Sustainability and welfare of Podolian cattle

The aim of the present study was to evaluate the sustainability and welfare of extensively farmed Podolian cattle. A trained interviewer visited 50 farms and filled in a checklist which included four cards corresponding to the following animal categories: calves, replacements, feeders and adults. The analysis of the farming system showed that animals were able to express their main behavioural patterns. In addition, recorded animal-related variables indicated that Podolian cattle could benefit from high standards of welfare. Sustainability of the Podolian farming system in terms of human edible returns was evaluated for two production systems producing 10-month-old calves (10 month) and 18-month-old young bulls (18 month), respectively. Edible returns for humans were low when all animal intakes were considered for both production systems. However, if returns were computed using not only the amount of food used by the animals but also consumable by humans, yields were much higher for 18-month systems [103% crude protein (CP) and 37.1% gross energy (GE)] and so high that they could not be computed for 10-month systems. These results indicate either a low degree of competition (18-month system) or no competition (10-month system) between humans and Podolian cattle. Perceptions of sustainability and welfare of Podolian cattle may promote a favourable positioning of products in premium-price markets and help preserving this breed and the related traditional farming system

Comparison of Iteration Schemes for the Solution of the Multidimensional Schrödinger-Poisson Equations

We present a fast and robust iterative method for obtaining self-consistent solutions to the coupled system of Schrödinger's and Poisson's equations in quantum structures. A simple expression describing the dependence of the quantum electron density on the electrostatic potential is used to implement a predictor – corrector type iteration scheme for the solution of the coupled system of differential equations. This approach simplifies the software implementation of the nonlinear problem, and provides excellent convergence speed and stability. We demonstrate the algorithm by presenting an example for the calculation ofthe two-dimensional bound electron states within the cross-section of a GaAs-AlGaAs based quantum wire. For this example, six times fewer iterations are needed when our predictor – corrector approach is applied, compared to a corresponding underrelaxation algorithm

Collective discussions for the development of Interpretative Knowledge in Mathematics Teacher Education

We start from the assumption that teachers need a deep and broad mathematical knowledge —called Interpretative Knowledge (IK)—that allows them to support students in building their mathematical knowledge from their own reasoning and productions. In the present study, we aimed to ascertain how collective discussions focusing on the interpretation of students’ productions engage Prospective Teachers (PTs) and impact their IK development. In particular, we observe how this form of collaborative work among PTs allows for the emergence of novel insights into the mathematical aspects of students’ productions that were not considered during previous individual work, and produce changes in PTs’ attitudes towards students’ productions

Hierarchy of Full Band Structure Models for Monte Carlo Simulation

This paper discusses the various hierarchy levels that are possible when the full band structure is considered. At the highest level, the scatterings are treated using complete k-k' transition rates, which entail extremely memory intensive computational applications. At the lowest level, the scattering anisotropy is neglected and the scattering rate is considered to be a constant average value on energy isosurfaces of the bandstructure. This model is more practical for device simulation. In between the two extremes, it is possible to design intermediate models which preserve some essential features of both. At all levels of the band structure hierarchy of models, there are similar issues of numerical noise, related to the sampling of real and momentum space that the Monte Carlo method necessarily performs with a relatively small number of particles. We discuss here computationally efficient approaches based on the assignment of variable weights to the simulated particles, in conjunction with careful gatherscatter procedures to split particles of large weight and combine particles of small weight

The hidden costs of livestock environmental sustainability: The case of Podolian Cattle

Life cycle assessment (LCA) is currently one of the most widely used methods for assessing the environmental impacts and performance of livestock products. According to this procedure, intensification of animal production is generally advocated to mitigate greenhouse gas emissions compared with extensive grazing systems due to the use of selected breeds, with enhanced productivity, and the significant reductions in CH4 emissions consequent to the use of concentrates rather than forages. In addition, the impact of intensive systems on land use is much lower. However, free-ranging Podolian cattle show a number of positive environmental effects, such as increased climate stability, improved soil functionality, water quality and footprint and preservation from fires along with maintaining an economically active social community in otherwise unproductive, marginal areas. Other beneficial effects of extensive Podolian farming system include low competition with human nutrition and high level of animal health and welfare. An economic evaluation of these non-commodity outputs should be indirectly estimated by the avoided costs (e.g. reduced veterinary interventions and therapy treatments) or the lack of profits (e.g. direct payments for the enhancement of environmental performance) that would have incurred in their absence. These economic evaluations should be used in order to allocate them as further outputs to be included in the LCA in order to achieve a more accurate estimation of the impact of the Podolian farming system

Sensory Properties and Consumer Liking of Buffalo Stracchino Cheese

The present study aims to characterize buffalo Stracchino cheese (BS) from a sensory point of view and verify how much consumers like it compared with the standard Stracchino cheese obtained from cow milk (CS). Nine panelists specifically trained to evaluate Stracchino cheese were used to conduct a quantitative descriptive sensory analysis, whereas 80 untrained consumers balanced for gender participated in the hedonic consumer test. Stracchino appearance was affected by milk type with higher intensities perceived for BS in terms of whiteness (P<0.0001) and shininess (P<0.001). As to taste and texture, BS showed higher sourness and oiliness intensities than CS, respectively (P<0.0001). Milk type did not affect the overall liking or the liking in terms of taste/flavor, texture, and appearance, but consumers rated both products at scores well above the neutral point. In addition, the liking expressed in blind conditions (i.e., without information on the milk type) was significantly lower as compared with the liking elicited by the expectations (i.e., based only on the information on the milk type) (P<0.05 and P<0.10, for CS and BS, respectively). We conclude that the good eating quality of buffalo Stracchino cheese as assessed by the consumer panel and the lack of differences between CS and BS in terms of a consumer may anticipate a possible good positioning of this novel product in the market of fresh cheese

Local Kernel Renormalization as a mechanism for feature learning in overparametrized Convolutional Neural Networks

Feature learning, or the ability of deep neural networks to automatically learn relevant features from raw data, underlies their exceptional capability to solve complex tasks. However, feature learning seems to be realized in different ways in fully-connected (FC) or convolutional architectures (CNNs). Empirical evidence shows that FC neural networks in the infinite-width limit eventually outperform their finite-width counterparts. Since the kernel that describes infinite-width networks does not evolve during training, whatever form of feature learning occurs in deep FC architectures is not very helpful in improving generalization. On the other hand, state-of-the-art architectures with convolutional layers achieve optimal performances in the finite-width regime, suggesting that an effective form of feature learning emerges in this case. In this work, we present a simple theoretical framework that provides a rationale for these differences, in one hidden layer networks. First, we show that the generalization performance of a finite-width FC network can be obtained by an infinite-width network, with a suitable choice of the Gaussian priors. Second, we derive a finite-width effective action for an architecture with one convolutional hidden layer and compare it with the result available for FC networks. Remarkably, we identify a completely different form of kernel renormalization: whereas the kernel of the FC architecture is just globally renormalized by a single scalar parameter, the CNN kernel undergoes a local renormalization, meaning that the network can select the local components that will contribute to the final prediction in a data-dependent way. This finding highlights a simple mechanism for feature learning that can take place in overparametrized shallow CNNs, but not in shallow FC architectures or in locally connected neural networks without weight sharing.Comment: 22 pages, 5 figures, 2 tables. Comments are welcom

RNA-based strategies for cancer therapy: in silico design and evaluation of ASOs for targeted exon skipping

Precision medicine in oncology has made significant progress in recent years by approving drugs that target specific genetic mutations. However, many cancer driver genes remain challenging to pharmacologically target ("undruggable"). To tackle this issue, RNA-based methods like antisense oligonucleotides (ASOs) that induce targeted exon skipping (ES) could provide a promising alternative. In this work, a comprehensive computational procedure is presented, focused on the development of ES-based cancer treatments. The procedure aims to produce specific protein variants, including inactive oncogenes and partially restored tumor suppressors. This novel computational procedure encompasses target-exon selection, in silico prediction of ES products, and identification of the best candidate ASOs for further experimental validation. The method was effectively employed on extensively mutated cancer genes, prioritized according to their suitability for ES-based interventions. Notable genes, such as NRAS and VHL, exhibited potential for this therapeutic approach, as specific target exons were identified and optimal ASO sequences were devised to induce their skipping. To the best of our knowledge, this is the first computational procedure that encompasses all necessary steps for designing ASO sequences tailored for targeted ES, contributing with a versatile and innovative approach to addressing the challenges posed by undruggable cancer driver genes and beyond