Welcome to WIRE

(Wolverhampton Intellectual Repository and E-Theses)

WIRE is an open access repository for the research publications and other outputs from postgraduate students and staff at the University of Wolverhampton.

Wolverhampton staff: to deposit your publication to WIRE, go to: https://www.wlv.ac.uk/lib/research/wire/

Use the search box above or the browse function on the left to discover publications from the research community at the University of Wolverhampton.

University students and staff can also search WIRE using LibrarySearch

For further information or help, contact the Scholarly Communications Team at wire@wlv.ac.uk

 

  • A framework for adopting solar energy governance in the Nigerian power sector

    Suresh, Subashini; Abdullahi, Dahiru (University of Wolverhampton, 2021-06)
    The Nigerian economy is almost exclusively dependent on oil and gas as more than 80% of its revenue is currently generated from this sector. However, lack of stable electricity from all sources has impacted the socio-economic growth over a long period of time. This research explores the drivers, barriers and benefits of implementing solar energy strategies. In doing so, a framework for adopting solar energy governance in the Nigerian power sector was developed based on literature review and findings from the semi-structured interview held with 25 top management officials of solar energy stakeholders in Nigeria. The philosophical position of this research is inductive approach and interpretivist paradigm. The qualitative data collection method was employed, data were interpreted and analysed using content analysis. Interpretive Structure Modelling (ISM) was used further to analyse the barriers for solar energy implementation in Nigeria. The study revealed that socio-cultural aspects, lack of financing and lack of awareness of the technology are the key barriers that has slowed the implementation of solar energy strategies. The power sector reform Act’s energy mix, synergy of private and public sector and lack of access to electricity were revealed as the key drivers for solar energy strategies to be implemented. While economic and environmental aspects were identified as key benefits for solar energy implementation. This identification and interconnectivity of the parameters helped in the development and evaluation of a framework for adopting solar energy governance in the Nigerian power sector.
  • Quantum metric and wave packets at exceptional points in non-Hermitian systems

    Solnyshkov, DD; Leblanc, C; Bessonart, L; Nalitov, A; Ren, J; Liao, Q; Li, F; Malpuech, G (American Physical Society, 2021-03-11)
    The usual concepts of topological physics, such as the Berry curvature, are not always relevant for non-Hermitian systems. We show that another object, the quantum metric, which often plays a secondary role in Hermitian systems, becomes a crucial quantity near exceptional points in non-Hermitian systems, where it diverges in a way that fully controls the description of wave-packet trajectories. The quantum metric behavior is responsible for a constant acceleration with a fixed direction, and for a nonvanishing constant velocity with a controllable direction. Both contributions are independent of the wave-packet size.
  • Optically controlled polariton condensate molecules

    Cherotchenko, ED; Sigurdsson, H; Askitopoulos, A; Nalitov, AV (American Physical Society, 2021-03-29)
    A condensed-matter platform for analog simulation of complex two-dimensional molecular bonding configurations, based on optically trapped exciton-polariton condensates is proposed. The stable occupation of polariton condensates in the excited states of their optically configurable potential traps permits emulation of excited atomic orbitals. A classical mean-field model describing the dissipative coupling mechanism between p-orbital condensates is derived, identifying lowest-threshold condensation solutions as a function of trap parameters corresponding to bound and antibound π and σ bonding configurations, similar to those in quantum chemistry.
  • Dynamics of spin polarization in tilted polariton rings

    Mukherjee, S; Kozin, VK; Nalitov, AV; Shelykh, IA; Sun, Z; Myers, DM; Ozden, B; Beaumariage, J; Steger, M; Pfeiffer, LN; et al. (American Physical Society, 2021-04-22)
    We have observed the effect of pseudomagnetic field originating from the polaritonic analog of spin-orbit coupling [transverse electric and transverse magnetic (TE-TM) splitting] on a polariton condensate in a ring-shaped microcavity. The effect gives rise to a stable four-leaf pattern around the ring as seen from the linear polarization measurements of the condensate photoluminescence. This pattern is found to originate from the interplay of the cavity potential, energy relaxation, and TE-TM splitting in the ring. Our observations are compared to the dissipative one-dimensional spinor Gross-Pitaevskii equation with the TE-TM splitting energy, which shows good qualitative agreement.
  • Robust Deep Identification using ECG and Multimodal Biometrics forIndustrial Internet of Things

    Alkeem, Ebrahim Al; Yeob Yeun, Chan; Yun, Jaewoong; Yoo, Paul D; Chae, Myungsu; Rahman, Arafatur; Asyhari, A Taufiq (Elsevier, 2021-06-12)
    The use of electrocardiogram (ECG) data for personal identification in Industrial Internet of Things can achieve near-perfect accuracy in an ideal condition. However, real-life ECG data are often exposed to various types of noises and interferences. A reliable and enhanced identification method could be achieved by employing additional features from other biometric sources. This work, thus, proposes a novel robust and reliable identification technique grounded on multimodal biometrics, which utilizes deep learning to combine fingerprint, ECG and facial image data, particularly useful for identification and gender classification purposes. The multimodal approach allows the model to deal with a range of input domains removing the requirement of independent training on each modality, and inter-domain correlation can improve the model generalization capability on these tasks. In multitask learning, losses from one task help to regularize others, thus, leading to better overall performances. The proposed approach merges the embedding of multimodality by using feature-level and score level fusions. To the best of our understanding, the key concepts presented herein is a pioneering work combining multimodality, multitasking and different fusion methods. The proposed model achieves a better generalization on the benchmark dataset used while the feature-level fusion outperforms other fusion methods. The proposed model is validated on noisy and incomplete data with missing modalities and the analyses on the experimental results are provided.

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