The emergence of interface states in graphene/transition metal dichalcogenides heterostructure with lateral interface

The relative strength of different proximity spin-orbit couplings in graphene on transition metal dichalcogenides (TMDC) can be tuned via the metal composition in the TMDC layer. While Gr/MoSe$_2$, has a normal gap, proximity to WSe$_2$ instead leads to valley-Zeeman-driven inverted bands. Although the $\mathbb{Z}_2$ index vanishes, these systems enable a concentration-dependent topological crossover with band gap closure when graphene is stacked on a composite or alloyed TMDC layer. This is due to a nonzero Berry curvature at the individual valleys and a change of the valley Chern index at a critical composition ratio. Therefore, inherently, we also expect that stacked heterostructures of graphene on composite TMDC layers should host localised boundary modes due to the presence of Gr/WSe$_2$- and Gr/MoSe$_2$-like domains with opposite valley Chern indices. In this study, we show that a Gr/(Mo-W)Se$_2$ heterostructure with a lateral interface in the TMDC layer can indeed host topologically protected in-gap propagating modes, similar to those at the border of commensurate AB and BA domains in biased minimally-twisted bilayer graphene. However, the stability of these modes depends crucially on the system size. We demonstrate that the electronic behaviour of Gr/(Mo-W)Se$_2$ heterostructures evolves from a homogeneous effective medium to a superposition of domain-localised bands and zero-energy branch crossings as the domain size in the alloyed TMDC layer is increased.Comment: 10 pages and 4 figure

Impact of strain on the excitonic linewidth in transition metal dichalcogenides

Monolayer transition metal dichalcogenides (TMDs) are known to be highly sensitive to externally applied tensile or compressive strain. In particular, strain can be exploited as a tool to control the optical response of TMDs. However, the role of excitonic effects under strain has not been fully understood yet. Utilizing the strain-induced modification of electron and phonon dispersion obtained by first principle calculations, we present in this work microscopic insights into the strain-dependent optical response of various TMD materials. In particular, we explain recent experiments on the change of excitonic linewidths in strained TMDs and predict their behavior for tensile and compressive strain at low temperatures.Comment: 7 pages, 7 figure

Improvement of effort estimation accuracy in software projects using a feature selection approach

In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become an inevitable demand. The high volumes of data, costs, and time necessary for gathering data , and also the complexity of the models used for effort estimation are all reasons to use the methods mentioned. Therefore, in this article, a genetic algorithm has been used for feature selection in the field of software project effort estimation. This technique has been tested on well-known data sets. Implementation results indicate that the resulting subset, compared to the original data set, has produced better outcomes in terms of effort estimation accuracy. This article showed that genetic algorithms are ideal methods for selecting a subset of features and improving effort estimation accuracy

Type-2 Fuzzy Logic Approach To Increase The Accuracy Of Software Development Effort Estimation

predicting the effort of a successful project has been a major problem for software engineers the significance of which has led to extensive investigation in this area. One of the main objectives of software engineering society is the development of useful models to predict the costs of software product development. The absence of these activities before starting the project will lead to various problems. Researchers focus their attention on determining techniques with the highest effort prediction accuracy or on suggesting new combinatory techniques for providing better estimates. Despite providing various methods for the estimation of effort in software projects, compatibility and accuracy of the existing methods is not yet satisfactory. In this article, a new method has been presented in order to increase the accuracy of effort estimation. This model is based on the type-2 fuzzy logic in which the gradient descend algorithm and the neuro-fuzzy-genetic hybrid approach have been used in order to teach the type-2 fuzzy system. In order to evaluate the proposed algorithm, three databases have been used. The results of the proposed model have been compared with neuro-fuzzy and type-1 fuzzy system. This comparison reveals that the results of the proposed model have been more favorable than those of the other two models

Dark exciton based strain sensing in tungsten-based transition metal dichalcogenides

The recent emergence of atomically thin two-dimensional (2D) materials has opened up possibilities for the design of ultrathin and flexible nanoelectronic devices. As truly 2D materials, they exhibit an optimal surface-to-volume ratio, which results in an extremely high sensitivity to external changes which can not be achieved by conventional semiconductors. This makes these materials optimal candidates for sensing applications. Here, we propose a dark exciton based concept for ultrasensitive strain sensors. By investigating both dark and bright excitons in tungsten-based monolayer transition metal dichalcogenides, we demonstrate that the dark-bright-exciton separation can be controlled by strain, which has a crucial impact on the activation of dark excitonic states. The predicted opposite strain-induced shifts for dark and bright excitons result in a pronounced change in photoluminescence stemming from dark excitons even at very small strain values. The predicted high optical gauge factors of up to 8000 are promising for the design of optical strain sensors

Foundation Metrics: Quantifying Effectiveness of Healthcare Conversations powered by Generative AI

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients' well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present an comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.Comment: 13 pages, 4 figures, 2 tables, journal pape

A comparative study of physical education curriculum in Iranian high schools with selected countries (USA, Germany, Australia, Japan)

The purpose of this study was to compare the physical education curriculum of Iranian high schools with some selected countries. The study adopted comparative research design, one of the qualitative methods. The countries of comparison were Japan, USA, Germany and Australia, which were selected via purposive sampling method. The data were collected from libraries, dissertations, databases, educational sites, books and publications. In the data analysis process, upon describing, interpreting and classifying the information, the curricula were compared and contrasted. The results showed that the most important goals of physical education course included promoting health, growth and development of motor skills, creating an active lifestyle. The most important contents of the physical education course were individual and social skills training, knowledge topics and sports training. Also, physical fitness tests, sports skills tests, written and oral tests and research projects were the most common evaluation methods. The sports equipment of the selected countries was standard and differed from that of Iran in terms of the number and the quality