The use of Rough Set and Spatial Statistic in evaluating the Periurban Fringe

The distinction among urban, peri-urban and rural areas inside a territory represents a classical example of uncertainty in land classification. The transition among the three classes is not much clear and can be described with Sorites Paradox, considering the residential buildings and the settlements. Peri-urban fringe can be considered as a transition zone between urban and rural areas, as an area with its own intrinsic organic rules, as a built area without formal organisation or as an abandoned rural area contiguous to urban centres. In any case, concepts as density of buildings, services and infrastructures or the degree of rural, residential and industrial activities, will lead to uncertainty in defining classes, due to the uncertainty in combining some properties. One of the methods which can be utilized is the rough sets theory, which represents a different mathematical approach to uncertainty capturing the indiscernibility. The definition of a set is connected to information knowledge and perception about phenomena. Some phenomena can be classified only in the context of the information available about them. Two different phenomena can be indiscernible in some contexts and classified in the same way (Pawlak 83). The rough sets approach to data analysis hinges on two basic concepts, the lower approximation which considers all the elements that doubtlessly belong to the class, and the upper approximation which includes all the elements that possibly belong to the class. The rough sets theory furthermore takes into account only properties which are independent. This approach has been tested in the case of study of Potenza Province. This area, located in Southern Italy, is particularly suitable to the application of this theory, because it includes 100 municipalities with different number of inhabitants, quantity of services and distance from the main road infrastructures.

Nationalism-as-Technology and Peace in Europe, 1815-1914

This study offers a theory in which nationalism is not only conducive to war -which is the conventional wisdom-, but also brings peace to entire groupings of states under a specific set of conditions. After the theory is laid out, a plausibility probe of 19th century Europe offers good justification for a continued research program of nationalism-as-technology and its effects. The theory's insight comes from seeing nationalism not as an ideology, but as a form of military technology. For such technologies, their effect on war depends on how widely all countries employ them. When everyone has the same technology (i.e. when all countries are similarly endowed with nationalism), peace is cemented because countries mutually deter each other from launching wars of conquest. They do this by building mass armies to offset that of their neighbors, and threaten would-be conquerors with costly guerrilla wars and insurgencies. (Conversely, if only a few states possess the technology, the temptation to abuse it in conflict does rise.) The theoretical section of this study first justifies this analytical possibility of seeing nationalism as a technology. Among other things, the absence of definitional stumbling blocks is discussed. That is, given how technology is broadly defined by leading technologists, there is nothing inherent in the concept of nationalism that prevents its consideration as a technology. The study then proceeds to derive a series of hypotheses about the curvilinear effects of nationalism on war across a given region. As mentioned, the primary case study is 19th century Europe (1815-1914), which lends itself to a plausibility probe. The results are corroborating. Napoleonic France first "discovered" nationalism as a technology with military applications - it formed the first mass armies and attempted continental conquest. Later on, other "early-adopters" also employed nationalism to take land from their neighbors. Sardinia, for instance, used Italian nationalism to build volunteer armies and fight Austria for control of northern Italy in 1859. But the early adopters were then followed by most other European countries, which took reins of their own nationalisms to build mass-armies and boost their defenses. In line with the theory, the widespread adoption of nationalism preceded two whole generations of European peace, from 1871 to 1914. (So rare was this long peace that it would not be equaled until after World War II.) In sum, the history of the 1800s seems to fit broadly with the theory, and gives good reason for continued research into the pacifying role of nationalism

Charlas de radio : Darío Arizmendi, Hernán Peláez, Félix de Bedout, Gustavo Gómez, Héctor Rincón y Jordi Finazzi hablan de radio exitosa en Colombia

Comunicador (a) SocialPregrad

Model-free Optimization of Trajectory And Impedance Parameters on Exercise Robots With Applications To Human Performance And Rehabilitation

This dissertation focuses on the study and optimization of human training and its physiological effects through the use of advanced exercise machines (AEMs). These machines provide an invaluable contribution to advanced training by combining exercise physiology with technology. Unlike conventional exercise machines (CEMs), AEMs provide controllable trajectories and impedances by using electric motors and control systems. Therefore, they can produce various patterns even in the absence of gravity. Moreover, the ability of the AEMs to target multiple physiological systems makes them the best available option to improve human performance and rehabilitation. During the early stage of the research, the physiological effects produced under training by the manual regulation of the trajectory and impedance parameters of the AEMs were studied. Human dynamics appear as not only complex but also unique and time-varying due to the particular features of each person such as its musculoskeletal distribution, level of fatigue,fitness condition, hydration, etc. However, the possibility of the optimization of the AEM training parameters by using physiological effects was likely, thus the optimization objective started to be formulated. Some previous research suggests that a model-based optimization of advanced training is complicated for real-time environments as a consequence of the high level of v complexity, computational cost, and especially the many unidentifiable parameters. Moreover, a model-based method differs from person to person and it would require periodic updates based on physical and psychological variations in the user. Consequently, we aimed to develop a model-free optimization framework based on the use of Extremum Seeking Control (ESC). ESC is a non-model based controller for real-time optimization which its main advantage over similar controllers is its ability to deal with unknown plants. This framework uses a physiological effect of training as bio-feedback. Three different frameworks were performed for single-variable and multi-variable optimization of trajectory and impedance parameters. Based on the framework, the objective is achieved by seeking the optimal trajectory and/or impedance parameters associated with the orientation of the ellipsoidal path to be tracked by the user and the stiffness property of the resistance by using weighted measures of muscle activations

Labeling Methods for Partially Ordered Paths

The landscape of applications and subroutines relying on shortest path computations continues to grow steadily. This growth is driven by the undeniable success of shortest path algorithms in theory and practice. It also introduces new challenges as the models and assessing the optimality of paths become more complicated. Hence, multiple recent publications in the field adapt existing labeling methods in an ad-hoc fashion to their specific problem variant without considering the underlying general structure: they always deal with multi-criteria scenarios and those criteria define different partial orders on the paths. In this paper, we introduce the partial order shortest path problem (POSP), a generalization of the multi-objective shortest path problem (MOSP) and in turn also of the classical shortest path problem. POSP captures the particular structure of many shortest path applications as special cases. In this generality, we study optimality conditions or the lack of them, depending on the objective functions' properties. Our final contribution is a big lookup table summarizing our findings and providing the reader an easy way to choose among the most recent multicriteria shortest path algorithms depending on their problem's weight structure. Examples range from time-dependent shortest path and bottleneck path problems to the fuzzy shortest path problem and complex financial weight functions studied in the public transportation community. Our results hold for general digraphs and therefore surpass previous generalizations that were limited to acyclic graphs

An A* Algorithm for Flight Planning Based on Idealized Vertical Profiles

The Flight Planning Problem is to find a minimum fuel trajectory between two airports in a 3D airway network under consideration of the wind. We show that this problem is NP-hard, even in its most basic version. We then present a novel A* heuristic, whose potential function is derived from an idealized vertical profile over the remaining flight distance. This potential is, under rather general assumptions, both admissible and consistent and it can be computed efficiently. The method outperforms the state-of-the-art heuristic on real-life instances