Predicting and controlling risks from human exposures to vibration and mechanical shock: flag waving and flag weaving

At work or in leisure activities, many people are exposed to vibration or mechanical shocks associated with risks of injury or disease. This paper identifies information that can be used to decide whether there may be a risk from exposure to hand-transmitted vibration or whole-body vibration and shock, and suggests actions that can control the risks. The complex and time-varying nature of human exposures to vibration and shock, the complexity of the different disorders and uncertainty as to the mechanisms of injury and the factors influencing injury have prevented the definition of dose–response relationships well proven by scientific study. It is necessary to wave a flag indicating when there is a need to control risks from exposure to vibration and shock while scientific enquiry provides understanding needed to weave a better flag. It is concluded that quantifying exposure severity is often neither necessary nor sufficient to either identify risks or implement measures that control the risks.Practitioner Summary: The identification of risks associated with exposure to vibration and mechanical shock cannot, and need not, rely solely on the quantification of exposure severity. Qualitative methods can provide a sufficient indication of the need for control measures, which should not be restricted to reducing standardised measures of exposure severity.     

An abstract formal basis for digital crowds

Distributed and Parallel Databases - Crowdsourcing, together with its related approaches, has become very popular in recent years. All crowdsourcing processes involve the participation of a digital...     

How do Individual‐Level Factors Affect the Creative Solution Formation Process of Teams?

The generation of creative solutions involves nonlinear dynamic procedures that can only be achieved through the creativity of individual team members. Thus, it is important to understand how the formation of individual‐level creativity factors influence the creative solution formation process. The creative solution formation process can be divided into four phases: idea generation, idea screening, idea development and solution verification. Prior research suggests that the creative process may be affected by motivation (intrinsic and extrinsic), knowledge stock (explicit and tacit), individual creativity (intelligence and divergent thinking), and pressure (challenge and time). We tested the effects of these eight factors on performance in the idea generation and idea development phases by conducting an experiment. Our results indicate that intrinsic motivation, intelligence and divergent thinking have a significant positive effect on both idea generation and idea development. Tacit knowledge and challenge pressure have a significant positive effect on idea generation. Time pressure has a significant negative effect on idea development. We also show that both idea generation and idea development have a significant impact on the quality of the final creative solution.     

The complexity of intersecting finite automata having few final states

The problem of determining whether several finite automata accept a word in common is closely related to the well-studied membership problem in transformation monoids. We raise the issue of limiting the number of final states in the automata intersection problem. For automata with two final states, we show the problem to be \({\oplus}\)L-complete or NP-complete according to whether a nontrivial monoid other than a direct product of cyclic groups of order 2 is allowed in the automata. We further consider idempotent commutative automata and (Abelian, mainly) group automata with one, two, or three final states over a singleton or larger alphabet, elucidating (under the usual hypotheses on complexity classes) the complexity of the intersection nonemptiness and related problems in each case.     

Modelling demand response in organized wholesale energy markets

We propose a bi-level optimization model for demand response in organized wholesale energy markets. In this model, the lower level performs the economic dispatch of energy and generates the price and the upper level minimizes the total amount of demand response subject to a net benefit requirement. In an economic sense, demand response is a trade of ‘consuming rights’ instead of a sale of energy. Therefore it must be traded separately from the energy market. Although a bi-level optimization model is very hard to solve in general, we demonstrate that realistic power networks have characteristics that can be exploited to reduce the effective size of the problem instance. In particular, we transform the nonconvex net benefit test constraint to an equivalent linear form, and reformulate the nonconvex complementarity conditions of doubly bounded variables using SOS2 constraints. For realistic instances of the MPEC, we employ a three-phase approach that exploits the fast local solution from a nonlinear programming solver as well as LP-based bound strengthening within a mixed integer/SOS2 formulation. The model is tested against various data cases and settings, and generates useful insight for demand response dispatch operations in practice.     

Large-scale information retrieval in software engineering - an experience report from industrial application

Software Engineering activities are information intensive. Research proposes Information Retrieval (IR) techniques to support engineers in their daily tasks, such as establishing and maintaining traceability links, fault identification, and software maintenance. We describe an engineering task, test case selection, and illustrate our problem analysis and solution discovery process. The objective of the study is to gain an understanding of to what extent IR techniques (one potential solution) can be applied to test case selection and provide decision support in a large-scale, industrial setting. We analyze, in the context of the studied company, how test case selection is performed and design a series of experiments evaluating the performance of different IR techniques. Each experiment provides lessons learned from implementation, execution, and results, feeding to its successor. The three experiments led to the following observations: 1) there is a lack of research on scalable parameter optimization of IR techniques for software engineering problems; 2) scaling IR techniques to industry data is challenging, in particular for latent semantic analysis; 3) the IR context poses constraints on the empirical evaluation of IR techniques, requiring more research on developing valid statistical approaches. We believe that our experiences in conducting a series of IR experiments with industry grade data are valuable for peer researchers so that they can avoid the pitfalls that we have encountered. Furthermore, we identified challenges that need to be addressed in order to bridge the gap between laboratory IR experiments and real applications of IR in the industry.     

Computing the Ramsey number R(4,3,3) using abstraction and symmetry breaking

The number R(4, 3, 3) is often presented as the unknown Ramsey number with the best chances of being found “soon”. Yet, its precise value has remained unknown for almost 50 years. This paper presents a methodology based on abstraction and symmetry breaking that applies to solve hard graph edge-coloring problems. The utility of this methodology is demonstrated by using it to compute the value R(4, 3, 3) = 30. Along the way it is required to first compute the previously unknown set \(\mathcal {R}(3,3,3;13)\) consisting of 78,892 Ramsey colorings.     

Summary Maps for Lifelong Visual Localization

Robots that use vision for localization need to handle environments that are subject to seasonal and structural change, and operate under changing lighting and weather conditions. We present a framework for lifelong localization and mapping designed to provide robust and metrically accurate online localization in these kinds of changing environments. Our system iterates between offline map building, map summary, and online localization. The offline mapping fuses data from multiple visually varied datasets, thus dealing with changing environments by incorporating new information. Before passing these data to the online localization system, the map is summarized, selecting only the landmarks that are deemed useful for localization. This Summary Map enables online localization that is accurate and robust to the variation of visual information in natural environments while still being computationally efficient. We present a number of summary policies for selecting useful features for localization from the multisession map, and we explore the tradeoff between localization performance and computational complexity. The system is evaluated on 77 recordings, with a total length of 30 kilometers, collected outdoors over 16 months. These datasets cover all seasons, various times of day, and changing weather such as sunshine, rain, fog, and snow. We show that it is possible to build consistent maps that span data collected over an entire year, and cover day‐to‐night transitions. Simple statistics computed on landmark observations are enough to produce a Summary Map that enables robust and accurate localization over a wide range of seasonal, lighting, and weather conditions.