Cinchona officinalis, known locally as cascarilla or cinchona, is a plant species native to South America. It was used as a source of quinine to combat malaria in the 17th century. The species is threatened by various anthropogenic activities. Further, the propagation of the species depends on seed dispersal and its germination capacity. Therefore, it is necessary to conserve and propagate this species. Because C. officinalis seeds have a low germination capacity, we determined the effect of arbuscular mycorrhizae (AM) on their germination and growth. A randomized design was employed with two treatments, one treated with mycorrhizae (CM) and another without mycorrhizae (SM). For each treatment, three replicates of 100 seeds were used. Germination, growth, and fungal characteristics were evaluated. In germination parameters, the CM treatment showed better performance, but the improvement was statistically insignificant. However, the application of AM significantly improved seedling height (cm), root length (cm), leaf area (cm²), and root number by 53.52, 28.72, 29.73, and 61.66%, respectively. Likewise, mycorrhization intensity (%), mycorrhization frequency (%), and extraradical mycelium length (cm) in the CM treatment were 37.13, 3.44, and 174.97% higher compared to the SM treatment, respectively. Therefore, the use of AM fungi proves to be advantageous in the propagation of C. officinalis, and these results provide a basis for the largescale and sustainable propagation of this species.

The objective of this research was to evaluate the unit generation rate of solid hospital waste at the Gustavo Lanatta Lujan Bagua Support Hospital (HAB). The calculations were based on the weights of hospital waste disposed periodically to the biosafety cell located 7 km from the city of Bagua since 2015. Nineteen services were identified and grouped into three areas: hospitalization area, emergency area and gynecology area, studied according to the group classification of Peruvian regulations. The results show that 92.77 kg/day-1 are generated, 62.26 % are biocontaminated waste, 26.45 % are common waste, 7.7 % are sharp-sharp waste and 3.6 % are special waste. It was also found that only 73.56 % is disposed of in the hospital's biosafety cell. In this sense, this study will contribute to promote the reduction of the entry of hazardous substances into the stream of an adequate waste management and save workers and the environment in the Amazon Region. Therefore, awareness should be raised at all levels of society through various means of communication and education, so that the risks of spreading health hazards can be minimized by improving hospital waste management. In addition, healthcare waste management should be implemented, taking into account the parameters to be monitored, the economic resources to be managed and the national plans for healthcare waste.

The inhabitants of Tunants and Yahuahua face water supply problems in terms of quantity and quality, leading to socio-environmental and health impacts in the areas. The objective of this research, therefore, is to determine the technical and economic feasibility of a proposal for a rainwater harvesting and treatment system for human consumption in the native communities. For the technical feasibility, monthly water demand per family was compared with the amount of water collected in the rainy and dry seasons. In addition, 16 physical, chemical, and microbiological parameters were evaluated at the inlet and outlet of the water system. The economic feasibility was determined by the initial investment and maintenance of the systems; with the benefits, we obtained the net present social value (NPSV), social internal rate of return (SIRR), and cost-effectiveness (CE). Technically, oxygenation and chlorination in the storage tanks allowed for water quality in physical, chemical, and microbiological aspects, according to the D.S. N° 031-2010-SA standard, in all cases. Finally, with an initial investment of S/2,600 and S/70.00 for annual maintenance of the system, it is possible to supply up to six people per family with an average daily consumption of 32.5 L per person. It is suggested that the system be used at scale in the context of native communities in north-eastern Peru.

Non-destructive methods that accurately estimate leaf area (LA) and leaf weight (LW) are simple and inexpensive, and represent powerful tools in the development of physiological and agronomic research. The objective of this research is to generate mathematical models for estimating the LA and LW of Cinchona officinalis leaves. A total of 220 leaves were collected from C. officinalis plants 10 months after transplantation. Each leaf was measured for length, width, weight, and leaf area. Data for 80% of leaves were used to form the training set, and data for the remaining 20% were used as the validation set. The training set was used for model fit and choice, whereas the validation set al.lowed assessment of the of the model’s predictive ability. The LA and LW were modeled using seven linear regression models based on the length (L) and width (Wi) of leaves. In addition, the models were assessed based on calculation of the following statistics: goodness of fit (R²), root mean squared error (RMSE), Akaike’s information criterion (AIC), and the deviation between the regression line of the observed versus expected values and the reference line, determined by the area between these lines (ABL). For LA estimation, the model LA = 11.521(Wi) − 21.422 (R² = 0.96, RMSE = 28.16, AIC = 3.48, and ABL = 140.34) was chosen, while for LW determination, LW = 0.2419(Wi) − 0.4936 (R² = 0.93, RMSE = 0.56, AIC = 37.36, and ABL = 0.03) was selected. Finally, the LA and LW of C. officinalis could be estimated through linear regression involving leaf width, proving to be a simple and accurate tool.

The relevance of scientific research to local challenges and the need to produce actionable knowledge that benefits local development have not been evaluated. This study evaluates whether scientific research focused on the Amazonas region is framed within its five regional components of the Concerted Regional Development Plan (CRDP) to achieve sustainable development. In this study, 386 scientific articles published during 1960–2021 focusing on the Amazonas region were evaluated. Although Amazonas is the third poorest region in Peru, scientific production in this region has largely increased (CAGR_2001-2021 = 16.4%). However, women and indigenous authors are underrepresented suggesting a unilateral knowledge transfer. The highest scientific contribution was reported for component 1 of the CRDP (58%), centering on topics about the conservation of biodiversity and ecosystem services. Scientific research focusing on the Amazonas region fails to fully aboard the overall sustainable components of the CRDP. Social sciences are clearly understudied. It is the role of regional institutions (government, universities, industry, non-profit, etc.) to ensure the extension of research topics covering other dimensions of scientific knowledge and social needs. Conclusively, it is pending that local policymakers take into consideration emerging disciplines that can provide an updated perspective in developmental policies in the Amazonas region.

Spilocaea oleagina is a common and dangerous fungal disease in olive trees that significantly reduces olive production. The early and accurate detection of this disease is essential for implementing effective control measures. In this study, we propose the creation of a new meta-architecture for identifying peacock spots on olive leaves. This new meta-architecture integrates Xception and VGG16 as the basis for the methodology employed in this study. Furthermore, a machine learning approach was used to pretrain the model in an unsupervised manner, thereby improving its generalization capacity. Metrics such as Kappa (K), True Skill Statistic (TSS), Proportion of Expected Success (Pe), Threat Index (Ts), and the Heidke Skill Score (HSS) were used to validate the model results. Based on these indicators, we evaluated the robustness, accuracy, and ability of the model to identify peacock spots. The testing results showed that the suggested meta-architecture, named SSL-XceVNet, substantially outperformed the baseline XceVNet model (88.24%) in detecting peacock spots, with an accuracy of 95.22%. The validation measurement results (K, 73.57%; TSS, 72.66%; Pe, 55.06%; Ts, 90.71%; HSS, 90.44%) attested to the efficacy and resilience of the suggested model, underscoring its ability to generalize and produce accurate predictions under a variety of circumstances. In conclusion, the combination of Xception and VGG16 into a new meta-architecture added the strengths of both models, and self-supervised learning helped learn meaningful representations without the need for explicit labels.