Abstract:The mining industry accounts for significant greenhouse gas emissions, with copper production in Chile contributing substantially to national carbon footprints. This study evaluates the technical and economic feasibility of transitioning large-scale copper mining operations to solar-hydrogen energy systems. We conducted detailed assessments at three major mining sites in the Atacama Desert, analyzing solar irradiance data, energy consumption patterns, and operational requirements. Our proposed hybrid system combines 500 MW photovoltaic installations with green hydrogen production and storage facilities capable of providing 24/7 power supply. Technical simulations demonstrate that the system can meet 92% of mining energy demands, including haul truck electrification and mineral processing. Economic modeling reveals levelized cost of energy (LCOE) of $0.038/kWh, competitive with current diesel-based operations. The transition would reduce annual CO2 emissions by 1.2 million tonnes per site. Implementation roadmaps, regulatory considerations, and workforce transition strategies are presented. Sensitivity analysis confirms project viability under various commodity price and policy scenarios.
Abstract:The COVID-19 pandemic exacerbated pre-existing mental health disparities across Latin America, disproportionately affecting vulnerable populations. This mixed-methods study examines social determinants of mental health in post-pandemic contexts across Argentina, Brazil, Colombia, and Mexico. Quantitative data from 8,500 survey respondents were complemented by 240 in-depth interviews with individuals from marginalized communities including informal workers, indigenous populations, migrants, and LGBTQ+ individuals. Results reveal that economic precarity (OR=3.2), housing instability (OR=2.8), and limited healthcare access (OR=2.5) significantly predict depression and anxiety disorders. Qualitative findings highlight the compounding effects of social stigma, discrimination, and inadequate social protection systems. Notably, community-based support networks emerged as protective factors, reducing mental health symptom severity by 35% among participants with strong social connections. Based on these findings, we propose an integrated policy framework addressing structural determinants while strengthening community mental health services. Pilot implementation in two municipalities demonstrated 28% improvement in mental health outcomes over 18 months.
Abstract:Early detection of crop diseases is crucial for food security in tropical regions where agriculture forms the economic backbone. This research presents a comparative analysis of machine learning algorithms for detecting diseases in coffee, cacao, and banana crops using smartphone-captured images. We developed and trained five deep learning models (ResNet-50, VGG-19, EfficientNet-B4, MobileNetV3, and a custom hybrid architecture) on a dataset of 125,000 annotated images collected from plantations across Central America and the Caribbean. The custom hybrid model achieved 94.7% accuracy in identifying 23 distinct disease conditions, outperforming existing solutions by 8.3%. Real-world deployment through a mobile application reached 15,000 farmers, enabling disease identification within 3 seconds per image. Economic impact analysis indicates potential savings of $2,400 per hectare annually through early intervention. The study also addresses challenges of model deployment in low-connectivity rural areas through edge computing optimization, reducing model size by 75% while maintaining 91% accuracy.
Abstract:Microplastic pollution in high-altitude freshwater ecosystems remains poorly understood despite growing concerns about global plastic contamination. This comprehensive study examines microplastic distribution across 85 lakes and rivers in the Andean region, spanning altitudes from 2,500 to 4,800 meters across Bolivia, Peru, and Chile. Water, sediment, and biota samples were analyzed using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). Results reveal alarming contamination levels, with microplastic concentrations ranging from 0.5 to 12.3 particles per liter, comparable to heavily industrialized lowland regions. Polyethylene and polypropylene fragments dominated (68%), primarily originating from agricultural plastics and tourism activities. Bioaccumulation studies in native fish species (Orestias and Trichomycterus) showed microplastic presence in 73% of specimens examined. We propose and test three remediation strategies: constructed wetlands, biofilm-based filtration, and community-based waste management programs. The integrated approach reduced microplastic input by 65% in pilot watersheds over 24 months.
Abstract:This longitudinal study investigates neuroplastic changes associated with second language acquisition in adult learners using immersive virtual reality (VR) environments. Sixty participants aged 25-45 underwent a 12-month Spanish-English language learning program, with half using traditional methods and half using VR immersion. Functional magnetic resonance imaging (fMRI) scans were conducted at baseline, 6 months, and 12 months. Results reveal significantly greater activation in Broca's area and the left inferior parietal lobule in VR learners, correlating with superior language proficiency outcomes. The VR group demonstrated 40% faster vocabulary acquisition and 35% better pronunciation accuracy. Diffusion tensor imaging (DTI) analysis showed increased white matter integrity in language-related pathways for VR participants. These findings suggest that immersive VR environments enhance neuroplastic adaptation during adult language learning, challenging traditional critical period hypotheses. Implications for educational technology and cognitive rehabilitation are discussed.
Abstract:Counterfeit pharmaceuticals pose significant public health risks globally, with developing nations particularly vulnerable. This study presents the design, implementation, and evaluation of a blockchain-based supply chain management system deployed across pharmaceutical distribution networks in Colombia, Peru, and Ecuador. The system utilizes a permissioned blockchain architecture with smart contracts to ensure immutable tracking of medications from manufacturing to patient delivery. Over an 18-month implementation period involving 45 pharmaceutical companies and 2,300 pharmacies, the system processed 12 million transactions with 99.97% uptime. Our analysis reveals a 78% reduction in counterfeit drug incidents and a 45% decrease in supply chain inefficiencies. The research also addresses regulatory compliance challenges, proposing a harmonized framework for cross-border pharmaceutical tracking. Cost-benefit analysis demonstrates return on investment within 2.5 years, making the solution economically viable for medium-sized distributors.
Abstract:Urban food security remains a critical challenge in rapidly growing Latin American megacities. This research investigates the feasibility and efficiency of integrating vertical farming systems with solar and wind energy sources in metropolitan areas. We conducted a comprehensive study across five major cities: São Paulo, Mexico City, Buenos Aires, Lima, and Bogotá. Our findings reveal that hybrid renewable energy systems can power vertical farms with 85% energy autonomy, reducing operational costs by 40% compared to grid-dependent facilities. The study employed life cycle assessment (LCA) methodology to evaluate environmental impacts, demonstrating a 60% reduction in carbon footprint compared to conventional agriculture. Additionally, we developed an optimization algorithm for crop selection based on local nutritional needs, market demand, and energy availability. Results indicate that strategically located vertical farms could supply 15% of fresh vegetable demand in target neighborhoods while creating 2,500 green jobs per installation.
Abstract:This study presents a novel deep learning framework for predicting extreme weather events with unprecedented accuracy. By integrating convolutional neural networks (CNNs) with long short-term memory (LSTM) architectures, we developed a hybrid model capable of processing multi-dimensional atmospheric data. Our approach was validated using 30 years of meteorological records from 150 weather stations across South America. Results demonstrate a 23% improvement in prediction accuracy for hurricanes and a 31% improvement for flash flood events compared to traditional numerical weather prediction models. The framework successfully identified precursor patterns 72 hours before extreme events, providing crucial lead time for emergency response. Furthermore, we introduce a novel attention mechanism that highlights the most influential atmospheric variables, enhancing model interpretability. This research contributes to climate resilience strategies and disaster risk reduction in vulnerable regions.
Abstract:Focusing on high-endization, intelligentization, and greenization development in manufacturing, this study constructs a comprehensive evaluation index system for the integrated development of Chinese manufacturing’s “three transformations”. It measures the integrated development level of these transformations across 30 provinces in China using the CRITIC-entropy weight method and the coupling coordination degree model, and it explores the regional differences and dynamic evolution of this integrated development level by means of approaches such as kernel density estimation and spatial Markov chains. The findings reveal that (1) the integrated development level of the “three transformations” in regional manufacturing shows an overall upward trend across all regions, with green development significantly outpacing both intelligent and high-end development, and (2) there are notable disparities in the convergence levels across regions. The central and northeastern regions show a trend of multipolarization, while the internal differences in the eastern and western regions have expanded but no bipolarization has emerged. (3) Finally, the convergence of the “three transformations” exhibits club convergence characteristics, with a low probability of state transitions but significant spatial spillover effects. Based on these findings, policy recommendations are proposed to promote the integrated development of the “three transformations” in China’s manufacturing sector, providing insights for high-quality development.
Abstract:Zebrafish have remarkable regenerative abilities, and the caudal fin amputation model has long been a cornerstone for studying tissue regeneration. However, amputation-based models mainly represent acute mechanical injury and do not capture the complex and progressive pathology often seen in clinical tissue damage. In this study, we establish a photochemically induced caudal fin injury model in adult zebrafish by combining photosensitization with localized light exposure to create controlled vascular damage and secondary tissue necrosis. This approach reliably produces localized and reproducible fin lesions that undergo robust regeneration, closely mimicking the structural recovery observed in clinical conditions. Moreover, the model responds consistently to pharmacological treatments, demonstrating its potential for drug evaluation and mechanistic studies of tissue repair. By integrating pathological relevance with regenerative assessment, this photochemical model offers a simple, reproducible, and physiologically relevant system for exploring regeneration and screening pro-regenerative compounds.