China
Africa
Explore chapters and articles related to this topic
Measurement of Exposure and Dose
Published in Samuel C. Morris, Cancer Risk Assessment, 2020
The measurement of DNA adducts is not as simple as might appear, however. DNA adducts can be measured in white blood cells or, since some adducts are removed from cellular DNA and excreted, the measurement can be made in urine. Initially, DNA adducts were measured using experimental exposure to radiotagged carcinogens, but it has become possible to quantify DNA adducts from specific environmental exposures using monoclonal antibodies with highly sensitive immunoassays (Santella et al., 1985). If the chemical nature and stability of the adducts and their excretion rates have been fully characterized (not a simple task), qualitative and quantitative identification of adduct levels can, in principle, provide an estimate of recent exposure history and biologically effective dose. There are complexities in interpretation and practical application, however (Wogan and Tannenbaum, 1987): carcinogens vary in structural complexity and form covalent bonds at a variety of nucleophilic sites on all four DNA bases as well as on the phosphate backbone of DNA, and adducts are removed from DNA at different rates for each tissue or even for the same adduct in different types of cells in the same tissue. The amount of DNA adduct also depends on the dynamic metabolic balance between carcinogen activation and deactivation and DNA repair rates. This varies by individual; a 50- to 150-fold variation in adduct formation and several-fold variation in repair has been measured in cultured human tissues (Harris, 1985). While the promise is great, much work is still needed before DNA adducts are a fully useful and practical tool for exposure assessment.
Pre-programmed Self-assembly
Published in Klaus D. Sattler, st Century Nanoscience – A Handbook, 2019
Carlos I. Mendoza, Daniel Salgado-Blanco
Although DNA is nowadays widely used to self-assemble artificial nanostructures with a high degree of structural complexity, there are limits to the materials that can be made using it. DNA itself has no remarkable electrical, optical, or thermal properties, and it remains costly to produce large-scale quantities of DNA-based materials (Pinheiro et al. 2011). To overcome these difficulties, the integration of other components in addition to DNA to build materials has been proposed.
Framing the interplay mechanisms between structural and dynamic complexity in supply chains
Published in Production Planning & Control, 2022
Pablo Fernández Campos, Luisa Huaccho Huatuco, Paolo Trucco
The complexity types of structural and dynamic complexity have been studied in previous research for decades (Casti 1979; Park and Okudan Kremer 2015; Serdarasan 2013; Bozarth et al. 2009; Wu, Frizelle, and Efstathiou 2007; Sivadasan et al. 2002; Dittfeld, Scholten, and Van Donk 2018). Structural complexity (also known as detail complexity; Bozarth et al. 2009; Aitken, Bozarth and Garn 2016) stems from the number, variety and interconnections between system components; thus, in a supply chain context, it is driven by the diversity of elements involved (products, processes, customers, suppliers, etc.) and by the dependencies and relationships between them. Dynamic complexity stems from the ‘system’s dynamical motion’ (Casti 1979) and involves time and uncertainty (Serdarasan 2013; Bozarth et al. 2009). Hence, in an SC context, dynamic complexity is driven by the dynamics of SC operations (Sivadasan et al. 2002) and by the pace of change of SC elements or of the relationships between these (Collinson and Jay 2012; Maylor, Vidgen, and Carver 2008).
The Complexity Register: A Collaborative Tool for System Complexity Evaluation
Published in Engineering Management Journal, 2022
Matthew Potts, David Harvey, Angus Johnson, Seth Bullock
Structural complexity posits that the number and heterogeneity of system components and their connections is the most important contributor to difficulty in engineering a system. Sinha and de Weck (Sinha, 2014; Sinha & de Weck, 2013, 2016) provide a metric for the structural complexity of an engineering system relying on a graph (or network) representation of a system/product architecture to provide a quantifiable measure. However, in taking such an approach, an engineering manager must exercise caution as the structural complexity of a network representation of a product or system architecture is not necessarily the structural complexity of the engineered system itself, as it depends on the modeling assumptions used to represent the architecture as a network (e.g., the level of abstraction used, what constituents an entity or relationship in the architecture, etc.). Further, the constituent terms in the complexity metric rely on estimates of individual component and interface complexity which are themselves disputed properties. Thus, the quantification of the structural complexity of an engineered system using this approach is perhaps more clearly stated as an estimation of structural complexity of a particular representation of the engineered system.
Exploring Configurations of Knowledge Management Strategy in Information and Communication Technology Firms: A Qualitative Comparative Approach
Published in Engineering Management Journal, 2022
Budi Hartono, Yulisyah P. Daulay, Hilya M. Arini
We follow the recommended steps by Berg-Schlosser et al. (2009) and Berg-Schlosser, Meur, et al. (2009) as follows: Key predictors were first selected. As this reported study focuses on the topic of KM strategy, two prominent KM strategies as indicated in the Literature Review in Chapter 2 were selected as two key conditions: personalization (people-to-people) and codification (people-to-system). With the QCA, the presence or absence of strategy could be investigated simultaneously. Thus, the study allows the test of two competing propositions on the effective approach, i.e., complementary/balanced (with the presence of both strategies) and universal/focused (one is present, the other is absent).Favorable or unfavorable conditions to outcome were chosen to extend the basic model, which is discussed in step (a) and to form a conjunctural model. For our study, “project complexity” was selected for this type of condition because: (a) it offers a strong analytical lens to investigate knowledge strategy in a project setting (Hartono, 2018), (b) it provides an important insight into the contingent nature of analysis [c.f. (Hartono et al., 2019)], and (c) its effect on the efficacy of KM is supported by empirical evidence. In this study, we relate “project complexity” to the level of complexity of project portfolios, which are typically performed by a particular ICT firm. The complexity concept follows a definition of project complexity by Hartono (2018, p. 734): “a key characteristic of any project in which problems, challenges, and opportunities pertaining to the management of projects can be analyzed and addressed.” The concepts of project complexity have been evolving remarkably over time (Geraldi et al., 2011; Maylor & Turner, 2017). Due to the abstract and broad concept of “project complexity,” scholars identified different aspects of this project attribute. In this study, two aspects of project complexity are considered: “structural complexity” and “uncertainty in goals.” Structural complexity reflects the project challenges due to scale and interdependency among elements of the project. Uncertainty in goals is related to challenges due to the limited definition of project objectives.Perspectives were widened by adding other relevant condition(s) to provide a business setting as suggested by contingency theory. In our study, the firm’s business strategy is chosen for the purpose. Earlier studies have offered a partial understanding of the alignment between business strategy, KM strategy, and performance (Asoh, 2004, 2004; (Bagnoli & Vedovato, 2014, 2014; Wu & Lin, 2009). By including “business strategy” in the analysis, an essential contingency variable is considered within its theoretical scope. Miles et al. (1978) suggest four (mutually exclusive) types of business strategy: prospector, analyzer, defender, and reactor. A firm may have a particular dominant strategy by operating one of the four strategies.