Diffusion in higher dimensional SYK model with complex fermions

We construct a new higher dimensional SYK model with complex fermions on bipartite lattices. As an extension of the original zero-dimensional SYK model, we focus on the one-dimension case, and similar Hamiltonian can be obtained in higher dimensions. This model has a conserved U(1) fermion number Q and a conjugate chemical potential \mu. We evaluate the thermal and charge diffusion constants via large q expansion at low temperature limit. The results show that the diffusivity depends on the ratio of free Majorana fermions to Majorana fermions with SYK interactions. The transport properties and the butterfly velocity are accordingly calculated at low temperature. The specific heat and the thermal conductivity are proportional to the temperature. The electrical resistivity also has a linear temperature dependence term.Comment: 15 pages, 1 figure, add 4 references and fix some typos in this versio

One-Step Preparation of High Performance TiO 2 /CNT/CQD Nanocomposites Bactericidal Coating with Ultrasonic Radiation

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).As an environmental semiconductor material, TiO2 has important applications in the fields of environmental protection and water treatment. The preparation of P25 particles into nano-functional material films with a high specific surface area has always been a bottleneck limiting its large-scale application. In this paper, a one-step method of preparing TiO2 nanocomposites by doping carbon nanotube (CNT) and carbon quantum dots (CQD) with tetrabutyltitanate and P25 TiO2 under ultrasonic radiation is proposed to synthesize a novel antifouling material, which both eliminates the bacterium of Escherichia coli and shows good photoelectric properties, indicating a great value for the industrial promotion of TiO2/CNT. This mesoporous composite exhibits a high specific surface area of 78.07 M2/g (BET) and a tested pore width range within 10–120 nm. The surface morphology of this composite is characterized by TEM and the microstructure is characterized through XRD. This preparation method can fabricate P25 particles into a nano-functional material film with a high specific surface area at a very low cost.Peer reviewe

Three-Dimensional Simulation of the Shrinkage Behavior of Injection-Molded Poly Lactic Acid (PLA): Effects of Temperature, Shear Rate and Part Thickness

The effects of injection temperature, shear and part thickness on the linear shrinkage of injection-molded poly (lactic acid) (PLA) were intensively analyzed using the Autodesk Moldflow software. The obtained results showed that both melt temperature and shear rate had obvious effects on the linear shrinkage of PLA, i.e., the linear shrinkage of PLA increases significantly with the increase of melt temperature and shear rate. In addition, the shrinkage of high-crystallinity PLA was remarkably larger than that of low-crystallinity PLA, and thin-walled parts was larger than thick-walled ones in shrinkage

Pyruvate Kinase regulates the Pentose-Phosphate pathway in Response to Hypoxia in Mycobacterium tuberculosis

In response to the stress of infection, Mycobacterium tuberculosis (Mtb) reprograms its metabolism to accommodate nutrient and energetic demands in a changing environment. Pyruvate kinase (PYK) is an essential glycolytic enzyme in the phosphoenolpyruvate–pyruvate–oxaloacetate node that is a central switch point for carbon flux distribution. Here we show that the competitive binding of pentose monophosphate inhibitors or the activator glucose 6-phosphate (G6P) to MtbPYK tightly regulates the metabolic flux. Intriguingly, pentose monophosphates were found to share the same binding site with G6P. The determination of a crystal structure of MtbPYK with bound ribose 5-phosphate (R5P), combined with biochemical analyses and molecular dynamic simulations, revealed that the allosteric inhibitor pentose monophosphate increases PYK structural dynamics, weakens the structural network communication, and impairs substrate binding. G6P, on the other hand, primes and activates the tetramer by decreasing protein flexibility and strengthening allosteric coupling. Therefore, we propose that MtbPYK uses these differences in conformational dynamics to up- and down-regulate enzymic activity. Importantly, metabolome profiling in mycobacteria reveals a significant increase in the levels of pentose monophosphate during hypoxia, which provides insights into how PYK uses dynamics of the tetramer as a competitive allosteric mechanism to retard glycolysis and facilitate metabolic reprogramming toward the pentose-phosphate pathway for achieving redox balance and an anticipatory metabolic response in Mtb

A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries

Lithium-ion battery is a core component of many systems such as satellite, spacecraft, and electric vehicles and its failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Remaining useful life (RUL) prediction of lithium-ion batteries before the future failure event is extremely crucial for proactive maintenance/safety actions. This study proposes a hybrid prognostic approach that can predict the RUL of degraded lithium-ion batteries using physical laws and data-driven modeling simultaneously. In this hybrid prognostic approach, the relevant vectors obtained with the selective kernel ensemble-based relevance vector machine (RVM) learning algorithm are fitted to the physical degradation model, which is then extrapolated to failure threshold for estimating the RUL of the lithium-ion battery of interest. The experimental results indicated that the proposed hybrid prognostic approach can accurately predict the RUL of degraded lithium-ion batteries. Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs) like the conventional backpropagation (BP) algorithm and support vector machines (SVMs). In addition, an investigation is also conducted to identify the effects of RVM learning algorithm on the proposed hybrid prognostic approach

Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm

The rolling element bearing is a core component of many systems such as aircraft, train, steamboat, and machine tool, and their failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Due to misoperation, manufacturing deficiencies, or the lack of monitoring and maintenance, it is often found to be the most unreliable component within these systems. Therefore, effective and efficient fault diagnosis of rolling element bearings has an important role in ensuring the continued safe and reliable operation of their host systems. This study presents a trace ratio criterion-based kernel discriminant analysis (TR-KDA) for fault diagnosis of rolling element bearings. The binary immune genetic algorithm (BIGA) is employed to solve the trace ratio problem in TR-KDA. The numerical results obtained using extensive simulation indicate that the proposed TR-KDA using BIGA (called TR-KDA-BIGA) can effectively and efficiently classify different classes of rolling element bearing data, while also providing the capability of real-time visualization that is very useful for the practitioners to monitor the health status of rolling element bearings. Empirical comparisons show that the proposed TR-KDA-BIGA performs better than existing methods in classifying different classes of rolling element bearing data. The proposed TR-KDA-BIGA may be a promising tool for fault diagnosis of rolling element bearings