Search Results (70)

Cotton is the most widely cultivated natural fiber crop worldwide, yet it is highly susceptible to various diseases and pests that significantly compromise both yield and quality. To enable rapid and accurate diagnosis of cotton diseases and pests—thus supporting the development of effective control strategies and facilitating genetic breeding research—we propose a lightweight model, the Resource-efficient Cotton Network (RF-Cott-Net), alongside an open-source image dataset, CCDPHD-11, encompassing 11 disease categories. Built upon the MobileViTv2 backbone, RF-Cott-Net integrates an early exit mechanism and quantization-aware training (QAT) to enhance deployment efficiency without sacrificing accuracy. Experimental results on CCDPHD-11 demonstrate that RF-Cott-Net achieves an accuracy of 98.4%, an F1-score of 98.4%, a precision of 98.5%, and a recall of 98.3%. With only 4.9 M parameters, 310 M FLOPs, an inference time of 3.8 ms, and a storage footprint of just 4.8 MB, RF-Cott-Net delivers outstanding accuracy and real-time performance, making it highly suitable for deployment on agricultural edge devices and providing robust support for in-field automated detection of cotton diseases and pests. Full article

Background/Objectives: Screen-related parenting practices (e.g., restriction, coviewing) influence the way children use screen devices. Although children start using screen devices (e.g., televisions [TV], tablets) in the first few years of life, rigorously developed measures of screen-related parenting practices for parents of toddlers do not exist. The objective of this study was to develop culturally and contextually relevant survey measures of screen-related parenting practices for use in Mexican American families with toddlers. Methods: Measures were developed using an exploratory sequential mixed methods (qualitative → quantitative) approach. Mexican American mothers of toddlers (15–26 months of age) participated in semi-structured interviews. Using the interview findings, domains of parenting practices across screen device types were identified, and survey items were developed. Items were administered by phone to 384 Mexican American mothers. Analyses included evaluation of the factor structure and psychometric properties of nine domains of parenting practices and evaluations of correlations between each scale and demographic characteristics. Results: Factor analyses supported a one-factor solution for each parenting practice as follows: Restrict TV Time (8 items), Coview TV (10 items), Behavioral Regulation with TV (12 items), Restrict Mobile Device Time (8 items); Coview Mobile Devices (10 items); Behavioral Regulation with Mobile Devices (16 items), Restrict Screen Content (8 items), Allow Screen Use Around Sleep (6 items), and Allow Screen Use While Eating (6 items). The reliabilities were acceptable (Cronbach’s alphas > 0.80). Hispanic acculturation, maternal age, and child age were correlated with many of the scales of parenting practices. Conclusions: The measures developed in this study offer a way to evaluate the use and impact of screen-related parenting practices in Mexican American families with toddlers. The use of these measures will enable investigators to identify relationships among parenting practices, screen use, and child well-being, which could inform the design of early childhood interventions promoting healthy screen use in this population. Full article

Herein, we present a method for detecting and determining the corrosion level of hydraulic metal structure surfaces through images while reducing the difficulty of dataset annotation. To achieve accurate detection of corrosion targets, the MobileViTv3 block is integrated into YOLOv10, resulting in the proposed YOLOv10-vit for corrosion target detection. Based on YOLOv10-vit, the YOLOv10-vit-cls classification network is introduced for corrosion level determination. This network leverages the pre-trained parameters of YOLOv10-vit to more quickly learn the features of different corrosion levels. To avoid subjective factors in the corrosion level annotation process and reduce annotation difficulty, a cascaded corrosion detection architecture combining YOLOv10-vit and YOLOv10-vit-cls is proposed. Finally, based on the proposed corrosion detection architecture, we achieve accurate corrosion detection and level determination for hydraulic metal structures. Full article

The pervasive use of screens, averaging nearly 7 h per day globally between mobile phones, computers, notebooks and TVs, has sparked a growing desire to minimize reflections from ambient lighting and enhance readability in harsh lighting conditions, without the need to increase screen brightness. This demand highlights a significant need for advanced anti-glare (AG) technologies, to increase comfort and eventually reduce energy consumption of the devices. Currently used production technologies are limited in their texture designs, which can lead to suboptimal performance of the anti-glare texture. To overcome this design limitation and improve the performance of the anti-glare feature, this work reports a new, cost-effective, high-volume production method that enables much needed design freedom over a large area. This is achieved by combining mastering via large-area Laser Beam Lithography (LBL) and replication by Nanoimprint Lithography (NIL) processes. The environmental impact of the production method, such as regards material consumption, are considered, and the full cycle from design to final imprint is discussed. Full article

This study focuses on the physiological response of lettuce grown on Technosols designed for the remediation of soils polluted by potentially harmful elements (PHEs: As, Cd, Cu, Fe, Pb, and Zn). Lettuce plants were grown in five treatments: recovered (RS) and polluted soil (PS) as controls, and three Technosols (TO, TS, and TV) consisting of 60% PS mixed with 2% iron sludge, 20% marble sludge, and 18% organic wastes (TO: composted olive waste, TS: composted sewage sludge, and TV: vermicompost of garden waste). The main soil properties and PHE solubility were measured, together with physiological parameters related to phytotoxicity in lettuce such as growth, photosynthetic capacity, oxidative stress, and antioxidant defence. All Technosols improved unfavourable conditions of PS (i.e., neutralised acidity and enhanced OC content), leading to a significant decrease in Cd, Cu, and Zn mobility. Nevertheless, TV was the most effective as the reduction in PHEs mobility was higher. Furthermore, lettuce grown on TV and TO showed higher growth (+90% and +41%) than PS, while no increase in TS. However, lower oxidative stress and impact on photosynthetic rate occurred in all Technosols compared to PS (+344% TV, +157% TO, and +194% TS). This physiological response of lettuce proves that PHE phytotoxicity is reduced by Technosols. Thus, this ecotechnology constitutes a potential solution for soil remediation, with effectiveness of Technosols depending largely on its components. Full article

Today, energy and performance efficiency have become a crucial factor in modern computing environments, such as high-end mobile devices, desktops, and enterprise servers, because data volumes in cloud datacenters increase exponentially. Unfortunately, many researchers and engineers neglect the power consumption and internal performance incurred by storage devices. In this paper, we present a renewable-zoned namespace (ReZNS), an energy and performance-optimal mechanism based on emerging ZNS SSDs. Specifically, ReZNS recycles the remaining capacity of zones that are no longer used by adding a renewable concept into the mapping mechanism. We implemented a prototype of ReZNS based on NVMeVirt and performed comprehensive experiments with diverse workloads from synthetic to real-world workloads to quantitatively confirm power and performance benefits. Our evaluation results present that ReZNS improves overall performance by up to 60% and the total power consumption by up to 3% relative to the baseline on ZNS SSD. We believe ReZNS creates new opportunities to prolong the lifespan of various consumer electronics, such as TV, AV, and mobile devices, because storage devices play a crucial role in their replacement cycle. Full article

MHealth strategies have been used in various health areas, and mobile apps have been used in the context of health self-management. They can be considered an adjuvant intervention in oral health literacy, mainly for people with special health needs. Thus, the aim of this study was to identify the improvement of oral health literacy in patients with special needs when using digital platforms. A systematic literature review, based on the Joanna Briggs Institute (JBI) guidelines, was the main research method employed in this study. A search was undertaken in PubMed/MEDLINE and Cochrane Central Register of Controlled Trials (CENTRAL) databases, according to the relevant Mesh descriptors, their synonyms, and free terms (Entry Terms). Studies published between the years 2012 and 2023 were included. Two researchers independently assessed the quality of the included studies by completing the Newcastle–Ottawa Quality Assessment Scale questionnaire. The analysis corpus comprised 5 articles among the 402 articles selected after applying the inclusion/exclusion criteria (k = 0.97). The evidence from the considered articles is consensual regarding the effectiveness of using new technologies and innovations in promoting oral health literacy in patients with special health needs. The interventions were based on using the Illustration Reinforcement Communication System, inspired by the Picture Exchange Communication System, Nintendo^® Wii™ TV, virtual reality, smartphones, with software applications to read messages sent, Audio Tactile Performance technique, and Art package. One study had a low-quality assessment, and four had a high quality. The evidence from the articles included in this systematic review is consistent regarding the effectiveness of using new technologies and innovations in promoting oral health literacy in patients with special health needs. Full article

The diversity and complexity of the agricultural environment pose significant challenges for the collection of pest and disease data. Additionally, pest and disease datasets often suffer from uneven distribution in quantity and inconsistent annotation standards. Enhancing the accuracy of pest and disease recognition remains a challenge for existing models. We constructed a representative agricultural pest and disease dataset, FIP6Set, through a combination of field photography and web scraping. This dataset encapsulates key issues encountered in existing agricultural pest and disease datasets. Referencing existing bounding box regression (BBR) loss functions, we reconsidered their geometric features and proposed a novel bounding box similarity comparison metric, DDRIoU, suited to the characteristics of agricultural pest and disease datasets. By integrating the focal loss concept with the DDRIoU loss, we derived a new loss function, namely Focal-DDRIoU loss. Furthermore, we modified the network structure of YOLOV7 by embedding the MobileViTv3 module. Consequently, we introduced a model specifically designed for agricultural pest and disease detection in precision agriculture. We conducted performance evaluations on the FIP6Set dataset using mAP75 as the evaluation metric. Experimental results demonstrate that the Focal-DDRIoU loss achieves improvements of 1.12%, 1.24%, 1.04%, and 1.50% compared to the GIoU, DIoU, CIoU, and EIoU losses, respectively. When employing the GIoU, DIoU, CIoU, EIoU, and Focal-DDRIoU loss functions, the adjusted network structure showed enhancements of 0.68%, 0.68%, 0.78%, 0.60%, and 0.56%, respectively, compared to the original YOLOv7. Furthermore, the proposed model outperformed the mainstream YOLOv7 and YOLOv5 models by 1.86% and 1.60%, respectively. The superior performance of the proposed model in detecting agricultural pests and diseases directly contributes to reducing pesticide misuse, preventing large-scale pest and disease outbreaks, and ultimately enhancing crop yields. These outcomes strongly support the promotion of sustainable agricultural development. Full article

Background: Screen time, involving activities like watching television (TV), and using tablets, mobile phones, and computers (electronic devices), is associated with the consumption of unhealthy foods. This study aimed to analyze the association between prolonged leisure screen time and healthy and unhealthy food consumption indicators among Brazilian adults (≥18 years). Methods: Data from the National Health Survey (NHS), conducted in 2019 (n = 88,531), were used. Prolonged leisure screen time (screen time ≥ 3 h/day) was analyzed in three dimensions: watching TV; use of electronic devices; and total screen time (TV and electronic devices). Food consumption was analyzed in two dimensions: healthy (in natura and minimally processed foods) and unhealthy (ultra-processed foods). Poisson regression models were used to calculate prevalence ratios (crude and adjusted (PRa)) by sociodemographic factors (sex, age, schooling, income, area of residence, and race/color) and health factors (weight status, self-rated health, and presence of noncommunicable disease), to assess the association between prolonged screen time and food consumption indicators. Results: Among Brazilian adults, the prevalence of prolonged screen time was 21.8% for TV and 22.2% for other electronic devices for leisure. The highest frequency of watching TV for a prolonged time was observed among women, older adults, and those with a lower income and schooling. Prolonged use of electronic devices was more common among young adults and those with intermediate schooling and income. Prolonged screen time was associated with an unhealthy diet, due both to the higher consumption of unhealthy foods (PRa = 1.35 for TV, PRa = 1.21 for electronic devices, and PRa = 1.32 for both types) and the lower consumption of healthy foods (PRa = 0.88 for TV, PRa = 0.86 for electronic devices, and PRa = 0.86 for both). Conclusions: Prolonged screen time was negatively associated with the consumption of healthy foods and favored the consumption of unhealthy foods among Brazilian adults. Full article

Detecting early plant diseases autonomously poses a significant challenge for self-navigating robots and automated systems utilizing Artificial Intelligence (AI) imaging. For instance, Botrytis cinerea, also known as gray mold disease, is a major threat to agriculture, particularly impacting significant crops in the Cucurbitaceae and Solanaceae families, making early and accurate detection essential for effective disease management. This study focuses on the improvement of deep learning (DL) segmentation models capable of early detecting B. cinerea on Cucurbitaceae crops utilizing Vision Transformer (ViT) encoders, which have shown promising segmentation performance, in systemic use with the Cut-and-Paste method that further improves accuracy and efficiency addressing dataset imbalance. Furthermore, to enhance the robustness of AI models for early detection in real-world settings, an advanced imagery dataset was employed. The dataset consists of healthy and artificially inoculated cucumber plants with B. cinerea and captures the disease progression through multi-spectral imaging over the course of days, depicting the full spectrum of symptoms of the infection, ranging from early, non-visible stages to advanced disease manifestations. Research findings, based on a three-class system, identify the combination of U-Net++ with MobileViTV2-125 as the best-performing model. This model achieved a mean Dice Similarity Coefficient (mDSC) of 0.792, a mean Intersection over Union (mIoU) of 0.816, and a recall rate of 0.885, with a high accuracy of 92%. Analyzing the detection capabilities during the initial days post-inoculation demonstrates the ability to identify invisible B. cinerea infections as early as day 2 and increasing up to day 6, reaching an IoU of 67.1%. This study assesses various infection stages, distinguishing them from abiotic stress responses or physiological deterioration, which is crucial for accurate disease management as it separates pathogenic from non-pathogenic stress factors. The findings of this study indicate a significant advancement in agricultural disease monitoring and control, with the potential for adoption in on-site digital systems (robots, mobile apps, etc.) operating in real settings, showcasing the effectiveness of ViT-based DL segmentation models for prompt and precise botrytis detection. Full article

The microphase separation of high-molecular-weight block copolymers into nanostructured films is strongly dependent on the surface fields. Both, the chain mobility and the effective interaction parameters can lead to deviations from the bulk morphologies in the structures adjacent to the substrate. Resolving frustrated morphologies with domain period L₀ above 100 nm is an experimental challenge. Here, solvothermal annealing was used to assess the contribution of elevated temperatures of the vapor T_v and of the substrate T_s on the evolution of the microphase-separated structures in thin films symmetric of polystyrene-b-poly(2vinylpyridine) block copolymer (PS-PVP) with L₀ about 120 nm. Pronounced topographic mesh-like and stripe patterns develop on a time scale of min and are attributed to the perforated lamella (PL) and up-standing lamella phases. By setting T_v/T_s combinations it is possible to tune the sizes of the resulting PL patterns by almost 10%. Resolving chemical periodicity using selective metallization of the structures revealed multiplication of the topographic stripes, i.e., complex segregation of the component within the topographic pattern, presumably as a result of morphological phase transition from initial non-equilibrium spherical morphology. Reported results reveal approaches to tune the topographical and chemical periodicity of microphase separation of high-molecular-weight block copolymers under strong confinement, which is essential for exploiting these structures as functional templates. Full article

Accurate weed detection is essential for the precise control of weeds in wheat fields, but weeds and wheat are sheltered from each other, and there is no clear size specification, making it difficult to accurately detect weeds in wheat. To achieve the precise identification of weeds, wheat weed datasets were constructed, and a wheat field weed detection model, YOLOv8-MBM, based on improved YOLOv8s, was proposed. In this study, a lightweight visual converter (MobileViTv3) was introduced into the C2f module to enhance the detection accuracy of the model by integrating input, local (CNN), and global (ViT) features. Secondly, a bidirectional feature pyramid network (BiFPN) was introduced to enhance the performance of multi-scale feature fusion. Furthermore, to address the weak generalization and slow convergence speed of the CIoU loss function for detection tasks, the bounding box regression loss function (MPDIOU) was used instead of the CIoU loss function to improve the convergence speed of the model and further enhance the detection performance. Finally, the model performance was tested on the wheat weed datasets. The experiments show that the YOLOv8-MBM proposed in this paper is superior to Fast R-CNN, YOLOv3, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLOv9, and other mainstream models in regards to detection performance. The accuracy of the improved model reaches 92.7%. Compared with the original YOLOv8s model, the precision, recall, mAP1, and mAP2 are increased by 10.6%, 8.9%, 9.7%, and 9.3%, respectively. In summary, the YOLOv8-MBM model successfully meets the requirements for accurate weed detection in wheat fields. Full article

This paper introduces an autonomous robot designed for in-pipe structural health monitoring of oil/gas pipelines. This system employs a 3D Optical Laser Scanning Technical Vision System (TVS) to continuously scan the internal surface of the pipeline. This paper elaborates on the mathematical methodology of 3D laser surface scanning based on dynamic triangulation. This paper presents the mathematical framework governing the combined kinematics of the Mobile Robot (MR) and TVS. It discusses the custom design of the MR, adjusting it to use of robustized mathematics, and incorporating a laser scanner produced using a 3D printer. Both experimental and theoretical approaches are utilized to illustrate the formation of point clouds during surface scanning. This paper details the application of the simple and robust mathematical algorithm RANSAC for the preliminary processing of the measured point clouds. Furthermore, it contributes two distinct and simplified criteria for detecting defects in pipelines, specifically tailored for computer processing. In conclusion, this paper assesses the effectiveness of the proposed mathematical and physical method through experimental tests conducted under varying light conditions. Full article

(1) Background: Environmental risks such as air pollutants pose a threat to human health and must be communicated to the affected population to create awareness, such as via health literacy (HL); (2) Methods: We analyzed HL in the context of environmental health risks, including sources of information and prior knowledge, in a sample from the German general population using Kendall’s rank correlations, regression analyses, and explorative parallel mediation analysis; (3) Results: The survey included 412 German participants aged between 18 and 77. HL was found to be problematic to inadequate. The internet, family and friends, and newspapers were the most frequently cited sources of information. Mobile apps were mostly unknown but were requested by sample subjects. Although subjects expressed environmental concerns and exhibited rather good levels of knowledge, the majority perceived no risk to human health and rated air quality quite positively. Knowledge on particulate matter, the term “ultrafine particles”, and protective measures was found to be rather low. HL was associated with the use of newspapers and commercials as sources of information. The relationship between age and HL is fully mediated by the use of newspapers and information from TV commercials; (4) Conclusions: HL should be promoted by raising awareness of the health effects of environmental pollutants. In particular, the information channels preferred by the affected population should be used and further information opportunities such as apps should be publicized, e.g., through campaigns. An improved HL can assist policy makers in creating a healthier environment by empowering individuals to become more environmentally aware and protect their own health. This, in turn, has the potential to reduce health-related costs. Full article

Novel architectures incorporating transport networks and artificial intelligence (AI) are currently being developed for beyond 5G and 6G technologies. Given that the interfacing mobile and transport network nodes deliver high transactional packet volume in downlink and uplink streams, 6G networks envision adopting diverse transport networks, including non-terrestrial types of transport networks such as the satellite network, High-Altitude Platform Systems (HAPS), and DOCSIS cable TV. Hence, there is a need to match the traffic to the transport network. This paper focuses on such a matching problem and defines a method that leverages machine learning and mixed-integer linear programming. Consequently, the proposed scheme in this paper is to develop a traffic steering capability based on types of transport networks, namely, optical, satellite, and DOCSIS cable. Novel findings demonstrate a more than 90% accuracy of steered traffic to respective types of transport networks for dedicated transport network resources. Full article