Training and evaluating machine learning algorithms for ocean microplastics classification through vibrational spectroscopy

Microplastics are contaminants of emerging concern - not only environmental, but also to human health. Characterizing them is of fundamental importance to evaluate their potential impacts and target specific actions aiming to reduce potential harming effects. This study extends the exploration of machine learning classification algorithms applied to FTIR spectra of microplastics collected at sea. A comparison of successful classification models was made in order to evaluate prediction performance for 13 classes of polymers. A rigorous methodology was applied using a pipeline scheme to avoid bias in the training and selection phases. The application of an oversampling technique also contributed by compensating unbalanceness in the dataset. The log-loss was used as the minimization function target and to assess performance. In our analysis, Support Vector Machine Classifier provides a good relationship between simplicity and performance, for a fast and useful automatic characterization of microplastics

Humanity's Last Exam

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai

Intersections between materials science and marine plastics to address environmental degradation drivers: a machine learning approach

Plastics are an integral part of the material structure in modern societies. However, their widespread contamination in the environment raises concerns regarding the sustainable use of these materials. Plastic pollution research has accelerated rapidly in the past 20 years and developed as a broad and multidisciplinary field, due to its complex nature. Research on the fate of plastics in the environment, specifically their dispersal and degradation, is one of the pillars in the field. In this study, we used machine learning and text mining tools to bridge the marine plastics community to the existing knowledge from polymer science regarding degradation drivers. Topic modelling enabled visualising hot topic trends in marine plastics research and filtering for relevant publications with minimal expert intervention. The recurrence of drivers was verified in the literature, indicating particular areas of focus on the marine degradation of plastics. The results show weathering conditions to be more recurrent than polymer properties and the latter to be rarely discussed in depth. Also, biodegradation is found to be a hot topic in the field, while degradation caused by abiotic factors is less addressed. This may be necessary as polymer engineering is traditionally less concerned about the end-of-life of plastic products. Finally, we argue that not only a deep understanding of plastics from polymer science should aid fundamental degradation studies by the marine plastics community, but also that the latter has the opportunity to largely contribute to the former by filling the gaps it has left

The Profiles of Mass and Heat Transfer during Pinewood Conversion

AbstractPulverised biomass is an alternative to fossil fuels recognized by both academic and industrial communities as it can effectively generate heat and electricity by different conversion processes in a carbon neutral manner. Among the forest biomass options, pine already represents 20% of planted forests as it has numerous industrial applications. This study concentrates on the conversion of pulverized particles of pinewood under fast and slow heating regimes in both air and nitrogen atmospheres. The results reveal that (i) at low heating rates the morphology of the pinewood particles (250 to 75μm) remains practically with the same aspect during all conversion (ash ∼ 2wt.%), while at high heating rates the network structure of the pinewood changes into a more spherical shape; (ii) during conversion, the temperature profiles show a single semi-plateau around 350°C for pyrolysis and two plateaux for direct combustion around 300 and 470°C, close to the maximum reaction rate and half-way of the conversion degree for the main reactions; and (iii) combustion reactivity of pinewood chars at 425°C augmented from 35min to 9min, as morphology becomes more uniform and size reduces