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Redox homeostasis in poultry/animal production.
No full textCommercial animal/poultry production is associated with a range of stresses, including physiological, environmental, technological, nutritional, and internal/immunological stresses. It is practically impossible to avoid these stresses under the commercial conditions of poultry/animal production and the development of strategies for stress protection has become a hot topic in recent years [1]. Accumulating evidence indicates that, at the molecular level, most commercially relevant stresses in poultry and farm animals, including pigs and cows, are related to redox imbalances, compromised antioxidant defences, the overproduction of free radicals, and oxidative stress. Importantly, the poor reputation of reactive oxygen species (ROS) has been challenged and their involvement in redox signalling has become an important topic of current research and applications. It should be emphasised that stress adaptation is related to the activation of various transcription factors, including Nrf2 and NF-κB, and vitagenes. In general, maintaining an optimal redox status is a key task for the integrated antioxidant defence network. In the stress-inducing conditions of commercial animal/poultry production, the internal antioxidant defence system is often incapable of mitgating the overproduction of ROS and needs external assistance, which can be provided by the dietary supplementation of traditional antioxidants such as vitamin E or other nutrients possessing relevant regulatory functions, including selenium, taurine, carnitine, polyphenolics, and others. An important task for nutritionists is to find an optimal balance of dietary antioxidants to provide animals with maximum antioxidant defences, effective stress signalling, and adaptation which are vital elements in gut health maintenance, immunocompetence, inflammation control, and the maintenance of a high productive and reproductive performance in animals/poultry
Adapting domestic regulations to the ILO decent work agenda: a review of Nigeria's non-standard and informal sector.
No full textThe nature and world of work have evolved globally due to various factors, including technological advancements, globalisation, and the Covid-19 pandemic. This has led to the emergence of various types of employment relationships and work arrangements not envisaged by traditional labour laws. This challenge is particularly glaring in a developing country like Nigeria, where a significant proportion of the workforce is absorbed by the informal sector due to high rates of unemployment. Additionally, a significant percentage of workers are engaged in non-standard forms of employment, often characterised by precarious working conditions, limited access to quality jobs, and lack of social security. The ILO's Decent Work Agenda provides an integrated, comprehensive framework for the protection of all workers irrespective of type of employment or categories of workers. In light of this, it is imperative to evaluate Nigeria's labour framework to determine whether it aligns with the Decent Work Agenda particularly for the protection of informal and non-standard workers. To achieve this aim, the research employed a doctrinal legal methodology to examine the relevant labour laws, case laws and policy documents to access how well Nigeria's labour framework aligns with the Decent Work Agenda. A comparative analysis of South Africa, India, Germany and the United Kingdom was also undertaken to illustrate international best practices that can be applied to Nigeria. The approach adopted by the study involves the comprehensive analysis of the Nigerian labour framework parallel to the Decent Work Agenda to identify the gaps in existing labour laws, while proposing actionable recommendations for reforms. The findings reveal that the Nigerian labour legislative framework is primarily geared towards formal workers in traditional work settings, while overlooking informal and non-standard workers. The research recommends a comprehensive review of Nigerian labour laws to better align it with the Decent Work Agenda. This is crucial for the protection of all Nigerian workers and for achieving economic sustainability, as outlined by the UN Sustainable Development Goals 2030
Natural counterfactual explanations with causal awareness and actionable recourse for black-box models.
No full textThe escalating complexity of artificial intelligence (AI) models, particularly black-box systems, poses substantial challenges to transparency, user trust, and actionable recourse, especially in high-stakes decision-making domains. Counterfactual (CF) explanations, which articulate the minimal input feature alterations necessary to achieve a desired model outcome, offer a promising avenue to mitigate these issues. However, prevailing CF methods often generate explanations that are linguistically unnatural, practically infeasible for end-users, or neglect underlying causal relationships within the data, thereby limiting their real-world utility and trustworthiness. This thesis systematically addresses these critical limitations by developing and validating a multi-faceted framework for generating natural language CF explanations that are simultaneously actionable, causally coherent, and user-centric. The research unfolds in three main thrusts: First, to enhance the comprehensibility and trustworthiness of CF explanations, user studies were conducted to identify effective linguistic constructs. These insights informed the development of the n-XAIT method, which uniquely combines a novel Feature Actionability Taxonomy (FAT), categorising features by their mutability and sensitivity, with templatebased natural language generation (NLG) to produce CFs that are both feasible and clearly articulated. Second, to ensure CFs are not only actionable but also reflect real-world causal mechanisms, the PICACHU approach was developed. This method integrates causal discovery (learning causal graphs and estimating Individual Treatment Effects - ITEs) with the FAT. This synergy ensures that suggested changes are causally plausible and considers the downstream consequences of interventions. This work was further extended by exploring multineighbour strategies and the Plausibility and Actionability ontology to improve solution coverage and domain compliance. Third, to foster user engagement and adaptability, an interactive agentic system was designed and implemented. This system orchestrates the NLG, FAT, and causal awareness components into a conversational workflow, enabling users to iteratively refine constraints and receive dynamically updated explanations, thereby personalizing the recourse process. The primary contributions of this thesis are: (1) an empirically-grounded methodology for generating natural language CF explanations tailored to user understanding and feature actionability; (2) a novel framework (PICACHU) for producing causally-aware and actionable CFs by robustly integrating causal knowledge with actionability constraints; and (3) a proof-of-concept interactive agentic system demonstrating enhanced user-centricity in the explanation process. Evaluations, including user studies across multiple domains (finance, healthcare, education), validate the efficacy of the proposed methods in improving the clarity, feasibility, acceptability, and trustworthiness of CF explanations. This research advances the field of explainable AI (XAI) by providing more human-aligned, actionable, and causally-aware CF explanations for interpreting and interacting with complex AI decision-making systems
Transforming organisational change in polypharmacy management within healthcare in Oman: planning for the development and implementation of a strategic framework.
No full textThis doctoral research was conducted in two stages using a mixed-methods approach. The first stage involved a scoping review, while the second one consisted of two data collection phases. The first utilised a semi-structured qualitative interview approach, while the second phase employed an online Delphi approach. The doctoral research is presented in six chapters that are outlined below: Chapter one: The chapter is an introduction to the thesis, providing a comprehensive overview of polypharmacy management at the patient level as well as the organisational level, with the presentation of organisational change models. It also provides a brief overview regarding global initiatives that address polypharmacy management, such as the World Health Organization's Third Global Patient Safety Challenge "Medication Without Harm", Oman's healthcare system and outlines the overall aim and objectives of this doctoral research. Chapter two: This chapter focuses on research paradigms, research philosophies, methodologies and methods. The chapter explains the rationality of selecting particular methodologies for the doctoral research and provides information and background regarding various types of reviews and the use of theoretical frameworks specifically the use in this research. The chapter also provides information on reflexivity, rigour and robustness of research. Chapter three: The chapter provides a comprehensive review of polypharmacy management implementation frameworks at organisational level. The findings of the review revealed a lack of research evidence on the implementation framework for organisational level change in polypharmacy management, with identification of barriers and facilitators for implementation of the framework and strategies. Chapter four: This chapter demonstrates the findings of the semi-structured qualitative interviews with key stakeholders in the Ministry of Health Oman using the Consolidated Framework of Implementation Research (CFIR) as a theoretical framework. The main aim was the identification of the factors that affect the implementation of a polypharmacy management framework, including barriers and facilitators at the organisational level. The findings indicate the availability of more facilitators that enable implementation over barriers that may hinder implementation. Chapter five: The chapter illustrates the results of an online Delphi study with stakeholders in the ministry with an objective of determining the structure and content of an implementation framework for managing polypharmacy for Ministry of Health Oman. The Delphi results also highlighted the challenges that must be addressed prior to the implementation of the framework. Chapter six: This chapter concludes the thesis of this doctoral research and presents a comprehensive summary of the findings, strengths, and limitations. The chapter also discusses the originality and impact of the research. This chapter outlines various possibilities for future research as a continuation of this doctoral study. It is anticipated that this doctoral research will make a valuable contribution to the establishment of an evidence-based implementation framework for polypharmacy management in Oman. Upon completing this work, the doctoral student plans to seek additional work and establish a collaboration with the World Health Organization (WHO) Regional Office for the Eastern Mediterranean (EMRO) in partnership with Robert Gordon University
Analytical model for laser cutting in porous media.
Get PDFLaser cutting in porous media presents both challenges and opportunities for various applications. Laser cutting requires a deep understanding of heat transfer, materials and laser physics, and can involve nonlinear and transient effects. There is a lack of literature regarding modelling laser cutting in porous media. Using conservation of energy principle, an analytical model has been developed to calculate the total laser power required to cut a porous media without the need for complex numerical simulations. The model incorporates a new correlation for calculating waste power due to heat transfer into the surrounding during laser cutting as well as modifications to the energy balance equation to incorporate the effect of porosity and fluid saturation. The model has been validated with experimental data of cutting porous media (carbonate rock) and corroborated well. Model validation showed around 10% accuracy for fluid-saturated rock samples and around 17% accuracy for dry rock samples. Also it has been observed that the level of accuracy improves with lower Peclet number (i.e. lower cutting speed). The proposed analytical model has been used to examine the effect of Peclet number, porosity, fluid saturation, rock type and material thickness on laser cutting performance. The methodology described in this article can be used as a guideline to calculate laser power requirements and size the laser equipment at an early stage prior to the manufacturing process
Impact of COVID-19 on air quality in major cities of Bangladesh: a temporal analysis (2018–2023).
Get PDFBangladesh is among the countries with the highest concentrations of particulate matter and other air pollutants according to the World Health Organization. The Department of Environment of Bangladesh has installed air monitoring systems in Dhaka, Chattogram, Khulna, Rajshahi, Barishal, Gazipur, and Narayanganj to observe daily gaseous pollutants and particulate matter (PM) concentrations in those cities. This study analyzed the concentration of gaseous and particulate pollutants from 2018 to 2023 in these urban areas of Bangladesh. Ambient air concentrations of PM2.5, PM10, carbon monoxide (CO), oxides of nitrogen (NOx) and ozone (O3) were monitored in the dry and wet seasons for these cities. Temporal variability including hourly, day of the week, monthly, and seasonal variations of particulate matter and gaseous pollutants (except seasonal variation) were assessed. Both PM2.5 and PM10 exceeded the Bangladesh National Ambient Air Quality Standard (BNAAQS) and the World Health Organization (WHO) air quality guidelines during the observed period for all the observed regions. PM2.5 mean concentration was maximum in Narayanganj i.e., 109.7 µg/m3 (313% of the limit of the air quality standard), and PM10 mean concentration was maximum in Narayanganj i.e., 203.3 µg/m3 (407%). Among the observed cities, Khulna had the better air quality although it was not satisfactory at all. However, the gaseous pollutants were within permissible limits. The temporal patterns suggested that vehicles, brick kilns, and industries were responsible for the poor air quality in Bangladesh. The air quality during the period in which COVID-19 was most prevalent (2020–2021) was compared to the two years prior and post to that interval. In general, air quality improved during the COVID period, but in the post-COVID period, they returned to concentrations similar to the pre-COVID period. After identifying possible pollutant sources, these results will assist decision-makers in taking action and implementing policies to control air pollution in cities
Evolutionary computation and explainable AI: a roadmap to understandable intelligent systems.
Get PDFArtificial intelligence methods are being increasingly applied across various domains, but their often opaque nature has raised concerns about accountability and trust. In response, the field of explainable AI (XAI) has emerged to address the need for human-understandable AI systems. Evolutionary computation (EC), a family of powerful optimization and learning algorithms, offers significant potential to contribute to XAI, and vice versa. This paper provides an introduction to XAI and reviews current techniques for explaining machine learning models. We then explore how EC can be leveraged in XAI and examine existing XAI approaches that incorporate EC techniques. Furthermore, we discuss the application of XAI principles within EC itself, investigating how these principles can illuminate the behavior and outcomes of EC algorithms, their (automatic) configuration, and the underlying problem landscapes they optimize. Finally, we discuss open challenges in XAI and highlight opportunities for future research at the intersection of XAI and EC. Our goal is to demonstrate EC's suitability for addressing current explainability challenges and to encourage further exploration of these methods, ultimately contributing to the development of more understandable and trustworthy ML models and EC algorithms
The beau idéal has been disconnected: a technology-capitalist realism perspective on immediate modernity's anxiety pandemic.
Get PDFWithin immediate modernity, increases in the multitude of psychological, social, emotional and behavioural disruptions affecting human actors, categorised under the (reductive) label of "anxiety" are reaching an apex. This peak can be conceptualised both by frequency of disruptions, which may be defined as ever-present, and by the volume of negative effects over humans' ability to exist in present reality. Some scholars have argued the permanence of contemporary anxiety represents rebound, a "hangover" from prolonged technological social change and innovation, which alongside positive advances has heralded unintended, unavoidable and unpredictable uncertainties. Others have focussed on impacts arising from impermeable associations between evolving modern life and accelerated capitalist agenda, associated sensemaking, and the intensified embedding of capitalist principles within macro and micro social interactions. Associations occur within and between actors and the technological connective mediums utilised by humans in routine modern life. Despite some existing scholarship in the above domains, few efforts attempt to deconstruct and apply theories holistically as a mechanism of interrogating and contextualising a contemporary anxiety pandemic. To react to a gap in social sensemaking, this transdisciplinary scholarship approaches this task: firstly, by conceptualising contemporary reality using the term immediate modernity, giving language and definition to the unique pro-anxiety social landscape within which human actors are presently situated; secondly, by evaluating and distilling selected theoretical fragments salient for comprehending rapid technological societal advances and their human effects; thirdly, Mark Fisher's notions of capitalist realism are reconfigured using the synthesised perspectives and applied to interrogate the theme of technological-mediated anxiety in immediate modernity. Drawing theoretical synthesis together, some novel perspectives are presented on immediate modernity's anxiety pandemic as the beau idéal disconnect theory, describing the hegemonic culture within immediate modernity where anxiety is ever-present and inexorably interlinked with technology-capitalism, yet enduringly and reductively defined as "progress". Applications for theory to further interrogate, visualise and give language to linkages between anxiety and technology-capitalism within contemporary and rapidly accelerating society are put forward
A multimodel-based screening framework for C-19 using deep learning-inspired data fusion.
Get PDFIn recent times, there has been a notable rise in the utilization of Internet of Medical Things (IoMT) frameworks particularly those based on edge computing, to enhance remote monitoring in healthcare applications. Most existing models in this field have been developed temperature screening methods using RCNN, face temperature encoder (FTE), and a combination of data from wearable sensors for predicting respiratory rate (RR) and monitoring blood pressure. These methods aim to facilitate remote screening and monitoring of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and COVID-19. However, these models require inadequate computing resources and are not suitable for lightweight environments. We propose a multimodal screening framework that leverages deep learning-inspired data fusion models to enhance screening results. A Variation Encoder (VEN) design proposes to measure skin temperature using Regions of Interest (RoI) identified by YoLo. Subsequently, the multi-data fusion model integrates electronic records features with data from wearable human sensors. To optimize computational efficiency, a data reduction mechanism is added to eliminate unnecessary features. Furthermore, we employ a contingent probability method to estimate distinct feature weights for each cluster, deepening our understanding of variations in thermal and sensory data to assess the prediction of abnormal COVID-19 instances. Simulation results using our lab dataset demonstrate a precision of 95.2%, surpassing state-of-the-art models due to the thoughtful design of the multimodal data-based feature fusion model, weight prediction factor, and feature selection model
Innovative and sustainable advances in polymer composites for additive manufacturing: processing, microstructure, and mechanical properties.
Get PDFAdditive manufacturing (AM) has revolutionised the production of customised components across industries such as the aerospace, automotive, healthcare, electronics, and renewable energy industries. Offering unmatched design freedom, reduced time-to-market, and minimised material waste, AM enables the fabrication of high-quality, customised products with greater sustainability compared to traditional methods like machining and injection moulding. Additionally, AM reduces energy consumption, resource requirements, and CO2 emissions throughout a material's lifecycle, aligning with global sustainability goals. This paper highlights insights into the sustainability of AM polymers, comparing bio-based and traditional polymers. Bio-based polymers exhibit lower carbon footprints during production but may face challenges in durability and mechanical performance. Conversely, traditional polymers, while more robust, require higher energy inputs and contribute to greater carbon emissions. Polymer composites tailored for AM further enhance material properties and support the development of innovative, eco-friendly solutions. This Special Issue brings together cutting-edge research on polymer composites in AM, focusing on processing techniques, microstructure–property relationships, mechanical performance, and sustainable manufacturing practices. These advancements underscore AM's transformative potential to deliver versatile, high-performance solutions across diverse industries
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