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Outside board directors’ expertise and intellectual capital disclosure: evidence from FTSE 350 companies
No full textRecent amendments to statutory reporting regime require the approval of strategic report (SR) by board of directors. As the guidance on SR encourages narrative content of firms’ value creation processes, regulators were concerned about the impact of board of directors on corporate transparency. Using content analysis approach upon a sample of nonfinancial UK firms listed in the FTSE 350, this study aims to examine whether expertise diversity of outside directors (ENEDs) on the board promotes intellectual capital (IC) disclosure. Drawing on the dual functions of boards of directors (monitoring and advising), we find that cross-directorship, nonaccounting and academic background are positively associated with level of IC disclosure, in line with agency and resource dependence perspectives. However, this is not the case for firms with more accounting ENEDs on the board. In addition, prior empirical studies have largely focused on IC disclosure in a static sense, while we find that it is the nonaccounting and academic ENEDs that matter to IC disclosure narratives connecting with corporate strategies. Results are robust to the use of alternative variables in board expertise. Our evidence suggests the needs of policymakers to better understand the role of boards of directors in the increasingly rich and complex information environment of corporate voluntary-based reporting. By adopting multiple attributes of IC disclosure narratives, this paper is distinct from what the extant disclosure literature has examined on the association with IC
The impact of touchscreen digital exposure on children’s social development and communication: a systematic review
No full textTouchscreen technologies—ranging from smartphones and tablets to interactive whiteboards and tabletops—are increasingly embedded in the daily lives of young children, shaping how they communicate and interact socially. The present systematic review aims to understand aspects of touchscreen use that support, hinder or explain changes in how young children (ages 1–6) develop social functioning skills such as peer interaction, cooperation, collaboration and communication. Specifically, it explores: (1) the types of touchscreen devices used, (2) their influence on peer interaction, cooperation and collaboration, (3) their effects on communication skills and (4) the developmental, educational, and policy recommendations emerging from the literature. A comprehensive search across Web of Science, ERIC, and Scopus yielded 365 studies, of which 82 met inclusion criteria following PRISMA guidelines. Using a theoretically grounded definition of social functioning and communication, we conducted a content analysis of empirical studies across social sciences, psychology, art and humanities, and computer science. Findings reveal a pervasive presence of touchscreen media in early childhood, with varied impacts shaped by device type, content quality, adult mediation, and contextual factors. This review offers evidence-based insights for educators, parents, and policymakers, emphasizing the value of interactive engagement, teacher training, public education efforts, and community-based approaches in promoting meaningful digital experiences. It stressed the importance of intentional, guided and contextual use of touchscreen technologies in early childhood and family settings. This review offers evidence-based insights for educators, parents, and policymakers, emphasizing the value of interactive engagement, teacher training, public education efforts, and community-based approaches in promoting meaningful digital experiences. It stressed the importance of intentional, guided and contextual use of touchscreen technologies in early childhood and family settings
Empathy-Led Readiness Toolkit for Digital Adoption
No full textThe paper aims to demonstrate the value of an empathy-led toolkit that categorises individuals’ digital adoption readiness levels. The toolkit is a self-diagnostic measure for individuals within any organisation to check their readiness to adopt digital technology and seek support as technology reshapes their roles. The web-based toolkit is built to understand and measure an individual’s emotional readiness level towards a transition before the introduction of digital technology. This toolkit is created by combining four key methods from the disciplines of design, psychology and computing, i.e. empathy-based storytelling, perception-action model, empathy mapping and empathy algorithm. The paper highlights the value of such a toolkit for the manufacturing and built environment sectors, where emerging technologies are implemented in day-to-day processes. The toolkit’s research and development has been funded by InterAct (Economic Social Research Council, UK), and the toolkit has been tested with the global community of professionals and students at the Building Information Modelling in Series at Leicester
A Process-Informed Approach to Network Intrusion Detection for Industrial Control System
No full textThe highly-connected nature of Industrial Control Systems (ICS) has significantly increased the possibility of cybersecurity threats to these systems. Waterfall company’s 2023 report showed 218 ICS security incidents, with 25% resulting in tangible consequences, including operational disruptions and equipment damage. This data underscores the criticality of robust ICS security measures. Given that ICS manage essential services, potential compromises could lead to severe disruptions, impacting public health and safety and economic stability. Network Intrusion Detection System (NIDS) are crucial for securing ICS, providing early threat detection, enhanced network visibility, and invaluable support during incident response. Machine Learning (ML) significantly enhances NIDS capabilities by analysing vast amounts of data to discern normal network behaviour and identify attack patterns. This enables ML-powered NIDS to adapt to evolving threats and identify anomalies with greater accuracy than traditional rule-based systems, all while reducing the occurrence of false positives.
This thesis investigates the potential of integrating both network traffic data and physical process data in the training of ML-based network intrusion detection model. It is hypothesised that this combined approach will yield a more effective detection performance compared to models trained solely on network traffic data. To enable the network intrusion detection model to function solely on network traffic during runtime, the Learning Using Privilege Information (LUPI) paradigm is adapted as a key element of the proposed Process Informed Network Intrusion Detection for Industrial Control Systems (PINIDS) framework. The initial phase involves supervised training of a network intrusion detection model using both network traffic and process data. Subsequently, the trained model can be deployed to detect potential intrusions by analysing network data during runtime.
The effectiveness of PINIDS framework for intrusion detection is evaluated using the SWaT dataset, focusing on brute force and unauthorised command message attacks. Various machine learning techniques adopted to the LUPI paradigm are investigated, including Knowledge Transfer (SVM+), Margin Transfer, Transfer Learning, and Distillation. The findings demonstrate enhanced precision and recall balance, leading to improved detection accuracy and reduced false positives and false negatives. Notably, SVM+ achieved a significant 21.47% improvement in F1-score and 49.19% in precision compared to classical ML models, exhibiting consistent performance across experimental runs. While Margin Transfer yielded a modest average improvement in F1-score and precision of 3.3%, it lacked robustness. Distillation proved highly effective, particularly for the DNN model, with a 12.23% F1-score improvement and substantial precision enhancement. Both distilled Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models demonstrated robust performance. Although pre-trained and baseline CNN models performed comparably, the former exhibited a 7.058% F1-score improvement, reduced detection time, and greater stability. These results highlight the potential of transfer learning techniques for enhancing intrusion detection systems.
While Deep Learning algorithms, such as CNN, generally outperform ML algorithms like Support Vector Machines, our findings demonstrate that Machine Learning-based LUPI methods surpass Deep Neural Network-based LUPI approaches in ICS application with limited training data. The feature-based teaching method employed by SVM+ contributes to its superior performance compared to Deep Neural Network models in this study, effectively leveraging input variable influence for decision-making
Repetition, Patterns, Accumulation: Southeast Asian Perspectives within Compositional Practices
No full textThis practice-based research comprises a portfolio of eight pieces composed between 2020 and 2024 that investigate the interaction between repetition and the manipulation of reductive micro-resources, resulting in unpredictability and complexity within autoethnographic musical environments. The compositions in the portfolio reflect my own experiences as a Southeast Asian composer, incorporating Western and Southeast Asian conceptions of repetition into musical compositions while evoking complex dynamics of contextual, aesthetic, and cultural dialogues.
The notion of repetition and a conscious discipline for the economy of material has always served as a guiding inspiration for my own compositional approaches. Repetition serves as a foundation of musical structure in Western and Southeast Asian traditions, providing a framework for musical development and coherence. It is also the spectra of unpredictability and complexity of the nuances within the respective traditions that I will seek to uncover through a congruence of art, architecture, and culture, which I aim to incorporate into my overall creative compositional approach to achieve emotional resonance and expressivity.
As a result, my core research question focuses on how I use repetition through structural, psychological, and emotional experience woven into a journey from the past to the present, incorporating elements of personal narrative and cultural identity in search of the transformative potential of integrating both Western and Southeast Asian tenets within autoethnographic musical landscapes.
The portfolio of compositions explores repetition across three domains: i) patterns, through considerations of form and continuity resulting in a series of related works; ii) place, by engaging with cultural references within Southeast Asian contexts; and iii) people, through the evocation of memory and personal interconnection. Acting as a bridge, the commentary accompanying my portfolio reflects the creative process, contextualises the compositions within pertinent theoretical and historical contexts, presents analysis, and contributes to scholarly discussions in composition studies
Key Drivers in Flood and Drought Risk Assessment: Unveiling Risk Factors through a Multi-Metric Network Approach
No full textFlood and drought risks in river basins are driven by a complex interplay of natural and anthropogenic factors, making their assessment and management particularly challenging. This paper presents a novel approach to systematically identify and evaluate the most influential parameters contributing to these risks. We employed Interpretive Structural Modelling (ISM) and Causal Loop Diagrams (CLD) to construct a comprehensive framework of 116 interconnected parameters. By applying 11 network metrics, including betweenness centrality, PageRank, and closeness centrality, we identified the key parameters that act as critical influencers within the system [4]. The Cross-Entropy method was then utilized to refine this set, pinpointing the most significant 30 percent of parameters across all metrics. This analysis led to the development of causal networks for flood and drought risks, highlighting the dynamic relationships and critical drivers in each context. The findings provide a robust foundation for decision-makers to prioritize resources, optimize risk management strategies, and predict future risks. This work offers valuable insights for policymakers and river basin managers to converge socio-environmental planning and transboundary water management, while also supporting the proactive mitigation of flood and drought impacts
Development and Characterization of Bioboards from Abura Sawdust using Response Surface Methodology
No full textParticleboards and fibreboards are commonly produced using synthetic binders which are harmful to humans and the environment. Replacing synthetic-based particleboards with renewable alternatives, such as binderless boards, are safer and promote circular economy. The best processing parameters need to be determined in order to produce high quality binderless boards from biomass materials. Limited studies exist on the use of abura wood for production of biodegradable boards. This study developed empirical models to predict physicomechanical characteristics of biodegradable boards produced from abura (Mitragyna ciliata) sawdust using a laboratory hot press. The independent variables were pressure (10-16 MPa), temperature (100-170°C) and pressing time (5-15 min). Regression models were suitable to predict the density (R2 =82.12%), Modulus of Rupture, MOR (R2 = 82.59%), Modulus of Elasticity, MOE (R2 = 68.55%), and Internal Bonding strength, IBS (R2 = 71.16%) of the bioboards. From the results, pressure and temperature significantly determined the density and MOE of abura sawdust bioboards. The interaction between pressure and temperature was significant to MOE. The highest values of the density, MOR, MOE and IBS were 699.2 kg/m3, 1.1 MPa, 100.4 MPa and 0.049 MPa, respectively. This study found that pressure of 16 MPa, pressing temperature of 170 °C, and pressing time of 15 min resulted in the highest values of density, MOE, MOR and IBS. The findings from this study are useful in understanding and improving the manufacturing process of bioboards
A Multi-objective Optimization Approach for Feature Selection in Gentelligent Systems
No full textThe integration of advanced technologies, such as Artificial Intelligence (AI), into manufacturing processes is attracting significant attention, paving the way for the development of intelligent systems that enhance efficiency and automation. This paper uses the term ”Gentelligent system” to refer to systems that incorporate inherent component information (akin to genes in bioinformatics—where manufacturing operations are likened to chromosomes in this study) and automated mechanisms. By implementing reliable fault detection methods, manufacturers can achieve several benefits, including improved product quality, increased yield, and reduced production costs. To support these objectives, we propose a hybrid framework with a dominance-based multi-objective evolutionary algorithm. This mechanism enables simultaneous optimization of feature selection and classification performance by exploring Pareto-optimal solutions in a single run. This solution helps monitor various manufacturing operations, addressing a range of conflicting objectives that need to be minimized together. Manufacturers can leverage such predictive methods and better adapt to emerging trends. To strengthen the validation of our model, we incorporate two real-world datasets from different industrial domains. The results on both datasets demonstrate the generalizability and effectiveness of our approach
Development of an Analytical Framework to Facilitate the Transition Towards a Circular Construction Economy
No full textThe Anthropocene has redirected global economic development toward sustainable models to avoid destabilising Earth’s natural systems. Major contributing industries have been identified as a target for innovation; the largest of which is the construction industry. A leading innovation advocated by researchers and policymakers to address the problem is the Circular Economy (CE), which aims to create circular (as opposed to linear) material flows and enhance the economic potential of virgin materials entering the system. By circulating materials within an economy, the sustainability of its systems is improved; however, material thermodynamics will persist, and the CE will only delay the inevitable generation of waste. Due to the size, lifespan, and socio-economic importance of buildings developed by the construction industry, minor circular innovations can produce a considerable impact on the sustainability of the wider economy.
A core problem within the existing development of the CE within the construction industry is a lack of clarity and cohesiveness amongst policymakers, practitioners, and researchers. The development of the CE within the construction industry is trailing other industries, such as car manufacturing. CE practices within the construction industry are being applied on an ad hoc basis, reducing or mitigating the benefits of holistic applications. Although CE scholarship in construction has grown rapidly, United Kingdom (UK) based studies and syntheses remain comparatively few, and the international knowledge base is fragmented. Studies that seek to coalesce the CE’s practices are incomplete and lack cohesiveness with the wider body of knowledge. The fragmentation within studies identifying and categorising the practices of the CE insinuates a lack of holistic and comprehensive research to synthesise and standardise the approach of the CE within the construction industry. Furthermore, research into the drivers and barriers of implementing CE practices is in its infancy and faces similar impediments. Overall, the fragmentation and early-stage development of the Drivers, Barriers and Practices (DBPs) of the literature highlight a gap in knowledge towards the holistic, comprehensive, standardised, and systematic adoption of the CE within the UK construction industry. Therefore, this research aims to encapsulate the CE’s DBPs into a comprehensive framework to facilitate a systematic transition within the UK construction industry to a Circular Construction Economy (CCE).
This research adopts a pragmatist philosophy to identify and adopt methods to generate action within the construction industry to facilitate the UK’s transition to a CE. The study employs an abductive approach consisting of deductive and inductive stages. The DBPs of the CE were identified through a systematic literature review for the development of a theoretical framework in the form of a comprehensive taxonomy, which was then deductively and inductively investigated within the empirical studies. Two supply chains were identified as case studies to conduct semi-structured interviews. A snowball sample was used, following the purposive selection of highly experienced and influential practitioners within each supply chain, resulting in 27 participants. The data on the practices of the CE were collected qualitatively and analysed using discourse, comparative, and content analyses, which were later used to generate quantitative data to be analysed through descriptive statistics. The drivers and barriers of the CE were collected and analysed quantitatively using measures of central tendency and descriptive statistics, which were subjected to a discourse and comparative analysis.
The findings of the empirical studies found that both supply chains had different levels of development and faced different drivers and barriers. Commonalities between DBPs that were present throughout each group and supply chain were: laws and regulations, guidance/best practices, frameworks, and business models. These commonalities suggest that, above all, the supply chains require guidance through a model that can assess their circular needs and prescribe practices that can assist them to develop in a tailored fashion to achieve their organisational targets, resulting in the development of the Analytical Circular Construction Economy (ACCE) framework. The ACCE framework was then evaluated and validated in two focus groups, one within each supply chain, and through two external specialist interviews.
There are several contributions to knowledge and practice within this PhD study. Firstly, the bibliometric and scientometric analyses of the extant literature provide a cross-sectional view of the current body of knowledge surrounding the CE within the construction industry. From the fragmented literature, a comprehensive taxonomy was developed to coalesce the identified DBPs into one unified theoretical framework. This contribution to theory can establish a baseline for future research and empirical studies into the DBPs of the CE and provide guidance to practitioners investigating the potential options for developing their practice. The development of the ACCE framework presents a novel and structured list of DBPs that can be systematically compared to support a comprehensive analysis of CE development. Its simplicity and analytical structure make it scalable across various sample sizes, such as disciplines, organisational divisions, companies, supply chains, or entire industries, allowing it to effectively inform both practice and the advancement of CE theory. The findings of the ACCE framework’s application developed a foundational benchmark for the advancement of the CE within its respective supply chain. Although the findings are non-parametric case studies and cannot be generalised for the construction industry, the empirical findings provide a case for the development and guidance of the CE within other projects, academic or practical. Additionally, the proof of concept of the ACCE framework allows for future benchmarks to be developed in practice for the comparison of projects or the collection of generalisable industry-wide data for a holistic assessment of the construction industry
Prevalence of rifampicin resistance in pulmonary tuberculosis: A laboratory-based study
No full textTuberculosis (TB) remains a significant global public health challenge, particularly in low- and middle-income countries. The emergence of rifampicin-resistant tuberculosis further complicates control efforts. Despite national efforts, there are limited data on the prevalence of Mycobacterium tuberculosis and their rifampicin resistance in Ghana, especially at the facility level. This study aimed to determine the prevalence of Mycobacterium tuberculosis and rifampicin resistance among presumptive tuberculosis cases at the Ho Teaching Hospital over a three-year period. The study used a retrospective design and collected secondary data from the Microbiology Laboratory Unit of Ho Teaching Hospital between 2022 and 2024. Data on patient demographics, Mycobacterium tuberculosis detection, and rifampicin resistance were retrieved and analysed using descriptive and inferential statistics. Mycobacterium tuberculosis and rifampicin resistance were identified using the GeneXpert MTB/RIF assay. Statistical analyses were performed using STATA version 15 with statistical significance set at p < 0.05. Out of 2,225 presumptive tuberculosis cases, 203 tested positive for the infection, resulting in an overall prevalence of 9.1% (95% CI: 7.9–10.4). The prevalence was significantly higher among males (12.4%) compared to females (5.7%), and highest among young adults aged 18–24 years (12.8%). Of the 166 Mycobacterium tuberculosis- positive cases tested for rifampicin susceptibility, 3 (1.8%) were resistant. Although rifampicin resistance was more common among females and adults, the differences were not statistically significant. Although the detection rate of rifampicin resistance among newly diagnosed Mycobacterium tuberculosis cases was low (1.8%), it remains a significant public health concern, hence the need for enhancing surveillance systems and prioritising early detection strategies