Relationships, sustainability and agri-food purchasing behaviour in farmer markets in Italy

PurposeThe study aims to demonstrate that farmers' markets can represent a model of environmental, social and governance reference for modern agri-food systems facing the challenge of post COVID-19 pandemic reconstruction, responding to consumer expectations in terms of health, safety and wholesomeness of agri-food products.Design/methodology/approachA sample of consumers was surveyed in farmers' markets and social network analysis (SNA) was adopted as a methodological approach to reconstruct the links between the worlds of production and consumption and to derive the relative importance attributed to various factors that promote relational structures.FindingsThe work demonstrates the importance of sustainability – as a productive and behavioural model of firms – for the construction of efficient and durable relationship systems in two farmer markets in Sicily. In particular, four fundamental components emerge in the construction of networks represented by consumer sensitivity to sustainability processes, the individual behavioural model of purchasing and consumption, the expectation of political direction and the level and factors of knowledge of the firm. The clustering elements of the relationships were found to be the territory and local products, the environmentalist attitude and the protection of resources, as well as the adoption of a rational waste disposal policy, the fight against food waste, the encouragement of healthier and more sustainable consumption styles, clear and transparent communication and the activation of sustainable supply chain processes in line with the Sustainable Development Goals (SDGs).Originality/valueThe paper aims to demonstrate how alternative food systems can become a useful model for large enterprises, which are committed to rebuilding their business strategy to overcome the current crisis.

Prospectiva del nuevo mundo educativo

Alrededor del mundo se perciben cambios acelerados en todos los ámbitos que forman la cotidianidad, quienes modulan las nuevas estructuras relacionales entre personas y de éstas con su entorno. Estos cambios germinan de la acelerada transformación tecnológica, de la masificación de los medios de producción y de la globalización económica y social. Sin embargo, el ecosistema educativo parece haber sufrido una parálisis evolutiva, o al menos sus cambios no son percibidos con la misma intensidad que los derivados de los aspectos antes mencionados. Por lo tanto, es necesario dar una lectura aproximada de las nuevas realidades que se despliegan en la educación contemporánea. La presente investigación trata de abordar las ideas principales que se desarrollan en el libro “La educación en su laberinto: análisis y propuestas para una salida”, el mismo que esboza las concepciones del denominado “Nuevo Mundo Educativo”. Estos postulados representan la propuesta para el desarrollo de un modelo educativo que sea adaptable a los diferentes paradigmas educativos, sin llegar a denominarse una nueva teoría educativa. Se analizó la disputa hegemónica de la educación actual, la pedagogía que representa un equilibrio entre las nuevas ignorancias y los nuevos aprendizajes, y el detalle de la reconstrucción de la biografía docente. Accelerated changes are perceived around the world in all the areas that make up daily life, which modulate the new relational structures between people and between people and their environment. These changes germinate from the accelerated technological transformation, from the massification of the means of production and from economic and social globalization. However, the educational ecosystem seems to have suffered an evolutionary paralysis, or at least its changes are not perceived with the same intensity as those derived from the aspects. Therefore, it is necessary to give an approximate reading of the new realities that are unfolding in contemporary education. The present investigation tries to approach the main ideas that are developed in the book “Education in its labyrinth: analysis and proposals for a way out”, the same one that outlines the conceptions of the so-called “New Educational World”. These postulates represent the proposal for the development of an educational model that is adaptable to the different educational paradigms, without being called a new educational theory. The hegemonic dispute of current education, the pedagogy that represents a balance between new ignorance and new learning, and the detail of the reconstruction of the teacher biography were analyzed.

A-GHN: Attention-based Fusion of Multiple GraphHeat Networks for Structural to Functional Brain Mapping

Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous fluctuations of the brain activity during the resting-state as captured by functional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural connectome. Among the modeling approaches, graph diffusion-based methods with single and multiple diffusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks along with graph diffusion have been shown to handle complex relational structures while preserving global information. In this paper, we propose a novel attention-based fusion of multiple GraphHeat networks (A-GHN) for mapping SC-FC. A-GHN enables us to model multiple heat kernel diffusion over the brain graph for approximating the complex Reaction Diffusion phenomenon. We argue that the proposed deep learning method overcomes the scalability and computational inefficiency issues but can still learn the SC-FC mapping successfully. Training and testing were done using the rsfMRI data of 100 participants from the human connectome project (HCP), and the results establish the viability of the proposed model. Furthermore, experiments demonstrate that A-GHN outperforms the existing methods in learning the complex nature of human brain function.

Secure Deep Graph Generation with Link Differential Privacy

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sensitive, we aim to seek effective approaches to generate utility-preserved yet privacy-protected structured data. In this paper, we leverage the differential privacy (DP) framework to formulate and enforce rigorous privacy constraints on deep graph generation models, with a focus on edge-DP to guarantee individual link privacy. In particular, we enforce edge-DP by injecting designated noise to the gradients of a link reconstruction based graph generation model, while ensuring data utility by improving structure learning with structure-oriented graph discrimination. Extensive experiments on two real-world network datasets show that our proposed DPGGAN model is able to generate graphs with effectively preserved global structure and rigorously protected individual link privacy.

On the Power of Symmetric Linear Programs

We consider families of symmetric linear programs (LPs) that decide a property of graphs (or other relational structures) in the sense that, for each size of graph, there is an LP defining a polyhedral lift that separates the integer points corresponding to graphs with the property from those corresponding to graphs without the property. We show that this is equivalent, with at most polynomial blow-up in size, to families of symmetric Boolean circuits with threshold gates. In particular, when we consider polynomial-size LPs, the model is equivalent to definability in a non-uniform version of fixed-point logic with counting (FPC). Known upper and lower bounds for FPC apply to the non-uniform version. In particular, this implies that the class of graphs with perfect matchings has polynomial-size symmetric LPs, while we obtain an exponential lower bound for symmetric LPs for the class of Hamiltonian graphs. We compare and contrast this with previous results (Yannakakis 1991), showing that any symmetric LPs for the matching and TSP polytopes have exponential size. As an application, we establish that for random, uniformly distributed graphs, polynomial-size symmetric LPs are as powerful as general Boolean circuits. We illustrate the effect of this on the well-studied planted-clique problem.

On a Theorem of Lovász that (&sdot, H ) Determines the Isomorphism Type of H

Graph homomorphism has been an important research topic since its introduction [20]. Stated in the language of binary relational structures in that paper [20], Lovász proved a fundamental theorem that, for a graph H given by its 0-1 valued adjacency matrix, the graph homomorphism function G ↦ hom( G , H ) determines the isomorphism type of H . In the past 50 years, various extensions have been proved by many researchers [1, 15, 21, 24, 26]. These extend the basic 0-1 case to admit vertex and edge weights; but these extensions all have some restrictions such as all vertex weights must be positive. In this article, we prove a general form of this theorem where H can have arbitrary vertex and edge weights. A noteworthy aspect is that we prove this by a surprisingly simple and unified argument. This bypasses various technical obstacles and unifies and extends all previous known versions of this theorem on graphs. The constructive proof of our theorem can be used to make various complexity dichotomy theorems for graph homomorphism effective in the following sense: it provides an algorithm that for any H either outputs a P-time algorithm solving hom(&sdot, H ) or a P-time reduction from a canonical #P-hard problem to hom(&sdot, H ).

Constraint Satisfaction Problems and Their Reduction to Directed Graphs

Constraint satisfaction problems present a general framework for studying a large class of algorithmic problems such as satisfaction of Boolean formulas, solving systems of equations over finite fields, graph colourings, as well as various applied problems in artificial intelligence (scheduling, allocation of cell phone frequencies, among others.) CSP (Constraint Satisfaction Problems) bring together graph theory, complexity theory and universal algebra. It is a well known result, due to Feder and Vardi, that any constraint satisfaction problem over a finite relational structure can be reduced to the homomorphism problem for a finite oriented graph. Until recently, it was not known whether this reduction preserves the type of the algorithm which solves the original constraint satisfaction problem, so that the same algorithm solves the corresponding digraph homomorphism problem. We look at how a recent construction due to Bulin, Deli´c, Jackson, and Niven can be used to show that the polynomial solvability of a constraint satisfaction problem using Datalog, a programming language which is a weaker version of Prolog, translates from arbitrary relational structures to digraphs.

Constraint Satisfaction Problems and Their Reduction to Directed Graphs

Constraint satisfaction problems present a general framework for studying a large class of algorithmic problems such as satisfaction of Boolean formulas, solving systems of equations over finite fields, graph colourings, as well as various applied problems in artificial intelligence (scheduling, allocation of cell phone frequencies, among others.) CSP (Constraint Satisfaction Problems) bring together graph theory, complexity theory and universal algebra. It is a well known result, due to Feder and Vardi, that any constraint satisfaction problem over a finite relational structure can be reduced to the homomorphism problem for a finite oriented graph. Until recently, it was not known whether this reduction preserves the type of the algorithm which solves the original constraint satisfaction problem, so that the same algorithm solves the corresponding digraph homomorphism problem. We look at how a recent construction due to Bulin, Deli´c, Jackson, and Niven can be used to show that the polynomial solvability of a constraint satisfaction problem using Datalog, a programming language which is a weaker version of Prolog, translates from arbitrary relational structures to digraphs.

Algebras Describing Pseudocomplemented, Relatively Pseudocomplemented and Sectionally Pseudocomplemented Posets

In order to be able to use methods of universal algebra for investigating posets, we assigned to every pseudocomplemented poset, to every relatively pseudocomplemented poset and to every sectionally pseudocomplemented poset, a certain algebra (based on a commutative directoid or on a λ-lattice) which satisfies certain identities and implications. We show that the assigned algebras fully characterize the given corresponding posets. A certain kind of symmetry can be seen in the relationship between the classes of mentioned posets and the classes of directoids and λ-lattices representing these relational structures. As we show in the paper, this relationship is fully symmetric. Our results show that the assigned algebras satisfy strong congruence properties which can be transferred back to the posets. We also mention applications of such posets in certain non-classical logics.