Time-resolved observation of spin-charge deconfinement in fermionic Hubbard chains

Elementary particles such as the electron carry several quantum numbers, for example, charge and spin. However, in an ensemble of strongly interacting particles, the emerging degrees of freedom can fundamentally differ from those of the individual constituents. Paradigmatic examples of this phenomenon are one-dimensional systems described by independent quasiparticles carrying either spin (spinon) or charge (holon). Here we report on the dynamical deconfinement of spin and charge excitations in real space following the removal of a particle in Fermi-Hubbard chains of ultracold atoms. Using space- and time-resolved quantum gas microscopy, we track the evolution of the excitations through their signatures in spin and charge correlations. By evaluating multi-point correlators, we quantify the spatial separation of the excitations in the context of fractionalization into single spinons and holons at finite temperatures

Improving the acceptability of canned mackerel tuna (Euthynnus affinis)

Methods for improving the colour and flavour of canned mackerel tuna (Euthynnus affinis) and modifications in the canning process are reported

Design and Implementation of Open Journal System (OJS) for Rajagiri Journals: A Review

Open Access (OA) is an alternative business model for the publication of scholarly journals. It makes articles freely available to readers on the Internet and covers the costs associated with publication through means other than subscriptions. Online Journal System (OJS) is an end to end publishing management platform offered by Public Knowledge Project (PKU) which will help Journal publishers and content developers to manage its journal website along with managing pre-publishing editorial activities including manuscript management, peer review process & publishing process. The OJS platform will cover all aspects of online journal publishing, from establishing a journal website to operational tasks such as the author\u27s submission process, peer review, editing, publication, archiving, and indexing of the journal. It also helps to manage the people facets of organizing a journal, including keeping track of the articles, the work done by the editors, reviewers, and authors, notifying readers, and assisting with the communication. In this paper, we try to discuss the practical challenges and way to overcome it which we implemented Rajagiri Journals through OJS platfor

Pattern matching techniques to automatically detect range of movement tests from wearable sensors

Wearable sensor technology has steadily grown in availability within a wide variety of well-established consumer and medical devices. Wearable sensors have been used in many healthcare applications to monitor patients at home and throughout their rehabilitation. Data collected from wearable sensors allow monitoring of patient recovery during rehabilitation and assist clinicians in diagnosing. Activities of Daily Living (ADL) is considered as an assessment criterion for various disease conditions. Wearable devices enable the collection of information associated with different range of movement (ROM) tests that measure ADL. In an ambulatory monitoring setting, the volume of data collected by wearable sensors can become complex and challenging to process. Extraction of ROM tests can be laboursome, and often fraught with misclassification of movement. Hence it is difficult to analyse and make conclusions/predictions from movement datasets using manual assessment techniques. This paper examines whether ROM tests can be automatically detected and extracted from wearable sensor data using Artificial Intelligence (AI) techniques.This research examines and discusses clinical trial data collected from patients suffering from Axial SpondyloArthritis (AxSpA). AxSpA is a disease that affects spinal cord mobility. In this trial, Inertial Measurement Unit (IMU) sensors are attached to the lower back and neck of the patient, and data corresponding to clinical trial movements are recorded. An AI system is trained and tested using these datasets, and the prediction accuracy of the system is examined. The system will be capable of detecting ROM tests within long-term datasets once the AI system used in this analysis is sufficiently trained by an adequate amount of data for efficient pattern recognition