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From experience to modelling: a Teaching-Learning sequence for studying surface phenomena in liquids through experiments and mesoscopic simulations
No full textThis doctoral dissertation investigates the teaching and learning of surface phenomena in liquids through an integrated approach that combines macroscopic observations, microscopic models, and mesoscopic simulations. Phenomena such as surface tension and capillarity provide a fertile educational context for developing skills in observation, analysis, and modelling, and for fostering deep and lasting learning. The research is situated within a constructivist theoretical framework, informed by the paradigm of Educational Reconstruction and by the ISLE (Investigative Science Learning Environment) approach, both of which emphasise active learning, conceptual change, and scientific modelling as means of integrating theory and experience. From this perspective, students are guided to gradually build knowledge starting from their initial conceptions, through engagement with experimental data, multiple representations, and iterative modelling processes.The project was developed around a carefully designed Teaching-Learning Sequence (TLS) aimed at promoting autonomous construction of meaning through a coherent set of macroscopic activities, microscopic conceptual models, and mesoscopic simulations based on the Smoothed Particle Hydrodynamics (SPH) method. This sequence was implemented within a teaching intervention involving fourth-year students from a state secondary school specialising in scientific studies.The initial research hypothesis, namely that students hold mixed cognitive models, was subsequently confirmed by the data analysis, highlighting the need for instructional interventions capable of supporting the integration and negotiation of different forms of knowledge. To address this need, the TLS was designed as a progressive pathway beginning with direct experience and gradually guiding students towards more abstract and formalised forms of representation.The sequence begins with a set of macroscopic activities that serve as the first conceptual exploration ground. These qualitative experiments provide immediate contact with surface phenomena, stimulate accurate descriptions, and encourage the emergence of spontaneous questions. The experiences collected in this phase generate productive cognitive conflicts and serve as essential anchors for naturally introducing modelling practices. These modelling concepts are not intended to offer direct explanations of invisible processes, but rather to prepare students to interpret, in an informed manner, what they will encounter at the subsequent level.Mesoscopic simulations play a crucial role as an epistemic bridge between the observable and the invisible. Through the SPH method, otherwise inaccessible processes are rendered visible and manipulable, enabling students to explore emergent dynamics, vary parameters, and observe the effects of interparticle forces. This interactive experience allows macroscopic observations to be reinterpreted in light of processes situated between the perceptible world and more abstract theoretical models, thereby facilitating the transition from intuitive conceptions to formalised representations. In doing so, the mesoscopic level not only provides operational access to what is otherwise invisible, but also creates the conditions for microscopic modelling, introduced as conceptual tools, to be understood, contextualised, and integrated in a more mature and scientifically grounded manner.To thoroughly document the effects of this integrated pathway, from macroscopic experience to mesoscopic modelling, the teaching intervention was supported by a systematic collection of both qualitative and quantitative data, gathered through questionnaires, worksheets, clinical interviews, researcher diaries, and students’ spontaneous feedback. The qualitative analysis was conducted on two levels: a phenomenographic approach applied to the questionnaires, designed to identify distinct epistemological profiles within students’ mental models, and a thematic analysis of the remaining instruments, which made it possible to trace the complexity of the cognitive, social, affective, and metacognitive processes activated by the TLS. The integration of multiple qualitative methods provided a rich and multifaceted account of the conceptual transformations and interactive dynamics promoted by the sequence. In parallel, the quantitative analysis based on Likert-scale data offered insights into students’ satisfaction, perceived impact, and level of engagement with the proposed pathway, thus providing meaningful triangulation with the qualitative evidence.The findings show significant progress in conceptual understanding, in the ability to connect theoretical models with experimental observations, and in the development of metacognitive, collaborative, and reflective competences. The use of mesoscopic simulations emerged as particularly effective in supporting the conceptualisation of complex processes, in increasing students’ participation, and in promoting active and informed learning. Overall, the TLS contributed to strengthening students’ ability to adopt a scientific perspective in interpreting phenomena, while enhancing their autonomy in the process of knowledge construction.Finally, the study acknowledges several limitations, mainly related to the composition of the sample, the specificity of the educational context, and the technical resources required for the simulations. These elements suggest the need for further research exploring applications in more diverse contexts, the replicability of the approach in other educational and academic settings, and the potential extension of the analysis to additional observables included within the dimensions of learning. Future perspectives also include the refinement of modelling tools, integration with emerging technologies, and investigation of the role of collaborative modelling as a means of developing transversal competences. In this sense, the study provides a significant contribution to contemporary science education and to the design of instructional interventions grounded in robust empirical evidence, outlining a promising trajectory for the innovation of physics teaching
Implementation and Experimental Validation of a PLC-Based Infrastructure for Distributed Generation and Storage Systems Remote Management
No full textThis article presents new devices and communication architecture for monitoring and controlling distributed generation (DG) and energy storage systems (ESS) in a smart grid. Different communication means, including power line communications, and protocols are presented, which can be adopted for the distribution power system, where DG and ESS are usually connected. The new devices allow the distribution system operator (DSO) to remotely monitor all DGs and ESSs connected to a secondary substation and to remotely interact with each of them with Modbus commands. The proposed communication link was tested, measuring the communication latency, success rate, and bit error rate. Moreover, a test of the whole architecture was carried out, including the power converter and ESS. The results show how DSO can change the power flow, injecting or storing energy in a very short time, confirming the possible contribution of ESS to distribution network management and stability
Long-term efficacy and safety of lomitapide in patients with familial chylomicronemia syndrome: Data from an expanded access program
No full textBACKGROUND: Familial chylomicronemia syndrome (FCS) is a rare, severe, autosomal recessive dis-
order characterized by extremely high triglyceride (TG) levels and an increased risk of acute and/or re-
current pancreatitis. Lomitapide, a microsomal triglyceride transfer protein (MTP) inhibitor, is approved
for the treatment of homozygous familial hypercholesterolemia. The open-label, single-arm LOCHNES
study (EudraCT 2018-002911-80) investigated lomitapide in adult patients with genetically confirmed
FCS and a historyMETHODS: Fourteen patients previously enrolled in the LOCHNES study were admitted to the Lomi-
tapide Expanded Access Program 2 months after study completion. They were followed every 3 months
over a nearly 3-year period (median follow-up: 33 months), continuing lomitapide at the maximum tol-
erated dose established during the trial. Evaluations included lipid profile, liver function tests, hepatic fat
content, and liver stiffness.
RESULTS: At the start of the follow-up period (after a 2-month lomitapide washout), median TG
levels were 1899.5 mg/dL (range: 1013.5-2572 mg/dL). At the last observation, median fasting TGs were
reduced to 376.5 mg/dL (range: 195-1328 mg/dL), representing a 80.2% decrease; 9 patients achieved
TG levels ≤750 mg/dL. Adverse events were mostly mild-to-moderate, predominantly gastrointestinal
(n = 11). Two patients experienced an episode of acute pancreatitis during follow-up. Liver enzymes
≥3 ×the upper limit of normal were observed in 2 patients. Hepatic fat content increased in 3 patients,
while median liver stiffness remained within the normal range.
CONCLUSIONS: Lomitapide effectively and safely reduced TG levels in FCS patients with a history of
pancreatitis over a nearly 3-year follow-up period. These findings are consistent with those of the open-
label trial, despite the use of a lower median daily dose (27 mg). No new safety signals were observed. of pancreatitis, demonstrating its efficacy and tolerability
Low awareness of medication-related osteonecrosis of the jaw among dentists: a systematic review with a medical and bioethical perspective
No full textIntroduction
Medication-related osteonecrosis of the Jaw (MRONJ) is a serious adverse drug reaction that can seriously affect the quality of life of patients if not promptly diagnosed and treated. To date, preventive measures have been the most effective strategy for reducing the incidence of MRONJ. The role of dental practitioners in managing patients treated with Bone Modifying Agents (BMAs) varies across countries, with some healthcare systems providing a more structured approach than others. This systematic review aims to investigate the knowledge and awareness of MRONJ among dentists, interpreting the findings from a medical and bioethical perspective.
Materials and methods
The protocol for this study was designed following the PRISMA guideline and registered on PROSPERO (CRD420251015032). A systematic review search was conducted in PubMed, Scopus, Web of Science, and Cochrane Database to answer the PICo question: Are dentists well-informed and aware of the risk of MRONJ in patients receiving BMAs? Furthermore, to address potential geographic differences, a country-specific literature review was conducted to compare the level of MRONJ awareness between patients receiving BMAs and dental professionals within the same country. Studies published up to March 2025 were screened.
Results
Twenty-two studies were included in this systematic review. Among the 10,536 dentists involved, 62.8% were well-informed about MRONJ, but only 9.7% were aware of its risk factors. The most common sources of information were university education, followed by scientific journals and specific training courses or conferences. Studies performed in the same countries investigating MRONJ awareness among patients taking BMAs were analyzed. Country-based analysis revealed significant discrepancies between dentists’ (68.5%) and patients’ (19.5%) knowledge and awareness of MRONJ in the USA, Saudi Arabia, Bulgaria, and Brazil. Finally, a bioethical analysis of European legal frameworks on informed consent for pharmacological therapies revealed that adequately informing patients about treatment risks is both an ethical obligation and a legal duty, rooted in constitutional and legislative principles.
Conclusion
This systematic review reveals a low level of awareness despite an acceptable level of knowledge among dentists, highlighting an alarming discrepancy. Beyond knowledge of the disease, awareness of its risk factors and preventive strategies is essential for dentists to adopt a personalized and safe workflow for these patient groups. Ensuring personalized therapy requires a structured and collaborative approach based on ethical principles and shared knowledge. Strengthening interprofessional training and teamwork is essential to improving prevention and patient empowerment
Maximum principle for a class of nonlinear elliptic systems
No full textWe prove a maximum principle result for a class of nonlinear elliptic systems of the form−div(A(x,u(x),Du(x)))=0x∈Ω. The result is obtained by assuming a componentwise sign condition
Electrodeposited NiFe-succinate for the oxygen evolution reaction in anion exchange membrane water electrolysis
No full textThis study proposes Platinum Group Metal-free (PGM-free) electrocatalysts for the Oxygen Evolution Reaction (OER) to be used in Anion Exchange Membrane Water Electrolyzers (AEMWEs). NiFe-based electrodes were synthesized via an optimized electrodeposition process in the presence of succinic acid onto a low-cost 304 stainless steel (SS) mesh, resulting in an active and durable inorganic–organic complex. Morphological characterization confirmed the formation of high-surface-area electrodes, with a catalyst layer composed of Ni and Fe ions coordinated by organic carboxylic groups. X-ray Photoemission Spectroscopy (XPS) proved the formation of NiFe2O4 as the active species for OER, showing improved electrochemical performance compared to Ni-based and Fe-based electrodes. Notably, 240 mV and 306 mV were recorded as onset overpotential and overpotential at 10 mA cm−2, respectively, with a low Tafel slope of approximately 50 mV dec−1, using electrodes with total catalyst layer mass loading lower than 1 mg cm−2. Density Functional Theory (DFT) calculations were performed to gain more insight into the OER mechanism on NiFe2O4 species, obtaining simulated polarization curves in very good agreement with experimental data. Finally, the best-performing electrode was tested in a single-cell AEMWE, achieving a maximum current density of 1.86 A cm−2 at 2.2 V and 60 °C, and demonstrating good stability after a 40-h chronoamperometric test conducted at 2 V
Artificial Intelligence in Hemodialysis: from failures detection to patient-therapy analysis
No full textIn recent decades, technological innovation has accelerated rapidly, leading to the spread of advanced devices and technologies. This trend has also had a profound impact on the biomedical sector, where such tools are increasingly being used to optimize clinical activities, improve prevention, diagnosis, and treatment, and reduce healthcare costs. As a result, research has progressively shifted toward the development of innovative, technologically advanced, yet economically sustainable investigative solutions.Artificial Intelligence (AI) has become a valuable tool for the analysis of biomedical data and for supporting the development of advanced medical technologies. The main advantage of AI methods, when applied to physiological signals or clinical information, lies in their ability to automatically learn meaningful patterns and correlations from large datasets without requiring extensive manual processing or controlled laboratory experiments. This brings considerable benefits in terms of time, costs, and reduction of the operational burden associated with traditional analysis workflows. Additionally, AI techniques enable the precise and quantitative characterization of biomedical data, allowing for an exploration of how changes in model architecture, training strategies, or input features impact predictive accuracy and reliability.In recent years, equipment manufacturers have shown a growing interest in directing their scientific efforts toward topics that can help address practical problems in the field. In the present Ph.D. project, the use of artificial intelligence is proposed as a tool to improve hemodialysis therapies, both from the dialysis machine side and the patient side.Hemodialysis is a blood-purification treatment performed outside the body and prescribed to patients with advanced kidney dysfunction. The therapy relies on dialyzers, which are devices containing bundles of hollow synthetic fibers. These fibers act as semi-permeable barriers that facilitate the transfer of unwanted solutes and metabolic by-products, such as urea and creatinine and many others, from the bloodstream, thereby helping to restore biochemical balance.With the purpose to fill some major gaps in the literature, the specific activities performed in this thesis propose the use of artificial intelligence to develop smarter dialysis systems, particularly those related to enhancing prediction, early identification, and overall management of clinical conditions. In this context, AI approaches hold considerable potential in nephrology, as they can enhance diagnostic accuracy, support therapeutic decision-making, and contribute to a more effective and personalized management of renal replacement therapies
Towards robot affective appraisal linking inner speech and emotion
No full textRecent studies in Robotics and AI suggest that robots “thinking out loud”
can foster positive human feedback and support collaborative goal achievement.
By externalizing their internal reasoning, robots enhance transparency and ex-
plainability, which are crucial for trust and robustness in human–robot interac-
tion.
This work investigates the role of robot inner speech in supporting affective
appraisal, focusing on the emergence, coherence, and interpretability of emo-
tionally grounded evaluations. While the relationship between inner speech and
architecture for affective appraisal.
Grounded in appraisal theories, the proposed model employs inner speech to
simulate internal reflection, enabling the identification and evaluation of contex-
tual variables relevant to affective assessment. Through this internal dialogue,
the robot structures its appraisal process and externalizes it, allowing human
partners to access and interpret the underlying affective reasoning.
The model is evaluated by comparing its appraisal dynamics with norma-
tive emotional patterns observed in adults under stress, and by assessing the
interpretability of the robot’s affective behavior through human observation.
Results indicate that the model produces coherent and context-sensitive eval-
uations, improving upon a widely adopted computational model of emotion in
terms of plausibility and transparency
Experimental analysis of an upscaled reverse electrodialysis unit featuring electrode segmentation
No full textReverse electrodialysis (RED) is a technology that produces renewable energy from salinity gradients. In 2024, the EU has identified the RED process as a potential soon-to-be marketable technology for osmotic energy exploitation. For this purpose, high power densities and energy efficiencies are essential to achieve to increase the technology readiness level (TRL) of this technology. In this context, it is crucial to study the transition from lab-scale set-ups to pilot-scale units with larger membrane area. In this work, an upscaled RED unit (10 × 80 cm2), equipped with segmented electrodes, was employed in an extensive experimental study where (i) flow velocities, (ii) high saline solution concentrations (up to 5.0 mol/L NaCl), and (iii) electrode configurations were varied to assess the influence of channels length on power generation and energy efficiency. Larger energy efficiencies obtained with long channels improved the overall power output and resources exploitation, reaching yield values of ∼0.5 kWh/m3, the highest ever reported in the literature. The electrode segmentation feature, explored for the first time with hypersaline solutions, allowed current density to be optimized across different sections of the unit, yielding up to a 21% increase in power density (maximum net power density of 4.1 W/m2) compared to undivided electrodes. Results marked useful indications for future RED commercial implementation