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Optimizing a Cutting Work Center: Multi-Criteria Approach to Pattern and Single Cut Sequencing
No full textThis work presents a multi-criteria optimization framework developed in collaboration with a leading company in the production of automatic machines for iron manufacturing. The study focuses on optimizing the workflow of a cutting machine and its unloading system, aiming to enhance efficiency and minimize idle times.
The manufacturing process involves several sequential steps. Initially, iron bars are cut according to pre-computed cutting stock patterns and transported via a conveyor belt to two temporary buffers. From there, a portal equipped with pliers relocates the bars through lengthwise movements into a depot consisting of identical parallel buffers, where they are consolidated by order. The finalized orders are then transferred to unloading tracks upon request from downstream processing steps. The first step essentially resembles a single-machine batch scheduling problem, whereas portal management incorporates features from several classical problems, such as dynamic berth allocation, virtual machine packing, and dynamic bin packing.
A key challenge arises from potential idle periods of the sequential cutter when the unloading stations reach capacity, leading to machine blocking. To address this, a three-step optimization approach, combining local search techniques and enumeration, is proposed. First, a local search minimizes order spread by optimizing the sequencing of cutting patterns. Second, a branch-and-bound procedure determines the optimal dispatching of parts to the unloading stations, reducing
cutter breaks and portal movements. Finally, a sequential value correction heuristic dynamically allocates consolidated lots into the depot, balancing the minimization of portal translations with the reduction of fragmented orders.
Computational experiments on real-world industrial instances validate the effectiveness of the
proposed methodology, demonstrating improvements in cutter utilization, order consolidation, and
overall workflow efficiency
Comunicazione politica e ricerca sociale della verità
No full textL’autrice esplora il trinomio comunicazione politica, verità e democrazia mostrando come la ricerca sociale della verità influenzi la qualità dell’azione pubblica democratica. Allontanandosi da posture relativiste e basandosi su una teoria sociologico-cognitiva del rapporto tra verità e democrazia, lo studio ricostruisce come la comunicazione politica influenzi la democrazia non solo attraverso l’evoluzione tecnologica dei suoi strumenti ma coevolvendo con profonde trasformazioni del senso comune democratico. Dapprima le religioni secolari, più recentemente le solitudini dei cittadini globali contribuiscono con la platform society, in modo variegato al mutare dei contesti nazionali, a rappresentazioni ontologizzate della verità. L’analisi sociologica di queste trasformazioni, osservate nel nostro Paese, nei movimenti essenziali assunti nel corso dell’ultimo secolo, rivela il nesso strettissimo esistente tra le condizioni di vita materiali degli individui e la loro possibilità di partecipare all’elaborazione di un progetto di costituzione democratica della società. In questo scenario, la regressione identitaria e comunitaria e la proliferazione di verità individuali ed effimere assumono un significato unitario e invitano il ricercatore sociale ad assegnare una funzione logico-pratica alla ricerca della verità. La possibilità di ricostruire le ragioni degli altri, anche quando si manifestano in credenze false, delinea un metodo genealogico-ermeneutico che guida la ricerca sociale non solo in una direzione diagnostica, dando vita a insospettate logiche della scoperta, ma in una direzione pratica, facendo emergere il senso pubblico di nuove domande di ricerca. In quali luoghi e, in particolare, in quali contesti professionali e attraverso quali tecniche, quali forme comunicative, è oggi possibile suscitare e curare la ricerca sociale della verità e, con essa, chances di partecipazione alla vita activa il più possibile equamente distribuite tra gli individui
Merico, M., Romeo, A. e Tirino, M. (a cura di) (2022), "Sport, pratiche culturali e processi educativi"
No full textEEG-based motor imagery recognition via novel explainable ensemble learning architecture
No full textBrain–computer interfaces (BCIs) are interactive machines using implicit neurophysiological signals, with applications ranging from medical rehabilitation to smart prostheses and entertainment. In this context, the need for high recognition performance demands increasingly complex machine learning (ML) architectures. Generally, the more complex the architecture, the less transparent its reasoning. This leads to difficulties in motivating their outputs and validating their internal model. Moreover, explainability is explicitly required by recent regulations on personal data processing, which advise against black box modeling. Here, a novel ensemble learning model is proposed aiming to effectively balance recognition performances and explainability. The proposed architecture employs different multilayer perceptrons, each one specialized to distinguish a single pair of classes and to provide counterfactual explanations and the minimal feature changes resulting in a classification shift. Subsequently, their outcomes are weighted to minimize the contribution of the non-competent classifiers and combined to address a multiclass classification problem. Results were gathered from two publicly available datasets on multiclass electroencephalography-based motor imagery and demonstrate that the proposed architecture overcomes state-of-the-art recognition performance while providing information on the most discriminant brain areas and power bands. For the sake of reproducibility, the implementation of the proposed approach is made publicly available
Analysis of best performances of front crawl swimming: a case study
No full textPurpose: The aim of this work was to: 1) measure the energy cost of front crawl swimming at various speeds (range .8 -1.8
m·s-1 in elite and medium level swimmers; in addition, 2) estimate the maximal aerobic power and maximal anaerobic
capacity from the relationship between overall energy spent and individual best times over distances from 50m to 1500m in
one elite athlete. It will also be shown, that 3) the so obtained values are rather close to the actually measured ones.
Methods: Oxygen consumption was measured on 13 medium level and 5 elite swimmers (best time over 100m, 51.50±3.54s),
swimming the front crawl in a 25m indoor pool in the speed range .8-1.8 m·s-1. So, the energy cost of front crawl (Csw) could
be calculated.
Results: In both groups Csw increased with the speed as a second order polynomial. In the elite group it was ≈ 17 to ≈ 34 %
smaller than in medium level swimmers, (P= .005). Knowledge of Csw allowed us to estimate the overall energy expenditure
(Etot) during actual competitions as a function of the corresponding performance time (tr) in one elite swimmer. The results
show that (Etot) increased linearly with tr. For tr ≥ 100s the slope and y intercept of the resulting linear regressions yield
V̇ O2max and maximal anaerobic capacity, respectively. These turned out to be close to the directly determined values.
Conclusions: If the energy cost of swimming as a function of the speed is known, this approach yields reasonable estimates
of the swimmers’ maximal oxygen consumption and maximal anaerobic capacity
The TEACH-AI project: Co-designing Practices for Socio-Educational Services
No full textThe TEACH-AI project (Transformative Educational Approaches for Civic and Human-centered AI) addresses these challenges by examining the role and implications of Generative Artificial Intelligence within Italian socio-educational services. Led by the CREDDI research group at eCampus University, the study adopts an action-research approach to investigate current uses of Generative AI. In close collaboration with participating organisations, the project aims to co-develop strategies, skill frameworks and policy recommendations for the ethical and sustainable adoption of AI. The aim is to promote AI literacy and produce shared guidelines to ensure that technology serves to enhance, rather than undermine, the quality of human interactions. Expected outcomes include the formulation of foundational organisational principles designed to support a socially responsible transition to AI-enabled practices. Artificial Intelligence (AI) can support and improve the effectiveness of social and educational services in various ways, focusing on optimising professional practices and maintaining the centrality of the human dimension. AI is recognised as a tool capable of increasing the effectiveness, accessibility and quality of professional interventions
Scuola dell'infanzia laboratorio di startup. Educazione all'imprenditorialità: come il "terzo porcellino" costruì la sua casa
No full textL’idea di scrivere un volume sull’educazione all’imprenditorialità scaturisce nel corso di alcuni incontri di aggiornamento sul tema della progettazione didattica con le/gli insegnanti della scuola dell’infanzia, impegnate/i a dettagliare gli scomparti che costituiscono i vari format di micro progettazione, utilizzati presso gli istituti scolastici sparsi sul territorio nazionale.
Mi riferisco alla formulazione di una serie di quesiti che, una volta raccolti, sono diventati traiettorie di ricerca e riflessione. Nello specifico: “Quali competenze chiave per l’apprendimento permanente (2018) possiamo sostenere nello sviluppo di questo lesson plan?”, ma anche “Quali e quante competenze chiave risultano maggiormente coinvolte nell’articolazione dell’apprendimento di questo percorso?” o ancora “Come declinare la competenza imprenditoriale con le bambine e i bambini dai 3 ai 6 anni, all’interno di questo microlearning?”.
Alla luce di questo scenario, il proposito di sostenere la cultura imprenditoriale trova svolgimento lungo la successione dei tre capitoli e nell’area appendice che compongono il volume.
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Assessment literacy and lifelong learning: the evaluative postures of teaching staff.
No full textLa valutazione riveste un ruolo centrale nella professionalità docente, evolvendo dai modelli centrati sulla misurazione dei risultati (assessment of learning) a quelli formativi (assessment for learning) e riflessivi (assessment as learning). Questo studio esplora le posture valutative di 12.384 docenti italiani, considerando concezioni della valutazione, pratiche di feedback e formazione specifica. Le cluster analyses hanno identificato quattro profili per ciascun ambito, mostrando una relazione significativa tra concezioni e pratiche di feedback. La distribuzione dei profili varia in base a grado scolastico, età e anzianità di servizio, con maggiore orientamento all’assessment as learning nella scuola dell’Infanzia e Primaria e tra docenti più esperti. Gli approcci certificativi privilegiano strumenti sommativi, mentre quelli formativi e riflessivi adottano pratiche partecipative e metacognitive. I risultati sottolineano l’urgenza di percorsi formativi integrati, capaci di sviluppare competenze tecniche e consapevolezza pedagogica, promuovendo valutazioni centrate sullo studente e orientate all’autoregolazione e alla costruzione condivisa del significato
Region-aware Minimal Counterfactual Rules for Model-agnostic Explainable Classification
No full textThe increasing demand for transparency in machine learning has spurred the development of techniques that provide faithful explanations for complex black-box models. In this work, we introduce RaMiCo (Region Aware Minimal Counterfactual Rules), a model-agnostic method that extracts global counterfactual rules by mining instances from diverse regions of the input space. RaMiCo focuses on single-feature substitutions to generate minimal and region-aware rules that encapsulate the overall decision-making process of the target model. These global rules can be further localised to specific input instances, enabling users to obtain tailored explanations for individual predictions. Comprehensive experiments on multiple benchmark datasets demonstrate that RaMiCo achieves competitive fidelity in replicating black-box behaviour and exhibits high coverage in capturing the intrinsic structure of white-box classifiers. RaMiCo supports the development of trustworthy and secure machine learning systems by providing transparent, human-understandable explanations in the form of concise global rules. This design enables users to verify and inspect the model’s decision logic, reducing the risk of hidden biases, unintended behaviours, or adversarial exploitation. These features make RaMiCo particularly suitable for applications where the reliability, safety, and verifiability of automated decisions are essential