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Políticas públicas para afrontar sequías en el Perú
(Revista de Climatología, 2022-06-29) Garrido Campaña,Zadith Nancy
El presente estudio se refirió a las Políticas Públicas para afrontar sequías en el Perú, aludiendo a la Estrategia Nacional de Lucha Contra la Desertificación y la Sequía en el Perú (ENLCDS) por ser un importante instrumento de gestión que promueve la participación y movilización de actores públicos y privados, para ejecutar acciones orientadas a promover el manejo sostenible de la tierra (MST), siendo este artículo de valía porque trae acotación el llamado a las instituciones peruanas responsables de esta problemática a que puedan priorizarlo por ser el Perú uno de los países con mayor extensión de tierras secas en América del Sur, en donde la sequía puede causar mayores consecuencias; describiéndose las sequías meteorológicas en la provincia de Candarave, Departamento de Tacna; Perú.
The convolutional neural network as a tool to classify electroencephalography data resulting from the consumption of juice sweetened with caloric or non-caloric sweeteners
(2022-07-19) Arteaga Miñano, Hubert Luzdemio
Sweetener type can influence sensory properties and consumer’s acceptance and preference for low-calorie products. An ideal sweetener does not exist, and each sweetener must be used in situations to which it is best suited. Aspartame and sucralose can be good substitutes for sucrose in passion fruit juice. Despite the interest in artificial sweeteners, little is known about how artificial sweeteners are processed in the human brain. Here, we applied the convolutional neural network (CNN) to evaluate brain signals of 11 healthy subjects when they tasted passion fruit juice equivalently sweetened with sucrose (9.4 g/100 g), sucralose (0.01593 g/100 g), or aspartame (0.05477 g/100 g). Electroencephalograms were recorded for two sites in the gustatory cortex (i.e., C3 and C4). Data with artifacts were disregarded, and the artifact-free data were used to feed a Deep Neural Network with tree branches that applied a Convolutions and pooling for different feature filtering and selection. The CNN received raw signal as input for multiclass classification and with supervised training was able to extract underling features and patterns from the signal with better performance than handcrafted filters like FFT. Our results indicated that CNN is an useful tool for electroencephalography (EEG) analyses and classification of perceptually similar tastes.
Sistema Experto para Orientar a los Usuarios del Corredor Turístico de las Provincias de Jaén, San Ignacio y Utcubamba en Cajamarca, Perú
(International Journal of Professional Business Review, 2022-08-29) Arce Fernández,Nilthon
Purpose: Develop a rule-based expert system to guide the users of the tourist corridor of the provinces of Jaén, San Ignacio and Utcubamba (Peru). Theoretical framework: Hussein and Aqel (2015), developed a rule-based expert system in Jordan to choose the best tour package based on time, budget, and preferences. In Peru, Ramos and Valdivia (2017), proposed an expert system to promote tourism in the Lambayeque region. Methodology: To develop the system, the methodology of Nicolás Kemper was used. Tourism experts from the provinces participated in the development of the knowledge base. The evaluation was carried out with an expert different from those who prepared the aforementioned base. Findings: In the evaluation, the expert system and the human expert agreed on the recommendation of tourist attractions by 80%. Concluding that this system helps tourists in making decisions about which places to visit in the tourist corridor. Contributions: The system helps improve the dissemination of local tourist information. To develop the knowledge base, tourism resources were systematized. New variables can be incorporated into the knowledge base in order to obtain more personalized tourist recommendations. Originality/value: This research is innovative because there is no expert system to guide tourists who want to travel to these places; it has social relevance as it helps to boost the local economy.
Detection of Rust Emergence in Coffee Plantations using Data Mining: A Systematic Review
(OnLine Journal of Biological Sciences, 2022-09-03) Ocaña Zúñiga,Candy Lisbeth; Quiñones Huatangari,Lenin; Huaccha Castillo,Annick Estefany; Milla Pino,Manuel Emilio
Hemileia vastatrix is a fungus that causes coffee rust disease and, depending on the level of severity, reduces the photosynthetic capacity of the plant and of new shoots, leading to low coffee yields and even death; its symptoms are visible on the leaf. Systems based on computer algorithms have been developed to predict diseases and pests in coffee. The objective of the manuscript was to analyse the detection of rust occurrence in coffee plantations, through field determinations of climatological, agronomic and crop management variables using data mining algorithms. A systematic review of studies published from 2001 to 2021 was carried out in the Scopus, Ebsco Host and Scielo databases, considering as an inclusion criterion the works that used experimental design in data collection. The studies included in this review were 22, 64% of which came from the top two coffee-roducing countries in Latin America (Brazil and Colombia); the analysis of these studies revealed that the input variables were climatic, soil fertility properties, management and physical properties of the crops. In addition, they used supervised (decision tree, artificial neural networks, multiple linear regression, among others) and unsupervised (clustering) algorithms, with the support of experts in the study of the fungus and used statistics such as coefficient of determination, root mean square error, among others, to validate the proposals. Overall, this systematic review provides evidence of the effectiveness of data mining algorithms implemented to detect the occurrence of rust in coffee plantation
Application of the Greenhouse Gas Protocol (GHG Protocol) and the ISO 14064-1: 2006 standard for the estimation of the carbon footprint at the National University of Jaen in 2021
(Universidad Nacional de Colombia||DYNA, 2023-05-31) Ocaña Zúñiga,Candy Lisbeth
The objective of the study is to estimate the Carbon Footprint of the National University of Jaen (UNJ), for the period 2021. The direct Scope 1 (fuel consumption) and indirect Scope 2 (electricity consumption) greenhouse gas (GHG) emissions were calculated from CO2, CH4 and N2O produced in 29 administrative offices of the university campus. The methodology used was proposed by the GHG Protocol and ISO 14064-1:2006. For fuel emission factors, the indicators established by the Intergovernmental Panel on Climate Change (IPCC) were used, and for electrical energy: 1.56E-01 tCO2/MWh, 9.70E-06 tCH4/MWh, 1.20E-06 tN2O/MWh, and specific conversion factors established by the Ministry of the Environment (MINAM) were used. The results show that a total of 29.3937 tCO2eq were emitted, being CO2 the predominant GHG (23.1364 t). Scope 1 contributed 15,6827 tCO2eq, occupying the highest participation with 53.35 %
Gis and fuzzy logic approach for forest fire risk modeling in the Cajamarca region, Peru
(Growing Science, 2023-06-03) Ocaña Zúñiga,Candy Lisbeth
Forest fires are a potential threat to life, as they contribute to reducing forest areas, impact on the services we expect from ecosystems, the health of the inhabitants is affected by smoke and the economic costs for the recovery of affected areas is high. The objective of the study is to apply fuzzy logic to model the risk of forest fires in the Cajamarca-Peru region, incorporating variables that represent biological, topographic, socioeconomic, and meteorological factors. The analysis was based on the acquisition, editing and rasterization of the database, application of fuzzy membership functions and image fuzzification, fuzzy superposition and spatial reclassification of forest fire risk. The results obtained show that 71.68% of the area is under very low or medium forest fire risk. However, 28.32% of the study area has a high to very high fire risk, which makes the occurrence of fires susceptible to the lack of rain and water in the soil. It was found that biological, topographic, and socioeconomic factors with their respective variables are directly influenced by meteorological factor variables such as temperature, rainfall and water availability. Fuzzy logic offered flexibility in modeling wildfire risk in the region, proving to be a useful tool for predicting and mapping wildfire risk.
Optimization Model that Minimizes the Penalty Caused by Delayed Delivery of Construction Projects
(International Journal of Professional Business Review, 2023-07-20) Arce Fernández,Nilthon
Purpose: This work aims to minimize the penalty generated by delays in delivering construction projects operated by a single machine. Theoretical framework: The fundamental issues to develop this work are optimization and its application to delays in construction projects operated in their initial stage by a single machine. Design/Methodology/Approach: We presented a construction case study, developed an optimization model, implemented a computational optimization tool and obtained the optimal sequence to perform the tasks. Findings: The numerical results demonstrated the model’s usefulness in minimizing the penalty generated by delays in the delivery of projects. Research, practical & social implications: The benefit of this study is to help managers or decision-makers schedule their construction projects with limited resources and deadlines per activity to minimize penalty costs of delay. Originality/Value: Considering that the construction sector generates the largest labor force in Peru, the study has an important social value by providing a tool to improve operations and incentivize construction companies to continue operating. In addition, it provides a substantial basis for future work by applying optimization in a specific area of civil engineering. To our knowledge, no researcher or company in Peru has yet addressed this study.
Estabilidad del nivel del agua en un tanque con variaciones generadas por la demanda del servicio mediante modelos matemáticos
(Revista Científica Pakamuros, 2023-09-13) Arce Fernández,Nilthon
El objetivo de la investigación fue mantener estable el nivel del agua en un tanque con variaciones generadas por la demanda del servicio. Se emplearon dos modelos matemáticos que permitieron estudiar la estabilidad, el modelo no lineal representado por una ecuación diferencial y el modelo lineal por una función de transferencia. Se utilizó el software Matlab/Simulink mediante un método numérico de Runge-Kutta para modelar y simular el proceso. Con las condiciones iniciales del 90 % en la apertura de la válvula de entrada y del 50 % en la apertura de la válvula de salida, la solución de ambos modelos alcanzó un nivel estacionario de h=1,8m. Debido a la demanda del servicio, la válvula de entrada se reajustó en +5 %, alcanzando la solución del sistema un nivel estacionario de h=2,005 m para el modelo no lineal y h=1,999 m para el modelo lineal. Finalmente, la válvula de salida se reajustó en +3 %, alcanzando la solución del sistema un nivel estacionario de h=1.785 m para el modelo no lineal y h=1.784m para el modelo lineal. Los modelos matemáticos empleados, permitieron estimar en menor tiempo con resultados esperados el nivel del agua, logrando así identificar la estabilidad del sistema.
Minimización de la penalidad generada por los retrasos en la entrega de proyectos mediante un modelo de programación lineal entera
(Revista Científica Pakamuros, 2023-09-17) Arce Fernández,Nilthon
La presente investigación tuvo por objetivo minimizar la penalidad total generada por el retraso en la entrega de proyectos operados por una máquina (retroexcavadora). Se empleó el método doble simplex de paso largo con el software GNU Octave (versión 6.2.0). Para estimar la fecha y el orden de entrega de los proyectos antes de la ejecución, se implementó un plan de trabajo para la máquina. La empresa Consultores & Ejecutores Jhothiza S.R.L. verificó los plazos de entrega ejecutando el modelo y concluyendo que la penalidad óptima fue de S/. 8100.
Current and Future Spatial Distribution of the Genus Cinchona in Peru: Opportunities for Conservation in the Face of Climate Change
(Sustainability- MDPI, 2023-09-23) Ocaña Zúñiga,Candy Lisbeth; Vergara Anticona,Alex Joel; Cieza Tarrillo,Dennis Alvarino; Quiñones Huatangari,Lenin; Idrogo Vasquez,Guillermo; Muñoz Astecker,Lucas Dalvil; Auquiñivin Silva,Erick Aldo; Cruzalegui Fernandez,Robert Javier; Arbizu Berrocal,Carlos Irvin
The genus Cinchona belongs to the Rubiaceae family and comprises native Peruvian tree species distributed in tropical areas. It is currently endangered due to human disturbance and overexploitation for medicinal, forestry and food uses. To date, the current and future distribution of Cinchona spp. under the climate change scenario is unknown. Here, we modeled the present and future spatial distribution of the genus Cinchona using bioclimatic, edaphic and topographic variables using the maximum entropy algorithm (MaxEnt). The results indicate that 8.08% (103,547.89 km2) and 6.02% (77,163.81 km2) of the surface of Peru possesses areas with high and moderate distribution probabilities, respectively, to host the genus Cinchona, distributed mainly in the departments of Cusco, Amazonas, San Martín and Cajamarca. Furthermore, according to future climate scenarios, the areas of high suitability will increase their extension for the years 2050 and 2070 by 3.65% and 3.9%, respectively. Since Peru seeks to promote the forest sector to be the other force for its development, this study can be considered as a basis for the establishment of priority zones for the conservation, restoration, reforestation and sustainable management of Cinchona spp. species in Peru.
Comparison of Collinearity Indices for Linear Models in Agricultural Trials
(Journal of Biological Sciences, 2023-12-28) Garrido Campaña,Zadith Nancy
The deleterious consequences of collinearity in linear regression on the precision of estimators of regression coefficients and the interpretability of the fitted model are widely recognized. In this study, we compare several methodologies for assessing collinearity in linear models and explore the effect of outliers on collinearity. The robustness of collinearity measures (individual and overall) is validated through two detailed Monte Carlo simulation study which also considers the effect of outliers on collinearity indices. The methods are illustrated with two real-world agricultural and fish morphology l data sets to show potential applications. The results do not provide any evidence for an effect from outliers on collinearity identification using the collinearity indices (individual and overall). The FG and Fj collinearity indices more robust as both sample size and collinearity degree increase. The VIF (individual measure) had a better performance on the fitted model with a greater number of parameters.
Small and in-country herbaria are vital for accurate plant threat assessments: A case study from Peru
(Universidad Nacional de Jaén, 2024-01-28) Marcelo Peña,José Luis
Herbaria can be considered plant libraries, each holding collections of dried specimens documenting plant diversity in space and time. For many plant species, these are our only evidence of their existence and the only means of assessing their conservation status. Specimens in all herbaria, especially those in small and often under-resourced herbaria in megadiverse countries, are key to achieving accurate estimates of the conservation status of the world's plant species. They are also part of a country's shared heritage and critical contributions to knowledge of the world's diversity.
Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology
(Universidad Nacional de Jaén, 2024-01-28) Marcelo Peña,José Luis
In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics.
Estimation of the Physical Progress of Work Using UAV and BIM in Construction Projects
(Civil Engineering Journal, 2024-02-01) Piedra Tineo,José Luis; Cayatopa Calderon,Billy Alexis
The delay in the physical progress of construction creates additional costs, missed deadlines, and quality issues. The research aimed to estimate the physical progress of the project by using unmanned aerial vehicles (UAVs) and building information modeling (BIM). The methodology comprised capturing 848 high-resolution images of the Civil Engineering Laboratory construction site at the National University of Jaen, Cajamarca, Peru, using the Phantom 4 RTK drone. The photographs were processed using Agisoft 2.0.1 software, resulting in a point cloud. This was then imported into ReCap Pro 2023 software, which was used to assess the quality of the points. The Revit 2023 software was subsequently utilized to establish the phase parameters, linking the BIM model with the point cloud, filtering the model, and eventually exporting it to the Power BI 2023 software. The work's estimated progress utilizing the proposed methodology was 42.82%, which was not statistically significant compared to the Public Works Information System (INFOBRAS) of 43.14%. This allows for the automation of customary processes, the identification of crucial issues, and prompt decision-making. The study's originality lies in the suggestion of integrating aerial imagery with drones and BIM modeling for the real-time and precise estimation of work progression. This method provides a precise and effective substitute for traditional techniques for gauging the tangible advancement of projects.
Using pulsed magnetic fields to improve the quality of frozen blueberry: A bio-impedance approach
(Universidad Nacional de Jaén, 2024-02-26) Arteaga Miñano,Hubert Luzdemio
Freezing assisted by pulsed magnetic field (PMF) is an emerging technology that could be used in food preservation. This paper evaluates how PMF-assisted freezing affects the preservation of blueberry and its bioactive compounds. Blueberries were subjected to 8 PMF-assisted freezing treatments: T1 (36.8 mT/30 Hz), T2 (36.8 mT/60 Hz), T3 (36.8 mT/90 Hz), T4 (36.8 mT/20 Hz), T5 (44.7 mT/30 Hz), T6 (44.7 mT/60 Hz), T7 (44.7 mT/90 Hz), and T8 (44.7 mT/120 Hz). In treatment T9 (control), the blueberries were subjected to conventional freezing to −35 °C; T10 represents fresh blueberries. Compared to conventional freezing (T9), PMF-assisted freezing (T1 to T8) gave different parameters of temperature, nucleation time, degree of supercooling, and phase change time. The parameters achieved with T7 evidenced better behavior: smaller crystals were formed, allowing the cellular structure to be preserved, as confirmed by the electrical parameters (Re, Ri, and Cm) obtained from electrical impedance data. Moreover, T7 preserved anthocyanins and polyphenols, promoting the highest antioxidant capacity among the blueberries subjected to PMF-assisted freezing. Meanwhile, conventional freezing and PMF-assisted freezing reduced the polyphenol oxidase and peroxidase activities. In conclusion, at the laboratory level, PMF-assisted freezing preserves the blueberry cellular structures and bioactive compounds.
A sequential approach to reduce sodium chloride in freshwater fish burgers considering chemical, texture, and consumer sensory responses
(Universidad Nacional de Jaén, 2024-02-26) Rios Mera, Juan Dario
The objective of this work was to determine the effect of the reduction and substitution of salt (NaCl) in pacu (Piaractus brachypomus) burgers, an Amazonian freshwater fish. In the first stage, five treatments with NaCl concentrations from 0.5 to 1.5 g/100 g were evaluated for proximal composition, instrumental texture, cooking losses, sensory profile, overall liking, and lipid oxidation for eight weeks. The results suggest a 50% reduction in NaCl content without affecting the parameters of burgers. In the second stage, NaCl was replaced up to 50% by potassium chloride (KCl) or calcium chloride (CaCl2), observing that CaCl2 at 50% substitution of NaCl presents better compatibility with the product in the chemical aspect, instrumental texture, sensory profile and overall liking, with the improvement in the decrease of the lipid oxidation compared to the product with only NaCl. The NaCl reductions in the two stages reached up to 75% NaCl reduction in the burger, showing the salience of studying first the NaCl reduction and then the incorporation of NaCl substitutes.
Improving Behavior Monitoring of Free-Moving Dairy Cows Using Noninvasive Wireless EEG Approach and Digital Signal Processing Techniques
(Universidad Nacional de Jaén, 2024-02-26) Arteaga Miñano, Hubert Luzdemio; -
Electroencephalography (EEG) is the most common method to access brain information. Techniques to monitor and extract brain signal characteristics in farm animals are not as developed as those for humans and laboratory animals. The objective of this study was to develop a noninvasive method for monitoring brain signals in cattle, allowing the animals to move freely, and to characterize these signals. Brain signals from six Holstein heifers that could move freely in a paddock compartment were acquired. The control group consisted of the same number of bovines, contained in a climatic chamber (restrained group). In the second step, the signals were characterized by Power Spectral Density, Short-Time Fourier Transform, and Lempel–Ziv complexity. The preliminary results revealed an optimal electrode position, referred to as POS2, which is located at the center of the frontal region of the animal’s head. This positioning allowed for attaching the electrodes to the front of the bovine’s head, resulting in the acquisition of longer artifact-free signal sections. The signals showed typical EEG frequency bands, like the bands found in humans. The Lempel–Ziv complexity values indicated that the bovine brain signals contained random and chaotic components. As expected, the signals acquired from the retained bovine group displayed sections with a larger number of artifacts due to the hot 32 degree C temperature in the climatic chamber. We present a method that helps to monitor and extract brain signal features in unrestrained bovines. The method could be applied to investigate changes in brain electrical activity during animal farming, to monitor brain pathologies, and to other situations related to animal behavior.
Pijuayo (Bactris gasipaes) Pulp and Peel Flours as Partial Substitutes for Animal Fat in Burgers: Physicochemical Properties
(Universidad Nacional de Jaén, 2024-02-26) Arteaga Miñano, Hubert Luzdemio; Rios Mera, Juan Dario
This study aimed to evaluate the incorporation of peach palm (PP) pulp and peel flours as substitutes for animal fat (25 and 50% substitution) in beef-based burgers. Incorporation of PP flours reduced hardness, springiness, cohesiveness, chewiness, fat, cooking losses, and diameter reduction. Burgers made with PP peel flour stood out for having low values of lipid oxidation in the two levels of fat substitution (0.14–0.23 malondialdehyde/kg) (p < 0.05). PP fruit has the potential to be utilized as a new ingredient in burgers, but future studies are needed regarding detailed sensory trials and consumer acceptance.
A Comparative Study of Freshwater Fish Burgers Made from Three Amazonian Species: Omega 3 Fortification and Sodium Reduction
(Universidad Nacional de Jaén, 2024-02-26) Rios Mera, Juan Dario
This study aimed to formulate burgers made from three Amazonian fish species: pacu (Pyaractus brachypomus), boquichico (Prochilodus nigricans), and bujurqui (Chaetobranchus flavescens), focusing on sodium reduction and fortification with fish oil microparticles (FOM) rich in eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). The proximal composition, sodium and calcium content, instrumental texture profile, fatty acid profile, sensory profile, and overall liking were evaluated. Differences in proximal composition and fatty acid profiles between the fillets were reflected in the burgers. Fortification with FOM increased EPA and DHA in the burgers; thus, they can be considered “high in omega-3 fatty acids” and reduced the n-6/n-3 ratio below 4. There were sensory attributes that could be related to lipid oxidation but reduced overall liking for less than 10% of consumers. Nevertheless, certain sensory attributes (grilled, characteristic, aromatic, tasty, tender, and juicy) had a positive impact on the overall liking of more than 20% of consumers, yielding adequate scores (between 5.60 and 5.71) on the 9-point hedonic scale. The production process must be optimized by knowing the fish fillet quality in depth, improving the FOM and burgers’ oxidative stability, and achieving an adequate sensory and hedonic profile by employing consumers’ vocabulary to characterize new products.
Mapping density, diversity and species-richness of the Amazon tree flora
(Universidad Nacional de Jaén, 2024-02-28) Marcelo Peña,José Luis
Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution.

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