There is a growing consensus that one of the most common effects of climate change will be an increase in the frequency and severity of extreme weather events. In regions with distinct climatic regimes and complex morphological settings, notably in the Mediterranean basin, this trend underscores the urgency of implementing timely and effective strategies to mitigate geohazards induced by extreme rainfall. Although the literature on this topic has expanded in recent years, research on how clast shape, size, and weight affect transport distances, using direct field-based instruments and in situ observations, remains limited. This study focuses on a debris-flow event triggered by extreme rainfall in gully channels on Akziyaret Hill in Şanlıurfa province, southeastern Türkiye, in May 2012. The factors influencing the travel distance of 60 clasts, varying in size, weight, and shape, that were previously painted and placed in gully channels were analyzed. In addition, sediments accumulated in two traps placed in the channels were collected after each rainy season and subjected to laboratory sieve analysis to examine the storage characteristics of debris-flow-transported sediment. Multiple Linear Regression analysis showed that clast-shape characteristics were significant predictors of transport distance, whereas weight was not. When the grain-size distribution curves of the sediments captured in the traps were considered alongside the statistical analyses, both the percentile metrics and the trap locations showed marked differences in their central tendency and variability. Moreover, further research is required to elucidate how slope gradients and hydrodynamic conditions within gully channels contribute to the observed differences in grain-size distributions during deposition.
Companies trading in mineral resources must monitor supply chains rigorously, as minerals may originate from conflict-affected areas and present risks like human rights abuses, environmental damage, and corruption. Beyond regulatory pressures mandating ethical sourcing and transparency, consumers, NGOs, employees, and investors increasingly demand robust due diligence and detailed information on material origin. A central element of these practices is traceability, which enables companies to verify that their sourcing aligns with corporate standards and sustainability goals. Despite progress, traceability initiatives remain fragmented, often addressing isolated issues rather than forming a unified framework. The complexity of mineral supply chains, involving numerous actors and diverse requirements, further complicates the implementation of traceability. This study proposes an integrated conceptual framework for mineral supply chain traceability, developed through an extensive review of academic literature, technical and regulatory documents, and refined through expert validation. The framework organises traceability into enabling conditions and core dimensions across five interdependent categories, consisting of a total of twenty factors: Governance and compliance, Supply chain management, Social and environmental impacts, Technology and analytics, and Performance and evaluation. It is emphasised that effective traceability is a systemic task that requires solid institutional structures, coordinated operational practices, meaningful social and environmental commitments, appropriate technological tools, and continuous evaluation mechanisms. Technologies such as blockchain or digital product passports only prove their effectiveness when integrated into favourable conditions. The proposed framework provides policymakers, industry actors and certification bodies with a coherent structure for designing, assessing and strengthening traceability across diverse mineral supply chains.
Table Extraction (TE) consists in extracting tables from PDF documents, in a structured format enabling automatic processing. While numerous TE tools exist, the variety of methods and techniques makes it difficult for users to choose the most appropriate one. We propose a novel benchmark for assessing end-to-end TE methods (from PDF to the final table) over 86k pages. We contribute an analysis of TE evaluation metrics, and a novel, rigorous evaluation process, which allows scoring each TE sub-task as well as end-to-end TE, and captures model uncertainty. Along with prior datasets, our benchmark comprises two new heterogeneous datasets of 39k samples. We run our benchmark on diverse models, including off-the-shelf libraries, tools, computer vision-based models and modern approaches using general and specialized vision language models. The results demonstrate that TE remains challenging: current methods suffer from a lack of generalizability when facing heterogeneous data, and from limitations in robustness and interpretability.
The global prevalence of organic pollutants presents a significant environmental challenge, necessitating sustainable remediation strategies. In situ biodegradation emerges as a cost-effective and eco-friendly solution. However, the real-time monitoring of in situ bacterial activities, particularly biodegradation processes, remains a challenge due to the limitations of traditional intrusive methods, including issues of representativeness, reproducibility and high-associated costs. Spectral induced polarization (SIP) has shown sensitivity to surface changes in subsurface environments, especially for biogeochemical reactivity monitoring including those associated with biodegradation. Despite this potential, advances have to be made to quantitatively link SIP parameters to in situ biodegradation processes. This study addresses this gap by conducting controlled biogeophysical experiments on a sand-packed column undergoing biodegradation facilitated by Rhodococcus wratislaviensis IFP 2006. SIP measurements were paired with bacterial growth kinetics to develop a quantitative model estimating bacterial growth. The results demonstrate that SIP, coupled with routine laboratory measurements, can effectively and quantitatively assess bacterial growth and the biodegradation of organic pollutants. These findings highlight the potential of SIP as a non-intrusive and reliable method for monitoring biodegradation in contaminated subsurface environments.
