the transmission levels will need to be reduced by orders of magnitude to bring the impact on radio astronomy to potentially manageable levels. Conclusions. Our results indicate that both intended and unintended radiation from Starlink satellites will be detrimental to key SKA science goals without mitigation. A continued conversation with SpaceX could potentially result in future mitigations which the EDA2 instrument could efficiently monitor and characterise at the SKA-Low site.