Voting is an important operation in multichannel computation paradigm and realization of ultrareliable and real-time control systems that arbitrates among the results of N redundant variants. These systems include N-modular redundant (NMR) hardware systems and diversely designed software systems based on N-version programming (NVP). Depending on the characteristics of the application and the type of selected voter, the voting algorithms can be implemented for either hardware or software systems. In this paper, a novel voting algorithm is introduced for real-time fault-tolerant control systems, appropriate for applications in which N is large. Then, its behavior has been software implemented in different scenarios of error-injection on the system inputs. The results of analyzed evaluations through plots and statistical computations have demonstrated that this novel algorithm does not have the limitations of some popular voting algorithms such as median and weighted; moreover, it is able to significantly increase the reliability and availability of the system in the best case to 2489.7% and 626.74%, respectively, and in the worst case to 3.84% and 1.55%, respectively.
The stability of clusters is a serious issue in mobile ad hoc networks. Low stability of clusters may lead to rapid failure of clusters, high energy consumption for reclustering, and decrease in the overall network stability in mobile ad hoc network. In order to improve the stability of clusters, weight-based clustering algorithms are utilized. However, these algorithms only use limited features of the nodes. Thus, they decrease the weight accuracy in determining node's competency and lead to incorrect selection of cluster heads. A new weight-based algorithm presented in this paper not only determines node's weight using its own features, but also considers the direct effect of feature of adjacent nodes. It determines the weight of virtual links between nodes and the effect of the weights on determining node's final weight. By using this strategy, the highest weight is assigned to the best choices for being the cluster heads and the accuracy of nodes selection increases. The performance of new algorithm is analyzed by using computer simulation. The results show that produced clusters have longer lifetime and higher stability. Mathematical simulation shows that this algorithm has high availability in case of failure.
Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential application in more realistic noise environments. Therefore, finding a feasible and accurate handwritten numeral recognition method that is accurate in the more practical noisy environment is crucial. To this end, this paper proposes a new scheme for handwritten numeral recognition using Hybrid orthogonal polynomials. Gradient and smoothed features are extracted using the hybrid orthogonal polynomial. To reduce the complexity of feature extraction, the embedded image kernel technique has been adopted. In addition, support vector machine is used to classify the extracted features for the different numerals. The proposed scheme is evaluated under three different numeral recognition datasets: Roman, Arabic, and Devanagari. We compare the accuracy of the proposed numeral recognition method with the accuracy achieved by the state-of-the-art recognition methods. In addition, we compare the proposed method with the most updated method of a convolutional neural network. The results show that the proposed method achieves almost the highest recognition accuracy in comparison with the existing recognition methods in all the scenarios considered. Importantly, the results demonstrate that the proposed method is robust against the noise distortion and outperforms the convolutional neural network considerably, which signifies the feasibility and the effectiveness of the proposed approach in comparison to the state-of-the-art recognition methods under both clean noise and more realistic noise environments.
A novel one-pot [3+2]-cycloaddition reaction of (E)-3-arylidene-1-phenyl-succinimides, cyclic 1,2-diketones (isatin, 5-chloro-isatin and acenaphtenequinone), and diverse α-aminoacids such as 2-phenylglycine or sarcosine is reported. The reaction provides succinimide-substituted dispiropyrrolidine derivatives with high regio- and diastereoselectivities under mild reaction conditions. The stereochemistry of these N-heterocycles has been confirmed by four X-ray diffraction studies. Several synthetized compounds show higher inhibition on acetylcholinesterase (AChE) than butyrylcholinesterase (BChE). Of the 17 synthesized compounds tested, five exhibit good AChE inhibition with IC50 of 11.42 to 22.21 µM. A molecular docking study has also been undertaken for compound 4n possessing the most potent AChE inhibitory activity, disclosing its binding to the peripheral anionic site of AChE enzymes.
Biallelic loss-of-function mutations in the sorbitol dehydrogenase (SORD) gene cause the most common recessive type of Charcot-Marie-Tooth disease (CMT), CMT-SORD. However, the full genotype-phenotype spectrum and progression of the disease remain to be defined. Notably, a multicenter phase 2/3 study to test the efficacy of govorestat (NCT05397665), a new aldose reductase inhibitor, is currently ongoing. Diagnosing CMT-SORD will become imperative when disease-modifying therapies become available. In this cross-sectional multicentre study, we identified 144 patients from 126 families, including 99 males (69%) and 45 females (31%). Patients represented multiple ancestries, including European, Hispanic, Chinese, Near Eastern, and Northern African. We confirmed c.757delG (p.Ala253GlnfsTer27) as the most common pathogenic allele, followed by c.458C>A (p.Ala153Asp), while other variants were identified mostly in single cases. The average sorbitol level in CMT-SORD patients was significantly higher compared to controls and heterozygous carriers, independently from serum storage duration, sex, or variant type. Two-thirds of cases were diagnosed with CMT2 while one-third had distal hereditary motor neuropathy (dHMN). Disease onset was usually in the second decade of life. Although foot dorsiflexion was the most affected muscle group, dorsal and plantar flexion had a similar degree of weakness in most cases (difference of Medical Research Council score ≤ 1). One fourth of patients used ankle foot orthoses, usually in their 30s, but most patients maintained independent ambulation later in life. Nerve conduction studies (NCS) were suggestive of a motor predominant axonal neuropathy, with reduced conduction velocities in the intermediate range in one fourth of the cases. Sensory conductions in the upper limbs appeared more frequently affected than in the lower limbs. Foot dorsiflexion and plantar flexion decreased significantly with age. Male sex was significantly associated with the severity of distal lower limb weakness (plantar flexion) and a larger change over time (dorsiflexion). In conclusion, CMT-SORD is a frequent recessive form of axonal, motor predominant CMT, with prominent foot dorsiflexion and plantar flexion involvement. Fasting serum sorbitol is a reliable biomarker of the condition that can be utilized for pathogenicity assessment of identified rare SORD variants.