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A study on the effect of feed load on mortality of Pacific white shrimp 'Litopenaeus vannamei'
Get PDFIn recirculating aquaculture systems (RAS), understanding the relationship between feed load and its impact on water quality and overall shrimp health is important. This study utilised an indoor marine RAS for the intensive culture of Pacific white shrimp Litopenaeus vannamei. The shrimp were stocked into the grow-out system at an average body weight of 0.25 g and a stocking density of 347 shrimp/m3. Water quality parameters of temperature, salinity, total dissolved solids, pH, and dissolved oxygen (DO) were measured twice daily. Total ammonia nitrogen (TAN), nitrite and nitrate were measured once weekly or when needed. Sampling was conducted to calculate the daily feed ratio based on the total estimated biomass. Mortality was recorded as a means of evaluating the overall shrimp health. The data obtained were analysed using Pearson correlation (r) analysis and multi-linear regression with a significant difference accepted p < 0.05. Correlation (r) established relationships among the water quality parameters, feed load and mortality. The TAN level of 24.20 mg/L was recorded when the feed load increased by 81.2% resulting in the mortality of 40% of shrimp. A negative correlation between TAN and DO resulted in a synergistic effect causing a massive consumption of DO in the water, reducing its availability to the shrimp and leading to a drastic change in the shrimp's behaviour. Overfeeding can lead to an accumulation of uneaten feed and waste, causing ammonia spikes and oxygen depletion in the water. Monitoring and adjusting feed rates accordingly can help maintain optimal water conditions for shrimp growth and health. Therefore, it is essential to use appropriate feed rates in recirculating systems because feed load can influence water quality parameters that can be detrimental to shrimp culture
How to extend pilot innovation in public services: a case of children's social care innovation
Get PDFThere is considerable investment by government policymakers in supporting pilot innovation in public services, following which pilots prove difficult to sustain. Our 4-year longitudinal study of three pilot innovations in England, which seek to support the transition of care leavers into adulthood, provides insight into how such pilots can be sustained. Conceiving innovation as a journey, our study first identifies the dynamics of innovation around five key ingredients: the role of senior managers in cultivating a receptive context for innovation, distributed leadership, user co-production, measurement of outcomes, and innovation adaption. Second, our study highlights some ingredients are more important as implementation of innovation is initiated and may fade in importance as the innovation journey proceeds. Third, our study shows innovation ingredients are shaped by organizational contingencies of performance and financial pressures. Finally, we suggest a need for a contextualized implementation science framework to examine innovation in social care
Decoding organisational attractiveness: a fuzzy multi-criteria decision-making approach
Get PDFPurpose- High-skilled employees are crucial for sustained competitive advantage of organisations. In the "war for talent", organisations must position themselves as attractive employers. This study introduces a unified framework to systematically identify and prioritise Organisational Attractiveness (OA) components, focusing on the extreme context of the airline industry. Design/methodology/approach- Treating OA as a Multi-Criteria Decision Making (MCDM) situation, the study employs the Fuzzy Delphi Method (FDM) to validate key OA factors and the Fuzzy Analytical Hierarchy Process (FAHP) to prioritise them based on experts’ judgements. Findings- The study identifies five criteria and 22 sub-criteria for OA, with job characteristics and person-job fit as most critical. These elements signal employment quality and skill-job alignment, reducing information asymmetry and attracting talent. Practical implications- This research provides a practical framework for airline managers to identify and prioritise key aspects of OA to enhance their value proposition and attract and retain qualified employees. For policymakers, applying the OA framework supports informed policy decisions on employment standards and workforce development. Originality- This research introduces a fuzzy OA index and a framework that enhances OA. By incorporating signalling theory into a fuzzy MCDM approach, it systematically addresses key OA components, offering a strategic method to boost OA
Probing action potentials of single beating cardiomyocytes using atomic force microscopy
Get PDFThis paper presents a method for using atomic force microscopy to probe action potentials of single beating cardiomyocytes at the nanoscale. In this work, the conductive tip of an atomic force microscope (AFM) was used as a nanoelectrode to record the action potentials of self-beating cardiomyocytes in both the non-constant force contact mode and the constant force contact mode. An electrical model of a tip–cell interface was developed and the indentation force effect on the seal of an AFM conductive tip–cell membrane was theoretically analyzed. The force feedback of AFM allowed for the precise control of tip–cell contact, and enabled reliable measurements. The feasibility of simultaneously recording the action potentials and force information during the contraction of the same beating cardiomyocyte was studied. Furthermore, the AFM tip electrode was used to probe the differences of action potentials using different drugs. This method provides a way at the nanoscale for electrophysiological studies on single beating cardiomyocytes, neurons, and ion channels embedded within the cell membrane in relation to disease states, pharmaceutical drug testing and screening
Understanding healthcare professionals’ responses to patient complaints in secondary and tertiary care in the UK: a systematic review and behavioural analysis using the Theoretical Domains Framework
No full textBackground The path of a complaint and patient satisfaction with complaint resolution is often dependent on the responses of healthcare professionals (HCPs). It is therefore important to understand the influences shaping HCP behaviour. This systematic review aimed to (1) identify the key actors, behaviours and factors influencing HCPs’ responses to complaints, and (2) apply behavioural science frameworks to classify these influences and provide recommendations for more effective complaints handling behaviours. Methods A systematic literature review of UK published and unpublished (so-called grey literature) studies was conducted (PROSPERO registration: CRD42022301980). Five electronic databases [Scopus, MEDLINE/Ovid, Embase, Cumulated Index to Nursing and Allied Health Literature (CINAHL) and Health Management Information Consortium (HMIC)] were searched up to September 2021. Eligibility criteria included studies reporting primary data, conducted in secondary and tertiary care, written in English and published between 2001 and 2021 (studies from primary care, mental health, forensic, paediatric or dental care services were excluded). Extracted data included study characteristics, participant quotations from qualitative studies, results from questionnaire and survey studies, case studies reported in commentaries and descriptions, and summaries of results from reports. Data were synthesized narratively using inductive thematic analysis, followed by deductive mapping to the Theoretical Domains Framework (TDF). Results In all, 22 articles and three reports met the inclusion criteria. A total of 8 actors, 22 behaviours and 24 influences on behaviour were found. Key factors influencing effective handling of complaints included HCPs’ knowledge of procedures, communication skills and training, available time and resources, inherent contradictions within the role, role authority, HCPs’ beliefs about their ability to handle complaints, beliefs about the value of complaints, managerial and peer support and organizational culture and emotions. Themes mapped onto nine TDF domains: knowledge, skills, environmental context and resources, social/professional role and identity, social influences, beliefs about capability, intentions and beliefs about consequences and emotions. Recommendations were generated using the Behaviour Change Wheel approach. Conclusions Through the application of behavioural science, we identified a wide range of individual, social/organizational and environmental influences on complaints handling. Our behavioural analysis informed recommendations for future intervention strategies, with particular emphasis on reframing and building on the positive aspects of complaints as an underutilized source of feedback at an individual and organizational level
Reviewing perceptions and attitudes towards the existing Misuse of Drugs Act 1971: public perceptions of drug legislation
Get PDFReviewing perceptions and attitudes towards the existing Misuse of Drugs Act 1971. PROSPERO 2024 CRD42024608647 The key aim of this systematic review is to explore the attitudes and perceptions of existing Misuse of Drugs Use Act (MDA, 1971) and consequences and policy implications
Integrating metadiscourse analysis with transformer-based models for enhancing construct representation and discourse competence assessment in L2 writing: a systemic multidisciplinary approach
No full textIn recent years, large-scale language test providers have developed or adapted automated essay scoring systems (AESS) to score L2 writing essays. While the benefits of using AESS are clear, they are not without limitations, such as over-reliance on frequency counts of vocabulary and grammar variables. Discourse competence is one important aspect of L2 writing yet to be fully explored in AEE application. Evidence of discourse competence can be seen in the use of Metadiscourse Markers (MDM) to produce reader-friendly texts. The article presents a multidisciplinary study to explore the feasibility of expanding the construct representation of automated scoring models to assess discourse competence in L2 writing. Combining machine learning, automated textual analysis and corpus-linguistic methods to examine 2000 scripts across two tasks and five proficiency levels, the study investigates (1) in addition to frequency and range, whether accuracy of MDM is worth pursuing as a predictive feature in L2 writing, and (2) how identification and classification of MDM use might be fed into developing an automated scoring model using machine learning techniques. The contributions of this study are three-fold. Firstly, it offers valuable insights within the context of Explainable AI. By integrating MDM usage and accuracy into the scoring framework, this research moves beyond frequency-based evaluation. This study also makes significant contributions to the current understanding of L2 writing development that even lower-proficiency learners exhibit evidence of discourse competence through their accurate use of MDMs as well as their choice of MDMs in response to genre. From the perspective of expanding the construct representation in automated scoring systems, this study provides a critical examination of the limitations of many AEE models, which have heavily relied on vocabulary and grammar features. By exploring the feasibility of incorporating MDMs as predictive features, this research demonstrates the potential for construct expansion of L2 AEE. The results would support test providers in developing competence tests in various contexts and domains including manufacturing, medicine and so on
Development of ultrasound inversion methods for characterising features in 3D woven composite materials
Get PDFThere is an increasing interest in the use of 3D woven composites in applications that require improved strength-to-weight ratios. In addition, the use of these structures helps to reduce CO2 emissions. Woven composites offer many benefits including many possible architectures with high ratios of strain to failure. Woven composites are structures made by interlacing some continuous fibres (known as wefts) in one direction and other continuous fibres (known as warps) in a perpendicular direction. In the case of 3D woven composites, the third direction is reinforced by other continuous fibres known as binder. This study deals with the development of ultrasound inversion methods to characterize features in 3D woven composite materials. The study focuses on orthogonal weave-type only. Both theoretical (simulated) and measured data are analysed and used to calculate features such as the warps, wefts, and binder locations. The analytical-signal response, including the definition of three instantaneous parameters, is analysed and their capabilities to calculate the warp, weft and binder locations are demonstrated. These instantaneous parameters are the instantaneous amplitude, phase and frequency. The simulated data is obtained from a 3D time domain Finite Element model whereas the measured data is acquired from scanning a built specimen using an ultrasound immersion tank. The inversion techniques developed in this study can be extended to other 3D woven weave-types
On the application of machine learning techniques to map porosity across carbon fibre reinforced polymer layers
Get PDFCarbon Fibre Reinforced Polymer (CFRP) composites are extensively used in the Automotive industries due to their excellent structural and mechanical properties. However, the occurrence of porosity within these materials can significantly affect their performance and durability. Porosity, defined as void inclusion, often occurs during the manufacturing process for these materials. Even for small amounts of porosity, this defect can alter the composite’s mechanical properties by reducing its inter-laminar shear strength. It is therefore important to characterise and accurately map this defect, characterising the porosity distribution within CFRP layers. In this work, a Finite Element method that accounts for circular cross-section pores subjected to an ultrasound excitation is developed. This simulated data is then used to apply a Machine Learning (ML) technique such as Convolutional Neural Networks (CNN) to characterise the porosity within the CFRP sample. This technique leverages the capabilities of ML algorithms to analyse and interpret ultrasound data for porosity detection. By training the ML model on a dataset of ultrasound images and corresponding porosity measurements, the model can learn patterns and features indicative of porosity. Results obtained for the simulation data are presented and discussed. The application of CNN in processing ultrasound data has shown exceptional potentials in identifying and quantifying porosity. Results obtained after applying this technique to real ultrasound data measured with an immersion tank are also presented. CNN technique shows interesting capabilities for extracting defects such as porosity from complex ultrasound data. This work contributes to a vast project that aims at underpinning the design of more efficient composite structures
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