An integrated approach project for the revaluation of a traditional sourdough bread production chain

The influence of organic and conventional farming systems on the performance of a panel of old and modern Italian bread wheat varieties has been evaluated, with the aim to individuate an agronomic protocol suitable for the production of a sourdough bread traditionally prepared in a hill zone of Emilia-Romagna. The agronomic and technological characterisation of the wheat samples obtained in organic and conventional farming conditions has been done and the sensorial qualities of the sourdough bread obtained have been evaluated

Collective deformation modes promote fibrous self-assembly in protein-like particles

The self-assembly of particles into organized structures is a key feature of living organisms and a major engineering challenge. While it may proceed through the binding of perfectly matched, puzzle-pieces-like particles, many other instances involve ill-fitting particles that must deform to fit together. These include some pathological proteins, which have a known propensity to form fibrous aggregates. Despite this observation, the general relationship between the individual characteristics of the particles and the overall structure of the aggregate is not understood. To elucidate it, we analytically and numerically study the self-assembly of two-dimensional, deformable ill-fitting particles. We find that moderately sticky particles tend to form equilibrium self-limited aggregates whose size is set by an elastic boundary layer associated with collective deformations that may extend over many particles. Particles with a soft internal deformation mode thus give rise to large aggregates. Besides, when the particles are incompressible, their aggregates tend to be anisotropic and fiber-like. Our results are preserved in a more complex particle model with randomly chosen elastic properties. This indicates that generic protein characteristics such as allostery and incompressibility could favor the formation of fibers in protein aggregation, and suggests design principles for artificial self-assembling structures.Comment: 21 pages, 12 figure

LSTM neural networks: Input to state stability and probabilistic safety verification

The goal of this paper is to analyze Long Short Term Memory (LSTM) neural networks from a dynamical system perspective. The classical recursive equations describing the evolution of LSTM can be recast in state space form, resulting in a time-invariant nonlinear dynamical system. A sufficient condition guaranteeing the Input-to-State (ISS) stability property of this class of systems is provided. The ISS property entails the boundedness of the output reachable set of the LSTM. In light of this result, a novel approach for the safety verification of the network, based on the Scenario Approach, is devised. The proposed method is eventually tested on a pH neutralization process

Residual stress of as-deposited and rolled Wire + Arc Additive Manufacturing Ti–6Al–4V components

Wire + arc additive manufacturing components contain significant residual stresses, which manifest in distortion. High-pressure rolling was applied to each layer of a linear Ti–6Al–4V wire + arc additive manufacturing component in between deposition passes. In rolled specimens, out-of-plane distortion was more than halved; a change in the deposits' geometry due to plastic deformation was observed and process repeatability was increased. The Contour method of residual stresses measurements showed that although the specimens still exhibited tensile stresses (up to 500 MPa), their magnitude was reduced by 60%, particularly at the interface between deposit and substrate. The results were validated with neutron diffraction measurements, which were in good agreement away from the baseplate

Optimum horizontal well length considering reservoir properties and drainage area

The length of a horizontal well increases and so does its drainage area. The efficiency of the long horizontal well is no longer proportional to the length of the well, since the rise in the length of horizontal well output segment also tends to causes frictional pressure losses in the well. Nevertheless, there are currently no reliable standards which take into account quantitatively the parameters necessary to determine the optimum well length of horizontal drilling. A new strategy to the basic productivity index is introduced, taking into account the friction losses under influx conditions in a long manufacturing segment. The consequence of this special productivity index is the constant state flow in an anisotropic structure of a very compressible fluid. This paper presents a technique developed to achieve an optimum length of horizontal pool based on the shift in the overall economics and productivity index (PI) in the long horizontal wellbore with frictional loosing results. In order to achieve optimal overall efficiency in a horizontal well project, an integrated method is proposed for numerical analysis of the parameters that affect profitability using Computer Modelling Group reservoir simulation (CMG)

Wind turbine systematic yaw error: Operation data analysis techniques for detecting IT and assessing its performance impact

The widespread availability of wind turbine operation data has considerably boosted the research and the applications for wind turbine monitoring. It is well established that a systematic misalignment of the wind turbine nacelle with respect to the wind direction has a remarkable impact in terms of down-performance, because the extracted power is in first approximation proportional to the cosine cube of the yaw angle. Nevertheless, due to the fact that in the wind farm practice the wind field facing the rotor is estimated through anemometers placed behind the rotor, it is challenging to robustly detect systematic yaw errors without the use of additional upwind sensory systems. Nevertheless, this objective is valuable because it involves the use of data that are available to wind farm practitioners at zero cost. On these grounds, the present work is a two-steps test case discussion. At first, a new method for systematic yaw error detection through operation data analysis is presented and is applied for individuating a misaligned multi-MW wind turbine. After the yaw error correction on the test case wind turbine, operation data of the whole wind farm are employed for an innovative assessment method of the performance improvement at the target wind turbine. The other wind turbines in the farm are employed as references and their operation data are used as input for a multivariate Kernel regression whose target is the power of the wind turbine of interest. Training the model with pre-correction data and validating on post-correction data, it is estimated that a systematic yaw error of 4◦ affects the performance up to the order of the 1.5% of the Annual Energy Production