Search Results (63)

Background: Age-related macular degeneration (AMD) is one of the leading causes of permanent vision loss in the elderly, particularly in higher-income countries. Fundus autofluorescence (FAF) imaging is a widely used, non-invasive technique that complements structural imaging in the assessment of retinal pigment epithelium (RPE) integrity. While optical coherence tomography (OCT) remains the gold standard for retinal imaging due to its high-resolution cross-sectional visualization, FAF offers unique metabolic insights. Among the FAF modalities, blue light FAF (B-FAF) is more commonly employed, whereas green light FAF (G-FAF) provides subtly different image characteristics, particularly improved visualization and contrast in the central macula. Despite identical acquisition times and nearly indistinguishable workflows, G-FAF is notably underutilized in clinical practice. Objectives: This narrative review critically compares green and blue FAF in terms of their diagnostic utility relative to OCT, with a focus on lesion detectability, macular pigment interference, and clinical decision-making in retinal disorders. Methods: A comprehensive literature search was performed using the PubMed database for studies published prior to February 2025. The search utilized the keywords fundus autofluorescence and age-related macular degeneration. The primary focus was on short-wavelength FAF and its clinical utility in AMD, considering three aspects: diagnosis, follow-up, and prognosis. The OCT findings served as the reference standard for anatomical correlation and diagnostic accuracy. Results: Both FAF modalities correlated well with OCT in detecting RPE abnormalities. G-FAF demonstrated improved visibility of central lesions due to reduced masking by macular pigment and enhanced contrast in the macula. However, clinical preference remained skewed toward B-FAF, driven more by tradition and device default settings than by evidence-based superiority. G-FAF’s diagnostic potential remains underrecognized despite its comparable practicality and subtle imaging advantages specifically for AMD patients. AMD stages were accurately characterized, and relevant images were used to highlight the significance of G-FAF and B-FAF in the examination of AMD patients. Conclusions: While OCT remains the gold standard, FAF provides complementary information that can guide management strategy. Since G-FAF is functionally equivalent in acquisition, it offers slight advantages. Broader awareness and more frequent integration of G-FAF that could optimize multimodal imaging strategies, particularly in the intermediate stage, should be developed. Full article

Implementation of green supply chain management (GSCM) has gained increasing attention as businesses seek to balance economic, social, and environmental sustainability. However, its adoption remains uneven across countries, particularly in developing economies such as Indonesia. This study aims to identify the key factors influencing the implementation of GSCM in Indonesian logistics companies using a qualitative approach. Data were collected via structured interviews with 14 senior management professionals from various logistics and supply chain companies. The findings reveal that, while awareness of GSCM exists, its implementation is hindered by high costs, regulatory limitations, inadequate infrastructure, and a lack of shared understanding or strategic prioritization among stakeholders, which points to a deeper organizational and policy disconnect regarding sustainability goals. Conversely, cost efficiency, brand image enhancement, and compliance with emerging regulations are identified as primary drivers of GSCM adoption. The study highlights the need for stronger government policies, financial incentives, and industry-wide collaboration to accelerate the adoption of sustainable supply chain practices. These insights contribute to both theoretical discussions on sustainable supply chain management and practical strategies for improving GSCM implementation in developing economies. Full article

This study used Chinese data to discover the causal relationship between the cultural and historical preservation and foreign tourism consumption and development. China has increased its cultural and historical protection investments and has made significant efforts in terms of environmental protection after economic growth. Tourism as an industry that develops with local environmental protection while providing economic growth is believed to be highly sustainable and attractive for many provinces to restructure their economic growth in China. This research uses empirical data from 2011 to 2019 and the regression method to show that cultural investment and environmental protection efforts have increased the amount of foreign visitors as well as the destination’s image and reputation. The results show that more cultural tourism resources and larger protection investments lead to greater tourism consumption. The cultural and historical protections have attracted foreign visitors from countries with completely different cultural backgrounds than China, particularly visitors from countries geographically far from China. Furthermore, the local service and hospitality industry grows with the development of tourism, and green innovation policies, which improve the local environment, increase tourism motivation, and develop the local economy by increasing foreign tourism consumption. This study contributes to the literature by connecting regional preservation, tourism development, and green innovation and motivates future policy decisions by demonstrating that the green policy effect stimulates tourism development; such development could alleviate the negative impact of the green innovation process on economic structural changes. Further details of cultural and historical interests from foreign visitors could aid in better understanding the tourism demand and increasing a destination’s reputation. Full article

The Normalized Difference Vegetation Index (NDVI) is an important remote sensing index that is widely used to assess vegetation coverage, monitor crop growth, and predict yields. Traditional NDVI calculation methods often rely on multispectral or hyperspectral imagery, which are costly and complex to operate, thus limiting their applicability in small-scale farms and developing countries. To address these limitations, this study proposes an NDVI estimation method based on low-cost RGB (red, green, and blue) UAV (unmanned aerial vehicle) imagery combined with deep learning techniques. This study utilizes field data from five major crops (cotton, rice, maize, rape, and wheat) throughout their whole growth periods. RGB images were used to extract conventional features, including color indices (CIs), texture features (TFs), and vegetation coverage, while convolutional features (CFs) were extracted using the deep learning network ResNet50 to optimize the model. The results indicate that the model, optimized with CFs, significantly enhanced NDVI estimation accuracy. Specifically, the R² values for maize, rape, and wheat during their whole growth periods reached 0.99, while those for rice and cotton were 0.96 and 0.93, respectively. Notably, the accuracy improvement in later growth periods was most pronounced for cotton and maize, with average R² increases of 0.15 and 0.14, respectively, whereas wheat exhibited a more modest improvement of only 0.04. This method leverages deep learning to capture structural changes in crop populations, optimizing conventional image features and improving NDVI estimation accuracy. This study presents an NDVI estimation approach applicable to the whole growth period of common crops, particularly those with significant population variations, and provides a valuable reference for estimating other vegetation indices using low-cost UAV-acquired RGB images. Full article

Urbanization has undeniably improved human living conditions but has also significantly altered the natural landscape, leading to increased Urban Heat Island (UHI) effects. While many studies have examined these impacts in other countries, research on this topic in Sri Lanka remains limited. This study aimed to evaluate the effects of changes in built-up areas (BAs) and Vegetation Cover (VC) on UHI and environmental criticality (EC) in Matara cityCity, Sri Lanka, utilizing Landsat data. This study employed the commonly used remote-sensing (RS) indices such as the land surface temperature (LST), the UHI Index, and the Environmental Criticality Index (ECI). Various techniques were utilized including supervised image classification, Urban–Rural Gradient Zone (URGZ) analysis, grid-based analysis, UHI profiles, and regression analysis. The results revealed that built-up areas increased by 12.21 km², while vegetation cover decreased by 9.94 km², and this urban expansion led to a 2.7 °C rise in mean LST over 26 years. By 2023, newly developed BA showed the highest LST and environmental criticality, with mean LST values ranging from 25 °C to 21 °C in URGZs 1 to 15 near the city center, and lower values of 15 °C to 16 °C in URGZs 40 to 47 further from the core. The correlation analysis highlighted a strong positive relationship between the NDBI and LST, underscoring the significant impact of BA expansion on LST. Consequently, high-density built-up areas are experiencing high environmental criticality. To minimize these effects, planning agencies should prioritize green urban planning strategies, particularly in high LST and environmental criticality zones. This approach can also be applied to other cities to assess the UHI and LST phenomena, with the goal of protecting the natural environment and promoting the health of urban dwellers. Full article

Background: Front-of-pack-labels (FOPLs) on packaged foods provide essential information to help consumers make informed dietary choices. However, evidence on their effectiveness, particularly in low- and middle-income countries like Kenya, is limited. Objective: This study assessed the effectiveness of three FOPLs in helping consumers identify nutrients of concern in packaged food products and influencing their purchase intention in Kenya. Methods: A total of 2198 shoppers from supermarkets in Nairobi, Mombasa, Kisumu, and Garissa were randomized into three groups: Red and Green Octagon label (RG), Red and Green Octagon with icons (RGI), and Black Octagon Warning label (WL). In the control phase, participants were shown unlabeled images of packaged foods, followed by questions. In the experimental phase, the same images were presented with one assigned FOPL, and participants responded again to the same set of questions. Differences in correct identification of nutrients of concern and changes in purchase intention were analyzed using frequency tables and Chi-Square tests, while modified Poisson regression assessed FOPL effectiveness. Results: FOPLs significantly improved correct nutrient identification and reduced the intention to purchase unhealthy foods, with the WL proving most effective. Conclusions: These findings highlight the potential of FOPLs, particularly the WL, as an effective regulatory tool for promoting healthier food choices in Kenya. Full article

Nephrite is a very precious gemstone material. As a non-renewable resource, the discovery of new nephrite deposits and the study of the genesis of nephrite have aroused great interest. A new occurrence of nephrite known as Xinyu nephrite was discovered in Xinyu Country, Jiangxi province, China. Field investigations reveal that nephrite appears in a contact zone between the Mengshan composite granitic pluton and Permian carbonate rock. The carbonate rock is calcic marble that underwent diopsidization and tremolitization. Nephrites have a light yellow-green color, weak greasy luster, are slightly-translucent to translucent, and are fine-grained. Their refractive index (RI) ranges from 1.60 to 1.61, and their specific gravity (SG) value ranges from 2.90 to 2.91, falling within the range of nephrites from Xinjiang, China. Their Mohs hardness (Hm) ranges from 5.78 to 5.83. Petrographic observations and electron probe micro analyzer (EPMA) data indicated that analyzed nephrites mainly comprise tremolite, with minor diopside, calcite, quartz, and apatite. Tremolite has a ratio of Mg/(Mg + Fe²⁺) greater than 0.99. The tremolite grains show microscopic fibrous-felted and columnar textures. Scanning electron microscope (SEM) images show some tremolite fibers interwoven in different crystallographic orientations, and some arranged in parallel. Fourier transform infrared and Raman spectroscopy features reveal the bands of minerals typical for nephrite composition. The petrographic characteristics and geological background of the Mengshan area indicate that nephrite formed through a replacement of calcic marble, which differs from the two known types (D-type: dolomite-related; S-type: serpentinite-related). Mineral replacements were common in nephrite, including diopside by tremolite, calcite by tremolite, and recrystallization of coarse by fine tremolite grains. The discovery of Xinyu nephrite occurrence complements the resource and provides an updated case for the in-depth study of the diversity of nephrite deposits. Full article

Jakarta holds the distinction of being the largest capital city among ASEAN countries and ranks as the second-largest metropolitan area in the world, following Tokyo. Despite numerous studies examining the diverse urban land use and land cover patterns within the city, the recent state of urban green spaces has not been adequately assessed and mapped precisely. Most previous studies have primarily focused on urban built-up areas and manmade structures. In this research, the first-ever detailed map of Jakarta’s urban green spaces as of 2023 was generated, with a resolution of three meters. This study employed a combination of supervised classification and evaluated two machine learning algorithms to achieve the highest accuracy possible. To achieve this, various satellite images were utilized, including VV and VH polarizations from Sentinel-1, multiple bands from Sentinel-2, and eight bands from Planet. The Planet data were subsequently transformed into the Red-Edge Triangulated Vegetation Index and Red-Edge Triangulated Wetness Index. The data training and testing samples for urban green spaces were obtained using the Street View images available on Google Maps. The results revealed that using the Random Forest classifier algorithm and only eight bands of Planet images achieved an accuracy rate of 84.9%, while a combination of multiple images achieved an impressive 95.9% accuracy rate. Jakarta’s urban areas cover approximately 33.2% of green spaces. This study provides unprecedented insights into the type, size, and spatial distribution of Jakarta’s urban green spaces, enabling urban residents and stakeholders to explore and promote healthier living and better manage these green areas. Additionally, a previously unexplored concept, Jakarta’s urban green belt, is introduced. Full article

Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients’ lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively. Full article

Lymphedema is a progressive chronic condition affecting approximately 250 million people worldwide, a number that is currently underestimated. In Western countries, the most common form of lymphedema of the extremities is cancer-related and less radical surgical intervention is the main option to prevent it. Standardized protocols in the areas of diagnosis, staging and treatment are strongly required to address this issue. The aim of this study is to review the main diagnostic methods, comparing new emerging procedures to lymphoscintigraphy, considered as the golden standard to date. The roles of Magnetic Resonance Lymphangiography (MRL) or indocyanine green ICG lymphography are particularly reviewed in order to evaluate diagnostic accuracy, potential associations with lymphoscintigraphy, and future directions guided by AI protocols. The use of imaging to treat lymphedema has benefited from new techniques in the area of lymphatic vessels anatomy; these perspectives have become of value in many clinical scenarios to prevent cancer-related lymphedema. Full article

The use of new technologies to monitor and evaluate the management of coffee crops allowed for a significant increase in productivity. Precision coffee farming has leveraged the development of this commodity by using remote sensing and Unmanned Aerial Vehicles (UAVs). However, the success of coffee farming in the country also resulted from management practices, including liming management in the soils. This study aimed to evaluate the response of coffee seedlings transplanted to areas subjected to deep liming in comparison to conventional (surface) liming, using vegetation indices (VIs) generated by multispectral images acquired using UAVs. The study area was overflown bimonthly by UAVs to measure the plant height, crown diameter, and chlorophyll content in the field. The VIs were generated and compared with the data measured in the field using linear time graphs and a correlation analysis. Linear regression was performed to predict the biophysical parameters as a function of the VIs. A significant difference was found only in the chlorophyll content. Most indices were correlated with the biophysical parameters, particularly the green chlorophyll index (GCI) and the canopy area calculated via vectorization. Therefore, UAVs proved to be effective coffee monitoring tools and can be recommended for coffee producers. Full article

In recent years, ecological design has emerged as an innovative approach for landscape designs to address urban environmental issues such as biodiversity protection and the promotion of ecosystem services. However, in developing countries like Vietnam, an ecological approach is still in its early stages and requires more research and practical application. This study aims to explore stakeholder perspectives and identify suitable ecological landscape approaches through semi-structured interviews based on designed images. The findings reveal various challenges to implementing ecological designs in the public green spaces of Hue City, such as the prioritisation of short-term goals over ecosystem services, solely focusing on increasing green per capita, the lack of market interest, and the lack of motivation among different departments responsible for the design and management of public green spaces. In addition, the study also finds that stakeholders are willing to accept a hybrid ecological landscape approach in combination with ‘cues to care’ landscapes, such as buffer zones of well-managed vegetation or regularly cut lawns. Results highlight the necessity of prioritising ecosystem services in decision-making, policy, and planning development concerning urban green spaces in Vietnamese cities. In addition, education and awareness campaigns are needed for the public and stakeholders to increase acceptance of ecological design. Full article

In order for a country’s economy to grow, agricultural development is essential. Plant diseases, however, severely hamper crop growth rate and quality. In the absence of domain experts and with low contrast information, accurate identification of these diseases is very challenging and time-consuming. This leads to an agricultural management system in need of a method for automatically detecting disease at an early stage. As a consequence of dimensionality reduction, CNN-based models use pooling layers, which results in the loss of vital information, including the precise location of the most prominent features. In response to these challenges, we propose a fine-tuned technique, GreenViT, for detecting plant infections and diseases based on Vision Transformers (ViTs). Similar to word embedding, we divide the input image into smaller blocks or patches and feed these to the ViT sequentially. Our approach leverages the strengths of ViTs in order to overcome the problems associated with CNN-based models. Experiments on widely used benchmark datasets were conducted to evaluate the proposed GreenViT performance. Based on the obtained experimental outcomes, the proposed technique outperforms state-of-the-art (SOTA) CNN models for detecting plant diseases. Full article

Starting with European colonization, African natural resources in particular and nature in general have been coveted and exploited mainly in the interest of Euro-American industrialized countries, with China as a recent major player from Asia. Interestingly, the incessant quest by some Western NGOs, institutions, and governments to protect and conserve African nature not only are inspired by ecological and climatic concerns but also often tend to propagate a false image of Africa as the last Eden of the earth in order to control Africa’s resources. Using literary texts, this article argues that some Euro-American transnational NGOs and some of their governments sometimes conspire with some African governments to spread global capitalism and green colonialism under the pretext of oxymoronic sustainable development as they attempt to conserve a mythical African Eden. Utilizing three novels and one play from the Congo Basin, namely In Koli Jean Bofane’s Congo Inc.: Le Testament de Bismarck (2014), Assitou Ndinga’s Les Marchands du développement durable (2006), Étienne Goyémidé’s Le Silence de la forêt ([1984] 2015), and Ekpe Inyang’s The Last Hope (2011), I contend that such Euro-American environmental NGOs and their governments sometimes impose and sustain fortress conservation (creation of protected areas) in the Congo Basin as a hidden means of coopting Africa’s nature and Africans into neoliberal capitalism. For the most part, instead of protecting the Congo Basin, green colonialists and developmentalists sell sustainable development, undermine alternative ways of achieving human happiness, and perpetuate epistemicide, thus leading to poverty and generating resentment among local and indigenous populations. As these literary texts suggest, nature conservation and sustainable development in the Congo Basin should not be imposed upon from the outside; they should emanate from Africans, tapping into local expertise, and indigenous and other knowledge systems. Full article

Lymphedema is a chronic progressive disorder that significantly compromises patients’ quality of life. In Western countries, it often results from cancer treatment, as in the case of post-radical prostatectomy lymphedema, where it can affect up to 20% of patients, with a significant disease burden. Traditionally, diagnosis, assessment of severity, and management of disease have relied on clinical assessment. In this landscape, physical and conservative treatments, including bandages and lymphatic drainage have shown limited results. Recent advances in imaging technology are revolutionizing the approach to this disorder: magnetic resonance imaging has shown satisfactory results in differential diagnosis, quantitative classification of severity, and most appropriate treatment planning. Further innovations in microsurgical techniques, based on the use of indocyanine green to map lymphatic vessels during surgery, have improved the efficacy of secondary LE treatment and led to the development of new surgical approaches. Physiologic surgical interventions, including lymphovenous anastomosis (LVA) and vascularized lymph node transplant (VLNT), are going to face widespread diffusion. A combined approach to microsurgical treatment provides the best results: LVA is effective in promoting lymphatic drainage, bridging VLNT delayed lymphangiogenic and immunological effects in the lymphatic impairment site. Simultaneous VLNT and LVA are safe and effective for patients with both early and advanced stages of post-prostatectomy LE. A new perspective is now represented by the combination of microsurgical treatments with the positioning of nano fibrillar collagen scaffolds (BioBridgeTM) to favor restoring the lymphatic function, allowing for improved and sustained volume reduction. In this narrative review, we proposed an overview of new strategies for diagnosing and treating post-prostatectomy lymphedema to get the most appropriate and successful patient treatment with an overview of the main artificial intelligence applications in the prevention, diagnosis, and management of lymphedema. Full article