1,721,328 research outputs found
New insights on the influence of free d-aspartate metabolism in the mammalian brain during prenatal and postnatal life
Full text linkFree d-aspartate is abundant in the mammalian embryonic brain. However, following the postnatal onset of the catabolic d-aspartate oxidase (DDO) activity, cerebral d-aspartate levels drastically decrease, remaining constantly low throughout life. d-Aspartate stimulates both glutamatergic NMDA receptors (NMDARs) and metabotropic Glu5 receptors. In rodents, short-term d-aspartate exposure increases spine density and synaptic plasticity, and improves cognition. Conversely, persistently high d-Asp levels produce NMDAR-dependent neurotoxic effects, leading to precocious neuroinflammation and cell death. These pieces of evidence highlight the dichotomous impact of d-aspartate signaling on NMDAR-dependent processes and, in turn, unveil a neuroprotective role for DDO in preventing the detrimental effects of excessive d-aspartate stimulation during aging. Here, we will focus on the in vivo influence of altered d-aspartate metabolism on the modulation of glutamatergic functions and its involvement in translational studies. Finally, preliminary data on the role of embryonic d-aspartate in the mouse brain will also be reviewed
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dematerialization, Archiving and Recovery of Documents: A Proposed Tool Based on a Semantic Classifier and a Semantic Search Engine
No full textNowadays, every organization have to manage several documents and the number and volume of these will grow more and more, causing a substantial increase in management costs and slowing down the process of recovering useful information at the desired time. Document Management Systems (DMS) are currently a very hot topic because companies can significantly improve their efficiency and productivity through their adoption and use. The aim of this article is to present a document management solution based on a semantic classifier and a semantic search engine, able to support a company in the correct management, archiving and research of documents. A Case Study approach is used through which has been analyzed potential benefits for companies, such as the reduction of problems related to the loss of paper documents and the costs related to their management, the resolution for the recovery and archiving of paper and/or thimble documents and the reduction of search time
Branch-price-and-cut algorithms for the vehicle routing problem with stochastic and correlated travel times
Full text linkIn this paper, we consider a version of the capacitated vehicle routing problem (CVRP) where travel times are assumed to be uncertain and statistically correlated (CVRP-SCT). In particular, we suppose that travel times follow a multivariate probability distribution whose first and second moments are known. The main purpose of the CVRPCST is to plan vehicle routes whose travel times are reliable, in the sense that observed travel times are not excessively dispersed with respect to their expected value. To this scope we adopt a mean-variance approach, where routes with high travel time variability are penalized. This leads to a parametric binary quadratic program for which we propose two alternative set partitioning reformulations and show how to exploit the structure of the correlation matrix when there is correlation only between adjacent links. For each model, we develop an exact branch-price-and-cut algorithm, where the quadratic component is dealt with either in the column generation master problem or in its subproblem. We tested our algorithms on a rich collection of instances derived from well-known data sets. Computational results show that our algorithms can efficiently solve problem instances with up to 75 customers. Furthermore, the obtained solutions significantly reduce the time variability when compared with standard CVRP solutions. Copyright
Predicting the Consumer's Purchase Intention of Food Products
No full textAn important aspect of health monitoring is effective knowledge of food consumed. In this regard, a methodology of analysis is proposed in order to know in advance the food choices of the final consumers. This can give added value to the agricultural productions of the territory and of the farmers, facilitating the direct relationship between agriculture and the final consumer. To build a prediction model, web monitoring and traditional marketing analysis will be performed. The results of analysis can be used to sensitize the consumers towards a greater alimentary awareness, helping to manage of diet-related diseases like obesity, diabetes, and even cardiovascular diseases
Dynamic routing for the Electric Vehicle Shortest Path Problem with charging station occupancy information
Full text linkWe study the problem of an Electric Vehicle (EV) having to travel from an origin to a destination in the shortest amount of time. We focus on long-distance settings, where the shortest path between the origin and the destination has energy requirements exceeding the EV autonomy. The EV may charge its battery at public Charging Stations (CSs), which are subject to unknown arrivals of exogenous vehicles requiring uncertain charging times. Thus, the waiting times at CSs are uncertain. Similar to other contributions in the literature, we model CSs using appropriately defined queues, whose status is revealed upon the EV arrival. However, following recent technological advances, we also consider that the status of each CS is updated in real-time via binary Occupancy Indicator (OI) information signaling if a CS is busy or not. Therefore, we assume that the EV continuously receives OI updates on all CSs. At each update, we determine the sequence of CSs to visit along with associated charging quantities. We name the resulting problem as the Electric Vehicle Shortest Path Problem with charging station Occupancy Indicator information (EVSPP-OI). In this problem, we consider that the EV is allowed to partially charge its battery, and we model charging times via piecewise linear charging functions that depend on the CS technology.We propose a Markov Decision Process formulation for the EVSPP-OI, which aims at optimizing the EV routing and charging policy. To solve the problem, we develop a reoptimization algorithm that establishes the sequence of CS visits and charging amounts based on system updates. Specifically, we propose a simulation-based approach to estimate the waiting time of the EV at a CS as a function of its arrival time. As the path to a CS may consist of multiple intermediate CS stops, estimating the arrival times at each CS is fairly intricate. To this end, we propose an efficient heuristic that yields approximate lower bounds on the arrival time of the EV at each CS, which are used to derive an estimation of the waiting time at each CS. We use these estimations to define a compatible deterministic version of the EVSPP, which we solve with an existing algorithm. We conduct a comprehensive computational study and compare the performance of our methodology with a benchmark that observes the status of CSs only upon arrival (i.e., with no OI information). Results show that our method reduces waiting times and total trip duration by an average of 23.7%-95.4% and 1.4%-18.5%, respectively
- …
