Parthenope University of Naples
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Applying the Theory of Planned Behaviour to Predict Investment Intention in Energy Transition by Small Businesses' Owners
No full textEnergy transition (ET) is the core of sustainable development. Therefore, ET is a growing operational imperative for all types of
businesses. Until now, the majority of studies focus on the presence of specific factors that should encourage the adoption of ET
practices, while little attention has been paid to the factors supporting the emergence of the intention of companies to follow ET
practices. Intention is a stage that is upstream of the decision to perform a certain behaviour. This knowledge gap is more evident
for small businesses (SBs), although they are the major responsible for pollution and GHG emissions. Hence, while most research
focuses on identifying the factors that may encourage SBs to invest in ET without certainty that investments will materialise,
this study investigates the process through which the intention to invest in ET emerges among SBs. For this purpose, the theory
of planned behaviour (TPB) is applied to a specific population of SBs with a high propensity toward innovative investments. The
findings show that the TPB can effectively help to explain investment choices of SBs. Specifically, economic and financial expectations
seem to be the best factors that push SBs to invest in ET, even if great importance can be devoted to external requests
for sustainability practices that cannot be ignored by any company. Even managers/owners' environmental sensitivity plays a
relevant role and is somewhat shaped by the new environmental culture that is widespread around the EU. From this evidence,
various implications occur
Basketball Workshop and Development of Motor Coordination and Problem-Solving Skills in Lower Secondary School Pupils: A Qualitative and Quantitative Experimental Study.
No full textDART-Vetter: A Deep Learning Tool for Automatic Triage of Exoplanet Candidates
No full textIn the identification of new planetary candidates in transit surveys, the employment of deep learning models proved to be essential to efficiently analyze a continuously growing volume of photometric observations. To further improve the robustness of these models, it is necessary to exploit the complementarity of data collected from different transit surveys such as NASA’s Kepler, Transiting Exoplanet Survey Satellite (TESS), and, in the near future, the ESA Planetary Transits and Oscillation of stars mission. In this work, we present a deep learning model, named DART-Vetter, that is able to distinguish planetary candidates from false positives signals detected by any potential transiting survey. DART-Vetter is a convolutional neural network that processes only the light curves folded on the period of the relative signal, featuring a simpler and more compact architecture with respect to other triaging and/or vetting models available in the literature. We trained and tested DART-Vetter on several data sets of publicly available and homogeneously labelled TESS and Kepler light curves in order to prove the effectiveness of our model. Despite its simplicity, DART-Vetter achieves highly competitive triaging performance, with a recall rate of 91% on an ensemble of TESS and Kepler data, when compared to Exominer and Astronet-Triage. Its compact, open source, and easy to replicate architecture makes DART-Vetter a particularly useful tool for automatizing triaging procedures or assisting human vetters, showing a discrete generalization on threshold-crossing events with multiple event statistic > 20 and orbital period < 50 days
Analyzing the Drivers of Digital Innovation in Healthcare: A Panel Analysis from European Countries
No full textDigital health represents a significant innovation in healthcare that can enhance patient engagement and improve health outcomes. Various studies have highlighted the benefits of these technologies in healthcare settings, the improved diagnostics, personalized care, and real-time patient monitoring. Recent research emphasizes the importance of understanding both the internal and external drivers of digital innovation in healthcare, particularly in diverse geographical contexts. Our study aims to address the gap in the literature by investigating the macroeconomic variables influencing digital healthcare innovation, focusing on the disparities across European countries. By exploring these factors, we seek to provide insights into the strategies that can facilitate effective digital transformation in healthcare organizations. We used the quantitative methodology of the regression model to investigate the drivers of digital innovation in the healthcare industry. Our research contributes to identifying the drivers of digital innovation in the healthcare sector. In particular, the analysis results highlight additional factors that influence digital innovation in the healthcare industry that the researchers have not empirically investigated yet
Marine Biodiversity in a Coastal Periphery Revealed by a Community-Based Monitoring Approach Integrating Citizen Science and Environmental DNA Metabarcoding
No full textA Consensus-Driven Distributed Moving Horizon Estimation Approach for Target Detection Within Unmanned Aerial Vehicle Formations in Rescue Operations
No full textIn the last decades, the increasing employment of unmanned aerial vehicles (UAVs) in civil applications has highlighted the potential of coordinated multi-aircraft missions. Such an approach offers advantages in terms of cost-effectiveness, operational flexibility, and mission success rates, particularly in complex scenarios such as search and rescue operations, environmental monitoring, and surveillance. However, achieving global situational awareness, although essential, represents a significant challenge, due to computational and communication constraints. This paper proposes a Distributed Moving Horizon Estimation (DMHE) technique that integrates consensus theory and Moving Horizon Estimation to optimize computational efficiency, minimize communication requirements, and enhance system robustness. The proposed DMHE framework is applied to a formation of UAVs performing target detection and tracking in challenging environments. It provides a fully distributed architecture that enables UAVs to estimate the position and velocity of other fleet members while simultaneously detecting static and dynamic targets. The effectiveness of the technique is proved by several numerical simulation, including an in-depth sensitivity analysis of key algorithm parameters, such as fleet network topology and consensus iterations and the evaluation of the robustness against node faults and information losses
Oltre l’Etica Aziendale: la Natura Estetica dell'Azienda
No full textThis study explores the limitations of traditional business ethics in addressing complex global challenges. Positioned as a theoretical exercise, the research examines the philosophical coherence of business ethics and introduces aesthetics as a complementary framework to guide firms toward harmony, sustainability, and the common good. By integrating the classical concept of “beauty” into the corporate context, the study seeks to redefine organizational practices, aligning economic functions with societal and environmental dimensions through balance, order, and proportion. The research employs an integrative literature review, combining established discussions on business ethics with the emerging role of aesthetics. This interdisciplinary approach facilitates the development of a theoretical framework that integrates ethical and aesthetic values while reinterpreting them through the lens of Italian Economia Aziendale. Grounded in this doctrine, the study emphasizes the interconnectedness of economic and social functions, their alignment with the common good, and the firm's capacity to act as an institution that harmonizes diverse dimensions of value. Challenging the notion of firms as autonomous moral entities, the study argues that ethics is intrinsically tied to individual behavior within the “working community”. However, it posits that the broader purpose of firms can be more effectively conceptualized through aesthetics. Corporate aesthetics is presented as a model rooted in harmony, balance, and order, guiding managerial strategies that integrate economic objectives with societal well-being. Inspired by the classical notion of beauty, this framework transcends the limitations of traditional business ethics, providing a holistic lens to address contemporary challenges. The findings underscore the practical relevance of integrating ethics and aesthetics into business management. The research highlights the need for developing metrics to measure harmony, balance, and proportion in corporate practices, enabling a comprehensive assessment of their economic, social, and cultural impacts. It also outlines a research agenda, including multi-case studies, longitudinal experiments, and cross-cultural analyses, to test the applicability and effectiveness of the framework in diverse contexts. These insights encourage managers to adopt strategies that balance financial objectives with the broader goal of creating shared value for stakeholders. By reinterpreting ethics and aesthetics in light of Italian Economia Aziendale, this research bridges philosophical principles with managerial practices, offering a novel framework for reimagining firms as institutions that harmoniously integrate economic, ethical, and aesthetic dimensions. This approach not only promotes the creation of shared value and sustainable practices but also inspires further exploration of how firms can address global challenges in a responsible and holistic manner
The Ross Sea in the Context of Climate Change
No full textThe Ross Sea is a key region in the global climate system, producing a significant fraction of Antarctic Bottom Water (AABW) that ventilates the abyssal ocean and regulates heat and carbon storage. Long-term observations from the Italian MORSea observatory reveal multi-decadal variability in Dense Shelf Water (DSW) linked to tidal modulation, atmospheric forcing, and large-scale climate modes such as the Southern Annular Mode. Recent freshening and episodic salinity rebounds reflect the influence of ice-shelf melt and wind anomalies. These findings underscore the Ross Sea’s central role in connecting Antarctic processes to global ocean circulation and climate variability