Capacities and Games on Lattices: A Survey of Result

We provide a survey of recent developments about capacities (or fuzzy measures) and ccoperative games in characteristic form, when they are defined on more general structures than the usual power set of the universal set, namely lattices. In a first part, we give various possible interpretations and applications of these general concepts, and then we elaborate about the possible definitions of usual tools in these theories, such as the Choquet integral, the Möbius transform, and the Shapley value.capacity, fuzzy measure, game, lattice, Choquet integral,Shapley value

Characterization of Awassi lamb fattening systems: a Syrian case study

Intensive lamb fattening systems are evolving in developing Middle Eastern countries due to high demand for lambs at favorable prices; however, little is known about their characteristics and constraints. A survey was conducted in Syria involving 241 farmers to characterize the fattening production systems and main constraints, with emphasis on feeding, management, labor, and marketing. Most farmers (90%) considered the income from fattening to be from medium to high, and 57% expressed that lamb fattening along with alternative income sources compose the family's livelihood strategies. Fattening systems offer employment to family members. Market price was the main decision factor to buy and sell lambs, but this was only part of various marketing aspects. Male lambs usually bought at markets at the mean age of 4 months (mean weight of 31 kg) are sold after fattening at a 50–60 kg weight range. The average yearly fattening cycle was 2.7 batches, and the average number of lambs per batch was 232. For 65% (n = 241) of the farmers the major constraint to fattening was feeding cost, and for about a half of farmers (51%, n = 241), disease outbreaks and prices for veterinarian services constituted the second important constraint. Research on least-cost fattening diets and curbing disease problems to increase farmer's income margins is needed. It is expected that due to existing commonalities, the information emerging from this study regarding major constraints to Awassi lamb fattening systems could be useful for an across-synthesis on Awassi fattening production in the regionFil: Wiedemann Hartwell, Birgitte. International Center for Agricultural Research in Dry Areas; Siria. University of Natural Resources and Applied Life Sciences. Department of Sustainable Agricultural Systems. Division of Livestock Sciences; Austria. Fredensborg; DinamarcaFil: Iñiguez, Luis. International Center for Agricultural Research in Dry Areas; SiriaFil: Mueller, Joaquin Pablo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; ArgentinaFil: Wurzinger, Maria. University of Natural Resources and Applied Life Sciences. Department of Sustainable Agricultural Systems. Division of Livestock Sciences; AustriaFil: Knaus, W.F. University of Natural Resources and Applied Life Sciences. Department of Sustainable Agricultural Systems. Division of Livestock Sciences; Austri

Application of Statistical and Artificial Intelligence Techniques for Medium-Term Electrical Energy Forecasting: A Case Study for a Regional Hospital

Electrical energy forecasting is crucial for efficient, reliable, and economic operations of hospitals due to serving 365 days a year, 24/7, and they require round-the-clock energy. An accurate prediction of energy consumption is particularly required for energy management, maintenance scheduling, and future renewable investment planning of large facilities. The main objective of this study is to forecast electrical energy demand by performing and comparing well-known techniques, which are frequently applied to short-term electrical energy forecasting problem in the literature, such as multiple linear regression as a statistical technique and artificial intelligence techniques including artificial neural networks containing multilayer perceptron neural networks and radial basis function networks, and support vector machines through a case study of a regional hospital in the medium-term horizon. In this study, a state-of-the-art literature review of medium-term electrical energy forecasting, data set information, fundamentals of statistical and artificial intelligence techniques, analyses for aforementioned methodologies, and the obtained results are described meticulously. Consequently, support vector machines model with a Gaussian kernel has the best validation performance, and the study revealed that seasonality has a dominant influence on forecasting performance. Hence heating, ventilation, and air-conditioning systems cover the major part of electrical energy consumption of the regional hospital. Besides historical electrical energy consumption, outdoor mean temperature and calendar variables play a significant role in achieving accurate results. Furthermore, the study also unveiled that the number of patients is steady over the years with only small deviations and have no significant influence on medium-term electrical energy forecasting

The Geology of Kuwait

This open access book contains a set of chapters covering all aspects of geosciences related to Kuwait and adjacent regions, including Iran, Saudi Arabia and the Arab Gulf states. It covers basic information about the geology including a wide range of geoscientific disciplines such as marine geology, structural geology, hydrogeology and geophysics related to the region. This book is aimed at researchers and students, as well as professionals in the field of hazard mitigation and petroleum exploration

Observations of winter storms with a video disdrometer and polarimetric radar

Spring 2007.Includes bibliographical references (pages 104-110).With efforts to upgrade the operational NEXRAD radars to include polarimetric capability underway, there are growing interests in developing radar-based algorithms for classifying hydrometeor types, quantifying winter precipitation, and improving the parameterization of winter precipitation in numerical forecast models. The capabilities of polarimetric radars, such as to better quantify warm season precipitation, have been demonstrated in various studies. However, these tasks are further complicated for winter precipitation by the need to know hydrometeor phase and bulk density of ice particles. In this study, data collected with a two-dimensional video disdrometer and S-band dual polarization radar during the Winter Icing and Storms Project 2004 (WISP04) storms are examined in support of ongoing research to develop radar-based algorithms for cold season precipitation. The capability to match radar-measured and disdrometer-based calculations of radar reflectivity factor and differential reflectivity is essential for retrieving hydrometeor characteristics with radar. During the WISP04, the disdrometer provided detailed information regarding hydrometeor size, number concentration, terminal velocity, and shape during the precipitation events. In this study, bulk ice particle density is estimated using an empirical relationship derived from disdrometer measurements of precipitation volume and rain gauge measurements of precipitation mass. Reflectivity and differential reflectivity, as measured by radar and computed from disdrometer observations are compared, and the combined dataset is used to examine storm microphysical properties. The measurements and computed values show good agreement and reveal that the radar detected subtle changes in the characteristics of winter precipitation. Additionally, sensitivity of the scattering computations to assumed ice particle characteristics is examined, and particle size distributions from radar measurements are retrieved for comparisons with the disdrometer observations

Scheduling under Linear Constraints

We introduce a parallel machine scheduling problem in which the processing times of jobs are not given in advance but are determined by a system of linear constraints. The objective is to minimize the makespan, i.e., the maximum job completion time among all feasible choices. This novel problem is motivated by various real-world application scenarios. We discuss the computational complexity and algorithms for various settings of this problem. In particular, we show that if there is only one machine with an arbitrary number of linear constraints, or there is an arbitrary number of machines with no more than two linear constraints, or both the number of machines and the number of linear constraints are fixed constants, then the problem is polynomial-time solvable via solving a series of linear programming problems. If both the number of machines and the number of constraints are inputs of the problem instance, then the problem is NP-Hard. We further propose several approximation algorithms for the latter case.Comment: 21 page