Spin-flip reflection at the normal metal-spin superconductor interface

We study spin transport through a normal metal-spin superconductor junction. A spin-flip reflection is demonstrated at the interface, where a spin-up electron incident from the normal metal can be reflected as a spin-down electron and the spin $2\times \hbar/2$ will be injected into the spin superconductor. When the (spin) voltage is smaller than the gap of the spin superconductor, the spin-flip reflection determines the transport properties of the junction. We consider both graphene-based (linear-dispersion-relation) and quadratic-dispersion-relation normal metal-spin superconductor junctions in detail. For the two-dimensional graphene-based junction, the spin-flip reflected electron can be along the specular direction (retro-direction) when the incident and reflected electron locates in the same band (different bands). A perfect spin-flip reflection can occur when the incident electron is normal to the interface, and the reflection coefficient is slightly suppressed for the oblique incident case. As a comparison, for the one-dimensional quadratic-dispersion-relation junction, the spin-flip reflection coefficient can reach 1 at certain incident energies. In addition, both the charge current and the spin current under a charge (spin) voltage are studied. The spin conductance is proportional to the spin-flip reflection coefficient when the spin voltage is less than the gap of the spin superconductor. These results will help us get a better understanding of spin transport through the normal metal-spin superconductor junction.Comment: 11 pages, 9 figure

A Review: Peanut Fatty Acids Determination Using Hyper Spectroscopy Imaging and Its Significance on Food Quality and Safety

This paper is a review of determination of peanut fatty acids by using Hyper Spectral Imaging (HSI) methods as a non-destructive food quality and safety monitoring. The key spectral areas are the visual and near-infrared wavelengths. Few have been published on determination of peanut fatty acids by using HSI as an efficient and effective method for evaluating the quality and safety of oil. Providentially, the use of HSI has been observed to have positive effects on determination of food quality and safety (Smith B. 2012). It has gained a wide recognition as a non-destructive, fast, quality and safety analysis, and assessment method for a wide range of food products.  Literature shows that, HSI is not commonly and widely used therefore this paper aspires to emphasize the use of HSI on improving the quality and safety of peanut oil and its products based on the determination of peanut fatty acids. The authors predicted that even in its current imperfect on the affordability, maintenance and complexity on finding solutions or model approaches to their food quality problems from optics, imaging, and spectroscopy, yet HSI is the best method than other current existing methods, and can give an idea of how to better meet market and consumer needs on high food quality and safety for their better healthy. Key words: Hyper spectral imaging, Peanut (Arachis hypogaea), oil, Oleic and linoleic fatty acid, Food quality, food safety

The effect of supply chain risk management practices on resilience and performance: A systematic literature review

In today's competitive and rapidly changing economy, client expectations and desires constantly shift, raising the risk of supply chain interruption. To be effective in this circumstance, a business's supply chain must be resilient. Most enterprises in the world recognize that performance evaluation is necessary to achieve the end aim of developing a resilient supply chain. The purpose of this article is to identify several SCRM techniques and practices for enhancing an organization's overall operational resilience and performance. A systematic review of peer-reviewed journal publications published since 2010 was conducted. The literature review drew on various reputable databases, including Scopus, Web of science, Emerald insight, Sage journals, and Taylor and Francis. The findings reveal that most research has correlated supply chain resilience, SCRM practices, and firm performance. SCRM practices are frequently classified into four categories: identification, evaluation, mitigation, and monitoring. Lastly, the results may be used as guidance for future scholars conducting experimental data on SCRM, resilience, and success of the organization

Chaotification of Quasi-Zero Stiffness System via Direct Time-delay Feedback Control

This paper presents a chaotification method based on direct time-delay feedback control for a quasi-zero-stiffness isolation system. An analytical function of time-delay feedback control is derived based on differential-geometry control theory. Furthermore, the feasibility and effectiveness of this method was verified by numerical simulations. Numerical simulations show that this method holds the favorable aspects including the advantage of using tiny control gain, the capability of chaotifying across a large range of parametric domain and the high feasibility of the control implement

Sequential Recommendation with Latent Relations based on Large Language Model

Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods rely on capturing implicit collaborative filtering signals among items. Recent relation-aware sequential recommendation models have achieved promising performance by explicitly incorporating item relations into the modeling of user historical sequences, where most relations are extracted from knowledge graphs. However, existing methods rely on manually predefined relations and suffer the sparsity issue, limiting the generalization ability in diverse scenarios with varied item relations. In this paper, we propose a novel relation-aware sequential recommendation framework with Latent Relation Discovery (LRD). Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items. The motivation is that LLM contains abundant world knowledge, which can be adopted to mine latent relations of items for recommendation. Specifically, inspired by that humans can describe relations between items using natural language, LRD harnesses the LLM that has demonstrated human-like knowledge to obtain language knowledge representations of items. These representations are fed into a latent relation discovery module based on the discrete state variational autoencoder (DVAE). Then the self-supervised relation discovery tasks and recommendation tasks are jointly optimized. Experimental results on multiple public datasets demonstrate our proposed latent relations discovery method can be incorporated with existing relation-aware sequential recommendation models and significantly improve the performance. Further analysis experiments indicate the effectiveness and reliability of the discovered latent relations.Comment: Accepted by SIGIR 202

Common Sense Enhanced Knowledge-based Recommendation with Large Language Model

Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance. Nevertheless, the knowledge graphs used in previous work, namely metadata-based knowledge graphs, are usually constructed based on the attributes of items and co-occurring relations (e.g., also buy), in which the former provides limited information and the latter relies on sufficient interaction data and still suffers from cold start issue. Common sense, as a form of knowledge with generality and universality, can be used as a supplement to the metadata-based knowledge graph and provides a new perspective for modeling users' preferences. Recently, benefiting from the emergent world knowledge of the large language model, efficient acquisition of common sense has become possible. In this paper, we propose a novel knowledge-based recommendation framework incorporating common sense, CSRec, which can be flexibly coupled to existing knowledge-based methods. Considering the challenge of the knowledge gap between the common sense-based knowledge graph and metadata-based knowledge graph, we propose a knowledge fusion approach based on mutual information maximization theory. Experimental results on public datasets demonstrate that our approach significantly improves the performance of existing knowledge-based recommendation models.Comment: Accepted by DASFAA 202