Quasi-Quantum Planes and Quasi-Quantum Groups of Dimension p3p^3 and p4p^4

The aim of this paper is to contribute more examples and classification results of finite pointed quasi-quantum groups within the quiver framework initiated in \cite{qha1, qha2}. The focus is put on finite dimensional graded Majid algebras generated by group-like elements and two skew-primitive elements which are mutually skew-commutative. Such quasi-quantum groups are associated to quasi-quantum planes in the sense of nonassociative geomertry \cite{m1, m2}. As an application, we obtain an explicit classification of graded pointed Majid algebras with abelian coradical of dimension $p^3$ and $p^4$ for any prime number $p.$Comment: 12 pages; Minor revision according to the referee's suggestio

Generalized Clifford Algebras as Algebras in Suitable Symmetric Linear Gr-Categories

By viewing Clifford algebras as algebras in some suitable symmetric Gr-categories, Albuquerque and Majid were able to give a new derivation of some well known results about Clifford algebras and to generalize them. Along the same line, Bulacu observed that Clifford algebras are weak Hopf algebras in the aforementioned categories and obtained other interesting properties. The aim of this paper is to study generalized Clifford algebras in a similar manner and extend the results of Albuquerque, Majid and Bulacu to the generalized setting. In particular, by taking full advantage of the gauge transformations in symmetric linear Gr-categories, we derive the decomposition theorem and provide categorical weak Hopf structures for generalized Clifford algebras in a conceptual and simpler manner

The Green rings of pointed tensor categories of finite type

In this paper, we compute the Clebsch-Gordan formulae and the Green rings of connected pointed tensor categories of finite type.Comment: 14 page

A hybrid Particle Swarm Evolutionary Algorithm for Constrained Multi-Objective Optimization

In this paper, a hybrid particle swarm evolutionary algorithm is proposed for solving constrained multi-objective optimization. Firstly, in order to keep some particles with smaller constraint violations, a threshold value is designed, the updating strategy of particles is revised based on the threshold value; then in order to keep some particles with smaller rank values, an infeasible elitist preservation strategy is proposed in order to make the infeasible elitists act as bridges connecting disconnected feasible regions. Secondly, in order to find a set of diverse and well-distributed Pareto-optimal solutions, a new crowding distance function is designed for bi-objective optimization problems. It can assign larger crowding distance function values not only for the particles located in the sparse region but also for the particles located near to the boundary of the Pareto front. In this step, the reference points are given, and the particles which are near to the reference points are kept no matter how crowded these points are. Thirdly, a new mutation operator with two phases is proposed. In the first phase, the total force is computed first, then it is used as a mutation direction, searching along this direction, better particles will be found. The comparative study shows the proposed algorithm can generate widely spread and uniformly distributed solutions on the entire Pareto front

Transcranial Magnetic Stimulation Over the Right Posterior Superior Temporal Sulcus Promotes the Feature Discrimination Processing

Attention is the dynamic process of allocating limited resources to the information that is most relevant to our goals. Accumulating studies have demonstrated the crucial role of frontal and parietal areas in attention. However, the effect of posterior superior temporal sulcus (pSTS) in attention is still unclear. To address this question, in this study, we measured transcranial magnetic stimulation (TMS)-induced event-related potentials (ERPs) to determine the extent of involvement of the right pSTS in attentional processing. We hypothesized that TMS would enhance the activation of the right pSTS during feature discrimination processing. We recruited 21 healthy subjects who performed the dual-feature delayed matching task while undergoing single-pulse sham or real TMS to the right pSTS 300 ms before the second stimulus onset. The results showed that the response time was reduced by real TMS of the pSTS as compared to sham stimulation. N270 amplitude was reduced during conflict processing, and the time-varying network analysis revealed increased connectivity between the frontal lobe and temporo-parietal and occipital regions. Thus, single-pulse TMS of the right pSTS enhances feature discrimination processing and task performance by reducing N270 amplitude and increasing connections between the frontal pole and temporo-parietal and occipital regions. These findings provide evidence that the right pSTS facilitates feature discrimination by accelerating the formation of a dynamic network

A novel compound heterozygous variant of ECEL1 induced joint dysfunction and cartilage degradation: a case report and literature review

BackgroundDistal arthrogryposis type 5D (DA5D) represents a subtype of distal arthrogryposis (DA) characterized by congenital joint contractures in the distal extremities. DA5D is inherited in a rare autosomal recessive manner and is associated with the ECEL1 gene. In this report, we describe a case of an infant with bilateral knee contractures and ptosis, caused by a novel compound heterozygous mutation of ECEL1.Case presentationWe conducted DNA extraction, whole-exome sequencing analysis, and mutation analysis of ECEL1 to obtain genetic data on the patient. We subsequently analyzed the patient’s clinical and genetic data. The proband was a 6 months-old male infant who presented with significant bilateral knee contracture disorders and bilateral ptosis. MRI demonstrated cartilage degradation in knee joint. Whole-exome sequencing of the patient’s DNA revealed a compound heterozygous mutation of c.2152-15C>A and c.110_155del in ECEL1. Analysis with the MutationTaster application indicated that c.110_155del was pathogenic (probability = 1), causing frameshift mutations affecting 151 amino acids (p.F37Cfs*151). The truncated protein lost the substructure of a transmembranous site based on the predicted protein crystal structure AF-O95672-F1. The variant of c.2152-15C>A of ECEL1 was also predicted to be disease-causing (probability = 0.98) as it impaired the methylation of ECEL1 serving as an H3K27me3 modification site, which led to the dysfunction of the second topological domain. Therefore, we concluded that the compound heterozygous mutation caused the pathogenic phenotype of this proband.ConclusionThe present case highlights the usefulness of molecular genetic screening in diagnosing unexpected joint disorder. Identification of novel mutations in the ECEL1 gene broadens the mutation spectrum of this gene and adds to the genotype-phenotype map of DA5D. Furthermore, rapid whole-exome sequencing analysis enabled timely diagnosis of this rare disease, facilitating appropriate treatment and scheduled follow-up to improve clinical outcomes

Modeling and Simulation of Gas Emission Based on Recursive Modified Elman Neural Network

For the purpose of achieving more effective prediction of the absolute gas emission quantity, this paper puts forward a new model based on the hidden recurrent feedback Elman. The recursive part of classic Elman cannot be adjusted because it is fixed. To a certain extent, this drawback affects the approximation ability of the Elman, so this paper adds the correction factors in recursive part and uses the error feedback to determine the parameters. The stability of the recursive modified Elman neural network is proved in the sense of Lyapunov stability theory, and the optimal learning rate is given. With the historical data of mine actual monitoring to experiment and analysis, the results show that the recursive modified Elman neural network model can effectively predict the gas emission and improve the accuracy and efficiency of prediction compared with the classic Elman prediction model