Effective Interaction Force between an Electric Charge and a Magnetic Dipole and Locality (or Nonlocality) in Quantum Effects of the Aharonov–Bohm Type

Classical electrodynamics foresees that the effective interaction force between a moving charge and a magnetic dipole is modified by the time-varying total momentum of the interaction fields. We derive the equations of motion of the particles from the total stress-energy tensor, assuming the validity of Maxwell’s equations and the total momentum conservation law. Applications to the effects of Aharonov–Bohm type show that the observed phase shift may be due to the relative lag between interfering particles caused by the effective local force.

Molecular and Kinetic Characterization of the Fermentative Behavior of Saccharomyces cerevisiae Strains Isolated from North Patagonia

At present, Argentina does not count with a production of indigenous yeast strains with suitable technological and oenological features to be used in the regional winery industry. Isolation and molecular characterization of these microorganisms and its fermentation attributes would be relevant to the sustainable development of the activity in the country and to recognize and preserve the biodiversity of the region. Eight strains isolated from grapes and musts from the North Patagonian region and genetically identified as Saccharomyces cerevisiae, were studied for their fermentation behavior, emphasizing in hexose transport through the plasma membrane, which is the limiting step of the process. Thus, sugar consumption profiles were analyzed in different media at laboratory scale, to be subsequently applied to the fermentation of natural musts. Three of the eight initial strains were selected, named NNM10, NIF8 and NMN16 according to their fermentation profiles. The expression of hexose transporters during fermentations revealed interesting differences in the response of each strain to sugar consumption, where transporters HXT2 and HXT5 showed significant changes in expression in Patagonian strains, which are normally associated to endurance to culture stress conditions. The results obtained by combining the characteristics studied, at molecular and physiological level, are extremely encouraging. Native strain NMN16, showed a high potential for application in local winemaking. Assays carried out on a pilot scale will determine the feasibility of applying this strain with promising technological features at industrial scale.

More than Color:Pigments with Thermal Storage Capacity;Processing and Degradation Behavior

A standard ultramarine pigment was used to produce phase change material composites, by adsorbing n-hexadecane paraffin around the pigment surface with the aim of obtaining a pigment providing thermal storage capacity apart from color. Vacuum impregnation method was employed optimizing the process variables to maximize the latent heat of the hexadecane/pigment composite. In addition to the process optimization, the stability of the composite having the maximum latent heat was investigated. The hexadecane/pigment composite providing the highest latent heat has a Latent heat of fusion of 44 J/g (around a 20%wt. hexadecane adsorbed in the pigment). Durability of the material was tested by thermo-diffractometric measurements. The results indicate slow reduction of the area intensity up to 6.5% after the 100 cycles.

Neural Network for Estimating Daily Global Solar Radiation Using Temperature, Humidity and Pressure as Unique Climatic Input Variables

Solar radiation is one of the most important parameters for applications, development and research related to renewable energy. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly on the search for relationships between weather variables, such as temperature, humidity, precipitation, cloudiness, sunshine hours, etc. But, many of these are subjective and difficult to measure, and thus they are not always available. In this paper, we propose a method for estimating daily global solar radiation, combining empirical models and artificial neural networks. The model uses temperature, relative humidity and atmospheric pressure as the only climatic input variables. Also, this method is compared with linear regression to verify that the data have nonlinear components. The models are adjusted and validated using data from five meteorological stations in the province of Tucumán, Argentina. Results show that neural networks have better accuracy than empirical models and linear regression, obtaining on average, an error of 2.83 [MJ/m2] in the validation dataset.