The ignition of a 200 A atmospheric-pressure argon arc in a setup where the electrodes are initially cold but are rapidly heated by the arc is simulated with the use of the extended model of non-equilibrium near-cathode high-pressure plasma layers. Two limiting cases are considered: one in which the supplied power increases very slowly and the ignition process proceeds through a sequence of quasi-stationary states, and the case of fast ignition, in which the external circuit is instantly switched on at its nominal operating parameters. Three discharge models are used: a stand-alone model of the cathodic part of the discharge; a model with the arc bulk simulated in the local thermodynamic equilibrium (LTE) approximation; and a model with the arc bulk simulated in the two-temperature (2T) approximation. Boundary conditions for the plasma temperature in the LTE model are similar to those for the electron temperature in the 2T model, which is different from the conventional LTE models. The modeling results describe three phases of the arc ignition: the abnormal glow discharge, an arc with a cathode spot, and an arc with a diffuse cathode attachment. The temperature inside plasma balls (bright small plasma regions adjacent to cathode spots) reaches values of the order of 10 eV. The distributions of the plasma temperature given by the LTE model are relatively close to the distributions of the electron temperature given by the 2T model, including at low currents. Results on the glow-to-arc transition, given by the LTE and 2T models, are rather close as well.
Nanoparticles with intrinsic anti-angiogenic activity hold strong promise in cancer nanomedicine, as they can both help suppress metastasis and deliver therapeutics, offering a dual strategy for addressing the disease. In this study, fucoidans from F. vesiculosus and U. pinnatifida with two different molecular weights (MW) of each were initially screened for in vitro anti-angiogenic potential using the tube formation assay with HUVEC cells. The higher MW fucoidans, which represent native MW fucoidan, exhibited greater anti-angiogenic activity and were subsequently combined with 2,2-bis(hydroxymethyl)propionic acid (bis-MPA)-based dendrimers (generation 2) at different fucoidan/dendrimer (F/D) mass ratios to form self-assembled nanoparticles through electrostatic interactions. Optimisation yielded two nanoparticle types with positive (F/D = 1 : 2) and negative (F/D = 2 : 1) zeta potentials, which were characterised for their physicochemical properties, including hydrodynamic diameter, zeta potential, chemical composition (FTIR), and morphology (TEM). These nanoparticles presented a near-spherical shape, were cytocompatible, and did not cause haemolysis. Positively charged nanoparticles showed stronger anti-angiogenic activity than negatively charged ones. Since the dendrimers alone were also anti-angiogenic, the overall effect likely results from the combined contribution of both components, with nanoparticle assembly potentially playing a role. Overall, these findings highlight the potential of fucoidan/dendrimer nanoparticles as multifunctional platforms for cancer nanomedicine, by targeting angiogenesis and potentially serving as drug or gene delivery systems.
PurposeThis study aims to model the causal architecture of new venture survival from an enabling-conditions perspective. Design/methodology/approachUsing cognitive mapping and Interpretive Structural Modeling (ISM), the authors structure causal relations among 43 prioritized survival factors from a multidisciplinary expert panel and validate the hierarchy via MICMAC. FindingsProduct–market fit drives survival, human and strategic capabilities mediate it and financial outcomes are downstream consequences, reframing financial fragility as a symptom rather than a root cause. Originality/valueThe resulting framework supports diagnosis and intervention prioritization based on causal dependencies.
Biological invasions, driven by the spread of non-native species, have become a critical global issue because of their far-reaching ecological and socioeconomic impacts. Effective communication of the risks of biological invasions is essential for implementing robust policy and legislation and gaining public support for conservation efforts. However, current policies often suffer from fragmentation and ineffectiveness, largely due to inadequate risk communication and complex multi-level governance. To address this challenge, we develop a global framework designed to enhance clearer communication about biological invasion risks. The framework contextualizes key terms across three domains in invasion science: species invasiveness, risk analysis, and decision support tools. Using both diffusion-of-English and ecology-of-language paradigms, and following a three-step process involving preliminary consensus, AI querying, and ground-truthing with final consensus, we validate the framework in 70 non-English languages which, together with English, have official status in at least one country and collectively cover all 195 countries worldwide. Our findings reveal that while terminology for risk analysis is well established, terminology for species invasiveness and, especially, for decision support tools remains underdeveloped in many languages, hindering effective communication and policy implementation. Our framework underscores the importance of cultural and political neutrality. By promoting clearer risk communication among scientists, policymakers, and the public globally, we aim to reduce policy fragmentation and foster enhanced collaboration in risk mitigation. We recommend expanding multilingual decision support tools to include the full risk analysis process: risk identification, risk assessment, and risk management. This will support intergovernmental mitigation efforts and promote a unified global response to biological invasions.
Selective revision operators are well-known non-prioritized revision operators that allow for the acceptance of only part of the new information while rejecting the rest. In this paper, we introduce their contraction counterparts, which we refer to as selective contraction operators. These operators enable the removal of only part of the input information. As such, selective contractions can be viewed as a method for removing some of the beliefs that support the input belief without eliminating the belief itself. In this sense, selective contraction operators allow for a weakening of the input belief’s strength without discarding it. We provide representation theorems for various classes of selective contraction operators.
