A feasible feedstock for waste-to-energy applications is refuse-derived fuel (RDF), a diverse blend composed of plastic and waste from biomass. Nevertheless, there is still a lack of quantification regarding the combined impacts of short vapor residence durations and feedstock composition in fluidized-bed decompositions. In order to close this gap, this study compares two compositionally different RDFs in a standard laboratory-sized fluidized-bed reactor under fast thermal pyrolysis conditions (550-600 degrees C, 2-5 s). At 550 degrees C, plastic-rich RDF 1 yielded a maximum liquid yield of 58 wt%, which was made up of hydrocarbon-rich oil with a high thermal value (HHV(dry) = 37.8 MJ kg(-1)) and a significant aromatic content. On the other hand, biomass-rich RDF 2 produced 52 wt%, liquid at 570 degrees C, with somewhat lower HHVs (35.9-39.2 MJ kg(-1)) and a higher oxygen and water concentration. Detailed characterization of the feedstock, resulting bio-oils, gases, and chars was conducted using TGA, FT-IR, NMR, GC-MS, SEM, and XRD techniques to elucidate the influence of temperature, residence time, and feedstock composition on product yield and quality. For biomass-rich RDF 2, raising the temperature from 570 degrees C to 600 degrees C decreased liquid yield while boosting olefinic entities, gaseous hydrocarbons, and suggesting increased secondary fracturing processes. Hydrocarbon, phenolic substances, and oxygenate distributions are highly dependent on RDF composition, according to product composition studies, with biomass-rich RDF tends to produce more oxygenated compounds while plastic-rich RDF tends to produce more aromatic compounds. These findings show that while minor temperature spikes (30 degrees C) can reroute products from liquids to gases, feedstock composition can shift liquid yield by up to similar to 6 wt% and considerably impact fuel performance. In order to optimize RDF valorization in fluidized-bed systems, this work offers quantitative data that links RDF composition and fast pyrolysis parameters to product distribution.
This work investigates the effect of electroadhesion on the smooth soft contact mechanics between a silicone rubber ball and a glass slide, both functionalized to be electroactive and biocompatible. In particular, the influence of electroadhesion on the pull-off force, real contact area, and friction force is experimentally studied using a lab-made tribometer both in dry and wet contact conditions. The role of the applied voltage frequency, interface dielectric properties and crack viscoelasticity is also theoretically disclosed. The results show that the interface electric potential can be designed to precisely control the contact mechanics, including friction and adhesion, thanks to the additional Maxwell stresses applied to the contact, either in dry and lubricated conditions. This is of particular interest for tribotronics, soft robotics and biointerface technologies.
We collect several results related to the Symmetrized Fractional Variation model for signal and image denoising (shortly denoted SFV): a variational approach based on L1 fitting data term together with regularizing terms exploiting a distributional version of Riemann-Liouville fractional derivatives. We enhance the analysis of the one-dimensional case through the study of the space BV*s of admissible signals on a bounded interval, say the functions with bounded variation of both sides fractional derivatives for a prescribed real positive order s. We show that the embedding in BV*s of the Sobolev space of the same fractional order is strict. We exhibit some nontrivial borderline examples of admissible or non admissible functions in the space BV*s. We prove several relationships between related fractional calculus and the integral transforms. The SFV model is discretized based on a second-order consistent Grunwald Letnikov scheme and coupled with an automatic selection procedure of all model parameters relying on the whiteness principle: some numerical simulations are presented to show the efficacy of the proposed approach in denoising one-dimensional signals corrupted by impulsive noise modelled by the Laplace distribution.
This systematic review synthesizes more than 5 decades of research (1970–2025) on information sources and persuasion determinants among elderly consumers. It accounts for a structural discontinuity in the informational and persuasive environment of older consumers, driven by three main disruptive forces—technological advancements, societal fragmentation, and global instability—which enable identifying a pre-digital baseline (1970–2010) against which post-2010 transformations can be assessed and used as a benchmark for future research. Using PRISMA guidelines, this review identified, in marketing and consumer literature, three primary information channels (mass media, domestic relationships, and social interactions) and three key persuasion determinants (perceived image, risk perception, and sense of control), all influencing seniors’ beliefs, attitudes, and behaviors. Findings showed that, before the digital revolution, older consumers’ persuasion ecosystem was grounded in stability, familiarity, and interpersonal trust; whereas post-2010 disruptive forces transformed this architecture into dependency, fragmentation, and algorithmic mediation. Theoretically, innovation diffusion and self-regulation approaches explain how persuasion among older adults shifted after the digital revolution—from television centrality, restricted sources, and habitual patronage to technology-mediated manipulation, tribal capture, and crisis-driven compliance. Managerially, understanding this historical baseline and its contemporary evolution offers a foundation for designing more inclusive and ethical persuasive strategies in an era in which communication should bridge rather than divide generations.
TikTok's algorithm-driven feed is reshaping electoral communication, yet a clear understanding of its effects is lacking. This study synthesizes and appraises evidence on how the platform's design and governance shape political (dis)information and may affect electoral dynamics. Based on a systematic review of 140 studies published between 2021 and 2025, the paper identifies several recurring patterns. The literature suggests that the platform's algorithms tend to amplify sensational content and may increase youth exposure to political messaging. National security concerns over data sovereignty have prompted platform bans, and opaque "visibility moderation" policies may hinder efforts to correct misinformation. Current research is often limited by small samples and restricted data access, which affects the generalizability of findings. We conclude that TikTok functions as a fast-moving marketplace for political ideas in which algorithmic incentives may shape conditions relevant to electoral integrity. Therefore, transparent data access and continued cross-disciplinary research are important for addressing these challenges.
