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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]

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]

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]

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]

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]

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]

Noise suppression algorithms have been used in various tasks such as computer vision, industrial inspection, and video surveillance, among others. The robust image processing systems need to be fed with images closer to a real scene; however, sometimes, due to external factors, the data that represent the image captured are altered, which is translated into a loss of information. In this way, there are required procedures to recover data information closest to the real scene. This research project proposes a Denoising Vanilla Autoencoding (DVA) architecture by means of unsupervised neural networks for Gaussian denoising in color and grayscale images. The methodology improves other state-of-the-art architectures by means of objective numerical results. Additionally, a validation set and a high-resolution noisy image set are used, which reveal that our proposal outperforms other types of neural networks responsible for suppressing noise in images. [ABSTRACT FROM AUTHOR]

Over the past three decades, there has been a significant increase in political and media attention towards climate change. The media has been instrumental in shaping, reproducing, and influencing the political and cultural comprehension of this phenomenon. While previous research has concentrated primarily on the textual content of news articles, this study focuses on the use of images in climate communication. It is based on the belief that images can combine facts and emotions, engaging audiences and adding narrative complexity to verbal claims. With focus on climate imagery, a content analysis was conducted on 1010 images used by a Portuguese newspaper (Público) between January 2000 and May 2022 to visually cover climate change. The purpose of the analysis was to identify the visual frames used by the newspaper to frame the issue. The primary findings indicate that 35.5% of the images analyzed employ a frame that dramatizes the effects of climate change, evoking anxiety and vulnerability. However, there is evidence of a growing body of scientific literature that challenges and refutes the sensationalist and demoralizing narrative, resulting in the development of novel methods of communicating the phenomenon. Compared to the preceding period (2000–2005), the proportion of visual frames depicting potential solutions and adaptation strategies has increased substantially over the past three years (2020–2022) by 16.3%. [ABSTRACT FROM AUTHOR]

The close-up visual inspection of bridges faces several problems, including a lack of financial resources and human personnel. Hence, there has been increasing use of artificial intelligence (AI) and information and communications technology (ICT) to solve them. We previously investigated remote inspection—in which skilled engineers provided on-site support from a remote location—with the aim of reducing the labor required for on-site work and addressing the lack of personnel through the use of AI and ICT. Sharing images of bridges from inspection sites to remote locations via the Internet enables remote assessment of the sites and the ability to consider and diagnose damage. Mobile communications can be used to upload images, although the volume of image data required for inspection can be enormous and take considerable time to upload. Consequently, in this study, we investigated image uploads using 5G communication—that is, the fifth-generation technology standard for broadband cellular networks. Moreover, we measured the upload times when using 4G and 5G services and examined their operation based on differences in the communication environments. We concluded that the simulated remote inspection can be efficiently performed by adjusting the inspection method to the communication environment. [ABSTRACT FROM AUTHOR]

This research article is aimed at improving the efficiency of a computer vision system that uses image processing for detecting cracks. Images are prone to noise when captured using drones or under various lighting conditions. To analyze this, the images were gathered under various conditions. To address the noise issue and to classify the cracks based on the severity level, a novel technique is proposed using a pixel-intensity resemblance measurement (PIRM) rule. Using PIRM, the noisy images and noiseless images were classified. Then, the noise was filtered using a median filter. The cracks were detected using VGG-16, ResNet-50 and InceptionResNet-V2 models. Once the crack was detected, the images were then segregated using a crack risk-analysis algorithm. Based on the severity level of the crack, an alert can be given to the authorized person to take the necessary action to avoid major accidents. The proposed technique achieved a 6% improvement without PIRM and a 10% improvement with the PIRM rule for the VGG-16 model. Similarly, it showed 3 and 10% for ResNet-50, 2 and 3% for Inception ResNet and a 9 and 10% increment for the Xception model. When the images were corrupted from a single noise alone, 95.6% accuracy was achieved using the ResNet-50 model for Gaussian noise, 99.65% accuracy was achieved through Inception ResNet-v2 for Poisson noise, and 99.95% accuracy was achieved by the Xception model for speckle noise. [ABSTRACT FROM AUTHOR]

Borescope inspection is a labour-intensive process used to find defects in aircraft engines that contain areas not visible during a general visual inspection. The outcome of the process largely depends on the judgment of the maintenance professionals who perform it. This research develops a novel deep learning framework for automated borescope inspection. In the framework, a customised U-Net architecture is developed to detect the defects on high-pressure compressor blades. Since motion blur is introduced in some images while the blades are rotated during the inspection, a hybrid motion deblurring method for image sharpening and denoising is applied to remove the effect based on classic computer vision techniques in combination with a customised GAN model. The framework also addresses the data imbalance, small size of the defects and data availability issues in part by testing different loss functions and generating synthetic images using a customised generative adversarial net (GAN) model, respectively. The results obtained from the implementation of the deep learning framework achieve precisions and recalls of over 90%. The hybrid model for motion deblurring results in a 10× improvement in image quality. However, the framework only achieves modest success with particular loss functions for very small sizes of defects. The future study will focus on very small defects detection and extend the deep learning framework to general borescope inspection. [ABSTRACT FROM AUTHOR]

This paper, aligned with contemporary thinking in terms of patient-centered care and co-creation of patient care, highlights the limitations of the reductionist approaches to psychiatry, offering an alternative, "emergent" perspective and approach. Assuming that psychopathological phenomena are essentially relational, what kind of epistemological framework and 'logic of discovery' should be adopted? I review two standard methods I call 'ticking boxes' and 'drafting arrows'. Within the ticking boxes framework, the clinician's main goal is to discover whether a patient showing psychopathological phenomena meets pre-given diagnostic criteria. The process of discovery can be compared to two people assembling a puzzle where the patient has the pieces and the interviewer has the image of the completed design. Drafting arrows consists in constructing pathogenetic diagrams that display linear causative relationships between variables connected by an arrow to other nodes. These explanatory narratives include psychodynamic (motivational) and biological (causal) diagrams. I argue for a third approach called 'linking dots', a method of discovery based on the emergent properties of psychopathological phenomena. I build on and develop the approach to images and discovery devised by art historian Aby Warburg in his atlas of images Bilderatlas Mnemosyne. The visual constellations created by Warburg in the panels of the Bilderatlas can be understood as a method to reveal the layers of memory and the web of relationships manifested in them, inviting the viewer to participate in the production of meanings, forging ever new connections between the images. It is the viewer's acts of perception that draw relationships between singularities. I suggest that this method is of enormous significance in the context of today's socio-cultural transformation processes and related forms of psychopathological conditions, which can no longer be comprehended using the categories of existing knowledge systems. [ABSTRACT FROM AUTHOR]

There are numerous sociological and psychosocial studies, both classic and current, that have analysed the images and representations of older people and aging. If gender, intersectional and land perspectives are added, the literature consulted is only a few years old, particularly in Spanish. In addition, research based on fieldwork from virtual image banks is still scarce and recent. The objective of this paper is to evaluate the images from some free access image banks (like Freepik, Canva, Pixabay, or Storyblocks) of older people from a gender, intersectional and socio-spatial and land perspective. Methods: 150 images have been analysed following different selected criteria: 22 variables related to gender, activity, socio-spatial environment, natural space and land, among others, briefly describe the main methods or treatments applied. The key results show a stereotyped and barely diverse image of old age and aging around positive representations, with a notable absence of images related to loneliness as opposed to the presence of social relationships. A feminization has also been observed in the representations, with an imbalance in the activities that are carried out (care in the case of women and leisure in the case of men) and in the visible space (indoor among women and outdoor among men). Older people are still identified with a rural, traditional, and more defined territory and not with more diverse and ecological spaces, which are more frequently attributed to younger profiles. This evaluation contributes to linking this necessary connection of current issues and challenges to ageism, sexism and other exclusions derived from territory and socio-spatial aspects. However, more research is still needed, and, in fact, a second phase of the fieldwork is underway to broaden the sample and to expand further evaluations of images. [ABSTRACT FROM AUTHOR]

Due to the lockdown and restrictions on public activities and gatherings amid COVID-19, parks received renewed attention because of their importance and irreplaceable functions as healthy outdoor recreation. This study aims to explore the values reflected in Twitter content that discussed parks amid COVID-19 through a qualitative content analysis of text and images using ATLAS.ti. Specifically, a qualitative data (visual and textual) set of Twitter posts amid COVID-19 (from February 2020 to September 2022) that mentioned Everglades National Park was explored. The results from the social media content analysis of park values during COVID-19 revealed three themes: (1) nature, (2) recreational tourism, and (3) proximity. This study expands the application of social media via a qualitative content analysis of text and images using ATLAS.ti by providing methodological improvements. Practically, the findings of this study can also provide practitioners with useable knowledge for design strategies considering the value of protected areas as a model of sustainable destination management. [ABSTRACT FROM AUTHOR]

There are limited methods to assess how dietary patterns adhere to a healthy and sustainable diet. The aim of this study was to develop a theoretically derived Healthy and Sustainable Diet Index (HSDI). The HSDI uses 12 components within five categories related to environmental sustainability: animal-based foods, seasonal fruits and vegetables, ultra-processed energy-dense nutrient-poor foods, packaged foods and food waste. A maximum of 90 points indicates the highest adherence. The HSDI was applied to 4-day mobile food records (mFRTM) from 247 adults (18–30 years). The mean HSDI score was 42.7 (SD 9.3). Participants who ate meat were less likely to eat vegetables (p < 0.001) and those who ate non-animal protein foods were more likely to eat more fruit (p < 0.001), vegetables (p < 0.05), and milk, yoghurt and cheese (p < 0.05). After adjusting for age, sex and body mass index, multivariable regression found the strongest predictor of the likelihood of being in the lowest total HSDI score tertile were people who only took a bit of notice [OR (95%CI) 5.276 (1.775, 15.681) p < 0.005] or did not pay much/any attention to the health aspects of their diet [OR (95%CI) 8.308 (2.572, 26.836) p < 0.0001]. HSDI provides a new reference standard to assess adherence to a healthy and sustainable diet. [ABSTRACT FROM AUTHOR]

Ethos, the speaker's image in speech is one of the three means of persuasion e stablished by Aristotle's Rhetoric and is often studied in a loose way. Many scholars develop lists of self-images (ethos of a leader, modesty ethos, etc.), but few explain how one arrives at these types of ethos. This is precisely what the inferential approach described here intends to do. Considering, like many discourse analysts, that ethos is consubstantial with speech, this paper provides an overview of various types and subtypes of ethos and highlights how these can be inferred from the discourse. Mainly, we would like to point out that what the speaker says about him or herself is only a part of what has been called "said ethos": inferential processes triggered by what the speaker says about collectivities, opponents, or the audience also help construct an ethos. This tool will be applied to analyze a corpus of Donald Trump's tweets of 6 January 2021, the day of the assault on the Capitol. As the notion of inference is essential in creating ethos, the paper pleads for the integration of the study of this rhetorical notion in the field of pragmatics. [ABSTRACT FROM AUTHOR]

In the context of behavior recognition, the emerging bed-exit monitoring system demands a rapid deployment in the ward to support mobility and personalization. Mobility means the system can be installed and removed as required without construction; personalization indicates human body tracking is limited to the bed region so that only the target is monitored. To satisfy the above-mentioned requirements, the behavior recognition system aims to: (1) operate in a small-size device, typically an embedded system; (2) process a series of images with narrow fields of view (NFV) to detect bed-related behaviors. In general, wide-range images are preferred to obtain a good recognition performance for diverse behaviors, while NFV images are used with abrupt activities and therefore fit single-purpose applications. This paper develops an NFV-based behavior recognition system with low complexity to realize a bed-exit monitoring application on embedded systems. To achieve effectiveness and low complexity, a queueing-based behavior classification is proposed to keep memories of object tracking information and a specific behavior can be identified from continuous object movement. The experimental results show that the developed system can recognize three bed behaviors, namely off bed, on bed and return, for NFV images with accuracy rates of 95~100%. [ABSTRACT FROM AUTHOR]

Tourists are increasingly looking for more emotion in the promotion of destinations in order to make decisions about their holidays. Traditional promotional tools centred on images have advantages over other sensorial tools due to the consumer′s knowledge of them. Nevertheless, simultaneously, the continued use of images leads to the sophistication of the receiver, making it necessary to analyse their capacity to create emotions and their effects. The main objective of this study is to evaluate consumers′ emotional reactions to visual stimuli through conscious and unconscious responses. With a sample of 38 students, a within-subjects study is carried out to compare emotional reactions and behavioural intentions in a 2 × 3 factorial experimental design, facing two international destinations and three tourist typologies. The results show differences between the emotional values gathered in surveys and sweating data (EDA) and the relationships between these and behavioural intentions. The main contribution of this study is that, despite the emotional evocation capacity of images in young audiences, there are discrepancies in the conditions that provoke greater emotional and behavioural intentions in the different evaluation phases. The main conclusions are that it is necessary to create images that evoke conscious positive emotions to obtain better behavioural intentions results. [ABSTRACT FROM AUTHOR]

Simple Summary: Although images are more effective than words at communicating important conservation ideas, different aspects of these images have been demonstrated to have positive and negative effects on viewers' views towards wildlife and towards the organisation that posted the image. The most prevalent and engaging characteristics of wildlife photographs posted to Instagram in 2020 and 2021 were assessed using a quantitative content analysis, with Australian organisations as a case study. The findings show that conservation organisations can confidently share and post photographs that promote positive attitudes towards wildlife and the conservation organisation, and that Instagram posts can feature and promote a wide range of currently underrepresented species. Wildlife populations are vanishing at alarmingly high rates. This issue is being addressed by organisations around the world and when utilizing social media sites like Instagram, images are potentially more powerful than words at conveying crucial conservation messages and garnering public support. However, different elements of these images have been shown to potentially have either positive or negative effects on viewers' attitudes and behaviours towards wildlife and towards the organisation posting the image. This study used a quantitative content analysis to assess the most common and engaging elements of wildlife images posted to Instagram in 2020 and 2021, using Australian conservation organisations as a case study. A total of 670 wildlife images from the Instagram accounts of 160 conservation organisation Instagram accounts were coded and analysed. Results highlight that the most common image elements used included natural backgrounds, mammals and birds, and no human presence. In addition, it was found that the taxon of the animal featured in a post and the presence of humans did not impact engagement levels. Our findings highlight the potential for Instagram posts to feature and promote a wide range of currently underrepresented species, and for conservation organisations to be able to confidently share and post images that promote positive perceptions of both the animal and the conservation organisation. [ABSTRACT FROM AUTHOR]

In this paper we report on the development and field-testing of proposed food-based dietary guideline (FBDG) messages among Tanzanian consumers. The messages were tested for cultural appropriateness, consumer understanding, acceptability, and feasibility. In addition, comprehension of the messages was assessed using culturally representative images for low literacy audiences. Focus group discussions were used as method for data collection. Results indicate that the core meaning of the proposed FBDG messages and images were understood and acceptable to the general population. However, participants felt that nutrition education would be required for improved comprehension. Feasibility was affected by some cultural differences, lack of nutrition knowledge, time constraints, and poverty. Suggestions were made for some rewording of certain messages and editing of certain images. It is recommended that the field-tested messages and images, incorporating the suggested changes, should be adopted. Once adopted, the FBDGs can be used to inform and engage various stakeholders, including parents, caregivers, healthcare providers and educators on appropriate nutritional practices for children and adults. They can also be used to guide implementation of relevant policies and programmes to contribute towards the achievement of sustainable healthy diets and healthy dietary patterns. [ABSTRACT FROM AUTHOR]

The need for a smart city is more pressing today due to the recent pandemic, lockouts, climate changes, population growth, and limitations on availability/access to natural resources. However, these challenges can be better faced with the utilization of new technologies. The zoning design of smart cities can mitigate these challenges. It identifies the main components of a new smart city and then proposes a general framework for designing a smart city that tackles these elements. Then, we propose a technology-driven model to support this framework. A mapping between the proposed general framework and the proposed technology model is then introduced. To highlight the importance and usefulness of the proposed framework, we designed and implemented a smart image handling system targeted at non-technical personnel. The high cost, security, and inconvenience issues may limit the cities' abilities to adopt such solutions. Therefore, this work also proposes to design and implement a generalized image processing model using deep learning. The proposed model accepts images from users, then performs self-tuning operations to select the best deep network, and finally produces the required insights without any human intervention. This helps in automating the decision-making process without the need for a specialized data scientist. [ABSTRACT FROM AUTHOR]

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research. [ABSTRACT FROM AUTHOR]

Optical systems are used for analysing the internal composition and the external properties in food. The measurement of the lycopene content in fruits and vegetables is important because of its benefits to human health. Lycopene prevents cardiovascular diseases, cataracts, cancer, osteoporosis, male infertility, and peritonitis. Among the optical systems focused on the estimation and identification of lycopene molecule are high-performance liquid chromatography (HPLC), the colorimeter, infrared near NIR spectroscopy, UV-VIS spectroscopy, Raman spectroscopy, and the systems of multispectral imaging (MSI) and hyperspectral imaging (HSI). The main objective of this paper is to present a review of the current state of optical systems used to measure lycopene in fruits. It also reports important factors to be considered in order to improve the design and implementation of those optical systems. Finally, it was observed that measurements with HPLC and spectrophotometry present the best results but use toxic solvents and require specialized personnel for their use. Moreover, another widely used technique is colorimetry, which correlates the lycopene content using color descriptors, typically those of CIELAB. Likewise, it was identified that spectroscopic techniques and multispectral images are gaining importance because they are fast and non-invasive. [ABSTRACT FROM AUTHOR]

Extensive research has examined the influence of online product reviews on consumer behavior. However, few have investigated the influence of reviewer submitted images on consumer attitudes. This research examines consumer perceptions of trust, information quality and valence disparity using three simulated online reviews appearing on TripAdvisor.com: one for a hotel, one for a cruise ship and one for a fast food restaurant. Findings reveal that reviews with images are seen as more trustworthy, and that consumers perceive reviews with images as possessing higher information quality than reviews containing text only. Likewise, the findings showed that effective images should be consistent with review text (e.g., reviewers do not respond positively to negative reviews linked with positive images, or vice versa). [ABSTRACT FROM AUTHOR]

Changes in river pattern have been documented in the literature. The recognition of islands and vegetated patches, which is indicative of the evolution processes, requires simultaneous information at different points and at different times and field studies are still difficult and scarce. The present work, focusing the attention on a gravel-bed river, explores the possibility to conduct a first-step pattern changes analysis by identifying the evolution of the morphological features and taking information of islands and vegetated patches from satellite images easily available from Google Earth. The study is conducted by combining the information taken from both the satellite images and the field photos and the hydrological data. Here analysis concerns a reach of the Tagliamento River, in Italy, in a decadal timescale characterized by evident stream pattern changes. Results confirm that a combination of different factors determine favorable conditions for the river's evolution, but the frequency of occurrence of high-magnitude events is the most important factor to consider in a first-step analysis. This is because the frequency of occurrence of high-magnitude events affects, in turn, some of the other influencing factors. In particular, the present study highlights that different morphological features form in time periods characterized by different frequency of occurrence of high-magnitude events. On one hand, this could be related to the fact that high-magnitude events could influence the sediment transport and deposition mechanisms, modifying the ratio coarse-to-fine sediments and the grain-to-grain interactions, with consequences in the sediment flux variation and in the migration of the morphological features. On the other hand, the frequency of occurrence of high-magnitude events could affect the vegetation distribution and growth, contributing to determine a variation of the typology of the vegetation characteristics depending on the tolerances of the vegetation species. A sort of interface between the vegetated-dominated area, where the flow resistance is significant, and the flood-disturbance-dominated area, where the action by flow is significant, is determined with a progressive reduction of the number of active channels and development of the meandering bend. [ABSTRACT FROM AUTHOR]

Neural networks are increasingly being used in the field of dentistry. The aim of this literature review was to visualize the state of the art of artificial intelligence in dental applications, such as the detection of teeth, caries, filled teeth, crown, prosthesis, dental implants and endodontic treatment. A search was conducted in PubMed, the Institute of Electrical and Electronics Engineers (IEEE) Xplore and arXiv.org. Data extraction was performed independently by two reviewers. Eighteen studies were included. The variable teeth was the most analyzed (n = 9), followed by caries (n = 7). No studies detecting dental implants and filled teeth were found. Only two studies investigated endodontic applications. Panoramic radiographies were the most common image employed (n = 5), followed by periapical images (n = 3). Near-infrared light transillumination images were employed in two studies and bitewing and computed tomography (CT) were employed in one study. The included articles used a wide variety of neuronal networks to detect the described variables. In addition, the database used also had a great heterogeneity in the number of images. A standardized methodology should be used in order to increase the compatibility and robustness between studies because of the heterogeneity in the image database, type, neural architecture and results. [ABSTRACT FROM AUTHOR]

The paper offers insights into people's exploration of creative products shown on a computer screen within the overall task of capturing artifacts' original features and functions. In particular, the study presented here analyzes the effects of different forms of representations, i.e., static pictures and videos. While the relevance of changing stimuli's forms of representation is acknowledged in both engineering design and human-computer interaction, scarce attention has been paid to this issue hitherto when creative products are in play. Six creative products have been presented to twenty-eight subjects through either pictures or videos in an Eye-Tracking-supported experiment. The results show that major attention is paid by people to original product features and functional elements when products are displayed by means of videos. This aspect is of paramount importance, as original shapes, parts, or characteristics of creative products might be inconsistent with people's habits and cast doubts about their rationale and utility. In this sense, videos seemingly emphasize said original elements and likely lead to their explanation/resolution. Overall, the outcomes of the study strengthen the need to match appropriate forms of representation with different design stages in light of the needs for designs' evaluation and testing user experience. [ABSTRACT FROM AUTHOR]

This paper aims at tackling the task of fusion feature from images and their corresponding point clouds for 3D object detection in autonomous driving scenarios based on AVOD, an Aggregate View Object Detection network. The proposed fusion algorithms fuse features targeted from Bird's Eye View (BEV) LIDAR point clouds and their corresponding RGB images. Differing in existing fusion methods, which are simply the adoption of the concatenation module, the element-wise sum module or the element-wise mean module, our proposed fusion algorithms enhance the interaction between BEV feature maps and their corresponding image feature maps by designing a novel structure, where single level feature maps and utilize multilevel feature maps. Experiments show that our proposed fusion algorithm produces better results on 3D mAP and AHS with less speed loss compared to the existing fusion method used on the KITTI 3D object detection benchmark. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are now filling in the gaps between spaceborne and ground-based observations and enhancing the spatial resolution and temporal coverage of data acquisition. In the realm of hydrological observations, UAVs play a key role in quantitatively characterizing the surface flow, allowing for remotely accessing the water body of interest. In this paper, we propose a technology that uses a sensing platform encompassing a drone and a camera to determine the water level. The images acquired by means of the sensing platform are then analyzed using the Canny method to detect the edges of water level and of Ground Control Points (GCPs) used as reference points. The water level is then retrieved from images and compared to a benchmark value obtained by a traditional device. The method is tested at four locations in an artificial lake in central Italy. Results are encouraging, as the overall mean error between estimated and true water level values is around 0.05 m. This technology is well suited to improve hydraulic modeling and thus provides reliable support to flood mitigation strategies. [ABSTRACT FROM AUTHOR]

What has happened with the image of the working class? The hero in the construction of the Nordic Model was the labor movement (and the working class). For a long time, this was the dominant picture of the Norwegian working class. However, the societal trends of the past decades have, in a peculiar way, given the working class a central place in political discussions and in the public sphere, but now, a more ambivalent image emerges. In a somewhat paternalistic way, the worker image in political and academic debates as well as in part of the public sphere is typically that of a person unsuccessful in the educational system. Even a third image is identifiable in the public sphere--a prejudice imagery--in which the class is labelled as unhealthy, abusing the welfare system, culturally unsophisticated, and politically dangerous, moving toward right-wing populism. The ambition of the paper is to present these different images of the working class. [ABSTRACT FROM AUTHOR]

Earth Observations (EO) encompasses different types of sensors (e.g., Synthetic Aperture Radar, Laser Imaging Detection and Ranging, Optical and multispectral) and platforms (e.g., satellites, aircraft, and Unmanned Aerial Vehicles) and enables us to monitor and model geohazards over regions at different scales in which ground observations may not be possible due to physical and/or political constraints. EO can provide high spatial, temporal and spectral resolution, stereo-mapping and all-weather-imaging capabilities, but not by a single satellite at a time. Improved satellite and sensor technologies, increased frequency of satellite measurements, and easier access and interpretation of EO data have all contributed to the increased demand for satellite EO data. EO, combined with complementary terrestrial observations and with physical models, have been widely used to monitor geohazards, revolutionizing our understanding of how the Earth system works. This Special Issue presents a collection of scientific contributions focusing on innovative EO methods and applications for monitoring and modeling geohazards, consisting of four Sections: (1) earthquake hazards; (2) landslide hazards; (3) land subsidence hazards; and (4) new EO techniques and services. [ABSTRACT FROM AUTHOR]

Consideration of animal welfare is essential to address the consumers' demands and for the long term sustainability of commercial poultry. However, assessing welfare in large poultry flocks, to be able to detect potential welfare risks and to control or minimize its impact is difficult. Current developments in technology and mathematical modelling open new possibilities for real-time automatic monitoring of animal welfare and health. New technological innovations potentially adaptable to commercial poultry are appearing, although their practical implementation is still being defined. In this paper, we review the latest technological developments with potential to be applied to poultry welfare, especially for broiler chickens and laying hens. Some of the examples that are presented and discussed include the following: sensors for farm environmental monitoring, movement, or physiological parameters; imaging technologies such as optical flow to detect gait problems and feather pecking; infrared technologies to evaluate birds' thermoregulatory features and metabolism changes, that may be indicative of welfare, health and management problems. All these technologies have the potential to be implemented at the commercial level to improve birds' welfare and to optimize flock management, therefore, improving the efficiency of the system in terms of use of resources and, thus, long term sustainability. [ABSTRACT FROM AUTHOR]

Since the 1990s, surveillance camera images have experienced a function creep from their juridical uses into journalism and entertainment. In these contexts, the images have also become memory media. This article, for the first time, analyses CCTV images, meaning closed circuit surveillance camera images, as memory media and discusses the implications of our use of artefacts of control within a frame of mediated constructions of social memory. The article undertakes this work by analyzing remediations of the CCTV images of Diana Spencer and Dodi Al-Fayed in the Ritz Hotel in Paris on 30 August 1997 in television news and a documentary from 2007 and 2011, respectively. It is shown how social memory of Diana’s death is a contested site, in which the images play a specific role. [ABSTRACT FROM AUTHOR]

This personal essay describes what influenced my development as a creative writer, in my childhood and adolescence. It delineates the effect on my imagination of family story-telling and of images--paintings and prints. I grew up in Italy, where I spent the first thirty years of my life. [ABSTRACT FROM AUTHOR]

In many applications in metallography and analysis, many regions need to be considered and not only the current region. In cases where there are analyses with multiple images, the specialist should also evaluate neighboring areas. For example, in metallurgy, welding technology is derived from conventional testing and metallographic analysis. In welding, these tests allow us to know the features of the metal, especially in the Heat-Affected Zone (HAZ); the region most likely for natural metallurgical problems to occur in welding. The expanse of the Heat-Affected Zone exceeds the size of the area observed through a microscope and typically requires multiple images to be mounted on a larger picture surface to allow for the study of the entire heat affected zone. This image stitching process is performed manually and is subject to all the inherent flaws of the human being due to results of fatigue and distraction. The analyzing of grain growth is also necessary in the examination of multiple regions, although not necessarily neighboring regions, but this analysis would be a useful tool to aid a specialist. In areas such as microscopic metallography, which study metallurgical products with the aid of a microscope, the assembly of mosaics is done manually, which consumes a lot of time and is also subject to failures due to human limitations. The mosaic technique is used in the construct of environment or scenes with corresponding characteristics between themselves. Through several small images, and with corresponding characteristics between themselves, a new model is generated in a larger size. This article proposes the use of Digital Image Processing for the automatization of the construction of these mosaics in metallographic images. The use of this proposed method is meant to significantly reduce the time required to build the mosaic and reduce the possibility of failures in assembling the final image; therefore increasing efficiency in obtaining results and expediting the decision making process. Two different methods are proposed: One using the transformed Scale Invariant Feature Transform (SIFT), and the second using features extractor Speeded Up Robust Features (SURF). Although slower, the SIFT method is more stable and has a better performance than the SURF method and can be applied to real applications. The best results were obtained using SIFT with Peak Signal-to-Noise Ratio = 61.38, Mean squared error = 0.048 and mean-structural-similarity = 0.999, and processing time of 4.91 seconds for mosaic building. The methodology proposed shows be more promissory in aiding specialists during analysis of metallographic images. [ABSTRACT FROM AUTHOR]