Logistic regression for simulating damage occurrence on a fruit grading line

Many factors influence the incidence of mechanical damage in fruit handled on a grading line. This makes it difficult to address damage estimation from an analytical point of view. During fruit transfer from one element of a grading line to another, damage occurs as a combined effect of machinery roughness and the intrinsic susceptibility of fruit. This paper describes a method to estimate bruise probability by means of logistic regression, using data yielded by specific laboratory tests. Model accuracy was measured via the statistical significance of its parameters and its classification ability. The prediction model was then linked to a simulation model through which impacts and load levels, similar to those of real grading lines, could be generated. The simulation output sample size was determined to yield reliable estimations. The process makes it possible to derive a suitable line design and the type of fruit that should be handled to maintain bruise levels within European Union (EU) Standards. A real example with peaches was carried out with the aid of the software implementation SIMLIN®, developed by the authors and registered by Madrid Technical University. This kind of tool has been demanded by inter-professional associations and grading lines designers in recent year

The Role of Motivational Persistence and Resilience Over the Well-being Changes Registered in Time

The present study investigates the interaction between personal characteristics that are considered nowadays strengths used to face difficult events or transition period. A number of 200 married or living together participants completed self-reports for common goals, motivational persistence, resilience and well-being. Results show that persistence and resilience do interact with each other at an individual level but also from a family concept perspective. Moreover, maintaining apositive outlook and family spirituality do have an impact over the intensity and direction of the relationship between long term purposes pursuing and recurrence of unattained purposes and changes in well-being registered in time. Resiliency as a personal characteristic and family resilience show good psychometric qualities for this study. Although some of the results are descriptive, in-depth analyses of direction and intensity of the relationships lead the finalconclusions to suggestions for further research and implications for psychological practice

Upper critical field in {Ba1−x_{1-x}Kx_xBiO3_3}: magnetotransport versus magnetotunneling

Elastic tunneling is used as a powerful direct tool to determine the upper critical field $H_{c2}(T)$ in the high-$T_c$ oxide Ba$_{1-x}$K$_x$BiO$_3$. The temperature dependence of $H_{c2}$ inferred from the tunneling follows the Werthamer-Helfand-Hohenberg prediction for type-II superconductors. A comparison will be made with resistively determined critical field data.Comment: 4 pages incl. 5 figure

C.V.D. annual report: January, 1967 research project ru27-1 : analogue study of semiconductor device structures

The e::tension of the resistance network analogue method to the study of a M.O.S.T. structure is described. By means of an iterative technique, data regarding channel current, field distribution, surface charge and position of pinch-off point as function of gate and drain voltagen can be obtained which do not involve the usual 'gradual' channel approximation Results for a particular device geometry are presented. A discussion of a digital computer approach to the solution of semiconductor device current flow problems is included, together with preliminary results

Distortion of the Stoner-Wohlfarth astroid by a spin-polarized current

The Stoner-Wohlfarth astroid is a fundamental object in magnetism. It separates regions of the magnetic field space with two stable magnetization equilibria from those with only one stable equilibrium and it characterizes the magnetization reversal of nano-magnets induced by applied magnetic fields. On the other hand, it was recently demonstrated that transfer of spin angular momentum from a spin-polarized current provides an alternative way of switching the magnetization. Here, we examine the astroid of a nano-magnet with uniaxial magnetic anisotropy under the combined influence of applied fields and spin-transfer torques. We find that spin-transfer is most efficient at modifying the astroid when the external field is applied along the easy-axis of magnetization. On departing from this situation, a threshold current appears below which spin-transfer becomes ineffective yielding a current-induced dip in the astroid along the easy-axis direction. An extension of the Stoner-Wohlfarth model is outlined which accounts for this phenomenon.Comment: 8 pages, 6 figure

A revised uncertainty budget for measuring the Boltzmann constant using the Doppler Broadening Technique on ammonia

We report on our on-going effort to measure the Boltzmann constant, kB, using the Doppler Broadening Technique. The main systematic effects affecting the measurement are discussed. A revised error budget is presented in which the global uncertainty on systematic effects is reduced to 2.3 ppm. This corresponds to a reduction of more than one order of magnitude compared to our previous Boltzmann constant measurement. Means to reach a determination of kB at the part per million accuracy level are outlined

Machine learning plasma-surface interface for coupling sputtering and gas-phase transport simulations

Thin film processing by means of sputter deposition inherently depends on the interaction of energetic particles with a target surface and the subsequent particle transport. The length and time scales of the underlying physical phenomena span orders of magnitudes. A theoretical description which bridges all time and length scales is not practically possible. Advantage can be taken particularly from the well-separated time scales of the fundamental surface and plasma processes. Initially, surface properties may be calculated from a surface model and stored for a number of representative cases. Subsequently, the surface data may be provided to gas-phase transport simulations via appropriate model interfaces (e.g., analytic expressions or look-up tables) and utilized to define insertion boundary conditions. During run-time evaluation, however, the maintained surface data may prove to be not sufficient. In this case, missing data may be obtained by interpolation (common), extrapolation (inaccurate), or be supplied on-demand by the surface model (computationally inefficient). In this work, a potential alternative is established based on machine learning techniques using artificial neural networks. As a proof of concept, a multilayer perceptron network is trained and verified with sputtered particle distributions obtained from transport of ions in matter based simulations for Ar projectiles bombarding a Ti-Al composite. It is demonstrated that the trained network is able to predict the sputtered particle distributions for unknown, arbitrarily shaped incident ion energy distributions. It is consequently argued that the trained network may be readily used as a machine learning based model interface (e.g., by quasi-continuously sampling the desired sputtered particle distributions from the network), which is sufficiently accurate also in scenarios which have not been previously trained