19684 research outputs found
Sort by
What Do We Think About Women Who Kill? Validation of the Attitudes Towards Female Perpetrators of Intimate Partner Homicide Scale
No full textAlthough women who kill their intimate partners may be viewed in stereotypical ways, a method of measuring the extent of these stereotypical or biased attitudes about femaleperpetrators of intimate partner homicide did not previously exist. Prior beliefs may be utilised by jurors during decision-making alongside factual information presented during a trial, and characterisation of female defendants in the courtroom may have a potential influence on jury outcomes. To enable further exploration of the extent and impact of stereotypical beliefs amongst potential jurors, the Attitudes towards Female Perpetrators of Intimate Partner Homicide (AFPIPH) scale was developed. Initial previous validation of the AF-PIPH scale via Exploratory Factor Analysis suggested a 4-factor, 17-item structure. The aim of this study was to further test the structure of the scale via Confirmatory Factor Analysis in a new participant pool. 190 juryeligible participants aged between 18 and 75 were recruited to anonymously complete the AFPIPH scale. After analysis, the 4-factor structure was retained (Chi-square χ2 = 152.53, p = .008, RMSEA = .043, CFI = .969, TLI = .963, SRMR = .057, GFI = .918) over an alternative 3-factor model, with theoretical implications considered alongside measures of model fit. The AF-PIPH scale therefore has utility in identifying potential stereotypical or biased attitudes towards female homicide perpetrators, which may have benefits for development of education and training programmes across sectors as well as within the legal system. Limitations are discussed, along with implications for jury selection, legal effectiveness reviews and potential contributions of the scale towards future research
Exploring lean team development from the Tuckman’s model perspective
Full text linkThis study explores lean teams through the developmental stages of the Tuckman model. Twenty-seven interviewees commented on the teamwork of a lean programme deployed in their organisations. The results reveal that forming lean teams involves excitement, anticipation, and a desire for acceptance. However, frustration, competition, and a need for individual recognition follow in the storming phase. Training programmes that foster cooperation, compromise, and unity sometimes inadvertently create a 'them vs. us' divide in an organisation’s workforce in the norming phase. Additionally, work commitments hinder the development of shared mental models among team members. Lean teams achieve synergy, support, and goal focus in the performing phase, delivering six functions. However, challenges like prioritisation disagreements due to project overload still exist. The adjourning phase evokes mixed emotions: satisfaction with transitioning to a permanent team and sadness when the team disbands. The findings extend the Tuckman model to explain a lean team development lifecycle
Gender diversity in construction:demystifying the pipeline leaks in Australia, United States, United Kingdom and Brazil
No full textGlobally, the construction industry is a key contributor to the gross domestic product. However, compared to the gender diversity performance of the workforce in the world economy, historically, construction has been performing significantly poorly. Literature argued that these consistently poor performances in diversity, equity and inclusion were causing leaks in the education and career pipeline. However, a systematic investigation with evidence base was lacking. In this vacuum, the proposed study aims to explore the evolution of gender dynamics within the construction sector in Australia, United States, United Kingdom, and Brazil through quantitative evidence. This study collected industry gender representation data, gender pay gaps and tertiary degrees conferred from government agencies in four countries: Australia, the United States, the United Kingdom and Brazil. Quantitative data analysis was conducted with an exploration of factual figures, significant trajectories and fluctuations. Results were explored to understand local jurisdictions’ possible causal relationships and interventions. Delving into findings from the education pipeline revealed declining trends and alarming opportunities for the education institutions to take a lead role in moving from a “challenge leaky pipeline” towards a “shared solution space” through international cross-sectorial collaborations with the paradigm shift in the construction industry with the emerging fifth industrial revolution.</p
Investigation on morphological filtering via enhanced adaptive time-varying structural element for bearing fault diagnosis
No full textThe accurate extraction of machine fault-related information is the premise for implementing condition-based maintenance. In vibration analysis, morphological filtering is an effective method to detect bearing fault signatures, wherein the design of structural element and the construction of morphological operator are crucial to its performance. In this paper, a generalized morphological diagonal slice operator (GMDSO) framework is established for constructing new morphological operators with strong immunity to multi-source noise. Then, by introducing high-performance morphological operators into the GMDSO framework, a specific morphological gradient diagonal slice operator (MGDSO), is designed for extracting transient signatures. To optimize the signature excavation of morphological operators and attenuate the influence of noise in selecting structural element shape and length, an enhanced adaptive time-varying structural element (EATVSE) is proposed for more exact matching fault signatures. Finally, to accurately diagnose the early faults of rolling bearings, an enhanced adaptive time-varying morphological filtering (EATVMF) is proposed in combination with MGDSO and EATVSE. The fault diagnosis capability of EATVMF is testified on simulated signals, experimental signals, and bearing accelerated degradation datasets, and compared with five existing methods. The results demonstrate that EATVMF has excellent transient signature excavation and noise elimination capabilities under strong interference noise, and outperforms comparison methods.</p
Initial Teacher Education in England during the COVID-19 Pandemic:One University’s Experience - From Challenge to the New Normal
No full textIn late March 2020, university-based schools of education across England were forced to close and cease in-person teaching due to the COVID-19 pandemic. Online teaching and learning was quickly adopted, and while it was initially considered a provisional solution, it became apparent that such methods would remain for at least another academic year. The transition from traditional face-to-face teaching and learning to online environments required considerable modification of content, resources and the development of digital competences. Expectedly, concerns have been raised about the quality of provision and student support. Since the start of the crisis, there has been an increased interest in blended learning approaches to teacher education and different frameworks have been created to manage the new reality. However, research on the practical implications, the challenges that teacher educators face, and the innovative solutions embraced is scarce. Set in the English teacher education system, this chapter provides a critical overview of both the initial and continuing impact of the COVID-19 pandemic on initial teacher education in a large UK University
Recursive Remote State Estimation for Stochastic Complex Networks with Degraded Measurements and Amplify-and-Forward Relays
Full text linkThis paper is concerned with the remote state estimation problem for stochastic complex networks under the effects of degraded measurements and amplify-and-forward (AF) relays. Three sets of random variables are employed to describe the measurement degradation, the sensor transmission energy, and the relay transmission energy, respectively. The measurement from each node is transmitted to an AF relay and then forwarded to the remote estimator to facilitate the state estimation. A novel recursive estimator is constructed in the form of the extended Kalman filter. An upper bound of estimation error covariance is determined by solving Riccati-like difference equations based on the statistical information of the random variables, and such an upper bound is then minimized by choosing an appropriate estimator gain. Furthermore, sufficient conditions are established under which the estimation error is exponentially bounded in the sense of mean square. Finally, the effectiveness of the proposed estimation scheme is demonstrated by some numerical simulations
An unsupervised transfer network with adaptive input and dynamic channel pruning for train axle bearing fault diagnosis
Full text linkThe field of bearing fault diagnosis has witnessed remarkable advancements with cross-domain fault diagnosis techniques. Nonetheless, these existing methods suffer from two main drawbacks. First, the input length of these methods is fixed, such as 2048 sample points, irrespective of the diverse sampling frequencies, bearing structure parameters, and rotational speeds observed among transfer objects. Additionally, the transfer learning methods currently employed are not robust to noise, rendering them incapable of functioning optimally in contaminated target domains. To address the aforementioned challenges, this study presents an unsupervised transfer network for train axle bearing fault diagnosis. First, an adaptive input module is proposed, which enables the input length of the proposed network to be adaptively selected based on parameters such as sampling frequency and bearing structure. Then, an enhanced feature learning block with sharing parameters is designed to enhance the transfer learning feature extraction capability under noise condition. Next, a dynamic channel pruning module is proposed to optimize of the proposed network. Finally, the transferability of the proposed network is demonstrated through experiments involving two types of transfer learning tasks. The proposed network exhibits robustness to noise and outperforms existing methods by achieving higher diagnostic accuracy and stability.</p
High-fold optical subdivision blazed grating interferometer based on Mach-Zehnder interferometer
No full textA blazed grating interferometer with high-fold optical subdivision is proposed based on the optical path design of Mach-Zehnder interferometer. In the designed measurement system, multiple diffractions are achieved on the blazed grating surface, fully leveraging the high diffraction efficiency of the blazed grating and avoiding the presence of non-coplanar beams. Furthermore, in order to avoid beam deformation resulting from multiple diffractions, the light path structure returning along the same path is constructed, and this design doubles the optical subdivision fold factor. The experimental results show that 14-fold optical subdivision can be realized in this measurement system, and the maximum calibration difference can reach 34.47 nm within a 0.2 mm travel and 101.07 nm within a 2 mm travel. The designed blazed grating interferometer has the characteristics of a simple structural layout, high-fold optical subdivision and high measurement performance, which lay the groundwork for actual product development.</p
Optimising thermal efficiency in high-temperature tube furnaces:An investigation on thermal insulations
No full textEffective thermal management is crucial for the efficiency of high-temperature reactors, particularly those designed for industrial decarbonisation. However, optimising thermal insulation in these reactors remains a challenge due to limited methods for accurately assessing insulation performance under operational conditions. Current approaches lack the precision needed to guide material selection for minimising energy losses, leaving a critical gap in reactor design and energy efficiency. Advanced simulations were combined with experimental validation to evaluate and optimise thermal insulation materials within a specialised high-temperature reactor. A novel quantitative method based on temperature gradient analysis within the reactor was introduced, providing a robust framework for assessing insulation effectiveness. Additionally, a comprehensive simulation-based case study quantifies energy losses, validating the practical benefits of the optimised materials. These findings connect the existing gap in thermal insulation evaluation, offering key insights into enhancing energy efficiency. This work not only advances reactor design but also lays the groundwork for improved thermal management strategies across a wide range of high-temperature industrial applications.</p
Dynamics of liquid infiltration into an espresso bed using time-resolved micro-computed tomography:Insights from experiment and modeling
No full textIn espresso brewing, soluble content is extracted from a bed of ground coffee beans by forcing hot water through the bed at high pressure. An important part of this process is the infiltration stage in which water permeates the initially dry bed. This process is investigated by a combination of x-ray tomography and fluid mechanical modeling. Tomography is used to track the infiltration front of the water via the contrast in density. The experimental data are compared with a one-dimensional unsaturated porous medium flow model, which divides the bed into wet and dry regions and incorporates the espresso pump dynamics. Good agreement is seen between the experimental data and the model predictions
Access Repository Dashboard
Do you manage Huddersfield Research Portal? Access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard!