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Background: Post-harvest physiological deterioration (PPD) poses a significant challenge to the cassava industry, leading to substantial economic losses. This study aims to address this issue by developing a comprehensive framework in collaboration with cassava breeders. Results: Advanced deep learning (DL) techniques such as Segment Anything Model (SAM) and YOLO foundation models (YOLOv7, YOLOv8, YOLOv9, and YOLO-NAS), were used to accurately categorize PPD severity from RGB images captured by cameras or cell phones. YOLOv8 achieved the highest overall mean Average Precision (mAP) of 80.4%, demonstrating superior performance in detecting and classifying different PPD levels across all three models. Although YOLO-NAS had some instability during training, it demonstrated stronger performance in detecting the PPD_0 class, with a mAP of 91.3%. YOLOv7 exhibited the lowest performance across all classes, with an overall mAP of 75.5%. Despite challenges with similar color intensities in the image data, the combination of SAM, image processing techniques such as RGB color filtering, and machine learning (ML) algorithms was effective in removing yellow and gray color sections, significantly reducing the Mean Absolute Error (MAE) in PPD estimation from 20.01 to 15.50. Moreover, Artificial Intelligence (AI)-based algorithms allow for efficient analysis of large datasets, enabling rapid screening of cassava roots for PPD symptoms. This approach is much faster and more streamlined compared to the labor-intensive and time-consuming manual visual scoring methods. Conclusion: These results highlight the significant advancements in PPD detection and quantification in cassava samples using cutting-edge AI techniques. The integration of YOLO foundation models, alongside SAM and image processing methods, has demonstrated promising precision even in scenarios where experts struggle to differentiate closely related classes. This AI-powered model not only effectively streamlines the PPD assessment in the pre-breeding pipeline but also enhances the overall effectiveness of cassava breeding programs, facilitating the selection of PPD-resistant varieties through controlled screening. By improving the precision of PPD assessments, this research contributes to the broader goal of enhancing cassava productivity, quality, and resilience, ultimately supporting global food security efforts. [ABSTRACT FROM AUTHOR]

AbstractDental photography is gaining popularity in contemporary dentistry, but its knowledge was unregulated in most dental schools’ curriculum. The study aims to evaluate the level of photography practice and knowledge among dental students in Malaysia, acting as a baseline to include dental photography as a subject in the current curriculum. A series of questions were designed and validated by experts with a content validity index of 0.84. Questionnaires were distributed to the dental students with varied years of clinical experience. The level of knowledge was categorised into poor, weak, subpar, average, good and excellent. The common photography practice and courses attended were also tabulated. Among the 344 respondents, the most commonly used equipment was phone cameras (70.66%), whereas only 16.09% used digital single-lens reflex (DSLR) cameras. The majority of dental students did not attend any photography-related courses (69.48%). Based on the level of photography knowledge, dental students mostly scored ‘subpar’ (25.84%), with no statistically significant differences between the years of clinical experience (p = 0.236). Therefore, proper and comprehensive dental photography courses should be imparted in the current curriculum to improve students’ skills and knowledge. [ABSTRACT FROM AUTHOR]

With the rapidly growing number of images being captured by digital devices, e.g., cameras and mobile phones, the demand for finding similar images is increasing. However, traditional techniques that rely on investigating low-level features, such as edges, corners, and blobs, or color histograms, have become obsolete, as such information may be similar in unrelated images. Moreover, methods that rely on matching high-level features, e.g., objects in the image, may also produce false matches, as unrelated images may also contain similar objects in them. This gap in matching images is known as the semantic gap, which is caused by the reliance on low-level or high-level features in the images to match them. In this paper, a novel similarity measure is proposed to enhance the performance of content-based image retrieval. Initially, three similarity measures (high-level features, low-level features, and colors) are calculated per pair of images, which are then multiplied by three factors and used to adjust the weight of each of these similarities using the PSO algorithm and fused to a single similarity measure. Next, a database is generated based on the proposed similarity measure, in which the similarity measure of each pair of images is used as the training value for a neural network that takes the pair of images as input. Finally, we used ANNs to approach the complex computations of the proposed method, which allows the proposed method to be used in real-life applications. This model implementation is based on the Yolo V8 neural network. The proposed method is compared with recent state-of-the-art methods using the MIRFLICKR-25K, NUS-WIDE, MS-COCO, Pascal VOC2007, and Pascal VOC2012 datasets in terms of three well-known metrics (NDCG, ACG, and wMAP). As a result, the proposed method has been able to outperform all existing methods in terms of NDCG, with the highest of 0.98. These results illustrate the importance of the inclusion of the three similarity factors, the high- and low-level features as well as the color histogram, in retrieving more-relevant results to the query image, in addition to the ability of neural networks to approximate complex computations to reduce execution time while maintaining high-quality results. [ABSTRACT FROM AUTHOR]

This study aims to enhance maize water demand calculation. We calculate crop evapotranspiration (ETc) through mobile phone photography and meteorological parameters. In terms of crop coefficient (Kc) calculation, we utilize the mobile phone camera image driver to establish a real-time monitoring model of Kc based on plant canopy coverage (PGC) changes. The calculation of PGC is achieved by constructing a PGC classification network and a Convolutional Block Attention Module (CBAM)-U2Net is implemented by the segment network. For the reference crop evapotranspiration (ETo) calculation, we constructed a simplified ETo estimation model based on SVR, LSTM, Optuna LSTM, and GWO-SVM using a public meteorological data-driven program, and evaluated its performance. The results demonstrate that our method achieves high classification accuracy for the PGC 98.9% and segmentation accuracy for the CBAM-U2net-based segmentation network 95.68%. The Kc calculation model exhibits a root mean square error (RMSE) of 0.053. In terms of ETo estimation, the Optuna-LSTM model with four variables demonstrates the best estimation effect, with a correlation coefficient (R2) of 0.953. The final R2 between the estimated ETc value and the true value is 0.918, with an RMSE of 0.014. This method can effectively estimate the water demand of maize. [ABSTRACT FROM AUTHOR]

Miniature camera modules are widely used in mobile electronic devices such as mobile phones, personal digital assistants or tablet computers. The introduction of freeform surface in imaging systems provides numerous opportunities for enhanced performance, compactness and lightweight packaging. In this paper, we proposed a wide-angle, low-distortion and large-aperture optical system with freeform lens. A 110°full field of view, 1/1.8″ sensor size, 1.8 F number, 6.35 mm total track length (TTL) wide-angle high-resolution miniature camera lens was developed. The prototype exhibits a novel solution for wide-angle, ultra-short TTL lens in the application such as mobile phone cameras, wearable imaging systems and endoscopic systems. [ABSTRACT FROM AUTHOR]

Backgraund: To determine the potential relationship between age-related macular degeneration and iris freckles. Method: In this case-control study, iris photographs of 300 eyes of 300 patients diagnosed with age-related macular degeneration and 300 eyes of 300 healthy volunteers were obtained with the help of a high-resolution mobile phone camera. The evaluated iris photographs were classified according to the Descriptive Iris Color Classification Scale. Results: The average age of the AMD group is 73.05 ± 6.93, and the average age of the control group is 73.43 ± 5.72. (p = 0.124) While freckles were present in 200 (66.7%) of the patients in the AMD group, freckles were not observed in 100 patients (33.3%) of AMD group. While freckles were present in 142 (47.3%) of the patients in the control group, freckles were not observed in 158 of control group(52.7%). There was a significant difference in the presence of freckles between the two groups. (p < 0.001) The average number of freckles in the AMD group was 3.97 ± 3.07, and the number of freckles in the control group was 3.06 ± 2.55. (p = 0.001) Conclusion: We think that evaluation of iris details, especially the presence of iris freckles, should be used routinely in age-related macular degeneration screening. The risk of age-related macular degeneration can be predicted by evaluating iris details, which is an easy and inexpensive method. [ABSTRACT FROM AUTHOR]

This paper discusses how integrating digital technologies such as cell phones and digital cameras with face-to-face participatory methodologies can help enhance community engagement and cultural preservation in ICT for Development (ICTD) initiatives in remote regions with limited connectivity and digital literacy. This view from practice presents participatory methods in the ‘Leadership, Communication, and Climate Change’ program in the context of environmental conservation and indigenous community development in the Vaupés region of the Colombian Amazon. The primary goal of the program is to contribute to the formulation of indigenous community development projects that enhance environmental protection and improve the quality of life for participating communities, while respecting and promoting the cultural identity of indigenous peoples. The ‘Leadership, Communication, and Climate Change’ program aims to foster an exchange of knowledge between academics, environmental practitioners, and indigenous leaders from the region of Vaupes, in the eastern boundaries of the Colombian Amazon. [ABSTRACT FROM AUTHOR]

Complex systems often operate in equilibriums that can be disrupted under specific conditions, driving the system to undergo an irreversible phase transition (aka tipping point) into a new equilibrium. Disruptive technology, as introduced in the book Innovator's Dilemma, is a new emerging technology that can successfully displace incumbent technologies and push the market through phase transition into a new equilibrium. In this paper, the authors model the market disruption caused by a disruptive technology as a complex system, with dynamics that show a phase transition or tipping point, after which the system shifts into a new equilibrium aiming at assessing the success or failure of a disruptive technology. The purpose of this study is to mathematically model a tipping point measure of complex networks and a theoretical framework for disruptive technology dynamics. A predator–prey model is used to emulate the behavior of a disruptive technology versus an incumbent technology, and a resilience index is integrated to measure the tipping point in the market where the disruptive technology will overtake the incumbent technology. This methodology is applied to a historical case study of film versus digital versus cell phone cameras, demonstrating the application of this methodology. The study's contribution can be applied to both systems engineering and disruptive innovation management. The proposed approach can help stakeholders assess the impact of a potential new disruptive technology and use the resulting resilience index as a measurement for adjusting technology requirements and systems management approaches to achieve a desired outcome. [ABSTRACT FROM AUTHOR]

Background: Teledentistry is applied in oral medicine to help dental practitioners and specialists manage complex oral conditions. This scoping review aims to synthesize the available evidence regarding the technical requirements and the provision of security services, as well as the recommendations on standard oral cavity photography methods for using teledentistry in oral medicine. Method: The present scoping review was conducted in 2022 according to the Joanna Briggs Institute (JBI) manual. Four databases were systematically searched, including ISI Web of Science, PubMed, Scopus, and ProQuest, from January 1999 to December 2021. After finalizing the search strategy, all the original articles, reviews, editorials, letters, comments, and book chapters were included. All the dissertations uploaded in English with a full-text electronic file were also included to achieve a comprehensive picture of the available literature. Two types of analysis were applied: the quantitative descriptive analysis applying Excel 2016 and the qualitative thematic analysis using MAXQDA version 10. Results: The included papers were published during 1999–2021. Most of the included articles were from Brazil (n = 9, 21.43%), India (n = 6, 14.28%), the USA (n = 6, 14.28%), and the UK (n = 5, 11.90%). Social media usage as a teledentistry platform increased considerably between 2019 and 2021. Teledentistry platforms have been classified into four categories, including email, telephone, social network applications (WhatsApp, zoom, Google Meeting, Instagram, line Application, Viber, and Messenger), and teledentistry systems (teledentistry systems, which were designed specially to perform a teledentistry project). Teledentistry has been conducted to store and forward (n = 15, 30.71%), real-time (n = 9, 21.43%), or both (n = 12, 35.71%). According to the thematic results, basic technical requirements have been categorized into four, including teledentistry platform, type of interactions, internet connection, and photography tool. Other thematic results demonstrate seven categories of recommendations, including general factors, light, patient position, phone position, setting of a mobile phone camera, photo sending requirements, and photo visualization. Conclusion: According to the results, over the past 7 years, especially during the COVID-19 pandemic, social network applications such as WhatsApp and Zoom have been used more frequently. Also, photography recommendations with a mobile phone camera depend on several factors such as general factors, light, patient position, phone position, setting of the mobile phone camera, and photo-sending requirements. Given the increased use of mobile cameras for oral photography, these recommendations can help improve the quality of images and subsequently improve the reliability of clinical decision-making in managing patients' conditions. [ABSTRACT FROM AUTHOR]

Copyright of Fire Ecology is the property of Springer Nature 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 popularity of camera phones, the availability of photo-editing apps, and the rise of visually oriented social media platforms have made it convenient for citizens to produce and circulate visual content in contentious politics. While scholars have increasingly recognized the role of visuals in mobilizing social-mediated protests, how different types of visuals affect message engagement across different stages of protests remains underexplored. For this study, we analyzed approximately ten million tweets from Twitter for three social-mediated protests (Black Lives Matter, Stop Asian Hate, and Women's March). We found that posts with images and videos generally attracted more audience engagement than their textual counterparts. Unpacking the role of visual media across different modalities and stages of social-mediated protests, we found that the superior effects of visuals were generally more pronounced during the ignition phase of the protest than the periods before and after. By applying unsupervised image clustering on millions of protest visuals, we systematically established four common visual content categories: crowd-based protest photos, non-crowd-protest human photos, non-human photos, and non-photograph visuals. We revealed heterogeneous effects on audience engagement across content categories and protests, and explored these categories through qualitative analysis of most-engaged visuals. These findings enrich our understanding of the mobilizing power of visual media in social movements and shed light on effective communication strategies regarding social inequalities. [ABSTRACT FROM AUTHOR]

In Batseri Village of Sangla Valley, a rockfall incident occurred during the monsoon season on July 21, 2021. To assess the kinematics of this rockfall event, a comprehensive study was conducted, involving detailed field investigations, video frame analysis, and numerical modelling. During the rockfall, a resident captured 10 m3 elliptical rockfall boulder motion using a mobile phone camera providing crucial data for tracking and analysing its trajectory. The video of the boulder showed rotational motion along its minor axis after impacting an embedded boulder. The field investigation revealed that the rock mass exhibits three prominent sets of tectonic joints. The weathering along these tectonic joints and topography formed denudation joints at the top of the escarpment, which led to the rock mass failure. Through numerical simulations, it was determined that the potential threat section of the Sangla–Chitkul road at Batseri village spanned approximately 400 m. For boulders passing this road section, the maximum velocity reached 46 m/s, whilst the maximum kinetic energy peaked at 5.9 × 104 kJ, and the highest jump height was measured at 9 m. The analysis of kinetic energy and jump height provides essential parameters for designing effective protective measures. Understanding the rockfall characteristics and potential reach is crucial in developing strategies to safeguard vulnerable areas and mitigate the impact of future rockfall events in Batseri village. The combination of field observations, video frame analysis, and numerical modelling enhances our understanding of rockfall behaviour and informs protective design efforts for infrastructure and communities in the Batseri Village. [ABSTRACT FROM AUTHOR]

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office 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.)

Preference stability can be a useful and supportive indicator for making strategic and tactical marketing decisions as it can assist short- and longer-term choices regarding product/brand characteristics and attributes. We provide a measurement approach combined with a model for strategic product/brand development decisions and subsequent marketing actions. Managers could use such insights when contrasting different attributes (e.g., design vs. camera of mobile phones) and also when making decisions about characteristics (e.g., different degrees of camera resolution of mobile phones). Such insights shall guide managers on where to invest and on how to leverage consumer preferences more effectively to generate sales for the company. We illustrate the use of our model of preferences and preference stability of product characteristics in three complementary studies (assessing yoghurt, energy drinks, and mobile phones). [ABSTRACT FROM AUTHOR]

Critical facilities are generally located in areas with restricted or controlled access, making it difficult for experts to monitor the structural health of enclosed infrastructures. Hence, a case study was conducted in South Korea to evaluate the applicability of digital image photogrammetry using commercial imaging devices in order to quickly measure the structural deformations of infrastructures in such areas. The applicability evaluation involved measuring the displacement of mechanically stabilized earth (MSE) walls. In the experiment, the displacement of MSE walls was first measured using the traditional monitoring method and the results were compared with those obtained via digital image photogrammetry using commercial imaging devices such as digital cameras and cellphones. The measurement results obtained with the cellphone camera had a maximum error of approximately 20 mm when compared with the results of the traditional monitoring method. Because this is a significant error, even when considering the mechanical error in the traditional monitoring method's result, it was determined that monitoring using a cellphone camera is infeasible. However, the experimental results of digital image photogrammetry using a digital camera showed a maximum error of approximately 9 mm. Although this is a sizable error, the method was assessed to be technically feasible. [ABSTRACT FROM AUTHOR]

Natural gas extraction is a critical driver of the economy in western North America. Ecological reclamation is important to ensure surface disturbance impacts associated with natural gas development are not permanent and to assist native biota. Previous studies in semi-arid natural gas fields within Sublette County, Wyoming, USA have shown insects respond favorably to 1–3-year-old well pads undergoing reclamation compared to older successional reference vegetation communities dominated by Wyoming big sagebrush (Artemisia tridentata spp. Wyomingensis). Here, we examined well pads which were initially seed 5, 8, 10, 11, and 12 years prior to our study. We used a free, image-based software called SamplePointv. 1.60 to quantify vegetation on these well pads and adjacent reference areas from cell phone camera photographs. Insects were collected with a sweep net and identified to the family and morphospecies level. Statistical analyses were conducted to compare both vegetation and insect communities between reclamation sites and their paired reference area. We found little statistical difference between vegetation communities across our study but found significantly more insect abundance on reclaimed well pads than reference areas in 3 of 5 years and significantly higher family and morphospecies richness on reclaimed well pads in 4 of 5 years. A total of 2036 individual insects representing 270 species from 71 families across 11 orders were identified across this study. A total of 1557 individuals (76.5%) were found on reclamation sites, whereas 479 (23.5%) were found in reference areas across the entire study. A total of 233 species (86.3% of total) were found on reclamation sites, whereas 121 species (44.8% of total) were found in reference areas across the entire study. A total of 67 families (94.4% of total) were found on reclamation sites, whereas 45 families (63.4% of total) were found in reference areas across the entire study. All 11 orders found in the study were found on reclamation sites, whereas 9 orders were found in reference areas across the entire study. Our results suggest reclamation of natural gas well pads within an old successional stand of sagebrush continues to support higher levels of insect biodiversity and abundance for at least 12 years. As insects are the most diverse group of animals on Earth and because they provide a wide array of ecosystem services, our findings suggest ecological reclamation plays an important role in returning biodiversity and ecosystem functionality to a semi-arid and old successional sagebrush–steppe ecosystem. [ABSTRACT FROM AUTHOR]

Purpose: Paper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms' nutrient source. Mold not only can damage papers' structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper. Methods: Molds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model. Results & Conclusion: In the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation. [ABSTRACT FROM AUTHOR]

A non-contact-based system for estimating the respiration rate of individuals using a mobile phone camera is presented in this paper. Respiration rate is an important vital sign that indicates the number of breaths a person takes per minute and is commonly used in medical assessments. The method involves recording videos of the pit of the neck region using the mobile phone camera and analyzing the changes in light intensity within the video frames. By processing the obtained signals from the video frames, the system accurately estimates the respiration rate. The effectiveness of the proposed method and the estimated respiration rates are compared with rates obtained using two other methods: a spirometer, which is a device used to measure lung function and manual counting. The evaluation is conducted on a group of 34 participants who are instructed to breathe rapidly to simulate varying respiration rates. The results indicate that the presented method achieves an accuracy of over 80% compared with existing state-of-the-art methods. The standard deviation of ±3% suggests a relatively consistent performance across participants. The calculated error between the estimated respiration rates and the reference rates obtained from the spirometer and manual counting validates the reliability of the proposed approach. Additionally, the paper highlights the advantages of this system, including its cost-effectiveness and suitability for remote monitoring and mass screening. The non-contact nature of the method reduces the need for physical contact with patients, which could be especially beneficial in situations where hygiene and infection control are crucial. The paper also outlines plans for future work, which involve validating the method with larger sample sizes and exploring its potential applications in various clinical scenarios. [ABSTRACT FROM AUTHOR]

ANP (atrial natriuretic peptide) and BNP (brain natriuretic peptide) are important indicators for detecting heart failure (HF). However, traditional testing method had low real-time detecting and poor sensitivity, which limited the early diagnosis of HF. Herein, we prepare a low cost device based on upconversion microparticles (UCMPs) with porous structure, which has a high detecting sensitivity for ANP and BNP markers. The lowest detection concentration of ANP and BNP can reach 5 pg/mL with Yb/Tm/Sr/12CaO·7Al 2 O 3 (Yb/Tm/Sr/C12A7)-LFS and Yb/Er/Sr/C12A7-LFS, which are of one order lower than their clinical cutoff. The whole UCL intensity of UCMPs enhanced via Sr2+ ions doping. UCMPs not only have dual-color UCL, but also achieve good temperature sensing. The maximum S R of Yb/Tm/Sr/C12A7 and Yb/Er/Sr/C12A7 based on UCL ratio are 4.23 % K−1 and 3.36 % K−1, respectively. The UCMPs-LFS device has a low-cost laser diode, simplified laser and permits a mobile phone camera to collect the imaging, which is benefit for detecting ANP and BNP application. [ABSTRACT FROM AUTHOR]

One of the fundamental stages in recognizing people by their ears, which most works omit, is locating the area of interest. The sets of images used for experiments generally contain only the ear, which is not appropriate for application in a real environment, where the visual field may contain part of or the entire face, a human body, or objects other than the ear. Therefore, determining the exact area where the ear is located is complicated, mainly in uncontrolled environments. This paper proposes a method for ear localization in controlled and uncontrolled environments using MediaPipe, a tool for face localization, and YOLOv5s architecture for detecting the ear. The proposed method first determines whether there are cues that indicate that a face exists in an image, and then, using the MediaPipe facial mesh, the points where an ear potentially exists are obtained. The extracted points are employed to determine the ear length based on the proportions of the human body proposed by Leonardo Da Vinci. Once the dimensions of the ear are obtained, the delimitation of the area of interest is carried out. If the required elements are not found, the model uses the YOLOv5s architecture module, trained to recognize ears in controlled environments. We employed four datasets for testing (i) In-the-wild Ear Database, (ii) IIT Delhi Ear Database, (iii) AMI Ear Database, and (iv) EarVN1.0. Also, we used images from the Internet and some acquired using a Redmi Note 11 cell phone camera. An accuracy of 97% with an error of 3% was obtained with the proposed method, which is a competitive measure considering that tests were conducted in controlled and uncontrolled environments, unlike state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Surveillance, democracy, social shaming, surveillance capitalism, Panopticon, security cameras, chilling effect The bottom line is that while I think that the Florida and Texas laws were profoundly misguided, the fact that some states are even attempting to rein in the power of surveillance capitalism shows there's some movement by states to create rational, more protective regimes. Keywords: Surveillance; Panopticon; surveillance capitalism; democracy; social shaming; security cameras; chilling effect EN Surveillance Panopticon surveillance capitalism democracy social shaming security cameras chilling effect 243 248 6 09/12/22 20220901 NES 220901 Michel Paradis teaches courses at Columbia University Law School on national security law, international law, the constitution, and the law of war, and is a fellow at the Center on National Security and the National Institute for Military Justice. So I don't think we're at risk of that same kind of advertent, overweening surveillance state that you have in China. [Extracted from the article]

Taekwondo is a widely practised martial art and an Olympic sport. In Taekwondo, Poomsae movements are essential, as they form the foundation of the sport and are fundamental for success in competitions. The evaluation of Poomsae movements in Taekwondo has been a subjective process, relying heavily on human judgments. This study addresses the above issue by developing a systematic approach to evaluate Poomsae movements using computer vision. A long short-term memory-based (LSTM-based) machine learning (ML) model was developed and evaluated for its effectiveness in Poomsae movement evaluation. The study also aimed to develop this model as an assistant for self-evaluation, that enables Taekwondo players to enhance their skills at their own pace. For this study, a dataset was created specially by recording Poomsae movements of Taekwondo players from the University of Colombo. The technical infrastructure used to capture skeleton point data was cost-effective and easily replicable in other settings. Small video clips containing Taekwondo movements were recorded using a mobile phone camera and the skeleton point data was extracted using the MediaPipe Python library. The model was able to achieve 61% of accuracy when compared with the domain experts' results. Overall, the study successfully achieved its objectives of defining a selfpaced approach to evaluate Poomsae while overcoming human subjectivity otherwise unavoidable in manual evaluation processes. The feedback of domain experts was also considered to finetune the model for better performance. [ABSTRACT FROM AUTHOR]

Malayalam Handwriting Character Recognition(MHCR) is a challenging job because of its large number of similar or confusing characters. This is especially true in the case of Malayalam scripts with enormously large character sets with complex shapes. In this study, an in-depth integrated learning model is proposed that incorporates the use of the hand-crafted Histogram of Oriented Gradients(HOG) Feature Descriptor(FD), and the flexible learning features of Deep Convolutional Neural Networks(DCNN). An Malayalam character data set which divides Malayalam characters into Basic and Compound classes were used for training the proposed model with Multiview Augmentation(MVA). Highly perspective-dependent compound character images extracted through mobile phones and digital cameras can be correctly classified with MVA which generates more perspective view that avoid overfitting and makes the training more effective. Similarly high interclass similarities which lead to the misclassification can be addressed through Multiview Inference Fusion(MVIF) which is used to fuse the individual prediction generated from each view. The experimental results express that DCNN-only models achieve 95.07% and 94.57% testing accuracy on respective model implementations. Moreover, it is clear that the proposed system, which fuses HOG Feature Descriptor with a DCNN, attains over 97.79% and 97.09% test accuracy respectively on corresponding live models. [ABSTRACT FROM AUTHOR]

Helicobacter pylori (HPY) is a type of spiral bacterium. The International Agency for Research on Cancer (IARC) has classified HPY as a Group 1 carcinogen, indicating that it is the leading cause of stomach cancer. The traditional testing method for HPY is cumbersome and requires professional personnel to operate. Owing to advances in biochemical technology, rapid tests for HPY have been developed. The development of the rapid test is based on colloidal gold. The rapid test displays two lines when the result is positive. The color of the test result can vary due to changes in the size or concentration of colloidal particles. Users may misinterpret the results owing to the effect of ambient lighting or when the color is too close to the background color. In this study, we successfully developed a method for using a smartphone to evaluate the results and sample concentration in rapid tests for detecting HPY. The HPY rapid test is placed on a colorimetric board developed for this study, and an image is captured using a smartphone and evaluated using the image processing program designed in this research. The ArUco marker is used for positioning and color correction to ensure that it is not affected by the brand of the camera of the mobile phone and nonuniform ambient light sources. To confirm whether the proposed method correctly determines the result and sample concentration, experiments were conducted using multiple different mobile phones. In the experiment, the illumination and shooting height were fixed and several different sample concentrations were used. Printed simulated and real rapid test results were used in the experiment. The experimental results show that both positive and negative can be correctly determined, and there is good linearity when determining each concentration of samples from the image. For example, when using simulated rapid tests, the R² value is at least 0.9915, and the detection concentration threshold of the real HPY rapid tests is 5000 CFU/mL. Even with the concentration at 1250 CFU/mL, the R² value is still 0.9473. [ABSTRACT FROM AUTHOR]

Defect detection in mobile phone cameras constitutes a critical aspect of the manufacturing process. Nonetheless, this task remains challenging due to the complexities introduced by intricate backgrounds and low-contrast defects, such as minor scratches and subtle dust particles. To address these issues, a Bilateral Feature Fusion Network (BFFN) has been proposed. This network incorporates a bilateral feature fusion module, engineered to enrich feature representation by fusing feature maps from multiple scales. Such fusion allows the capture of both fine and coarse-grained details inherent in the images. Additionally, a Self-Attention Mechanism is deployed to garner more comprehensive contextual information, thereby enhancing feature discriminability. The proposed Bilateral Feature Fusion Network has been rigorously evaluated on a dataset of 12,018 mobile camera images. Our network surpasses existing state-of-the-art methods, such as U-Net and Deeplab V3+, particularly in mitigating false positive detection caused by complex backgrounds and false negative detection caused by slight defects. It achieves an F1-score of 97.59%, which is 1.16% better than Deeplab V3+ and 0.99% better than U-Net. This high level of accuracy is evidenced by an outstanding precision of 96.93% and recall of 98.26%. Furthermore, our approach realizes a detection speed of 63.8 frames per second (FPS), notably faster than Deeplab V3+ at 57.1 FPS and U-Net at 50.3 FPS. This enhanced computational efficiency makes our network particularly well-suited for real-time defect detection applications within the realm of mobile camera manufacturing. [ABSTRACT FROM AUTHOR]

Large horizontal displacements and tilts in tall structural elements such as towers and factory chimneys resulting from soil movement or earthquakes may lead to their collapse. The traditional methods of tilt monitoring (GPS, total stations, digital levels) require direct contact with the monitored buildings. Although the measurement results of sensors are accurate, sensors are typically cumbersome to deploy and can only provide local and relative deformation information. In this study, closerange photogrammetry based on the principle of Structure from Motion technique (SfM Photogrammetry) was used to monitor high structural elements. This method is safe and economic as it doesn’t require direct contact with the monitored building. In addition, it requires low effort, short practical time and low budget. The proposed methodology in this study was applied on the chimneys of the thermal station located at Tishreen University. Results were evaluated by comparing with the traditional survey methods based on the use of the total station measurements. The results show that the value of the maximum displacement at the highest level, determined by using an uncalibrated mobile phone built-in camera and SfM photogrammetry (0.081 m) is approximately equal to twice the displacement and inclination determined by traditional surveying (0.049 m); but through some precautions and conditions, the accuracy of this method can be increased. [ABSTRACT FROM AUTHOR]

Objective To describe and compare the dermoscopic features of various papulosquamous skin disorders. Methods This study is an observational descriptive comparative study performed on 169 patients with papulosquamous skin diseases including psoriasis, lichen planus, eczema, pityriasis rosea and pityriasis lichenoides chronica at the outpatient clinic at the dermatology department of AL-Sader teaching hospital in Basra during the period between April 2021 to September 2022. A detailed history was taken from each patient with a complete physical examination and all relevant investigations were done. The dermoscopic examination was done using a hand-held dermoscope (x10) and photos were taken with a mobile phone camera attached to the Dermlight DL200 dermoscope then data were collected and statistically analyzed. Results Dermoscopic examination of psoriasis showed that light red background, regularly dotted vessels and diffuse white scales were the most common findings. The most characteristic dermoscopic features of lichen planus was Wickham striae which was seen as radial streaming followed by a reticular pattern, the lesion was purple in color, non-scaly and non-vascular lesions, the scales if present were mainly peripheral white scales, while the vascular lesions of lichen planus were usually dotted peripheral blood vessels. Patchy dotted blood vessels on a dull red background covered by yellow-white scales in patchy distribution were the common findings seen in eczema. According to a study of patients with pityriasis rosea, the most significant dermoscopic features observed in patients were yellow background color, peripheral white scales, and avascular pattern. Pityriasis lichenoides chronica was found to be associated with peripheral dotted vessels on orangeyellow background covered by white scales on a peripheral or diffuse pattern. Conclusion Dermoscopy is a simple non-invasive safe bedside tool that provides important information to help in diagnosis or narrowing differential diagnosis of many papulosquamous skin diseases. [ABSTRACT FROM AUTHOR]

Our recent advancement employs lead sulfide (PbS) quantum dots (QDs) in short‐wave infrared (SWIR) photodetector towards low light night vision (LLNV). The as prepared QD SWIR photodetector showcases low dark current density(< 200 nA/cm2) and high efficiency (EQE > 50%), demonstrating QDs' potential for superior LLNV in mobile devices. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the reliability and validity of a novel method for remotely measuring trismus. Materials and methods: We recruited 60 volunteers who took three types of photographs at a fixed restricted jaw position mimicking limited mouth opening, including one selfie and one portrait with or without a reference frame. Additionally, the interincisal distance and the width of the upper central incisors were measured with a ruler, as per common practice. Measurements of trismus were made using image analysis software comparing different types of photos and calibration methods. Intraclass correlation coefficient (ICC) and 95% limits of agreement (LoA) with 95% confidence interval were calculated to evaluate reliability and validity. Results: The proposed method demonstrated high reliability (ICC 0.998; 95% CI 0.997, 0.999). Calibration of photographs using at least a baseline photograph with an external reference frame yielded unbiased measurements and minimised variability. The use of selfies compared to portrait photos also increased variability. Conclusion: The measurement of trismus can be performed using images taken remotely by patients using their mobile phone cameras. The proposed method is highly accurate, with best results obtained by using a reference frame for calibration of portrait photographs. Clinical relevance: We propose an easy, cheap, and accurate method that allows for remote and frequent monitoring of trismus in clinical studies using patients' mobile phones. [ABSTRACT FROM AUTHOR]