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Improved corrosion and cavitation erosion resistance of laser-based powder bed fusion produced Ti-6Al-4V alloy by pulsed magnetic field treatment
No full textThe application of pulsed magnetic field (PMF) treatment demonstrated enhanced corrosion resistance in saline solution and prolonged resistance to cavitation erosion in deionised water for Ti-6AI-4V alloy manufactured by laser-based powder bed fusion (LPBF) and conventional wrought processing methods. The observed outcomes were attributed to the formation of a denser protective surface oxide layer and microstructural changes, resulting in a reduction of the α’ phase by 0.13% and an increase in the presence of dislocations at the surface. Consequently, this led to an increase in the compressive residual stresses. Additionally, the application of this treatment resulted in the formation of highly refined and uniform precipitates, leading to a notable enhancement in microhardness by 5.73% and 5.85% for the conventionally manufactured (CM) and LPBF samples, respectively
Inside the Black Box: Unpacking Employee Voice to Advance Employee-Centred CSR Research in Nigeria
No full textAbstract onl
Evaluating the impact of employability skill training on the self-efficacy of disadvantaged adults
No full textPurposeEmployability skills training programmes are an effective means for reducing unemployment rates. Such programmes also have the potential to improve the general well-being (e.g. self-efficacy) of disadvantaged individuals, however, reliable longitudinal evaluations of the psychological benefits of such programmes are limited. The present study evaluated the impact of an employability programme offered to disadvantaged adults in North-West England on self-efficacy. Additionally, the study aimed to identify risk factors for programme disengagement to identify at-risk groups that require further support.Design/methodology/approachSecondary longitudinal data pertaining to the background characteristics, programme engagement and self-efficacy scores (repeatedly measured on a monthly basis) of 308 programme users were analysed.FindingsResults demonstrated that employability programme engagement significantly increased self-efficacy scores. Additionally, the findings suggested that individuals with mental health and learning difficulties were more likely to disengage from the programme. The findings demonstrate that employability programmes can have a positive impact on the well-being of individuals from disadvantaged backgrounds, however, prolonged engagement is needed for which some individuals require further support with.Originality/valueThe present study analysed longitudinal data from a diverse sample of disadvantaged individuals to reliably evaluate psychological outcomes from employability training programmes
Analysis of bubble departure and lift-off boiling model using computational intelligence techniques and hybrid algorithms
No full textThe bubble departure and lift-off boiling (BDL) model was studied using computational intelligence techniques and hybrid algorithms. Quite a few studies have predicted the relationship between wall heat fluxes and wall temperature in the form of flow boiling curves. The output wall temperature is a performance indicator that depends on many operating parameters. The current study, therefore, analyses the predictability of the wall temperature in terms of operating pressure, bulk flow velocity, and wall heat flux, based on the BDL model developed by Zenginer, which included two suppression factors namely, flow-induced and subcooling factors, respectively. The soft computing techniques used for prediction were - the artificial neural network (ANN), and the Fuzzy Mamdani model, and the hybrid algorithms were adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained particle swarm optimization (ANN-PSO). In addition, the ANN-PSO conducted a parametric analysis to evaluate the best model configuration by considering various factors. The comparison of all four techniques showed that the ANFIS model exhibited the prediction performance for wall temperature. Moreover, the results obtained from the ANFIS model have been compared with the different flow boiling curves from the literature and observed that the curve fitted well for higher bulk flow velocities with an MSE and R2 was found to be 0.85 % and 0.9933, respectively
An overview of the applied use of chatbots for substance use disorder: past, present and future
No full textAimThe purpose of this paper was to critically evaluate the use of chatbots as an eHealth resource for substance use disorder. It considers their past, present and future use, including the progression of the new wave of generative chatbots.FindingsThis paper finds chatbots for substance use disorder to be an underutilised resource, noting that there are few solutions and that these have limitations. It identifies that, along with limitations with general chatbot design, there are also serious ethical concerns given the vulnerabilities of the target population.DiscussionHow a collaborative design can address some of the limitations is discussed, as well as the restrictions this could pose to leveraging the latest technological advances. The ethical concerns presented in the applied use of both existing and prospective chatbots are explored, and how there is a need to safeguard users in matters of mental health support
Compare and contrast two different methods of data collection and analysis from a critical realist ontology perspective in studying my lived experience of conversion therapy and disfellowshipment
No full textWhen managing a research project, it is imperative to decide which ontological view the researcher-participant stands. Is the world viewed in absolute terms thereby having a positivist view only believing the observable constructions of the mind. From this stance then helps to decide epistemology informing both data methods of collection and analysis. This informed the lived experience researcher to follow logical and correct processes to uncover the post traumatic grief experience after gay conversion therapy. To best understand the lived experience self-studies are growing in popularity with the experience standing up to the rigor of peer review. A dual approach is taken to use autoethnography to explore the church culture via the lens of fundamentalism, working class, gay lens, and auto-hermeneutics to address the conversion and disfellowship phenomenon. With the focus of trying to get ideas, attitudes, felt senses of grief and trauma following conversion therapy to curb suicide, depression, and minority stress
A machine learning model for Alzheimer’s disease prediction
No full textAlzheimer’s disease (AD) is a neurodegenerative disorder that mostly affects old aged people. Its symptoms are initially mild, but they get worse over time. Although this health disease has no cure, its early diagnosis can help to reduce its impacts. In this paper, a methodology SMOTE-RF is proposed for AD prediction. Alzheimer’s is predicted using machine learning (ML) algorithms. Performance of three algorithms decision tree (DT), extreme gradient boosting (XGB), and random forest (RF) are evaluated in prediction. Open Access Series of Imaging Studies (OASIS) longitudinal dataset available on Kaggle is used for experiments. Dataset is balanced using synthetic minority oversampling technique (SMOTE). Experiments are done on both imbalanced and balanced datasets. DT obtained 73.38% accuracy, XGB obtained 83.88% accuracy and RF obtained a maximum 87.84% accuracy on the imbalanced dataset. DT obtained 83.15% accuracy, XGB obtained 91.05% accuracy and RF obtained maximum 95.03% accuracy on the balanced dataset. Maximum accuracy of 95.03% is achieved with SMOTE-R
Fundamental Functions for Smart Grid Control based on Power Electronic Devices
No full textDecarbonisation of the electric power industry using Renewable Energy Sources (RESs), such as wind and solar, is progressing towards achieving global net-zero emission targets. Unlike conventional fossil fuel power plants connected at the Transmission System Operator (TSO) level, a significant share of RESs is connected at the Distribution System Operator (DSO) level, causing a major change in the power supply system structure. This transition introduces challenges related to power quality and stability, including voltage profile, frequency control, system inertia, three-phase asymmetry, and harmonics. These issues are expected to intensify and require proactive solutions.To address these challenges, the Energy Supply Laboratory at South Westphalia University of Applied Sciences, led by Prof. Dr.-Ing. Egon Ortjohann, developed the Clustering Power System Approach (CPSA). CPSA focuses on transferring dynamic control and ancillary service functions from the TSO level to the DSO level using a decentralised control structure. This research identifies four essential measurement functions to support this transition:1.Power Quality Analyser (PQA) Functions: Necessary for system-wide monitoring of DSO networks and network condition assessment.2.Phasor Measurement Unit (PMU) Functions: Crucial for advanced monitoring to observe quasi-steady-state network variables and enhance dynamic control.3.Real-time detection of DSO network conditions: Enables power electronic devices to counteract power quality issues and interact with the DSO grid down to sub-transient periods.4.Grid dynamic response identification function: Important for improving control and understanding the dynamic response of DSO networks.This thesis presents the development, implementation, and field testing of these measurement functions. The results illustrate their effectiveness in supporting the transfer of ancillary services to the DSO level in conjunction with other system components based on CPSA
Sentiment Analysis of Intra-Regional Trade in Africa: A Case Study of COMESA
No full textUnderstanding the sentiment nature of Trade Policydocuments is a crucial step in both fostering global and regionaltrade. Moreso, the sentiments present in trade policy documentsoffer valuable insights into the direction, attitudes, and politicalatmosphere amongst member countries. This research aims toprovide a sentiment analysis of one of Africa’s crucial tradingblocs, COMESA, using Natural Language Processing. By usingMachine Learning techniques, we arrived at the conclusionthat the COMESA Trade Policy document has mostly positivesentiments with preferences for ’Member States’, ’Transit ofGoods’ and ’Common Market’ to name a few. The researchwill lay the groundwork for future sentiment analysis of otherglobal and regional trading blocs