A DOUBLE-SHRINK AUTOENCODER FOR NETWORK ANOMALY DETECTION

The rapid development of the Internet and the wide spread of its applications has affected many aspects of our life. However, this development also makes the cyberspace more vulnerable to various attacks. Thus, detecting and preventing these attacks are crucial for the next development of the Internet and its services. Recently, machine learning methods have been widely adopted in detecting network attacks. Among many machine learning methods, AutoEncoders (AEs) are known as the state-of-the-art techniques for network anomaly detection. Although, AEs have been successfully applied to detect many types of attacks, it is often unable to detect some difficult attacks that attempt to mimic the normal network traffic. In order to handle this issue, we propose a new model based on AutoEncoder called Double-Shrink AutoEncoder (DSAE). DSAE put more shrinkage on the normal data in the middle hidden layer. This helps to pull out some anomalies that are very similar to normal data. DSAE are evaluated on six well-known network attacks datasets. The experimental results show that our model performs competitively to the state-of-the-art model, and often out-performs this model on the attacks group that is difficult for the previous methods

Cyclization of some N-Arylidene-2-(Acetamido)-3-(4-Chlorophenyl)Acrylohydrazides to 1-Arylideneamino-4-(4-Chlorobenzylidene)-2-Methyl-1h-Imidazolin-5(4h)-Ones

Cyclization of the N-arylidene-2-(acetamido)-3-(4-chlorophenyl)acrylohydrazides, which were prepared from 4-chlorobenzaldehyde and acetylglycine via 4-chlorobenzylidene-2-methyl-(4H)-oxazol-5-one and then 2-(acetamido)-3-(4-chlorophenyl)acrylohydrazide, on treatment with acetic anhydride gave seven corresponding compounds namely 1-arylideneamino-4-(4-chlorobenzylidene)-2-methyl-1H-imidazolin-5(4H)-ones but did not give 3-acetyl-2-aryl-5-[1-acetamido-2-(4-chlorophenyl)vinyl]-1,3,4-oxadiazolines. The structure of the imidazoline-5-one compounds was confirmed by IR, 1H-NMR and MS spectral data

Optimal Design of V-Shaped Fin Heat Sink for Active Antenna Unit of 5G Base Station

The active antenna unit (AAU) is one of the main parts of the 5G base station, which has a large size and a high density of chipsets, and operates at a significantly high temperature. This systematic study presents an optimal design for the heat sink of an AAU with a V-shaped fin arrangement. First, a simulation of the heat dissipation was conducted on two designs of the heat sink – in-line and V-shaped fins – which was validated by experimental results. The result shows that the heat sink with V-shaped fins performed better compared to conventional models such as heat sinks with in-line fins. Secondly, computational fluid dynamics (CFD) and the Lagrange interpolation method were applied to find out an optimal set of design parameters for the heat sink. It is worth noting that the optimal parameters of the orientation angle and fin spacing considerably affected the heat sink’s performance.  

Optimal Design of V-Shaped Fin Heat Sink for Active Antenna Unit of 5G Base Station

The active antenna unit (AAU) is one of the main parts of the 5G base station, which has a large size and a high density of chipsets, and operates at a significantly high temperature. This systematic study presents an optimal design for the heat sink of an AAU with a V-shaped fin arrangement. First, a simulation of the heat dissipation was conducted on two designs of the heat sink – in-line and V-shaped fins – which was validated by experimental results. The result shows that the heat sink with V-shaped fins performed better compared to conventional models such as heat sinks with in-line fins. Secondly, computational fluid dynamics (CFD) and the Lagrange interpolation method were applied to find out an optimal set of design parameters for the heat sink. It is worth noting that the optimal parameters of the orientation angle and fin spacing considerably affected the heat sink’s performance.  

Study of Using Cassava Pulp to Produce Livestock Feed Pellet

In Vietnam, the cassava production capacity is about 10 million tons annually. Indeed, it eliminates approximate 4 million tons of cassava pulp from the cassava starch factories. This amount of cassava is usually dried to feed cattle or fertilizer. However, drying of cassava pulp has its disadvantages such as air pollution, difficult storage and transportation. This study has proposed and successfully tested a line of equipment used to produce pellets from the utilization of fresh cassava pulp bringing from cassava starch processing plants. The processing includes a mixing of fresh cassava pulp with dried cassava starch, pressing and drying of pellets. The experiment results show that when the mixing ratio between cassava starch and cassava residue is 1:10 – 1:5, the cassava pulp pellets after drying achieved the required technical specifications. Hence, it has high volume density, the dried specific weight of the pellets is about 700 kg / m3, the breaking strength of the pellets is greater than 2 kG, the tanning time of the pellets is greater than 116 minutes in water, the moisture content of the pellets is remained in 13% after drying in 5-5.5 hours

Detecting familial defective apolipoprotein B-100 R3500Q in Vietnamese patients by PCR-sequencing

Familial defective apolipoprotein B-100 (FDB) is an autosomal codominant disorder associated with hypercholesterolemia, caused by mutations in and around codon 3500 of the Apolipoprotein (Apo) B gene, which encodes Apo B-100. The first mutation occurred in Arginine codons to be described, and the most characterized, is caused by a G→A transition at nucleotide 10,708 and results in the substitution of Arginine by Glutamine at codon 3500 (ApoB R3500Q). In this study, we have identified 27 R3500Q mutations in known FDB patients using PCRSequencing method. As the result, most of the patients carried heterozygous mutation R3500Q. PCR-Sequencing method that we have applied in this study proved consistent and so easily identified mutations correctly

Effects of second litter syndrome on reproductive performance in sows

Background and Aim: The effects of second litter syndrome (SLS) on subsequent reproductive performance remain poorly understood. This study examined the impact of SLS on reproductive parameters such as piglets born alive (PBA), accumulative number of PBA (APBA), farrowing interval (FI), and risk of decreased PBA (DPBA) up to parity 5. Materials and Methods: Data on 5,464 litters were recorded from 1,507 sow cards collected on five swine farms in northern Vietnam. A linear mixed-effect model was used to analyze the effect of SLS on the PBA, APBA, and FI. A generalized linear mixed model was used to analyze the effect of DPBA in parity n on the risk of DPBA in parity n + 1. Results: About 47.8% of the sows contracted SLS (720/1507). Only APBA1-2 was significantly decreased by SLS. The APBA3-5 in SLS sows was comparable to that in non-SLS sows (41.8 vs. 41.9). Non-DPBA2 upped the risk for DPBA3 by 3.6-fold (95% confidence interval [CI]: 2.8–4.6). Moreover, non-DPBA3 increased the risk of DPBA4 (odds ratio [OR] = 2.7, 95% CI = 2.1–3.7), and non-DPBA4 increased the risk of DPBA5 (OR = 3.2, 95% CI = 2.3–4.7). The risks of developing DPBA4 and DPBA5 remained unchanged following SLS (p > 0.05). About 98.4% of sows underwent PBA fluctuations during their first five parities. Conclusion: SLS does not appear to detrimentally affect PBA, APBA, and FI in subsequent parities. Therefore, SLS sows do not necessarily have future low reproductive performance or be culled. Future investigations should explore the mechanism of alternate decrease/increase patterns in PBA

Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting

This article presents a research approach to enhancing the quality of short-term power output forecasting models for photovoltaic plants using a Long Short-Term Memory (LSTM) recurrent neural network. Typically, time-related indicators are used as inputs for forecasting models of PV generators. However, this study proposes replacing the time-related inputs with clear sky solar irradiance at the specific location of the power plant. This feature represents the maximum potential solar radiation that can be received at that particular location on Earth. The Ineichen/Perez model is then employed to calculate the solar irradiance. To evaluate the effectiveness of this approach, the forecasting model incorporating this new input was trained and the results were compared with those obtained from previously published models. The results show a reduction in the Mean Absolute Percentage Error (MAPE) from 3.491% to 2.766%, indicating a 24% improvement. Additionally, the Root Mean Square Error (RMSE) decreased by approximately 0.991 MW, resulting in a 45% improvement. These results demonstrate that this approach is an effective solution for enhancing the accuracy of solar power output forecasting while reducing the number of input variables

Enabling Technologies for Web 3.0: A Comprehensive Survey

Web 3.0 represents the next stage of Internet evolution, aiming to empower users with increased autonomy, efficiency, quality, security, and privacy. This evolution can potentially democratize content access by utilizing the latest developments in enabling technologies. In this paper, we conduct an in-depth survey of enabling technologies in the context of Web 3.0, such as blockchain, semantic web, 3D interactive web, Metaverse, Virtual reality/Augmented reality, Internet of Things technology, and their roles in shaping Web 3.0. We commence by providing a comprehensive background of Web 3.0, including its concept, basic architecture, potential applications, and industry adoption. Subsequently, we examine recent breakthroughs in IoT, 5G, and blockchain technologies that are pivotal to Web 3.0 development. Following that, other enabling technologies, including AI, semantic web, and 3D interactive web, are discussed. Utilizing these technologies can effectively address the critical challenges in realizing Web 3.0, such as ensuring decentralized identity, platform interoperability, data transparency, reducing latency, and enhancing the system's scalability. Finally, we highlight significant challenges associated with Web 3.0 implementation, emphasizing potential solutions and providing insights into future research directions in this field

Furanosesterterpenes from the marine sponge Ircinia echinata (Keller, 1889)

Four furanosesterterpene, (7E,12E,20Z,18β)-variabilin (1), (12E,20Z,18β)-8-hydroxyvariabilin (2), (7E,11E,3β)-3,7,11-trimethyl-14-(furan-3-yl)tetradec-7,11-dienoic acid (3), and furoscalarol (4), and one sterol, 3β-hydroxycholest-5-en-7-one (5) were isolated from the methanol extract of the sponge Ircinia echinata (Keller, 1889). Their structures were elucidated by 1D and 2D-NMR spectra and in comparison with those reported in the literature. Keywords. Sponge, Ircinia echinata, furanosesterterpene, sterol