The min-dist location selection and facility replacement queries

We propose and study a new type of location optimization problem, the min-dist location selection problem: given a set of clients and a set of existing facilities, we select a location from a given set of potential locations for establishing a new facility, so that the average distance between a client and her nearest facility is minimized. The problem has a wide range of applications in urban development simulation, massively multiplayer online games, and decision support systems. We also investigate a variant of the problem, where we consider replacing (instead of adding) a facility while achieving the same optimization goal. We call this variant the min-dist facility replacement problem. We explore two common approaches to location optimization problems and present methods based on those approaches for solving the min-dist location selection problem. However, those methods either need to maintain an extra index or fall short in efficiency. To address their drawbacks, we propose a novel method (named MND), which has very close performance to the fastest method but does not need an extra index. We then utilize the key idea behind MND to approach the min-dist facility replacement problem, which results in two algorithms names MSND and RID. We provide a detailed comparative cost analysis and conduct extensive experiments on the various algorithms. The results show that MND and RID outperform their competitors by orders of magnitude.     

Polypropylene photostabilization by hindered amines in the presence of acidic species

Some volatile acids were found to dramatically reduce the effectiveness of additives based on 2,2,6,6-tetramethylpiperidine as photostabilizers for polypropylene films. Strong acids such as HCl, HBr, and HNO₃ had the largest effect with sulfurous acid somewhat less detrimental. Weak organic acids did not impair the effectiveness of the hindered amine light stabilizers. The role of acid concentration and contact time were explored for the HCl–piperidyl additive system. Secondary and tertiary amines were included in the study as well as oligomeric additives and an N-oxyl derivative. The latter is less basic than the free amines, and it was correspondingly less effected by acid exposures. The possibilities for acid exposure during the compounding, fabrication, and use of stabilized polyolefin articles is discussed as well as the effects of acids in terms of proposed stabilization mechanisms for the hindered amines.     

Margarine oil formulation and control

The formulation and control of margarine oils and margarines is based on an understanding of the relation between various physical measurements and the composition of the oils and margarines. Solid-to-liquid-fat ratios are determined by dilatometry or by nuclear magnetic resonance spectroscopy. Oils are chosen for their crystal habit under conditions of processing and finishing. Some margarine test methods involve appearance, oral melting characteristics, oil-off, slump or collapse, get-away, penetration, and spreadability. Many measurements are effective only when they describe conditions over a range of temperatures. These include dilatometry and consistency determinations which require multipoint measurements. Presented at the American Oil Chemists' Society Short Course on “Processing and Quality Control of Fats and Oils,” Aug. 29–Sept. 1, 1966, Michigan State University, East Lansing, Mich.     

Pilot proteomic analysis of cerebrospinal fluid in Alzheimer's disease

Proteomic analysis of cerebrospinal fluid (CSF) holds great promise in understanding the progression of neurodegenerative diseases, including Alzheimer's disease (AD). As one of the primary reservoirs of neuronal biomolecules, CSF provides a window into the biochemical and cellular aspects of the neurological environment. CSF can be drawn from living participants allowing the potential alignment of clinical changes with these biochemical markers. Using cutting-edge mass spectrometry technologies, we perform a streamlined proteomic analysis of CSF. We quantify greater than 700 proteins across 10 pairs of age- and sex-matched participants in approximately one hour of analysis time each. Using the paired participant study structure, we identify a small group of biologically relevant proteins that show substantial changes in abundance between cognitive normal and AD participants, which were then analyzed at the peptide level using parallel reaction monitoring experiments. Our findings suggest the utility of fractionating a single sample and using matching to increase proteomic depth in cerebrospinal fluid, as well as the potential power of an expanded study.     

A Hybrid Color Quantization Algorithm Incorporating a Human Visual Perception Model

Color quantization is a common image processing technique where full color images are to be displayed using a limited palette of colors. The choice of a good palette is crucial as it directly determines the quality of the resulting image. Standard quantization approaches aim to minimize the mean squared error (MSE) between the original and the quantized image, which does not correspond well to how humans perceive the image differences. In this article, we introduce a color quantization algorithm that hybridizes an optimization scheme based with an image quality metric that mimics the human visual system. Rather than minimizing the MSE, its objective is to maximize the image fidelity as evaluated by S‐CIELAB, an image quality metric that has been shown to work well for various image processing tasks. In particular, we employ a variant of simulated annealing with the objective function describing the S‐CIELAB image quality of the quantized image compared with its original. Experimental results based on a set of standard images demonstrate the superiority of our approach in terms of achieved image quality.     

Open to a Select Few? Matching Partners and Knowledge Content for Open Innovation Performance

The purpose of the paper is to illuminate the costs and benefits of crossing firm boundaries in inbound open innovation (OI) by determining the relationships among partner types, knowledge content and performance. The empirical part of the study is based on a survey of OI collaborations answered by R&D managers in 415 Italian, Finnish and Swedish firms. The results show that the depth of collaboration with different partners (academic/consultants, value chain partners, competitors and firms in other industries) is positively related to innovation performance, whereas the number of different partners and size have negative effects. The main result is that the knowledge content of the collaboration moderates the performance outcomes and the negative impact of having too many different kinds of partners. This illustrates how successful firms use selective collaboration strategies characterized by linking explorative and exploitative knowledge content to specific partners, to leverage the benefits and limit the costs of knowledge boundary crossing processes.     

SleepExplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors

Getting enough quality sleep is a key part of a healthy lifestyle. Many people are tracking their sleep through mobile and wearable technology, together with contextual information that may influence sleep quality, like exercise, diet, and stress. However, there is limited support to help people make sense of this wealth of data, i.e., to explore the relationship between sleep data and contextual data. We strive to bridge this gap between sleep-tracking and sense-making through the design of SleepExplorer, a web-based tool that helps individuals understand sleep quality through multi-dimensional sleep structure and explore correlations between sleep data and contextual information. Based on a two-week field study with 12 participants, this paper offers a rich understanding on how technology can support sense-making on personal sleep data: SleepExplorer organizes a flux of sleep data into sleep structure, guides sleep-tracking activities, highlights connections between sleep and contributing factors, and supports individuals in taking actions. We discuss challenges and opportunities to inform the work of researchers and designers creating data-driven health and well-being applications.     

Improved Quality Output through Computer‐Based Training: An Automotive Assembly Field Study

In this article, two field experiments, conducted in an automotive assembly plant, evaluate how computer‐based training of operational sequences and related quality information can support the assembly performance of the operators. The experiments were performed during the launch of a new vehicle. A comparison was made of learning progress and quality performance between a reference group of operators that only had regular training and a test group for which some of the regular training was replaced with individual computer‐based training. Both quantitative measures of the quality output and questionnaires and observations were used to evaluate the effects of computer‐based training. The results show a clear positive difference in learning progress and improvements in quality output for the test group compared with the reference group. This combined with positive attitudes expressed by the operators and their team leaders shows that this type of training is an effective way to train operators during launches of new vehicles in automotive production.     

On matrices, automata, and double counting in constraint programming

Matrix models are ubiquitous for constraint problems. Many such problems have a matrix of variables $\mathcal{M}$ , with the same constraint C defined by a finite-state automaton $\mathcal{A}$ on each row of $\mathcal{M}$ and a global cardinality constraint $\mathit{gcc}$ on each column of $\mathcal{M}$ . We give two methods for deriving, by double counting, necessary conditions on the cardinality variables of the $\mathit{gcc}$ constraints from the automaton $\mathcal{A}$ . The first method yields linear necessary conditions and simple arithmetic constraints. The second method introduces the cardinality automaton, which abstracts the overall behaviour of all the row automata and can be encoded by a set of linear constraints. We also provide a domain consistency filtering algorithm for the conjunction of lexicographic ordering constraints between adjacent rows of $\mathcal{M}$ and (possibly different) automaton constraints on the rows. We evaluate the impact of our methods in terms of runtime and search effort on a large set of nurse rostering problem instances.