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SWOT Analysis of Metaverse Integration in Engineering Education: Case Study – Faculty of Engineering and Technologies at Trakia University, Bulgaria
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Positional Overload: Positional Debiasing and Context Window Extension for Large Language Models Using Set Encoding
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A systematic exploration of current limitations of cognate-based phylogenetic inference
No full textBackground
Computational tools for phylogenetic inference are now routinely applied to data from historical linguistics, especially cognate data.
Methods
We initially provide an overview of the cognate datasets that are publicly available at present and compare the amount of cognate data with the available masses of molecular data. Then, we outline the drawbacks of the standard binary cognate data representation and introduce an alternative representation that alleviates some of these disadvantages. We also introduce dedicated, parameter-rich evolutionary models for this novel representation. We implement the model and investigate its behavior. In addition, we conduct an orthogonal experiment to investigate whether machine learning-based approaches can be used for cognate data.
Results
Our experiments show that our newly introduced models can currently not be applied, as they exhibit clear indications for overparameterization due to the small size of the available cognate datasets. We demonstrate that, for the same reason, the applicability of emerging machine learning-based approaches to cognate data is highly limited.
Conclusion
We conclude that it is necessary to collect more data, investigate potential data sources, and also consider alternative types of data. Historical linguistics will be able to benefit from recent advances in phylogenetics if the amount of available datasets can be substantially increased, both, in terms of number of datasets, and dataset sizes
Environmental, economic, and social trade-offs in biochar and biosurfactant-based soil remediation: A critical review based on mass flow analysis
No full textBiochar and biosurfactants are emerging bio-based materials for remediating contaminated soils. While their pollutant removal mechanisms are well studied, broader environmental, economic, and social implications remain underexplored. Existing studies often rely on a 1 kg functional unit, limiting direct comparisons. This critical review evaluates and quantifies the multidimensional sustainability trade-offs of using biochar and biosurfactants to remediate one hectare of contaminated land, based on real-world applications. Use of common functional unit (1 hectare of land treatment) enables direct, meaningful comparison.
Material flow analysis reveals biochar’s superior energy efficiency (net output for the grid ∼290 GJ/ha) and economic returns, despite higher production emissions (100 kg CH, 55 kg NO, 38 kg PM, and 1.7 kg PAHs/ha). In contrast, biosurfactants emit negligible direct pollutants but demand significantly more energy (2340 GJ/ha). Both materials offer social benefits, such as enhanced rural livelihoods and health, yet face challenges like land use conflicts and patent barriers. Policy measures are proposed to mitigate these issues. Finally, the synergistic use of biochar and biosurfactants is highlighted as a promising avenue for future research in sustainable soil remediation