Variational approach to p-Laplacian fractional differential equations with instantaneous and non-instantaneous impulses

In this paper, we examine the existence of solutions of p-Laplacian fractional differential equations with instantaneous and non-instantaneous impulses. New criteria guaranteeing the existence of infinitely many solutions are established for the considered problem. The problem is reduced to an equivalent form such that the weak solutions of the problem are defined as the critical points of an energy functional. The main result of the present work is established by using a variational approach and a mountain pass lemma. Finally, an example is given to illustrate our main result

Low-Frequency Repetitive Transcranial Magnetic Stimulation Ameliorates Cognitive Function and Synaptic Plasticity in APP23/PS45 Mouse Model of Alzheimer’s Disease

Alzheimer’s disease (AD) is a chronic neurodegenerative disease leading to dementia, which is characterized by progressive memory loss and other cognitive dysfunctions. Recent studies have attested that noninvasive repetitive transcranial magnetic stimulation (rTMS) may help improve cognitive function in patients with AD. However, the majority of these studies have focused on the effects of high-frequency rTMS on cognitive function, and little is known about low-frequency rTMS in AD treatment. Furthermore, the potential mechanisms of rTMS on the improvement of learning and memory also remain poorly understood. In the present study, we reported that severe deficits in spatial learning and memory were observed in APP23/PS45 double transgenic mice, a well known mouse model of AD. Furthermore, these behavioral changes were accompanied by the impairment of long-term potentiation (LTP) in the CA1 region of hippocampus, a brain region vital to spatial learning and memory. More importantly, 2-week low-frequency rTMS treatment markedly reversed the impairment of spatial learning and memory as well as hippocampal CA1 LTP. In addition, low-frequency rTMS dramatically reduced amyloid-β precursor protein (APP) and its C-terminal fragments (CTFs) including C99 and C89, as well as β-site APP-cleaving enzyme 1 (BACE1) in the hippocampus. These results indicate that low-frequency rTMS noninvasively and effectively ameliorates cognitive and synaptic functions in a mouse model of AD, and the potential mechanisms may be attributed to rTMS-induced reduction in Aβ neuropathology

Divergence of a genomic island leads to the evolution of melanization in a halophyte root fungus

AbstractUnderstanding how organisms adapt to extreme living conditions is central to evolutionary biology. Dark septate endophytes (DSEs) constitute an important component of the root mycobiome and they are often able to alleviate host abiotic stresses. Here, we investigated the molecular mechanisms underlying the beneficial association between the DSE Laburnicola rhizohalophila and its host, the native halophyte Suaeda salsa, using population genomics. Based on genome-wide Fst (pairwise fixation index) and Vst analyses, which compared the variance in allele frequencies of single-nucleotide polymorphisms (SNPs) and copy number variants (CNVs), respectively, we found a high level of genetic differentiation between two populations. CNV patterns revealed population-specific expansions and contractions. Interestingly, we identified a ~20 kbp genomic island of high divergence with a strong sign of positive selection. This region contains a melanin-biosynthetic polyketide synthase gene cluster linked to six additional genes likely involved in biosynthesis, membrane trafficking, regulation, and localization of melanin. Differences in growth yield and melanin biosynthesis between the two populations grown under 2% NaCl stress suggested that this genomic island contributes to the observed differences in melanin accumulation. Our findings provide a better understanding of the genetic and evolutionary mechanisms underlying the adaptation to saline conditions of the L. rhizohalophila–S. salsa symbiosis.</jats:p

Kinetic inhibition performance of N-vinyl caprolactam/isopropylacrylamide copolymers on methane hydrate formation

Low dosage kinetic hydrate inhibitors play an important role in flow assurance for oil and gas industry. New polymers especially based on N-vinyl caprolactam (NVCap) are widely designed to serve as potential inhibitors. In this work, a series of random copolymers of NVCap with hydrophobic monomer isopropylacrylamide (NIPAM) were synthesized. The effect of molecular weight on inhibition performance of newly copolymers (PVCap-co-NIPAM)s on CH4 hydrate formation were firstly examined and compared with N-vinyl caprolactam homopolymer (PVCap). The macroscopic kinetic tests indicated that all the copolymers were more powerful than PVCap as nucleation inhibitors under the same conditions. Significant reductions in the hydrate growth rates by 1.0 wt% inhibitors were also observed. Copolymers with the lowest molecular weight possessed the best suppression performance. Powder X-ray diffraction and Raman spectra indicated neither PVCap nor PVCap-co-NIPAM affected the hydrate structure due to their too large molecular size to match the hydrate cages. However, cage-dependent gas occupancy calculated from Raman data proved that the polymers preferred to hinder CH4 molecules from being trapped by large cages (5(1,2)6(2)). A possible inhibition mechanism of PVCap-co-NIPAM was also proposed. These results could be helpful to develop synergistic kinetic hydrate inhibitors for guaranteeing pipeline fluids transportation safety. (C) 2021 Elsevier Ltd. All rights reserved

Machine learning driven rationally design of amorphous alloy with improved elastic models

Rational design of amorphous alloys from the viewpoint of elasticity can be helpful as it offers close correlations with glass forming ability (GFA), thermal stability, mechanical properties and so on. Here, by separately employing composition and structure descriptors as input, we successfully optimized, generated and interpreted the elastic predictive models via various machine learning (ML) approaches, which exhibit distinct advantages of high accuracy, simple operation, wide applicability and good interpretability relative to that of previously reported elastic models. Meanwhile, the performances of our developed elastic models were well verified via GFA and plasticity prediction in two ternary amorphous alloy systems. Finally, based on the above improved elastic models, we proposed a general framework for rational design of amorphous alloys using four steps strategy. Our results demonstrate the great potential to accelerate the composition screening and property optimization of amorphous alloys

Accelerated discovery of Fe-based amorphous/nanocrystalline alloy through explicit expression and interpretable information based on machine learning

The intricate interplay between the characteristics and properties of amorphous/nanocrystalline alloys, specifically saturation flux density (Bs) and Curie temperature (Tc), has long been a perplexing task, despite their remarkable properties. However, recent developments in the field of machine learning (ML) have offered a promising paradigm for the accelerated discovery of these elusive alloys. Notably, it has become clear that their properties depend not only on their chemical composition but also on their thickness and annealing process. To further advance this paradigm, 5 ML methods were employed to predict Bs and Tc based on original features (OFs) and polynomial features (PFs). Impressively, it was discovered that ML model with the largest R2 score on OFs displayed an outstanding capability, with interpretable method, which could be of great aid to alloy design. Further analysis revealed that the explicit formulas based on PFs quantitatively provided direction for optimization. Finally, the Fe81.4Si3.4B11.4Cu0.8Nb3 alloy, with appropriate annealing time (AT1), annealing temperature (AT2) and thickness (THK), demonstrated promising Bs = 1.79 T and Tc = 661.6 K. This work strongly suggests that the discovery paradigm has the potential to quantitatively explore the relationship between features and properties and might provide some insight into the discovery of new Fe-based amorphous/nanocrystalline alloy

Genome Analysis of an Alphabaculovirus Isolated from the Larch Looper, Erannis ankeraria

The larch looper, Erannis ankeraria Staudinger (Lepidoptera: Geometridae), is one of the major insect pests of larch forests, widely distributed from southeastern Europe to East Asia. A naturally occurring baculovirus, Erannis ankeraria nucleopolyhedrovirus (EranNPV), was isolated from E. ankeraria larvae. This virus was characterized by electron microscopy and by sequencing the whole viral genome. The occlusion bodies (OBs) of EranNPV exhibited irregular polyhedral shapes containing multiple enveloped rod-shaped virions with a single nucleocapsid per virion. The EranNPV genome was 125,247 bp in length with a nucleotide distribution of 34.9% G+C. A total of 131 hypothetical open reading frames (ORFs) were identified, including the 38 baculovirus core genes and five multi-copy genes. Five homologous regions (hrs) were found in the EranNPV genome. Phylogeny and pairwise kimura 2-parameter analysis indicated that EranNPV was a novel group II alphabaculovirus and was most closely related to Apocheima cinerarium NPV (ApciNPV). Field trials showed that EranNPV was effective in controlling E. ankeraria in larch forests. The above results will be relevant to the functional research on EranNPV and promote the use of this virus as a biocontrol agent

Experimental Study and Discrete Analysis of Compressive Properties of Glass Fiber-Reinforced Polymer (GFRP) Bars

Glass fiber-reinforced polymer (GFRP) has superior characteristics over traditional steel, such as lightweight, high strength, corrosion resistance and high durability. GFRP bars can be a useful alternative to steel bars in structures, specifically those in highly corrosive environments, as well as structures subjected to high compressive pressure such as bridge foundations. Digital image correlation (DIC) technology is used to analyze the strain evolution of GFRP bars under compression. It can be seen from using DIC technology that the surface strain of GFRP reinforcement is uniformly distributed and increases approximately linearly, and brittle splitting failure of GFRP bars happens due to locally occurring high strain at the failure stage. Moreover, there are limited studies on the use of distribution functions to describe the compressive strength and elastic modulus of GFRP. In this paper, Weibull distribution and gamma distribution are used to fit the compressive strength and compressive elastic modulus of GFRP bars. The average compressive strength is 667.05 MPa and follows Weibull distribution. Moreover, the average compressive elastic modulus is 47.51 GPa and follows gamma distribution. In order to verify that GFRP bars still have certain strength under compressive conditions, this paper provides a parameter reference for their large-scale application

Optimization method of PMU placements base on costs and risk assessments of state estimation

In the context of vigorous development of new energy sources, optimizing the arrangement of phase measurement units (PMUs) is crucial to realize real-time data monitoring for distribution network (DN) condition assessment. Firstly, we propose a multi-objective model for optimal PMU placements. Secondly, we classified the buses based on the Jacobi matrix and the grid structure, and calculated the unobservable risk probability of the system by estimating the state of the PMUs installed on the buses, so as to obtain the observability of the system. Finally, two simulations of PMU placements in the DN implementing the IEEE39 bus are designed and the placement schemes are determined through solving the non-dominated Pareto solution sets by NSGA-II. The verifications show that the model reduces the placement number of PMUs, resulting in a cost reduction of 35.4% and an unobservable risk indicator δ reduction of 13.8%, and achieves an optimal placement of PMUs