Abstract
Hyperparameter optimization (HPO) is critical for enhancing the predictive performance of machine learning models in credit risk assessment for peer-to-peer (P2P) lending. This study evaluates four HPO methods, Grid Search, Random Search, Hyperopt, and Optuna, across four models, Logistic Regression, Random Forest, XGBoost, and LightGBM, using three real-world datasets (Lending Club, Australia, Taiwan). We assess predictive accuracy (AUC, Sensitivity, Specificity, G-Mean), computational efficiency, robustness, and interpretability. LightGBM achieves the highest AUC (e.g., 70.77% on Lending Club, 93.25% on Australia, 77.85% on Taiwan), with XGBoost performing comparably. Bayesian methods (Hyperopt, Optuna) match or approach Grid Search's accuracy while reducing runtime by up to 75.7-fold (e.g., 3.19 vs. 241.47 min for LightGBM on Lending Club). A sensitivity analysis confirms robust hyperparameter configurations, with AUC variations typically below 0.4% under +/- 10% perturbations. A feature importance analysis, using gain and SHAP metrics, identifies debt-to-income ratio and employment title as key default predictors, with stable rankings (Spearman correlation > 0.95, p<0.01) across tuning methods, enhancing model interpretability. Operational impact depends on data quality, scalable infrastructure, fairness audits for features like employment title, and stakeholder collaboration to ensure compliance with regulations like the EU AI Act and U.S. Equal Credit Opportunity Act. These findings advocate Bayesian HPO and ensemble models in P2P lending, offering scalable, transparent, and fair solutions for default prediction, with future research suggested to explore advanced resampling, cost-sensitive metrics, and feature interactions.

Abstract
This paper presents a semantic characterization of leakage-freeness through timing side-channels for Jasmin programs. Our characterization covers probabilistic Jasmin programs that are not constant-time. In addition, we provide a characterization in terms of probabilistic relational Hoare logic and prove the equivalence between both definitions. We also prove that our new characterizations are compositional and relate our new definitions to existing ones from prior work, which could only be applied to deterministic programs. To provide practical evidence, we use the Jasmin framework to develop a rejection sampling algorithm and provide an EasyCrypt proof that ensures the algorithm's implementation is leakage-free while not being constant-time.

Abstract

Abstract
Active and abandoned mining sites are significant sources of heavy metals and metalloid pollution, leading to serious environmental issues. This study assessed the environmental risks posed by potentially toxic elements (PTEs), specifically arsenic (As) and antimony (Sb), in the Technosols (mining residues) of the former Pej & atilde;o coal mine complex in Northern Portugal, a site impacted by forest wildfires in October 2017 that triggered underground combustion within the waste heaps. Our methodology involved determining the pseudo-total concentrations of As and Sb in the collected heap samples using microwave digestion with aqua regia (ISO 12914), followed by analysis using hydride generation-atomic absorption spectroscopy (HG-AAS). The concentrations of As an Sb ranging from 31.0 to 68.6 mg kg-1 and 4.8 to 8.3 mg kg-1, respectively, were found to be above the European background values reported in project FOREGS (11.6 mg kg-1 for As and 1.04 mg kg-1 for Sb) and Portuguese Environment Agency (APA) reference values for agricultural soils (11 mg kg-1 for As and 7.5 mg kg-1 for Sb), indicating significant enrichment of these PTEs. Based on average Igeo values, As contamination overall was classified as unpolluted to moderately polluted while Sb contamination was classified as moderately polluted in the waste pile samples and unpolluted to moderately polluted in the downhill soil samples. However, total PTE content alone is insufficient for a comprehensive environmental risk assessment. Therefore, further studies on As and Sb fractionation and speciation were conducted using the Shiowatana sequential extraction procedure (SEP). The results showed that As and Sb levels in the more mobile fractions were not significant. This suggests that the enrichment in the burned (BCW) and unburned (UCW) coal waste areas of the mine is likely due to the stockpiling of lithic fragments, primarily coals hosting arsenian pyrites and stibnite which largely traps these elements within its crystalline structure. The observed enrichment in downhill soils (DS) is attributed to mechanical weathering, rock fragment erosion, and transport processes. Given the strong association of these elements with solid phases, the risk of leaching into surface waters and aquifers is considered low. This work underscores the importance of a holistic approach to environmental risk assessment at former mining sites, contributing to the development of sustainable remediation strategies for long-term environmental protection.

Abstract
Abstract This paper investigates the ability of large language models (LLMs) to evaluate semantic relations between word pairs by examining their alignment with human-generated semantic ratings. Semantic relations represent the degree of connection (e.g., relatedness or similarity) between linguistic elements and are traditionally validated against human-annotated datasets. Due to the challenges of building such datasets and recent progress in LLMs’ capacity to model human-like understanding, we explore whether LLMs can serve as reliable substitutes for traditional human ratings. We conducted experiments using multiple LLMs from OpenAI, Google, Mistral, and Anthropic, evaluating their performance across diverse English and Portuguese semantic relations datasets. We included in the analysis PAP900, a recently published dataset of semantic relations in Portuguese, to examine the influence of prior exposure to the dataset on LLM training. The results show that the LLM predictions correlate strongly with human ratings. The findings reveal the potential of LLMs to supplement or replace traditional semantic measure algorithms and crowd-sourced human annotations in semantic tasks.

Abstract
Photovoltaic (PV) panels serve as a standard solution for the collection of solar energy. The flat photovoltaic solar plate design has been the most adopted by the market for its ease of installation. However, this design faces limitations due to geometric constraints and the sun's trajectory through the day. Inspiration was drawn from nature to overcome these limitations by utilizing the tridimensional hexagonal shape observed in honeycomb structures. The used approach aimed to explore a novel design that can reduce the constraints of flat PV panels while maximizing energy output. The unique 3D arrangement of the hexagonal pyramid enables the installation of mirrors inside to ease the reflection of photons and to increase energy production compared to flat panels. Furthermore, this design presents an opportunity to incorporate a water capture and heating system, thereby increasing the system's overall usage.