Search Results (1,254)

Background: Children with special needs often need tailored approaches to oral healthcare to address their unique needs effectively. It is essential to analyze the effectiveness of non-pharmacological management in reducing dental anxiety among special needs children during dental treatment. Methods: Five electronic databases, PubMed, Scopus, Web of Science, Embase, and Google Scholar, were searched from 2007 to August 2024 for randomized control trials and observational studies comparing the effectiveness of non-pharmacological techniques in reducing dental anxiety during invasive and noninvasive dental treatment. The primary outcomes of the studied intervention were reduced dental anxiety and improved behavior during dental treatment. The conceptual map was created to understand the need for assessment and behavior management for special needs children (SN). Results: Nineteen articles qualified for the final analysis from 250 screened articles. Included studies evaluated the effect of strategies applied clinically, such as audio–visual distraction, sensory-adapted environment, and virtual reality. The included studies measured the trivial to large effect of measured interventions and supported non-pharmacological interventions in clinical settings. Conclusions: Most basic non-pharmacological interventions showed a trivial to large reduction in dental anxiety among SN patients. The conceptual map developed in this study supports the need for non-pharmacological interventions as they are cost-effective and create a positive environment in dental clinics. However, more studies need to focus on non-pharmacological behavior interventions in SN children to support the findings of this scoping review. Full article

This study introduces an innovative, non-contact method for classifying the hardness of austenitic stainless steels (grade AISI 304) based on their intrinsic magnetic fields. Utilizing a 3 × 3 matrix sensor system, this research captures weak magnetic fields to produce precise 2D magnetic field maps of the samples. A key advancement is the application of a modified GoogleNet convolutional neural network, optimized with the stochastic gradient descent with momentum algorithm, which achieves exceptional classification accuracy, ranging from 95% to 100%, and median accuracies of 97.5% to 99%. This method stands out by revealing a novel correlation between annealing temperature and magnetic field strength, particularly a pronounced decline in magnetic properties at temperatures near 1000 °C. This observation underscores the sensitivity of magnetic profiles to heat treatments, offering a groundbreaking approach to material characterization. By enabling reliable, efficient, and fully automated hardness evaluation based on magnetic signatures, this work has the potential to transform materials engineering and manufacturing, setting a new benchmark for non-destructive material analysis techniques. Full article

Lebanon benefits from a rich biodiversity, with medicinal and aromatic plants (MAPs) representing an important part of the country’s natural wealth; however, limited data are available documenting medicinal plants being employed in eye health. This review is the first to document Lebanese medicinal plants with ophthalmic characteristics and phytochemistry that might be beneficial in the development of new, accessible, and efficient ocular medications. In this study, we searched for studies on ocular therapeutic plants using known resources, including PubMed, ScienceDirect, and Google Scholar, and confirmed these plants’ presence within the Lebanese flora. The efficacy of 52 species from 28 families, including two endemic species (Crepis libanotica and Salvia libanotica), has been documented. Their Latin names, regional names, ocular medical applications, the plant parts used, and preparation forms are detailed below. The largest number of species belongs to the Lamiaceae family (21%), followed by Asteraceae (14%) and Solanaceae (7%). The most commonly used plant parts are the stems, leaves, and seeds. Ocular treatments fall into several categories: inflammation, infection, irritation, dry-eye, eyewash, the prevention or delay of cataracts, and general eye problems. A significant percentage (68%) of the medicinal plants target the anterior part of the eye. Some of the reported plants can be harmful to the eyes and should be handled with caution. The Lebanese medicinal plants listed, constituting a local heritage with global importance, could be used for treating ophthalmic ailments and require special screening and preservation. Full article

Quantifying and mapping evapotranspiration (ET) from land surfaces is crucial in the context of climate change. For decades, remote sensing data have been utilized to estimate ET, leading to the development of numerous algorithms. However, their application is still non-trivial, mainly due to practical constraints. This paper introduces geeSSEBI, a Google Earth Engine implementation of the S-SEBI (Simplified Surface Energy Balance Index) model, for deriving ET from Landsat data and ERA5-land radiation. The source code and a graphical user interface implemented as a Google Earth Engine application are provided. The model ran on 871 images, and the estimates were evaluated against multiyear data of four eddy covariance towers belonging to the ICOS network, representative of both forests and agricultural landscapes. The model showed an RMSE of approximately 1 mm/day, and a significant correlation with the observed values, at all the sites. A procedure to upscale the data to monthly is proposed and tested as well, and its accuracy evaluated. Overall, the model showed acceptable accuracy, while performing better on forest ecosystems than on agricultural ones, especially at daily and monthly timescales. This implementation is particularly valuable for mapping evapotranspiration in data-scarce environments by utilizing Landsat archives and ERA5-land radiation estimates. Full article

The construction industry has endured high incident rates for many decades. Although multiple safety measures in the form of Safety I- and II-enabled safe systems of working (SSoWs) have been implemented, statistics reveal that a significant prevalence of incidents prevails worldwide. However, there is limited information available about the actual factors that are impeding these SSoWs. This study investigates and evaluates the barriers, bottlenecks and challenges (BB&Cs) that hinder the implementation of Safety I- and II-enabled SSoWs in the construction industry. Using a scoping review methodology, a thorough search of articles documenting the BB&Cs of implementing Safety I- and II-enabled SSoWs was carried out using Google Scholar, Scopus, and Web of Science databases. An initiative model was employed for categorising BB&C to implement Safety I and II, which includes micro- (site), meso- (organisation), and macro (environment)-thematic groupings, as a guiding framework for the mapping and analysis of results. The search yielded 98 articles that discussed the implementation of Safety I and II, with 54 of them specifically related to BB&Cs. Emergent results emphasised how there is scant literature on the BB&Cs of implementation Safety I- and II-enabled SSoWs across site, organisation and environment levels. Extensive global research is necessary to comprehensively understand the obstacles to implementing Safety I and II in practice as a first step towards reducing incidents and accidents on site. Cumulatively, the findings suggest that implementing Safety I- and II-enabled SSoWs should be based on removing BB&Cs and evaluating how they affect safety performance. Full article

This study explores the dynamics of urban green spaces in five major Indonesian cities—Central Jakarta, Bandung, Yogyakarta, Surabaya, and Semarang—using Sentinel-2 satellite imagery and vegetation indices, such as NDVI and EVI. As major urban areas expand and become more densely populated, development activities have significantly altered urban green spaces, necessitating comprehensive mapping through remote sensing technologies. The findings reveal significant variability in green space coverage among the cities over three periods (2019–2020, 2021–2022, 2023–2024), ensuring that the findings are comprehensive and up to date. This study demonstrates the utility of remote sensing for detailed urban analysis, emphasizing its effectiveness in identifying, quantifying, and monitoring changes in green spaces. Integrating advanced techniques, such as NDVI and EVI, offers a nuanced understanding of urban vegetation dynamics and their implications for sustainable urban planning. Utilizing Sentinel-2 data within the Google Earth Engine (GEE) framework represents a contemporary and innovative approach to urban studies, particularly in rapidly urbanizing environments. The novelty of this research lies in its method of preserving and enhancing green infrastructure while supporting the development of effective strategies for sustainable urban growth. Full article

In urban environments, eating and drinking out (EDO) is a widespread activity among residents and visitors, generating a wealth of digital footprints that reflect consumer experiences. These digital traces provide businesses with opportunities to enhance their services and guide entrepreneurs in selecting optimal locations for new establishments. This study investigates the relationship among urban spatial features, pedestrians and digital consumer interactions at EDO venues. It highlights the utility of integrating urban mobility and spatial data to model digital consumer behavior, offering potential urban planning and business strategies. By analyzing Melbourne’s city center, we evaluate how factors, such as pedestrian count by sensors on the streets, residential density, the centralities and geometric properties of streets, and place-specific characteristics, influence consumer reviews and ratings on Google Maps. The study employs a random forest machine learning model to predict review volumes and ratings, categorized into high and low classes. The results indicate that pedestrian counts and residential density are key predictors for both metrics, while centrality measures improve the prediction of visitor scores but negatively impact review volume predictions. The geometric features of streets play varying roles across different prediction tasks. The model achieved a 65% F1-score for review volume classifications and a 62% for visitor score. These findings not only provide actionable understanding for urban planners and business stakeholders but also contribute to a deeper understanding of how spatial dynamics affect digital consumer behavior, paving the way for more sustainable urban development and data-driven decision-making. Full article

Land cover mapping is essential to understanding global land-use patterns and studying biodiversity composition and the functioning of eco-systems. The introduction of remote sensing technologies and artificial intelligence models made it possible to base land cover mapping on satellite imagery in order to monitor changes, assess ecosystem health, support conservation efforts, and reduce monitoring time. However, significant challenges remain in managing large, complex satellite imagery datasets, acquiring specialized datasets due to high costs and labor intensity, including a lack of comparative studies for the selection of optimal deep learning models. No less important is the scarcity of aerial datasets specifically tailored for agricultural areas. This study addresses these gaps by presenting a methodology for semantic segmentation of land covers in agricultural areas using satellite images and deep learning models with pre-trained backbones. We introduce an efficient methodology for preparing semantic segmentation datasets and contribute the “Land Cover Aerial Imagery” (LICAI) dataset for semantic segmentation. The study focuses on the Franciacorta area, Lombardy Region, leveraging the rich diversity of the dataset to effectively train and evaluate the models. We conducted a comparative study, using cutting-edge deep-learning-based segmentation models (U-Net, SegNet, DeepLabV3) with various pre-trained backbones (ResNet, Inception, DenseNet, EfficientNet) on our dataset acquired from Google Earth Pro. Through meticulous data acquisition, preprocessing, model selection, and evaluation, we demonstrate the effectiveness of these techniques in accurately identifying land cover classes. Integrating pre-trained feature extraction networks significantly improves performance across various metrics. Additionally, addressing challenges such as data availability, computational resources, and model interpretability is essential for advancing the field of remote sensing, in support of biodiversity conservation and the provision of ecosystem services and sustainable agriculture. Full article

In location-based augmented reality (LAR) applications, a simple and effective authoring tool is essential to create immersive AR experiences in real-world contexts. Unfortunately, most of the current tools are primarily desktop-based, requiring manual location acquisitions, the use of software development kits (SDKs), and high programming skills, which poses significant challenges for novice developers and a lack of precise LAR content alignment. In this paper, we propose an intuitive in situ authoring tool with visual-inertial sensor fusions to simplify the LAR content creation and storing process directly using a smartphone at the point of interest (POI) location. The tool localizes the user’s position using smartphone sensors and maps it with the captured smartphone movement and the surrounding environment data in real-time. Thus, the AR developer can place a virtual object on-site intuitively without complex programming. By leveraging the combined capabilities of Visual Simultaneous Localization and Mapping(VSLAM) and Google Street View (GSV), it enhances localization and mapping accuracy during AR object creation. For evaluations, we conducted extensive user testing with 15 participants, assessing the task success rate and completion time of the tool in practical pedestrian navigation scenarios. The Handheld Augmented Reality Usability Scale (HARUS) was used to evaluate overall user satisfaction. The results showed that all the participants successfully completed the tasks, taking 16.76 s on average to create one AR object in a 50 m radius area, while common desktop-based methods in the literature need 1–8 min on average, depending on the user’s expertise. Usability scores reached 89.44 for manipulability and 85.14 for comprehensibility, demonstrating the high effectiveness in simplifying the outdoor LAR content creation process. Full article

The extensive peatlands of the Tibetan Plateau (TP) play a vital role in sustaining the global ecological balance. However, the distribution of peatlands across this region and the related environmental factors remain poorly understood. To address this issue, we created a high-resolution (10 m) map for peatland distribution in the TP region using 6146 Sentinel-1 and 23,730 Sentinel-2 images obtained through the Google Earth Engine platform in 2023. We employed a random forest algorithm that integrated spatiotemporal features with field training samples. The overall accuracy of the peatland distribution map produced is high, at 86.33%. According to the classification results, the total area of peatlands on the TP is 57,671.55 km², and they are predominantly located in the northeast and southwest, particularly in the Zoige Protected Area. The classification primarily relied on the NDVI, NDWI, and RVI, while the DVI and MNDWI were also used in peatland mapping. B11, B12, NDWI, RVI, NDVI, and slope are the most significant features for peatland mapping, while roughness, correlation, entropy, and ASM have relatively slight significance. The methodology and peatland map developed in this work will enhance the conservation and management of peatlands on the TP while informing policy decisions and supporting sustainable development assessments. Full article

Land use and land cover (LULC) changes are significantly impacting the natural environment. Human activities and population growth are negatively impacting the natural environment. This negative impact directly relates to climate change, sustainable agriculture, inflation, and food security at local and global levels. Remote sensing and GIS tools can provide valuable information about change detection. This study examines the correlation between population growth rate and LULC dynamics in three districts of South Punjab, Pakistan—Multan, Bahawalpur, and Dera Ghazi Khan—over a 30-year period from 2003 to 2033. Landsat 7, Landsat 8, and Sentinel-2 satellite imagery within the Google Earth Engine (GEE) cloud platform was utilized to create 2003, 2013, and 2023 LULC maps via supervised classification with a random forest (RF) classifier, which is a subset of artificial intelligence (AI). This study achieved over 90% overall accuracy and a kappa value of 0.9 for the classified LULC maps. LULC was classified into built-up, vegetation, water, and barren classes in Multan and Bahawalpur, with an additional “rock” class included for Dera Ghazi Khan due to its unique topography. LULC maps (2003, 2013, and 2023) were prepared and validated using Google Earth Engine. Future predictions for 2033 were generated using the MOLUSCE model in QGIS. The results for Multan indicated substantial urban expansion as built-up areas increased from 8.36% in 2003 to 25.56% in 2033, with vegetation and barren areas displaying decreasing trends from 82.96% to 70% and 7.95% to 3.5%, respectively. Moreover, areas containing water fluctuated and ultimately changed from 0.73% in 2003 to 0.9% in 2033. In Bahawalpur, built-up areas grew from 1.33% in 2003 to 5.80% in 2033, while barren areas decreased from 79.13% to 74.31%. Dera Ghazi Khan expressed significant increases in built-up and vegetation areas from 2003 to 2033 as 2.29% to 12.21% and 22.53% to 44.72%, respectively, alongside reductions in barren and rock areas from 32.82% to 10.83% and 41.23% to 31.2%, respectively. Population projections using a compound growth model for each district emphasize the demographic impact on LULC changes. These results and findings focus on the need for policies to manage unplanned urban sprawl and focus on environmentally sustainable practices. This study provides critical awareness to policy makers and urban planners aiming to balance urban growth with environmental sustainability. Full article

Google Earth Engine (GEE) is a cloud-based platform revolutionizing geospatial analysis by providing access to vast satellite datasets and computational capabilities for monitoring environmental and societal issues. It incorporates machine learning (ML) techniques and algorithms as part of its tools for analyzing and processing large geospatial data. This review explores the diverse applications of GEE in monitoring and mitigating greenhouse gas emissions and uptakes. GEE is a cloud-based platform built on Google’s infrastructure for analyzing and visualizing large-scale geospatial datasets. It offers large datasets for monitoring greenhouse gas (GHG) emissions and understanding their environmental impact. By leveraging GEE’s capabilities, researchers have developed tools and algorithms to analyze remotely sensed data and accurately quantify GHG emissions and uptakes. This review examines progress and trends in GEE applications, focusing on monitoring carbon dioxide (CO₂), methane (CH₄), and nitrous oxide/nitrogen dioxide (N₂O/NO₂) emissions. It discusses the integration of GEE with different machine learning methods and the challenges and opportunities in optimizing algorithms and ensuring data interoperability. Furthermore, it highlights GEE’s role in pinpointing emission hotspots, as demonstrated in studies monitoring uptakes. By providing insights into GEE’s capabilities for precise monitoring and mapping of GHGs, this review aims to advance environmental research and decision-making processes in mitigating climate change. Full article

The needs of night communities and the barriers they face in accessing diverse urban amenities are underexplored in urban planning research. Focus is primarily given to the needs of cultural consumers, frequently overlooking the challenges faced by regular nighttime communities, including night workers. Through a GIS-based analysis, the aim of this research is to shed light on differences in accessibility to core urban services between day and night in the city of Milan. The spatiotemporal analysis was performed using a customized version of the 15-min City Score Toolkit, an open-source, Python-based proprietary tool developed to automate the 15 min access metric estimation. Proprietary Point-Of-Interest (POI) data that were retrieved, sorted and filtered from the Google Places API are used to simulate time-variant walkability maps based on opening hour information contained in the dataset. The research reveals significant differences in walkability potential, both in spatial and temporal terms, and highlights gaps in nighttime service availability. The work presents an innovation on the 15 min city approach that highlights the impact of 24-h urban rhythms on real walkability outcomes. The quality limitations of the Google data are extensively explored in the article, providing further insight into the replicability and scalability of the methodology for future research. Full article

Background: Universal health coverage (UHC) is a global priority, with the goal of ensuring that everyone has access to high-quality healthcare without suffering financial hardship. In Africa, most governments have prioritized UHC over the last two decades. Despite this, the transition to UHC in Africa is seen to be sluggish, with certain countries facing inertia. This study sought to examine the progress of UHC-focused health reform implementation in Africa, investigating the approaches utilized, the challenges faced, and potential solutions. Method: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines, we scoped the literature to map out the evidence on UHC adoption, roll out, implementation, challenges, and opportunities in the African countries. Literature searches of the Cochrane database of systematic reviews, PUBMED, EBSCO, Eldis, SCOPUS, CINHAL, TRIP, and Google Scholar were conducted in 2023. Using predefined inclusion criteria, we focused on UHC adoption, rollout, implementation, and challenges and opportunities in African countries. Primary qualitative, quantitative, and mixed-methods evidence was included, as well as original analyses of secondary data. We employed thematic analysis to synthesize the evidence. Results: We found 9633 documents published between May 2005 and December 2023, of which 167 papers were included for analysis. A significant portion of UHC implementation in Africa has focused on establishing social health protection schemes, while others have focused on strengthening primary healthcare systems, and a few have taken integrated approaches. While progress has been made in some areas, considerable obstacles still exist. Financial constraints and supply-side challenges, such as a shortage of healthcare workers, limited infrastructure, and insufficient medical supplies, remain significant barriers to UHC implementation throughout Africa. Some of the promising solutions include boosting public funding for healthcare systems, strengthening public health systems, ensuring equity and inclusion in access to healthcare services, and strengthening governance and community engagement mechanisms. Conclusion: Successful UHC implementation in Africa will require a multifaceted approach. This includes strengthening public health systems in addition to the health insurance schemes and exploring innovative financing mechanisms. Additionally, addressing the challenges of the informal sector, inequity in healthcare access, and ensuring political commitment and community engagement will be crucial in achieving sustainable and comprehensive healthcare coverage for all African citizens. Full article

Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on flood classification, efficiently and automatically processing multi-source imagery to generate reliable flood inundation maps remains challenging. In this study, a new automatic flood monitoring method, utilizing optical and Synthetic Aperture Radar (SAR) imagery, was developed based on the Google Earth Engine (GEE) cloud platform. The Normalized Difference Flood Vegetation Index (NDFVI) was innovatively combined with the Edge Otsu segmentation method, utilizing SAR imagery, to enhance the initial accuracy of flood area mapping. To more effectively distinguish flood areas from non-seasonal water bodies, such as lakes, rivers, and reservoirs, pre-flood Landsat-8 imagery was analyzed. Non-seasonal water bodies were classified using multi-index methods and water body probability distributions, thereby further enhancing the accuracy of flood mapping. The method was applied to the catastrophic floods in Poyang Lake, Jiangxi Province, in 2020, and East Dongting Lake, Hunan Province, China, in 2024. The results demonstrated classification accuracies of 92.6% and 97.2% for flood inundation mapping during the Poyang Lake and East Dongting Lake events, respectively. This method offers efficient and precise information support to decision-makers and emergency responders, thereby fully demonstrating its substantial potential for practical applications. Full article