Challenges and drivers for data mining in the AEC sector

Purpose: This paper explores the current challenges and drivers for data mining in the AEC sector. Design/methodology/approach: Following a comprehensive literature review, the data mining concept was investigated through a workshop with industry experts and academics. Findings: The results showed that the key drivers for using data mining within the AEC sector is associated with the sustainability, process improvement, market intelligence, cost certainty and cost reduction, performance certainty and decision support systems agendas in the sector. As for the processes with the greatest potential for data mining application, design, construction, procurement, forensic analysis, sustainability and energy consumption and reuse of digital components were perceived as the main process areas. While the key challenges were perceived as being, data issues due to the fragmented nature of the construction process, the need for a cultural change, IT systems used in silos, skills requirements and having clearly defined business goals. Originality/value: With the increasing abundance of data, business intelligence and analytics and its related concepts, data mining and big data have captured the attention of practitioners and academics for the last 20 years. On the other hand, and despite the growing amount of data in its business context, the AEC sector still lags behind in utilising those concepts in its end products and daily operations with limited research conducted to explore those issues at the sector level. This paper investigates the main opportunities and barriers for Data Mining in the AEC sector with a practical focus. Keywords: Business analytics, Data Mining, Data Analytics, AEC, Facilities Managemen

TGF-beta(2)- and H2O2-Induced Biological Changes in Optic Nerve Head Astrocytes Are Reduced by the Antioxidant Alpha-Lipoic Acid

Background/Aims: The goal of the present study was to determine whether transforming growth factor-beta(2) (TGF-beta(2))- and oxidative stress-induced cellular changes in cultured human optic nerve head (ONH) astrocytes could be reduced by pretreatment with the antioxidant alpha-lipoic acid (LA). Methods: Cultured ONH astrocytes were treated with 1.0 ng/ml TGF-beta(2) for 24 h or 200 mu M hydrogen peroxide (H2O2) for 1 h. Lipid peroxidation was measured by a decrease in cis-pari-naric acid fluorescence. Additionally, cells were pretreated with different concentrations of LA before TGF-beta 2 or H2O2 exposure. Expressions of the heat shock protein (Hsp) alpha B-crystallin and Hsp27, the extracellular matrix (ECM) component fibronectin and the ECM-modulating protein connective tissue growth factor (CTGF) were examined with immunohistochemistry and real-time PCR analysis. Results: Both TGF-beta(2) and H2O2 increased lipid peroxidation. Treatment of astrocytes with TGF-beta(2) and H2O2 upregulated the expression of alpha B-crystallin, Hsp27, fibronectin and CTGF. Pretreatment with different concentrations of LA reduced the TGF-beta(2)- and H2O2-stimulated gene expressions. Conclusion: We showed that TGF-beta(2)- and H2O2-stimulated gene expressions could be prevented by pretreatment with the antioxidant LA in cultured human ONH astrocytes. Therefore, it is tempting to speculate that the use of antioxidants could have protective effects in glaucomatous optic neuropathy. Copyright (C) 2012 S. Karger AG, Base

Novel role for the innate immune receptor toll-like receptor 4 (TLR4) in the regulation of the wnt signaling pathway and photoreceptor apoptosis

Recent evidence has implicated innate immunity in regulating neuronal survival in the brain during stroke and other neurodegenerations. Photoreceptors are specialized light-detecting neurons in the retina that are essential for vision. In this study, we investigated the role of the innate immunity receptor TLR4 in photoreceptors. TLR4 activation by lipopolysaccharide (LPS) significantly reduced the survival of cultured mouse photoreceptors exposed to oxidative stress. With respect to mechanism, TLR4 suppressed Wnt signaling, decreased phosphorylation and activation of the Wnt receptor LRP6, and blocked the protective effect of the Wnt3a ligand. Paradoxically, TLR4 activation prior to oxidative injury protected photoreceptors, in a phenomenon known as preconditioning. Expression of TNFα and its receptors TNFR1 and TNFR2 decreased during preconditioning, and preconditioning was mimicked by TNFα antagonists, but was independent of Wnt signaling. Therefore, TLR4 is a novel regulator of photoreceptor survival that acts through the Wnt and TNFα pathways. © 2012 Yi et al

Disclosure Of EVA Use In Corporate Financial Reports: A Descriptive Analysis

This descriptive study examines a sample of 269 firms that mentioned EVA in their public disclosures.  The key findings of our study are: (1) the use of EVA is found in a cross-section of the industries; (2) the most commonly used source of disclosure is the proxy statement; (3) a majority of the firms use only EVA rather than EVA in combination with other traditional measures; (4) a majority of the sample firms apply EVA at the corporate level alone; (5) three-fourths of the sample firms use EVA as an incentive compensation tool; (6) most firms apply EVA and other metrics only at the executive level for compensation and performance evaluation; and (7) two common modes of compensation using EVA determination are bonus plans and stock options.  The results of our study indicate that firms are steadily adopting EVA as one component of their value management system.  In a related decision context, investors estimate the cost of equity capital to arrive at the intrinsic value of the firm.  Firms can help investors reduce this estimation error by reporting their own estimate of the cost of equity capital, in turn, reducing the valuation error. Our findings have implications for the Securities and Exchange Commission and the Financial Accounting Standards Board in that they should recognize the need to address this issue thereby enhancing the decision usefulness of public reporting

Predictive digital twin technologies for achieving net zero carbon emissions: a critical review and future research agenda

Purpose: Predictive digital twin technology, which amalgamates digital twins (DT), the internet of Things (IoT) and artificial intelligence (AI) for data collection, simulation and predictive purposes, has demonstrated its effectiveness across a wide array of industries. Nonetheless, there is a conspicuous lack of comprehensive research in the built environment domain. This study endeavours to fill this void by exploring and analysing the capabilities of individual technologies to better understand and develop successful integration use cases. Design/methodology/approach: This study uses a mixed literature review approach, which involves using bibliometric techniques as well as thematic and critical assessments of 137 relevant academic papers. Three separate lists were created using the Scopus database, covering AI and IoT, as well as DT, since AI and IoT are crucial in creating predictive DT. Clear criteria were applied to create the three lists, including limiting the results to only Q1 journals and English publications from 2019 to 2023, in order to include the most recent and highest quality publications. The collected data for the three technologies was analysed using the bibliometric package in R Studio. Findings: Findings reveal asymmetric attention to various components of the predictive digital twin’s system. There is a relatively greater body of research on IoT and DT, representing 43 and 47%, respectively. In contrast, direct research on the use of AI for net-zero solutions constitutes only 10%. Similarly, the findings underscore the necessity of integrating these three technologies to develop predictive digital twin solutions for carbon emission prediction. Practical implications: The results indicate that there is a clear need for more case studies investigating the use of large-scale IoT networks to collect carbon data from buildings and construction sites. Furthermore, the development of advanced and precise AI models is imperative for predicting the production of renewable energy sources and the demand for housing. Originality/value: This paper makes a significant contribution to the field by providing a strong theoretical foundation. It also serves as a catalyst for future research within this domain. For practitioners and policymakers, this paper offers a reliable point of reference

Evaluation of continuous improvement programmes

The study began with the problem posed by an organisation for a group of researchers in the UK. There was a need to carry out an in-depth study to evaluate the continuous improvement programmes in the context of Lean Construction, and the following question emerged: How to evaluate the continuous improvement programme? This paper aims to understand how the literature on continuous improvement, including quality circles (QCs), small group activities (SGAs), and continuous improvement cells (CICs), can help to conduct the evaluation of continuous improvement programmes. The paper includes a literature review to gain an understanding of the problem from a theoretical perspective. Continuous improvement techniques are assessed in the framework of the TFV theory, with the main focus on the flow and the waste concepts. A logic model framework is used to synthesize the literature review findings and to establish an initial proposal for the evaluation of continuous improvement programmes in the Lean Construction context. This paper does not include any empirical study or actual measure and cannot ascertain the definitive benefits of continuous improvement techniques. Also, the paper does not propose any definitive procedure on how to evaluate continuous improvement techniques

Visual Management (VM) supporting collaborative practices in infrastructure engineering design

Managing the design of complex engineering systems requires an organisational structure and an information system to support collaboration among all stakeholders. Technological developments in information management have the potential to facilitate interactions across physical boundaries, even more during the Covid-19 pandemic. Visual Management (VM) is an information management strategy, as well as a means for communication between individuals, supporting collaborative work. However, there is a lack of effective understanding of how digital VM can support infrastructure engineering design. The adoption of digital collaborative VM in the context addressed is new, under rapid evolution, and there is limited understanding of how the users embrace VM while interacting with it. The aim of the paper is to explore the adoption of VM, focusing on digital whiteboards, to support collaborative practices in design processes. The ongoing investigation is carried out in collaboration with an infrastructure design and consultancy company, and follows the action research approach. The VM effectiveness was investigated by analysing the whiteboards applicability to diverse functions and comparing digital and manual implementations. Initial findings include understanding digital whiteboards as a means for collaboration among individuals with different perceptions to establish a common point of view, as it allows the information to be transferred across time and space, identifies abnormalities, and supports problem-solving. By creating a common ground, it has the potential to support complex and emergent interactions in the collaborative space