Workers’ Power in Resisting Precarity: Comparing Transport Workers in Buenos Aires and Dar es Salaam

The growing precariousness of employment across the world has radically altered the conditions upon which the representation of workers’ interests has traditionally been built, as it has posed challenges for established trade unions: individualized employment and fragmented identities have displaced the centrality of the workplace and the employee–employer relationship in framing collective issues of representation. In this article, we compare the processes of collective organization of two groups of precarious workers in the transport and delivery sector of Buenos Aires and Dar es Salaam. Through this comparison we investigate how existing trade union structures, industrial relations frameworks, socio-political contexts and labour processes interact with the processes of workers’ organization that take place even in the harsher conditions of informal work, critically engaging with the argument that the growing precariousness of work represents the end of trade unionism as we know it

Post-disaster dynamics in inner areas. An Italian hypothesis for transition management

The city can be defined as a balanced relation among polis, civitas and urbs (Salzano, 1998). Disasters impact this balance. Undermining the link between the components as well as the component themselves, the disaster can lead a city to the death, especially if the balance is already damaged (Edgington, 2010), at the same time it can represent the opportunity for changing the development trajectory of the territory (May and Williams, 2012). The catalyst effect of a disaster, and in particular of an earthquake, emerges more evident in inner areas where generally there are ongoing negative demographic and socio-economic trends (Barca, 2014). With this premise, the chapter proposes an overview of Italian reconstruction processes from the post-war period until today with the main aim of highlighting the dynamics of disaster governance and community organization, which are often less visible in the ordinary circumstances. The approach to reconstruction used seems not to be able to stem these phenomena and to reverse trends in order to “revitalize” the territories. The chapter aims to show the possible application of a flexible tool, such as the Transition Management approach, to the issue of post-disaster management in inner areas. Basing our study on transition management theories and (Rotmans et al., 2001; Bosch and Rotmans, 2008) disaster and post-disaster literature, the research uses the window of opportunity concept to connect the concepts of development trajectory, transition, trajectory break and trajectory reshape. Finally, the aims of the research are explained under the light of the ultimate goal of contributing to resilience-building vocation of the National Strategy (Barca et al., 2013)

Fruit ripeness classification: A survey

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by field experts, making it a labor-intensive and error-prone process. Thus, there is an arising need for automation in fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on

Dispersione dei modi di polarizzazione di fibre ottiche sottoposte a spin ed avvolte in bobina

L'obbiettivo della tesi è di ottimizzare i moduli per la compensazione della dispersione cromatica in funzione della curvatura e, in particolare, trovare le combinazioni di ampiezza A0 e periodo p dello spin sinusoidale e del raggio di curvatura R che contrastino gli effetti negativi della birifrangenza da curvatura e diano un DGD più piccolo possibile. Dopo l'introduzione del primo capitolo, nel secondo capitolo si andranno ad introdurre tutte le basi teoriche su cui si basano le simulazioni i cui esiti saranno ampiamente mostrati e commentati nel terzo capitolo; infine nel quarto capitolo si trarranno le conclusioniope

Horocyclic harmonic Bergman spaces on homogeneous trees

The main focus of this contribution is on the harmonic Bergman spaces $\mathcal{B}_{\alpha}^{p}$ on the $q$-homogeneous tree $\mathfrak{X}_q$ endowed with a family of measures $\sigma_\alpha$ that are constant on the horocycles tangent to a fixed boundary point and turn out to be doubling with respect to the corresponding horocyclic Gromov distance. A central role is played by the reproducing kernel Hilbert space $\mathcal{B}_{\alpha}^{2}$ for which we find a natural orthonormal basis and formulae for the kernel. We also consider the atomic Hardy space and the bounded mean oscillation space. Appealing to an adaptation of Calder\'on-Zygmund theory and to standard boundedness results for integral operators on $L^p_\alpha$ spaces with H\"ormander-type kernels, we determine the boundedness properties of the Bergman projection

Ranking Models for the Temporal Dimension of Text

Temporal features of text have been shown to improve clustering and organization of documents, text classification, visualization, and ranking. Temporal ranking models consider the temporal expressions found in text (e.g., “in 2021” or “last year”) as time units, rather than as keywords, to define a temporal relevance and improve ranking. This paper introduces a new class of ranking models called Temporal Metric Space Models (TMSM), based on a new domain for representing temporal information found in documents and queries, where each temporal expression is represented as a time interval. Furthermore, we introduce a new frequency-based baseline called Temporal BM25 (TBM25). We evaluate the effectiveness of each proposed metric against a purely textual baseline, as well as several variations of the metrics themselves, where we change the aggregate function, the time granularity and the combination weight. Our extensive experiments on five test collections show statistically significant improvements of TMSM and TBM25 over state-of-the-art temporal ranking models. Combining the temporal similarity scores with the text similarity scores always improves the results, when the combination weight is between 2% and 6% for the temporal scores. This is true also for test collections where only 5% of queries contain explicit temporal expressions

Cascading Convolutional Temporal Colour Constancy

Computational Colour Constancy (CCC) consists of estimating the colour of one or more illuminants in a scene and using them to remove unwanted chromatic distortions. Much research has focused on illuminant estimation for CCC on single images, with few attempts of leveraging the temporal information intrinsic in sequences of correlated images (e.g., the frames in a video), a task known as Temporal Colour Constancy (TCC). The state-of-the-art for TCC is TCCNet, a deep-learning architecture that uses a ConvLSTM for aggregating the encodings produced by CNN submodules for each image in a sequence. We extend this architecture with different models obtained by (i) substituting the TCCNet submodules with C4, the state-of-the-art method for CCC targeting images; (ii) adding a cascading strategy to perform an iterative improvement of the estimate of the illuminant. We tested our models on the recently released TCC benchmark and achieved results that surpass the state-of-the-art. Analyzing the impact of the number of frames involved in illuminant estimation on performance, we show that it is possible to reduce inference time by training the models on few selected frames from the sequences while retaining comparable accuracy

A Risk-based Path Planning Strategy to Compute Optimum Risk Path for Unmanned Aircraft Systems over Populated Areas

The large diffusion of Unmanned Aircraft Systems (UAS) requires a suitable strategy to design safe flight missions. In this paper, we propose a novel path planning strategy to compute optimum risk path for UAS over populated areas. The proposed strategy is based on a variant of the RRT* (Rapidly-exploring Random Tree "Star") algorithm, performing a risk assessment during the path planning phase. Like other RRT-based algorithms, the proposed path planning explores the state space by constructing a graph. Each time a new node is added to the graph, the algorithm estimates the risk level involved by the new node, evaluating the flight direction and velocity of the UAS placed in the analyzed node. The risk level quantifies the risk of flying over a specific location and it is defined using a probabilistic risk assessment approach taking into account the drone parameters and environmental characteristics. Then, the proposed algorithm computes an asymptotically optimal path by minimizing the overall risk and flight time. Simulation results in realistic environments corroborate the proposed approach proving how the proposed risk-based path planning is able to compute an effective and safe path in urban areas