A new fast motion estimation algorithm using fast mode decision for high-efficiency video coding standard

High-efficiency video coding is the latest standardization effort of the International Organization for Standardization and the International Telecommunication Union. This new standard adopts an exhaustive algorithm of decision based on a recursive quad-tree structured coding unit, prediction unit, and transform unit. Consequently, an important coding efficiency may be achieved. However, a significant computational complexity is resulted. To speed up the encoding process, efficient algorithms based on fast mode decision and optimized motion estimation were adopted in this paper. The aim was to reduce the complexity of the motion estimation algorithm by modifying its search pattern. Then, it was combined with a new fast mode decision algorithm to further improve the coding efficiency. Experimental results show a significant speedup in terms of encoding time and bit-rate saving with tolerable quality degradation. In fact, the proposed algorithm permits a main reduction that can reach up to 75 % in encoding time. This improvement is accompanied with an average PSNR loss of 0.12 dB and a decrease by 0.5 % in terms of bit-rate.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Online collaboration: Collaborative behavior patterns and factors affecting globally distributed team performance

Studying the collaborative behavior of online learning teams and how this behavior is related to communication mode and task type is a complex process. Research about small group learning suggests that a higher percentage of social interactions occur in synchronous rather than asynchronous mode, and that students spend more time in task-oriented interaction in asynchronous discussions than in synchronous mode. This study analyzed the collaborative interaction patterns of global software development learning teams composed of students from Turkey, US, and Panama. Data collected from students’ chat histories and forum discussions from three global software development projects were collected and compared. Both qualitative and quantitative analysis methods were used to determine the differences between a group’s communication patterns in asynchronous versus synchronous communication mode. K-means clustering with the Ward method was used to investigate the patterns of behaviors in distributed teams. The results show that communication patterns are related to communication mode, the nature of the task, and the experience level of the leader. The paper also includes recommendations for building effective online collaborative teams and describes future research possibilities.     

A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.     

Combining docking, scoring and molecular field analyses to probe influenza neuraminidase-ligand interactions

In this project, several docking conditions, scoring functions and corresponding protein-aligned molecular field analysis (CoMFA) models were evaluated for a diverse set of neuraminidase (NA) inhibitors. To this end, a group of inhibitors were docked into the active site of NA. The docked structures were utilized to construct a corresponding protein-aligned CoMFA models by employing probe-based (H+, OH, CH3) energy grids and genetic partial least squares (G/PLS) statistical analysis. A total of 16 different docking configurations were evaluated, of which some succeeded in producing self-consistent and predictive CoMFA models. However, the best model coincided with docking the ionized ligands into the hydrated form of the binding site via PLP1 scoring function (r2LOO=0.735, r2PRESS against 24 test compounds=0.828). The highest-ranking CoMFA models were employed to probe NA-ligand interactions. Further validation by comparison with a co-crystallized ligand-NA crystallographic structure was performed. This combination of docking/scoring/CoMFA modeling provided interesting insights into the binding of different NA inhibitors.     

Polymer Membrane Electrodes for Sensitive Potentiometric Determination of β-blockers

ABSTRACT

The construction of PVC matrix-type β-blockers (sotalol, carvedilol, and betaxolol) ion selective electrodes and their use for direct potentiometry of their respective species are described. The proposed sensors are based on the complex ion associates of β-blockers with tungstophosphate (TP) and Ammonium Reineckate (Rein) ionophoris in poly vinyl chloride membrane (PVC) with Dioctylphthalate (DOP) plasticizer. The four electrodes (Beta-TP), (Sota-TP), (Carve-TP), and (Cave-Rein) show stable potential response with near Nernstian slope of 50.8, 33.7, 32.35, and 33 mv per decade, range of concentration 10?2–10?7 M β-blockers. Selectivity coefficients data obtained for 11 different organic and inorganic ions are presented. The electrodes have fast response time (30 and 40 s) and were used over wide range of pH 4.5–8.5. Validation of the method according to the quality assurance standers shows suitability of proposed sensors for use in the quality control assessment of these drugs. The results obtained for the determination of β-blockers with the proposed electrodes show average recoveries of 100.78% and a mean standard deviation of ±1.2. The nominal are obtained. The data agree well with those obtained by standard methods.     

Anticonvulsant Profiles of Certain New 6-Aryl-9-substituted-6,9-diazaspiro-[4.5]decane-8,10-diones and 1-Aryl-4-substituted-1,4-diazaspiro[5.5]undecane-3,5-diones

Synthesis and anticonvulsant potential of certain new 6-aryl-9-substituted-6,9-diazaspiro[4.5]decane-8,10-diones (6a–l) and 1-aryl-4-substituted-1,4-diazaspiro[5.5]undecane-3,5-diones (6m–x) are reported. The intermediates 1-[(aryl)(cyanomethyl)amino]cycloalkanecarboxamides (3a–f) were prepared via adopting Strecker synthesis on the proper cycloalkanone followed by partial hydrolysis of the obtained nitrile functionality and subsequent N-cyanomethylation. Compounds 3a–f were subjected to complete nitrile hydrolysis to give the respective carboxylic acid derivatives 4a–f which were cyclized under mild conditions to give the spiro compounds 5a–f. Ultimately, compounds 5a–f were alkylated or aralkylated to give the target compounds 6a–i and 6m–u. On the other hand, compounds 6j–l and 6v–x were synthesized from the intermediates 5a–f through alkylation, dehydration and finally tetrazole ring formation. Anticonvulsant screening of the target compounds 6a–x revealed that compound 6g showed an ED₅₀ of 0.0043 mmol/kg in the scPTZ screen, being about 14 and 214 fold more potent than the reference drugs, Phenobarbital (ED₅₀ = 0.06 mmol/kg) and Ethosuximide (ED₅₀ = 0.92 mmol/kg), respectively. Compound 6e exhibited an ED₅₀ of 0.019 mmol/kg, being about 1.8 fold more potent than that of the reference drug, Diphenylhydantoin (ED₅₀ = 0.034 mmol/kg) in the MES screen. Interestingly, all the test compounds 6a–x did not show any minimal motor impairment at the maximum administered dose in the neurotoxicity screen.     

Innovative hydrogen release from sodium borohydride hydrolysis using biocatalyst-like Fe2O3 nanoparticles impregnated on Bacillus simplex bacteria

For the first time in this innovative study, microorganisms such as Bacillus simplex bacteria, mostly used in biological activity studies, are used as a bio-supporter agent of iron to release hydrogen from sodium borohydride hydrolysis at 25.0 ± 0.1 °C. The goal is to investigate thoroughly sodium borohydride hydrolysis catalyzed by Fe₂O₃ nanoparticles impregnated on microorganism such as Bacillus simplex (BS) bacteria (Fe₂O₃@BS NPs) known with strong antibacterial properties, which makes innovative them a candidate for hydrolysis reaction. This study was focused on the preparation, identification, and catalytic use of biocatalyst-like Fe₂O₃@BS NPs for hydrogen release from the sodium borohydride hydrolysis at 25.0 ± 0.1 °C. The characterization results made after and before hydrolysis reaction using by SEM/SEM-EDX, FT-IR, XRD, UV–vis, XPS, DLS, ELS Zeta potential, ESR, and TEM techniques reveal the formation of highly active, stable, durable, and long-lived biocatalysts-like Fe₂O₃@BS NPs.