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Salma Lasram Souheib Oueslati Ana Valero Sonia Marin Abdelwahed Ghorbel Vicente Sanchis 《Journal of food science》2010,75(2):M89-M97
ABSTRACT: This study investigated the effects of temperature (15 to 37 °C) and water activity (0.90 to 0.99) on the growth and production of ochratoxin A (OTA) by Aspergillus carbonarius cultured on synthetic nutrient medium (SNM) after 5 and 10 d of incubation. Total of 8 ochratoxigenic A. carbonarius, isolated from vineyards located in different regions of Tunisia, were used. Growth data were modeled by the flexible model of Baranyi and growth rates at each set of conditions were obtained. For both growth and OTA production, optimal water activity was 0.99; however, optimal temperature varied. The optimal temperature for growth was 30 °C. At 37 °C, the growth rate decreased significantly (P < 0.05). Maximum toxin production occurred at temperatures in the range of 15 to 25 °C with the optimum one depending on the isolate tested. Significant amounts of OTA were produced after only 5 d of incubation. Our results showed that A. carbonarius isolated from Tunisian grapes behave as those from European and Australian grapes, as reported in the literature, although some differences in trends for growth and OTA production were observed. 相似文献
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Mesfer Al Duhayyim Heba G. Mohamed Fadwa Alrowais Fahd N. Al-Wesabi Anwer Mustafa Hilal Abdelwahed Motwakel 《计算机系统科学与工程》2023,46(2):1293-1310
The Internet of Things (IoT) has gained more popularity in research because of its large-scale challenges and implementation. But security was the main concern when witnessing the fast development in its applications and size. It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats. Additionally, machine learning (ML) techniques optimally use a colossal volume of data generated by IoT devices. Deep Learning (DL) related systems were modelled for attack detection in IoT. But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations. This study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network (AAA-ODBN) Enabled Ransomware Detection in an IoT environment. The presented AAA-ODBN technique mainly intends to recognize and categorize ransomware in the IoT environment. The presented AAA-ODBN technique follows a three-stage process: feature selection, classification, and parameter tuning. In the first stage, the AAA-ODBN technique uses AAA based feature selection (AAA-FS) technique to elect feature subsets. Secondly, the AAA-ODBN technique employs the DBN model for ransomware detection. At last, the dragonfly algorithm (DFA) is utilized for the hyperparameter tuning of the DBN technique. A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm. The experimental values indicate the significant outcome of the AAA-ODBN model over other models. 相似文献
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This article presents the design and implementation of a controller scheme for efficient resource management in Advanced Life Support Systems. In the proposed approach, a switching hybrid system model is used to represent the dynamics of the system components and their interactions. The operational specifications for the controller are represented by utility functions, and the corresponding resource management problem is formulated as a safety control problem. The controller is designed as a limited-horizon online supervisory controller that performs a limited forward search on the state-space of the system at each time step, and uses the utility functions to decide on the best action. The feasibility and accuracy of the online algorithm can be assessed at design time. We demonstrate the effectiveness of the scheme by running a set of experiments on the Reverse Osmosis (RO) subsystem of the Water Recovery System (WRS). 相似文献
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We report the synthesis of polyurethane‐urea elastomers containing 1,2,4,5‐tetrazine covalently reacted within the main chains of the polymers. Our study investigates the synthesis of 3,6‐diamino‐1,2,4,5‐tetrazine (DAT), the polymerization reaction conditions for reacting DAT into the backbone of segmented polyurethane elastomers, and the metal‐complexation capabilities of tetrazine‐containing elastomers with cobalt (II) chloride. Tetrazines are highly colored and electro‐active heterocyclic moieties, which have a very high electron affinity which make them reducible at high to very high potentials. Upon complexation with metals, we observed a strong color shift of the polymers from deep red to blue indicating the binding efficacy for the polymers. We quantified the metal‐complexation capability of the tetrazine elastomers and determined a molar ratio of approximately two metal atoms per tetrazine allowing us to provide a plausible complexation mechanism for the active polymers. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009 相似文献
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Node localization is one of the most critical issues for wireless sensor networks, as many applications depend on the precise location of the sensor nodes. To attain precise location of nodes, an improved distance vector hop (IDV-Hop) algorithm using teaching learning based optimization (TLBO) has been proposed in this paper. In the proposed algorithm, hop sizes of the anchor nodes are modified by adding correction factor. The concept of collinearity is introduced to reduce location errors caused by anchor nodes which are collinear. For better positioning coverage, up-gradation of target nodes to assistant anchor nodes has been used in such a way that those target nodes are upgraded to assistant anchor nodes which have been localized in the first round of localization. For further improvement in localization accuracy, location of target nodes has been formulated as optimization problem and an efficient parameter free optimization technique viz. TLBO has been used. Simulation results show that the proposed algorithm is overall 47, 30 and 22% more accurate than DV-Hop, DV-Hop based on genetic algorithm (GADV-Hop) and IDV-Hop using particle swarm optimization algorithms respectively and achieves high positioning coverage with fast convergence. 相似文献
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Anwer Mustafa Hilal Siwar Ben Haj Hassine Souad Larabi-Marie-Sainte Nadhem Nemri Mohamed K. Nour Abdelwahed Motwakel Abu Sarwar Zamani Mesfer Al Duhayyim 《计算机、材料和连续体(英文)》2022,72(1):713-726
The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data. This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT. The proposed Machine Learning based Block Chain Technology malware detection scheme (MLBCT-Mdetect) is implemented in three steps namely: feature extraction, Classification and blockchain. Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight. Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes. Furthermore, third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy. ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate. 相似文献
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Anwer Mustafa Hilal Jaber S. Alzahrani Ibrahim Abunadi Nadhem Nemri Fahd N. Al-Wesabi Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani 《计算机、材料和连续体(英文)》2022,72(1):333-348
In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims at identifying the existence of intrusions in network. Further, PPBDL-IIoT technique also involves the design of Chaos Game Optimization (CGO) with Bidirectional Gated Recurrent Neural Network (BiGRNN) technique for both detection and classification of intrusions in the network. Besides, CGO technique is applied to fine tune the hyperparameters in BiGRNN model. CGO algorithm is applied to optimally adjust the learning rate, epoch count, and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model. Moreover, Blockchain enabled Integrity Check (BEIC) scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system. The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack (ICSCA) dataset and the outcomes were analysed under various measures. The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%. 相似文献
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Khaled Mohamad Almustafa Taiseer Abdalla Elfadil Eisa Amani Abdulrahman Albraikan Mesfer Al Duhayyim Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Muhammad Imran Babar 《计算机、材料和连续体(英文)》2022,72(2):2483-2499
In recent years, Software Defined Networking (SDN) has become an important candidate for communication infrastructure in smart cities. It produces a drastic increase in the need for delivery of video services that are of high resolution, multiview, and large-scale in nature. However, this entity gets easily influenced by heterogeneous behaviour of the user's wireless link features that might reduce the quality of video stream for few or all clients. The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences. Besides, multicast routing protocol with multiple constraints in terms of Quality of Service (QoS) is a Nondeterministic Polynomial time (NP) hard problem which can be solved only with the help of metaheuristic optimization algorithms. With this motivation, the current research paper presents a new Improved Black Widow Optimization with Levy Distribution model (IBWO-LD)-based multicast routing protocol for smart cities. The presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients receive. Besides, a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints. A detailed experimental analysis was carried out to ensure the outcomes improved under different aspects. The results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods. 相似文献