Draft genome sequence of Enterobacter sp. from wheat grain.

Here, we report the draft genome sequence of an isolate from the Enterobacter cloacae species complex. Enterobacter spp. are plant growth-promoting microbes and biocontrol agents. Analyses of this genome will serve as a useful resource for future studies of similar microbes isolated from grain.

Corrigendum: A survey of Fusarium species and ADON genotype on Canadian wheat grain.

[This corrects the article DOI: 10.3389/ffunb.2022.1062444.].

A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism.

In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms-XGboost and random forest (RF)-used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities.

An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain.

In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.

An Intelligent Automated System for Detecting Malicious Vehicles in Intelligent Transportation Systems.

The exponential growth of intelligent vehicles(IVs) development has resulted in a complex network. As the number of IVs in a network increases, so does the number of connections. As a result, a great deal of data is generated. This complexity leads to insecure communication, traffic congestion, security, and privacy issues in vehicular networks (VNs). In addition, detecting malicious IVs, data integration, and data validation are major issues in VNs that affect network performance. A blockchain-based model for secure communication and malicious IV detection is proposed to address the above issues. In addition, this system also addresses data integration and transaction validation using an encryption scheme for secure communication. A multi-chain concept separates the legitimate and malicious data into two chains: the Integrity chain (I-chain) and Fraud chain (F-chain). This multi-chain mechanism solves the storage problem and reduces the computing power. The integration of blockchain in the proposed model provides privacy, network security, transparency, and immutability. To address the storage issue, the InterPlanetary File System (IPFS) is integrated with Certificate Authority (CA). A reputation mechanism is introduced to detect malicious IVs in the network based on ratings. This reputation mechanism is also used to prevent Sybil attack. The evaluation of the proposed work is based on the cost of smart contracts and computation time. Furthermore, two attacker models are presented to prevent the selfish mining attack and the Sybil attack. Finally, a security analysis of the proposed smart contracts with their security vulnerabilities is also presented.

A survey of Fusarium species and ADON genotype on Canadian wheat grain.

Introduction: Wheat is a staple food that is important to global food security, but in epidemic years, fungal pathogens can threaten production, quality, and safety of wheat grain. Globally, one of the most important fungal diseases of wheat is Fusarium head blight (FHB). This disease can be caused by several different Fusarium species with known differences in aggressiveness and mycotoxin-production potential, with the trichothecene toxin deoxynivalenol (DON) and its derivatives being of particular concern. In North America, the most predominant species causing FHB is F. graminearum, which has two distinct sub-populations that are commonly classified into two main chemotypes/genotypes based on their propensity to form trichothecene derivatives, namely 15-acetyldeoxynivalenol (15-ADON) and 3-acetyldeoxynivalenol (3-ADON). Materials and methods: We used a panel of 13 DNA markers to perform species and ADON genotype identification for 55, 444 wheat kernels from 7, 783 samples originating from across Canada from 2014 to 2020. Results and discussion: Based on single-seed analyses, we demonstrate the relationships between Fusarium species and trichothecene chemotype with sample year, sample location, wheat species (hexaploid and durum wheat), severity of Fusarium damaged kernels (FDK), and accumulation of DON. Results indicate that various Fusarium species are present across wheat growing regions in Canada; however, F. graminearum is the most common species and 3-ADON the most common genotype. We observed an increase in the occurrence of the 3-ADON genotype, particularly in the western Prairie regions. Our data provides important information on special-temporal trends in Fusarium species and chemotypes that can aid with the implementation of integrated disease management strategies to control the detrimental effects of this devastating disease.

Profiling of Bacillus cereus on Canadian grain.

Microorganisms that cause foodborne illnesses challenge the food industry; however, environmental studies of these microorganisms on raw grain, prior to food processing, are uncommon. Bacillus cereus sensu lato is a diverse group of bacteria that is common in our everyday environment and occupy a wide array of niches. While some of these bacteria are beneficial to agriculture due to their entomopathogenic properties, others can cause foodborne illness; therefore, characterization of these bacteria is important from both agricultural and food safety standpoints. We performed a survey of wheat and flax grain samples in 2018 (n = 508) and 2017 (n = 636) and discovered that B. cereus was present in the majority of grain samples, as 56.3% and 85.2%, in two years respectively. Whole genome sequencing and comparative genomics of 109 presumptive B. cereus isolates indicates that most of the isolates were closely related and formed two genetically distinct groups. Comparisons to the available genomes of reference strains suggested that the members of these two groups are not closely related to strains previously reported to cause foodborne illness. From the same data set, another, genetically more diverse group of B. cereus was inferred, which had varying levels of similarity to previously reported strains that caused disease. Genomic analysis and PCR amplification of genes linked to toxin production indicated that most of the isolates carry the genes nheA and hbID, while other toxin genes and gene clusters, such as ces, were infrequent. This report of B. cereus on grain from Canada is the first of its kind and demonstrates the value of surveillance of bacteria naturally associated with raw agricultural commodities such as cereal grain and oilseeds.

Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids.

In the smart grid (SG) environment, consumers are enabled to alter electricity consumption patterns in response to electricity prices and incentives. This results in prices that may differ from the initial price pattern. Electricity price and demand forecasting play a vital role in the reliability and sustainability of SG. Forecasting using big data has become a new hot research topic as a massive amount of data is being generated and stored in the SG environment. Electricity users, having advanced knowledge of prices and demand of electricity, can manage their load efficiently. In this paper, a recurrent neural network (RNN), long short term memory (LSTM), is used for electricity price and demand forecasting using big data. Researchers are working actively to propose new models of forecasting. These models contain a single input variable as well as multiple variables. From the literature, we observed that the use of multiple variables enhances the forecasting accuracy. Hence, our proposed model uses multiple variables as input and forecasts the future values of electricity demand and price. The hyperparameters of this algorithm are tuned using the Jaya optimization algorithm to improve the forecasting ability and increase the training mechanism of the model. Parameter tuning is necessary because the performance of a forecasting model depends on the values of these parameters. Selection of inappropriate values can result in inaccurate forecasting. So, integration of an optimization method improves the forecasting accuracy with minimum user efforts. For efficient forecasting, data is preprocessed and cleaned from missing values and outliers, using the z-score method. Furthermore, data is normalized before forecasting. The forecasting accuracy of the proposed model is evaluated using the root mean square error (RMSE) and mean absolute error (MAE). For a fair comparison, the proposed forecasting model is compared with univariate LSTM and support vector machine (SVM). The values of the performance metrics depict that the proposed model has higher accuracy than SVM and univariate LSTM.