Weak Anti-Localization Effect in Topological Ni3_3In2_2S2_2 Single Crystal

Ni$_3$In$_2$S$_2$ is the most recent entrant into the family of topological insulator (TI) materials, the same exhibits very high MR in a low-temperature regime. Here, we report the crystal growth, the structural, micro-structural, and magneto-transport study of Ni$_3$In$_2$S$_2$ down to 2.5K under an applied field of up to 14Tesla. The phase purity and growth direction of a single crystal is studied by performing XRD on both powder and flake and further Rietveld analysis is also carried out. The electrical transport measurements are studied and the grown crystal showed metallic behaviour down to 2.5K, with an R300K/R2K ratio of around 7. A significant variation in magnetoresistance (MR) values is observed as the temperature is increased from 2.5K to 200K under an applied field of up to 14 Tesla. Interestingly the low T (2.5K), MR shows a clear V-type characteristic TI cusp. Magnetoconductivity data at low fields (1Tesla) is fitted with the Hikami Larkin Nagaoka (HLN) model, which showed the presence of a weak anti-localization effect in the synthesized Ni$_3$In$_2$S$_2$ crystal at low temperatures. We have successfully grown near single-phase Ni$_3$In$_2$S$_2$ and its TI behavior is demonstrated by magneto-transport measurements.Comment: 12 Pages TEXT + Figs: Accepted Journal of Materials Science: Materials in Electronic

MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification

Chest X-ray imaging is a critical diagnostic tool for identifying pulmonary diseases. However, manual interpretation of these images is time-consuming and error-prone. Automated systems utilizing convolutional neural networks (CNNs) have shown promise in improving the accuracy and efficiency of chest X-ray image classification. While previous work has mainly focused on using feature maps from the final convolution layer, there is a need to explore the benefits of leveraging additional layers for improved disease classification. Extracting robust features from limited medical image datasets remains a critical challenge. In this paper, we propose a novel deep learning-based multilayer multimodal fusion model that emphasizes extracting features from different layers and fusing them. Our disease detection model considers the discriminatory information captured by each layer. Furthermore, we propose the fusion of different-sized feature maps (FDSFM) module to effectively merge feature maps from diverse layers. The proposed model achieves a significantly higher accuracy of 97.21% and 99.60% for both three-class and two-class classifications, respectively. The proposed multilayer multimodal fusion model, along with the FDSFM module, holds promise for accurate disease classification and can also be extended to other disease classifications in chest X-ray images.Comment: 19 page

Consumer online purchase behaviour: perception versus expectation

Conceptualising online customer behaviour is very important, as more and more customers are interested in buying products through online. To capture online customer behaviour, this study has conducted empirical research in Bangladesh among general online customers who have experience in online buying or have an intention to buy from online boutique websites in Bangladesh. In this regard, the quality-purchase interaction model that was developed, based on both customer perception and the expectation of buying online from business-to-consumer electronic-commerce in Bangladesh, was used to capture actual customer behaviour or behavioural intention for online purchasing. We conducted path analysis through LISREL to reveal the causal relation between independent and dependent variables. There are some significant differences between online buying behaviour and the behavioural intention to buy online, that is between customers who have experience of online buying from a boutique website and those who have the intention to buy online but have not yet gotten an online buying experience

Role of time-varying magnetic field on QGP equation of state

The phase diagram of quantum chromodynamics (QCD) and its associated thermodynamic properties of quark gluon plasma (QGP) are studied in the presence of time dependent magnetic field. The study plays a pivotal role in the field of cosmology, astrophysics, and heavy ion collisions. In order to explore the structure of quark gluon plasma to deal with the dynamics of quarks and gluons, we investigate the equation of state (EoS) not only in the environment of static magnetic field but also in the presence of time-varying magnetic fields. So, for determining the equation of state of QGP at non zero magnetic fields, we revisited our earlier model where the effect of time varying magnetic field was not taken into consideration. Using the phenomenological model, some appealing features are noticed depending upon the three different scales; effective mass of quark, temperature, and time independent as well as time-dependent magnetic field. Earlier the effective mass of quark was incorporated in our calculations and in the current work, it is modified for static and time-varying magnetic fields. Thermodynamic observables including pressure, energy density, entropy, etc. are calculated for a wide range of temperature and time-dependent as well as time-independent magnetic fields. Finally, we claim that the EoS are highly affected in the presence of a magnetic field. Our results are notable compared to other approaches and found to be advantageous for the measurement of QGP equation of state. These crucial findings with and without time-varying magnetic field could have phenomenological implications in various sectors of high energy physics.Comment: 17 pages, 4 figures, accepted in Advances in High Energy Physic

Service delivery through mobile-government (mGov): Driving factors and cultural impacts

The mobile-Government (mGov) service system is conducted through an open network, and it is virtual. This service mode and pattern change inevitably necessitates a behavioral change in citizen attitudes and intentions. Nevertheless, this new pattern of service delivery through mGov has hardly been systematically investigated by any researchers. The objective of this current research is twofold. First, we attempt to reveal the sources of beliefs for developing intention toward the mGov (ITM) system. Then, as the second objective, we investigate cultural influence as the reason for a difference in consumer attitudes and intentions toward mGov. In this regard, the empirical study was conducted in Bangladesh and the USA, which have potential differences in the cultural traits listed by Hofstede. From our statistical analysis, we have identified the sources of beliefs for both Bangladeshi and USA consumers.We observed clear differences in sources of beliefs and their influence on attitudes leading to intention, which demonstrates support for our second objective which was designed to verify the cultural impacts on belief-attitude relations.We understand that these different sources of beliefs influence cognitive, affective, and connative attitudes toward mGov in different ways

WIRELESS TRAFFIC ANALYSIS AND ANOMALY DETECTION USING DEEP LEARNING

Presented herein are innovative techniques for analyzing network traffic and identifying anomalous patterns using Artificial Intelligence (AI). In particular, the techniques presented herein map the network traffic into pictures and use advanced image recognition AI to detect anomalies in those pictures. The solution uses Transfer Learning from pre-trained models such as RESNET50. Since the models are pre-trained, the amount of new training data and time is reduced drastically