Search Results (13)

Edible bird’s nests have a variety of biological activities, the main components of which are sialic acids. Sialic acids are a group of nine-carbon N-acetylated derivatives of neuraminic acid containing a keto group at position C2 and play important roles in many biological processes. To verify whether the oral administration of edible bird’s nests would change the content and distribution of sialic acid components in vivo, a liquid chromatography–mass spectrometry method for the quantitative analysis of sialic acid levels in serum and tissues was developed. In the negative ion mode, the mobile phases consist of 0.1% formic acid in water (A) and acetonitrile (v/v) (B). Isocratic elution was performed with 60% B for 0−15 min. The chromatographic separation was performed on a Morphling HILIC Amide column (2.1 mm × 150 mm, 5 μm) at a flow rate of 0.5 mL min⁻¹. The results showed that the correlation coefficients of the typical calibration curves were all higher than 0.995, exhibiting good linearity. The levels of free and conjugated forms of N-glycolylneuraminic acid (Neu5Gc), N-acetylneuraminic acid (Neu5Ac), and 2-keto-3-deoxy-D-glycero-D-galactonononic acid (KDN) in the serum and different tissues were simultaneously detected after the oral administration of the edible bird’s nests at a daily dose of 300 and 700 mg Kg⁻¹ for seven days in mice. Our study found that the oral administration of edible bird’s nests can significantly increase the concentration of total sialic acids (Neu5Gc + Neu5Ac + KDN) in serum and spleen and lungs tissues, which may be related to the anti-inflammatory and immune function of edible bird’s nest, but further studies are needed to verify this. Neu5Ac was the dominant sialic acid in brain tissue, and Neu5Gc was the dominant sialic acid in serum and other tissues, including heart, liver, spleen, lungs, and kidney. Moreover, we found that the forms of Neu5Ac and Neu5Gc were mainly conjugated in all groups except liver tissue. In conclusion, the method we established had good linearity and accuracy; it allowed the analytes to be effectively separated from the matrix and endogenous substances in serum or tissues, so it could effectively detect the distribution and concentration of free and conjugated forms of sialic acids in serum and tissues, which was beneficial to the research and exploitation of edible bird’s nests and sialic acids. Full article

Knowledge-Defined Networking (KDN) necessarily consists of a knowledge plane for the generation of knowledge, typically using machine learning techniques, and the dissemination of knowledge, in order to make knowledge-driven intelligent network decisions. In one way, KDN can be recognized as knowledge-driven Software-Defined Networking (SDN), having additional management and knowledge planes. On the other hand, KDN encapsulates all knowledge-/intelligence-/ cognition-/machine learning-driven networks, emphasizing knowledge generation (KG) and dissemination for making intelligent network decisions, unlike SDN, which emphasizes logical decoupling of the control plane. Blockchain is a technology created for secure and trustworthy decentralized transaction storage and management using a sequence of immutable and linked transactions. The decision-making trustworthiness of a KDN system is reliant on the trustworthiness of the data, knowledge, and AI model sharing. To this point, a KDN may make use of the capabilities of the blockchain system for trustworthy data, knowledge, and machine learning model sharing, as blockchain transactions prevent repudiation and are immutable, pseudo-anonymous, optionally encrypted, reliable, access-controlled, and untampered, to protect the sensitivity, integrity, and legitimacy of sharing entities. Furthermore, blockchain has been integrated with knowledge-based networks for traffic optimization, resource sharing, network administration, access control, protecting privacy, traffic filtering, anomaly or intrusion detection, network virtualization, massive data analysis, edge and cloud computing, and data center networking. Despite the fact that many academics have employed the concept of blockchain in cognitive networks to achieve various objectives, we can also identify challenges such as high energy consumption, scalability issues, difficulty processing big data, etc. that act as barriers for integrating the two concepts together. Academicians have not yet reviewed blockchain-based network solutions in diverse application categories for diverse knowledge-defined networks in general, which consider knowledge generation and dissemination using various techniques such as machine learning, fuzzy logic, and meta-heuristics. Therefore, this article fills a void in the content of the literature by first reviewing the diverse existing blockchain-based applications in diverse knowledge-based networks, analyzing and comparing the existing works, describing the advantages and difficulties of using blockchain systems in KDN, and, finally, providing propositions based on identified challenges and then presenting prospects for the future. Full article

Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks. Full article

Free enzymes often face economic problems because of their non-repeatability and variability, which limit their application in industrial production. In this study, KDN lipase was immobilized with the macroporous resin LXTE-1000 and glutaraldehyde. The optimal conditions of enzyme immobilization were defined by a single factor experiment and response surface methodology (RSM). The concentration of the cross-linking agent glutaraldehyde was 0.46% (v/v), the cross-linking temperature was 25.0 °C, and the cross-linking time was 157 min. The enzyme activity of the immobilized KDN lipase after adsorption/cross-linking was 291.36 U/g, and the recovery of the enzyme activity was 9.90%. The optimal conditions for the synthesis of isopropyl myristate were catalyzed by the immobilized KDN lipase in a solvent-free system: immobilized enzyme 53 mg, reaction temperature 36.1 °C, myristic acid 228.4 mg, isopropanol 114 µL, and reaction time 18 h. The yield of isopropyl myristate was 66.62%. After ten cycles, the activity of the immobilized KDN lipase preserved more than 46.87% of its initial enzyme activity, and it demonstrated high tolerance to solvents compared to free KDN lipase. Full article

Sialic acids (Sias) are acidic monosaccharides and red meat is a notable dietary source of Sia for humans. Among the Sias, N-acetylneuraminic acid (Neu5Ac) and 2-keto-3-deoxy-D-glycero-D-galacto-2-nonulosonic acid (KDN) play multiple roles in immunity and brain cognition. On the other hand, N-glycolylneuraminic acid (Neu5Gc) is a non-human Sia capable of potentiating cancer and inflammation in the human body. However, their expression within the animal kingdom remains unknown. We determined Neu5Ac and KDN in skeletal muscle and organs across a range (n = 9) of species using UHPLC and found that (1) caprine skeletal muscle expressed the highest Neu5Ac (661.82 ± 187.96 µg/g protein) following by sheep, pig, dog, deer, cat, horse, kangaroo and cattle; (2) Among organs, kidney contained the most Neu5Ac (1992–3050 µg/g protein) across species; (3) ~75–98% of total Neu5Ac was conjugated, except for in dog and cat muscle (54–58%); (4) <1% of total Sia was KDN, in which ~60–100% was unconjugated, with the exception of sheep liver and goat muscle (~12–25%); (5) Neu5Ac was the major Sia in almost all tested organs. This study guides consumers to the safest red meat relating to Neu5Ac and Neu5Gc content, though the dog and cat meat are not conventional red meat globally. Full article

Estimation of spacecraft pose is essential for many space missions, such as formation flying, rendezvous, docking, repair, and space debris removal. We propose a learning-based method with uncertainty prediction to estimate the pose of a spacecraft from a monocular image. We first used a spacecraft detection network (SDN) to crop out the rectangular area in the original image where only spacecraft exist. A keypoint detection network (KDN) was then used to detect 11 pre-selected keypoints with obvious features from the cropped image and predict uncertainty. We propose a keypoints selection strategy to automatically select keypoints with higher detection accuracy from all detected keypoints. These selective keypoints were used to estimate the 6D pose of the spacecraft with the EPnP algorithm. We evaluated our method on the SPEED dataset. The experiments showed that our method outperforms heatmap-based and regression-based methods, and our effective uncertainty prediction can increase the final precision of the pose estimation. Full article

The purpose of this study is to conduct an economic evaluation of a photovoltaic-energy storage system (PV–ESS system) based on the power generation performance data of photovoltaic operations in Korea, and to calculate the optimal capacity of the energy storage system. In this study, PV systems in Jeju-do and Gyeongsangnam-do were targeted, PV systems in this area were assumed to be installed on a general site, and the research was conducted by applying weights based on the facility’s capacity. All the analyses were conducted using the actual amount of Korea power exchange (KPX) transactions of PV systems in 2019. In order to calculate the optimal capacity of the power conditioning system (PCS) and the battery energy storage system (BESS) according to global horizontal irradiation (GHI), PV systems with a minimum/maximum/central value were selected by comparing the solar radiation before the horizontal plane for three years (2017–2019) in the location where the PV systems was installed. As a result of the analysis, in Jeju-do, if the renewable energy certificate(REC) weight decreased to 3.4 when there was no change in the cost of installing a BESS and a PCS, it was more economical to link to the BESS than the operation of the PV system alone. In Gyeongsangnam-do, it was revealed that if the REC weight was reduced to 3.4, it was more likely to link to the BESS than the operation of the PV system alone. Full article

Leaving the current 4th generation of mobile communications behind, 5G will represent a disruptive paradigm shift integrating 5G Radio Access Networks (RANs), ultra-high-capacity access/metro/core optical networks, and intra-datacentre (DC) network and computational resources into a single converged 5G network infrastructure. The present paper overviews the main achievements obtained in the ALLIANCE project. This project ambitiously aims at architecting a converged 5G-enabled network infrastructure satisfying those needs to effectively realise the envisioned upcoming Digital Society. In particular, we present two networking solutions for 5G and beyond 5G (B5G), such as Software Defined Networking/Network Function Virtualisation (SDN/NFV) on top of an ultra-high-capacity spatially and spectrally flexible all-optical network infrastructure, and the clean-slate Recursive Inter-Network Architecture (RINA) over packet networks, including access, metro, core and DC segments. The common umbrella of all these solutions is the Knowledge-Defined Networking (KDN)-based orchestration layer which, by implementing Artificial Intelligence (AI) techniques, enables an optimal end-to-end service provisioning. Finally, the cross-layer manager of the ALLIANCE architecture includes two novel elements, namely the monitoring element providing network and user data in real time to the KDN, and the blockchain-based trust element in charge of exchanging reliable and confident information with external domains. Full article

With the expansion of advanced metering infrastructure (AMI) installations, various additional services using AMI data have emerged. However, some data is lost in the communication process of data collection. Hence, to address this challenge, the estimation of the missing data is required. To estimate the missing values in the time-series data generated from smart meters, we investigated four methods, ranging from a conventional method to an estimation method applying long short-term memory (LSTM), which exhibits excellent performance in the time-series field, and provided the performance comparison data. Furthermore, because power usages represent estimates of data that are missing some values in the middle, rather than regular time-series estimation data, the simple estimation may lead to an error where the estimated accumulated power usage in the missing data is larger than the real accumulated power usage appearing in the data after the end of the missing data interval. Therefore, this study proposes a hybrid method that combines the advantages of the linear interpolation method and the LSTM estimation-based compensation method, rather than those of conventional methods adopted in the time-series field. The performance of the proposed method is more stable and better than that of other methods. Full article

Partial discharge (PD) detection studies aiming at the fault diagnosis for facilities and power cables in transmission networks have been conducted over the years. Recently, the deep learning models for PD detection have been used to diagnose the PD fault of facilities and cables. Most PD studies have been conducted in the field, such as gas-insulated switchgear (GIS) and power cables for high voltage transmission networks. There are few studies of PD fault detection for on-site low-voltage distribution networks. Additionally, there are few studies of PD detection algorithms for improving the accuracy of the deep learning models using small real PD data only. In this study, a PD online detection system and a model for long-term operational sustainability of on-site low voltage distribution networks are proposed using convolutional neural network (CNN) transfer-learning. The proposed PD online system makes it possible to acquire as many real PD data as possible through continuous monitoring of PD occurrence. The PD detection accuracy results showed that the proposed CNN transfer-learning models are more effective models for obtaining improved accuracy (97.4%) than benchmark models, such as CNN and support vector machine (SVM) using only small real PD data acquired from PD online detection system. Full article

Sialic acids in the structural matrix of biofilms developing in engineered water systems constitute a potential target in the battle against biofouling. This report focuses specifically on the presence of sialic acids as part of the extracellular polymeric substances (EPS) of biofilms forming in cooling towers and the potential effect of nutrient starvation on sialic acid presence and abundance. Two cooling water compositions were compared in parallel pilot-scale cooling towers, one poor in nutrients and one enriched in nutrients. Fresh deposits from the two cooling towers were collected after a five-week operation period. EPS extractions and analyses by Fourier transform infrared spectroscopy (FTIR) and high-resolution mass spectrometry (MS), along with 16S rRNA gene amplicon sequencing were performed. The results of MS analyses showed the presence of pseudaminic/legionaminic acids (Pse/Leg) and 2-keto-3-deoxy-d-glycero-d-galacto-nononic acid (KDN) in both biofilm EPS samples. FTIR measurements showed the characteristic vibration of sialic acid-like compounds ν(C=O)OH in the nutrient poor sample exclusively. Our findings, combined with other recent studies, suggest that bacterial sialic acids are common compounds in environmental biofilms. Additionally, the conservation of sialic acid production pathways under nutrient starvation highlights their importance as constituents of the EPS. Further in-depth studies are necessary to understand the role of sialic acids in the structural cohesion and protection of environmental biofilm layer. Full article

Heavy-Hitters (HHs) are large-volume flows that consume considerably more network resources than other flows combined. In SDN-based DCNs (SDDCNs), HHs cause non-trivial delays for small-volume flows known as non-HHs that are delay-sensitive. Uncontrolled forwarding of HHs leads to network congestion and overall network performance degradation. A pivotal task for controlling HHs is their identification. The existing methods to identify HHs are threshold-based. However, such methods lack a smart system that efficiently identifies HH according to the network behaviour. In this paper, we introduce a novel approach to overcome this lack and investigate the feasibility of using Knowledge-Defined Networking (KDN) in HH identification. KDN by using Machine Learning (ML), allows integrating behavioural models to detect patterns, like HHs, in SDN traffic. Our KDN-based approach includes mainly three modules: HH Data Acquisition Module (HH-DAM), Data ANalyser Module (HH-DANM), and APplication Module (HH-APM). In HH-DAM, we present the flowRecorder tool for organizing packets into flows records. In HH-DANM, we perform a cluster-based analysis to determine an optimal threshold for separating HHs and non-HHs. Finally, in HH-APM, we propose the use of MiceDCER for routing non-HHs efficiently. The per-module evaluation results corroborate the usefulness and feasibility of our approach for identifying HHs. Full article

Load forecasting techniques can be an essential method to save energy and shave peak loads in order to improve energy efficiency and maintain the stability of a power grid. To achieve this goal, machine learning-based approaches have been proposed recently. Before moving toward the long-term and ultimate solution such as machine learning, we propose a simple and efficient method to forecast electricity usage patterns and the duration of maximum electrical load using a small data set. The proposed algorithm can forecast maximum electrical load duration using random sampling and a cumulative slope index. To verify the algorithm, we utilized electricity data (from 2015.11 to 2016.12) obtained from a building with a constant lifestyle and electricity pattern. The performance of the algorithm was evaluated using electricity bills, the discharging condition of an energy storage system, and the cumulative slope index. It was found that the proposed algorithm could provide electricity cost savings of 0.62–2.28% compared with other, conventional electricity prediction techniques, such as the moving average method and exponential smoothing. In near future research, it is expected that this algorithm could be applied to electrical big data to handle real-time data processing. Full article