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Direct-to-patient mobile teledermoscopy is a feasible and useful adjunct to smartphone imaging for monitoring patient-identified lesions of concern, achieving comparable diagnostic and management accuracy as in-office dermatology.

In recent years, artificial intelligence (AI), especially deep learning models, has increasingly been integrated into diagnosing and treating diabetic retinopathy (DR). From delving into the singular realm of ocular fundus photography to the gradual development of proteomics and other molecular approaches, from machine learning (ML) to deep learning (DL), the journey has seen a transition from a binary diagnosis of "presence or absence" to the capability of discerning the progression and severity of DR based on images from various stages of the disease course. Since the FDA approval of IDx-DR in 2018, a plethora of AI models has mushroomed, gradually gaining recognition through a myriad of clinical trials and validations. AI has greatly improved early DR detection, and we're nearing the use of AI in telemedicine to tackle medical resource shortages and health inequities in various areas. This comprehensive review meticulously analyzes the literature and clinical trials of recent years, highlighting key AI models for DR diagnosis and treatment, including their theoretical bases, features, applicability, and addressing current challenges like bias, transparency, and ethics. It also presents a prospective outlook on the future development in this domain. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) has become an important tool for gastrointestinal examination due to its non-invasive nature and minimal patient discomfort. However, the quality of WCE images is often limited by built-in lighting and the complex gastrointestinal environment, particularly in the region filled with small intestinal villi. Additionally, the morphology of these villi usually serves as a crucial indicator for related diseases. To address this, we propose a novel method to enhance the clarity of small intestinal villi in WCE images. Our method uses a guided filter to separate the low- and high-frequency components of WCE images. Illumination gain factors are calculated from the low-frequency components, while gradient gain factors are derived from Laplacian convolutions on different regions. These factors enhance the high-frequency components, combined with the original image. This approach improves edge detail while suppressing noise and avoiding edge overshoot, providing clearer images for diagnosis. Experimental results show that our proposed method achieved a 45.47% increase in PSNR compared to classical enhancement algorithms, a 12.63% improvement in IRMLE relative to the original images, and a 31.84% reduction in NIQE with respect to the original images. [ABSTRACT FROM AUTHOR]

Background/objectives: The way in which topics like suicide, mental health concerns and alcohol and other drug use are communicated matters. It has the potential to have either a positive or negative impact on people and communities, particularly those with a lived experience of these concerns. This article draws on the findings of a qualitative study designed to explore the experiences and perceptions of stakeholders on the imagery and language used to depict suicide, mental health concerns or alcohol and other drug use. Methods: The focus group method was used as a form of participatory action research to gain an in-depth understanding of the experiences and views of those who use or are impacted by language and imagery about suicide, mental ill-health and AOD use, including those with lived experiences of these topics. Results: A series of 10 focus groups were created in February and March 2022 with media and other professional communicators; people identifying as having a lived experience of suicide, mental ill-health or alcohol and other drug use; mental health and suicide prevention sector professionals; and people from priority populations (n = 49). From these focus groups, principles were developed as well as exemplars of helpful and less helpful depictions. Rather than prescriptive or static rules, the participants indicated that safe representations require an ongoing engagement with the principle of "do no harm". Conclusions: A positive conclusion arose—that words and images have the potential to promote help-seeking, challenge stigma or stereotypes and create change. [ABSTRACT FROM AUTHOR]

This article aims to study how historical documentaries handle the development of a tale. The initial premise is that documentaries are not a mere compilation and recounting of data but rather a narration constructed based on the perspective of the audiovisual language. To understand how filmmakers create the story, we studied documentary feature-length films on the Spanish Civil War between 2000 and 2014. The sequence has been taken as the unit of analysis, and a breakdown has been made of the types of images and sounds used according to duration, order, and predominance of plot or characters. The research bears on the concept of narrative strategy, which helps to understand the format's internal mechanism, considering three issues: the production approach, the visual solution, and the narrative decision. The study of the selected corpus detected eleven narrative strategies-eleven ways to portray the same event from the past, with different purposes and intentions. [ABSTRACT FROM AUTHOR]

Copyright of National Palace Museum Research Quarterly is the property of National Palace Museum and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article analyses expedition sketches of professional artist Lyudmila Mikhailovna Pozdeeva from the collections of the National Museum of the Republic of Tatarstan and the “Miraskhane” Written Heritage Center of G. Ibragimov Institute of Language, Literature and Art of the Republic of Tatarstan Academy of Sciences, which were made by the master during the expeditions of 1926 and 1927 under the leadership of N. I. Vorobyev. Costume complexes of Tatars of different subethnic and ethnographic groups are analysed. Particular attention is paid to the scientific component of L. M. Pozdeeva’s work, the consistency of the sketches to written sources, museum materials and expedition photographs. The author of the article identified nine watercolours and one graphite pencil sketch, which depict the clothes of men and women of different ages. The pieces were made in the Chelny, Chistopol, Sviyazhsk, and Tetyush cantons, in settlements such as Nizhneye Bishevo, Kainly, Akbulatovo, Stary Tatarsky Adam, Molkeevo, Stary Kurbash, and Bakorchi (Bakrchi). Some images are duplicated by photographs, which became the visual accompaniment to the texts of N.I. Vorobyev’s academic publications. The sketches by L.M. Pozdeeva found by the author of the article fully correspond to the photographic images, which is evidence of the precise reproduction of what she saw during the expeditions. The artist chose different angles of the characters in national clothing, thereby creating not just an ethnographic copy, but an artistic image.

If images were once conceived either as subordination or oppositions of words, the advent of the Internet has started new relationship dynamics between them. Within this digital scenario, vision and textuality intensely intertwine in the form of captions, posts, hyperlinks or other kind of social media content, daily handled by users. In this context, autobiographical narratives will take on a major role, shaping the way in which the subject conceives, forges and registers his or her identity in relation to the reader/viewer. This article aims to address the question, more present than ever, of how certain autobiographical narratives are disseminated through social networks, and how image and narrative dialogue within this framework. By addressing specific cases found on platforms such as Instagram, we will try to elucidate how these publications point to an ambivalent relationship with visual tropes or narratives associated with certain life experiences, either questioning it or contributing to the creation of a collective narrative. A complex interplay of image and narrative that will affect not only how we share life, but also how we understand it and construct it together with others in a changing world.

This article looked at how the ‘Ghanaian Times’ newspaper, using stories sourced from the British Broadcasting Corporation (BBC), framed Africa to the rest of the world. The study which utilized the mixed approach also employed the gatekeeping, framing and cultural imperialist theories in its attempt to find out which of the regional blocs in Africa dominated the coverage and whether the portrayal was positive or negative. The study found that the majority of the stories that the ‘Ghanaian Times’ sourced from the BBC were negative about Africa hence framing Africa as an unhealthy place for living. Out of the 154 stories the newspaper sourced from the BBC, only 52 stories focused on the positive happenings in Africa while 94 stories painted a catastrophic image about Africa. Also, political unrest and crime were the two dominant themes Africa was associated with. In all, the general image of Africa as portrayed by the newspaper was negative as the editors focused more on negative stories of Africa compared to the positives amidst the abundance of stories published by the BBC. This arguably means that Africa’s negative image could only be corrected if editors are deliberate in selecting more positive stories about Africa for publication. This work will indeed add to the literature on the framing of Africa in the media especially the contribution of the African media to the dominant negative image Africa continues to be associated with.

Brain tumor (BT) is an awful disease and one of the foremost causes of death in human beings. BT develops mainly in 2 stages and varies by volume, form, and structure, and can be cured with special clinical procedures such as chemotherapy, radiotherapy, and surgical mediation. With revolutionary advancements in radiomics and research in medical imaging in the past few years, computer-aided diagnostic systems (CAD), especially deep learning, have played a key role in the automatic detection and diagnosing of various diseases and significantly provided accurate decision support systems for medical clinicians. Thus, convolution neural network (CNN) is a commonly utilized methodology developed for detecting various diseases from medical images because it is capable of extracting distinct features from an image under investigation. In this study, a deep learning approach is utilized to extricate distinct features from brain images in order to detect BT. Hence, CNN from scratch and transfer learning models (VGG-16, VGG-19, and LeNet-5) are developed and tested on brain images to build an intelligent decision support system for detecting BT. Since deep learning models require large volumes of data, data augmentation is used to populate the existing dataset synthetically in order to utilize the best fit detecting models. Hyperparameter tuning was conducted to set the optimum parameters for training the models. The achieved results show that VGG models outperformed others with an accuracy rate of 99.24%, average precision of 99%, average recall of 99%, average specificity of 99%, and average f 1-score of 99% each. The results of the proposed models compared to the other state-of-the-art models in the literature show better performance of the proposed models in terms of accuracy, sensitivity, specificity, and f 1-score. Moreover, comparative analysis shows that the proposed models are reliable in that they can be used for detecting BT as well as helping medical practitioners to diagnose BT. [ABSTRACT FROM AUTHOR]

Corrosion negatively impacts the functionality of civil structures. This paper introduces a new methodology that combines the fractality of vibration signals with a data processing stage utilizing autoencoders to detect corrosion damage in a truss-type bridge. Firstly, the acquired vibration signals are analyzed using six fractal dimension (FD) algorithms (Katz, Higuchi, Petrosian, Sevcik, Castiglioni, and Box dimension). The obtained FD values are then used to generate a gray-scale image. Then, autoencoders analyze these images to generate a damage indicator based on the reconstruction error between input and output images. These indicators estimate the damage probability in specific locations within the structure. The methodology was tested on a truss-type bridge model placed at the Vibrations Laboratory from the Autonomous University of Queretaro, Mexico, where three damage corrosion levels were evaluated, namely incipient, moderate, and severe, as well as healthy conditions. The results demonstrate that the proposal is a reliable tool to evaluate the condition of truss-type bridges, achieving an accuracy of 99.8% in detecting various levels of corrosion, including incipient stages, within the elements of truss-type structures regardless of their location. [ABSTRACT FROM AUTHOR]

Increased urbanization coupled with climate change is increasing the number and intensity of stormwater flooding events. Implementing efforts to successfully manage stormwater flooding depends on understanding how people perceive these events. While images of stormwater flooding abound, how these images influence perceptions about flooding events or management options remains understudied. Our objective is to contribute to the general understanding of how various types of images depicting stormwater runoff and stormwater related flooding influence individual and group interpretations of causes of events, major impacts of those events, and responsibility for managing stormwater related events. To this end, we convened focus groups, gave participants numerous photos of stormwater flooding, asked them to identify which images were most concerning, and to then discuss the specific aspects of the photos that prompted concern. We also tested whether a priming image implicating climate change or development as a cause of stormwater flooding influenced viewer reactions. Finally, we asked participants about preferences for who should manage stormwater. Our results revealed that photo location, the water's appearance, and what people were doing in the photo influenced levels of concern. We also found that priming seems to affect opinions regarding urban stormwater management. Finally, there is some evidence that the absence of people in the photo may affect beliefs about who should manage stormwater. [ABSTRACT FROM AUTHOR]

The role that visual discriminative ability plays among giant pandas in social communication and individual discrimination has received less attention than olfactory and auditory modalities. Here, we used an eye-tracker technology to investigate pupil fixation patterns for 8 captive male giant pandas Ailuropoda melanoleuca. We paired images (N =  26) of conspecifics against: 1) sympatric predators (gray wolves and tigers), and non-threatening sympatric species (golden pheasant, golden snub-nosed monkey, takin, and red panda), 2) conspecifics with atypical fur coloration (albino and brown), and 3) zookeepers/non-zookeepers wearing either work uniform or plain clothing. For each session, we tracked the panda's pupil movements and measured pupil first fixation point (FFP), fixation latency, total fixation count (TFC), and duration (TFD) of attention to each image. Overall, pandas exhibited similar attention (FFPs and TFCs) to images of predators and non-threatening sympatric species. Images of golden pheasant, snub-nosed monkey, and tiger received less attention (TFD) than images of conspecifics, whereas images of takin and red panda received more attention, suggesting a greater alertness to habitat or food competitors than to potential predators. Pandas' TFCs were greater for images of black-white conspecifics than for albino or brown phenotypes, implying that familiar color elicited more interest. Pandas reacted differently to images of men versus women. For images of women only, pandas gave more attention (TFC) to familiar combinations (uniformed zookeepers and plain-clothed non-zookeepers), consistent with the familiarity hypothesis. That pandas can use visual perception to discriminate intra-specifically and inter-specifically, including details of human appearance, has applications for panda conservation and captive husbandry. [ABSTRACT FROM AUTHOR]

Experiments in affective computing are based on stimulus datasets that, in the process of standardization, receive metadata describing which emotions each stimulus evokes. In this paper, we explore an approach to creating stimulus datasets for affective computing using generative adversarial networks (GANs). Traditional dataset preparation methods are costly and time consuming, prompting our investigation of alternatives. We conducted experiments with various GAN architectures, including Deep Convolutional GAN, Conditional GAN, Auxiliary Classifier GAN, Progressive Augmentation GAN, and Wasserstein GAN, alongside data augmentation and transfer learning techniques. Our findings highlight promising advances in the generation of emotionally evocative synthetic images, suggesting significant potential for future research and improvements in this domain. [ABSTRACT FROM AUTHOR]

English as a Foreign Language (EFL) textbook image resources provide important materials and ways of learning for EFL students. Previous studies on the meaning and function of images in Chinese EFL textbooks tend to take pedagogy and cognitive psychology as main perspectives. Based on visual grammar, this article analyzes the image resources in Chinese junior high school EFL textbooks from a multimodal perspective, attempting to explore how the images realize three meta-meanings. It is found that cartoon, photo, table, and diagram are four main types of images in Yilin EFL textbooks. Quantitative analysis and qualitative analysis show that the images in the textbooks realized the representational meaning, interactive meaning and compositional meaning, but there are still some problems in image design, such as insufficient image types, lack of images promoting knowledge comprehension, and inadequate realization of compositional meaning of images. The research results offer some implications for Chinese EFL textbook editors to design textbooks, and also provide some suggestions for Chinese EFL teachers' teaching practice and students' learning. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Babylon Center for Humanities Studies is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article elaborates on opportunities and challenges related to implementing critical visual literacy (CVL) in English as a foreign language (EFL) classrooms in Norwegian secondary schools as an approach to preparing learners to face the complexity and diversity of today's textual landscapes. The study draws on a doctoral research project which focused on the meaning-making processes learners engage in when discussing images from the perspective of intercultural learning and expands on this by giving voice to the participating students and teachers. Data consisted of researcher field notes and learner responses from a 16-week intervention in which 83 upper secondary EFL learners in Norway were introduced to CVL practices as an integrated part of their EFL classes. Using thematic analysis, the data was analysed with a focus on the opportunities and challenges that arose from teachers and learners enacting CVL in the EFL classroom. Three themes were identified and discussed, including 'Changing classroom practices', 'Co-construction processes' and 'Reading and talking about images'. [ABSTRACT FROM AUTHOR]

Ischemic brain strokes are severe medical conditions that occur due to blockages in the brain's blood flow, often caused by blood clots or artery blockages. Early detection is crucial for effective treatment. This study aims to improve the detection and classification of ischemic brain strokes in clinical settings by introducing a new approach that integrates the stroke precision enhancement, ensemble deep learning, and intelligent lesion detection and segmentation models. The proposed hybrid model was trained and tested using a dataset of 10,000 computed tomography scans. A 25-fold cross-validation technique was employed, while the model's performance was evaluated using accuracy, precision, recall, and F1 score. The findings indicate significant improvements in accuracy for different stages of stroke images when enhanced using the SPEM model with contrast-limited adaptive histogram equalization set to 4. Specifically, accuracy showed significant improvement (from 0.876 to 0.933) for hyper-acute stroke images; from 0.881 to 0.948 for acute stroke images, from 0.927 to 0.974 for sub-acute stroke images, and from 0.928 to 0.982 for chronic stroke images. Thus, the study shows significant promise for the detection and classification of ischemic brain strokes. Further research is needed to validate its performance on larger datasets and enhance its integration into clinical settings. [ABSTRACT FROM AUTHOR]

The fish are incorporated with ice to preserve their freshness when sold on the market. Ordinary people can only detect its freshness with some basic freshness knowledge. Therefore, non-destructive fish freshness inspection is an innovative solution to help. This dataset provides a medium to develop a system for non-destructive detection of fish freshness. There are three data variations: sensor data, images, and organoleptic examination. This dataset includes three fish species: mackerel, tilapia, and tuna, using 21 fish of each species. Data generation was carried out for 11 days, where 800 MQ (Metal Oxide) 135 and TGS (Taguchi Gas Sensor) 2602 sensor data and 80 images were generated every day. Organoleptic examinations were carried out using the Indonesian National Standard (SNI) 2729-2013 on six parameters: eyes, gills, body surface mucus, meat, smell, and body textures. This dataset can be used to develop a fish freshness detection system, regression modeling to estimate the deterioration in fish freshness, and standard grouping of freshness classes.

ABSTRACTImpacts of the surrounding land use land cover changes on Suba Sabeta Forest over the past three decades from 1990 to 2020 and associated community perception were assessed employing an integrated approach of Landsat images analysis, household survey, key informants interview and focus group discussion. The study involved collection of both quantitative and qualitative data which were analyzed using descriptive statistics. The results revealed that forests and shrub declined from 9,220 ha and 1335 ha to 2702 ha and 783 ha while settlement and bare land increased from 866 ha and 273 ha to 5,589 ha and 3,978 ha, respectively. Cultivated land increased from 12,162 ha in 1990 to 14,329 ha in 2005 and then declined to 10,811 ha by 2020. Respondents’ responses also indicated a drastic decline in the forest cover related to fuel wood collection (81.6%), settlement (13.8%), cutting trees for construction (3.3%) and expansion of cultivated land (1.3%). According to the respondents poverty (79.08%) and population growth (20.92%) were the underlying causes of the forest cover decline. Responses further revealed disappearance of indigenous plants (biodiversity loss) (73.2%), soil erosion (18%) and decline in agricultural production (8.8%) related to the decline in forest cover. Thus, protection of the remnant forest, reforestation and developing renewable alternative energy sources might help to mitigate further decline in Suba Sebeta Forest cover and associated impacts.

This article analyses the semantics of εἴδωλον, εἰκών, and ὁμοίωμα in the LXX in light of Greek literary and documentary evidence. By addressing the issue of the relationship between the vocabulary of images and the vocabulary of idols, (1) it deconstructs some oppositions inherited from early Christian interpretations of biblical passages (especially regarding Genesis 1). Moreover (2) it shows the interest of including evidence from the LXX in a broader discussion on the role of visual representation in antiquity.

As manifested in Jean Baudrillard’s notoriously provoking claim that “the Gulf War did not take place,” mediatization of war has long been associated with illusion. Today, war images that circulate online are increasingly judged by their proximity to ‘truth,’ eliciting a skepticism towards their ‘evidentiary’ value. By juxtaposing Baudrillard’s reading of the mediatization of the Gulf War with the contemporary image theories of e.g. Cecilia Sjöholm and Matthew Fuller and Eyal Weizman, the article explores how this skepticism is expressed in a contemporary context. Through visual analysis of a YouTube video of a press conference held at the bombed Al-Ahli Baptist Hospital in Gaza, it examines the relationship between the f orm through which the war is perceived (the images) and their cont ent (the ‘realities’ of war). Through a lens oered by Georges Didi-Huberman, the article concludes by suggesting that by expanding what I term the snapshot logic of war images to embrace a scenography of war, the press conference video gives form to the condition of desperation and suering in Gaza.

In recent years, artificial intelligence (AI), especially deep learning models, has increasingly been integrated into diagnosing and treating diabetic retinopathy (DR). From delving into the singular realm of ocular fundus photography to the gradual development of proteomics and other molecular approaches, from machine learning (ML) to deep learning (DL), the journey has seen a transition from a binary diagnosis of “presence or absence” to the capability of discerning the progression and severity of DR based on images from various stages of the disease course. Since the FDA approval of IDx-DR in 2018, a plethora of AI models has mushroomed, gradually gaining recognition through a myriad of clinical trials and validations. AI has greatly improved early DR detection, and we’re nearing the use of AI in telemedicine to tackle medical resource shortages and health inequities in various areas. This comprehensive review meticulously analyzes the literature and clinical trials of recent years, highlighting key AI models for DR diagnosis and treatment, including their theoretical bases, features, applicability, and addressing current challenges like bias, transparency, and ethics. It also presents a prospective outlook on the future development in this domain.

Background. Research into the formation of algorithms for obtaining MRI images is highly relevant. The purpose of the work is to analyze advances in the development of a software algorithm for obtaining MRI images of the brain. Materials and methods. Found 17 articles in specialized databases Cyberleninka, eLibrary, PubMed, ScienceDirect. Methods of analysis, synthesis and induction were used. Results. The best way to suppress noise in brain MRI images is the Gaussian Filter, the improvement of which has been achieved through the evolution of neural networks. Automatic segmentation achieved performance comparable to manual segmentation by using a combined system with integrated modules to eliminate the influence of noise and background, to identify image features and edge information. The Sobel operator allows the bright edges of an MRI image to be more clearly identified for removal. For volumetric visualization of brain images, due to its labor-intensive nature, the use of layer-by-layer representation of MRI data is proposed. Watershed segmentation and the K-nearest neighbor classification algorithm resulted in an MRI image accuracy of only 89 %; the wavelet transform was performed without calculating the accuracy. Support Vector Machine (SVM) using the GLCM algorithm showed an accuracy of up to 93 %, but only 36 images were used for training. Based on 150 MRI images of the brain, their classification was performed using the MATLAB 2018a software package (Matrix Laboratory) and a testing accuracy of 96,7 % was achieved. Conclusions. Improved algorithms for removing noise and bright edges from MRI brain images, segmenting them, and creating them with volumetric visualization are being created, including effective software modules for automatic segmentation based on convolutional neural networks.

This paper addresses the classification of images depicting the eruptive activity of Mount Etna, captured by a network of ground-based thermal cameras. The proposed approach utilizes Convolutional Neural Networks (CNNs), focusing on pretrained models. Eight popular pretrained neural networks underwent systematic evaluation, revealing their effectiveness in addressing the classification problem. The experimental results demonstrated that, following a retraining phase with a limited dataset, specific networks such as VGG-16 and AlexNet, achieved an impressive total accuracy of approximately 90%. Notably, VGG-16 and AlexNet emerged as practical choices, exhibiting individual class accuracies exceeding 90%. The case study emphasized the pivotal role of transfer learning, as attempts to solve the classification problem without pretrained networks resulted in unsatisfactory outcomes.

This research aimed to study the changing Liannichang’s images in The Bride with White Hair, a Swordman novel published in 1996, and The Bride with White Hair TV series in 2012. The theory of adaptation was used for analysis. The result showed that Liannichang’s images in the novel have brutal, helpful, sympathetic, grateful, and faithful images. Liannichang’s images in the TV series have brutal, helpful, sacrificeable, and faithful images. The researcher found that Liannichang’s family background persisted throughout in that she did not have a family and grew up with a wolf. When adapted as a TV series, it was revealed that Liannichang had symptoms of sickness (she did not control herself). This was reflected in a brutal image. Character relationships have conventions, extensions, reductions, and modifications in some situations, and these factors reflect helpful, sacrificeable, grateful, and faithful images. When adapted to the TV series, these reduced sympathetic and grateful images, but appeared instead to be sacrificeable because the TV series modified Tiefeilong in the negative role. These images still reflect the five constant virtues of Confucianism; they showed that the helpful and sympathetic images reflected benevolence, and they can expand the grateful image of propriety. The faithful image reflects fidelity. The last factors that reflect on the changing Liannichang’s images are the era and the limitations of the story

Similarly to China, Japan has a long history of worshiping fortune gods. The act of making offerings and praying to these deities has been practiced since ancient times. Fortune gods are figures in Japanese folk religion that are believed to bring happiness, hope, and good luck. When speaking of fortune gods in Japan, people will first think of the Seven Lucky Gods. Apart from them, there are also some local fortune gods such as Fukusuke and Sendai Shiro. These gods share some common traits and also have connections with the Japanese folk belief in Fukuko (fortune child). This study adopts a comparative methodology to compare Japan's Seven Lucky Gods with the local Japanese fortune gods as well as Fukuko, and then analyze their similarities. This article argues that the Japanese fortune gods have two major common characteristics: the super power to bring good fortune, and their distinctive appearance. By systematically analyzing the common features of Japanese fortune gods, this study will clarify the mechanism behind their deification as fortune deities and also help us to gain a better insight into the Japanese conceptions of deities and spirits. [ABSTRACT FROM AUTHOR]

This paper addresses the classification of images depicting the eruptive activity of Mount Etna, captured by a network of ground-based thermal cameras. The proposed approach utilizes Convolutional Neural Networks (CNNs), focusing on pretrained models. Eight popular pretrained neural networks underwent systematic evaluation, revealing their effectiveness in addressing the classification problem. The experimental results demonstrated that, following a retraining phase with a limited dataset, specific networks such as VGG-16 and AlexNet, achieved an impressive total accuracy of approximately 90 % . Notably, VGG-16 and AlexNet emerged as practical choices, exhibiting individual class accuracies exceeding 90 % . The case study emphasized the pivotal role of transfer learning, as attempts to solve the classification problem without pretrained networks resulted in unsatisfactory outcomes. [ABSTRACT FROM AUTHOR]

Featured Application: This framework can be applied to evaluate plant density and health in different types of green stormwater infrastructures, agriculture land, and forests. Over the past few decades, there has been a notable surge in interest in green stormwater infrastructure (GSI). This trend is a result of the need to effectively address issues related to runoff, pollution, and the adverse effects of urbanization and impervious surfaces on waterways. Concurrently, umanned aerial vehicles (UAVs) have gained prominence across applications, including photogrammetry, military applications, precision farming, agricultural land, forestry, environmental surveillance, remote-sensing, and infrastructure maintenance. Despite the widespread use of GSI and UAV technologies, there remains a glaring gap in research focused on the evaluation and maintenance of the GSIs using UAV-based imagery. This study aimed to develop an integrated framework to evaluate plant density and health within GSIs using UAV-based imagery. This integrated framework incorporated the UAV (commonly known as a drone), WebOpenDroneMap (WebDOM), ArcMap, PyCharm, and the Canopeo application. The UAV-based images of GSI components, encompassing trees, grass, soil, and unhealthy trees, as well as entire GSIs (e.g., bioretention and green roofs) within the Morgan State University (MSU) campus were collected, processed, and analyzed using this integrated framework. Results indicated that the framework yielded highly accurate predictions of plant density with a high R2 value of 95.8% and lower estimation errors of between 3.9% and 9.7%. Plant density was observed to vary between 63.63% and 75.30% in the GSIs at the MSU campus, potentially attributable to the different types of GSI, varying facility ages, and inadequate maintenance. Normalized difference vegetation index (NDVI) maps and scales of two GSIs were also generated to evaluate plant health. The NDVI and plant density results can be used to suggest where new plants can be added and to provide proper maintenance to achieve proper functions within the GSIs. This study provides a framework for evaluating plant performance within the GSIs using the collected UAV-based imagery. [ABSTRACT FROM AUTHOR]

In this article, we present methods for the use of eye-tracking in interviews in order to reflect on visual perception and practices of looking as part of an ethnography of the senses. The methods are based on two multi-year ethnographic studies involving eye trackers. In the first one, researchers used mobile eye trackers to study how art museum visitors approach digital image technologies. In the other, they relied on stationary eye trackers to investigate practices on digital image platforms. We discuss how video recordings of participants' eye movements were made and describe the process of conducting ethnographic interviews based on the videos. The eye-tracking interviews can be used 1. to make participants aware of and think about practices of looking; 2. to verbalize in dialogue sensory and interpretative processes regarding museum objects and digital image technologies; and 3. to surface individuals' aesthetic preferences and incorporated knowledge. [ABSTRACT FROM AUTHOR]

Copyright of Odeere is the property of Edicoes UESB and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ensuring that marginalized populations become empowered in agriculture is vital to the success of global agricultural development goals. However, these populations have reported lacking access, power, and voice. Perhaps one strategy researchers can use to address this issue is through visual Q methodology. In this methodological paper, I argue that by combining the tenets of Q and visual methodologies, researchers can offer empirically grounded findings that evoke powerful, rich insight into the perspectives of marginalized populations in agriculture who may lack the communication skills to articulate their perspectives through words. To this point, however, the approach has lacked clear guidance, which has led to diminished quality in the published literature on visual Q methodology. In response, I offer six principles to guide visual Q methodological studies moving forward: (a) relationship-building with participants, (b) participant training, (c) concourse development, (d) Q set sampling, (e) data collection, and (f) data analysis and interpretation. [ABSTRACT FROM AUTHOR]

After retiring from the field of drama therapy in 2018, I resumed my career as an artist, making theatre, music and visual art. Since that time I have focused most on creating both actual and digital images through photography. In February 2024, I completed an exhibition of 60 images launched on an international platform, Art without Boundaries, in March 2024. The exhibition concerns a series of photographs I created in the late 1970s of people wearing masks that I made on my face. The early photographs were very much about my fascination with masks as self-portraiture. I paired the old images with new ones of new masks of my face on some of the same people 45 years later, and some new people. In this creative contribution, I present my artist statement for the exhibition and then one photograph that was in the show. And below that is a playful image visually expressing an idea that has been with me for more than fifty years – that a human being is multiplicitous and that, like an actor onstage, has the potential to play many roles. [ABSTRACT FROM AUTHOR]

We were invited by Gitanjali Dang and Christopher Schenker to participate in the Draft Project, an initiative that explores contemporary art that produces, contributes or provokes public debate. The project involved nine teams around the world that worked for twelve months in their local contexts: Beijing, Cairo, Cape Town, Hamburg, Honk Kong, Mexico City, Mumbai, St. Petersburg and Zurich. In our case, Mexico City was conformed by Helena Chávez Mac Gregor, Cuauhtémoc Medina and the artistic collective Teatro Ojo. For us, one of the questions raised by the Draft project was how to intervene within a public sphere swamped by images of violence that unlike creating a space to produce a collective thought subdues into the effects of its own violence. The local configuration of the public sphere happens as if we are isolated, staring at the catastrophe without any words to say. The images that circulate, normalized, are no longer able to open up our eyes. Its saturation no longer lights up anything. Teatro Ojo’s intervention attempts to produce a different order, sense or formation, by circulating publicly a bunch of different images. Montages trying to turn back to the public sphere –either TV or social network- and appear with the shape of lightning bolts, to establish in its sequence possible new relations. This text is the recounting of the process that lead us to At night, lightening.

The aim of this article is to reflect on the presence of colonized women in photographs and other representations, such as drawings, posters, postcards, exhibition catalogues, newspapers and magazines, which were disseminated in the context of the Portuguese colonial expositions, and in exhibition spaces conceived by the Portuguese with a colonial component. Generally speaking, the exhibitions sought to put forward the progress, taking into account land, rail and sea transport, but also roads, communications, trade, industry, arts, architecture, culture, and the most recent advances in science and medicine. The exhibitions were also places where the logic of colonial models was staged, showing a clear relationship between colonial domination and gender representation. The research includes several materials produced throughout the 1930s (a fertile period regarding the Portuguese participation in this kind of international events) intended to publicize these exhibitions or serve as a complement to them. These materials may include art works or merely propagandistic works, or works that combine both components. The analysis will include materials associated with several exhibitions between 1931 and 1940, such as the International Colonial Exhibition of Paris (1931), the Lisbon Industrial Exhibition (1932), the Portuguese Colonial Exhibition in Porto (1934), the Exhibition of the Portuguese World in Lisbon (1940), and the Portugal of the Little Ones (Portugal dos Pequenitos) in Coimbra (1940). The contexts in which women appear and the way they are represented — as active beings (performing tasks), as contemplative beings (as in natural landscapes) or as objects of sexual desire, revealing the context of power (legislative, administrative, male and colonial) in which the images and the representations were produced — will be analyzed.

In this paper we present some modifications to and extensions of a Wordnet for Bulgarian designed to make it more appropriate for applications in the area of language learning. However, in order to support education, we need to ensure the appropriate selection of sets of synonyms (synsets) from BTB- Wordnet, depending on the education level of the learners, and various types of exercises based on integration of the learning topic and semantic information within Wordnet. For this purpose, our focus is mainly on the combination of the lexemes (lemmas), with their meanings and examples, and the specially designed pictures as illustrations of those meanings within the synsets. We report on our preliminary results.

This article elaborates on opportunities and challenges related to implementing critical visual literacy (CVL) in English as a foreign language (EFL) classrooms in Norwegian secondary schools as an approach to preparing learners to face the complexity and diversity of today’s textual landscapes. The study draws on a doctoral research project which focused on the meaning-making processes learners engage in when discussing images from the perspective of intercultural learning and expands on this by giving voice to the participating students and teachers. Data consisted of researcher field notes and learner responses from a 16-week intervention in which 83 upper secondary EFL learners in Norway were introduced to CVL practices as an integrated part of their EFL classes. Using thematic analysis, the data was analysed with a focus on the opportunities and challenges that arose from teachers and learners enacting CVL in the EFL classroom. Three themes were identified and discussed, including ‘Changing classroom practices’, ‘Co-construction processes’ and ‘Reading and talking about images’.

VGG16 is a convolutional neural network model used for image recognition. It is unique in that it only has 16 weighted layers, rather than relying on a large number of hyperparameters. It is considered one of the best vision model architectures. However, several things need to be improved to increase the accuracy of image recognition. In this context, this work proposes and investigates two ensemble CNNs using transfer learning and compares them with state-of-the-art CNN architectures. This study compares the performance of (rectified linear unit) ReLU and sigmoid activation functions on CNN models for animal classification. To choose which model to use, we tested two state-of-the-art CNN architectures: the default VGG16 with the proposed method VGG16. A dataset consisting of 2,000 images of five different animals was used. The results show that ReLU achieves a higher classification accuracy than sigmoid. The model with ReLU in fully connected and convolutional layers achieved the highest precision of 97.56% in the test dataset. The research aims to find better activation functions and identify factors that influence model performance. The dataset consists of animal images collected from Kaggle, including cats, cows, elephants, horses, and sheep. It is divided into training sets and test sets (ratio 80:20). The CNN model has two convolution layers and two fully connected layers. ReLU and sigmoid activation functions with different learning rates are used. Evaluation metrics include accuracy, precision, recall, F1 score, and test cost. ReLU outperforms sigmoid in accuracy, precision, recall, and F1 score. This study emphasizes the importance of choosing the right activation function for better classification accuracy. ReLU is identified as effective in solving the vanish-gradient problem. These findings can guide future research to improve CNN models in animal classification.

The present study examined the usage of deep convolutional neural networks (DCNNs) for the classification, segmentation, and detection of the images of surface defects in heritage buildings. A survey was conducted on the building surface defects in Gulang Island (a UNESCO World Cultural Heritage Site), which were subsequently classified into six categories according to relevant standards. A Swin Transformer- and YOLOv5-based model was built for the automated detection of surface defects. Experimental results suggested that the proposed model was 99.2% accurate at classifying plant penetration and achieved a mean intersection-over-union (mIoU) of over 92% in relation to moss, cracking, alkalization, staining, and deterioration, outperforming CNN-based semantic segmentation networks such as FCN, PSPNet, and DeepLabv3plus. The Swin Transformer-based approach for the segmentation of building surface defect images achieved the highest accuracy regardless of the evaluation metric (with an mIoU of 90.96% and an mAcc of 95.78%), when contrasted to mainstream DCNNs such as SegFormer, PSPNet, and DANet.

There is increasing recognition of the importance of the humanities and arts in medical and psychiatric training. We explore the poetry of Charles Baudelaire (1821–1867) and its evocations of depression through themes of mood, time and self-consciousness and discuss their relation to images of ‘spleen’, the ‘snuffling clock’ and the ‘sinister mirror’. Following the literary critical commentaries of Walter Benjamin (1892–1940) and Jean Starobinski (1920–2019) we identify some of their roots in the poet's experience of the rapid and alienating urbanisation of 19th-century Paris. Appreciation of the rich vocabulary of poetry and the images it generates adds depth to clinical practice by painting vivid pictures of subjective experience, including subjective experience of the ‘social’ as part of the biopsychosocial constellation.

Abstract Objectives Phenotyping plants in a field environment can involve a variety of methods including the use of automated instruments and labor-intensive manual measurement and scoring. Researchers also collect language-based phenotypic descriptions and use controlled vocabularies and structures such as ontologies to enable computation on descriptive phenotype data, including methods to determine phenotypic similarities. In this study, spoken descriptions of plants were collected and observers were instructed to use their own vocabulary to describe plant features that were present and visible. Further, these plants were measured and scored manually as part of a larger study to investigate whether spoken plant descriptions can be used to recover known biological phenomena. Data description Data comprise phenotypic observations of 686 accessions of the maize Wisconsin Diversity panel, and 25 positive control accessions that carry visible, dramatic phenotypes. The data include the list of accessions planted, field layout, data collection procedures, student participants’ (whose personal data are protected for ethical reasons) and volunteers’ observation transcripts, volunteers’ audio data files, terrestrial and aerial images of the plants, Amazon Web Services method selection experimental data, and manually collected phenotypes (e.g., plant height, ear and tassel features, etc.; measurements and scores). Data were collected during the summer of 2021 at Iowa State University’s Agricultural Engineering and Agronomy Research Farms.

Wireless capsule endoscopy (WCE) has become an important tool for gastrointestinal examination due to its non-invasive nature and minimal patient discomfort. However, the quality of WCE images is often limited by built-in lighting and the complex gastrointestinal environment, particularly in the region filled with small intestinal villi. Additionally, the morphology of these villi usually serves as a crucial indicator for related diseases. To address this, we propose a novel method to enhance the clarity of small intestinal villi in WCE images. Our method uses a guided filter to separate the low- and high-frequency components of WCE images. Illumination gain factors are calculated from the low-frequency components, while gradient gain factors are derived from Laplacian convolutions on different regions. These factors enhance the high-frequency components, combined with the original image. This approach improves edge detail while suppressing noise and avoiding edge overshoot, providing clearer images for diagnosis. Experimental results show that our proposed method achieved a 45.47% increase in PSNR compared to classical enhancement algorithms, a 12.63% improvement in IRMLE relative to the original images, and a 31.84% reduction in NIQE with respect to the original images.

Case PresentationAn 85-year-old male presented to the emergency department after a motor vehicle collision and developed progressive neurological deficits.  CT imaging demonstrated epidural thickening from C2-C7, and MRI was notable for a cervicothoracic epidural hematoma.  The patient underwent emergent decompression with a favorable outcome.DiscussionCases of traumatic spinal epidural hematomas are rarely seen in the emergency department.  These are part of a small subset of operative neurological emergencies that benefit from urgent operative intervention.

The aim of the study is to describe the images that accompanied information about the Covid-19 pandemic, vaccination and treatment of the virus in the following digital news media: ABC, Deia, EITB.eus, El Correo, elDiario.es, El Mundo, La Razón, La Vanguardia, Naiz and Público (20202022). A sample of 15,654 single images was studied, of which 15 clusters were identified with Artificial Intelligence techniques, including the algorithm Inception V3. The analysis is based on the combined use of a series of innovative techniques within the field of Communication. This methodology involves a large data set of images, along with the use of AI techniques such as embeddings. We opted for an unsupervised strategy, typical of exploratory and inductive research. Among the results, the identification of different typologies of images used by the medical and health-oriented media, representations of death and of the human drama caused by the pandemic, linked to the more political and economic dimension of the vaccination campaign, stands out. The images analyzed are part of 'political communication': a communication model that seeks and reinforces the relationship between the government and the media, and vice versa, to generate confidence in the management and in the system itself. The diversity of approaches observed is a positive value as it contributes to establish a more multifaceted and richer operational social reality. The media show different preferences when it comes to visually representing the issues, which translates into an unequal distribution of the same. The study has identified the different narratives revealing different uses of emotionally charged images that pose a complex scenario. [ABSTRACT FROM AUTHOR]

The protection of children is a priority obligation in our societies. This study focuses on the delicate field of photographic representation of minors to analyze how their images achieve connections with the people who follow the publications of these organizations. The research aims to question and analyze the secondary victimization of vulnerable minors and the relationship of this treatment with public engagement. A descriptive analysis methodology is applied to the photographs published in the posts of the official Facebook pages of four NGOs dedicated to children. The research finds that boys and girls usually appear as protagonists of the information, but occupying almost 20 percent of the cases the role of victims. The image of girls is used more than boys, and their representation as victims and sufferers is the one that provokes the most engagement. However, the study shows that what generates the most engagement with audiences is merely illustrative and non-dramatic photography of minors, one that avoids symbolic revictimization and allows visual cultures of peace in which children can inhabit positively. [ABSTRACT FROM AUTHOR]

This study, titled "True Faith and False Faith," discusses what true faith is and other related topics, including how work can be done in the name of faith and how faith can grow and decline. Then, it addresses several forms of erroneous faith and the grounds for their invalidity. These forms are split into what represents life and death through witnessing persecution, devastation, gurgling, or the sun rising from the West. I ended my research by discussing the causes of falling into the various types of false faith and strategies to prevent it. We pray to God for success and sincerity in everything we say and do. [ABSTRACT FROM AUTHOR]