Search Results (77)

The rising number of Hyaluronic acid (HA) filler applications has led to an increase in the incidence of complications, which can range from undesired aesthetic outcomes to severe vascular occlusion events. Hyaluronidase (Hyal) hydrolyzes HA molecules and represents the preferred treatment for managing complications associated with HA fillers. Although Hyal formulations are safe and effective, Hyal use for treating complications of aesthetic procedures remains off-label in many countries due to the lack of standardized protocols and varying recommendations on dosing strategies. Here, we review the use of Hyal in aesthetic medicine, focusing on the available formulations, including galenic preparations, and the associated risks. We provide an update on the current clinical practice for Hyal administration by reviewing cases reported in the literature from 2020 to 2025. We summarize the current dosage strategies and administration techniques for treating different complications, with details on newly developed protocols, the implementation of imaging guidance, and adjunctive treatments. Despite the great variability in dosage and protocols, Hyal administration is a safe and effective treatment to manage complications and undesired aesthetic outcomes caused by HA fillers. Future efforts should focus on developing standardized protocols to facilitate the decision-making process, reduce response time, and ensure successful outcomes. Full article

Background: The mortality and morbidity rates of cardiovascular disease (CVD) are rising sharply in many developed and developing countries. CVD is a fatal disease that requires early and timely diagnosis to prevent further damage and ultimately save patients’ lives. In recent years, numerous studies have explored the automated identification of different categories of CVDs using various deep learning classifiers. However, they often rely on a substantial amount of data. The lack of representative training samples in real-world scenarios, especially in developing countries, poses a significant challenge that hinders the successful training of accurate predictive models. In this study, we introduce a framework to address this gap. Methods: The core novelty of our framework is the combination of Multi-Resolution Wavelet Features with Scale-Invariant Feature Transform (SIFT) keypoint density maps and a lightweight residual attention neural network (ResAttNet). Our hybrid approach transforms one-dimensional ECG signals into a three-channel image representation. Specifically, the CWT is used to extract hidden features in the time-frequency domain to create the first two image channels. Subsequently, the SIFT algorithm is implemented to capture additional significant features to generate the third channel. These three-channel images are then fed to our custom residual attention neural network to enhance classification performance. To tackle the challenge of class imbalance present in our dataset, we employed a hybrid strategy combining the Synthetic Minority Over-sampling Technique (SMOTE) with Edited Nearest Neighbors (ENN) to balance class samples and integrated Focal Loss into the training process to help the model focus on hard-to-classify instances. Results: The performance metrics achieved using five-fold cross-validation are 99.60% accuracy, 97.38% precision, 98.53% recall, and 97.37% F1-score. Conclusions: The experimental results showed that our proposed method outperforms current state-of-the-art methods. The primary practical implication of this work is that by combining a novel, information-rich feature representation with a lightweight classifier, our framework offers a highly accurate and computationally efficient solution, making it a significant step towards developing accessible and scalable computer-aided screening tools. Full article

Globalization and market saturation have led Portuguese textile companies to seek international markets not only for growth but also to contribute to their country’s international image. This study aims to explore how the internationalization of the Portuguese textile sector into the Chinese market contributes to Portugal’s destination image and identify the critical success factors in this process. The research follows an inductive, qualitative methodology based on semi-structured interviews with two groups of companies: those already operating in China (n = 5) and those preparing to enter the market (n = 5). The interviews were thematically analyzed to extract key patterns and insights. The findings reveal that successful companies operate in the luxury segment, rely on prior international experience, and often use local intermediaries. Firms planning to internationalize highlight quality differentiation, brand authenticity, and innovation as strategic advantages. These insights support the role of niche positioning and cultural adaptation in building both commercial success and a refined international image of Portugal. This study contributes to the literature by linking internationalization and destination branding through industry-specific case evidence and offers practical implications for managers targeting emerging markets like China. Full article

As major countries around the world have successively proposed the construction goal of “Beautiful National Land Space”, how to effectively integrate urban design with spatial control in specific geographical environments and use urban design to achieve efficient spatial control has become a new research trend. The process of planning the national territory is constrained by the legal framework, involving multiple planning stages and multiple stakeholders. In an ideal state, these planning stages and stakeholders should coordinate with each other, but during the actual implementation of the plan, these factors are often not coordinated enough, making it difficult for the plan to play a role. In this study, Mile City in Yunnan Province, a representative city in the unique intermontane basin area of Southwestern China, was used as a case to explore how to use urban design methods in territorial spatial planning to achieve more efficient spatial control. This study provides scientific support for establishing an indicator control system for urban design methods by combining multiple data collection methods such as text analysis, image analysis, and interview methods. The distinctive features of Mile City have been further enhanced by optimizing its spatial layout through urban design, and it has been scientifically integrated into the territorial spatial planning system. The results indicate that the successful implementation of urban design highly relies on the reform willingness of local governments, clear control frameworks, and the coordinated integration of regional ecological resources and landscape features. This study proposed a set of urban design control methods suitable for intermontane basin-type cities and formed a comprehensive control framework including city, town, and landscape. In addition, it will provide methodological support and references for improving the scientific management of “Beautiful Land” in the special geographical environment of Southwest China. Full article

President John F Kennedy faced an impending crisis upon taking office in January 1961. The revolutionary threat of Cuba held the potential to spread to several neighboring countries. This crisis was the product of decades of neglect from successive US presidents, that ultimately invited the USSR into the region and fell to Kennedy during his first year as President. Kennedy sought to recast the image of the US in the hemisphere to inoculate against the example of Cuba. The cornerstone of this plan was the Alliance for Progress, a substantial program of economic assistance from the US to Latin America. However, that program has widely been criticized as a failure. Rather than reflect on the economic and social limitations of the Alliance for Progress, this paper will evaluate the diplomatic impact of Kennedy’s approach in forming the anti-Cuban coalition in the first year of his presidency. Kennedy successfully changed the Latin American attitude towards the US prior to the releasing of any substantial economic aid. Therefore, this paper will argue that “The Kennedy Plan” was a diplomatic success that reduced the threat of Castro’s Cuba in the context of the Cold War. Full article

Early detection of skin cancer is crucial for successful treatment and improved patient outcomes. Medical images play a vital role in this process, serving as the primary data source for both traditional and modern diagnostic approaches. This study aims to provide an overview of the significant role of medical images in skin cancer detection and highlight developments in the use of deep learning for early diagnosis. The scope of this survey includes an in-depth exploration of state-of-the-art deep learning methods, an evaluation of public datasets commonly used for training and validation, and a bibliometric analysis of recent advancements in the field. This survey focuses on publications in the Scopus database from 2019 to 2024. The search string is used to find articles by their abstracts, titles, and keywords, and includes several public datasets, like HAM and ISIC, ensuring relevance to the topic. Filters are applied based on the year, document type, source type, and language. The analysis identified 1697 articles, predominantly comprising journal articles and conference proceedings. The analysis shows that the number of articles has increased over the past five years. This growth is driven not only by developed countries but also by developing countries. Dermatology departments in various hospitals play a significant role in advancing skin cancer detection methods. In addition to identifying publication trends, this study also reveals underexplored areas to encourage new explorations using the VOSviewer and Bibliometrix applications. Full article

Rice is a vital staple in many countries, and as the demand for food diversity rises, the focus has shifted towards improving rice quality rather than just yield. This shift in breeders’ goals has led to the development of breeding populations aimed at comprehensively assessing rice grain appearance quality. In this regard, we developed an F₁₁ rice recombinant inbred line population derived from a cross between the IR28 and Shahpasand (SH) varieties and assessed the grain appearance characteristics of 151 lines and seven varieties using a computer vision system and a new generation of phenotyping tools for rapidly and accurately evaluating all grain quality-related traits. In this method, characteristics such as area, perimeter, length, width, aspect ratio, roundness, whole kernel, chalkiness, red stain, mill rate, and brown kernel were measured very quickly and precisely. To select the best lines, considering multiple traits simultaneously, we used the multi-trait genotype ideotype distance index (MGIDI) as a successful selection index. Based on the MGIDI and a 13% selection intensity, we identified 17 lines and three varieties as superior genotypes for their grain appearance quality traits. Line 59 was considered the best due to its lowest MGIDI value (0.70). Lines 19, 31, 32, 45, 50, 59, 60, 62, 73, 107, 114, 122, 125, 135, 139, 144, and 152 exhibited superior grain quality traits compared to the parents, making them high-quality candidates and indicating transgressive segregation within the current RIL population. In conclusion, the image processing technique used in this study was found to be a fast and precise tool for phenotyping in large populations, helpful in the selection process in plant breeding. Additionally, the MGIDI, by considering multiple traits simultaneously, can help breeders select high-quality genotypes that better match consumer preferences. Full article

According to data from the World Health Organization (WHO), lung cancer is becoming a global epidemic. It is particularly high in the list of the leading causes of death not only in developed countries, but also worldwide; furthermore, it holds the leading place in terms of cancer-related mortality. Nevertheless, many breakthroughs have been made the last two decades regarding its management, with one of the most prominent being the implementation of artificial intelligence (AI) in various aspects of disease management. We included 473 papers in this thorough review, most of which have been published during the last 5–10 years, in order to describe these breakthroughs. In screening programs, AI is capable of not only detecting suspicious lung nodules in different imaging modalities—such as chest X-rays, computed tomography (CT), and positron emission tomography (PET) scans—but also discriminating between benign and malignant nodules as well, with success rates comparable to or even better than those of experienced radiologists. Furthermore, AI seems to be able to recognize biomarkers that appear in patients who may develop lung cancer, even years before this event. Moreover, it can also assist pathologists and cytologists in recognizing the type of lung tumor, as well as specific histologic or genetic markers that play a key role in treating the disease. Finally, in the treatment field, AI can guide in the development of personalized options for lung cancer patients, possibly improving their prognosis. Full article

Self-esteem is a central part of personality, related to self-perceptions and evaluation of oneself compared to other people. Self-esteem could be global self-esteem, concerning the whole personality, or partial self-esteem, concerning the different aspects of personality and performance. Global self-esteem, as well as feelings of inferiority and supremacy, were compared among 197 athletes and 198 non-athletes in Bulgaria based on three self-reported questionnaires—the inferiority complex shortened scale COMPIN-10, the superiority complex shortened scale SUCOMP-10, and the single-item self-esteem scale. It was found that the athletes had significantly higher self-esteem and more strongly expressed feelings of superiority, as well as fewer experienced feelings of inferiority than the non-athletes. This may be due to athletes’ achievements and their recognition by society, as well as the social image imposed by media as rich, successful, and representatives of their country. High self-esteem is necessary for athletes to be confident in their ability to perform well during sports competitions. More years of sport experience correlated with a decrease in inferiority feelings and an increase in self-esteem. There were not any statistically significant differences between the athletes practicing individual sports and the athletes practicing team sports in their inferiority feelings, superiority feelings, or self-esteem. Full article

Background/Objectives: As reported by the World Health Organization, Mpox (monkeypox) is an important disease present in 110 countries, mostly in South Asia and Africa. The number of Mpox cases has increased rapidly, and the medical world is worried about the emergence of a new pandemic. Detection of Mpox by traditional methods (using test kits) is a costly and slow process. For this reason, there is a need for methods that have high success rates and can diagnose Mpox disease from skin images with a deep-learning-based autonomous method. Methods: In this work, we propose a multi-class, fast, and reliable autonomous disease diagnosis model using transformer-based deep learning architectures and skin lesion images, including for Mpox disease. Our other aim is to investigate the effects of self-supervised learning, self-distillation, and shifted window techniques on classification success when multi-class skin lesion images are trained with transformer-based deep learning architectures. The Mpox Skin Lesion Dataset, Version 2.0, which was publicly released in 2024, was used in the training, validation, and testing processes of the study. Results: The SwinTransformer architecture we proposed in our study achieved about 8% higher accuracy evaluation metric classification success compared to its closest competitor in the literature. ViT, MAE, DINO, and SwinTransformer architectures achieved 93.10%, 84.60%, 90.40%, and 93.71% accuracy classification success, respectively. Conclusions: The results obtained in the study showed that Mpox disease and other skin lesion images can be diagnosed with high success and can support doctors in decision-making. In addition, the study provides important results that can be used in other medical fields where the number of images is low in terms of transformer-based architecture and technique to use. Full article

Introduction: Antenatal diagnosis of cardiac abnormalities and counselling parents about postnatal care require a multidisciplinary team, which includes a paediatric cardiologist, a neonatologist, and a fetal medicine physician. Some of these kinds of expertise are not available in all centres with fetal medicine expertise. However, with modern technology, this could be provided remotely. Our objective was to assess the feasibility and outcomes of prenatal multidisciplinary tele-echocardiography diagnostic and counselling services. Materials and Methods: Two centres based in separate countries provided a joint diagnostic and counselling service over a period of 14 months. The primary centre performed the fetal echocardiography with a Voluson E10 machine, and images were transmitted live using Zoom OPS system with video-consultation and counselling. The fetal echo was performed using the ISUOG Guidelines check list. Results: There was an initial feasibility period of 2 months during which 10 women whose fetuses had normal hearts were scanned to test the workability of the system. Over a period of 12 months, 513 high-risk fetuses were then scanned, and out of these, 27 had congenital malformations. The most common were hypoplastic left heart syndrome (HHLS) and atrio-ventricular septal defect. Tele-echocardiography and counselling were successful in all the cases. Satisfaction with the service was 3.8/4, with the main limitation being the need for further referral to a tertiary centre for delivery. Conclusions: Tele-echocardiography is reliable, and when combined with live counselling and support from a paediatric cardiologist, it is an option acceptable to patients. The greatest benefit was from being counselled by a team of experts at a single consultation rather than having to travel to another centre for consultation. With rapidly evolving technology, making video transmission easier and less expensive, we feel that consideration should be given not only to the development of tele-echocardiography but also to extending it to other aspects of fetal medicine. Full article

The evaluation of crowdfunding campaigns varies from person to person; some investors are more interested in the project’s creativity, and others are more concerned with the profiles of entrepreneurs. The study investigated how entrepreneurs’ face disclosure influenced the success of crowdfunding. Secondary data were collected from multiple crowdfunding platforms for projects in Africa. That is, cross-country data from 54 African countries, to overcome data limitations from a single country. An econometrics analysis revealed that the facial disclosure of entrepreneurs increases the probability of crowdfunding success by 3%. Images, videos, and backers had a positive influence on the success of crowdfunding. On the contrary, the duration of the crowdfunding campaign was negatively associated with its success. To reduce the knowledge asymmetry between creators and backers, those prepared to start a crowdfunding project must provide as much information as possible to show their abilities. This study contributes to understanding the role of disclosing an entrepreneur’s profile on economic exchanges to the success of online crowdfunding. Full article

As the primary grain crop in China, wheat holds a significant position in the country’s agricultural production, circulation, consumption, and various other aspects. However, the presence of imperfect grains has greatly impacted wheat quality and, subsequently, food security. In order to detect perfect wheat grains and six types of imperfect grains, a method for the fast and non-destructive identification of imperfect wheat grains using hyperspectral images was proposed. The main contents and results are as follows: (1) We collected wheat grain hyperspectral data. Seven types of wheat grain samples, each containing 300 grains, were prepared to construct a hyperspectral imaging system for imperfect wheat grains, and visible near-infrared hyperspectral data from 2100 wheat grains were collected. The Savitzky–Golay algorithm was used to analyze the hyperspectral images of wheat grains, selecting 261 dimensional effective hyperspectral datapoints within the range of 420.61–980.43 nm. (2) The Successive Projections Algorithm was used to reduce the dimensions of the 261 dimensional hyperspectral datapoints, selecting 33 dimensional hyperspectral datapoints. Principal Component Analysis was used to extract the optimal spectral wavelengths, specifically selecting hyperspectral images at 647.57 nm, 591.78 nm, and 568.36 nm to establish the dataset. (3) Particle Swarm Optimization was used to optimize the Support Vector Machines model, Convolutional Neural Network model, and MobileNet V2 model, which were established to recognize seven types of wheat grains. The comprehensive recognition rates were 93.71%, 95.14%, and 97.71%, respectively. The results indicate that a larger model with more parameters may not necessarily yield better performance. The research shows that the MobileNet V2 network model exhibits superior recognition efficiency, and the integration of hyperspectral image technology with the classification model can accurately identify imperfect wheat grains. Full article

Remote sensing data represent one of the most important sources for automized yield prediction. High temporal and spatial resolution, historical record availability, reliability, and low cost are key factors in predicting yields around the world. Yield prediction as a machine learning task is challenging, as reliable ground truth data are difficult to obtain, especially since new data points can only be acquired once a year during harvest. Factors that influence annual yields are plentiful, and data acquisition can be expensive, as crop-related data often need to be captured by experts or specialized sensors. A solution to both problems can be provided by deep transfer learning based on remote sensing data. Satellite images are free of charge, and transfer learning allows recognition of yield-related patterns within countries where data are plentiful and transfers the knowledge to other domains, thus limiting the number of ground truth observations needed. Within this study, we examine the use of transfer learning for yield prediction, where the data preprocessing towards histograms is unique. We present a deep transfer learning framework for yield prediction and demonstrate its successful application to transfer knowledge gained from US soybean yield prediction to soybean yield prediction within Argentina. We perform a temporal alignment of the two domains and improve transfer learning by applying several transfer learning techniques, such as ${\mathrm{L}}^2$-SP, BSS, and layer freezing, to overcome catastrophic forgetting and negative transfer problems. Lastly, we exploit spatio-temporal patterns within the data by applying a Gaussian process. We are able to improve the performance of soybean yield prediction in Argentina by a total of 19% in terms of RMSE and 39% in terms of ${\mathrm{R}}^2$ compared to predictions without transfer learning and Gaussian processes. This proof of concept for advanced transfer learning techniques for yield prediction and remote sensing data in the form of histograms can enable successful yield prediction, especially in emerging and developing countries, where reliable data are usually limited. Full article

Two economic metrics demonstrate the actual, practical judgment of a country: on the one hand, the number of arriving tourists and overnight stays and their share within the GDP; on the other hand, the increase in FDI arriving in the country. The impact of macroenvironmental elements is becoming more and more intense in the life of each country. How a country deals with the issue of sustainability greatly influences the perception of a given destination among travelers. In other words, sustainability has become a serious element of a country’s image. Impressions of a country formulated by others build on the image the country authentically reflects about itself. This image, if successful, boosts countries’ competitiveness in all sectors. This study focuses on the liaison between tourism and a country’s brand. Researchers examined the body of networks and the interactions thereof. For this reason, the authors established a model to explain the relationship and coaction of a country’s brand and tourism. To build up research models, the authors used data from publicly available international public databases. The researchers detected areas where the improvement achieved increases the value of the country’s brand in addition to its competitiveness in tourism. Full article