Hydrolysis lignins (HL) produced from cellulosic ethanol biorefineries can be transformed into high-value activated carbons (ACs) for capturing CO2, offering more lucrative options compared to using them as combustion fuels. Maximizing gravimetric yield and CO2 adsorption performance of ACs made from lignin through chemical activation requires a thorough understanding and control of lignin's reactivity under activation conditions. This study introduces a methodological framework that merges analytical thermobalance reactivity assessments with experimental design principles to simultaneously optimize AC yield and CO2 adsorption during the chemical activation of hydrolysis lignin with K2CO3 in a batch, gram-scale furnace. Using this structured strategy revealed ways to minimize the activator amount and temperature needed, while still achieving high gravimetric yields and high CO2 uptake. Furthermore, building on a solid-state kinetic model describing the chemical activation of polyphenols with K2CO3, we propose a new chemical activation severity factor (SCA) that combines temperature, K2CO3-to-lignin ratio, and reaction time into a single metric that correlates with the CO2 adsorption performance of ACs derived from hydrolysis lignin. The results demonstrate that reactivity tests and chemical activation models from analytical thermobalance experiments can guide the design of lignin-based activated carbons in a more systematic, engineering-driven manner rather than relying on trial-and-error approaches.
This study examines whether patterns of divergence between large language model (LLM) firm classifications and the Statistical Classification of Economic Activities in the European Community (NACE) are more consistent with between-model inconsistency, information-availability effects, or taxonomic ambiguity. Using a registry dataset of 43,931 Finnish firms, of which 29,565 received at least one Experience Industry classification, we compare GPT-3.5, GPT-4, Gemini 1.5 Flash, and Llama 3 70B. LLMs were prompted using company name and identifier only, and the resulting keywords were mapped to sector profiles, enabling comparison between model outputs and formal taxonomic assignments without external descriptive text. We evaluate agreement with Krippendorff’s α, cosine similarity, and normalized HHI-based concentration measures, and examine variation by firm revenue, size, and sector-level keyword reliability. Using raw LLM-derived profiles, inter-LLM similarity is high (cosine 0.89–0.95), whereas LLM–NACE alignment is substantially lower (0.19–0.24). Confidence-level adjustment increases LLM–NACE similarity, as expected, because adjusted profiles partly incorporate NACE information. Agreement increases in revenue-weighted and larger-firm analyses, but declines in sectors with low keyword reliability and diffuse boundaries. Across all weighting schemes and size classes, inter-LLM similarity exceeds LLM–NACE similarity. The findings suggest that LLM–NACE divergence is more consistent with information-availability effects and taxonomic ambiguity than with large between-model inconsistency. The study contributes a multi-metric framework for evaluating classification under unstable category systems and shows how LLM-based profiling can complement formal industry coding in hybrid sectors.
The use of unmanned aerial vehicles (UAVs) for a variety of commercial, civilian, and defense applications has increased many folds in recent years.While UAVs are expected to transform future air operations, there are instances where they can be used for malicious purposes.In this context, the detection, classification, and tracking (DCT) of UAVs (DCT-U) for safety and surveillance of national air space is a challenging task when compared to DCT of manned aerial vehicles.In this survey, we discuss the threats and challenges from malicious UAVs and we subsequently study three radio frequency (RF)-based systems for DCT-U.These RF-based systems include radars, communication systems, and RF analyzers.Radar systems are further divided into conventional and modern radar systems, while communication systems can be used for joint communications and sensing (JC&S) in active mode and act as a source of illumination to passive radars for DCT-U.The limitations of the three RF-based systems are also provided.The survey briefly discusses non-RF systems for DCT-U and their limitations.Future directions based on the lessons learned are provided at the end of the survey.
At present, polymer barrier-coated packaging board waste is processed with other paperboard waste, where the cellulose fiber fraction is separated through a re-pulping process and recycled, while the residual plastic-rich fraction containing aluminum is combusted for energy production. This paper reports on experimental research on gasification of aluminum-containing plastic reject for the generation of synthesis gas, which can subsequently be converted to methanol and then olefins, used as feedstock in polymer manufacturing. Gasification experiments were performed using a bench-scale bubbling fluidized bed gasifier equipped with a hot filter and catalytic reformer. The temperature for gasification was 650–670 °C to avoid aluminum melting in the gasifier. Sand was used as the initial bed material. Aluminum was separated in the filter unit, and the tar and hydrocarbon-containing gas that passed through the filter was sent to the catalytic reformer. The gasifier operated efficiently and achieved over 99
Despite growing interest in monitoring cognitive states, current studies inadequately address individual differences in physiological reactions. Whereas prior works require extensive data from each individual to personalize the model, the current article explores personalization approaches operating with minimal baseline data. We propose three novel methods to personalize the model with only baseline data available for personalization. Further, we systematically compare those to an existing baseline calibration method, a non-personalized model, and a model using all available data for personalization. We conduct experiments with four open datasets with a total of 170 participants, classifying the cognitive states with a prevalent feature-based approach and a recent large time-series foundation model, MOMENT. The experiments target stress and cognitive load detection in realistic classification tasks, which require models to adapt to a new person. The best classification scores after personalizing with minimal data were around 0.7−0.9 and 0.7 balanced accuracy in binary and three-class tasks, respectively. Two of the proposed personalization methods outperformed the non-personalized model in most cases with the feature-based approach, especially in classification tasks with more than two classes, although their performance remained lower than that of the model using all data for personalization. MOMENT showed little benefit from personalization and performed comparably to the feature-based approach even with a non-personalized model. The findings provide a critical overview of the generalizability and necessity of model personalization with little data, and valuable insights into the development of personalized cognition-aware applications.
