EFFICIENCY OF THE ITALIAN AGRI-FOOD INDUSTRY: AN ANALYSIS OF "DISTRICTS EFFECT"

In the course of the past decades major transformations in the Italian food industry induced considerable structural changes: on one hand, the creation of large industrial groups, with substantial input of foreign capital and, on the other, the concentration and geographic specialisation of numerous small and medium enterprises, with the formation of specific and typical agri-food local system (districts). To take account of the presence of food districts the analysis of the Italian food industry could not be conducted at national and regional level but it has to be studied at province and local level. It is also useful to analyse the food industry with reference to the different sub-sectors. We will conduct an in-depth analysis of the local systems (districts) of two sectors meat and fruit & vegetables processing. We will use balance-sheet data of the processing firms that were active in the meat (446 firms) and fruit & vegetables (227 firms) sub-sector in the years from 1996 to 1999. The analysis will consider the most significant balance sheet ratios, such as returns, productivity and labour cost in these local systmems. Then, we will consider the economic assessment of the firms belonging to local systems of the two sectors and compare with the non district firms. For the efficiency analysis will estimate a stochastic frontier production function in order to determine the "district effect". This analysis will also be directly correlated with investment in technological innovation. The economic and efficiency analysis confirms for many aspects the presence of a "district effect" in the Italian food industry.Food industry's efficiency, Food districts, Local development, Stochastic frontier production function, Meat and fruit sectors, Agribusiness, Industrial Organization,

Buoyancy-induced convection of water-based nanofluids in differentially-heated horizontal Semi-Annuli

A two-phase model based on the double-diffusive approach is used to perform a numerical study on natural convection of water-based nanofluids in differentially- heated horizontal semi-annuli, assuming that Brownian diffusion and thermophoresis are the only slip mechanisms by which the solid phase can develop a significant relative velocity with respect to the liquid phase. The system of the governing equations of continuity, momentum, and energy for the nanofluid, and continuity for the nanoparticles, is solved by the way of a computational code which incorporates three empirical correlations for the evaluation of the effective thermal conductivity, the effective dynamic viscosity, and the thermophoretic diffusion coefficient, all based on a wide number of literature experimental data. The pressure-velocity coupling is handled through the SIMPLE-C algorithm. Numerical simulations are executed for three different nanofluids, using the diameter and the average volume fraction of the suspended nanoparticles, the cavity size, the average temperature, and the temperature difference imposed across the cavity, as independent variables. It is found that the impact of the nanoparticle dispersion into the base liquid increases remarkably with increasing the average temperature, whereas, by contrast, the other controlling parameters have moderate effects. Moreover, at temperatures of the order of room temperature or just higher, the heat transfer performance of the nanofluid is significantly affected by the choice of the solid phase material

Natural convection from a pair of differentially-heated horizontal cylinders aligned side by side in a nanofluid-filled square enclosure

A two-phase model based on the double-diffusive approach is used to perform a numerical study on natural convection from a pair of differentially-heated horizontal cylinders set side by side in a nanofluid-filled adiabatic square enclosure. The study is conducted under the assumption that Brownian diffusion and thermophoresis are the only slip mechanisms by which the solid phase can develop a significant relative velocity with respect to the liquid phase. The system of the governing equations of continuity, momentum and energy for the nanofluid, and continuity for the nanoparticles, is solved by the way of a computational code which incorporates three empirical correlations for the evaluation of the effective thermal conductivity, the effective dynamic viscosity, and the thermophoretic diffusion coefficient, all based on a wide number of literature experimental data. The pressure-velocity coupling is handled through the SIMPLE-C algorithm. Simulations are executed for three different nanofluids, using the diameter and the average volume fraction of the suspended nanoparticles, as well as the cavity width, the inter-cylinder spacing, the average temperature of the nanofluid, and the temperature difference imposed between the cylinders, as controlling parameters, whose effects are thoroughly analyzed and discussed. It is found that the impact of the nanoparticle dispersion into the base liquid increases remarkably with increasing the average temperature, whereas it increases just moderately as the nanoparticle size decreases, as well as the imposed temperature difference and the cavity width increase. Conversely, the distance between the cylinders seems to have marginal effects. Moreover, an optimal particle loading for maximum heat transfer is detected for most configurations investigated

The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots

Deep networks have brought significant advances in robot perception, enabling to improve the capabilities of robots in several visual tasks, ranging from object detection and recognition to pose estimation, semantic scene segmentation and many others. Still, most approaches typically address visual tasks in isolation, resulting in overspecialized models which achieve strong performances in specific applications but work poorly in other (often related) tasks. This is clearly sub-optimal for a robot which is often required to perform simultaneously multiple visual recognition tasks in order to properly act and interact with the environment. This problem is exacerbated by the limited computational and memory resources typically available onboard to a robotic platform. The problem of learning flexible models which can handle multiple tasks in a lightweight manner has recently gained attention in the computer vision community and benchmarks supporting this research have been proposed. In this work we study this problem in the robot vision context, proposing a new benchmark, the RGB-D Triathlon, and evaluating state of the art algorithms in this novel challenging scenario. We also define a new evaluation protocol, better suited to the robot vision setting. Results shed light on the strengths and weaknesses of existing approaches and on open issues, suggesting directions for future research.Comment: This work has been submitted to IROS/RAL 201

Buoyancy-induced convection of water-based nanofluids in differentially-heated horizontal Semi-Annuli

A two-phase model based on the double-diffusive approach is used to perform a numerical study on natural convection of water-based nanofluids in differentially- heated horizontal semi-annuli, assuming that Brownian diffusion and thermophoresis are the only slip mechanisms by which the solid phase can develop a significant relative velocity with respect to the liquid phase. The system of the governing equations of continuity, momentum, and energy for the nanofluid, and continuity for the nanoparticles, is solved by the way of a computational code which incorporates three empirical correlations for the evaluation of the effective thermal conductivity, the effective dynamic viscosity, and the thermophoretic diffusion coefficient, all based on a wide number of literature experimental data. The pressure-velocity coupling is handled through the SIMPLE-C algorithm. Numerical simulations are executed for three different nanofluids, using the diameter and the average volume fraction of the suspended nanoparticles, the cavity size, the average temperature, and the temperature difference imposed across the cavity, as independent variables. It is found that the impact of the nanoparticle dispersion into the base liquid increases remarkably with increasing the average temperature, whereas, by contrast, the other controlling parameters have moderate effects. Moreover, at temperatures of the order of room temperature or just higher, the heat transfer performance of the nanofluid is significantly affected by the choice of the solid phase material

Best Sources Forward: Domain Generalization through Source-Specific Nets

A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions. The problem has been formalized as a covariate shift among the probability distributions generating the training data (source) and the test data (target) and several domain adaptation methods have been proposed to address this issue. While these approaches have considered the single source-single target scenario, it is plausible to have multiple sources and require adaptation to any possible target domain. This last scenario, named Domain Generalization (DG), is the focus of our work. Differently from previous DG methods which learn domain invariant representations from source data, we design a deep network with multiple domain-specific classifiers, each associated to a source domain. At test time we estimate the probabilities that a target sample belongs to each source domain and exploit them to optimally fuse the classifiers predictions. To further improve the generalization ability of our model, we also introduced a domain agnostic component supporting the final classifier. Experiments on two public benchmarks demonstrate the power of our approach

AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs

The ability to categorize is a cornerstone of visual intelligence, and a key functionality for artificial, autonomous visual machines. This problem will never be solved without algorithms able to adapt and generalize across visual domains. Within the context of domain adaptation and generalization, this paper focuses on the predictive domain adaptation scenario, namely the case where no target data are available and the system has to learn to generalize from annotated source images plus unlabeled samples with associated metadata from auxiliary domains. Our contributionis the first deep architecture that tackles predictive domainadaptation, able to leverage over the information broughtby the auxiliary domains through a graph. Moreover, we present a simple yet effective strategy that allows us to take advantage of the incoming target data at test time, in a continuous domain adaptation scenario. Experiments on three benchmark databases support the value of our approach.Comment: CVPR 2019 (oral

Robust Place Categorization With Deep Domain Generalization

Traditional place categorization approaches in robot vision assume that training and test images have similar visual appearance. Therefore, any seasonal, illumination, and environmental changes typically lead to severe degradation in performance. To cope with this problem, recent works have been proposed to adopt domain adaptation techniques. While effective, these methods assume that some prior information about the scenario where the robot will operate is available at training time. Unfortunately, in many cases, this assumption does not hold, as we often do not know where a robot will be deployed. To overcome this issue, in this paper, we present an approach that aims at learning classification models able to generalize to unseen scenarios. Specifically, we propose a novel deep learning framework for domain generalization. Our method develops from the intuition that, given a set of different classification models associated to known domains (e.g., corresponding to multiple environments, robots), the best model for a new sample in the novel domain can be computed directly at test time by optimally combining the known models. To implement our idea, we exploit recent advances in deep domain adaptation and design a convolutional neural network architecture with novel layers performing a weighted version of batch normalization. Our experiments, conducted on three common datasets for robot place categorization, confirm the validity of our contribution

Learning Deep NBNN Representations for Robust Place Categorization

This paper presents an approach for semantic place categorization using data obtained from RGB cameras. Previous studies on visual place recognition and classification have shown that, by considering features derived from pre-trained Convolutional Neural Networks (CNNs) in combination with part-based classification models, high recognition accuracy can be achieved, even in presence of occlusions and severe viewpoint changes. Inspired by these works, we propose to exploit local deep representations, representing images as set of regions applying a Na\"{i}ve Bayes Nearest Neighbor (NBNN) model for image classification. As opposed to previous methods where CNNs are merely used as feature extractors, our approach seamlessly integrates the NBNN model into a fully-convolutional neural network. Experimental results show that the proposed algorithm outperforms previous methods based on pre-trained CNN models and that, when employed in challenging robot place recognition tasks, it is robust to occlusions, environmental and sensor changes