Nottingham Trent University

About: Nottingham Trent University is a education organization based out in Nottingham, United Kingdom. It is known for research contribution in the topics: Population & Addiction. The organization has 4702 authors who have published 12862 publications receiving 307430 citations. The organization is also known as: NTU & Trent Polytechnic.

Perspective: Quantum Hamiltonians for optical interactions

Abstract: The multipolar Hamiltonian of quantum electrodynamics is extensively employed in chemical and optical physics to treat rigorously the interaction of electromagnetic fields with matter. It is also widely used to evaluate intermolecular interactions. The multipolar version of the Hamiltonian is commonly obtained by carrying out a unitary transformation of the Coulomb gauge Hamiltonian that goes by the name of Power-Zienau-Woolley (PZW). Not only does the formulation provide excellent agreement with experiment, and versatility in its predictive ability, but also superior physical insight. Recently, the foundations and validity of the PZW Hamiltonian have been questioned, raising a concern over issues of gauge transformation and invariance, and whether observable quantities obtained from unitarily equivalent Hamiltonians are identical. Here, an in-depth analysis of theoretical foundations clarifies the issues and enables misconceptions to be identified. Claims of non-physicality are refuted: the PZW transformation and ensuing Hamiltonian are shown to rest on solid physical principles and secure theoretical ground.

Psychometric properties of the problematic online gaming questionnaire short-form and prevalence of problematic online gaming in a national sample of adolescents

Abstract: The rise and growing popularity of online games has led to the appearance of excessive gaming that in some cases can lead to physical and psychological problems. Several measures have been developed to explore the nature and the scale of the phenomenon. However, few measures have been validated psychometrically. The aim of the present study was to test the psychometric properties of the 12-item Problematic Online Gaming Questionnaire Short-Form (POGQ-SF) and to assess the prevalence of problematic online gaming. Data collection was carried out to assess the prevalence of problematic online gaming in a national representative adolescent sample by using an offline (pen and pencil) method. A total of 5,045 secondary school students were assessed (51% male, mean age 16.4 years, SD=0.9 years) of which 2,804 were gamers (65.4% male, mean age 16.4 years, SD=0.9 years). Confirmatory factor analysis was applied to test the measurement model of problematic online gaming, and latent profile analysis was used to identify the proportion of gamers whose online game use can be considered problematic. Results showed that the original six-factor model yielded appropriate fit to the data, and thus the POGQ-SF has appropriate psychometric properties. Latent profile analysis revealed that 4.6% of the adolescents belong to a high risk group and an additional 13.3% to a low risk group. Due to its satisfactory psychometric characteristics, the 12-item POGQ-SF appears to be an adequate tool for the assessment of problematic online gaming.

Comparing generalized and specific problematic smartphone/internet use: Longitudinal relationships between smartphone application-based addiction and social media addiction and psychological distress

Abstract: Background and aims The literature has proposed two types of problematic smartphone/internet use: generalized problematic use and specific problematic use However, longitudinal findings on the associations between the two types of problematic use and psychological distress are lacking among East-Asians The present study examined temporal associations between both generalized and specific problematic use of the smartphone/internet, and psychological distress Methods Hong Kong University students (N = 308; 100 males; mean age = 2375 years; SD ± 515) were recruited with follow-ups at three, six, and nine months after baseline assessment All participants completed the Smartphone Application-Based Addiction Scale (for generalized problematic smartphone/internet use), the Bergen Social Media Addiction Scale (for specific problematic smartphone/internet use), and the Hospital Anxiety and Depression Scale (for psychological distress) in each assessment Latent growth modeling (LGM) was constructed to understand temporal associations between generalized/specific problematic use and psychological distress Results The LGM suggested that the intercept of generalized problematic use was significantly associated with the intercept of psychological distress (standardized coefficient [β] = 032; P < 001) The growth of generalized problematic use was significantly associated with the growth of psychological distress (β = 051; P < 001) Moreover, the intercept of specific problematic use was significantly associated with the intercept of psychological distress (β = 028; P < 001) and the growth of psychological distress (β = 037; P < 001) Conclusion The initial level of problematic use of smartphone/internet increased the psychological distress among university students Helping young adults address problematic use of the smartphone/internet may prevent psychological distress

Social Group Optimization-Assisted Kapur's Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images.

Abstract: The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning-based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19-affected CTI using social group optimization-based Kapur's entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis-based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection.

Psychology of Computer Use: XLIII. Some Comments on ‘Addictive Use of the Internet’ by Young

Abstract: This paper adds further comments to a case description by Young on addictive use of the Internet. Brief comments relating to the Internet as a behavioural addiction and the problem of to what users are actually addicted, are discussed.