Superconducting edge states in a topological insulator

We study the stability of multiple conducting edge states in a topological insulator against perturbations allowed by the time-reversal symmetry. A system is modeled as a multi-channel Luttinger liquid, with the number of channels equal to the number of Kramers doublets at the edge. Assuming strong interactions and weak disorder, we first formulate a low-energy effective theory for a clean translation invariant system and then include the disorder terms allowed by the time-reversal symmetry. In a clean system with N Kramers doublets, N − 1 edge states are gapped by Josephson couplings and the single remaining gapless mode describes collective motion of Cooper pairs synchronous across the channels. Disorder perturbation in this regime, allowed by the time reversal symmetry is a simultaneous backscattering of particles in all N channels. Its relevance depends strongly on the parity if the number of channel N is not very large. Our main result is that disorder becomes irrelevant with the increase of the number of edge modes leading to the stability of the edge states superconducting regime even for repulsive interactions.

Observing non-ergodicity due to kinetic constraints in tilted Fermi-Hubbard chains

The thermalization of isolated quantum many-body systems is deeply related to fundamental questions of quantum information theory. While integrable or many-body localized systems display non-ergodic behavior due to extensively many conserved quantities, recent theoretical studies have identified a rich variety of more exotic phenomena in between these two extreme limits. The tilted one-dimensional Fermi-Hubbard model, which is readily accessible in experiments with ultracold atoms, emerged as an intriguing playground to study non-ergodic behavior in a clean disorder-free system. While non-ergodic behavior was established theoretically in certain limiting cases, there is no complete understanding of the complex thermalization properties of this model. In this work, we experimentally study the relaxation of an initial charge-density wave and find a remarkably long-lived initial-state memory over a wide range of parameters. Our observations are well reproduced by numerical simulations of a clean system. Using analytical calculations we further provide a detailed microscopic understanding of this behavior, which can be attributed to emergent kinetic constraints.

Renewable Power Generation: A Supply Chain Perspective

In recent years, the transition to a more sustainable and clean system has focused on the accelerated development of renewable energy technologies. This transition can be perceived as a major priority, especially with the current environmental concerns, threatening various aspects of human life. The objective of this article is, therefore, to highlight the role of the supply chain in the renewable power generation sector. In this context, a detailed assessment of the supply chain contribution to the renewable energy sector is presented. Next, the performance of the renewable energy supply chain is qualitatively evaluated by illustrating the various barriers against continuing development, and the key measures are recommended to overcome these barriers. Then, the main factors influencing the performance of the supply chain are identified and key performance indicators related to the renewable energy supply chain are established to achieve high efficiency and sustainability performances in the power sector.

Mesoporous Silica Nanoparticles Supported Atomically Precise Palladium Nanoclusters Catalyzed Aerobic Oxidation of Alcohols in Water

The first mesoporous silica nanoparticles (MSNs) supported atomically precise palladium nanoclusters catalyzed alcohol oxidation reactions in water have been achieved. The catalysts was synthesized with simple impregnation method and well characterized by TEM, FT-IR, XPS anddiffuse reflectance optical spectrum and the results proved that the Pd nanoclustersimmobilized into the pores of MSNs.The as-prepared catalyst show excellent activity for the alcohol oxidation reactions with high yield under extremely mild aqueous conditions utilizes 1 atmosphere of molecular oxygen as sole oxidant. The features of clean system, gram-scale oxidation and easy recovery catalyst make this method cost effectively and environmentally benign.

THE RELATION OF CORRUPTION POTENTIAL PERCEPTION WITH BUSINESS INTEGRATION, PUBLIC INTEGRATION, AND LOCAL INTEGRATION SYSTEM (FROM CORRUPTION PERCEPTION SURVEY ANALYSIS)

Potential of corruption is a condition that allows corruption criminal to occur. This paper aims to see whether perceptions of potential corruption have a relationship with business integrity, public integrity, and local integrity systems. The research method used is analysis by looking at the relationship between variables using Rank Spearman test. The results indicate that perceptions of potential corruption have relationship with local integration systems, local integration systems have relationship with the perceptions of business integrity, and the perceptions of business integrity have relationship with public integrity. Thus there are findings that, the eradication of corruption can not be initiated from public integrity, but must be initiated by the improvement of a clean system.

Prediction of Cleaning Loss of Combine Harvester Based on Neural Network

This paper explores the performance and obtains a reasonable cleaning effect of the cleaning system of combine harvester and studies the relationship between the cleaning effect of the combine harvester cleaning system and its influencing factors. We established a neural network model between the cleaning loss rate and the clean system parameters. First, we tested the results of the cleaning performance of each group under different combinations of conditions, and analyzed the direct or indirect relationship between the cleaning loss rate and the parameters in the experiment under each working condition. Then, according to the experimental data obtained in the experiment, we predict the clearance loss rate for several sets of conditions by this model. The experimental results show that the prediction results of the model can meet the experimental requirements under the condition that the accuracy is not very high.

Hybrid Analysis Technique to detect Advanced Persistent Threats

Advanced persistent threats (APT) are major threats in the field of system and network security. They are extremely stealthy and use advanced evasion techniques like packing and behaviour obfuscation to hide their malicious behaviour and evade the detection methods. Existing behavior-based detection technique fails to detect the APTs due to its high persistence mechanism and sophisticated code nature. Hence, a novel hybrid analysis technique using Behavior based Sandboxing approach is proposed. The proposed technique consists of four phases namely, Static, Dynamic, Memory and System state analysis. Initially, static analysis is performed on the sample which involves packer detection and signature verification. If the sample is found stealthy and remains undetected, then it is executed inside a sandbox environment to analyze its behavior. Further, memory analysis is performed to extract memory artefacts of the current system state. Finally, system state analysis is performed by correlating clean system state and infected system state to determine whether the system is compromised