This study evaluates radon exhalation rates and assesses the potential radiological risks of external exposure to primordial radionuclides in building materials employed in the Ica region of Peru, particularly those with high uranium content. The radon exhalation rates are currently measured using a combination of a closed chamber and an active monitor. We proposed a novel method that effectively ensured a hermetic seal for the closed chamber and guaranteed that the efficient maintenance of secular equilibrium. The obtained results ranged from below the detection limit (BDL) to a maximum of 52.3 mBq · kg⁻¹h⁻¹. Gamma spectrometry was employed to measure the concentrations of radionuclides by utilizing a 3′ × 3′ NaI detector. The analysis of cement samples revealed a strong positive correlation between the activity concentration of radium and the radon exhalation rate. The activity concentrations for radionuclides varied, with values ranging from BDL to 60.6 mBq · kg⁻¹h⁻¹ for ²²⁶Ra, BDL to 22.3 mBq · kg⁻¹h⁻¹ for ²³²Th, and BDL to 1074 mBq · kg⁻¹h⁻¹ for ⁴⁰K. These findings contribute valuable insight to decision-making processes in the Peruvian construction industry, particularly regarding material safety and radiological risk management.

We consider a scenario inspired by natural supersymmetry, where neutrino data is explained within a low-scale seesaw scenario. We extend the Minimal Supersymmetric Standard Model by adding light right-handed neutrinos and their superpartners, the R-sneutrinos, and consider the lightest neutralinos to be higgsino-like. We consider the possibilities of having either an R-sneutrino or a higgsino as lightest supersymmetric particle. Assuming that squarks and gauginos are heavy, we systematically evaluate the bounds on slepton masses due to existing LHC data.

Background: The increasing complexity of global supply chains has intensified the demand for transparency, traceability, security, and sustainability in logistics and operations. Blockchain technology enables decentralized, immutable frameworks that improve data integrity, automate transactions via smart contracts, and integrate seamlessly with the IoT and AI. Methods: This bibliometric review analyzes 559 peer-reviewed publications retrieved from Scopus and Web of Science using a PRISMA-guided protocol. Data were processed with Bibliometrix and Biblioshiny to examine scientific production, contributing institutions, author countries, collaboration patterns, thematic clusters, and keyword evolution. Results: The analysis reveals a 400% increase in publications after 2020, with China, India, and the USA leading in output but with limited international collaboration. Keyword co-occurrence and thematic mapping reveal dominant topics, including smart contracts, food supply chain traceability, and sustainability, as well as emerging themes such as decentralization, privacy, and the circular economy. Conclusions: The field is marked by interdisciplinary growth, yet it remains thematically and geographically fragmented. This review maps the intellectual structure of blockchain-enabled sustainable supply chains, offering insights for policymakers, developers, and industry leaders and outlining future research avenues centered on global cooperation, platform efficiency, and ethical and regulatory dimensions.

Artificial intelligence is increasingly used to support public safety by predicting events, uncovering patterns, and informing decisions. In Latin America, where crime burdens are high and data systems are heterogeneous, a region-focused synthesis is needed to assess progress, identify gaps, and clarify operational implications. Accordingly, this PRISMA-guided, multilingual (English, Spanish, and Portuguese) bibliometric review synthesizes 146 peer-reviewed journal articles (2010–October 2025) to examine trends, methods, and application domains. Since 2018, publication output accelerated, peaking in 2024–2025. Regionally, Brazil leads within a multi-hub co-authorship network linking Latin American nodes to the United States and Spain; additional hubs include Colombia, Chile, Mexico, Ecuador, and Peru. Methodologically, three motifs dominate: temporal-dependence modeling; ensemble learners with cost-sensitive decision rules; and multimodal integration of remote sensing and computer vision with administrative data. At the application level, four families prevail: utility and fiscal-fraud analytics; environmental offenses with temporal modeling; cyber and platform-based analytics; and sensing, geospatial, and forensic workflows. However, evaluation practices are heterogeneous, with frequent risks of spatial or temporal leakage; moreover, reporting on fairness, accountability, and transparency is limited. In order to support responsible scaling, research directions include interoperable data governance, leakage-controlled and cost-sensitive evaluation, domain adaptation that accounts for spatial dependence, open and auditable benchmarks, and broader regional participation. To our knowledge, this review is one of the first multilingual, region-centered syntheses of artificial intelligence and crime in Latin America, and it establishes a reproducible baseline and an actionable evidence map that enable comparable, leakage-controlled evaluation and inform research, funding, and public safety policy in the region.

Background: Blockchain and distributed ledger technologies are increasingly proposed to strengthen traceability, governance, visibility, and coordination in mining and mineral supply chains, but mining-specific evidence remains fragmented. Methods: We conducted a systematic mapping review of peer-reviewed articles indexed in Scopus and Web of Science to examine application contexts, functional roles, technical architectures, evidence types, and adoption constraints of blockchain-enabled systems in these settings. Results: The review shows that blockchain is used across five functional domains: traceability and provenance; governance and secure data control; operational monitoring and inspection; energy and market coordination; and sustainability and environmental surveillance. Permissioned and consortium-based architectures predominated and were commonly combined with sensors, external storage, identity mechanisms, and smart contracts. Evidence was strongest for technical feasibility under simulated, experimental, comparative, or bounded pilot conditions, whereas durable economic, social, and governance outcomes remained less substantiated. Conclusions: Blockchain is most credible in mining contexts when it supports controlled coordination, auditable recordkeeping, and process integrity. Its practical value depends on reliable physical-to-digital data capture, workable governance arrangements, interoperability, and validation under real institutional and operational conditions.

This study evaluates the effectiveness of advanced statistical and geospatial methods for analyzing radon concentration distributions in indoor environments, using the district of San Martín de Porres, Lima, Peru, as a case study. Radon levels were monitored using LR-115 nuclear track detectors over three distinct measurement periods between 2015 and 2016, with 86 households participating. Detectors were randomly placed in various rooms within each household. Normality tests (Shapiro–Wilk, Anderson–Darling, and Kolmogorov–Smirnov) were applied to assess the fit of radon concentrations to a log-normal distribution. Additionally, analysis of variance (ANOVA) was used to evaluate the influence of environmental and structural factors on radon variability. Non-normally distributed data were normalized using a Box–Cox transformation to improve statistical assumptions, enabling subsequent geostatistical analyses. Geospatial interpolation methods, specifically Inverse Distance Weighting (IDW) and Kriging, were employed to map radon concentrations. The results revealed significant temporal variability in radon concentrations, with geometric means of 146.4 Bq·${\text{m}}^{-3}$, 162.3 Bq·${\text{m}}^{-3}$, and 150.8 Bq·${\text{m}}^{-3}$, respectively, across the three periods. Up to 9.5% of the monitored households recorded radon levels exceeding the safety threshold of 200 Bq·${\text{m}}^{-3}$. Among the interpolation methods, Kriging provided a more accurate spatial representation of radon concentration variability compared to IDW, allowing for the precise identification of high-risk areas. This study provides a framework for using advanced statistical and geospatial techniques in environmental risk assessment.