We develop a model that successfully learns social and organizational human network structure using ambient sensing data from distributed plug load energy sensors in commercial buildings. A key goal for the design and operation of commercial buildings is to support the success of organizations within them. In modern workspaces, a particularly important goal is collaboration, which relies on physical interactions among individuals. Learning the true socio-organizational relational ties among workers can therefore help managers of buildings and organizations make decisions that improve collaboration. In this paper, we introduce the Interaction Model, a method for inferring human network structure that leverages data from distributed plug load energy sensors. In a case study, we benchmark our method against network data obtained through a survey and compare its performance to other data-driven tools. We find that unlike previous methods, our method infers a network that is correlated with the survey network to a statistically significant degree (graph correlation of 0.46, significant at the 0.01 confidence level). We additionally find that our method requires only 10 weeks of sensing data, enabling dynamic network measurement. Learning human network structure through data-driven means can enable the design and operation of spaces that encourage, rather than inhibit, the success of organizations.

Recommendations of sustainable design methods are usually based on theory, not empirical industry tests. Furthermore, since professionals often mix components of different design methods, recommending whole methods may not be relevant. It may be better to recommend component activities or mindsets. To provide empirical grounding for recommendations, this study performed 23 workshops on three sustainable design methods involving over 172 professionals from 27 companies, including consultancies and manufacturers in three industries (consumer electronics, furniture and clothing). The design methods tested were The Natural Step, Whole System Mapping and Biomimicry. Participants were surveyed about what components in each design method drove perceived innovation, sustainability or other value, and why. The most valued components only partially supported theoretical predictions. Thus, recommendations should be more empirically based. Results also found unique and complementary value in components of each method, which suggests recommending mixed methods for sustainable design. This may help design professionals find more value in green design practices, and thus integrate sustainability more into their practice.

Earlier reports revealed oxysterol metabolites of Opisthorchis spp. liver fluke origin conjugated with DNA bases, suggesting that the generation of these DNA-adducts may underlie the mutagenicity and carcinogenicity of the infection with these food-borne pathogens. Here, we employed liquid chromatography-mass spectrometry to investigate, compare and contrast spectrograms of soluble extracts from Fasciola hepatica adult worms from bile ducts of cattle with those from O. viverrini and O.felineus from experimentally infected hamsters. F. hepatica and Opisthorchis spp. shared common compounds including oxysterol-like metabolites, bile acids and DNA-adducts, but the spectrometric profiles of F. hepatica included far fewer compounds than Opisthorchis species. These findings support the postulate that parasitic oxysterol-like metabolites could be related to carcinogenesis associated to infection and they point to a molecular basis for the differences among major groups of liver flukes concerning infection-induced malignancy.

Chordoma is a rare bone cancer for which there are no approved drugs. Surgery is the principle treatment but complete resection can be challenging due to the location of the tumours in the spine and therefore finding an effective drug treatment is a pressing unmet clinical need. A major recent study identified the transcription factor Brachyury as the primary vulnerability and drug target in chordoma. Previously, all-trans retinoic acid (ATRA) has been shown to negatively influence expression of the Brachyury gene, TBXT. Here we extend this finding and demonstrate that ATRA lowers Brachyury protein levels in chordoma cells and reduces proliferation of the chordoma cell line U-CH1 as well as causing loss of distinctive chordoma cell morphology. ATRA is available as a generic drug and is the first line treatment for acute promyelocytic leukaemia (APL). This study implies ATRA could have therapeutic value if repurposed for chordoma.

Bollobás and Nikiforov (J. Combin. Theory Ser. B. 97 (2007) 859–865) conjectured the following. If G is a Kr+1-free graph on at least r+1 vertices and m edges, then ${\rm{\lambda }}_1^2(G) + {\rm{\lambda }}_2^2(G) \le (r - 1)/r \cdot 2m$, where λ1 (G)and λ2 (G) are the largest and the second largest eigenvalues of the adjacency matrix A(G), respectively. In this paper we confirm the conjecture in the case r=2, by using tools from doubly stochastic matrix theory, and also characterize all families of extremal graphs. Motivated by classic theorems due to Erdös and Nosal respectively, we prove that every non-bipartite graph of order and size contains a triangle if one of the following is true: (i) ${{\rm{\lambda }}_1}(G) \ge \sqrt {m - 1} $ and $G \ne {C_5} \cup (n - 5){K_1}$, and (ii) ${{\rm{\lambda }}_1}(G) \ge {{\rm{\lambda }}_1}(S({K_{[(n - 1)/2],[(n - 1)/2]}}))$ and $G \ne S({K_{[(n - 1)/2],[(n - 1)/2]}})$, where $S({K_{[(n - 1)/2],[(n - 1)/2]}})$ is obtained from ${K_{[(n - 1)/2],[(n - 1)/2]}}$ by subdividing an edge. Both conditions are best possible. We conclude this paper with some open problems.

Supporting designers is one of the main motivations for design research. However, there is an ongoing debate about the ability of design research to transfer its results, which are often provided in form of design methods, into practice. This article takes the position that the transfer of design methods alone is not an appropriate indicator for assessing the impact of design research by discussing alternative pathways for impacting design practice. Impact is created by different means – first of all through the students that are trained based on the research results including design methods and tools and by the systematic way of thinking they acquired that comes along with being involved with research in this area. Despite having a considerable impact on practice, this article takes the position that the transfer of methods can be improved by moving from cultivating method menageries to facilitating the evolution of method ecosystems. It explains what is understood by a method ecosystem and discusses implications for developing future design methods and for improving existing methods. This paper takes the position that efforts on improving and maturing existing design methods should be raised to satisfy the needs of designers and to truly support them.

In order to understand queueing performance given only partial information about the model, we propose determining intervals of likely values of performance measures given that limited information. We illustrate this approach for the mean steady-state waiting time in the $GI/GI/K$ queue. We start by specifying the first two moments of the interarrival-time and service-time distributions, and then consider additional information about these underlying distributions, in particular, a third moment and a Laplace transform value. As a theoretical basis, we apply extremal models yielding tight upper and lower bounds on the asymptotic decay rate of the steady-state waiting-time tail probability. We illustrate by constructing the theoretically justified intervals of values for the decay rate and the associated heuristically determined interval of values for the mean waiting times. Without extra information, the extremal models involve two-point distributions, which yield a wide range for the mean. Adding constraints on the third moment and a transform value produces three-point extremal distributions, which significantly reduce the range, producing practical levels of accuracy.

Aiming to help researchers capture output from the early stages of engineering design projects, this article presents a new research tool for digitally capturing physical prototypes. The motivation for this work is to collect observations that can aid in understanding prototyping in the early stages of engineering design projects, and this article investigates if and how digital capture of physical prototypes can be used for this purpose. Early-stage prototypes are usually rough and of low fidelity and are thus often discarded or substantially modified through the projects. Hence, retrospective access to prototypes is a challenge when trying to gather accurate empirical data. To capture the prototypes developed through the early stages of a project, a new research tool has been developed for capturing prototypes through multi-view images, along with metadata describing by whom, why, when, and where the prototypes were captured. Over the course of 17 months, this research tool has been used to capture more than 800 physical prototypes from 76 individual users across many projects. In this article, one project is shown in detail to demonstrate how this capturing system can gather empirical data for enriching engineering design project cases that focus on prototyping for concept generation. The authors also analyze the metadata provided by the system to give understanding into prototyping patterns in the projects. Lastly, through enabling digital capture of large quantities of data, the research tool presents the foundations for training artificial intelligence-based predictors and classifiers that can be used for analysis in engineering design research.