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Fully bio-based flax/furan versus carbon/glass epoxy composites: scope and limitations in terms of fire and physico-mechanical performances
No full textFire and mechanical performances of a bio-based flax/furan resin composite are evaluated and, in order to assess their commercial potential, compared with those of conventional carbon/glass fiber-reinforced composites. Fire retardant (FR) variants of flax/furan were obtained by adding FRs to the resin and using (i) flax or (ii) FR-treated flax fabrics. With (i), the fire hazard of the composite could be reduced to minimum, without detrimental effect on the mechanical properties. However, use of FR-flax fabric (ii) led to impairment of mechanical properties. This indicated that for optimized fire and mechanical properties, use of a FR in the resin matrix suffices; there is no advantage in using a FR-treated flax fabric. Natural aging of the samples for 10 years followed by water aging indicated that water absorption in flax/furan composites was much higher than in comparable carbon/epoxy composites. While there was evidence of released acidic components such as acetic acid, oxalic acid, and so forth, in flax/furan composites, mainly from oxidative degradation of furan resin, there was no evidence of leaching of FR additives from the matrix. However, FR treatment of flax fabric affected the fiber-matrix interfacial adhesion, leading to considerable water absorption during aging and disintegration of the reinforcement.Highlights Furan resins are naturally fire retardant, burn only under forced combustion. FR flax/furan composites obtained by adding FRs to the resin or the flax fabric. FR treatment of the flax impairs mechanical properties and water tolerance. FRs in the resin neither affect mechanical properties nor leach out in water.Fabrication of fire retardant (FR) flax/furan composites by adding FR to the resin and using FR-treated flax fabric, and their fire safety indices as compared to conventional carbon/glass fibre - reinforced composites. imag
Investigation of the impact of Neuro-Linguistic Programming (NLP) on educational leadership
No full textThe purpose of this study was to explore the impact of Neuro-Linguistic Programming (NLP)on educational leadership. In particular, it aimed to investigate the impact of NLP practitioner trainingon educational leaders’ personal and professional development. A mixed-method study was conductedusing a questionnaire, semi-structured interviews and focus groups with educational leaders in theGreater Manchester area of the UK. A quantitative method was initially used, followed by qualitativeresearch drawing on semi-structured interviews and focus groups. The overall conclusion is that NLPtechniques can have a positive impact on educational leaders’ day-to-day work. The findings suggestthat educational leaders find a range of NLP principles and techniques helpful in their personal andprofessional development. In particular, NLP rapport building techniques, or NLP communicationmodelling were ranked the highest by all the research participants
Post-traumatic growth from grief - a narrative literature review
No full textPurpose The purpose of this paper is to give an overview of existing literature on post-traumatic growth (PTG), particularly in the ways that it relates to grief. Design/methodology/approach This narrative literature review brings together 125 sources and presents them in a readable way. Findings There is a great deal of evidence to suggest that PTG can come from grief. This is not always the case, however. Research limitations/implications This review presents only a selection of the existing literature - the review is not systematic. However, this allows for a narrative to be crafted, to aid readability. Practical implications Suggestions for future research are made throughout, and potential therapeutic applications are mentioned. Social implications This paper discusses stigma, in the form of "disenfranchised grief". In this, social pressures and expectations affect how a person processes their grief psychologically. While movements to increase discourse and reduce stigma are on the rise, more is needed. Originality/value This review guides readers through existing literature, providing a wide overview of the topic of PTG in grief
Deep temporal convolutional neural network for predicting electricity consumption
No full textThis study addresses the critical research domain of electricity consumption prediction, emphasizing its importance in energy production, distribution, and related aspects such as load balancing, cost optimization, energy efficiency, and carbon emissions reduction. Various models have been explored to tackle the challenges of prediction accuracy. The research introduces a Temporal Convolution Network (TCN) as a base model, aiming to enhance accuracy in predicting electricity consumption using a United Kingdom (UK) dataset spanning 2009 to 2023. Models like ARIMA, Linear Regression (LR), Random Forest (RF), Support Vector Regression (SVR), LR-SVR Hybrid, and Long Short-Term Memory (LSTM) were compared using Mean Absolute Error (MAE), with the proposed TCN demonstrating superior accuracy over other model
Assessing the capabilities of ChatGPT in recognising customer intent in a small training data scenario
No full textThis study addresses the issue of recognising customer intent when only limited training data is available. The performance of ChatGPT was evaluated in this scenario, and it was found to be better than traditional machine learning algorithms and the Bidirectional Encoder Representations from Transformers (BERT) model, which performed the worst in this case. While Random Forest with PCA was objectively the best among traditional models when the training examples were randomly selected, a qualitative evaluation showed that ChatGPT had better generalisation ability and could produce contextually correct outputs. Our research found that to improve ChatGPT?s performance on small data classification tasks, it is essential to utilise stratified sampling to select representative examples for few-shot learning. This research provides valuable insights into using ChatGPT in customer-facing applications with limited training data. Knowing the strengths and limitations of ChatGPT can enhance response accuracy, customer satisfaction, and loyalt
Necessity the mother of (Re) invention - a scoping review
No full textPurpose - The purpose of this scoping review is to find studies testing out psychological interventions to help victims of conversion therapy. Life after conversion therapy can be devastating; nonetheless, what treatment modalities are available for this population? Design/methodology/approach - This study adopts scoping review process using JBI protocol. Findings - There are minimal results to conclude upon. The paper presents discussion on future research and inquiry. The author introduces a positive autoethnography, adapting the model created by Tedeschi and Calhoun (2004) to create the post-conversion recovery process to aid recovery. Research limitations/implications - Eye movement desensitization and reprocessing and positive autoethnography offer valuable insights, but further research is needed to help survivors. Practical implications - To reduce the current death-by-suicide trends, more education and training are needed to help this specialised group. Social implications - The suicide rates for sexual minority conversion therapy victims are eight times higher than those of other sexual minority groups and isolation levels. A single point of entry pathway for conversion therapy survivors is needed. Originality/value - To the best of the authors' knowledge, this is the first review addressing gay conversion therapy and disfellowship. It requires further attention, and there are gaps in the knowledge that need to be filled
Statistical modelling of depth milling in Ti-6AL4V using abrasive water jet machining
No full textMulti-objective grey relational analysis optimization technique and multiple regression analysis were employed to determine the optimum values for depth of cut, surface roughness ( Ra), and kerf at entry and exit ([Formula: see text] and [Formula: see text]), for abrasive waterjet machining of Ti6AL4V materials. This method highlights a new process to extend the grey relational analysis technique for determining the optimum conditions for obtaining the best quality characteristics. The input parameters of the study were water pressure ( Wp), transverse speed ( Ts), abrasive mass flow rate ( Amf), abrasive orifice size ( Aos), nozzle/orifice diameter ratio ( N/Odia). The experiments were conducted as per the Taguchi-based L27orthogonal array. The grey relational analysis technique found that Tswas the most significant parameter on the combined outputs. The regression models developed had an R2of 81.58%, 79.79%%, 77.20%, and 74.39% for depth of cut, Ra, [Formula: see text] and [Formula: see text], respectively. Additionally, the analysis of variance showed that Wpand Aoshad a significant influence on the output parameters. The predicted values were found to be reasonably close with the experimental values, and the maximum average deviation was 8.15% for [Formula: see text]
Design of smart waste management system using IoT
No full textThe concept of automation in the context of hygiene and cleanliness, particularly in waste management systems, serves as the foundation for this study. Garbage dumping on public streets and in open spaces is a prevalent practice in developing nations, mostly contributing to environmental degradation and unsanitary conditions. In order to address these issues an idea known as "smart dustbin" combines hardware and software innovations, such as attaching a Wi-Fi system to a regular trash can to give users free internet access for a set amount of time. By rewarding the user for maintaining a clean environment, the technology contributes to effective waste management in a community. The goal of this project is to transform garbage disposal and monitoring procedures by introducing a Smart garbage Management System that makes use of the Internet of Things (IoT). The system offers real-time monitoring and data analytics capabilities by integrating sensor technologies with a strong IoT infrastructure. One of the sys-tem's main features is smart bins that include ultrasonic sensors to measure garbage levels. These sensors provide a full view of garbage accumulation throughout the monitored locations by wirelessly transmitting data to a centralized IoT platform. The gathered data is processed by sophisticated analytics algorithms, which then improve garbage collection routes to guarantee prompt and effective removal. The implementation of Smart Waste Management Systems not only addresses immediate concerns regarding cleanliness and hygiene but also contributes significantly to sustainable development goals. By efficiently managing waste disposal, these systems reduce the environmental footprint associated with improper waste handling, including pollution of water bodies and soil degradation. Moreover, the optimized waste collection routes minimize fuel consumption and greenhouse gas emissions, aligning with efforts to combat climate change. Beyond its technical functionalities, the Smart Garbage Management System fosters community engagement and education
Feature subset selection using heuristic and metaheuristic approaches for diabetes prediction on a binary encoded dataset
No full textThe Machine Learning (ML) models are prone to a curse of dimensionality. The dataset with a greater number of features involves more computational cost and it may lead to low performance in the context of prediction accuracy. Therefore, in this research work we have predicted diabetes with more accuracy by using a smaller number of features. The heuristic methods Sequential Forward Selection (SFS), Sequential Backward Selection (SBS) and metaheuristic evolutionary methods - Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA) are used for performing feature subset selection. The Gini index is also used as a filter evaluator. The performance of the feature subsets is analyzed by applying three different types of ML models, Random Forest (RF), Multi-Layer Perceptron (MLP) and K-Nearest Neighbor (KNN). We have predicted type-2 diabetes with an accuracy of 96.82%. Also, we have reduced the number of features up to 67.44% i.e., identified 32.56% most relevant features
Can you credit it? Towards a process for ascribing credit to apprenticeships in England
No full textPurpose: Apprenticeships in England, while defined by level and typical duration, are not quantified regarding the number of learning hours required to achieve the outcomes specified, as with other regulated qualifications and accredited programmes. This paper proposes an approach to ascribe credit to apprenticeships recognising both on-and-off-the-job learning to remove some of the existing barriers to accessing higher education (HE) and the professions.Design/methodology/approachA mixed methodological approach resulting in a total learning hours/credit value was proposed.FindingsThere is significant HE-wide confusion regarding the amount of learning/training that is required to complete apprenticeships in England. Whilst sector guidance made it clear that there was no prescribed method to ascribe credit to qualifications, programmes, modules, units or apprenticeships by drawing out the core principles within current practice, a key outcome of this project was the development of a method to ascribe a credit value to apprenticeships.Research limitations/implicationsThere is potential to support further research into the recognition of prior learning as a specialised pedagogy and for reflecting on apprenticeship practice in other roles and sectors.Practical implicationsWhilst the project underpinning this paper focused on the healthcare sector, the method used to ascribe credit to the level-3 healthcare support worker apprenticeship was not sector specific and can therefore be applied to apprenticeships within other contexts providing more widespread benefits to workforce development. Social implications: Policy makers must ensure that employers and providers are clear that the minimum statutory off-the-job hours constitute an apprentice employment entitlement, which must not be conflated with total apprenticeship learning hours requirements. This recommended policy clarification could assist in simplifying the process required for ascribing credit to apprenticeships and at the same time support a move towards better and more consistent recognition of the value of apprenticeship learning. Originality/value: It is a first attempt to ascribe a credit value to an apprenticeship in England for the specific purpose of facilitating progression to HE