Nonorthogonal multiple access (NOMA) increases spectrum efficacy by permitting multiple devices to share link resources. It can be used to provide convenient offloading computing services for edge devices (EDs) in unmanned aerial vehicle (UAV) and mobile-edge computing (MEC) networks. However, due to the Line-of-Sight (LoS) of UAV transmission, NOMA-based UAV-MEC systems are susceptible to information eavesdropping. In this paper, we investigate a secure offloading model for a NOMA-based UAV-aided MEC in Internet of Things (IoT) network concerning an aerial eavesdropper (EAV) that considers imperfect channel state information (ipCSI) and imperfect successive interference cancellation (ipSIC).We derive the expression of secrecy successful computation probability (SSCP) across the entire system to analyze EAV’s impact on the performance of the NOMA-based UAV-aided MEC in IoT networks. In addition, we present a formulation of an optimization problem that optimizes the SSCP through the optimization of the UAV’s altitude and location, as well as the offloading ratio. To address this issue, a genetic algorithm (GA)-based approach was implemented. The results of our study were corroborated by the Monte Carlo simulations, which assessed system performance by considering multiple system parameters including the UAV’s location, altitude, average transmit signal-to-noise ratio (SNR), and offloading ratio.

A convolution graph attention model based on self-distillation convolutional graph attention network (SDC-GAT) is proposed for multi-channel EEG emotion recognition. Firstly, two-dimensional feature matrix based on EEG time-domain features are constructed, and the matrix is fed into the graph attention neural network to learn the internal connections between electrical brain channels located in different brain regions. Meanwhile, the three-dimensional feature matrix is constructed according to the relative positions of the electrode channels, and the self-distillation network is employed to extract local high-level abstract features containing electrode spatial position information from the three-dimensional feature matrix. Finally, outputs of the two networks are integrated to determine the emotional states. Experiments were performed on the DEAP dataset. The experimental results show that the spatial domain information of the electrode channel and the internal connection relationship between different channels are beneficial for emotion recognition. In addition, the proposed model can effectively fuse these information to improve the performance of multi-channel EEG emotion recognition.

UNESCO is dedicated to identifying, protecting, and preserving global heritage. The designation of a site as a World Heritage Site, recognized as a prestigious brand, significantly enhances its appeal, drawing an increasing number of visitors to these locations. The study sought to determine visitors' perceptions and attitudes of World Heritage emblem, in sync with UNESCO's overall objectives. Concentrating on Hoi An, Vietnam, as a case study, unstructured interviews were conducted with over 30 travelers. The interview sampling was non-probability, using the snowball sampling method to examine awareness amongst visitors’ awareness of the Hoi An WHS emblem and designation, with the goal of providing essential insights for both locals and tourists to help preserve the genuine spirit endorsed by UNESCO regulations. The findings revealed that visitors' impressions were more aligned with the World Heritage Site (WHS) designation than with the emblem itself. Only a small percentage of visitors were aware of Hoi An’s WHS status before their visit and were able to recognize the logo and its association with UNESCO. The concept of a World Heritage site evoked numerous positive product attributes, such as "night, street, friendly people," and some negative associations, such as "river, garbage. crowded". This research idea offers valuable insights for professionals and policymakers in their future applications.

Convolutional Neural Networks (CNNs) have emerged as a prominent research area in deep learning in recent years. U-Net, an essential model within CNNs, has gradually become a research focus in the field of medical image segmentation due to its remarkable segmentation performance. This paper presents a comprehensive overview of brain tumor segmentation methods based on CNNs. Firstly, it introduces common medical image datasets in the field of brain tumor segmentation. Secondly, it offers detailed reviews on the common improvements to 2D U-Net, 3D U-Net, and improvements based on other CNNs for brain tumor segmentation. Finally, it discusses the future development directions of CNNs for brain tumor segmentation.

This study examines the role of state management in promoting sustainable tourism accommodation development in Vietnam, focusing on balancing economic growth, environmental conservation, and social equity. The objectives are to assess the current state of sustainable tourism accommodation, identify challenges and opportunities in implementing sustainable practices, evaluate the effectiveness of existing government policies, and propose strategic recommendations for enhancing state management. Using quantitative surveys and qualitative interviews with stakeholders, the research reveals a growing commitment to sustainability among high-end accommodations but also highlights challenges such as high certification costs, inadequate regulatory frameworks, and limited stakeholder engagement. The findings underscore the critical role of government policies in fostering sustainable practices and recommend strengthening policy frameworks, providing financial incentives, enhancing stakeholder collaboration, and offering training programs to improve the skills and knowledge of tourism professionals. These steps are essential for Vietnam to achieve its sustainability goals and enhance the competitiveness of its tourism sector.

The evolution of 5G New Radio (NR) technology offers unprecedented speeds, ultra-low latency, and the capability to connect billions of devices. However, these advancements come with significant challenges, particularly in managing interference during uplink communication. This study presents a comprehensive investigation into the optimization of 5G NR uplink performance by focusing on two critical channels: the Physical Uplink Shared Channel (PUSCH) and the Physical Random Access Channel (PRACH). The research explores the impact of intra-cell and inter-cell interference on these channels, highlighting how various User Equipment (UE) and cell configuration parameters influence performance. Key Performance Indicators (KPIs) such as Block Error Rate (BLER) and Correct Detection Rate (CDR) are utilized to assess the effectiveness of proposed interference management strategies. Through rigorous simulations and empirical evaluations, the study provides valuable insights into optimizing 5G NR networks, aiming to enhance the robustness and reliability of uplink communication in diverse interference scenarios. The findings underscore the importance of adaptive resource allocation and interference mitigation techniques in achieving superior network performance and quality of service (QoS).

This paper introduces a method for determining men's trousers sizes using a fuzzy logic technique. The Sugeno model is employed in a MISO fuzzy system with three inputs and one output. The process begins by choosing primary dimensions from the size chart, specifically one horizontal and one vertical dimension, followed by defining the value ranges for the membership functions. The model results, based on a size chart that includes six different dimensions. In this study, waist girth and outseam are selected as the primary dimensions, acting as input variables for the simulation model. Fuzzy logic is utilized to determine the size based on the Min-Max rule, with the IF-THEN structure effectively implementing commands within this model. The result of this process is an optimal size selection that aligns more accurately with the individual's body measurements. Moreover, the application of fuzzy logic significantly reduces the time required for size determination compared to traditional methods. This approach offers an alternative method for size selection, one that accounts for the inherent variability in body measurements, thus providing a more tailored and accurate fit for consumers. The study underscores the potential of fuzzy logic to enhance the efficiency and effectiveness of garment sizing systems, offering a promising solution to the challenges posed by standardized sizing methods.  

This study introduces a novel lightweight Keyword Spotting (KWS) model optimized for deployment on resource-constrained microcontrollers, with potential applications in robotic control and end-effector operations. The proposed model employs inter-domain interaction to effectively extract features from both Mel-frequency cepstral coefficients (MFCCs) and temporal audio characteristics, complemented by an attention mechanism to prioritize relevant audio segments for enhanced keyword detection. Achieving a 93.70% accuracy on the Google Command v2-12 commands dataset, the model outperforms existing benchmarks. It also demonstrates remarkable efficiency in inference speed (0.359 seconds) and resource utilization (34.9KB peak RAM and 98.7KB flash memory), offering a 3x faster inference time and reduced memory footprint compared to the DS-CNN-S model. These attributes make it particularly suitable for real-time voice command applications in low-power robotic systems, enabling intuitive and responsive control of robotic arms, end-effectors, and navigation systems. In this work, however, the KWS model is demonstrated in a simple non-destructive testing system for controlling sensor movement. This research lays the groundwork for advancing voice-activated robotic technologies on resource-limited hardware platforms.

This paper investigates the changes in lift and drag coefficients, as well as the lift-to-drag ratio, of two airfoils designed for small wind turbines operating at low Reynolds numbers. The results include 2D simulations performed using the commercial software ANSYS Fluent 2019. The two selected airfoils, EPU-E387 and EPU-S1010, are newly developed and have not yet been studied for their aerodynamic performance. The research method involves varying the angle of attack to determine the optimal angle and identify which airfoil offers greater stability. The Reynolds numbers applied in the 2D simulations include 50,000, 60,000, 90,000, and 120,000. The simulation results indicate that the EPU-E387 airfoil achieves the highest lift-to-drag coefficient at the optimal angle of attack, which is 13.13% greater than that of the EPU-S1010 airfoil at Reynolds number 120,000. Additionally, the EPU-E387 airfoil demonstrates greater stability compared to the EPU-S1010 at higher angles of attack.

This article proposes a hybrid satellite-terrestrial relaying network (HSTRN) that integrates physical-layer security (PLS), Fountain codes (FCs), non-orthogonal multiple access (NOMA), and partial relay selection (PRS) to enhance system performance in terms of reliability, data rate, and security. In the proposed system, a satellite uses NOMA to simultaneously transmit Fountain packets to two clusters of terrestrial users. Data transmission is assisted by one of the terrestrial relay stations, selected by the PRS algorithm. We derive exact expressions for outage probability (OP) and system outage probability (SOP) at the legitimate users, as well as intercept probability (IP) and system intercept probability (SIP) at eavesdroppers. Monte Carlo simulations are realized to validate the accuracy of the analytical results, illustrate performance trends, and analyze the impact of key parameters on the considered performance.