Fusarium oxysporum produced maximum extracellular inulinase after 9 days of its growth at 25°C on a medium (pH 5.5) containing 3% fructan and 0.2% sodium nitrate. The level of this enzyme decreased on the addition of either glucose, fructose, galactose or sucrose to F. oxysporum already growing on a fructan-containing medium. A significant increase in invertase production which resulted in an increase of the invertase/inulinase (S/I) ratio, was observed on addition of inulin to this fungus growing on other carbon sources. Glycerol (10%) gave better protection to inulinase against thermal denaturation at 50°C compared to ethylene glycol and sorbitol. Inulinase immobilised in polyacrylamide gel retained 45% of its original activity. The immobilised enzyme showed a higher optimum temperature (45°C) compared to free enzyme (37°C). The immobilised enzyme after storage at 25°C for 96 h showed 58% activity. Thermal stability of entrapped inulinase increased in the presence of inulin. 相似文献
The anti-oxidant activity of extracts from 36 vegetables was evaluated by using a model system consisting of β-carotene and linoleic acid. The total phenolics of the extracts was determined spectrophotometrically according to the Folin–Ciocalteau procedure and ranged from 34 to 400 mg (100 g)−1 on a fresh weight basis. Mint, aonla, black carrots, chenopodium, fenugreek, kachnar and ginger had high phenolic contents. The anti-oxidant activity expressed as per percent inhibition of oxidation ranged from a high of 92% in turmeric extracts to a low of 12.8% in long melon. Other vegetables found to have high anti-oxidant activity (>70%) were kachnar, aonla, ginger, fenugreek, mint, beetroot, black carrots, Brussels sprouts, broccoli, lotus stem, yam, coriander and tomato. Anti-oxidant activity correlated significantly and positively with total phenolics ( r 2=0.6578, P < 0.05). The results indicate that vegetables containing high phenolics may provide a source of dietary anti-oxidants. 相似文献
Calcium silicates are very stable and good hosts for luminescent materials. These calcium silicates are synthesized using cost-effective agro-food wastes such as rice husk ash and eggshell powder along with doping of samarium oxide [Ca3?xSi2O7:xSm3+(x(%)?=?0.25, 0.50, 0.75, and 1.00)] via solid-state reaction method. X-ray diffraction confirms that the Ca3Si2O7 phase co-exists with the monoclinic-Ca2SiO4 phase. An increase in doping concentration of Sm3+ enhances the Ca2SiO4 phase content. Two types of morphology can be seen in the SEM micrographs confirming the presence of two phases. Photoluminescence emission spectra contain peaks in the visible region. Characteristic emission peaks of Sm3+ are present along with strong peaks due to the titanium ions present in agro-food wastes. Commission International de'Eclairage (CIE) co-ordinates correspond to the green region, which is significantly different from the CIE co-ordinates of Sm3+ doped samples derived from mineral oxides. This study presents an alternate use of agro-food wastes for synthesizing visible light-emitting phosphors and presents a mechanism for stabilizing Ca2SiO4 in waste-derived samples.
This study introduces a comprehensive framework designed for detecting and mitigating fake and potentially threatening user communities within 5G social networks. Leveraging geo-location data, community trust dynamics, and AI-driven community detection algorithms, this framework aims to pinpoint users posing potential harm. Including an artificial control model facilitates the selection of suitable community detection algorithms, coupled with a trust-based strategy to effectively identify and filter potential attackers. A distinctive feature of this framework lies in its ability to consider attributes that prove challenging for malicious users to emulate, such as the established trust within the community, geographical location, and adaptability to diverse attack scenarios. To validate its efficacy, we illustrate the framework using synthetic social network data, demonstrating its ability to distinguish potential malicious users from trustworthy ones. 相似文献
Software-defined networking (SDN) is an advanced networking paradigm that decouples forwarding control logic from the data plane. Therefore, it provides a loosely-coupled architecture between the control and data plane. This separation provides flexibility in the SDN environment for addressing any transformations. Further, it delivers a centralized way of managing networks due to control logic embedded in the SDN controller. However, this advanced networking paradigm has been facing several security issues, such as topology spoofing, exhausting bandwidth, flow table updating, and distributed denial of service (DDoS) attacks. A DDoS attack is one of the most powerful menaces to the SDN environment. Further, the central data controller of SDN becomes the primary target of DDoS attacks. In this article, we propose a Kafka-based distributed DDoS attacks detection approach for protecting the SDN environment named K-DDoS-SDN. The K-DDoS-SDN consists of two modules: (i) Network traffic classification (NTClassification) module and (ii) Network traffic storage (NTStorage) module. The NTClassification module is the detection approach designed using scalable H2O ML techniques in a distributed manner and deployed an efficient model on the two-nodes Kafka Streams cluster to classify incoming network traces in real-time. The NTStorage module collects raw packets, network flows, and 21 essential attributes and then systematically stores them in the HDFS to re-train existing models. The proposed K-DDoS-SDN designed and evaluated using the recent and publically available CICDDoS2019 dataset. The average classification accuracy of the proposed distributed K-DDoS-SDN for classifying network traces into legitimate and one of the most popular attacks, such as DDoS_UDP is 99.22%. Further, the outcomes demonstrate that proposed distributed K-DDoS-SDN classifies traffic traces into five categories with at least 81% classification accuracy. 相似文献
Agriculture has been affected by several global trends raising the concern for food security. Agri-food demands are amplifying due to the ever-escalating population numbers. Thus, the notion of extending the use of smart innovative technology in managing agricultural practices has emerged rapidly over the last decade. Technological innovations have contributed significantly to shape modern agriculture as smart agriculture. Smart agriculture unfolds various benefits such as increased production, real time data and production insights and remote monitoring. The rising advancements in Information and Communication Technologies (ICT) have paved the way for researchers to use these technologies in managing agricultural practices, leading to greater benefits for farmers and society. These innovations are the key to establishing agriculture as a research discipline. The purpose of this article is to conduct a scientometric analysis to study the structure and evolution of research activities in the field of smart agriculture. The scientometric analysis aims to empirically map the scientific knowledge and identify any possible challenges in the field. This study performs elementary analysis to study publication growth over the years, impact analysis to assess the leading journals, authors, and countries, and articles analysis for findings patterns among the citations over the years and among the keyword-based clusters. There has been a considerable increase of more than 200% in the number of publications from 2011 to 2022. However, around 60% of authors have contributed with a single publication. The findings of the study reveal the research trends and hot topics for future research fronts. Deep learning, digital agriculture, object detection, blockchain, and semantic segmentation have been identified as trending topics in smart agriculture. Comprehensively, an intellectual view of the agriculture domain is presented as a scientific field in this article. 相似文献