How To Persuade Non-Mobile Shoppers Into Mobile Shoppers: A Trust Enhancing Perspective

Mobile shopping is getting popular and pervasive. However, the number of mobile users is not parallel to the number of mobile shoppers, because consumers frequently concern about security while conducting mobile transactions. The current study aims to elaborate in what trust enhancing message designs can be used to persuade non-mobile shoppers into mobile shoppers. Drawing on social judgment theory and the model of persuasion, our study has the potential revealing that consumers’ negative attitudes toward ubiquitously using credit cards over the air can be improved by persuasive messages if they are added into the checkout page of a shopping website

Persistent currents in a graphene ring with armchair edges

A graphene nano-ribbon with armchair edges is known to have no edge state. However, if the nano-ribbon is in the quantum spin Hall (QSH) state, then there must be helical edge states. By folding a graphene ribbon to a ring and threading it by a magnetic flux, we study the persistent charge and spin currents in the tight-binding limit. It is found that, for a broad ribbon, the edge spin current approaches a finite value independent of the radius of the ring. For a narrow ribbon, inter-edge coupling between the edge states could open the Dirac gap and reduce the overall persistent currents. Furthermore, by enhancing the Rashba coupling, we find that the persistent spin current gradually reduces to zero at a critical value, beyond which the graphene is no longer a QSH insulator

Quantum mechanical Gaussian wavepackets of single relativistic particles

We study the evolutions of selected quasi-(1+1) dimensional wavepacket solutions to the Klein-Gordon equation for a relativistic charged particle in uniform motion or accelerated by a uniform electric field in Minkowski space. We explore how good the charge density of a Klein-Gordon wavepacket can be approximated by a Gaussian state with the single-particle interpretation. We find that the minimal initial width of a wavepacket for a good Gaussian approximation in position space is about the Compton wavelength of the particle divided by its Lorentz factor at the initial moment. Relativistic length contraction also manifests in the spreading of the wavepacket's charge density.Comment: 15 pages, 8 figure

Metalloporphyrin-incorporated diphosphine ligands for metal ion-binding

Poster: no. P48Diphosphine ligands have been widely used in organometallic chemistry and catalysis.1 By incorporation of functional units such as metallomacrocycles, the resulting functionalized diphosphines could exhibit unusual properties or binding behavior. In this study, we prepared several examples of ruthenium porphyrin phosphine complexes [RuII(Por)(dppm)2] (1; Por = TTP, 4-MeO-TPP, F20-TPP; dppm = bis(diphenylphosphino)methane) by a similar method to that previously reported for their congeners.2 Reaction of complexes 1 with a number of metal …published_or_final_versio

Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation

The process of transforming input images into corresponding textual explanations stands as a crucial and complex endeavor within the domains of computer vision and natural language processing. In this paper, we propose an innovative ensemble approach that harnesses the capabilities of Contrastive Language-Image Pretraining models

catena-Poly[[bis­(pyridine-κN)nickel(II)]-μ-oxalato-κ4 O 1,O 2:O 1′,O 2′]

The title compound, [Ni(C2O4)(C5H5N)2]n, was synthesized under hydro­(solvo)thermal conditions. The NiII atom, lying on a twofold rotation axis, has an octa­hedral coordination geometry involving two N atoms from two pyridine ligands and four O atoms from two oxalate ligands. The Ni atoms are connected by the tetra­dentate bridging oxalate ligands into a one-dimensional zigzag chain

AutoML-GPT: Large Language Model for AutoML

With the emerging trend of GPT models, we have established a framework called AutoML-GPT that integrates a comprehensive set of tools and libraries. This framework grants users access to a wide range of data preprocessing techniques, feature engineering methods, and model selection algorithms. Through a conversational interface, users can specify their requirements, constraints, and evaluation metrics. Throughout the process, AutoML-GPT employs advanced techniques for hyperparameter optimization and model selection, ensuring that the resulting model achieves optimal performance. The system effectively manages the complexity of the machine learning pipeline, guiding users towards the best choices without requiring deep domain knowledge. Through our experimental results on diverse datasets, we have demonstrated that AutoML-GPT significantly reduces the time and effort required for machine learning tasks. Its ability to leverage the vast knowledge encoded in large language models enables it to provide valuable insights, identify potential pitfalls, and suggest effective solutions to common challenges faced during model training