1,721,986 research outputs found
Understanding and Targeting the Colon Cancer Pathogenesis: A Molecular Perspective
No full textColorectal cancer (CRC) one of the leading cause of cancer-related deaths worldwide. With the presently available knowledge on CRC, it is understood that the underlying is a complex process. The complexity of CRC lies in aberrant activation of several cellular signaling pathways that lead to activation and progression of CRC. In this context, recent studies have pointed towards the role of developmental pathways like; hedgehog (HH), wingless-related integration site (WNT/??-catenin) and Notch pathways that play a crucial role in maintenance and homeostasis of colon epithelium. Moreover, the deregulation of these signaling pathways has also been associated with the pathogenesis of CRC. Therefore, in the search for better therapeutic options, these pathways have emerged as potential targets. The present review attempts to highlight the role of HH, WNT/??-catenin and Notch pathways in colon carcinogenesis
Modelling of strengthening in Al-Cu-Mg alloys during isothermal and non-isothermal treatments
No full textThis present work consists of developing and testing a model for the prediction ofprecipitation kinetics and strengthening in Al-Cu-Mg alloys with composition in the. + S phase field. The model is applied to a range of conditions including isothermaland non-isothermal treatments. The non-isothermal treatments include controlled slowheating and cooling cycles and rapid heating and cooling cycles as experienced duringfusion welding. In these Al-Cu-Mg alloys the Cu:Mg ratio is close to 1 and the Cu:Mgco-clusters and the S phase precipitates are the dominant strengthening phases. Themodel consists of two integrated modules, one for the prediction of themicrostructural evolution of the Cu:Mg co-clusters and the S phase precipitates andthe other for the prediction of yield strength or hardness.The modelling of precipitation kinetics of S phase is based on the Kampmann andWagner (KW) numerical model. The major predictions of the microstructural modelare the volume fraction and average radius of the S phase precipitates and the volumefraction of the Cu:Mg co-clusters evolving during the isothermal and non-isothermaltreatments. The modelling of the thermal profile representing fusion welding is basedon the Rosenthal’s thin plate solution for two dimensional heat flow.In the strength model the total critical resolved shear stress (CRSS) of the grains isevaluated by including contributions from the precipitates, solid solution, dislocationsand the aluminium matrix. The strengthening due to the Cu:Mg co-clusters is basedon the modulus strengthening mechanism and the strengthening due to the S phaseprecipitates is modelled using the Orowan looping mechanism. The predicted CRSS isthen converted to yield strength and hardness data in order to compare with theexperimental results.The testing of the model predictions is carried out by experimental data on 2024T351 aluminium alloys. Some of the experimental data has been taken from otherpublished works. The model is tested not only by the strength and hardness data butalso by heat flow measured by the calorimetry experiments and the S phase averagesize measured from the transmission electron microscopy (TEM) micrographs.The predictions of the model correspond well with the experimental results for allthe three models (one isothermal and two non-isothermal)
The Effect of Occupancy Patterns on the Energy Performance of Hotel Guestroom Area
No full textA Hotel industry, being one of the vital elements of the commercial buildings, consumes a considerable portion of energy used in this sector. With regard to this specific field, this paper presents a case study focused on the energy consumption of a hotel by analyzing the monitored data of occupancy in the guestroom area and corresponding energy consumption during the period July06-May08. Starting from statistical methods and energy simulations, the aim of this study is to suggest an optimization protocol for placement/reservation of guests in the guestroom area for efficient final energy use in the hotel. The statistical methods are first applied to investigate the effect of occupancy on energy consumption and to evaluate their correlations; then a specific study has been carried out to find the floor of the hotel which gives weak relationship between the two aforesaid variables. The results from statistical methods show that in general there is a strong direct relationship between occupancy and energy consumption, and the third floor is the one which shows weak relationship between the two variables. This relationship gets stronger with the severity in ambient weather conditions. Moreover, in order to investigate the statistical results from the monitoring data and optimize the energy consumption, different simulations for the last ten rooms on the east wing of the third floor are being performed using a calibrated model. The simulation results not only validate the relationship between occupancy and energy consumption but also shows that energy consumption varies by changing the position of room on different sides and wing
Fault detection analysis of building energy consumption using Data Mining techniques
Get PDFThis study describes three different data mining techniques for detecting abnormal lighting energy consumption using hourly recorded energy consumption and peak demand (maximum power) data. Two outliers' detection methods are applied to each class and cluster for detecting abnormal consumption in the same data set. In each class and cluster with anomalous consumption the amount of variation from normal is determined using modified standard scores. The study will be helpful for building energy management systems to reduce operating cost and time by not having to detect faults manually or diagnose false warnings. In addition, it will be useful for developing fault detection and diagnosis model for the whole building energy consumption
Fault detection analysis using data mining techniques for a cluster of smart office buildings
No full textThere is an increasing need for automated fault detection tools in buildings. The total energy request in buildings can be significantly reduced by detecting abnormal consumption effectively. Numerous models are used to tackle this problem but either they are very complex and mostly applicable to components level, or they cannot be adopted for different buildings and equipment. In this study a simplified approach to automatically detect anomalies in building energy consumption based on actual recorded data of active electrical power for lighting and total active electrical power of a cluster of eight buildings is presented. The proposed methodology uses statistical pattern recognition techniques and artificial neural ensembling networks coupled with outliers detection methods for fault detection. The results show the usefulness of this data analysis approach in automatic fault detection by reducing the number of false anomalies. The method allows to identify patterns of faults occurring in a cluster of bindings; in this way the energy consumption can be further optimized also through the building management staff by informing occupants of their energy usage and educating them to be proactive in their energy consumption. Finally, in the context of smart buildings, the common detected outliers in the cluster of buildings demonstrate that the management of a smart district can be operated with the whole buildings cluster approach
Journey Planning Algorithms for Massive Delay-Prone Transit Networks
Get PDFThis paper studies the journey planning problem in the context of transit networks. Given the timetable of a schedule-based transportation system (consisting, e.g., of trains, buses, etc.), the problem seeks journeys optimizing some criteria. Specifically, it seeks to answer natural queries such as, for example, “find a journey starting from a source stop and arriving at a target stop as early as possible”. The fastest approach for answering to these queries, yielding the smallest average query time even on very large networks, is the Public Transit Labeling framework, proposed for the first time in Delling et al., SEA 2015. This method combines three main ingredients: (i) a graph-based representation of the schedule of the transit network; (ii) a labeling of such graph encoding its transitive closure (computed via a time-consuming pre-processing); (iii) an efficient query algorithm exploiting both (i) and (ii) to answer quickly to queries of interest at runtime. Unfortunately, while transit networks’ timetables are inherently dynamic (they are often subject to delays or disruptions), ptl is not natively designed to handle updates in the schedule—even after a single change, precomputed data may become outdated and queries can return incorrect results. This is a major limitation, especially when dealing with massively sized inputs (e.g., metropolitan or continental sized networks), as recomputing the labeling from scratch, after each change, yields unsustainable time overheads that are not compatible with interactive applications. In this work, we introduce a new framework that extends ptl to function in delay-prone transit networks. In particular, we provide a new set of algorithms able to update both the graph and the precomputed labeling whenever a delay affects the network, without performing any recomputation from scratch. We demonstrate the effectiveness of our solution through an extensive experimental evaluation conducted on real-world networks. Our experiments show that: (i) the update time required by the new algorithms is, on average, orders of magnitude smaller than that required by the recomputation from scratch via ptl; (ii) the updated graph and labeling induce both query time performance and space overhead that are equivalent to those that are obtained by the recomputation from scratch via ptl. This suggests that our new solution is an effective approach to handling the journey planning problem in delay-prone transit networks
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
- …
