Multi-level planning for semi-autonomous vehicles in traffic scenarios based on separation maximization

The planning of semi-autonomous vehicles in traffic scenarios is a relatively new problem that contributes towards the goal of making road travel by vehicles free of human drivers. An algorithm needs to ensure optimal real time planning of multiple vehicles (moving in either direction along a road), in the presence of a complex obstacle network. Unlike other approaches, here we assume that speed lanes are not present and that different lanes do not need to be maintained for inbound and outbound traffic. Our basic hypothesis is to carry forward the planning task to ensure that a sufficient distance is maintained by each vehicle from all other vehicles, obstacles and road boundaries. We present here a 4-layer planning algorithm that consists of road selection (for selecting the individual roads of traversal to reach the goal), pathway selection (a strategy to avoid and/or overtake obstacles, road diversions and other blockages), pathway distribution (to select the position of a vehicle at every instance of time in a pathway), and trajectory generation (for generating a curve, smooth enough, to allow for the maximum possible speed). Cooperation between vehicles is handled separately at the different levels, the aim being to maximize the separation between vehicles. Simulated results exhibit behaviours of smooth, efficient and safe driving of vehicles in multiple scenarios; along with typical vehicle behaviours including following and overtaking.

Delivery of AAV2/9-microdystrophin genes incorporating helix 1 of the coiled-coil motif in the C-terminal domain of dystrophin improves muscle pathology and restores the level of α1-syntrophin and α-dystrobrevin in skeletal muscles of mdx mice. Level of α1-Syntrophin and α-Dystrobrevin in Skeletal Muscles of mdx Mice

Duchenne muscular dystrophy is a severe X-linked inherited muscle wasting disorder caused by mutations in the dystrophin gene. Adeno-associated virus (AAV) vectors have been extensively used to deliver genes efficiently for dystrophin expression in skeletal muscles. To overcome limited packaging capacity of AAV vectors (<5 kb), truncated recombinant microdystrophin genes with deletions of most of rod and carboxyl-terminal (CT) domains of dystrophin have been developed. We have previously shown the efficiency of mRNA sequence–optimized microdystrophin (ΔR4-23/ΔCT, called MD1) with deletion of spectrin-like repeat domain 4 to 23 and CT domain in ameliorating the pathology of dystrophic mdx mice. However, the CT domain of dystrophin is thought to recruit part of the dystrophin-associated protein complex, which acts as a mediator of signalling between extracellular matrix and cytoskeleton in muscle fibers. In this study, we extended the ΔR4-23/ΔCT microdystrophin by incorporating helix 1 of the coiled-coil motif in the CT domain of dystrophin (MD2), which contains the α1-syntrophin and α-dystrobrevin binding sites. Intramuscular injection of AAV2/9 expressing CT domain–extended microdystrophin showed efficient dystrophin expression in tibialis anterior muscles of mdx mice. The presence of the CT domain of dystrophin in MD2 increased the recruitment of α1-syntrophin and α-dystrobrevin at the sarcolemma and significantly improved the muscle resistance to lengthening contraction–induced muscle damage in the mdx mice compared with MD1. These results suggest that the incorporation of helix 1 of the coiled-coil motif in the CT domain of dystrophin to the microdystrophins will substantially improve their efficiency in restoring muscle function in patients with Duchenne muscular dystrophy.

Business level strategy and performance: the moderating effects of environment and structure

Purpose – This study aims to examine the moderating effects of external environment and organisational structure in the relationship between business-level strategy and organisational performance. Design/methodology/approach – The focus of the study is on manufacturing firms in the UK belonging to the electrical and mechanical engineering sectors, and respondents were CEOs. Both objective and subjective measures were used to assess performance. Non-response bias was assessed statistically and appropriate measures taken to minimise the impact of common method variance (CMV). Findings – The results indicate that environmental dynamism and hostility act as moderators in the relationship between business-level strategy and relative competitive performance. In low-hostility environments a cost-leadership strategy and in high-hostility environments a differentiation strategy lead to better performance compared with competitors. In highly dynamic environments a cost-leadership strategy and in low dynamism environments a differentiation strategy are more helpful in improving financial performance. Organisational structure moderates the relationship of both the strategic types with ROS. However, in the case of ROA, the moderating effect of structure was found only in its relationship with cost-leadership strategy. A mechanistic structure is helpful in improving the financial performance of organisations adopting either a cost-leadership or a differentiation strategy. Originality/value – Unlike many other empirical studies, the study makes an important contribution to the literature by examining the moderating effects of both environment and structure on the relationship between business-level strategy and performance in a detailed manner, using moderated regression analysis.

A dwelling-level investigation into the physical and socio-economic drivers of domestic energy consumption in England

The UK Government's Department for Energy and Climate Change has been investigating the feasibility of developing a national energy efficiency data framework covering both domestic and non-domestic buildings. Working closely with the Energy Saving Trust and energy suppliers, the aim is to develop a data framework to monitor changes in energy efficiency, develop and evaluate programmes and improve information available to consumers. Key applications of the framework are to understand trends in built stock energy use, identify drivers and evaluate the success of different policies. For energy suppliers, it could identify what energy uses are growing, in which sectors and why. This would help with market segmentation and the design of products. For building professionals, it could supplement energy audits and modelling of end-use consumption with real data and support the generation of accurate and comprehensive benchmarks. This paper critically examines the results of the first phase of work to construct a national energy efficiency data-framework for the domestic sector focusing on two specific issues: (a) drivers of domestic energy consumption in terms of the physical nature of the dwellings and socio-economic characteristics of occupants and (b) the impact of energy efficiency measures on energy consumption.

Pediastrum species as potential indicators of lake-level change in tropical South America

We explored the potential for using Pediastrum (Meyen), a genus of green alga commonly found in palaeoecological studies, as a proxy for lake-level change in tropical South America. The study site, Laguna La Gaiba (LLG) (17°45′S, 57°40′W), is a broad, shallow lake located along the course of the Paraguay River in the Pantanal, a 135,000-km2 tropical wetland located mostly in western Brazil, but extending into eastern Bolivia. Fourteen surface sediment samples were taken from LLG across a range of lake depths (2-5.2 m) and analyzed for Pediastrum. We found seven species, of which P. musteri (Tell et Mataloni), P. argentiniense (Bourr. et Tell), and P. cf. angulosum (Ehrenb.) ex Menegh. were identified as potential indicators of lake level. Results of the modern dataset were applied to 31 fossil Pediastrum assemblages spanning the early Holocene (12.0 kyr BP) to present to infer past lake level changes qualitatively. Early Holocene (12.0-9.8 kyr BP) assemblages do not show a clear signal, though abundance of P. simplex (Meyen) suggests relatively high lake levels. Absence of P. musteri, characteristic of deep, open water, and abundance of macrophyte-associated taxa indicate lake levels were lowest from 9.8 to 3.0 kyr BP. A shift to wetter conditions began at 4.4 kyr BP, indicated by the appearance of P. musteri, though inferred lake levels did not reach modern values until 1.4 kyr BP. The Pediastrum-inferred mid-Holocene lowstand is consistent with lower precipitation, previously inferred using pollen from this site, and is also in agreement with evidence for widespread drought in the South American tropics during the middle Holocene. An inference for steadily increasing lake level from 4.4 kyr BP to present is consistent with diatom-inferred water level rise at Lake Titicaca, and demonstrates coherence with the broad pattern of increasing monsoon strength from the late Holocene until present in tropical South America.

Making the most of PAS data: Macro- and micro-level studies of Romano-British settlement

The Portable Antiquities Scheme (PAS) database includes records of over 225,000 artefacts of Roman date, with a wide geographical coverage and the potential to contribute to our understanding of Romano-British landscapes and settlement at several scales of analysis. This paper draws upon the author’s doctoral research to describe regional case studies from six counties (Wiltshire, Worcestershire, Warwickshire, Northamptonshire, North Lincolnshire and Cumbria) on the use of PAS data. The data have value nationally and regionally as general guides to ancient settlement patterns, but it is arguably at the micro-scale that they have the most potential. With reference to detailed landscape studies from parts of Warwickshire and Wiltshire, the paper argues that sites represented by PAS data are often rural settlements that show evidence for continued activity throughout the Roman period. The paper demonstrates that with an appropriate methodology PAS data can be an immensely valuable archaeological resource, particularly when interpreted at multiple scales, and can be considerably more than a guide to broad distributions of Roman finds.

Content validation for level of use of feature rich systems: a Delphi study of electronic medical records systems

Introduction. Feature usage is a pre-requisite to realising the benefits of investments in feature rich systems. We propose that conceptualising the dependent variable 'system use' as 'level of use' and specifying it as a formative construct has greater value for measuring the post-adoption use of feature rich systems. We then validate the content of the construct as a first step in developing a research instrument to measure it. The context of our study is the post-adoption use of electronic medical records (EMR) by primary care physicians. Method. Initially, a literature review of the empirical context defines the scope based on prior studies. Having identified core features from the literature, they are further refined with the help of experts in a consensus seeking process that follows the Delphi technique. Results.The methodology was successfully applied to EMRs, which were selected as an example of feature rich systems. A review of EMR usage and regulatory standards provided the feature input for the first round of the Delphi process. A panel of experts then reached consensus after four rounds, identifying ten task-based features that would be indicators of level of use. Conclusions. To study why some users deploy more advanced features than others, theories of post-adoption require a rich formative dependent variable that measures level of use. We have demonstrated that a context sensitive literature review followed by refinement through a consensus seeking process is a suitable methodology to validate the content of this dependent variable. This is the first step of instrument development prior to statistical confirmation with a larger sample.

Graph-based algorithms for comparison and prediction of household-level energy use profiles

We present an efficient graph-based algorithm for quantifying the similarity of household-level energy use profiles, using a notion of similarity that allows for small time–shifts when comparing profiles. Experimental results on a real smart meter data set demonstrate that in cases of practical interest our technique is far faster than the existing method for computing the same similarity measure. Having a fast algorithm for measuring profile similarity improves the efficiency of tasks such as clustering of customers and cross-validation of forecasting methods using historical data. Furthermore, we apply a generalisation of our algorithm to produce substantially better household-level energy use forecasts from historical smart meter data.