Politecnio die Bari - Catalogo di prodotti della Ricerca
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A Novel Sliding Mode Control for the Speed Regulation in Permanent Magnet Synchronous Generators
No full textMulti-criteria quality assessment of parts produced by single point incremental forming process: application of the ELECTRE method
No full textThe Single Point Incremental Forming (SPIF) process is being considered an ideal candidate in several industrial sectors thanks to its flexibility, versatility and capability to target the paradigm of the product customization. Moreover, shorter development cycle and lower cost, considering that the die is not essential, make the process attractive even from the environmental point of view. Nonetheless, the product customization is still an open question; therefore, several indicators, sometimes even conflicting, are generally defined to quantitatively evaluate the quality of the final part. Moreover, the SPIF process is influenced by a large set of parameters, which combined with the multiple indicators, excludes the possibility of identifying unique optimal conditions and opens the way to a less straightforward multi-objective optimization. In such complex scenario, the Elimination and Choice Expressing Reality (ELECTRE) technique has been selected to identify different combinations of the process parameters to simultaneously satisfy the main output indicators of the process (i.e., the maximization of final depth, and minimization of the maximum load and the surface roughness of the formed part). Thanks to the ELECTRE capability of building outranking-based relations and by giving different combinations of weights to the output indicators, a multi-criteria evaluation and prioritization approach was effectively applied to the SPIF process allowing the identifications of different optimal manufacturing conditions
A System for Automated Unit Test Generation using Large Language Models and Assessment of Generated Test Suites
No full textUnit tests are fundamental for ensuring software correctness but are costly and time-intensive to design and create. Recent advances in Large Language Models (LLMs) have shown potential for automating test generation, though existing evaluations often focus on simple scenarios and lack scalability for real-world applications. To address these limitations, we present AgoneTest, an automated system for generating and assessing complex, class-level test suites for Java projects. Leveraging the Methods2Test dataset, we developed Classes2Test, a new dataset enabling the evaluation of LLM-generated tests against human-written tests. Our key contributions include a scalable automated software system, a new dataset, and a detailed methodology for evaluating test quality
Dynamic Inductor Switching and Frequency Control to Enhance Efficiency in Buck Converters for CubeSat EPS
No full textThe Electrical Power System (EPS) is a critical satellite subsystem that must meet mission requirements to operate effectively and ensure mission success, including supporting various operational modes during the required satellite lifetime. The power distribution of an EPS relies on Point of Load (POL) converters, which supply energy to all other subsystems. The efficiency of these DC/DC converters is crucial, especially when the satellite depends solely on batteries. High-efficiency converters help to minimize power losses, thereby potentially reducing the required battery size. This is particularly important for nanosatellite standardized platforms like CubeSats, which have strict limitations on space and weight. This paper introduces a versatile POL converter architecture based on switched inductors and switching frequency adjustment, aiming to optimize efficiency under different operating conditions. The effectiveness of the proposed method is demonstrated through a measurement campaign, and the preliminary results show that, with the proposed method, the converter can adapt its efficiency under both heavy and light load conditions. Our method exhibits an efficiency enhancement up to more than 25% compared to solutions that are based on the standard circuit parameters sizing, providing notable advantages for applications where power consumption is critical
A Cloud-Edge Framework Combining AI, IoT and Blockchain for Smart Farming and Agrifood Traceability
No full textThe growing need for process visibility, sustainability and responsiveness in the agrifood sector spurs the adoption of digital technologies throughout the value chain. This paper introduces a unified cloud-edge architecture combining Internet of Things, Artificial Intelligence and Blockchain technologies to support Smart Farming and end-to-end Product Traceability Management. In contrast to existing frameworks limited to specific value chain segments, the proposed architecture enables end-to-end process integration and technological convergence within a unified digital ecosystem. It is designed to be modular, interoperable and scalable, enabling data-driven monitoring, decision-making and secure certification of agricultural practices. A real-world use case focused on olive and grape cultivation in the Apulia region demonstrates the applicability of the architecture. The use case highlights the integration of multispectral imaging on tractors, edge-side inference for crop disease detection, microservice-based orchestration in the cloud, and blockchain-backed traceability, also considering interoperability with national registries
Evaluation of the Real Estate Dynamics in the Italian University Rental Market
No full textWith reference to the student housing market segment in five Italian cities, the present research aims to (i) verify the potential influence of a university center on rental prices, and (ii) analyze the intrinsic factors that mostly affect the formation of rental fees. In particular, in the paper a two-step methodological approach for the achievement of the two goals is proposed. Starting from the collection of a set of rented residential units in the selected cities, in the first step the detection of intrinsic and extrinsic factors taken into account by owners and potential tenants is carried out. The implementation of an econometric technique—called Evolutionary Polynomial Regression—allows to identify the most influential variables and to quantify each contribution on rental prices. In the second step, for cities where the impact of the university's presence has been confirmed by the outputs of the first step, from the initial dataset, new clusters of rented residential properties located in the range of 2 km from the main poles are defined. By excluding the extrinsic factors from the initially considered set as their negligible influence due to the limited distance of the properties, a new econometric model is determined. Therefore, the most influencing factors on local rental dynamics are analyzed and their weight is calculated
Holistic Design Optimization of 350 kW High-Speed Permanent Magnet-Assisted Synchronous Reluctance Machine for Heavy-Duty Electric Vehicle
No full textThe widely adopted “high-speed machine + high-ratio gear” solutions for passenger electric vehicle (EV) drivetrains are yet to be explored for demanding heavy-duty applications. This article will investigate 350-kW level high-speed traction motor development based on permanent magnet (PM)-assisted synchronous reluctance machine (PMaSyRM) topology. With the overall target of boosting active power density under threshold of materials’ performance boundaries, multiphysics solvers are configured by both analytical and simulation tools to tackle design challenges in electromagnetic, mechanical, and thermal domains. To deal with multiple design parameters and performance indicators, a three-stage hierarchical development platform is proposed and implemented to feature not only comprehensiveness but also balanced computation resource consumption and accuracy. Apart from globally parametrized machine geometry, the usually predefined slot number and pole number are looked into in the first and second stages, respectively, and are downselected due to their substantial influence on material usage and loss distributions. In the final stage, a novel mechanical stress design concept is proposed, which significantly accelerates the “electromagnetic + mechanical” coupled rotor design process. Moreover, three typical cooling strategies are quantitatively evaluated for further downselection of the most suitable thermal management. The finalized design is validated by a 1:1 PMaSyRM prototype with 580-Nm peak torque and 15 000-r/min peak speed, which features an active power density of 6.3 kW/kg
Consumer behavioral intention for sustainable garments: do materials used and the level of garment's visibility and skin contact matter?
No full textSustainable fashion consumption can be promoted only by understanding the motivation behind consumers' decision to purchase sustainable clothing. This study explores the determinants of consumers' purchasing intentions for two clothing items with different functions, characterized by different levels of visibility and skin contact (underwear and jacket) made with two sustainable materials (biobased and recycled). A conceptual framework was tested using the SEM technique on data collected through a questionnaire administered to 768 Italian consumers. Sustainable fashion knowledge, availability of sustainable garments, influence of celebrities and influencers, and environmental concerns significantly affected purchase intentions for the four product categories investigated. Moreover, gender and age significantly influenced purchase intention. The findings highlighted that purchase intentions and their determinants vary based on the levels of visibility and skin contact associated with the products and the type of sustainable materials used. Several contributions to the theory and managerial implications are provided
Human-Centric Ergonomics in Industry 5.0: A Preliminary Literature Review
No full textIndustry 5.0 is a new manufacturing paradigm where the integration of humans and advanced technologies is aimed not only at efficiency but, above all, at the well-being of the operators. In this context, predictive ergonomics and human-centric technologies, including artificial intelligence (AI), offer innovative tools to improve ergonomic conditions, reduce the risk of musculoskeletal disorders (MSD), and decrease work stress levels, contributing to greater safety and productivity. Through real-time monitoring and analysis of physiological parameters and operating conditions, new technologies enable safer and more adaptive work environments. This study proposes a review of the literature on human-centric applications for ergonomics assessment and optimization, explore the methodologies used to prevent musculoskeletal hazards and improve operator well-being. Tools such as Digital Human Modelling (DHM), Digital Twins (DT), Learning Algorithms (ML), as well as electroencephalography (EEG), electrooculography (EOG), heart rate variability (HRV) and electrodermal activity (EDA) sensors, are used to collect biometric data, predict fatigue and optimize workstation design, allowing work conditions to be adapted to individual needs. The study highlights both the benefits and challenges of adopting these solutions and emphasize the need for a multidisciplinary approach to their implementation. The conclusions outline future perspectives for the adoption of smarter, safer, and human-centric working environments in line with the principles of Industry 5.0