Partial Cubes and Fibonacci Dimension: Insights and Perspectives

A partial cube is a graph G that can be isometrically embedded into a hypercube Qk, with the minimum of such k called the isometric dimension, idim(G), of G. A Fibonacci cubeΓk excludes strings containing 11 from the vertices. Any partial cube G embeds into some Γd, defining Fibonacci dimension, fdim(G), as the minimum of such d. It holds idim(G)≤fdim(G)≤2·idim(G)-1. While idim(G) is computable in polynomial time, check whether idim(G)=fdim(G) is NP-complete. We survey the properties of partial cubes and Generalized Fibonacci Cubes and present a new family of graphs G for which idim(G)=fdim(G). We conclude with some open problems

Efficacy and safety of European Medicines Agency (EMA)-approved pharmacological, endoscopic, and surgical treatments in different classes of obesity: A network meta-analysis of randomised controlled trials for the development of the SIO (Società Italiana Obesità) Italian guidelines for the diagnosis and treatment of overweight and obesity

Aims: We aimed at comparing different approved strategies (obesity management medications—OMM, endoscopic bariatric procedures—EBP, and metabolic bariatric surgery—MBS) with lifestyle intervention/placebo/no therapy (LSI/Pbo/NT) for the treatment of different BMI-based classes of obesity (i.e., overweight—BMI: 25–29.9 kg/m2; class I—BMI: 30–34.9 kg/m2; class II—BMI: 35–39.9 kg/m2; class III—BMI >39.9 kg/m2). Materials and Methods: This systematic review (SR) and network meta-analysis (NMA) included randomised clinical trials (RCTs) comparing OMM, EBP, and MBS versus either LSI/Pbo/NT or active comparators in individuals with overweight or obesity. A Medline and Embase search was performed up to 31st January 2025 for RCTs on EMA (European Medicines Agency)-approved weight-loss interventions in adults with overweight/obesity. The primary endpoint was total body weight loss (TBWL%), analysed at different time points: 26–52, 53–104, 105–156, and ≥156 weeks. Secondary endpoints included all-cause mortality, quality of life, and serious adverse events (SAE). Weighted mean difference and 95% confidence intervals (WMD, 95% CI) for continuous variables and Mantel–Haenszel odds ratio (MH-OR, 95% CI) for categorical variables were calculated using random effect models. The study was registered on the PROSPERO website (CRD42024625338). Results: In trials enroling subjects in class I of obesity, tirzepatide resulted in equal effectiveness to both OAGB and RYGB, and it was significantly superior to all the other comparisons. In trials on class II of obesity, tirzepatide was significantly superior to all the other comparisons and inferior to both OAGB and RYGB. Semaglutide was associated with a higher TBWL% than the other OMMs (with the notable exception of tirzepatide), and it was equally effective to EBP, GCP, and LAGB. In trials enroling patients with a mean BMI >40 kg/m2, the procedure with the highest estimated weight loss was BPD. Semaglutide was statistically less effective than SG and gastric bypass, but not inferior to GCP and LAGB. Both RYGB and OAGB were superior to SG. Conclusion: In patients affected by mild to moderate obesity, newer OMMs (i.e., tirzepatide and semaglutide) appear to be valid alternatives to EBP and MBS. They could be preliminarily chosen as a first-line option based on similar efficacy and greater safety and tolerability. Higher degrees of obesity could be more effectively treated with MBS, the efficacy of which, with the notable exception of LAGB and GCP, appears superior to other treatments, especially in the long term

MDR: an ontology vocabulary and registry service for dataset catalogs

The web has establshed itself as a worldwide data hub facilitating the publication and unification of data. Nonetheless, machines still seem not ready to exploit this data for independently executing complex tasks. In pursuit of fulfilling the unachieved promise of the Semantic Web to facilitate machine functionality, we have focused on one particular aspect: ensuring a comprehensive experience in any consuming application. To this end, we have investigated how the appropriate reuse and exploitation of metadata can realize this vision. We have thus defined a metadata model combining an interpretation of existing metadata vocabularies with a new lightweight ontology concerned with dataset accessibility. Then, we have developed a metadata registry and a set of associated services that complement the proposed model in satisfying our elicited requirements. As tangible evidence of our solution's effectiveness and influence, we describe and examine the implementation of the metadata registry in three distinct, open-source applications

Demonstrating black-diamond-based high-temperature solar cells

Efficient high-temperature solar cells are feasible through the photon-enhanced thermionic emission (PETE) mechanism. The development of defect-engineered black-diamond layers, combined with micro-graphitized electrodes fabricated within p-type/intrinsic structures, represents the key technology for sunlight interaction of 0.3-eV electron-affinity PETE diamond cathodes, characterized by excellent electron emission. The resulting PETE converters demonstrate energy generation under concentrated radiation. At operating temperatures ranging from 600 to 900 K, the PETE operational regime is revealed, whereas photoemission and thermionic emission are found to be predominant at lower and higher temperatures, respectively. Cathode thickness emerges as the primary factor limiting the present performance of black-diamond technology. The generation-recombination analytical model applied to the device allows predicting a quantum efficiency of 30.3% for a 300-nm-thick black-diamond cathode operating at 700 K, today attainable with advanced diamond membrane technologies, and a solar-to-electric conversion efficiency of 14.5% for the resulting PETE converter

Boosting oxygen evolution reaction activity descriptors in LaNiO3 perovskite oxide via one-pot synthesis for alkaline electrolysis

Perovskite oxides have emerged as promising alternatives to platinum group metal based state-of-the-art electrocatalysts for alkaline oxygen evolution reaction, such as RuO2 and IrO2. Specifically, LaNiO3 is a highly efficient OER electrocatalyst in alkaline environment, nevertheless, its performance is strongly dependent on the preparation conditions. In this work, a one-pot solution combustion synthesis (SCS) is presented as a straightforward method to enhance LaNiO3 OER descriptors. The process involves a clever combination of low-cost, easily available fuels: glycine (Gly) and citric acid monohydrate (CAM). The SCS Gly +CAM LaNiO3 is compared to other LaNiO3 samples obtained via sustainable wet-chemistry techniques like coprecipitations and other SCS methods. Although all samples show comparable XRD patterns, the SCS Gly +CAM route ensures superior textural properties and optimal Ni3+/Ni2+ratio, resulting in higher intrinsic OER activity. SCS Gly +CAM displays the lowest overpotential, 377 ±7 mV, further decreasing to 350 ±5 mV after 25 cycles, due to surface reconstruction. Over 25 cycles, SCS Gly +CAM LaNiO3 outperformes the state-of-the-art RuO2. To the best of our knowledge, the Gly +CAM combination has not been explored for LaNiO3 and presents a cost-effective alternative for scaled-up production of high-performance OER electrocatalysts

Enhancing interpretability and automation in data-driven energy modelling: an analytical approach to change-point regression models

Reliable, automated Measurement & Verification (M&V) and portfolio-scale energy analytics need models that are both accurate and interpretable. Current practice often relies on change-point regressions whose balance points are found via grid search or optimisation. As an alternative, an analytical formulation for simplified and automated identification of three-parameter heating (3PH), three-parameter cooling (3PC), and five-parameter (5P) models as defined in ASHRAE Guideline 14:2023 is proposed in this paper, with the goal of preserving interpretability also in more sophisticated workflows at the state of the art, which can use this novel formulation at different temporal scales (monthly, daily, and hourly). Standardised test datasets (39 in total) for 3PH, 3PC, and 5P models' testing and the Inverse Modelling Toolkit (IMT) have been used, showing comparable results in the large majority of cases and minor discrepancies in the others. The total batch runtime has been markedly reduced compared to the original implementation. Moreover, datasets from prior studies have been employed to evaluate applicability in real-world scenarios, demonstrating analogous results in this instance as well. While the current formulation is tested with monthly and daily interval data, its incorporation in hourly and sub-hourly resolution modelling workflows can promote further research developments in the area of interpretable data-driven analytics towards the “digital twins” paradigm, where interpretability of machine learning techniques and physical interpretation of underlying parameters is relevant to deliver effective and trusted solutions. Open-source code and datasets are made available to encourage further research on robust, transparent, and scalable data-driven energy modelling methodologies based on M&V principles. In this regard, additional efforts may be pursued to expand the concepts presented for the analytical formulation's development to encompass various automated processes with different objective functions (e.g., lasso, elastic net regression, etc.), model formulations, and constraints (e.g., physics-based interpretation of slopes and change points)

Human–AI synergy: finding cognitive balance in idea generation for product innovation

This study examines how innovators and AI work together during idea generation for product innovation. It examines how varying levels of reliance on AI impact cognitive engagement and, in turn, influence the quantity, originality and feasibility of ideas as well as innovators’ overconfidence. The study highlights AI’s role as a cognitive amplifier, showing how human intuition and AI's analytical power interact to support creativity and innovation. A controlled experiment was conducted with 123 product innovators, testing three conditions: no AI, moderate AI assistance and high AI assistance, to measure cognitive engagement, number of ideas generated, originality, feasibility and innovator overconfidence. ANOVA, polynomial regression and mediation tests were performed to determine the effects of AI assistance on innovative idea generation. The results reveal an inverted U relationship between AI assistance, cognitive engagement and the generation of ideas for product innovation. Moderate AI assistance optimally enhances cognitive engagement, producing the highest number of original and feasible ideas. In contrast, excessive AI assistance may foster automation bias, reducing originality and increasing overconfidence. At the same time, the absence of AI constrains idea generation due to cognitive limitations in relying only on human abilities. The findings show that moderate AI use maximizes the quantity, originality and feasibility of ideas while minimizing overconfidence. Innovation managers should structure ideation sessions to cap AI interactions, promote critical evaluation of AI outputs and combine them with human insight. This balanced approach enables firms to optimize cognitive engagement and generate higher-quality product innovations. This research uniquely contributes to product innovation literature by explicitly focusing on human–AI synergy, highlighting AI’s optimal role as a cognitive enhancer rather than a substitute. It elucidates conditions that maximize innovative outcomes through balanced human–AI collaboration, providing actionable managerial guidelines for structuring AI integration to amplify creativity and mitigate biases in idea generation for product innovation