Media Images Research Articles

STATISTICAL INDICATORS OF THE CORRESPONDENCE OF THE LANGUAGE OF REGIONAL NEWSPAPERS IN KOSTROMA REGION TO ALL-RUSSIAN STANDARDS: A SYNTACTIC APPROACH

The paper is devoted to the analysis of complex sentences with subordinate clauses of condition, cause, goal, concession, consequence in regional newspapers of Kostroma Region in comparison with the data of similar studies conducted on the material of all-Russian periodicals. The grammar of the language, which is the backbone of the entire language system, is the most stable, standardised, which should be manifested at the syntactic level. The description of linguistic phenomena in the materials of regional periodicals in comparison with the all-Russia’s ones contributes to the forming of a general picture of the syntax of the modern Russian language. This paper shows on quantitative research data the correspondence of the syntax of regional newspapers with the language of all-Russia’s periodicals in the following aspects – the distribution of complex sentences with various types of subordinate clauses in the general system of conditionality and the peculiarities of using the means of their construction. It is shown that in the texts of regional newspapers of Kostroma Region, at the syntactic level, there are all the structural and semantic features of complex sentences with the meaning of conditionality – distribution by their types, the use of typed and non-typed means of communication, the phenomena of gradation, inversion, parcellation, phraseologisation of subordinate clauses. On the example of functioning of the most popular, semantically capacious and significant complex sentences, it is concluded that the syntactic media image of Kostroma Region reflects the general picture in modern Russian journalism, which indicates the stability of the system of complex sentences at the current stage of language development.

Media image transmission and privacy protection for internet of things security

Internet of Things (IoT) is becoming more popular with the increase in advancements of technology and is widely used in various sectors. The data are collected from the real environment and then transmitted over the networks. Due to their restricted resources, processing capacity, and memory, IoT gadgets are susceptible to certain security risks. Several encryption methodologies were established to provide safe communication between IoT devices with minimal computing cost and bandwidth consumption, due to the rise in media date. This research proposes a revolutionary compact pixel crypto (CPC) technique that can accommodate the modest transmission speeds of IoT gadgets. The suggested techniques reduces the computational burden and data quantity by encrypting the image data using pixel driven selective encryption and bloke scanning compression. The suggested method’s effectiveness is examined using the network simulator (NS-2) platform. According to the findings of the experiments, the suggested method outperforms the previous approaches in terms of both packet rate and energy usage. The proposed approach shortens the time needed for node side encryption and decryption operations with improving the system’s energy effectiveness and connectivity performance.

Analisis Bahan Ajar Berbasis Media Gambar untuk Meningkatkan Kemampuan Membaca Siswa Kelas III Mata Pelajaran Bahasa Indonesia di MI Birrul Walidain NW 2 Rensing

One of the best role models for encouraging kids to learn is visual media, which may be utilized to accomplish educational objectives. Teachers can increase students' interest in the lecture by using image media, which can help with comprehension and memory consolidation. This study aimed to ascertain the type of instructional materials utilized in Indonesian language courses and if image media instructional materials may enhance reading proficiency in Indonesian language courses in class III MI. Rensing of Birrul Walidain NW. Data for the qualitative descriptive study design was gathered through documentation, interviews, and observation. Four steps encompass the data analysis process: gathering data, reducing data, presenting data, and making conclusions. The study's findings demonstrate: Two types of visual aids frequently utilized in Indonesian language courses are 1) visual aids in the form of posters and 2) visual aids in the form of serial photos. Regarding media image, there are four ways that it may help with reading comprehension in Indonesian language courses: a) media usage; b) learning media; c) student reading abilities; and d) teacher training and improvement of student reading skills.

Research on the Female Media Image Shaping of the Mainstream Media

In this paper, the articles on WeChat official account Southern Weekend are taken as the research object, using a content analysis method and focusing on the female media image-building in the mainstream media. It is found that the female image construction in mainstream media is still influenced by historical culture, patriarchal culture, and other factors, and there are hidden prejudices, inequalities and imbalances, which do not correctly guide the shaping of female media images from the root.

Reconstruction of reservoir rock using attention-based convolutional recurrent neural network

The digital reconstruction of reservoir rock or porous media is important as it enables us to visualize and explore their real internal structures. The reservoir rocks (such as sandstone and carbonate) contain both spatial and temporal characteristics, which pose a big challenge in their characterization through routine core analysis or x-ray microcomputer tomography. While x-ray micro-computed tomography gives us three-dimensional images of the porous media, it is often impossible to quantify the variability of the pore, grains, structure, and orientation experimentally. Recently, machine learning has successfully demonstrated the reconstruction ability of reservoir rock images or any porous media. These reservoir rock images are crucial for the digital characterization of the reservoir. We propose a novel algorithm consisting of the convolutional neural network, an attention mechanism, and a recurrent neural network for the reconstruction of reservoir rock or porous media images. The attention-based convolutional recurrent neural network (ACRNN) can reconstruct a representative sample of reservoir rocks. The reconstructed image quality was checked by comparing them with the original Parker sandstone, Leopard sandstone, carbonate shale, Berea sandstone, and sandy medium images. We evaluated the reconstruction by measuring pore and throat properties, two-point probability function, and structural similarity index. Results show that ACRNN can reconstruct reservoir rock or porous media of different scales with approximately the same geometrical, statistical, and topological parameters of the reservoir rock images. This deep learning method is computationally efficient, fast, and reliable for synthetic image realizations. The model was trained and validated on real images, and the reconstructed images showed excellent concordance with the real images having almost the same pore and grain structures. The deep learning-based digital rock reconstruction of reservoir rock or porous media images can aid in rapid image generation to better understand reservoir rock or subsurface formation.

Isolating the effects of body size and sexualisation in social media images on body image-related constructs among young women

This study systematically separated the effects of body size and sexualisation in social media images on women’s body image. Young women (18–25 years, N = 194) viewed 10 social media images of either (1) sexualised women in larger bodies; (2) non-sexualised women in larger bodies; (3) sexualised women in smaller bodies; or (4) non-sexualised women in smaller bodies. Participants completed pre- and post-exposure measures of body image and related constructs. Thin ideal internalisation was examined as a moderator of those effects. The body size of the women in the images was more influential than how sexualized their bodies were presented. Regardless of sexualisation, viewing images of women with smaller bodies was negative for appearance satisfaction, negative mood, and body appreciation, and viewing women with larger bodies was positive for body appreciation and neutral for appearance satisfaction and mood. The impact of body size on appearance satisfaction was stronger for those high in thin ideal internalisation. Women with larger bodies were perceived more positively than those with smaller bodies. However, participants’ self-objectification increased in all conditions. Thus, while viewing images of women with larger bodies provides promise for improving social media, further research is needed before it is recommended broadly.

Development of Local Culture-Based Image Media to Improve Student Learning Outcomes on the Material of Arithmetic Operations of Multiplication of Natural Numbers

The aim of this research is to develop Image Media learning media to calculate the multiplication of natural numbers for class II students at SDN Tejamari, Baros District. This research uses the type of research and development (R&D) from S. Thiagarajan's development model, which has 4 stages of development, namely defining, designing, developing and disseminating. The media developed is validated by material experts and media experts before conducting initial field trials on students. Data was collected using questionnaires from media experts, material experts, class II teacher questionnaires, student questionnaires, interview observations and documentation. Data resulting from research were analyzed using quantitative and qualitative analysis techniques. Based on the validation results, material experts got a score of 46 with a percentage of 83% with the criteria "Good", validation from media experts with a score of 44 with a percentage of 88% with the criteria "Very Good", and the results of class II teacher validation with a score of 99 with a percentage 94% with "Very Good" criteria. As for the results of the initial field trials, the percentage was 90% - 100% with the criteria "Very Good". Overall, the learning media Image Media is suitable for use by making it easier to calculate multiplication of natural numbers by students who have difficulty calculating multiplication using direct observation. This image media makes it easier for students to learn multiplication. Based on the results of the Pre-Test before using image media and the Post-Test using image media, the average score was 0.898 with high criteria. And obtained an N-Gain percentage score of 89.833 in the effective category. So, the learning media Image Media has been effectively used for students in improving learning outcomes for the arithmetic operation of multiplying natural numbers.

Construction of the Historical and Cultural Media Landscape in Regional Media

The cultural space of the city is one of the most modern and relevant areas of research into the possibilities of identifying modern cities. Today, the media play a key role in the construction of the cultural media space, which largely determines both the “face” and “soul” of a modern city (both a metropolis and a provincial outback). When designing and implementing regional cultural policy, the central place is given to the unique cultural content of the place. This factor determines the relevance of the work. The object of the study is the media texts of the regional cultural and educational magazine “Omsk Muse”. The purpose of the study is to identify the problematic and thematic originality and historical context of media texts that implement the processes of construction and reproduction of the cultural media space of Omsk. Empirical material is media texts from the magazine “Omsk Muse”. The chronological period of the study is from 2008 to 2020. A total of 30 issues of the journal were analyzed. The following research methods were used in the work: general scientifi c methods of observing the material and describing it, the method of historicism, the method of content analysis, the method of discourse analysis, the cultural method, the method of functional analysis. In the course of the study, the content of the concept “cultural space of the city” was outlined, the specifi cs of the media space and media images as factors of modern cultural identifi cation were considered; the functions of the media are highlighted, with the help of which the cultural “face” of the region is formed. Based on the study of empirical material using content analysis, the problematic and thematic features of publications devoted to the cultural and historical image of the city were identifi ed. The research materials can be used in the development of lectures and practical classes in the disciplines “Regional Media”, “Art Journalism”, “Leisure Journalism”. Keywords: media space, media landscape, cultural memory, cultural and historical values, urban culture, provincial town

Deep learning for pore-scale two-phase flow: Modelling drainage in realistic porous media

In order to predict phase distributions within complex pore structures during two-phase capillary-dominated drainage, we select subsamples from computerized tomography (CT) images of rocks and simulated porous media, and develop a pore morphology-based simulator (PMS) to create a diverse dataset of phase distributions. With pixel size, interfacial tension, contact angle, and pressure as input parameters, convolutional neural network (CNN), recurrent neural network (RNN) and vision transformer (ViT) are transformed, trained and evaluated to select the optimal model for predicting phase distribution. It is found that commonly used CNN and RNN have deficiencies in capturing phase connectivity. Subsequently, we develop a higher-dimensional vision transformer (HD-ViT) that drains pores solely based on their size, regardless of their spatial location, with phase connectivity enforced as a post-processing step. This approach enables inference for images of varying sizes and resolutions with inlet-outlet setup at any coordinate directions. We demonstrate that HD-ViT maintains its effectiveness, accuracy and speed advantage on larger sandstone and carbonate images, compared with the microfluidic-based displacement experiment. In the end, we train and validate a 3D version of the model.

An improved ResNet method for urban flooding water depth estimation from social media images

Social media images featuring high timeliness, large data volume, and low cost, is utilized for flooding depth estimation. Common flood depth estimation methods are to segment each reference instance, resulting in local water depths and contradictory results. This study added Coordinate Attention into Residual Neural Network (ResNet), and proposed Coordinate Attention-Residual Neural Network (CA-ResNet) to acquire global submerged water depths. The 2021 Henan rainstorm in China was used as case study and a flooding dataset from Chinese Sina Microblog was constructed, with total 5676 images and 4189 flooded. All the flooded depths class 0–9 reflected from the images were annotated manually. The proposed CA-ResNet method was compared with VGGNet, ResNet, GoogleNet, and EfficientNet, both accuracy and F1 score improved by 1% − 3%. The average accuracy of level 0–––8 within the error range of CA-ResNet reached 83.14%. It is proved that CA-ResNet could achieve flood depth estimation, facilitating efficient decision support for flood responding.

The Contents of the Image of Iraqi Women in the Caricature of The Instagram Application As A Model - An Analytical Study

This study aimed to answer the question: What is the media image of Iraqi women in Instagram cartoons? The Hella Sketchz page was analyzed to uncover this image through a content analysis of the cartoons. The study employed a descriptive-analytical approach using a content analysis tool to analyze all cartoons on the Hella Sketchz page from January 1, 2022, to December 31, 2023, totaling 137 cartoons. At the end of the study, the researcher arrived at several key findings, including: Themes: The media images focused on diverse themes related to daily life, social and cultural issues, including women's, family, and health issues, reflecting the variety of concerns and challenges faced by Iraqi women. Classifications of the media image: The media images can be classified into several main categories, including comedy, awareness, and social and political criticism, highlighting the role of cartoons in conveying messages in an innovative and entertaining way. Nature of the media image: The media image is characterized by its distinctive and simple graphic style, which facilitates quick and effective understanding of the conveyed message and draws attention to the issues raised in a creative way.

Application of space renormalisation technique for spatiotemporal analysis of effective thermal conductivity in multiphase porous materials

Effective thermal conductivity (k¯) is a critical parameter influencing heat transfer in multiphase porous materials. This study introduces a computational approach using the space renormalisation technique to derive k¯ from segmented X-ray micro-CT images of porous media. By accounting for pore-scale heterogeneity, this approach enables detailed spatiotemporal analysis of k¯ in different porous structures. The application of this technique is demonstrated through the prediction and spatiotemporal analysis of k¯ in three different porous rock samples experiencing multiphase fluid flow: (i) steady-state two-phase flow in Estaillades carbonate, (ii) sequential flooding in Bentheimer sandstone, and (iii) immiscible three-phase flow in Ketton limestone. Results show distinct directional variations in k¯ according to phase saturations and redistributions, highlighting the sensitivity of the approach to the microstructural characteristics of porous media. The space renormalisation technique efficiently predicts k¯ with significantly reduced computational costs compared to traditional finite difference methods, making it suitable for real-time applications. The findings provide deeper insights into the dynamic heat transfer mechanisms in heterogeneous porous systems, with implications for various porous media applications such as enhanced oil recovery, geothermal energy extraction, and the design of heat exchangers in engineering systems.

How #Trad Catholics Challenge Current Constructions of Christian Nationalism: Counterknowledge, Masculinity, and Elite Aesthetics on Instagram

ABSTRACT I analyze traditionalist Catholic (or Trad) content on Instagram to add digital hyperconnectivity to current conversations about Christian nationalism in the United States. Building on studies of both epistemological and affective shifts in the age of digital hyperconnectivity, I argue that some pockets of right-wing Christian content are not anti-intellectual, but rather construct alternative knowledge bases to challenge mainstream authorities. This populist strategy of counterknowledge is mediatized in Trad Instagram, creating an epistemic community to counter those of the mainstream. The affordances of Instagram as a platform, characterized by its emphasis on visual content, brevity, and rapid consumption, have resulted in the decontextualized presentation of fragmented elements of Catholic philosophy and social teaching. This reconfiguration not only reshapes the public perception of Catholic thought but also fundamentally alters the grammar of public discourse. I draw on studies of affective polarization to argue that counterknowledge is not primarily about building a substantive alternative knowledge base, but rather about constructing an identity around the media image of an epistemic community under threat. The Trad aesthetic representation of intellectualism is distinctly gendered, presenting intellectual pursuit and reason as masculine virtues. I conclude by explaining how Trad Instagram contributes to and challenges current discourses on Christian nationalism by centering digital hyperconnectivity instead of a rationalist analysis of motivating beliefs, providing insights not only about shifts in authority and populism, but also moving beyond ideology to highlight aesthetic, affect, and the algorithmic curation of knowledge.

Inne kobiety: lethal lesbians i lesbian chic – oblicza i ewolucje wizerunku homoseksualnych kobiet w hollywoodzkich thrillerach lat dziewięćdziesiątych

The article showcases different images and aspects of lesbian characters in 1990s, with special focus on the media landscapes and Hollywood neo-noir thrillers. Stereotypical themes like lethal lesbians and their impact in American cinema are tied with early 90s trends of media portrayal of lesbian chic. The author also highlights the various restrictions of lesbian characters in mainstream cinema, that allows only the visually attractive images of homosexual body to be shown on the screen. Whole article also depicts evolutions of homosexual’s images in 90s media and cinema, the role of male gaze and society fears after the previous homophobic decade in these cultural constructions.

Making the most of big qualitative datasets: a living systematic review of analysis methods.

Qualitative data provides deep insights into an individual's behaviors and beliefs, and the contextual factors that may shape these. Big qualitative data analysis is an emerging field that aims to identify trends and patterns in large qualitative datasets. The purpose of this review was to identify the methods used to analyse large bodies of qualitative data, their cited strengths and limitations and comparisons between manual and digital analysis approaches. A multifaceted approach has been taken to develop the review relying on academic, gray and media-based literature, using approaches such as iterative analysis, frequency analysis, text network analysis and team discussion. The review identified 520 articles that detailed analysis approaches of big qualitative data. From these publications a diverse range of methods and software used for analysis were identified, with thematic analysis and basic software being most common. Studies were most commonly conducted in high-income countries, and the most common data sources were open-ended survey responses, interview transcripts, and first-person narratives. We identified an emerging trend to expand the sources of qualitative data (e.g., using social media data, images, or videos), and develop new methods and software for analysis. As the qualitative analysis field may continue to change, it will be necessary to conduct further research to compare the utility of different big qualitative analysis methods and to develop standardized guidelines to raise awareness and support researchers in the use of more novel approaches for big qualitative analysis. https://osf.io/hbvsy/?view_only=.

Research on the Media Image of Overseas Chinese in Latin American Countries based on the TV Series 'Surinam'

Research on the Media Image of Overseas Chinese in Latin American Countries based on the TV Series 'Surinam'

Artificial intelligence and machine learning applications for cultured meat.

Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological challenges remain which require time-and resource-intensive research and development efforts. Machine learning has the potential to accelerate cultured meat technology by streamlining experiments, predicting optimal results, and reducing experimentation time and resources. However, the use of machine learning in cultured meat is in its infancy. This review covers the work available to date on the use of machine learning in cultured meat and explores future possibilities. We address four major areas of cultured meat research and development: establishing cell lines, cell culture media design, microscopy and image analysis, and bioprocessing and food processing optimization. In addition, we have included a survey of datasets relevant to CM research. This review aims to provide the foundation necessary for both cultured meat and machine learning scientists to identify research opportunities at the intersection between cultured meat and machine learning.

A New Demon after Collapse: Jewish Social Media Images in the Israeli Palestinian ConflictShort Videos' Analysis Based on TikTok

From over two thousand years of exile history to post World War II Holocaust literature and film and television dramas, Jews have always appeared in various media as innocent victims with a mix of sadness and resilience, even making the "Jewish stance" somewhat politically correct. This article is based on the new round of Palestinian Israeli conflict that broke out on October 7, 2023. The research object is 729 short video data crawled on social media TikTok. Through analyzing the overall attitudes towards Jews presented in the videos, it is found that the ratio of positive and negative attitudes is about 1:12, and "genocide", "war criminals", and "terrorism" are the majority of accusations against Israeli Jews in the videos. The large number of negative evaluations on social media presented in the research conclusion strongly contrasts with the stereotypical impression of Jews in past media expressions. This finding is crucial for China's future diplomatic practice and how to enhance its ability to guide international public opinion.

Three-dimensional reconstruction of porous media by fusing multi-grid image features based on extended feature pyramid network

Microstructural analysis of porous media has significant research value in the study of their macroscopic properties. An accurate reconstruction of the digital microstructure model is a prerequisite for understanding their physical properties and transport properties. Deep learning can accurately extract features from training images for a fast and precise reconstruction of porous media. The reconstruction methods based on deep learning have yielded important achievements in the reconstruction of porous media. Aiming at the complex morphology and multi-scale image information of porous media, this paper proposes a generic end-to-end deep learning-based encoder–decoder generative method for the reconstruction of porous media from a single 2D image. This paper designs an extended feature pyramid network encoder to extract image features of different resolutions and different scales, and a generator network incorporating multi-scale image information. Through the training process of the network, the final three-dimensional images that meets certain requirements are generated by means of confrontation. By introducing the loss function of the pattern density function and the loss function of Wasserstein distance with gradient penalty, we constrain the reconstructed 3D structures. Finally, the reconstructed 3D structures and the target 3D structures are visualized to compare their similar morphologies. The morphological parameter indicators of the reconstructed 3D structures were line with those of the target 3D structure. The microstructure parameters of the 3D structures were also compared and analyzed. For complex porous media images, our method can achieve diverse, accurate, and stable 3D reconstruction.

ECT image reconstruction algorithm based on Res-SEUnet

A Res-SEUnet algorithm for ECT image reconstruction is proposed to address the problem of artifacts and blurring of media boundaries in ECT (capacitance tomography) reconstructed images of high-density and small-size media. A convolutional neural network is used to extract the detail features of the LBP image and recover the edge details of the ECT inverted image. Simulation experiments are performed on various complex multi-media datasets built in Comsol and Matlab. The results of the test set show that the Res-SEUnet based ECT reconstruction algorithm can by better reconstruction results on various complex multimedia datasets. [Display omitted]