This work is inspired by the method proposed in [1], where planar G2 interpolants based on seventh-degree PH biarcs were constructed to interpolate prescribed points, tangent directions, and curvature values while enforcing a desired arc length. Our aim is to investigate the possibility of achieving analogous results using quintic PH curves. The construction of the quintic biarc model with prescribed arc length is presented, along with the derivation of the associated equations and the strategies used to reduce the degrees of freedom. Numerical experiments are reported to show the validity, limitations, and general applicability of the proposed quintic approach.
Type 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by the destruction of pancreatic β-cells, leading to chronic hyperglycemia and lifelong dependence on exogenous insulin. Increasing evidence indicates that T1DM is a highly heterogeneous condition driven by complex interactions among genetic, immune, environmental, and metabolic factors, which collectively influence disease prediction, diagnosis, prevention, and treatment. To date, no curative or durable remission-inducing therapy for T1DM is available. Current therapeutic approaches, acknowledging both the central role of the immune system and the emerging contribution of β-cell stress to disease pathogenesis, include targeted immunotherapies, cytokine inhibitors, tolerance-inducing strategies, and β-cell–protective agents. Despite promising advances, highlighted by the approval of teplizumab, achieving the ultimate therapeutic goal in T1DM, namely preventing or halting disease progression, will require closing critical gaps in our understanding of disease etiopathogenesis and heterogeneity to enable individualized, stage-specific interventions. Consequently, the development, implementation, and clinical validation of novel, easily accessible, and measurable biomarkers, capable of stratifying individuals with T1DM and predicting therapeutic responses, represent a high priority. In this context, circulating microRNAs emerge as an attractive class of candidate biomarkers. In parallel, it is essential to integrate autoantibody screening into routine clinical care through structured, population-based programs.
Aim Pinus sylvestris is the most widely distributed Pinus species in the world, highlighting its ecological, economic and socio-cultural importance. The Iberian Peninsula marks its south-western distribution limit, whose extent has been significantly reduced since the Mid-Holocene. In this study, we investigated the current diversity of native Pinus sylvestris forests in the Iberian Peninsula and their postglacial distribution and history.Location Iberian Peninsula, south-western Europe.Taxon Pinus sylvestris L.Methods We compiled 1299 vegetation plots from native Pinus sylvestris forests and performed a numerical classification, modified TWINSPAN, to identify major forest types. We characterized their floristic composition, diversity and environmental drivers. Ecosystem Distribution Models were fitted using climatic and edaphic variables to estimate their potential distributions during the Last Glacial Maximum (21 ka BP), Mid-Holocene (6 ka BP) and present. Model outputs were validated with palaeobotanical records.Results We identified four different forest types: acidophilous oromediterranean, acidophilous temperate, basophilous, and thermophilous mixed forests. These forests host unique assemblages of endemic, relict and broadly distributed plant species. Ecosystem Distribution Models revealed that, among the three studied periods, present climatic conditions are the most suitable for the development of Pinus sylvestris forests. Yet, their present-day distribution is considerably more restricted than predicted, a mismatch that agrees with palaeobotanical records.Main Conclusions Native Pinus sylvestris forests in the Iberian Peninsula display a wide ecological range. Their current distribution is more restricted than expected by suitable climatic conditions, suggesting the key role of anthropogenic historical pressures. Conservation strategies should not only consider future climate scenarios but also integrate historical land-use legacies.
In this work, we report the design and synthesis of two new organic D-A-π-A dyes endowed with a common benzothiadiazole-dithienosilole (BTD-DTS) central core, and their evaluation as anodic sensitizers in dye-sensitized photoelectrochemical cells (DS-PEC) aimed at molecular hydrogen generation. Both dyes possess a cyanoacrylic acid as acceptor/anchoring group, but present two distinct donor groups bearing substituents of different hydrophilicity. Preliminary density functional theory (DFT) computational investigations indicated that the dyes presented the correct electronic structure and energy levels alignment for their desired application in devices. The compounds were then prepared by means of a concise synthetic sequence featuring a microwave-assisted Stille-Migita cross-coupling as the key step. Following their full spectroscopic and electrochemical characterization, the dyes were then employed to sensitize the nanocrystalline TiO2- or SnO2-based photoanodes of three-electrode DS-PECs, and the corresponding performances in terms of photocurrent production and hydrogen generation, as well as electrode stability, were assessed under several different conditions.
The development of emerging General-Purpose Technologies (GPTs) is fraught with uncertainty, particularly in identifying promising knowledge recombinations and application areas. Technological progress can stall when firms, universities, and independent inventors pursue “dead ends” in their search strategies, disrupting the trajectory of follow-up innovations. While prior research has largely examined GPT evolution at a macro level, this study investigates how organizational search strategies influence these trajectories. Focusing on Wearable Haptics Technology (WHT) as an emerging GPT, we analyze 1,261 patent-applicant pairs to explore the impact of knowledge recombination strategies. Our findings reveal that patents exploring entirely new technological domains are less likely to catalyze follow-up inventions. In contrast, patents that incorporate novel knowledge within the existing WHT ecosystem are more likely to drive subsequent innovation. A supplementary analysis further shows that university involvement is more beneficial in explorative and domain-pushing projects than in exploitative ones. Our findings contribute to research on entrepreneurial ecosystems by clarifying how different actors and search strategies shape knowledge dynamics in early-stage GPTs.
