Building Information Modelling [BIM] for energy efficiency in housing refurbishments

Building Information modelling offers potential process and delivery improvements throughout the lifecycle of built assets. However, there is limited research in the use of BIM for energy efficiency in housing refurbishments. The UK has over 300,000 solid wall homes with very poor energy efficiency. A BIM based solution for the retrofit of solid wall housing using lean and collaborative improvement techniques will offer a cost effective, comprehensive solution that is less disruptive, reduces waste and increases accuracy, leading to high quality outcomes. The aim of this research is to develop a BIM based protocol supporting development of 'what if' scenarios in housing retrofits for high efficiency thermal improvements, aiming to reduce costs and disruption for users. The paper presents a literature review on the topic and discusses the research method for the research project (S-IMPLER)

Time-Space Efficient Regression Testing for Configurable Systems

Configurable systems are those that can be adapted from a set of options. They are prevalent and testing them is important and challenging. Existing approaches for testing configurable systems are either unsound (i.e., they can miss fault-revealing configurations) or do not scale. This paper proposes EvoSPLat, a regression testing technique for configurable systems. EvoSPLat builds on our previously-developed technique, SPLat, which explores all dynamically reachable configurations from a test. EvoSPLat is tuned for two scenarios of use in regression testing: Regression Configuration Selection (RCS) and Regression Test Selection (RTS). EvoSPLat for RCS prunes configurations (not tests) that are not impacted by changes whereas EvoSPLat for RTS prunes tests (not configurations) which are not impacted by changes. Handling both scenarios in the context of evolution is important. Experimental results show that EvoSPLat is promising. We observed a substantial reduction in time (22%) and in the number of configurations (45%) for configurable Java programs. In a case study on a large real-world configurable system (GCC), EvoSPLat reduced 35% of the running time. Comparing EvoSPLat with sampling techniques, 2-wise was the most efficient technique, but it missed two bugs whereas EvoSPLat detected all bugs four times faster than 6-wise, on average.Comment: 14 page

Ab Initio Study of Phase Stability in Doped TiO2

Ab-initio density functional theory (DFT) calculations of the relative stability of anatase and rutile polymorphs of TiO2 were carried using all-electron atomic orbitals methods with local density approximation (LDA). The rutile phase exhibited a moderate margin of stability of ~ 3 meV relative to the anatase phase in pristine material. From computational analysis of the formation energies of Si, Al, Fe and F dopants of various charge states across different Fermi level energies in anatase and in rutile, it was found that the cationic dopants are most stable in Ti substitutional lattice positions while formation energy is minimised for F- doping in interstitial positions. All dopants were found to considerably stabilise anatase relative to the rutile phase, suggesting the anatase to rutile phase transformation is inhibited in such systems with the dopants ranked F>Si>Fe>Al in order of anatase stabilisation strength. Al and Fe dopants were found to act as shallow acceptors with charge compensation achieved through the formation of mobile carriers rather than the formation of anion vacancies

Improving International Climate Policy via Mutually Conditional Binding Commitments

This paper proposes enhancements to the RICE-N simulation and multi-agent reinforcement learning framework to improve the realism of international climate policy negotiations. Acknowledging the framework's value, we highlight the necessity of significant enhancements to address the diverse array of factors in modeling climate negotiations. Building upon our previous work on the "Conditional Commitments Mechanism" (CCF mechanism) we discuss ways to bridge the gap between simulation and reality. We suggest the inclusion of a recommender or planner agent to enhance coordination, address the Real2Sim gap by incorporating social factors and non-party stakeholder sub-agents, and propose enhancements to the underlying Reinforcement Learning solution algorithm. These proposed improvements aim to advance the evaluation and formulation of negotiation protocols for more effective international climate policy decision-making in Rice-N. However, further experimentation and testing are required to determine the implications and effectiveness of these suggestions.Comment: Presented at AI For Global Climate Cooperation Competition, 2023 (arXiv:cs/2307.06951

Making a Difference?: The Effects of Teach for America in High School

Uses longitudinal data from North Carolina to estimate the effectiveness, in terms of gains in student test scores, of TFA teachers relative to traditional teachers. Focuses on math and science teachers in the first study of TFA effects in high schools

Data mining of gene arrays for biomarkers of survival in ovarian cancer

The expected five-year survival rate from a stage III ovarian cancer diagnosis is a mere 22%; this applies to the 7000 new cases diagnosed yearly in the UK. Stratification of patients with this heterogeneous disease, based on active molecular pathways, would aid a targeted treatment improving the prognosis for many cases. While hundreds of genes have been associated with ovarian cancer, few have yet been verified by peer research for clinical significance. Here, a meta-analysis approach was applied to two care fully selected gene expression microarray datasets. Artificial neural networks, Cox univariate survival analyses and T-tests identified genes whose expression was consistently and significantly associated with patient survival. The rigor of this experimental design increases confidence in the genes found to be of interest. A list of 56 genes were distilled from a potential 37,000 to be significantly related to survival in both datasets with a FDR of 1.39859 × 10−11, the identities of which both verify genes already implicated with this disease and provide novel genes and pathways to pursue. Further investigation and validation of these may lead to clinical insights and have potential to predict a patient’s response to treatment or be used as a novel target for therapy

Overcoming extreme-scale reproducibility challenges through a unified, targeted, and multilevel toolset

pre-printReproducibility, the ability to repeat program executions with the same numerical result or code behavior, is crucial for computational science and engineering applications. However, non-determinism in concurrency scheduling often hampers achieving this ability on high performance computing (HPC) systems. To aid in managing the adverse effects of non-determinism, prior work has provided techniques to achieve bit-precise reproducibility, but most of them focus only on small-scale parallelism. While scalable techniques recently emerged, they are disparate and target special purposes, e.g., single-schedule domains. On current systems with O(106) compute cores and future ones with O(109), any technique that does not embrace a unied, targeted, and multilevel approach will fall short of providing reproducibility. In this paper, we argue for a common toolset that embodies this approach, where programmers select and compose complementary tools and can effectively, yet scalably, analyze, control, and eliminate sources of non-determinism at scale. This allows users to gain reproducibility only to the levels demanded by specific code development needs. We present our research agenda and ongoing work toward this goal