Effects of different feeds and stocking densities on growth and survival rates of mud crab (<em>Scylla paramamosain</em>) at the stage from megalopa to crablet-1

Mud crabs (Scylla genus) are luxury foods in high demand internationally. The efficient techniques for mud crab hatcheries are vital for providing breeds for their aquaculture, which is rapidly growing in many countries. This study aims to investigate the effects of different feeds and stocking densities on mud crabs' growth and survival rates (Scylla paramamosain) in the stage from megalopa to crablet-1 stage. Two separate experiments were conducted indoors in the 60-liter round plastic tanks (containing 50 liters of water at a 28‰ salinity). Experiment 1 investigated four feeds: frozen Artemia biomass, pureed shrimp meat, Lansy pellet feed (48% protein), and NRD pellet feed (55% protein). Megalopae (mean weight of 5.8 mg) were stocked at a density of 10/L. In experiment 2, the megalopae (mean weight of 5.4 mg) were stocked at densities of 20, 30, and 40/L and were fed the Lansy pellet feed, which was the best one selected from experiment 1. High survival rates were obtained at all four feeds (82.2–87.5%) and three stocking densities (88.4–90.1%). The growth performances in Lansy feed and frozen Artemia biomass were better than those in pureed shrimp meat and NRD pellet feed, which was seen through higher indicators of daily weight gain (DWG) and specific growth rate in weight (SGRw) (p p > 0.05). The investigated feeds and stocking densities suit the nursing mud crab (S. paramamosain) megalopa. In contrast, the Lansy pellet feeds had a stocking density of 20/L, resulting in the highest nursing efficiency

Outage performance analysis of non-orthogonal multiple access systems with RF energy harvesting

Non-orthogonal multiple access (NOMA) has drawn enormous attention from the research community as a promising technology for future wireless communications with increasing demands of capacity and throughput. Especially, in the light of fifth-generation (5G) communication where multiple internet-of-things (IoT) devices are connected, the application of NOMA to indoor wireless networks has become more interesting to study. In view of this, we investigate the NOMA technique in energy harvesting (EH) half-duplex (HD) decode-and-forward (DF) power-splitting relaying (PSR) networks over indoor scenarios which are characterized by log-normal fading channels. The system performance of such networks is evaluated in terms of outage probability (OP) and total throughput for delay-limited transmission mode whose expressions are derived herein. In general, we can see in details how different system parameters affect such networks thanks to the results from Monte Carlo simulations. For illustrating the accuracy of our analytical results, we plot them along with the theoretical ones for comparison

An efficient approach to measure the difficulty degree of practical programming exercises based on student performances

oai:ojs.www.rev-jec.org:article/282This study examines the generality of easy to hard practice questions in programming subjects. One of the most important contributions is to propose four new formulas for determining the difficulty degree of questions. These formulas aim to describe different aspects of difficulty degree from the learner's perspective instead of the instructor's subjective opinions. Then, we used clustering technique to group the questions into three easy, medium and difficult degrees. The results will be the baseline to consider the generality of the exercise sets according to each topic. The proposed solution is then tested on the data set that includes the results of the two subjects: Programming Fundamentals, Data Structures and Algorithms from Ho Chi Minh City University of Technology. The most important result is to suggest the instructors complete various degrees according to each topic for better evaluating student's performance

Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, the Hai Phong city (Vietnam) using GIS-Based Kernel Logistic Regression

-The Cat Ba National Park area (Vietnam) with the tropical forest is recognized to be part of the world biodiversity conservation by United Nations Educational, Scientific and Cultural Oranization (UNESCO) and is a well-known destination for tourist with around 500,000 travellers per year. This area has been the site for many research projects; however no project has been carried out for the forest fire susceptibility assessment. Thus, protection of the forest including fire prevention is one of the main concerns of the local authority. This work aims to produce a tropical forest fire susceptibility map for the Cat Ba National Park area, which may be helpful for the local authority in the forest fire protection management. To obtain this purpose, first, historical forest fires and related factors were collected from various sources to construct a GIS database. Then a forest fire susceptibility model was developed using Kernel logistic regression. The quality of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), and five statistical evaluation measures. The usability of the resulting model is further compared with a benchmark model, the Support vector machine. The results show that the Kernel logistic regression model has high performance on both the training and validation dataset with a prediction capability of 92.2%. Since the Kernel logistic regression model outperform the benchmark model, we conclude that the proposed model is a promising alternative tool that should be considered for forest fire susceptibility mapping also for other areas. The result in this study is useful for the local authority in forest planning and management

A RESEARCH ON MULTI-OBJECTIVE OPTIMIZATION OF THE GRINDING PROCESS USING SEGMENTED GRINDING WHEEL BY TAGUCHI-DEAR METHOD

In this study, the mutil-objective optimization was applied for the surface grinding process of SAE420&nbsp;steel. The aluminum oxide grinding wheels that were grooved by 15&nbsp;grooves, 18&nbsp;grooves, and 20&nbsp;grooves were used in the experimental process. The Taguchi method was applied to design the experimental matrix. Four input parameters that were chosen for each experiment were the number of grooves in cylinder surface of grinding wheel, workpiece velocity, feed rate, and cutting depth. Four output parameters that were measured for each experimental were the machining surface roughness, the system vibrations in the three directions (X,&nbsp;Y,&nbsp;Z). The DEAR technique was applied to determine the values of the input parameters to obtaine the minimum values of machining surface roughness and vibrations in three directions. By using this technique, the optimum values of grinding wheel groove number, workpiece velocity, feed-rate, cutting depth were 18&nbsp;grooves, 15&nbsp;m/min, 2&nbsp;mm/stroke, and 0.005&nbsp;mm, respectively. The verified experimental was performed by using the optimum values of input parameters. The validation results of surface roughness and vibrations in X,&nbsp;Y,&nbsp;Z directions were 0.826&nbsp;(µm), 0.531&nbsp;(µm), 0.549&nbsp;(µm), and 0. 646&nbsp;(µm), respectively. These results were great improved in comparing to the normal experimental results. Taguchi method and DEAR technique can be applied to improve the quality of grinding surface and reduce the vibrations of the technology system to restrain the increasing of the cutting forces in the grinding process. Finally, the research direction was also proposed in this stud

Z-GMOT: Zero-shot Generic Multiple Object Tracking

Despite the significant progress made in recent years, Multi-Object Tracking (MOT) approaches still suffer from several limitations, including their reliance on prior knowledge of tracking targets, which necessitates the costly annotation of large labeled datasets. As a result, existing MOT methods are limited to a small set of predefined categories, and they struggle with unseen objects in the real world. To address these issues, Generic Multiple Object Tracking (GMOT) has been proposed, which requires less prior information about the targets. However, all existing GMOT approaches follow a one-shot paradigm, relying mainly on the initial bounding box and thus struggling to handle variants e.g., viewpoint, lighting, occlusion, scale, and etc. In this paper, we introduce a novel approach to address the limitations of existing MOT and GMOT methods. Specifically, we propose a zero-shot GMOT (Z-GMOT) algorithm that can track never-seen object categories with zero training examples, without the need for predefined categories or an initial bounding box. To achieve this, we propose iGLIP, an improved version of Grounded language-image pretraining (GLIP), which can detect unseen objects while minimizing false positives. We evaluate our Z-GMOT thoroughly on the GMOT-40 dataset, AnimalTrack testset, DanceTrack testset. The results of these evaluations demonstrate a significant improvement over existing methods. For instance, on the GMOT-40 dataset, the Z-GMOT outperforms one-shot GMOT with OC-SORT by 27.79 points HOTA and 44.37 points MOTA. On the AnimalTrack dataset, it surpasses fully-supervised methods with DeepSORT by 12.55 points HOTA and 8.97 points MOTA. To facilitate further research, we will make our code and models publicly available upon acceptance of this paper

Students and young university staff development in the context of e-learning and the fourth industrial revolution

This article aims at presenting the current state of students’ capacity for learning and competencies of young staff members in Vietnamese universities. Then, we imply some orientations to improve students’ capacity for learning and young lecturers’ capacity for teaching in the 4th industrial revolution such as: Application of technology in teaching - education management and international affair