Risks of Surface Water Pollution in Southern Vietnam

The study was carried out to assess surface water quality and ecological risks in water bodies in the southern region of Vietnam. The study used monitoring data at 58 locations, which were collected in March, May, June, July, August, October, November, and December of 2022, with 11 water quality parameters (temperature, pH, DO, TSS, BOD, COD, NH4+-N, NO3--N, Fe, Pb, and Cd). Comprehensive pollution index (CPI), ecological risk level, and multivariate statistical analysis methods were utilized. The values of CPI showed that the surface water quality was mildly polluted, moderately polluted, and severely polluted, accounting for 37.93, 46.93, and 15.52%, respectively. In particular, heavy pollution was concentrated in the water bodies of the Sai Gon and Vam Co Rivers. TSS, BOD, COD, NH4+-N, and Fe had a moderate to high level of risk, while water samples contaminated with NO3--N, Pb, and Cd had a level of risk from low to safe. High levels of risk were concentrated in the water bodies of the Sai Gon River and Vam Co River, typically BOD and COD. Based on the impact level, the positions were classified into five groups, with the locations on the Sai Gon River and Vam Co River (Groups 4 and 5) being affected by various waste sources in the inner city of Ho Chi Minh City. The PCA results presented three sources, such as discharge from residential areas, soil erosion, and agriculture, that have caused water quality fluctuations and increased the impact on the water quality of water bodies. Measures to protect water resources according to environmental protection laws must be implemented soon to minimize ecological risks from water-polluting sources. Doi: 10.28991/CEJ-2023-09-11-06 Full Text: PD

CAMELLIA THUANANA (CAMELLIA SECT. CHRYSANTHA) – A NEW SPECIES FROM THE CENTRAL HIGHLANDS, VIETNAM

Camellia thuanana, a new species of the genus Camellia L. (Theaceae) is described and illustrated from the Central Highlands, Vietnam. Morphological features of this species are small flowers and pedicellate; leaves stalked, anastomosing venation, blades oblong-elliptic to elliptic, sparsely hirsute along the midrib below; pedicel very short; bracteoles 2–3, triangular; sepals 4–(5) in opposite pairs; corolla light greenish-yellow color; petals 7–8, glabrous; androecium 190–200 stamens, light yellow, in 3–4 circles; gynoecium 3, ovary ovoid and pubescent; styles 3, free to the base, and glabrous. C. thuanana resembles C. thuongiana in some morphological characteristics. C. thuanana is classified into sect. Chrysantha by styles completely free, flowers yellow, ovaries 3–5 locular, and partially connate. The IUCN Redlist Category of C. thuanana was assessed as Critically Endangered (CR)

SURVEY RESULTS OF THE FACTORS AFFECTTING ORGANIC FOOD PURCHASE INTENTION OF CONSUMERS

The research examines factors that influence the organic food purchase intention of Vietnamese consumers. In accordance with theoretical behavioral models and organic food research, data was collected and analyzed via SMARTPLS. Inspecting results demonstrate that 4 factors have a positive influence on organic food purchase intention. Additionally, the research team evaluates the levels of influence of each factor through descriptive statistics of the factors’ average value. The evaluating results are the foundation to propose remedies that encourage the organic food purchase intention of Vietnamese consumers

THE DIVERSITY OF YELLOW CAMELLIAS IN THE CENTRAL HIGHLANDS, VIETNAM

The Central Highlands (Tây Nguyên) is a center of yellow camellia diversity in Vietnam and the world. The Central Highlands contains 18 of Vietnam’s yellow camellia species, accounting for 37% of yellow camellia species in Vietnam and 28% of yellow camellia species worldwide. Moreover, all 18 yellow camellia species in the Central Highlands are endemic to Vietnam. The camellias of the Central Highlands belong to nine sections, accounting for 75% of the world. The yellow colors occur in three groups: pale yellow, yellow, and yellow with compound colors. The yellow camellia distribution is dispersed at 500–1600 m elevation in evergreen broadleaf forests and mixed wood-bamboo forests

RELATIONSHIP BETWEEN SEAFOOD EXPORT AND VIETNAM'S ECONOMIC GROWTH IN THE PERIOD OF 2005 – 2022

This article studies the relationship between seafood exports and Vietnam's economic growth in the period 2005 - 2022.  To conduct the research, the authors collected data on seafood export turnover and economic growth rate - represented by real gross domestic product (GDPr) in the period from 2005 to 2022. Data is collected from the General Statistics Office of Vietnam (GSO) and international financial statistics sites (IFS, IMF).  After synthesizing and cleaning the data, the research team used Eviews 8 software to analyze the data series to examine the relationship between seafood export turnover and economic growth during the research period. Model results show that when seafood export turnover increases by 1%, economic growth increases by 1.041382%, so it can be confirmed that seafood export has a positive relationship with Vietnam's economic growth during the research period. From the research results, the authors have several proposals to promote the growth of Vietnamese seafood exports to the international market in the post-pandemic context and the volatile world seafood market situation

Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments

Continual semantic segmentation aims to learn new classes while maintaining the information from the previous classes. Although prior studies have shown impressive progress in recent years, the fairness concern in the continual semantic segmentation needs to be better addressed. Meanwhile, fairness is one of the most vital factors in deploying the deep learning model, especially in human-related or safety applications. In this paper, we present a novel Fairness Continual Learning approach to the semantic segmentation problem. In particular, under the fairness objective, a new fairness continual learning framework is proposed based on class distributions. Then, a novel Prototypical Contrastive Clustering loss is proposed to address the significant challenges in continual learning, i.e., catastrophic forgetting and background shift. Our proposed loss has also been proven as a novel, generalized learning paradigm of knowledge distillation commonly used in continual learning. Moreover, the proposed Conditional Structural Consistency loss further regularized the structural constraint of the predicted segmentation. Our proposed approach has achieved State-of-the-Art performance on three standard scene understanding benchmarks, i.e., ADE20K, Cityscapes, and Pascal VOC, and promoted the fairness of the segmentation model

A Fuzzy LQR PID Control for a Two-Legged Wheel Robot with Uncertainties and Variant Height

This paper proposes a fuzzy LQR PID control for a two-legged wheeled balancing robot for keeping stability against uncertainties and variant heights. The proposed control includes the fuzzy supervisor, LQR, PID, and two calibrations. The fuzzy LQR is conducted to control the stability and motion of the robot while its posture changes with respect to time. The fuzzy supervisor is used to adjust the LQR control according to the robotic height. It consists of one input and one output. The input and output have three membership functions, respectively, to three postures of the robot. The PID control is used to control the posture of the robot. The first calibration is used to compensate for the bias value of the tilting angle when the robot changes its posture. The second calibration is applied to compute the robotic height according to the hip angle. In order to verify the effectiveness of the proposed control, a practical robot with the variant height is constructed, and the proposed control is embedded in the control board. Finally, two experiments are also conducted to verify the balancing and moving ability of the robot with the variant posture

Taxonomy of the genus Paris L. (Melanthiaceae) in Vietnam

Paris L. is a small genus distributed widely in Eurasia. In Vietnam Paris occur in evergreen broad-leaved forests in some mountainous areas of the North and the Central highlands. Due to over-exploitation as well as habitat loss, populations of some Paris species are seriously declining. This genus has not been studied extensively in Vietnam. The aim of this study was to define the morphological characteristics of the genus Paris in Vietnam. Morphological description, dichotomous key for identification, ecology and distributions of the genus in Vietnam are reported. The results show that this genus in Vietnam comprises 8 species and 2 varieties, possesing unilocular ovary with parietal placenta

Identify aerodynamic derivatives of the airplane attitude channel using a spiking neural network

The paper proposes a method for identifying aerodynamic coefficient derivatives of aircraft attitude channel using spiking neural network (SNN) and Gauss-Newton algorithm based on data obtained from actual flights. Using SNN combination with Gauss-Newton iterative calculation algorithm allows the identification of aerodynamic coefficient derivatives in a nonlinear model for aerodynamic parameters with higher accuracy and faster calculation time. The paper proposes an algorithm to train the SNN multi-layer network by Normalized Spiking Error Back Propagation (NSEBP), in which, in the forward propagation period, the time of output spikes is calculating by solving quadratic equations instead of detection by traditional methods. The phase of propagation of errors backward uses the step-by-step calculation instead of the conventional gradient calculation method. The identification results are compared with the results when using the RBN network to prove the algorithm efficienc

Sensor clustering using a K-means algorithm in combination with optimized unmanned aerial vehicle trajectory in wireless sensor networks

We examine a general wireless sensor network (WSN) model which incorporates a large number of sensors distributed over a large and complex geographical area. The study proposes solutions for a flexible deployment, low cost and high reliability in a wireless sensor network. To achieve these aims, we propose the application of an unmanned aerial vehicle (UAV) as a flying relay to receive and forward signals that employ nonorthogonal multiple access (NOMA) for a high spectral sharing efficiency. To obtain an optimal number of subclusters and optimal UAV positioning, we apply a sensor clustering method based on K-means unsupervised machine learning in combination with the gap statistic method. The study proposes an algorithm to optimize the trajectory of the UAV, i.e., the centroid-to-next-nearest-centroid (CNNC) path. Because a subcluster containing multiple sensors produces cochannel interference which affects the signal decoding performance at the UAV, we propose a diagonal matrix as a phase-shift framework at the UAV to separate and decode the messages received from the sensors. The study examines the outage probability performance of an individual WSN and provides results based on Monte Carlo simulations and analyses. The investigated results verified the benefits of the K-means algorithm in deploying the WSN.Web of Science234art. no. 234