Spectrum map is the foundation of spectrum resource management, security governance and spectrum warfare. Aiming at the problem that the traditional spectrum mapping is limited to two-dimensional space, a three-dimensional spectrum data acquisition and mapping system architecture for the integration of space, sky and earth is presented, and a spectrum map reconstruction scheme driven by propagation model is proposed, which can achieve high-precision three-dimensional spectrum map rendering under the condition of sparse sampling. The spectrum map reconstructed by this method in the case of single radiation source and multiple radiation sources is in good agreement with the theoretical results based on ray tracing method. In addition, the measured results of typical scenes further verify the feasibility of this method.

North Korea has been isolated from the international community because of high-intensity sanctions. Nonetheless, research on North Korea should continue so that we are prepared not for contingencies that may occur because of sudden political changes in that country, as occurred after the unification of Germany and dissolution of the Soviet Union, and also to cope with future risks and threats wisely. This study conducted a quantitative survey regarding “inter-Korean cooperation in science and technology,” targeting experts at the Korean government-funded research institutes. As a qualitative survey, focus group interviews (FGI) were conducted to gain insights into the possibilities, considerations, and procedures for inter-Korean cooperation in science and technology. This study is the first to conduct quantitative research on inter-Korean exchange and cooperation in science and technology and shows significant statistical results.

This study was designed to study the push and pull motivational factors affecting the foreign backpackers travel behavior towards Full Moon Party in Koh Phangan District, Surat Thani Province. In the sample 300 foreign backpackers aged 18 or older were included, who came to attend the Full Moon Party solely for vacation purposes and not for any work or income generating activities. The study was executed using a structured questionnaire. The statistical tools for the analysis of the data included, but were not limited to, frequency counts, computed percentages, means, standard deviations, chi-square analysis, one- way ANOVA, and Pearson correlation at the 0.05 level of significance. The research demonstrated that with respect to the first-time foreign visitors in Thailand to attend the Full Moon Party, then, they have habitually stayed at the resorts and the bungalows. It was a general observation that such visitors preferred to seek out information on the Internet, social websites as well as tourism websites. Their activities included horse riding, general activities, seeing natural sights including waterfalls and mountains, going for mountain hikes, participating in physically hard and risky outdoors activities, and nighttime activities. Tourists are sufficiently motivated to visit Thailand for its various appealing attributes, as revealed by the analysis. Furthermore, 10 motivational components were identified with 24 variables; Push Motivation Components: (1) Escape and Novelty Seeking, (2) Feel Free, (3) Open the World, and (4) Social Need. Pull Motivation Components: (1) Party, (2) Unique, (3) Only for Myself, (4) Sea Lover, (5) Diversity, and (6) Loner. Demographic characteristics for example gender, age, marital status, education level, occupation, and place of residence were also studied. The push factors, as well as the pull factors of travel, were found to co-relate with the behavior of female foreign backpackers on the other hand where both were significant.

This study conducted a systematic review of the existing literature on rhythmic gymnastics. Through searching databases such as PubMed, Web of Science, and Scopus, 37 out of 2319 articles were selected, covering training and physical fitness, nutrition and metabolism, as well as sports injuries and rehabilitation. The findings revealed that: (1) Core physical training significantly enhanced athletes’ performance; (2) Inadequate nutritional intake was prevalent; (3) The incidence of sports injuries was high, particularly those resulting from overtraining. The conclusion emphasizes the need to enhance strength training, optimize nutritional management, and further investigate injury prevention and rehabilitation measures to enhance athletes’ performance and health status.

Retinal disorders, such as diabetic retinopathy, glaucoma, macular edema, and vein occlusions, are significant contributors to global vision impairment. These conditions frequently remain symptomless until patients suffer severe vision deterioration, underscoring the critical importance of early diagnosis. Fundus images serve as a valuable resource for identifying the initial indicators of these ailments, particularly by examining various characteristics of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. Traditionally, healthcare practitioners often rely on manual retinal vessel segmentation, a process that is both time-consuming and intricate, demanding specialized expertise. However, this approach poses a notable challenge since its precision and consistency heavily rely on the availability of highly skilled professionals. To surmount these challenges, there is an urgent demand for an automatic and efficient method for retinal vessel segmentation and classification employing computer vision techniques, which form the foundation of biomedical imaging. Numerous researchers have put forth techniques for blood vessel segmentation, broadly categorized into machine learning, filtering-based, and model-based methods. Machine learning methods categorize pixels as either vessels or non-vessels, employing classifiers trained on hand-annotated images. Subsequently, these techniques extract features using 7D feature vectors and apply neural network classification. Additional post-processing steps are used to bridge gaps and eliminate isolated pixels. On the other hand, filtering-based approaches employ morphological operators within morphological image processing, capitalizing on predefined shapes to filter out objects from the background. However, this technique often treats larger blood vessels as cohesive structures. Model-based methods leverage vessel models to identify retinal blood vessels, but they are sensitive to parameter selection, necessitating careful choices to simultaneously detect thin and large vessels effectively. Our proposed research endeavors to conduct a thorough and empirical evaluation of the effectiveness of automated segmentation and classification techniques for identifying eye-related diseases, particularly diabetic retinopathy and glaucoma. This evaluation will involve various retinal image datasets, including DRIVE, REVIEW, STARE, HRF, and DRION. The methodologies under consideration encompass machine learning, filtering-based, and model-based approaches, with performance assessment based on a range of metrics, including true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), negative predictive value (NPV), false discovery rate (FDR), Matthews's correlation coefficient (MCC), and accuracy (ACC). The primary objective of this research is to scrutinize, assess, and compare the design and performance of different segmentation and classification techniques, encompassing both supervised and unsupervised learning methods. To attain this objective, we will refine existing techniques and develop new ones, ensuring a more streamlined and computationally efficient approach.