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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. [ABSTRACT FROM AUTHOR]

Objectives: The performed hand-held echocardiography (HHE) was evaluated and interpreted by trained advanced practice providers (APPs) on hospitalized CHF patients for image quality and interpretation by comparing with expert echocardiographer and SE findings. Background: Congestive heart failure (CHF) is associated with increased hospital admissions and mortality. While a standard echocardiogram (SE) is the gold standard for cardiac assessment, it is not readily available. Hospitalized CHF patients require rapid assessment for expedited treatment. Methods: Over 6 months, five trained APPs performed HHE on hospitalized CHF patients and interpreted: (a) left ventricular (LV) size, (b) LV ejection fraction (LVEF), and (c) right atrial pressure (RAP). The study echocardiographer reviewed and blindly interpreted the HHE images and compared them with APPs and SE findings. Kappa statistics determined the degree of agreement between APPs and the study echocardiographer's interpretation of the HHE images and SE. Results: A total of 80 CHF patients (age 73 ± 14 years, 58% males; LVEF (by SE) 45 ± 19%; 36.3% body mass indexes ≥ 30 kg/m2) were enrolled. HHE interpretation by APPs had a good agreement for LVEF (kappa 0.79) with the study echocardiographer and SE (kappa 0.74) and a good agreement for RAP (kappa 0.67) with the study echocardiographer. The correlation between the absolute LVEF interpretation by the study echocardiographer on HHE and SE was r = 0.88 (p < 0.0001). Conclusions: Trained APPs obtained diagnostic-quality HHE images and interpreted the LV function and RAP in CHF patients in good agreement with the study echocardiographer. LVEF by HHE correlated with LVEF by SE. Our study suggests trained APPs can use HHE to evaluate LVEF and RAP in CHF patients, leading to expedited and optimized treatment. [ABSTRACT FROM AUTHOR]

Three-dimensional semantic segmentation is the foundation for automatically creating enriched Digital Twin Cities (DTCs) and their updates. For this task, prior-level fusion approaches show more promising results than other fusion levels. This article proposes a new approach by developing and benchmarking three prior-level fusion scenarios to enhance the outcomes of point cloud-enriched semantic segmentation. The latter were compared with a baseline approach that used the point cloud only. In each scenario, specific prior knowledge (geometric features, classified images, or classified geometric information) and aerial images were fused into the neural network's learning pipeline with the point cloud data. The goal was to identify the one that most profoundly enhanced the neural network's knowledge. Two deep learning techniques, "RandLaNet" and "KPConv", were adopted, and their parameters were modified for different scenarios. Efficient feature engineering and selection for the fusion step facilitated the learning process and improved the semantic segmentation results. Our contribution provides a good solution for addressing some challenges, particularly for more accurate extraction of semantically rich objects from the urban environment. The experimental results have demonstrated that Scenario 1 has higher precision (88%) on the SensatUrban dataset compared to the baseline approach (71%), the Scenario 2 approach (85%), and the Scenario 3 approach (84%). Furthermore, the qualitative results obtained by the first scenario are close to the ground truth. Therefore, it was identified as the efficient fusion approach for point cloud-enriched semantic segmentation, which we have named the efficient prior-level fusion (Efficient-PLF) approach. [ABSTRACT FROM AUTHOR]

The connection between translation and contemporary art is a hitherto under-researched avenue. Some scholars have analyzed the translation of exhibition catalogue essays (Krein-Kühle 2021) or the museum as a translation site (Sturge 2007; Neather 2008, 2012, 2021; Liao 2018, 2021). Although these are important and necessary research avenues, my aim here is different. I intend to study translation beyond the verb-centric tradition, looking at how artworks have been using translation for decades and how the images used by artists communicate through nonverbal semiotic systems. This will be done by (1) examining the artworks that use words as images (with special emphasis on John Baldessari, Lawrence Weiner, and Robert Barry), (2) by studying how many contemporary artists use translation in their artworks (Ghada Amer, Danica Dakić, Nalini Malani, Mona Hatoum, among others) and (3) by concentrating on exhibitions which deal with translation as a way of looking at the world. [ABSTRACT FROM AUTHOR]

Research has illustrated the role visual design plays in effective communication and instruction. However, more research has yet to investigate how online course designers (i.e., anyone who designs online courses) add visual appeal to online courses. This study explored the visual design process in online course development. It examines how educators utilize the Thinking, Looking, Doing, and Critiquing (TLD+C) method in online course design, identifying common barriers in applying visual design principles. The research highlights a gap in formal visual design training among instructors and emphasizes the need for institutional support in enhancing visual design in online education. Through semi-structured interviews with experienced educators, the study revealed how design thinking, despite its complexity, is crucial in creating effective and engaging online courses. [ABSTRACT FROM AUTHOR]

Population growth has led to an exponential demand for agricultural products, to meet this demand it is necessary to improve management and achieve efficient use of resources without compromising the sustainability of ecosystems, particularly agricultural ones. One of the technologies that facilitate these tasks is precision agriculture (PA), which focuses on the optimization of resources and inputs based on the compilation of precise and timely geo-information of variables of agricultural interest with high spatio-temporal variability, obtained through remote sensors of three types: images captured by satellites or airplanes, images obtained from manned and unmanned aerial vehicles (UAVs) and specific information with sensors mounted on machinery or in the field. These limitations were overcome by using multispectral images, which has increased applications for agricultural purposes. Currently, multispectral images allow quantifying soil moisture, monitoring the presence of droughts and the degree of crop water stress, estimating the temporal and spatial variability of evapotranspiration, monitoring phenology, detecting nutritional deficiencies, estimating the degree of weed infestation. and insects, calculate organic carbon and soil salinity, and estimate yields and agricultural production. The use of geospatial technologies in the PA has changed the paradigm of agriculture and today constitutes a viable alternative to face the challenges that food production demands in a world with high climate variability. [ABSTRACT FROM AUTHOR]

The article investigates images in communication processes as manifestations of social representations. Focusing on the conflict-affected society of Cyprus, it studies the images used in 76250 tweets related to the Cyprus problem during the intensification of the negotiations in the period between September 2016 – July 2017. Through social network visualisation we identify the most prominent images used to construct meaning about the Cyprus problem as well as the clusters created through the image tweeting. The results show that most of the images tweeted about the Cyprus problem are institutioalised manifestations of social representations that contribute to meanings that are not conducive to the peace process in Cyprus. They instead enhance the social imaginary of Cyprus being determined by a loop of unsuccessful peace efforts that are always distant from the grassroot of society and by the concept that a solution will arrive to Cyprus as an external force sourced from the political elites. [ABSTRACT FROM AUTHOR]

In recent years, the study of social representations through images, icons, drawings, and the like, has attracted the attention of researchers interested in analyzing the role visual devices play in the elaboration and interpretation of Social Representations and Social Imaginary. Along the same lines, in this paper we study some visual elements that can provide relevant information for exploring and reconstructing social representations. These visual units are called Memes “units of popular culture that are circulated, imitated, and transformed by individual Internet users, creating a shared cultural experience in the process” (Shifman, 2013, p. 367); they generate and shape the mindsets and significant forms of behavior and actions of a social group. Memes spread a particular idea as presented in images, animated GIFs, videos, written text, or some other units of cultural practices. They give rise to the materialization of the expression of a given social reality within which individuals and social groups live which is why we consider them as useful tool for communicating and reconstructing social representations. During the COVID-19 confinement, many internet memes circulated but we decided to study those on remote learning and teaching since education in that period was a great challenge. The corpus of study was gathered by manually tagged searches on social media such as Facebook, Reddit and Twitter using the following hashtags: #virtualeducation, #remoteteaching, #pandemicandschool. The internet memes analyzed show the difficulties students faced when they were confronted with remote teaching and the emotional states they experienced. These expressions and meanings allow us to reconstruct the social representation they have of this modality of teaching. [ABSTRACT FROM AUTHOR]

The present study aimed to establish the relationship between the characteristics of RGB images of tomato fruits of three cultivars and important indicators of the quality of these fruits. In the study, 30 fruits from three cultivars at different ripening stages were used. The digital images of each tomato from four sides were obtained using a document camera. The same fruits were analyzed for dry matter, total dyes, vitamin C, titr. organic acids, beta carotene and lycopene content. The data of each image's G, R, and B channels were extracted and averaged. The average data of grayscale were taken as well. Based on the color characteristics and the chemical analyses, a procedure of descriptive statistics, correlation, and regression analysis was performed. The image procedure of all obtained data was performed using Jupyter notebook. The highest correlation coefficient (-0.860) was obtained between the Gray channel and total dye content, followed by the strong correlation between the Green channel and lycopene content (-0.820). And the models with the highest predictive ability were the models for total dye by Gray channel values (R2 = 0.815) and lycopene by Green channel (R2 = 0.761). [ABSTRACT FROM AUTHOR]

To invoke Mnemosyne (goddess of memory in Greek mythology) is to invoke her daughter muses? If so then memory is always behind the imagination. The idea of the research grew out of interest to understand if architecture can be recognized as a physical manifestation of memory. The work is an attempt to demonstrate how remembering is experienced through architectural forms. Literature, psychological and architectural narratives provided a necessary guidance to understand where the conjunctions between the theory and empirical studies of memory lie. After the exploration of different theories comes the time to respond to the question whether memories can be matters of architecture and if they can become principal tools of form finding. That weightless transitional forces making the unconscious to travel back in time and space, somewhere and sometime that you are not anymore. What can we do with them? To seize the forces of our mind and later to submit them to control of our reason as Andre Breton was saying in "First Manifesto of Surrealism". Images, associations and all what is remembered, let them serve as paraphernalia for creative act. The discussion proceeds with a real act of memory connected to Armenian traces of Venice, an architectural experimentation with cultural, personal and contextual memories for definition of new forms on the island of San Lazzaro degli Armeni and in Armenian College Moorat-Raphael. [ABSTRACT FROM AUTHOR]

Copyright of Revista Tamoios is the property of Editora da Universidade do Estado do Rio de Janeiro (EdUERJ) 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 term sexting, coined in 2005, is still in its prime. Relying on a rather large number of image types (contextual pictures, inoffensive sexual pictures, joke images, offensive and unethical sexual pictures, private, or public selfies), sexting among youth may have many causes. This paper is an attempt at providing a school social worker’s job description by answering questions such as: Who sexts young people? What categories of young people are sexted? Where does sexting occur? When does sexting occur? How are young people sexted? Why are young people sexted? What are the consequences of sexting? And what should schools do? [ABSTRACT FROM AUTHOR]

Copyright of Journal of Modern Russian History & Historiography is the property of Brill Academic Publishers 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.)

Copyright of Journal of History / Habaršy Tarihi Seriâsy is the property of Al-Farabi Kazakh National University 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.)

Copyright of Croatica et Slavica Iadertina is the property of University of Zadar, Department of Croatian & Slavic Studies 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.)

Copyright of Itinerarios (1507-7241) is the property of University of Warsaw 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.)

Copyright of Anales de Investigación en Arquitectura is the property of Universidad ORT Uruguay, Facultad de Arquitectura 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.)

Common law is predicated historically upon a sense of the common, of custom and use time out of mind. The legal tradition has its roots in a guild, an elite community whose common opinion and conversations provided the substance and sensibility of the normative. Remediation of law, meaning here the changing media of legal transmission, the imaginal turn in the streamed and viral relays of law and its enforcement, confront a monochrome and linear textual tradition, the regimentations of the page, with the fragmentary and anarchic optics of online platforms and social media bytes and nibbles. Increased online visibility, this essay argues, forces the guild to face up to an expanded commons, the diversity of colors, the heuristics of the eye, and the nuances of viewing. [ABSTRACT FROM AUTHOR]

Here, a machine learning tool (YOLOv5) enables the detection of Cryptosporidium microorganisms using optical and phase contrast microscope images. The two databases were processed using 520 images (optical microscopy) and 1200 images (phase contrast microscopy). It used Python libraries to label, standardize the size, and crop the images to generate the input tensors to the YOLOv5 network (s, m, and l). It implemented two experiments using randomly initialized weights in optical and phase contrast microscope images. The other two experiments used the parameters for the best training time obtained before and after retraining the models. Metrics used to assess model accuracy were mean average accuracy, confusion matrix, and the F1 scores. All three metrics confirmed that the optimal model used the best epoch of optical imaging training and retraining with phase contrast imaging. Experiments with randomly initialized weights with optical imaging showed the lowest precision for Cryptosporidium detection. The most stable model was YOLOv5m, with the best results in all categories. However, the differences between all models are lower than 2%, and YOLOv5s is the best option for Cryptosporidium detection considering the differences in computational costs of the models. [ABSTRACT FROM AUTHOR]

Children's parents, caregivers, educators, and the community may have concerns about the impact of media on children. Violence, natural disasters, and other unsettling information can be traumatic for young children to experience through exposure to media that is not developmentally appropriate. The broad nature of media use in a child's life is a crucial consideration for their social emotional development. Advancing technology and expanding media usage pervades all aspects of modern life while understanding of the impact of media on growth and development in young children may be unclear. How do we talk to children about what they see in the media? This article explores ways to support children's social emotional development when they experience exposure to media in the early years. We provide information about considerations for media with regard to young children, concerns of media production for children, and guidance for children on traumatic events in media. [ABSTRACT FROM AUTHOR]

This paper focuses on intersemiotic translation of Yixiang, or image in western literary term, from poetry to painting by taking Tang Poetry in Paintings as a case to exemplify the translation strategies of Yixiang from verbal sign system to visual sign system. Furthermore, the meditative role of the visualized images in the target context will also be touched upon. A qualitative approach is applied in the study. Typical examples will be chosen for case analysis. It is found that the strategies of equivalence, focalization, integration, montage, addition, contrast and omission are employed by the painters. The effects of those visualized images varied in different cases. Overall, cooperating with the interlingual translations, visual languages such as color, shadow, body language of figures, picture structure are employed to help English readers better receive the poems. [ABSTRACT FROM AUTHOR]

Artificial intelligence presents different approaches, one of these is the use of neural network algorithms, a particular context is the farming sector and these algorithms support the detection of diseases in flowers, this work presents a system to detect downy mildew disease in roses through the analysis of images through neural networks and the correlation of environmental variables through an experiment in a controlled environment, for which an IoT platform was developed that integrated an artificial intelligence module. For the verification of the model, three different models of neural networks in a controlled greenhouse were experimentally compared and a proposed model was obtained for the training and validation sets of two categories of healthy roses and diseased roses with 89% training and 11% recovery. validation and it was determined that the relative humidity variable can influence the development and appearance of Downy Mildew disease when its value is above 85% for a prolonged period. [ABSTRACT FROM AUTHOR]

There is increasing pressure on young consumers to practice sustainable consumption. With young women being key agents in fashion consumption, switching their purchasing to secondhand clothing over new is instrumental to reducing textile waste. This study applied the Theory of Planned Behaviour and Prototype Willingness Model to identify key drivers informing young women's secondhand clothing purchasing decisions. Young Australian women (N = 48) completed qualitative surveys assessing their underlying attitudinal, normative, and control beliefs and perceived images of typical secondhand clothing shoppers. Thematic analysis indicated the main benefits of secondhand clothing purchasing to be the environmental impact and cost savings, with drawbacks being quality issues, reduced shopping experience, and greater effort required. Clothing diversity was both positive ('unique finds') and had a downside (limited sizes). Approvers of secondhand purchasing were mainly friends and family, with older relatives being less supportive. Key barriers were increased prices for quality items and the time required to locate them. Images of typical secondhand clothes shoppers were generally positive ('cool', 'thrifty', 'unique', 'eco-friendly'), while 'materialistic', 'upper-class', and 'ignorant' but also 'trendy' indicated mixed perceptions about those who did not. Crucial in our findings was clarifying the intersections and contextual context of participants' responses. Identifying the nuances in the underlying beliefs driving young women's fashion choices assists in theory-informed strategies to encourage sustainable consumption of clothing. [ABSTRACT FROM AUTHOR]

Deep learning is a powerful tool for automating the process of recognizing and classifying objects in images. In this study, we used ML.NET, a popular open-source machine learning framework, to develop a model for identifying tree species in images obtained from airborne mobile mapping. These high-resolution images can be used to create detailed maps of the landscape. They can also be analyzed and processed to extract information about visual features, including tree species recognition. The deep learning model was trained using ML.NET to classify two tree species based on the combination of airborne mobile mapping images. Our approach yielded impressive results, with a maximum classification accuracy of 93.9%. This demonstrates the effectiveness of combining imagery sources with deep learning tools in ML.NET for efficient and accurate tree species classification. This study highlights the potential of the ML.NET framework for automating object classification and can provide valuable insights and information for forestry management and conservation efforts. The primary objective of this research was to evaluate the effectiveness of an approach for identifying tree species through a model generated using a combination of ortho and oblique images captured by a mobile mapping system. [ABSTRACT FROM AUTHOR]

The Dynamic Laser Speckle (DLS) is a photonic phenomenon transformed into a technique to monitor tiny changes in many materials, particularly in biological ones (Biospeckle Laser - BSL). Its high sensitivity is key to monitor tiny changes in the tissues that can characterise biological features in medical and in agricultural samples. This work aimed to develop an environment at the world-wide-web to share raw data of BSL and to offer access to an interactively online analysis with tutorials. The gateway designed was implemented using PHP and JavaScript tools solving the challenges of managing data (images) and online interactive use. The result was an easy-to-use environment boosting the BSL research. The early results presented thousands of accesses and of more than one hundred different users from all around the world. [ABSTRACT FROM AUTHOR]

Between c.1825–1856, a French-born artist, Joseph Bouet, made approximately sixty pencil sketches in the criminal courtroom at Durham, of legal actors including judges, lawyers, and defendants. Our research is the first detailed analysis of these images by legal scholars. It is presented in two parts, which can be read as separate and independent pieces, but each gain from being read in conjunction with the other. In Part 1 of this series of two articles we discussed potential theoretical approaches to analysis of the images and their importance to socio-legal and legal historical scholarship (cross ref). In this Part 2, we explore Bouet's courtroom sketches of legal actors as the rare and unusual starting point for a microhistorical analysis examining individual interaction(s) with the criminal justice process in the mid-nineteenth century. This article demonstrates that with detailed research these previously overlooked images can offer a unique window into aspects of nineteenth century legal history, with much to tell us about legal institutions, the people who worked within them and the 'objects/subjects' of the law. This study makes an important contribution to the growing body of scholarship on the interface between history, law and the visual. [ABSTRACT FROM AUTHOR]

Between c.1825–1856, a French-born artist, Joseph Bouet, made approximately sixty pencil sketches of legal actors in the courtroom at Durham; including images of judges, lawyers, and defendants. Legal imagery from this period in North East England is rare and our research (presented in two parts) is the first detailed analysis of these sketches by legal scholars. This article introduces our preliminary analysis of Bouet's sketches. We explore potential theoretical approaches and demonstrate that the images show the law in practice in a specific nineteenth century context. The value of our analysis is in revealing what the images tell us about legal institutions, the people who worked within them and the 'objects/subjects' of the law. The study makes an important contribution to socio-legal scholarship in demonstrating the value of such images as an underused source in legal historical research. The sketches are also the subject of a second article, which presents three detailed case studies. The articles can be read as separate and independent pieces, but each benefit from being read in conjunction with the other. [ABSTRACT FROM AUTHOR]

Crops' health is affected by a varied range of diseases. Convenient and precise diagnosis plays a substantial role in preventing the loss of crop quality. In the past decade, deep learning (DL), particularly Convolutional Neural Networks (CNNs), has presented extraordinary performance for diverse applications involving crop disease (CD) detection. In this study, a comparison is drawn for the three pre-trained state-of-art architectures, namely, EfficientNet B0, ResNet50, and VGG19. An ensembled CNN has also been generated from the mentioned CNNs, and its performance has been evaluated over the original coloured, grey-scale, and segmented dataset. K-means clustering has been applied with six clusters to generate the segmented dataset. The dataset is categorized into three classes (two diseased and one healthy class) of bean crop leaves images. The model performance has been assessed by employing statistical analysis relying on the accuracy, recall, F1-score, precision, and confusion matrix. The results have shown that the performance of ensembled CNNs' has been better than the individual pre-trained DL models. The ensembling of CNNs gave an F1-score of 0.95, 0.93, and 0.97 for coloured, grey-scale, and segmented datasets, respectively. The predicted classification accuracy is measured as: 0.946, 0.938, and 0.971 for coloured, grey-scale, and segmented datasets, respectively. It is observed that the ensembling of CNNs performed better than the individual pre-trained CNNs. [ABSTRACT FROM AUTHOR]

Copyright of Estudos em Comunicação is the property of Labcom / Universidade da Beira Interior 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.)

Purpose: Packaging design has the power to convey persuasive and functional benefits, thereby inducing the brand preference as per empirical studies, but not much has been studied in detail about aesthetic aspects of packaging. This study is a careful attempt to understanding the influence of aesthetic elements—colour, illustrations/images/pictures and information representation's influence on consumer brand preference for ready-to-eat (RTE) product category of pav bhaji. Design/methodology/approach: The data for this study have been collected in two phases; phase 1 was a quantitative research where structure Google form was used while in order to elite consumer responses regarding their preferences structured interview was conducted in phase 2. Twenty-five respondents were selected using snowball sampling, who were approached for understanding how aesthetic element influenced their brand preference. Findings: This research could help in identifying various underlying subthemes within the three major themes of aesthetic element in packaging. By utilising these, packaging modifications could be made to increase brand preference. Originality/value: In case of RTE segment, in-store decisions are mostly take in spurn of minutes, hence making it one of essential topics of research. The findings of this research can contribute in placement of correct element in appropriate position to grab consumers' interest leading to brand preference. [ABSTRACT FROM AUTHOR]

Technology is becoming more portable, easy to use and helping the human beings in day to day life. Smart wearable devices are widely used in many fields nowadays. Wearables can deliver unique value to users and to others who access the data they collect. Wearable devices are gaining attention in capacities related to processing, sensing, storing and communication. It is the main reason that several publications are available on wearable technology, but still there is no survey manuscript available for camera based wearables. So, this work is focused on the camera based wearable devices and its applications in various areas. In camera based wearable devices, captured images are used as input and user is informed after processing the information. This paper provides classification of camera based wearables, a discussion on architectures, data processing aspects, and market position etc.. Finally, the survey highlights the Challenges, future trends and solutions of camera based wearables. [ABSTRACT FROM AUTHOR]

Copyright of Index: Revista de Arte Contemporaneo is the property of Pontificia Universidad Catolica del Ecuador 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.)

Abandoned houses have become a common feature of the local landscapes: the rising number of abandoned houses is a major challenge facing many counties in South Korea. Their presence negatively influences the neighborhood by undermining its aesthetic quality, depreciating the perception of safety in the neighborhood properties, and deepening the fiscal deficit of local financing. The detection of abandoned houses is the first step toward adequate housing management by local governments. This study aims to provide a cost-effective and prompt approach to identifying abandoned houses in rural areas. Multi-source data, that is, images and building registry data are utilized and a multi-input neural network is designed to adopt these heterogeneous datasets. Trained by the two source datasets, the proposed network achieves 86.2% accuracy in classifying abandoned houses, which is an acceptable performance level in administrative practice. The database of abandoned houses identified in this manner is expected to promote effective housing management by governments and ultimately contribute to mitigating vacancies in rural areas.

Abstract The effective integration of RGB and depth map features to improve the performance of RGB‐D salient object detection (SOD) has garnered significant research interest. The existing dual‐stream models can be used for high‐level feature fusion or unidirectionally transferring depth features to RGB features; however, they are unable to fully exploit the differences in modality. Furthermore, owing to the influence of image background information, the generated salient object is affected by background swallow. Herein, a three‐stream RGB‐D SOD method based on cross‐layer and cross‐modal dual‐attention (CMDA) fusion is proposed. In the encoding stage, the CMDA fusion module is used to fuse RGB and depth features layer by layer. Through this module, merged interactive features may be used to extract the richer features of salient objects, realize the commonality and complementarity of fusion features, and achieve effective cross‐modal fusion. In addition, for the decoding stage, a cross‐level feature fusion module that introduces global context features into the up‐sampling process, reduces the impact of salient objects being swallowed by the background, and helps to accurately detect salient areas is proposed. Three different branch features are used for simultaneous end‐to‐end training. The experimental results demonstrate that the proposed method outperforms other methods in terms of multiple evaluation metrics on four datasets. Furthermore, the authors visualize the precision–recall curve, F‐measure curve, and saliency map, which indicate that the detection effect of the proposed method is superior to those of other methods. During the testing stage, our model ran at 14 frames per second (FPS).

Case Presentation: An 85-year-old male who had been prescribed prasurgel presented to the emergency department (ED) after a motor vehicle collision and developed progressive neurological deficits. Computed tomography imaging demonstrated epidural thickening from the second through seventh cervical vertebrae, and magnetic resonance imaging was notable for a cervicothoracic epidural hematoma. The patient underwent emergent decompression with a favorable outcome.Discussion: Cases of traumatic spinal epidural hematomas are rarely seen in the ED. These are part of a small subset of operative neurological emergencies that benefit from urgent surgical intervention.

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.

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.

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.