Search

Faba beans are a promising emerging plant-based protein source to be used as a quality alternative to peas and soy. In this study, the potential health beneficial activities of three Canadian faba bean varieties (Fabelle, Malik and Snowbird) were investigated after in vitro gastrointestinal digestion and compared to two commonly used legumes (peas and soy). The results revealed that the faba beans had a higher antioxidant activity than peas when assessed with the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and the 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) assays, except for the Fabelle variety. In the oxygen radical absorbance capacity (ORAC) and the iron chelating assays, the faba beans had a lower antioxidant activity than soy. Interestingly, Fabelle and Snowbird showed a higher antioxidant effect than the peas and soy at the cellular level. The antihypertensive properties of Fabelle and Malik varieties were significantly higher than peas but lower than soy. The in vitro antidiabetic activity was higher for soy, but no differences were found at the cellular level. The faba bean peptides were further fractionated and sequenced by mass spectrometry. Eleven peptides with in silico predicted bioactivities were successfully identified in the faba bean digestate and support validating the health-promoting properties of peptides. The results demonstrate the bioactive potential of faba beans as a health-promoting food ingredient against non-communicable diseases.

An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various heart-related conditions in patients. An automated diagnostic tool is required to detect significant episodes in long-term ECG records. It is a very challenging task for cardiologists to analyze long-term ECG records in a short time. Therefore, a computer-based diagnosis tool is required to identify crucial episodes. Myocardial infarction (MI) and conduction disorders (CDs), sometimes known as heart blocks, are medical diseases that occur when a coronary artery becomes fully or suddenly stopped or when blood flow in these arteries slows dramatically. As a result, several researchers have utilized deep learning methods for MI and CD detection. However, there are one or more of the following challenges when using deep learning algorithms: (i) struggles with real-life data, (ii) the time after the training phase also requires high processing power, (iii) they are very computationally expensive, requiring large amounts of memory and computational resources, and it is not easy to transfer them to other problems, (iv) they are hard to describe and are not completely understood (black box), and (v) most of the literature is based on the MIT-BIH or PTB databases, which do not cover most of the crucial arrhythmias. This paper proposes a new deep learning approach based on machine learning for detecting MI and CDs using large PTB-XL ECG data. First, all challenging issues of these heart signals have been considered, as the signal data are from different datasets and the data are filtered. After that, the MI and CD signals are fed to the deep learning model to extract the deep features. In addition, a new custom activation function is proposed, which has fast convergence to the regular activation functions. Later, these features are fed to an external classifier, such as a support vector machine (SVM), for detection. The efficiency of the proposed method is demonstrated by the experimental findings, which show that it improves satisfactorily with an overall accuracy of 99.20% when using a CNN for extracting the features with an SVM classifier.

Cellulose represents the most abundant component of plant biomass on earth; it is degraded by cellulases, specific enzymes produced by microorganisms. However, cellulases of bacterial origin attract more interest due to their natural diversity and ability to inhabit a variety of niches, allowing the selection of cellulolytic strains resistant to environmental stresses. The screening of the cellulolytic activity of 398 bacteria isolated from various ecosystems in Algeria (cave, ruins, chott, thermal station, and rhizosphere of arid and semi-arid regions) was performed by the appearance of a hydrolysis zone on carboxymethylcellulose (CMC) medium. The cellulase activity on CMC (1%) broth allowed to select 26 strains among which 12 had the best activity (0.3 U/mL to 2.2 U/mL). Optimization of physicochemical parameters (salinity: 0–1 M NaCl; pH: 3, 4, 7, 9, and 11; temperature: 30, 45, and 50 °C; PEG8000: 0 and 30%) involved in growth and cellulose production showed that the majority of strains were mesophilic, neutrophilic, or alkali- tolerant and tolerant to 30% of PEG8000. The cellulase activity and stability under different stress allowed to retain five strains, which the most efficient. Based on the 16S-rRNA sequencing results, they belonged to the genus Bacillus. The physicochemical properties of cellulases (crude extract) showed a CMCase active over a wide range of pH (4 to 11), optimal at 50 °C and 60 °C. The inhibiting salinity effect on the activity was not detected and was negligible on the enzymatic stability. The residual CMCase activity remained between 40 and 70% in a temperature range between 40 and 70 °C, was stable over a wide range of saline concentrations (0–2000 mM), and was weakly affected at 30% of PEG8000. The crude enzyme extract was able to hydrolyze both soluble and insoluble cellulosic substrates. The evaluation of the hydrolysis capacity of lignocellulosic waste revealed the ability of tested strains to degrade wheat bran, barley bran, and corncob. In addition, the enzyme showed significant multi-stress resistance on solid and liquid media. By these characteristics, these cellulolytic bacteria could be attractive to be used in various industrial and biotechnology applications.

The critical task of recognizing human–object interactions (HOI) finds its application in the domains of surveillance, security, healthcare, assisted living, rehabilitation, sports, and online learning. This has led to the development of various HOI recognition systems in the recent past. Thus, the purpose of this study is to develop a novel graph-based solution for this purpose. In particular, the proposed system takes sequential data as input and recognizes the HOI interaction being performed in it. That is, first of all, the system pre-processes the input data by adjusting the contrast and smoothing the incoming image frames. Then, it locates the human and object through image segmentation. Based on this, 12 key body parts are identified from the extracted human silhouette through a graph-based image skeletonization technique called image foresting transform (IFT). Then, three types of features are extracted: full-body feature, point-based features, and scene features. The next step involves optimizing the different features using isometric mapping (ISOMAP). Lastly, the optimized feature vector is fed to a graph convolution network (GCN) which performs the HOI classification. The performance of the proposed system was validated using three benchmark datasets, namely, Olympic Sports, MSR Daily Activity 3D, and D3D-HOI. The results showed that this model outperforms the existing state-of-the-art models by achieving a mean accuracy of 94.1% with the Olympic Sports, 93.2% with the MSR Daily Activity 3D, and 89.6% with the D3D-HOI datasets.

The internet of things (IoT) concept has been extremely investigated in many modern smart applications, which enable a set of sensors to either process the collected data locally or send them to the cloud for remote processing. Unfortunately, cloud datacenters are located far away from IoT devices, and consequently, the transmission of IoT data may be delayed. In this paper, we investigate fog computing, which is a new paradigm that overcomes many issues of cloud computing. More importantly, dynamic task offloading in fog computing is a challenging problem that requires an optimal decision for processing the tasks that are generated in each time slot. Thus, exact optimization methods based on Lyapunov function have been widely used for solving dynamic offloading which represents an NP hard problem. To overcome the scalability issue of exact optimization techniques, we have explored famous population based meta-heuristics for optimizing the offloading process of a set of dynamic tasks using Orthogonal Frequency Division Multiplexing (OFDM) communication. Hence, a parallel multi-threading framework is proposed for generating the optimal offloading solution while selecting the best sub-carrier for each offloaded task. More importantly, our contribution associates a thread for each IoT device and generates a population of random solutions. Next, each population is updated and evaluated according to the proposed fitness function that considers a tradeoff between the delay and energy consumption. Upon the arrival of new tasks at each time slot, an evaluation is performed for maintaining some individuals of the previous population while generating new individuals based on some criteria. Our results have been compared to the results achieved using Lyapunov optimization. They demonstrate the convergence of the fitness function, the scalability of the parallel Particle Swarm Optimization (PSO) approach, and the performance in terms of the offline error and the execution cost.

Faba beans are emerging as sustainable quality plant protein sources, with the potential to help meet the growing global demand for more nutritious and healthy foods. The faba bean, in addition to its high protein content and well-balanced amino acid profile, contains bioactive constituents with health-enhancing properties, including bioactive peptides, phenolic compounds, GABA, and L-DOPA. Faba bean peptides released after gastrointestinal digestion have shown antioxidant, antidiabetic, antihypertensive, cholesterol-lowering, and anti-inflammatory effects, indicating a strong potential for this legume crop to be used as a functional food to help face the increasing incidences of non-communicable diseases. This paper provides a comprehensive review of the current body of knowledge on the nutritional and biofunctional qualities of faba beans, with a particular focus on protein-derived bioactive peptides and how they are affected by food processing. It further covers the adverse health effects of faba beans associated with the presence of anti-nutrients and potential allergens, and it outlines research gaps and needs.

Halotolerant plant-growth-promoting rhizobacteria (PGPR) could not only promote plant growth, but also help in counteracting the detrimental effects of salt stress. In the present study, a total of 76 bacteria were isolated from the rhizosphere, non-rhizospheric soil and endophytes of the halophyte Salsola tetrandra, collected from natural saline soils in Algeria. Phylogenetic analysis based on the 16S rDNA sequence of Gram-negative bacteria (n = 51) identified, showed seventeen representative isolates grouped into four genera (Pseudomonas, Acinetobacter, Enterobacter, and Providencia). These bacterial isolates that exhibited different PGPR traits were selected and tested for their ability to tolerate different abiotic stress (NaCl, PEG8000, and pH). The majority of isolates were drought tolerant (60% of PEG8000) and had an optimal growth at high pH values (pH 9 and 11) and some strains tolerated 2 M of NaCl. Strains identified as Enterobacter xiangfangensis BE1, Providencia rettgeri BR5 and Pseudomonas stutzeri MLR6 showed high capacity of adaptation on their PGP traits. The salt-tolerant isolates were finally chosen to promote growth and enhance salt tolerance, separately or combined, of Arabidopsis thaliana (Col-0) exposed or not to 0.1 M NaCl, by following fresh and root weight, primary root elongation and lateral root number. The best bacterial effect was recorded for the MLR6 strain in increasing shoot fresh weight and for BE1 in terms of root fresh weight in the absence of salt stress. At stressed conditions, all growth parameters declined. However, inoculation of Arabidopsis thaliana with the three bacterial strains (MLR6, BE1 and BR5), single or in co-culture, conferred an increase in the shoot weight, primary root length and lateral root number. The use of these strains separately or combined as biofertilizers seems to be a powerful tool in the development of sustainable agriculture in saline soils.

Mobile crowd-sensing (MCS) is a well-known paradigm used for obtaining sensed data by using sensors found in smart devices. With the rise of more sensing tasks and workers in the MCS system, it is now essential to design an efficient approach for task allocation. Moreover, to ensure the completion of the tasks, it is necessary to incentivise the workers by rewarding them for participating in performing the sensing tasks. In this paper, we aim to assist workers in selecting multiple tasks while considering the time constraint of the worker and the requirements of the task. Furthermore, a pricing mechanism is adopted to determine each task budget, which is then used to determine the payment for the workers based on their willingness factor. This paper proves that the task-allocation is a non-deterministic polynomial (NP)-complete problem, which is difficult to solve by conventional optimization techniques. A worker multitask allocation-genetic algorithm (WMTA-GA) is proposed to solve this problem to maximize the workers welfare. Finally, theoretical analysis demonstrates the effectiveness of the proposed WMTA-GA. We observed that it performs better than the state-of-the-art algorithms in terms of average performance, workers welfare, and the number of assigned tasks.

The soil-borne species Aneurinibacillus migulanus (A. migulanus) strains Nagano and NCTC 7096 were shown to be potent biocontrol agents active against several plant diseases in agricultural and forest ecosystems. Both strains produce the cyclic peptide gramicidin S (GS) that was described as the main weapon inhibiting some gram-negative and gram-positive bacteria and fungus-like organisms along with the production of biosurfactant and hemolysis activities. However, the contribution of the cyclic peptide gramicidin S (GS) to the biocontrol ability of A. migulanus has never been studied experimentally. In this paper, using a mutant of the A. migulanus Nagano strain (E1 mutant) impaired in GS biosynthesis we evaluated the contribution of GS in the biocontrol potential of A. migulanus against Phytophthora spp. The two strains of A. migulanus, Nagano and NCTC 7096, were tested in a pilot study for the inhibition of the growth of 13 Phytophthora species in dual culture assays. A. migulanus Nagano was significantly more inhibitory than NCTC 7096 to all species. Additionally, using apple infection assays, P. rosacearum MKDF-148 and P. cryptogea E2 were shown to be the most aggressive on apple fruits displaying clear infection halos. Therefore, the three A. migulanus strains, Nagano, NCTC 7096, and E1, were used in apple infection experiments to check their effect on infection ability of these two Phytophthora species. Treatment with A. migulanus Nagano significantly reduced the severity of symptoms in apple fruits compared with NCTC 7096. A. migulanus E1 mutant showed total loss of biocontrol ability suggesting that GS is a major actor in the biocontrol ability of A. migulanus Nagano strain.

The cloud computing paradigm is evolving rapidly to address the challenges of new emerging paradigms, such as the Internet of Things (IoT) and fog computing. As a result, cloud services usage is increasing dramatically with the recent growth of IoT-based applications. To successfully fulfill application requirements while efficiently harnessing cloud computing power, intelligent scheduling approaches are required to optimize the scheduling of IoT application tasks on computing resources. In this paper, the chimp optimization algorithm (ChOA) is incorporated with the marine predators algorithm (MPA) and disruption operator to determine the optimal solution to IoT applications’ task scheduling. The developed algorithm, called CHMPAD, aims to avoid entrapment in the local optima and improve the exploitation capability of the basic ChOA as its main drawbacks. Experiments are conducted using synthetic and real workloads collected from the Parallel Workload Archive to demonstrate the applicability and efficiency of the presented CHMPAD method. The simulation findings reveal that CHMPAD can achieve average makespan time improvements of 1.12–43.20% (for synthetic workloads), 1.00–43.43% (for NASA iPSC workloads), and 2.75–42.53% (for HPC2N workloads) over peer scheduling algorithms. Further, our evaluation results suggest that our proposal can improve the throughput performance of fog computing.

The development of smart network infrastructure of the Internet of Things (IoT) faces the immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The existing network security solutions of enterprise networks are significantly expensive and unscalable for IoT. The integration of recently developed Software Defined Networking (SDN) reduces a significant amount of computational overhead for IoT network devices and enables additional security measurements. At the prelude stage of SDN-enabled IoT network infrastructure, the sampling based security approach currently results in low accuracy and low DDoS attack detection. In this paper, we propose an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework. The proposed AMLSDM framework develops an SDN-enabled security mechanism for IoT devices with the support of an adaptive machine learning classification model to achieve the successful detection and mitigation of DDoS attacks. The proposed framework utilizes machine learning algorithms in an adaptive multilayered feed-forwarding scheme to successfully detect the DDoS attacks by examining the static features of the inspected network traffic. In the proposed adaptive multilayered feed-forwarding framework, the first layer utilizes Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbor (kNN), and Logistic Regression (LR) classifiers to build a model for detecting DDoS attacks from the training and testing environment-specific datasets. The output of the first layer passes to an Ensemble Voting (EV) algorithm, which accumulates the performance of the first layer classifiers. In the third layer, the adaptive frameworks measures the real-time live network traffic to detect the DDoS attacks in the network traffic. The proposed framework utilizes a remote SDN controller to mitigate the detected DDoS attacks over Open Flow (OF) switches and reconfigures the network resources for legitimate network hosts. The experimental results show the better performance of the proposed framework as compared to existing state-of-the art solutions in terms of higher accuracy of DDoS detection and low false alarm rate.

Abstract The Schwarz algorithm for a class of elliptic quasi-variational inequalities with nonlinear source terms is studied in this work. The authors prove a new error estimate in uniform norm, making use of a stability property of the discrete solution. The domain is split into two sub-domains with overlapping non-matching grids. This approach combines the geometrical convergence of solutions and the uniform convergence of variational inequalities.

Background: Due to the increasing complexity of embedded systems, system designers use higher levels of abstraction in order to model and analyse system performances. STARSoC (Synthesis Tool for Adaptive and Reconfigurable System-on-Chip) is a tool for hardware/software co-design and the synthesis of System-on-Chip (SoC) starting from a high level model using the StreamsC textual language. The process behaviour is described in the C syntax language, whereas the architecture is defined with a small set of annotation directives. Therefore, these specifications bring together a large number of details which increase their complexity. However, graphical modelling is better suited for visualizing system architecture. Objectives: In this paper, the authors propose a graphical modelling editor for STARSoC design tool which allows models to be constructed quickly and legibly. Its intent is to assist designers in building their models in terms of the UML Component-like Diagram, and in the automatic translation of the drawn model into StreamsC specification. Methods: To achieve this goal, the Model-Driven Engineering (MDE) approach and well-known frameworks and tools on the Eclipse platform were employed. Conclusion: Our results indicate that the use of the Model-Driven Engineering (MDE) approach reduces the complexity of embedded system design, and it is sufficiently flexible to incorporate new design needs.

Bacillus velezensis gram-positive bacterium, is frequently isolated from diverse niches mainly soil, water, plant roots, and fermented foods. B. velezensis is ubiquitous, non-pathogenic and endospore forming. Being frequently isolated from diverse plant holobionts it is considered host adapted microorganism and recognized of high economic importance given its ability to promote plant growth under diverse biotic and abiotic stress conditions. Additionally, the species suppress many plant diseases, including bacterial, oomycete, and fungal diseases. It is also able after plant host root colonization to induce unique physiological situation of host plant called primed state. Primed host plants are able to respond more rapidly and/or effectively to biotic or abiotic stress. Moreover, B. velezenis have the ability to resist diverse environmental stresses and help host plants to cope with, including metal and xenobiotic stresses. Within species B. velezensis strains have unique abilities allowing them to adopt different life styles. Strain level abilities knowledge is warranted and could be inferred using the ever-expanding new genomes list available in genomes databases. Pangenome analysis and subsequent identification of core, accessory and unique genomes is actually of paramount importance to decipher species full metabolic capacities and fitness across diverse environmental conditions shaping its life style. Despite the crucial importance of the pan genome, its assessment among large number of strains remains sparse and systematic studies still needed. Extensive knowledge of the pan genome is needed to translate genome sequencing efforts into developing more efficient biocontrol agents and bio-fertilizers. In this study, a genome survey of B. velezensis allowed us to (a) highlight B. velezensis species boundaries and show that Bacillus suffers taxonomic imprecision that blurs the debate over species pangenome; (b) identify drivers of their successful acquisition of specific life styles and colonization of new niches; (c) describe strategies they use to promote plant growth and development; (d) reveal the unlocked strain specific orphan secondary metabolite gene clusters (biosynthetic clusters with corresponding metabolites unknown) that product identification is still awaiting to amend our knowledge of their putative role in suppression of pathogens and plant growth promotion, and (e) to describe a dynamic pangenome with a secondary metabolite rich accessory genome.

El poema de Ibn al-Yāsamīn sobre el álgebra (m. 601/1204) se considera como uno de los textos matemáticos más difundidos en el Magreb a finales de la Edad Media. Fue objeto de trece comentarios entre los cuales encontramos el de Abū ˁUṯmān Saˁīd b. Muḥammad al-ˁUqbānī (m. 811/1408). Este último fue considerado perdido por los especialistas en la historia de las matemáticas árabes. Pero después de investigaciones en las bibliotecas privadas, encontramos una copia de este comentario en una colección de astronomía y matemáticas conservada en la zagüía al-ˁUṯmāniyya de Tolga en Argelia. Como ha sido escrito por varios especialistas, este texto didáctico testimonia la difusión de comentarios y resúmenes en la enseñanza de las matemáticas en el Occidente islámico.