The causal relationships between components of customer-based brand equity for a destination: Evidence from South Korean tourists in Danang city, Vietnam

The main purpose of this study is to examine the causal relationships between components of customer-based brand equity for a tourist destination. We have collected data from 252 South Korean tourists in Danang City and tested some hypotheses by applying structural equation modeling (SEM). Results show that: (1) destination brand awareness has a significant and positive effect on destination brand image, but not on destination perceived quality and destination brand loyalty; (2) destination brand image has positive and direct influences on destination perceived quality and destination brand loyalty; and (3) destination perceived quality has significant positive impacts on destination brand loyalty. Lastly, these findings have managerial implications for decision makers

COMPARISION OF THREE DIGESTION METHODS FOR SOIL ARSENIC DETERMINATION. APPLICATION FOR HO CHI MINH CITY SOIL ARSENIC ANALYSIS

Joint Research on Environmental Science and Technology for the Eart

Self-supervised few-shot learning for real-time traffic sign classification

Although supervised approaches for traffic sign classification have demonstrated excellent performance, they are limited to classifying several traffic signs defined in the training dataset. This prevents them from being applied to different domains, i.e., different countries. Herein, we propose a self-supervised approach for few-shot learning-based traffic sign classification. A center-awareness similarity network is designed for the traffic sign problem and trained using an optical flow dataset. Unlike existing supervised traffic sign classification methods, the proposed method does not depend on traffic sign categories defined by the training dataset. It applies to any traffic signs from different countries. We construct a Korean traffic sign classification (KTSC) dataset, including 6000 traffic sign samples and 59 categories. We evaluate the proposed method with baseline methods using the KTSC, German traffic sign, and Belgian traffic sign classification datasets. Experimental results show that the proposed method extends the ability of existing supervised methods and can classify any traffic sign, regardless of region/country dependence. Furthermore, the proposed approach significantly outperforms baseline methods for patch similarity. This approach provides a flexible and robust solution for classifying traffic signs, allowing for accurate categorization of every traffic sign, regardless of regional or national differences

Overcoming Fear of Developing Country: A Case Report of Retroperitoneal Laparoscopic Partial Nephrectomy for T3a Renal Cell Carcinoma

INTRODUCTION: Renal cell carcinoma poses significant challenges in kidney diseases, particularly in the context of the T3 stage, where treatment strategies remain controversial. The utilization of laparoscopic partial nephrectomy, particularly in developing countries, has been restricted for such patients, primarily due to limited infrastructure and concerns about recurrence risk and long-term pathologic outcomes. PRESENTATION OF CASE: In this report, we present a case of a 64-year-old male diagnosed with T3aN0M0 renal cell carcinoma (RCC). Abdominal computed tomography revealed a 5.2 × 5.2 × 5.1 cm mass on the right upper part of the kidney with a possible thrombus in the superior renal polar vein. The patient underwent successful treatment with retroperitoneal laparoscopic partial nephrectomy (LPN), leading to the preservation of kidney function with/min/1.73 m2 GFR reduced after one year postoperative (estimated GFR from 85 mL/min/1.73 m2 to 81.79 mL/min/1.73 m2). The patient was discharged after three days; no recurrence was observed during the follow-up. DISCUSSION: For stage T3a RCC, studies show that LPN induces comparable long-term outcomes to radical nephrectomy, with advantages such as preserved kidney function, reduced operative time, blood loss, and shorter hospital stays. However, due to infrastructure constraints and limited access to robotic-assisted surgery in our country, coupled with concerns about tumor recurrence, laparoscopic radical nephrectomy is predominantly employed for similar patients. Our case represents one of the very first cases in which we successfully treated a patient diagnosed with T3a RCC using retroperitoneal laparoscopic partial nephrectomy. CONCLUSION: Laparoscopic partial nephrectomy is a reliable choice for T3aN0M0 RCC with good long-term outcomes and preserved renal function, especially by the hands of an experienced laparoscopic surgeon

Gene Family Abundance Visualization based on Feature Selection Combined Deep Learning to Improve Disease Diagnosis

Advancements in machine learning in general and in deep learning in particular have achieved great success in numerous fields. For personalized medicine approaches, frameworks derived from learning algorithms play an important role in supporting scientists to investigate and explore novel data sources such as metagenomic data to develop and examine methodologies to improve human healthcare. Some challenges when processing this data type include its very high dimensionality and the complexity of diseases. Metagenomic data that include gene families often have millions of features. This leads to a further increase of complexity in processing and requires a huge amount of time for computation. In this study, we propose a method combining feature selection using perceptron weight-based filters and synthetic image generation to leverage deep-learning advancements in order to predict various diseases based on gene family abundance data. An experiment was conducted using gene family datasets of five diseases, i.e. liver cirrhosis, obesity, inflammatory bowel diseases, type 2 diabetes, and colorectal cancer. The proposed method provides not only visualization for gene family abundance data but also achieved a promising performance level