Media Images Research Articles

Study on Influencing Factors of Consumers Satisfaction with Sichuan Tourism in Post-epidemic Era

The purpose of this study is to deeply explore the satisfaction of consumers outside Sichuan province to Sichuan tourism and its influencing factors in the post-epidemic era. Through questionnaire survey and data analysis, this study analyzes the influence of real image, perceived image, media image and other factors on consumer satisfaction outside the province, and puts forward corresponding promotion strategies.

Target recognition and segmentation in turbid water using data from non-turbid conditions: a unified approach and experimental validation

Semantic segmentation of targets in underwater images within turbid water environments presents significant challenges, hindered by factors such as environmental variability, difficulties in acquiring datasets, imprecise data annotation, and the poor robustness of conventional methods. This paper addresses this issue by proposing a novel joint method using deep learning to effectively perform semantic segmentation tasks in turbid environments, with the practical case of efficiently collecting polymetallic nodules in deep-sea while minimizing damage to the seabed environment. Our approach includes a novel data expansion technique and a modified U-net based model. Drawing on the underwater image formation model, we introduce noise to clear water images to simulate images captured under varying degrees of turbidity, thus providing an alternative to the required data. Furthermore, traditional U-net-based modified models have shown limitations in enhancing performance in such tasks. Based on the primary factors underlying image degradation, we propose a new model which incorporates an improved dual-channel encoder. Our method significantly advances the fine segmentation of underwater images in turbid media, and experimental validation demonstrates its effectiveness and superiority under different turbidity conditions. The study provides new technical means for deep-sea resource development, holding broad application prospects and scientific value.

The Construction of Harbin’s Image in Chinese and Western Media: A Corpus-Based Critical Discourse Analysis

With the development of the ice and snow economy, Harbin has received great attention in domestic and international economic cooperation and cultural exchange. This paper conducts a corpus-based critical discourse analysis over the characteristics of Chinese and Western English media reports on Harbin, aiming to quantitatively and qualitatively study the discourse construction of Harbin’s city image. The results show that firstly, Harbin is mainly presented as “the city of ice and snow” in both media. Secondly, while domestic media focus more on shaping Harbin’s city image around tourism with the purpose of driving economic development, the focus of foreign media is more diverse and comprehensive. Thirdly, limitations exist in both media and need to be reduced.

A New Materialist Analysis of Health and Fitness Social Media, Gender and Body Disaffection: ‘You Shouldn’t Compare Yourself to Anyone… but Everyone Does’

Recent research has documented the relationship between the promotion of ‘ideal’, ‘fit’ bodies in social media, body image and associated body concerns and conditions. This article expands this scholarship, focusing specifically on gender, body disaffection and social media. Thus far, body disaffection has mostly been understood through a psychological framing, as a pathology residing within an individual and strongly associated with poor body image because of internalizing media images. In this paper, drawing on feminist new materialism, I offer a framing of body disaffection as a relational phenomenon. The paper draws on a mixed method study in England, with over 1000 young people examining their experiences with a range of digital health technologies. I focus specifically on their engagement with social media, to explore the relationship between ideal images and body concerns. Far from being a simple process of internalization of negative perceptions or image one has of their body, disaffection is formed through the body via a complex process of entanglement with social media and other elements. I outline how disaffection materialises as part of an assemblage of elements, including discourses, humans, bodies, digital objects and platforms. The paper reveals how entanglements with social media can generate powerful affects such as shame, pleasure and belonging along gendered lines, which may have significant implications for young people’s relationships with their bodies. I analyse how social media events focused on the ‘transformation’ of bodies generate powerful affects, which open or limit capacities for what ‘boys’ or ‘girls’ bodies might become in deeply gendered and sometimes harmful ways.

The Shifting Image of Sweden Abroad: Framings of the 2022 Swedish Election in Traditional and Far-Right Online Media from the United States, Germany, India, and China

The Shifting Image of Sweden Abroad: Framings of the 2022 Swedish Election in Traditional and Far-Right Online Media from the United States, Germany, India, and China

CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)

Strong gravitational lensing is a powerful tool for investigating dark matter and dark energy properties. With the advent of large-scale sky surveys, we can discover strong-lensing systems on an unprecedented scale, which requires efficient tools to extract them from billions of astronomical objects. The existing mainstream lens-finding tools are based on machine-learning algorithms and applied to cutout-centered galaxies. However, according to the design and survey strategy of optical surveys by the China Space Station Telescope (CSST), preparing cutouts with multiple bands requires considerable efforts. To overcome these challenges, we have developed a framework based on a hierarchical visual transformer with a sliding window technique to search for strong-lensing systems within entire images. Moreover, given that multicolor images of strong-lensing systems can provide insights into their physical characteristics, our framework is specifically crafted to identify strong-lensing systems in images with any number of channels. As evaluated using CSST mock data based on a semianalytic model named CosmoDC2, our framework achieves precision and recall rates of 0.98 and 0.90, respectively. To evaluate the effectiveness of our method in real observations, we have applied it to a subset of images from the DESI Legacy Imaging Surveys and media images from Euclid Early Release Observations. A total of 61 new strong-lensing system candidates are discovered by our method. However, we also identified false positives arising primarily from the simplified galaxy morphology assumptions within the simulation. This underscores the practical limitations of our approach while simultaneously highlighting potential avenues for future improvements.

Empowering Crisis Response Efforts: A Novel Approach to Geolocating Social Media Images for Enhanced Situational Awareness

Social media and online platforms play an important role in assessing the impact of a natural disaster, especially in the immediate aftermath of the event. The information from social media demonstrated the potential to accelerate the response to a crisis. Locating social media images is critically important to help the victims immediately, mobilize community support, and provide enhanced situational awareness. However, finding the location of relevant social media images still challenges humans and computational processes. This study introduces a social media image classifier aimed at enhancing crowdsourced geolocation. The model is trained using data annotated by experts in disaster risk management. Its main goal is to reduce the difficulty of geolocating images by detecting those that are ``easy'' to geolocate. We combined the classifier with an ad-hoc crowdsourcing platform and tested it with pictures posted during a crisis. The experimental results indicate that the proposed approach speeds up the geolocation process of social media images while increasing the level of location precision

QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1

Earthquake monitoring is necessary to promptly identify the affected areas, the severity of the events, and, finally, to estimate damages and plan the actions needed for the restoration process. The use of seismic stations to monitor the strength and origin of earthquakes is limited when dealing with remote areas (we cannot have global capillary coverage). Identification and analysis of all affected areas is mandatory to support areas not monitored by traditional stations. Using social media images in crisis management has proven effective in various situations. However, they are still limited by the possibility of using communication infrastructures in case of an earthquake and by the presence of people in the area. Moreover, social media images and messages cannot be used to estimate the actual severity of earthquakes and their characteristics effectively. The employment of satellites to monitor changes around the globe grants the possibility of exploiting instrumentation that is not limited by the visible spectrum, the presence of land infrastructures, and people in the affected areas. In this work, we propose a new dataset composed of images taken from Sentinel-1 and a new series of tasks to help monitor earthquakes from a new detailed view. Coupled with the data, we provide a series of traditional machine learning and deep learning models as baselines to assess the effectiveness of ML-based models in earthquake analysis.

INAPPROPRIATE CONTENT DETECTION IN IMAGE

The proliferation of social media platforms has led to a significant increase in the sharing of memes and other image-based content. However, some of these images may contain inappropriate or offensive text, which can be challenging to detect using traditional methods. Because the open-source existing solutions does not classify the text content in images. This project aims to develop a robust solution for detecting Not Safe for Work (NSFW) text content in images, with a particular focus on social media memes. The objectives of this project are to accurately extract text from images using optical character recognition (OCR) techniques, and to classify the extracted text as either NSFW or Safe for Work (SFW) using a fine-tuned natural language processing (NLP) model. The methodology employed in this project involves several key steps. First, the Keras OCR library is utilized to extract text from input images, as it has demonstrated superior performance compared to other OCR tools for social media images. Next, a preprocessing step is performed to align the extracted text in the correct sequence using coordinates, distance from the image origin, and the Pythagorean theorem. The pre-processed text is then passed to a fine-tuned BERT-BASE-UNCASED language model, which has been trained on a dataset of Reddit posts (NSFW and SFW) to classify the text as either NSFW or SFW. Key Words: OCR, Inappropriate, BERT, NSFW, SFW, Image, Text

Finding the Next Successor: Visual Message in Caricature Image

Caricature is a form of visual communication wherein messages are conveyed through exaggerated and often humorous distortions of characters, serving as a critique of societal issues. Caricature often serves to critique various aspects of society, including social, cultural, economic, legal, and political conditions. Similarly, the political landscape surrounding the 2024 general election has intensified, fostering active and diverse discussions through both direct and indirect media channels. Mass media visualizes the very busy pros and cons of the Indonesian future successors. The proponents and the supported people are arguing and the one that can represent them will be the leader of government in 2024-2029. Funny, amusing, and intriguing caricature images in National Mass Media are interesting to study to see how the interpretation on the form of a caricature object is using the meaningful visual signs. This research was limited to caricature images by a cartoonist, Kang Yuyun, that was published in TEMPO National mass media in January-June 2023. The object of illustrative image was limited using a purposive sample that is considered representing the objective of research. The data mining was carried out through observation, interview, document, and literature. Observation was conducted directly in Tempo’s Office, Jakarta; interview was conducted online with the cartoonist; document included the caricature images that have been published on Sunday; and literature came from scientific works such as journal, proceeding, and books. All data were collected to be validated using triangulation and then analyzed using Roland Barthes’ semiotic theory about denotation, connotation, and myth. The findings reveal that Kang Yuyun’s caricature images in TEMPO mass media during the 2024 General Election depict three groups supporting Ganjar Pranowo, Prabowo Subianto, and Anis Bawesdan as potential successors, emphasizing unity in nation-building and promotion. Keywords: caricature, mass media, the successor, semiotics

THE USE OF PICTURE TO IMPROVE STUDENTS’ ABILITY IN WRITING SKILL OF EIGHTH GRADE STUDENTS IN SMP NEGERI 1 MAKARTI JAYA BANYUASIN SUMATERA SELATAN

The topic of this study is “The use of pictures to improve the writing skills of eight grade students of SMPN 1 Makarti Jaya Banyuasin”. This discussion of this lesson is to answer the question of is there a significant improvement between students learning to use pictures learning to use pictures and not to use picture?In this study, researcher used quansi experiments using pre test and post test. The population is eighth grade,especially grade 8.1 and 8.5. One of the classes used as the experimental class is class of 8.5 and the control class is class of 8.1. The use of media image to improve students writing skills is applied to the experimental class, namely class of 8.5. While the control class does not use images. Each class held treatment and the research lasted for 16 meetings with different pictures at each meeting.From the results of the pretest and post test that have been carried out, the researcher gets the maximum result, namely the valueon the pretest 72% to 78% in the post test. Before holding the post test with the same picture,students were given treatment for 14 meetings. From the results of this study, this means that the use of images can improve students writing skills. The data collection was carried out using the SPSS application.

A multimodal transfer learning framework for the classification of disaster-related social media images

Rapidly classifying disaster-related social media (SM) images during a catastrophe event is critical for enhancing disaster response efforts. However, the biggest challenge lies in acquiring labeled data for an ongoing (target) disaster to train supervised learning-based models, given that the labeling process is both time-consuming and costly. In this study, we address this challenge by proposing a new multimodal transfer learning framework for the real-time classification of SM images of the target disaster. The proposed framework is based on Contrastive Language-Image Pretraining (CLIP) model, jointly pretrained on a dataset of image-text pairs via contrastive learning. We propose two distinct methods to design our classification framework (1) Zero-Shot CLIP: it learns visual representations from images paired with natural language descriptions of classes. By utilizing the vision and language capabilities of CLIP, we extract meaningful features from unlabeled target disaster images and map them to semantically related textual class descriptions, enabling image classification without training on disaster-specific data. (2) Linear-Probe CLIP: it further enhances the performance and involves training a linear classifier on top of the pretrained CLIP model’s features, specifically tailored to the disaster image classification task. By optimizing the linear-probe classifier, we improve the model’s ability to discriminate between different classes and achieve higher performance without the need for labeled data of the target disaster. Both methods are evaluated on a benchmark X (formerly Twitter) dataset comprising images of seven real-world disaster events. The experimental outcomes showcase the efficacy of the proposed methods, with Linear-Probe CLIP achieving a remarkable 7% improvement in average F1-score relative to the state-of-the-art methods.

Social Media Image Construction and Communication of Sports Athletes

With the development of social media, it has become more and more a distinctive phenomenon for sports athletes to present themselves and spread sports culture through operating personal social media accounts. As the national sport of China, table tennis has long enjoyed widespread attention, and the athletes are also the focus of the public. In this regard, this paper selected eight Chinese table tennis athletes in different growth periods in a balanced manner according to the demographic approach. With the content and text analysis methods, the paper collects data and text from their social media accounts represented by Weibo, and analyzes the content and effects from aspects of sports communication, health communication, brand communication, social value communication and entertainment daily communication. After analyzing the current operation and exposed shortcomings of the personal social media of sports athletes represented by them, the paper puts forward three feasible suggestions: “build a good media image, convey positive energy actively,” “raise the awareness of media management, explore the possibilities of cross-border” and “enhance the ability of media operation, create differentiated personalities,” to contribute media power to the in-depth implementation of the "Healthy China" Initiative and the development of China's sports industry.

Формирование образа Ирака в региональных СМИ Германии посредством концептуальных метафор

The article presents the results of the study of productive conceptual metaphors regularly used to create an image of Iraq in the German regional media. Metaphors are characterized with regard to their frequency, the amount of lexical means in metaphorical nominative fields, and the characteristics added to the formed country image. The sources of the material are the regional media Berliner Zeitung, Aachener-Zeitung, Saarbrücker Zeitung, Münchner Merkur, Berliner Morgenpost, Tagesspiegel. The sample includes publications from 2019 to the present. Contextual and definitional analysis and quantitative calculations are used. The following metaphors are identified as the most frequently used (in descending order of productivity): "Iraq is a living being", "Iraq is a non-whole whole", "Iraq is a place of uncontrolled power", "Iraq is a warrior". The listed metaphors are updated in the publications of the German regional media by the lexical units of the semantic fields "speech activity", "mental state", "physical state", "movement", "division", "mental manifestations", "weather manifestations", "war". The highest nominative density distinguishes the personifying metaphor "Iraq is a living being"; the lowest nominative density characterizes the metaphor "Iraq is a warrior". The metaphors "Iraq is a place of uncontrolled power" and "Iraq is a non-whole whole" reveal a similar degree of linguistic detail of the corresponding conceptual metaphorical spaces, an average degree of variability in the designations of the introduced semantic shades. The lexical units leiden, Teil, toben, kämpfen are attributed to the most frequent kernel means which implement the studied metaphors. The kernel of the Iraq image in the regional German media is constituted by such features of the country as suffering, divided, raging, struggling.

Wizerunek polityki zagranicznej kanclerza Olafa Scholza na łamach dziennika „Gazeta Wyborcza” w kontekście pełnoskalowej wojny Rosji w Ukrainie (2022–2023)

The aim of this article is to reconstruct the foreign policy press image of Chancellor Olaf Scholz in the period from 8.12.2021, i.e. from the day the politician was confirmed by the Bundestag as German Chancellor and on the eve of Russia's full-scale invasion of Ukraine (24.02.2022), to the end of the conference on the future of Ukraine taking place in Saudi Arabia in Jeddah (6.08.2023). The research used a content analysis method, supplemented by elements of text hermeneutics. The image of Scholz in the Gazeta Wyborcza newspaper in the area of his foreign policy in the context of the Russian Federation's war in Ukraine reflects conservative and prestige politics (according to Hans Morgenthau's typology). The media portrayal of German-Ukrainian relations furthermore highlights the German Chancellor's attitude of post-classical realism in Robert Gilpin's terms. In the journal analysed, the image of the head of the German government is that of a politician intent on maintaining businesslike relations with Russia and taking up a new business-as-usual policy with the Russian Federation. Scholz represents so-called Realpolitik and consequently does not realistically (apart from the declarative layer) support Ukraine's political aspirations related to its membership of NATO and the European Union. The article increases the cognitive value in the area of research on the media image of European political leaders in the context of the Russian Federation's war in Ukraine.

Wizerunek Ukraińców w ogólnoinformacyjnych portalach regionalnych na Podkarpaciu i Dolnym Śląsku od lutego 2022 r.

Russia's aggression against Ukraine and its consequences, e.g. in the form of the influx of refugees from Ukraine to neighbouring countries, highlighted the role of reliable sources of information. The aim of the research was to check how the media image of Ukrainians was created in the first year of the war on two regional websites (www.gazetawroclawska.pl and www.nowiny24.pl) belonging to Polska Press Sp. z o. o. The research units were verbal and visual content. A set of research questions was an attempt to answer the following questions: what was the frequency and dominant sources of origin of the surveyed units, what thematic and interpretative frameworks were present in them, and what functions were dominant in them. Interdisciplinary research methods were used – content analysis with elements of discourse analysis. The obtained results lead to the conclusion about the diverse media image of Ukrainians in the studied regions. The interpretative framework and the functions of headlines and photographs were the same, thematic framework and attendance were different.

Epidural, Inadvertent Subdural, and Combined Epidural-Subdural Anesthesia in Lumbar Spine Surgery: A Retrospective Analysis.

This study aimed to categorize contrast media images associated with epidural, subdural, and combined epidural-subdural anesthesia in patients who had undergone fluoroscopy-guided epidural anesthesia using contrast media combined with monitored anesthesia care (MAC) targeted at deep sedation, incorporating capnography over 5 years. Additionally, a correlation was established between the anesthetic effects and radiographic findings according to the categorized imaging appearances. This study included 628 patients who underwent endoscopic, open, or fusion surgery under epidural anesthesia at Nanoori Hospital in Gangnam between March 2018 and September 2023. Fluoroscopy-guided epidural anesthesia using contrast media combined with MAC and capnography was used. The dataset included detailed radiographic imaging, nursing, and anesthesia records. Distinct patterns of anesthesia administration were observed, with 49%, 19.6%, and 31% of patients receiving epidural, subdural, and combined epidural-subdural anesthesia, respectively. The incidence and duration of motor block were significantly different among the three groups. Additionally, subdural anesthesia displayed a higher incidence of motor block and a prolonged motor deficit duration than epidural anesthesia. Fluoroscopic guidance using a contrast medium for epidural and subdural anesthesia ensures precise space identification and prevents serious anesthetic complications. Our findings suggest the potential to achieve stable anesthesia, particularly using subdural and combined epidural-subdural anesthesia.

Assessing Rheology Effects and Pore Space Complexity in Polymer Flow Through Porous Media: A Pore‐Scale Simulation Study

AbstractNon‐Newtonian fluid flow within porous media, exemplified by polymer remediation of contaminated groundwater/aquifer systems, presents complex challenges due to the fluids' complex rheological behavior within 3D tortuous pore structures. This paper introduces a pore‐scale flow simulator based on the OpenFOAM open‐source library, designed to model shear‐thinning flow within porous media. Leveraging this developed solver, extensive pore‐scale flow simulations were conducted on μ‐CT images of various real and synthetic porous media with varying complexity for both power‐law and Cross‐fluid models. We focused on the macroscale‐averaged deviation between bulk viscosity and the in‐situ viscosity, commonly denoted by a shift factor. We provided an in‐depth evaluation of the shift factor's dependency on the fluid's rheological attributes and the rock's pore space complexity. The least‐squares fitted values of the shift factor fell in the range of 1.6–9.5. Notably, the most pronounced shift factor emerged for extreme flow behavior indices. Our findings highlight not just the critical role of rheological parameters, but also demonstrate how the shift factor fluctuates based on tortuosity, characteristic pore length, and the cementation exponent. In particular, less porous/permeable systems with smaller characteristic pore lengths exhibited larger shift factors due to higher variations of shear rate and local viscosity in narrower flow paths. Additionally, the shift factor increased as rock became more tortuous and heterogeneous. The introduced pore‐scale simulation proves instrumental in determining the macroscopic averaged shift factor. This, in consequence, is vital for precise computations of viscosity and pressure drop when dealing with non‐Newtonian fluid flow in porous media.

Fourier Finite Difference Migration Based Heterogeneous Photoacoustic Imaging in Irregular Layered Media

Fourier Finite Difference Migration Based Heterogeneous Photoacoustic Imaging in Irregular Layered Media

Effects of microscopic pore-throat structure on gas–liquid relative permeability: Porous media construction and pore-scale simulation

The porous media structure of the oil/gas reservoir changes greatly after long-term development, which subsequently influences the macroscopic relative permeability. To clarify the effects of microscopic pore-throat structure on macroscopic relative permeability, we first proposed a method to generate two-dimensional porous media images with adjustable structure parameters. The method is based on Delaunay triangulation and is similar to the pore-network generation process, which can provide binary images for direct numerical simulation of flow through porous media. Then, we established the single component multiphase Shan–Chen lattice Boltzmann method coupling the real gas equation of state. Finally, we discussed the effect of pore radius, coordination number, pore-throat ratio, pore shape, and wettability on the gas–liquid relative permeability curve using the lattice Boltzmann simulation. This study provides an effective method to generate porous media and explain the mechanism of relative permeability change at the pore scale.