Peruvian University of Applied Sciences

Calcium carbonate derived from scallop shells (SCC) represents a sustainable mineral filler for asphalt applications, offering environmental benefits through the valorisation of marine by-products generated by aquaculture and seafood-processing activities in Peru and other coastal countries. Hydrated lime (HL) is widely used to enhance adhesion and moisture resistance in asphalt mixtures; however, in Peru its acquisition and commercialisation are subject to administrative controls due to its regulated status. In this study, SCC was obtained through a physical processing route and evaluated against commercially available HL at the asphalt mastic (binder–filler) scale. Both fillers were characterised using Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), X-ray fluorescence (XRF), and X-ray diffraction (XRD). FTIR and TGA confirmed the chemical stability and thermal resistance of SCC under typical asphalt production temperatures, while SEM revealed compact granular particles favourable for binder–filler interaction within the asphalt mastic. The higher alkalinity of HL was associated with stronger chemical affinity, favouring adhesion and moisture resistance, whereas SCC exhibited high purity and a stable calcite structure, providing a physically compatible and thermally stable filler phase. Rheological characterisation of asphalt mastics using multiple stress creep recovery (MSCR) and Linear Amplitude Sweep (LAS) tests showed that SCC produced lower non-recoverable compliance (Jnr) and higher elastic recovery (R), indicating improved resistance to permanent deformation, while HL exhibited superior fatigue performance under cyclic loading. Overall, this study establishes a binder- and mastic-level predictive basis for assessing the mechanical behaviour of SCC-filled systems within a circular economy framework.

[This corrects the article DOI: 10.7759/cureus.88238.].

Peru is a developing country that has significantly improved the average of almost all health indicators. Specifically, in the past four decades, child mortality decreased tenfold. However, the same is not necessarily true of equality, which remains a challenge. Using microdata from Peru's population censuses in 1981, 1993, 2007, and 2017, we estimate the inequality in child mortality across different social groups. We estimate differences between ethnic groups, education levels, wealth quintiles, regions, and urban-rural groups and find that although inequality has decreased, it remains significantly high. The data show that inequality in child mortality increased between 1981 and 1993, declined between 1993 and 2007, and then increased between 2007 and 2017. Differences in education are the most crucial factor, associated with 45 % of the inequality in 1981 and 58 % in 2017. Differences between Lima and rural areas account for 27 % to 30 % of the inequality, while ethnicity contributes only 6 % in 1981 and 10 % in 2017.

In recent years, large-scale language models (LLMs) have emerged as innovative solutions in various areas of software development. The creation of automated acceptance tests traditionally requires specialized technical knowledge and the involvement of multiple roles, resulting in waiting times and a high coding effort. This study presents an integrated system based on GPT-4 and computer vision for the generation and execution of ‘acceptance tests’ from natural language instructions. The system has two AI agents: the first generates tests in Gherkin format from textual descriptions, and the second executes the tests, interacting with the browser using GPT-4 and computer vision. The results demonstrate that the system can generate and execute consistent, functional, and reliable tests, helping to accelerate and democratize the test automation process.

Athlete burnout has traditionally been examined as a phenomenon mainly associated with high-performance sport; however, growing evidence suggests that it may also affect amateur athletes. This study aimed to analyze burnout in high-performance and amateur athletes, integrating gender differences and a person-centered approach through cluster analysis. A cross-sectional design was applied to 511 athletes (38.0% high-performance and 62.0% amateur) using the Revised Athlete Burnout Inventory (IBD-R). Descriptive and inferential analyses examined differences by competitive level and gender, while hierarchical and k-means cluster analyses identified burnout profiles. Results showed similar levels of emotional exhaustion and depersonalization across competitive levels, with specific differences in reduced personal accomplishment. Four burnout profiles were identified: Healthy, Reduced Accomplishment, Emotional Exhaustion, and Depersonalization-dominant burnout, differentially distributed by gender and competitive level. These findings indicate that burnout is a transversal phenomenon in sport and highlight the value of person-centered approaches for prevention and intervention across competitive contexts.