Diabetes is a chronic metabolic disease affecting millions worldwide. It is characterized by a lack of insulin production or impaired insulin function, leading to elevated blood glucose levels. Conventional treatment methods for diabetes management typically include lifestyle changes and medications. However, alternative therapies have gained attention in recent years, including traditional medicine containing bioactive compounds, supplements like vitamin D and Omega-3 fatty acids, aromatherapy, and homeopathy. Diabetic complications are common in patients with uncontrolled diabetes and can lead to serious health problems, including diabetic retinopathy, impaired wound healing, kidney disease, nerve damage, and cardiovascular disease. Alternative remedies, such as traditional medicine containing bioactive compounds, supplements, and aromatherapy, have been studied for their potential benefits in managing these complications. Traditional medicines like bitter melon, cinnamon, and fenugreek have been shown to have anti-diabetic effects due to their bioactive compounds. Similarly, supplements like vitamin D and Omega-3 fatty acids have been found to improve glycemic control in patients with diabetes. Aromatherapy, which involves the use of essential oils, has also been explored for its potential benefits in diabetes management. Homeopathy, which uses highly diluted substances to stimulate the body's natural healing abilities, has been used to treat diabetes-related symptoms like neuropathy and wounds. Personalized care is essential in natural diabetes management because each person's body and health needs are unique. A holistic approach that addresses the individual's physical, emotional, and spiritual well-being is essential. As research in this field continues to expand, a more comprehensive understanding of diabetes management will lead to improved outcomes for those living with this condition.

Abstract In recent years, Visual Question Answering (VQA) has gained significant attention for its diverse applications, including intelligent car assistance, aiding visually impaired individuals, and document image information retrieval using natural language queries. VQA requires effective integration of information from questions and images to generate accurate answers. Neural models for VQA have made remarkable progress on large-scale datasets, with a primary focus on resource-rich languages like English.To address this, we introduce the ViCLEVR dataset, a pioneering collection for evaluating various visual reasoning capabilities in Vietnamese while mitigating biases. The dataset comprises over 26,000 images and 30,000 question-answer pairs (QAs), each question annotated to specify the type of reasoning involved. Leveraging this dataset, we conduct a comprehensive analysis of contemporary visual reasoning systems, offering valuable insights into their strengths and limitations.Furthermore, we present PhoVIT, a comprehensive multimodal fusion that identifies objects in images based on questions. The architecture effectively employs transformers to enable simultaneous reasoning over textual and visual data, merging both modalities at an early model stage. The experimental findings demonstrate that our proposed model achieves state-of-the-art performance across seven evaluation metrics.

Automated techniques for evaluating sports activities inside dynamic frames are highly dependent on advanced sports analysis by smart machines. The monitoring of individuals and the discerning of athletic pursuits has several potential applications. Monitoring individuals, detecting unusual behavior, identifying medical issues, and tracking patients within healthcare facilities are examples of these applications. An assessment of the feasibility of integrating smart real-time monitoring systems across a variety of athletic environments is provided in this study. Motion and activity detection for recording sporting events has advanced due to the need for a large amount of both real-time and offline data. Through the use of deformable learning approaches, we extend conventional deep learning models to accurately detect and analyze human behavior in sports. Due to its robustness, efficiency, and statistical analysis, the system is a highly suitable option for advanced sports recording detection frameworks. It is essential for sports identification and administration to have a comprehensive understanding of action recognition. An accurate classification of human activities and athletic events can be achieved through the use of a hybrid deep learning framework presented in this study. Using innovative methodologies, we conduct cutting-edge research on action recognition that prioritizes users’ preferences and needs. It is possible to reduce the error rate to less than 3% by using the recommended structure and the three datasets mentioned above. It is 97.84% accurate for UCF-Sport, 97.75% accurate for UCF50, and 98.91% accurate for YouTube. The recommended optimized networks have been tested extensively compared to other models for recognizing athletic actions.

The factors influencing stock returns have been extensively studied in the current academic sphere. In this study, the factor analysis method is employed to assess the investment value of stocks in the Science and Technology Innovation Board market (STAR Market) using indicators such as asset-liability ratio, current ratio, quick ratio, gross profit amount, YoY growth rate of net assets, asset return rate A, return on equity ratio ROEA, operating net profit margin, earnings per share of net assets, sales operating profit margin, and YoY growth rate of total assets. Factor scores are derived using principal component analysis. The obtained factor scores are ranked in descending order, revealing that Juchen Corporation holds a factor score of 1.11, ranking first among all companies and demonstrating the highest investment value. Following closely are EastMicro Semiconductor and Baochu Electronics. Additionally, this paper summarizes the stock performance of 20 other companies, offering investors a reference for value-based investment decisions. The quantitative findings of this study facilitate informed decision-making for investors, serving as a valuable reference for their investment strategies.

In recent years, unmanned aerial vehicles (UAVs) have undergoing experienced remarkable advancements. Nevertheless, the growing utilization of UAVs brings forth potential security threats to the public, particularly in private and sensitive locales. To address these emerging hazards, we introduce a low-cost, three-stage UAV detection framework for monitoring invading UAVs in vulnerable zones. This framework devised through an exhaustive investigation of the Chinese UAV market. Various scenarios were examined to evaluate the effectiveness of the framework, and it was subsequently implemented on a portable board. Experiments demonstrated that the proposed framework can detect invading UAVs at an early stage, even in stealthy mode. As such, the framework has the potential to be applied in the formulation of a portable surveillance system for a UAV-restricted region.

Over the past few decades, there have been several successful methods developed for steganography. One popular technique is the insertion method, which is favored for its simplicity and ability to hold a reasonable amount of hidden data. This study introduces an adaptive insertion technique based on the two-dimensional discrete Haar filter (2D DHF). The technique involves transforming the cover image into the wavelet domain using 2D DWT and selecting a predetermined number of coefficients to embed the binary secret message. The selection process is carried out by analyzing the cover image in two non-orthogonal domains: 2D discrete cosine transform and 2D DHF. An adaptive algorithm is employed to minimize the impact on the unrepresented parts of the cover image. The algorithm determines the weights of each coefficient in each domain, and coefficients with low weights are chosen for embedding. To evaluate the effectiveness of the proposed approach, samples from the BOSSbase and custom databases are used. The technique’s performance is measured using three metrics: mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Additionally, a visual inspection by humans is conducted to assess the resulting image. The results demonstrate that the proposed approach outperforms recently reported methods in terms of MSE, PSNR, SSIM, and visual quality.

BackgroundSMYD2 is a protein of the SET and MYND domain-containing family SMYD. It can methylate the lysine residue of various histone and nonhistone cancer-related proteins and plays a critical role in tumorigenesis. Although emerging evidence supports the association of SMYD2 in the progression of cancers, but its definitive effect is not yet clear. Therefore, further study of the gene in relation with cancer progression needs to be conducted. In the current study, investigators used TCGA data to determine the potential carcinogenic effect of SMYD2 in 11 cancer types. The transcriptional expression, survival rate, mutations, enriched pathways, and Gene Ontology of the SMYD2 were explored using different bioinformatics tools and servers. In addition, we also examined the correlation between SMYD2 gene expression and immunocyte infiltration in multiple cancer types. ResultsFindings revealed that higher expression of SMYD2 was significantly correlated with cancer incidents. In CESC and KIRC, the mRNA expression of SMYD2 was significantly correlated with overall survival (OS). In BRCA, KIRC, COAD, and HNSC, the mRNA expression of SMYD2 was significantly correlated with disease-free survival (DFS). We detected 15 missense, 4 truncating, 4 fusions, and 1 splice type of mutation. The expression of SMYD2 was significantly correlated with tumor purity and immunocyte infiltration in six cancer types. The gene GNPAT was highly associated with SMYD2. Significant pathways and Gene Ontology (GO) terms for co-expressed genes were associated to various processes linked with cancer formation. ConclusionCollectively, our data-driven results may provide reasonably comprehensive insights for understanding the carcinogenic effect of SMYD2. It suggests that SMYD2 might be used as a significant target for identifying new biomarkers for various human tumors.

ABSTRACT This research attempts to investigate corporate social responsibility (CSR) and consumers’ advocacy behavior (CAB) association in the hotel services sector of a developing economy. Furthermore, this study explores how hotel consumers’ emotions and values mediate and moderate their CAB. This is accomplished by introducing two mediating variables, consumer happiness and warm glow, and altruistic values as a moderator. The data were obtained from hotel consumers with the help of an adapted questionnaire. The structural analysis indicated that CSR communication on social media by hotel services organizations positively predicted CAB, while consumer happiness and warm glow served as mediators to explain this relationship. Our results also confirmed the moderating effect of altruistic values. Several theoretical and practical implications can be drawn from this study. For example, our study highlights the important role of social media as an effective medium to communicate CSR-related information with hotel consumers to bolster their advocacy intentions.

This study investigates the origin of the zonal asymmetry in stratospheric ozone trends at northern high latitudes, identified in satellite limb observations over the past two decades. We use a merged dataset consisting of ozone profiles retrieved at the University of Bremen from SCIAMACHY and OMPS-LP measurements to derive ozone trends. We also use TOMCAT chemical transport model (CTM) simulations, forced by ERA5 reanalyses, to investigate the factors which determine the asymmetry observed in the long-term changes. By studying seasonally and longitudinally resolved observation-based ozone trends, we find, especially during spring, a well-pronounced asymmetry at polar latitudes, with values up to +6 % per decade over Greenland and -5 % per decade over western Russia. The control CTM simulation agrees well with these observed trends, whereas sensitivity simulations indicate that chemical mechanisms, involved in the production and removal of ozone, or their changes, are unlikely to explain the observed behaviour. The decomposition of TOMCAT ozone time series and of ERA5 geopotential height into the first two wavenumber components shows a clear correlation between the two variables in the middle stratosphere and demonstrates a weakening and a shift in the wavenumber-1 planetary wave activity over the past two decades. Finally, the analysis of the polar vortex position and strength points to a decadal oscillation with a reversal pattern at the beginning of the century, also found in the ozone trend asymmetry. This further stresses the link between changes in the polar vortex position and the identified ozone trend pattern.