Artículos con filiación institucional UNACH en revistas indexadas en Scopus, Web of Science y SciELO

Permanent URI for this collectionhttps://repositorio.unach.edu.pe/handle/20.500.14142/667

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A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services.
(Hindawi Limited, 2022-01) Jain, Paras; F. Alsanie, Walaa Fahad; Oseda Gago, Dulio; Cieza Altamirano, Gilder; Sandoval Núñez, Rafaél Artidoro; Rizwan, A.; Asakipaam, Simon Atuah
ECG (electrocardiogram) identi es and traces targets and is commonly employed in cardiac disease detection. It is necessary for monitoring precise target trajectories. Estimations of ECG are nonlinear as the parameters TDEs (time delays) and Doppler shifts are computed on receipt of echoes where EKFs (extended Kalman thlters) and electrocardiogram have not been examined for computations. ECG, certain times, results in poor accuracies and low SNRs (signal-to-noise ratios), especially while encountering complicated environments. This work proposes to track online lter performances while using optimization techniques to enhance outcomes with the removal of noise in the signal. The use of cost functions can assist state corrections while lowering costs. A new parameter is optimized using IMCEHOs (Improved Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system nonlinearity where multiiterative function (Optimized Iterative UKFs) predicts a target’s unknown parameters. To obtain optimal solutions theoretically, multiiterative function takes less iteration, resulting in shorter execution times. De proposed multiiterative function provides numerical approximations, which are derivative-free implementations. Signals are updated in the cloud environment; the updates are received by the patients from home. The simulation evaluation results with estimators show better performances in terms of reduced NMSEs (normalized mean square errors), RMSEs (root mean squared errors), SNRs, variances, and better accuracies than current approaches. Machine learning algorithms have been used to predict the stages of heart disease, which is updated to the patient in the cloud environment. The proposed work has a 91.0% accuracy rate with an error rate of 0.05% by reducing noise levels.
A neuro Meyer wavelet neural network procedure for solving the nonlinear Leptospirosis model.
(Elsevier, 2023-06) Sabir, Zulqurnain; Raja, Muhammad Asif Zahoor; Ali, Mohamed R.; Sadat, Rahma; Fathurrochman, Irwan; Sandoval Núñez, Rafaél Artidoro; Bhat, Shahid Ahmad
The aim of such work is to design a Meyer wavelet neural network (WNN) for solving the mathematical form of the Leptospirosis disease model (LDM). The global and local search optimization schemes based on the genetic algorithm (GA) and active-set algorithm (ASA) are used in this study. Leptospirosis is an infection spread by rodents, which is found in the world and causes fatalities in humans. The mathematical LDM model form consists of susceptible-infected-recovered (SIR), which is based on the disease spread processes. A fitness function is designed by using the mathematical LMD and then optimized by the hybridization of the GAASA. For the correctness, and capability of the Meyer WNN along with the procedures of GAASA, the comparison of the obtained and reference results is provided. Moreover, the reducible absolute error provides the efficiency of the proposed Meyer WNN along with the procedures of GAASA. The statistical observations are also provided to authenticate the convergence of the stochastic Meyer WNN along with the procedures of GAASA.
A stochastic computational scheme for the computer epidemic virus with delay effects.
(American Institute of Mathematical Sciences, 2022-09) Weera, Wajaree; Botmart, Thongchai; La-Inchua, Teerapong; Sabir, Zulqurnain; Sandoval Núñez, Rafaél Artidoro; Abukhaled, Marwan I.; Guirao, Juan Luis García
This work aims to provide the numerical performances of the computer epidemic virus model with the time delay effects using the stochastic Levenberg-Marquardt backpropagation neural networks (LMBP-NNs). The computer epidemic virus model with the time delay effects is categorized into four dynamics, the uninfected S(x) computers, the latently infected L(x) computers, the breakingout B(x) computers, and the antivirus PC’s aptitude R(x). The LMBP-NNs approach has been used to numerically simulate three cases of the computer virus epidemic system with delay effects. The stochastic framework for the computer epidemic virus system with the time delay effects is provided using the selection of data with 11%, 13%, and 76% for testing, training, and verification together with 15 neurons. The proposed and data-based Adam technique is overlapped to execute the LMBP-NNs method’s exactness. The constancy, authentication, precision, and capability of the LMBP-NNs scheme are perceived with the analysis of the state transition measures, regression actions, correlation performances, error histograms, and mean square error measures.
A Stochastic Framework for Solving the Prey-Predator Delay Differential Model of Holling Type-III
(Tech Science Press, 2022-12) Ruttanaprommarin, Naret; Sabir, Zulqurnain; Sandoval Núñez, Rafaél Artidoro; Az-Zo’bi, Emad A.; Weera, Wajaree; T.; Botmart, Thongchai; Zamart, Chantapish
The current research aims to implement the numerical results for the Holling third kind of functional response delay differential model utilizing a stochastic framework based on Levenberg-Marquardt backpropagation neural networks (LVMBPNNs). The nonlinear model depends upon three dynamics, prey, predator, and the impact of the recent past. Three different cases based on the delay differential system with the Holling 3rd type of the functional response have been used to solve through the proposed LVMBPNNs solver. The statistic computing framework is provided by selecting 12%, 11%, and 77% for training, testing, and verification. Thirteen numbers of neurons have been used based on the input, hidden, and output layers structure for solving the delay differential model with the Holling 3rd type of functional response. The correctness of the proposed stochastic scheme is observed by using the comparison performances of the proposed and reference data-based Adam numerical results. The authentication and precision of the proposed solver are approved by analyzing the state transitions, regression performances, correlation actions, mean square error, and error histograms.
Adubos e coberturas orgânicas, fontes de nutrientes de liberação lenta na produção de acelga de colheitas múltiplas.
(Universidade Federal de Santa Catarina, 2019-07) Murga-Orrillo, Hipolito; Irigoin-Aguilar, Jhon Maber; Hilares-Vargas, Sharmely; Bardales-Lozano, Ricardo Manuel; De Almeida Lobo, Francisco
Objetivou-se avaliar o desempenho de coberturas e adubos orgânicos na estabilidade produtiva da acelga. O delineamento experimental foi de blocos casualizados, em arranjo fatorial e em parcelas subdivididas; usando húmus de minhoca (A1), guano de ilha (A2), e húmus de esterco de porquinho-daíndia (A3) como adubos, serragem de pinheiro-de-folhas-pêndulas (C1), casca de arroz (C2), e palha de aveia (C3) como coberturas, para três colheitas sucessivas. Avaliou-se, porcentagem de emergência (E), comprimento de folhas (CF), massa fresca de folhas (MFF) e massa seca de folhas (MSF). Nos resultados, as coberturas que propiciaram os maiores valores foram a C2 e C3 com 43,1 e 42,7 cm no CF, 209,9 e 215,5 g no MFF, respectivamente; nos adubos, o A3 proporcionou os maiores valores com 92,0% na E, 44,2 cm no CF, 231,6 g no MFF, e 154 g no MSF. As coberturas de C2 e C3 e, o adubo de A3 condicionaram os melhores desempenhos no CF e MFF em relação aos outros tratamentos; os rendimentos mantiveram-se na 1ª e 2a colheita baixando significativamente na 3a colheita; o húmus de esterco de porquinho-da-índia apresenta caraterísticas nutricionais destacáveis para seu uso na horticultura.
Aggregation-Based Dynamic Channel Bonding to Maximise the Performance of Wireless Local Area Networks (WLAN)
(Hindawi Limited, 2022-06) Parashar, Vivek; Kashyap, Ramgopal; Rizwan, A.; Karras, Dimitrios Alexios; Cieza Altamirano, Gilder; Dixit, Ekta; Ahmadi, Fardin
Channel bonding is considered by the IEEE 802.11ac amendment to improve wireless local area network (WLAN) performance. In this article, the channel bonding and aggregation method were proposed to increase wireless local area network performance (WLANs). It combines many channels (or lanes) to boost the capacity of modem traffic. Channel bonding is the combination of two neighbouring channels within a certain frequency band to increase wireless device throughput. Wi-Fi employs channel bonding, also known as Ethernet bonding. Channel bandwidth is equal to the uplink/downlink ratio multiplied by the operational capacity. A single 20 MHz channel is divided into two, four, or eight power channels. At 80 MHz, there are more main and smaller channels. Performance of short-range WLANs is determined through graph-based approach. The twochannel access techniques including channel bonding proposed for the IEEE 802.11ac amendment are analysed and contrasted. The novel channel sizing algorithm based on starvation threshold is proposed to expand the channel size to improve WLAN performance. Second-cycle throughput is estimated at 20 Mbps, much beyond the starvation threshold. Our test reveals access points (AP) 1, 2, and 4 have enough throughput. A four-AP WLAN with a 5-Mbps starvation threshold is presented. C160 = 1 since there is only one 160 MHz channel. MIR (3, 160 (a, a, a)) =0, indicating that AP 3’s predicted throughput is 0. The algorithm rejects the 160 MHz channel width since ST is larger than 0. The channel width in MHz is given by B =0,1 MIR. The MIR was intended to maximise simultaneous broadcasts in WLANs. The authors claim that aggregation with channel bonding outperforms so all WLAN APs should have a single-channel width. It usually outperforms fairness-based measures by 15% to 20%. Wi-Fi standards advise “channel bonding,” or using higher frequency channels. Later standards allow channel bonding by increasing bands and channel lengths. Wider channels enhance average WLAN AP throughput, but narrower channels reduce appetite. Finally, it is concluded that APs are more useful than STAs.
Alta capacidad de recuperación del crecimiento de los árboles de Eucalyptus grandis después de un período de 3 años con una reducción del 80 % en la caída de árboles.
(Elsevier, 2022-01) Chambi-Legoas, Roger; Tommasiello Filho, Mario Tommasiello; Perassolo Guedes, Fernanda Trisltz; Chaix, Gilles
Ya se han plantado plantaciones de eucalipto en regiones con escasez de agua y alto riesgo de sequías severas, o se están expandiendo hacia ellas. En un futuro con un clima más seco y variable, incluyendo fenómenos extremos, la capacidad de los árboles para recuperarse tras sequías severas se convierte en un factor crucial para la sostenibilidad de las plantaciones forestales. En Brasil se realizó un experimento original que implicó una reducción del 80% en la precipitación para comprender mejor las respuestas de los árboles de Eucalyptus grandis al déficit hídrico extremo prolongado (3 años) y la capacidad de esta especie para recuperarse tras el estrés hídrico. Nuestro estudio se centró en los cambios en el área basal, el radio del fuste y la altura total medidos mediante dendrómetros de alta resolución temporal y estudios periódicos de árboles afectados por una reducción del 80% en la precipitación (grupo de tratamiento) y en un grupo de control. Se compararon las diferencias en el área basal, el radio del fuste y la tasa de crecimiento de la altura total entre los grupos durante (i) 37 meses de reducción del 80% en la precipitación y (ii) 31 meses después del final de la reducción del 80%. Se determinaron las correlaciones entre las tasas de crecimiento, las fluctuaciones del radio del fuste y las variables meteorológicas en cada grupo para comprender mejor las respuestas de los árboles a las condiciones ambientales y el estado hídrico del fuste. La reducción del 80% en la precipitación a lo largo de 3 años redujo significativamente las tasas de crecimiento de los árboles en un 73% en área basal y un 95% en altura total. Sin embargo, con una disponibilidad hídrica normal tras la reducción de la precipitación, la tasa de crecimiento del área basal de los árboles sometidos a estrés hídrico fue un 97% mayor que la de los árboles de control, mientras que la tasa de crecimiento de la altura total fue solo un 8% mayor. A pesar del severo estrés hídrico, no se observó mortalidad de árboles. Los árboles recuperaron el 51% de su área basal durante el período de recuperación de 31 meses. En contraste, solo se recuperó el 5% de la altura total. En el grupo de tratamiento, se observaron respuestas rápidas a la variación en las precipitaciones durante el período de reducción del 80% en la precipitación. Asimismo, las correlaciones entre las fluctuaciones del radio del fuste y el déficit de presión de vapor indican un aumento de la transpiración tras la finalización de la reducción. Estas relaciones indican una alta conservación de la integridad del sistema vascular xilemático durante los tres años de reducción del 80% en la precipitación, un factor clave en la mayor resiliencia de los árboles. En ausencia de mortalidad de árboles, nuestros resultados sugieren que la reducción del 80% en la precipitación tuvo un impacto severo en el crecimiento de los árboles, pero demuestran una gran capacidad de recuperación de los árboles de Eucalyptus grandis en el crecimiento del área basal tras un déficit hídrico tan severo.
An Internet of Things (IoT) Based Block Chain Technology to Enhance the Quality of Supply Chain Management (SCM)
(Hindawi Limited, 2022-07) Rizwan, A.; Karras, Dimitrios Alexios; Mohan Kumar, Jitendra K.; Sánchez-Chero, Manuel Jesus; Mogollón Taboada, Marlon Martin; Cieza Altamirano, Gilder
Recent technological developments indicate possible advancements in supply chain management (SCM). ese innovations have attracted a lot of interest from industries including logistics, manufacturing, packaging, and transportation. e conventional systems, however, use centralised servers to control all operations, including the exchange of raw materials, making orders, dealing with buyers and sellers, and updating orders. e network’s supply chain may thus be insecure as a result of every activity being routed via centralised servers. e danger is additionally increased by a number of di culties, including scalability, data integrity, security, and availability. Block chain technology may be used in these circumstances to decentralise transaction processing and eliminate the need for a centralised controller. In this approach, the performance of the resource-constrained supply chain network is improved by the e ective use of edge computing and priority data access. e Intelligent K-Means (IKM) clustering algorithm is suggested across the edge nodes in the current research to categorise the priority level of each piece of data. is classi er determines if the edge node has received data that is high priority or low priority. Low priority data is recorded in the log les for future data analysis. en, to allow safe data ow in the open block chain while excluding outside parties, the High Priority Access based Smart Contract (HPASC) technique is deployed. e whole experiment was conducted in a Python environment, and variables including scalability, reaction time, throughput, and accuracy were studied. Current systems’ constrained block sizes and fork creation lengthen the time transactions must wait before being processed. e suggested methodology is quicker and uses less storage space than current block chain systems. e results show that the suggested approach works better than current blockchain technology to raise the standard of supply chain management.
Analysis of Germination Curves of Cinchona officinalis L. (Rubiaceae) Using Sigmoidal Mathematical Models.
(Hindawi Limited, 2023-01) Quiñones Huatangari, Lenin; Huaccha-Castillo, Annick Estefany; Fernandez-Zarate, Franklin Hitler; Morales-Rojas, Eli; Marrufo-Jiménez, Jenny Del Milagro; Mejía-Córdova, Leslie Lizbeth
Seed germination is the fundamental phenomenon that determines the successful growth and development of each plant species, even more so in Cinchona ofcinalis, which is a forest species that stands out for its medicinal importance. Te objective of this work was to determine the best sigmoidal mathematical model describing the germination of C. ofcinalis. For the germination test, a completely randomized design was used with six treatments and three replicates per treatment; 100°C. ofcinalis seeds were used per replicate, and 1800 seeds were needed in the trial. Gompertz sigmoidal, logistic, and von Bertalanfy models were used to analyse the germination curves of C. ofcinalis. Te results of these adjustments were analysed based on the graphic representation and statistical criteria (Akaike’s value (AIC), R2, and R2 ai). Te results suggest that the Gompertz and logistic models have a better graphic representation, showing values close to those observed, while the von Bertalanfy model shows negative germination values. According to the statistical criteria, the lowest AIC and the highest were obtained. R2 and R2 ai with the Gompertz model, followed by the logistic model and von Bertalanfy. It is concluded that the Gompertz model can represent the shape of the germination curves of C. ofcinalis for the six treatments of the test.
Analytical optimisation of eco-friendly soap production using hyperspectral imaging and chemometric modelling of physicochemical properties.
(Elsevier, 2025-08) Jara-Vélez, Joe Richard; Siche, Raúl;; Velásquez-Barreto, Frank Fluker; Salazar Campos, Juan Orlando; Lopez, Ysolina; Salazar-Campos, Johonathan
The pressing environmental imperative to curb petrochemical detergent pollution has driven the development of circular approaches that valorise waste lipids. In this work, we establish an integrated chemometric–hyperspectral framework to optimise bar soap production from used frying oils (UFOs). A fractional Taguchi screening, followed by a central composite rotatable design (CCRD), systematically evaluated the effects of NaOH concentration (14–22 % w/v) and NaOH/UFO ratio (0.30–0.70) on soap pH and mechanical hardness. The optimal formulation (14.08 % NaOH; ratio 0.30) yielded bars with pH 10.31 ± 0.02 and hardness 359.6 ± 5.2 g, alongside superior textural resilience and cohesion. Near-infrared hyperspectral imaging (896–1704 nm) coupled with partial least squares regression (PLSR) enabled non-invasive, real-time pH prediction (R2 = 0.83; SEP = 0.18), while a simplified multiple linear regression (MLR) model refined alkalinity forecasts to R2 = 0.87. Hardness modelling (R2 < 0.60) highlighted the need for advanced variable-selection and nonlinear strategies to capture complex microstructural dynamics. By uniting NIR-HSI with data-driven calibration, our methodology delivers rapid quality control, reduces reliance on laborious assays and demonstrates a scalable, sustainable template for eco-innovative personal-care manufacturing.
Analytical Solution to the Generalized Complex Duffing Equation.
(Hindawi Limited, 2022-11) Salas S., Alvaro H.; Cieza Altamirano, Gilder; Martínez H, Lorenzo J.
Future scientifc and technological evolution in many areas of applied mathematics and modern physics will necessarily depend on dealing with complex systems. Such systems are complex in both their composition and behavior, namely, dealing with complex dynamical systems using diferent types of Dufng equations, such as real Dufng equations and complex Dufng equations. In this paper, we derive an analytical solution to a complex Dufng equation. We extend the Krylov–Bogoli´ubov–Mitropolsky method for solving a coupled system of nonlinear oscillators and apply it to solve a generalized form of a complex Dufng equation.
Antimicrobial and production of hydrolytic enzymes potentials of bacteria and fungi associated with macroalgae and their applications: a review.
(Frontiers Media, 2023-05) Vega Portalatino, Edwin Jorge; Rosales-Cuentas, Miriam Marleni; Valdiviezo-Marcelo, Jaime; Arana-Torres, Nancy Maribel; Espinoza-Espinoza, Luis Alfredo; Moreno-Quispe, Luz A.; Cornelio-Santiago, Heber Peleg
Endophytic and epiphytic bacteria and fungi that live in association with macroalgae produce compounds that favor the growth of the host, being in some cases more efficient than those produced by the terrestrial microbiome. This review collects information from articles published in Scopus, ScienceDirect, PubMed, and Wiley Online Library. Articles were organized according to their antimicrobial properties, synthesis of hydrolytic enzymes, production of other bioactive compounds by bacteria and fungi, and their application. The information collected showed that bacteria and fungi associated with macroalgae have the ability to inhibit bacteria, fungi, yeasts, and protozoa that affect aquaculture, public health, and the food industry, reporting that the pyrenocines A, B, E, and S isolated from Phaeosphaeria sp. Inhibited pathogenic protozoa. Additionally, other compounds identified as alkaloids, steroids, triterpenoids, and flavonoids could act by altering the morphology and physiology of pathogenic microorganisms, which can be applied in the food, pharmaceutical, paper, chemical, textile, and cosmetic industries. In addition, these microorganisms can synthesize enzymes such as xylanase, amylase, cellulase, pectinase, agarase, lignocellulose, chitinase, gelatinase, asparaginase, glutaminase, and lipase, which can be used to reduce oxidation and enzymatic browning, improve digestibility and functionality of feed, synthesis of chitin oligomers with antimicrobial properties, bioremediation of agricultural residues and industrial effluents, and production of hydrolysates.
Application of Machine Learning in the Discrimination of Citrus Fruit Juices: Uses of Dielectric Spectroscopy.
(Institute of Electrical and Electronics Engineers, 2020-10) Chuquizuta, Tony Steven; Oblitas, Jimy; Arteaga, Hubert; Castro, Wilson Manuel
Nowadays, process control in the juice industry requires fast, safe and easily applicable methods. In this regard, the use of dielectric spectroscopy is being coupled to statistical methods such as machine learning in order to develop new methods to identify adulteration. However, there is a small number of scientific reports above the application of the aforementioned methods when citric fruit juices is being identified. Therefore, the objective of this research was to evaluate dielectric spectroscopy and four different classification techniques (Support Vector Machine - SVM, K-nearest neighbor-KNN, Linear Discriminat -LD and Quadratic Discriminat-QD) to discriminate between three citrus juices. For this purpose, samples of Citrus limetta, Citrus limettioides and Citrus reticulata were evaluated; obtaining its dielectric spectral profiles in the range of 5 to 9 GHz. Then from the spectral profiles the loss factor (e”) was calculated using the reflection coefficient. Next e” value was pretreated, reducing noise through a savitzky golay filter, and new variables created through Principal Component Analysis (PCA). Finally, the models for classification were constructed with the previously mentioned techniques and the principal components. The results shown that using four components the variance can be explained in 97%; likewise, the discrimination values vary between 88.9 and 100.0%, with SVM, LD and QD the best discrimination techniques all successfully at 100.0 %. Therefore; It is concluded that the technique of dielectric spectroscopy and machine learning presents potential for the discrimination of citrus fruit juices.
Artificial intelligent investigations for the dynamics of the bone transformation mathematical model.
(Elsevier, 2022-10) Cholamjiak, Watcharaporn; Sabir, Zulqurnain; Zahoor Raja, Muhammad Asif; Sánchez-Chero, Manuel Jesus; Oseda Gago, Dulio; Sánchez-Chero, José Antonio; Seminario-Morales, Maria Veronica; Oseda Gago, Marco Antonio; Agurto Cherre, Cesar Augusto; Cieza Altamirano, Gilder
In this study, the stochastic numerical solutions of the fractional myeloma bone disease system (FMBDS) have been presented. The fractional order investigation provides more accurate solutions of the FMBDS. The FMBDS is classified into three dynamics and the solution of each class is presented by using the artificial neural network enhanced by the scale conjugate gradient procedures (ANN-SCGPs). Three different fractional order performances have been used to present the solutions of the FMBDS by applying the ANN-SCGPs. The statics is chosen as 11%, 12% and 77% for training, testing and verification. Twelve number of hidden neurons with input and output layers have been proposed for the FMBDS. The comparison of proposed and reference solutions is performed that shows the accuracy of the ANN-SCGPs. The consistency, validity, precision, and capability of the ANN-SCGPs can be judged based on the state transitions values, regression actions, correlation behaviors, error histograms, and mean square error data.
Artificial neural network procedures for the waterborne spread and control of diseases.
(American Institute of Mathematical Sciences, 2022-11) Ruttanaprommarin, Naret; Sabir, Zulqurnain; Sandoval Núñez, Rafaél Artidoro; Salahshour, Soheil; García Guirao, Juan Luis; Weera, Wajaree; Botmart, Thongchai; Klamnoi, Anucha
bstract: In this study, a nonlinear mathematical SIR system is explored numerically based on the dynamics of the waterborne disease, e.g., cholera, that is used to incorporate the delay factor through the antiseptics for disease control. The nonlinear mathematical SIR system is divided into five dynamics, susceptible X(u), infective Y(u), recovered Z(u) along with the B(u) and Ch(u) be the contaminated water density. Three cases of the SIR system are observed using the artificial neural network (ANN) along with the computational Levenberg-Marquardt backpropagation (LMB) called ANNLMB. The statistical performances of the SIR model are provided by the selection of the data as 74% for authentication and 13% for both training and testing, together with 12 numbers of neurons. The exactness of the designed ANNLMB procedure is pragmatic through the comparison procedures of the proposed and reference results based on the Adam method. The substantiation, constancy, reliability, precision, and ability of the proposed ANNLMB technique are observed based on the state transitions measures, error histograms, regression, correlation performances, and mean square error values.
Artificial neural network-based heuristic to solve COVID-19 model including government strategies and individual responses.
(Elsevier, 2022-08) Botmart, Thongchai;; Sabir, Zulqurnain; Javeed, Shumaila; Sandoval Núñez, Rafaél Artidoro; Wajaree Weera; Ali, Mohamed R.; Sadat, Rahma
The current work aims to design a computational framework based on artificial neural networks (ANNs) and the optimization procedures of global and local search approach to solve the nonlinear dynamics of the spread of COVID-19, i.e., the SEIR-NDC model. The combination of the Genetic algorithm (GA) and active-set approach (ASA), i.e., GA-ASA, works as a global-local search scheme to solve the SEIR-NDC model. An error-based fitness function is optimized through the hybrid combination of the GA-ASA by using the differential SEIR-NDC model and its initial conditions. The numerical performances of the SEIR-NDC nonlinear model are presented through the procedures of ANNs along with GA-ASA by taking ten neurons. The correctness of the designed scheme is observed by comparing the obtained results based on the SEIR-NDC model and the reference Adams method. The absolute error performances are performed in suitable ranges for each dynamic of the SEIR-NDC model. The statistical analysis is provided to authenticate the reliability of the proposed scheme. Moreover, performance indices graphs and convergence measures are provided to authenticate the exactness and constancy of the proposed stochastic scheme.
Assessing Data Analytics Capabilities in Retail Organizations: Insights into Mining, Predictive Analytics and Machine Learning.
(World Scientific and Engineering Academy and Society., 2024-07) Pariona Luque, Rosario Blanca; Pacheco, Alex Abelardo; Vegas-Gallo, Edwin; Castanho, Rui Alexandre; Lema, Fabián; Huaman-Valle, Angela; Añaños-Bedriñana, Marco A.; Marín, Wilson; Felix-Poicon, Edwin Carlos Lenin; Loures, Ana
Nowadays, implementing data analytics is necessary to improve the collection, evaluation, analysis, and organization of data that allow the discovery of patterns, correlations, and trends that improve knowledge management, development of strategies, and decision-making in the organization. Therefore, this study aims to provide an accurate and detailed assessment of the current state of data analytics in the retail sector, identifying specific areas of improvement to strengthen knowledge management in organizations. The research is applied with a quantitative approach and non-experimental design at a descriptive and propositional level. The survey technique was used, and as a data collection instrument, a questionnaire addressed to 351 employees of companies in the retail sector concerning the variable data analysis with the dimensions of data extraction, predictive analysis, and machine learning and the variable management of the knowledge with the dimensions knowledge creation and knowledge storage. The results show that 52.99% of collaborators indicate that the level of data extraction is terrible, 57.83% indicate that the level of predictive analysis is wrong, and 54.99% express that the level of machine learning is average, which contributes to the implementation of innovative resources and solutions that promote the inclusion of a high-tech approach to address information management problems and contribution to the development of knowledge in an institution.
Caracteres morfométricos como indicadores de calidad de sitio de Tara spinosa (Leguminosae, Caesalpinioideae) en Cajamarca, Perú.
(Fundacion Miguel Lillo, 2022-04) Villena-Velásquez, Jim J.; Muñoz Chávarry, Pacífico; Seminario, Juan F.; Martínez Sovero, Gustavo
Tara spinosa (Molina) Britton & Rose [= Caesalpinia spinosa (Molina) Kuntze] (Fabaceae) es una especie económicamente importante para el Perú y particularmente para el departamento de Cajamarca. Presenta plasticidad adaptativa a gradientes altitudinales (0 a 3000 msnm) y tipos de suelo. Sin embargo, no existen estudios sobre su calidad de sitio, los cuales son importantes para entender los requerimientos ecológicos y de manejo. El objetivo de la presente investigación fue evaluar la expresión de los caracteres morfométricos como indicadores de la calidad de sitio de Tara spinosa, para esto se tomaron muestras procedentes de 15 localidades del departamento de Cajamarca, Perú. De 15 árboles elegidos al azar se midió la altura y se colectaron 50 frutos secos en planta, a las que se les midió el largo, ancho y peso. Alrededor de cada árbol evaluado se extrajo una muestra del suelo del horizonte A, para analizar en laboratorio 17 factores físico-químicos. Los datos climáticos promedio, se obtuvieron de la base del Servicio Nacional de Meteorología e Hidrología del Perú. El análisis de varianza indicó que existen diferencias estadísticas ntre los materiales y la prueba de Scott-Knott determinó los mejores materiales. El análisis de correlación lineal de Pearson (p < 0,05) entre 21 variables (17 de suelo y cuatro biométricas) identificó el grado de asociación entre las variables edáficas y las de las plantas. Se encontraron diez modelos de regresión lineal que explican el largo y el peso del fruto. Se concluye en que estos dos caracteres son buenos indicadores de la calidad de sitio de Tara spinosa.
Características de la fibra de alpaca huacaya de cotaruse, Apurímac, Perú
(Universidad Nacional Mayor de San Marcos, 2017-08) Machaca Machaca, Virgilio; Bustinza Choque, Víctor; Corredor Arizapana, F. A.; Paucara Ocsa, V.; Quispe Peña, E. C.; Machaca Machaca, R.
El objetivo del estudio fue establecer el perfil de las principales características físicas de la fibra de alpaca que pueden servir para su mejor comercialización y para fines de mejoramiento genético. Se hicieron mediciones de 145 muestras de colores blanco, intermedio y oscuro pertenecientes a alpacas de cinco comunidades del distrito de Cotaruse, Apurímac, Perú, utilizando el equipo ODFA 2000 para determinar el diámetro de fibra (DF), el coeficiente de variación (CV[DF]), el factor de confort (FC) y el índice de curvatura (IC). Se estimaron los efectos de la comunidad, el sexo, edad, color de la fibra y sitio de muestreo sobre el DF, CV(DF), FC e IC y la relación entre ellos. El promedio del diámetro de fibra (MDF) estuvo influenciado por la edad (p<0.01) (valores entre 21.61 y 24.32 µm), por color de fibra (22.30, 23.81 y 26.69 µm para blanco, intermedio y oscuro, respectivamente) y por comunidad (de 21.9 µm para Iscahuaca a 24.2 µm para San Miguel de Mestizas) y por sexo (p<0.05), siendo la fibra de las hembras 1 µm más fina que la de los machos; sin embargo, no hubo diferencias significativas por la zona corporal de la toma de la muestra. El CV(DF) mostró diferencias significativas por efecto de la edad (p<0.01) y por sexo y sitio de muestreo (p<0.05), pero sin diferencias por color y comunidad. El FC tuvo diferencias significativas (p<0.01) por efecto de la comunidad y color de la fibra, así como por edad, sexo y sitio de muestreo (p<0.05). El IC tuvo diferencias significativas debido a la comunidad (p<0.01), edad, sexo y color (p<0.05), pero no por el sitio de muestreo. La MDF presentó una alta y negativa correlación con FC (r=-0.99) e IC (r=-0.61) y la FC presentó una correlación positiva con IC (r=0.62). No se encontró una definición clara del sitio apropiado para el muestreo del vellón de alpaca, pero se puede realizar a la edad de 1 año. Las alpacas del distrito de Cotaruse, Apurímac, indistintamente del color del vellón, producen una buena calidad de fibra y hay un gran potencial de variabilidad para su mejoramiento genético.
Características tecnológicas de la fibra de llama (Lama glama) chaku antes y después de descerdar.
(Universidad Nacional Mayor de San Marcos, 2016-06) Laime Huarcaya, Flor de María; Pinares Huamaní, Rubén; Paucara Ocsa, Valeriano; Machaca Machaca, Virgilio; Quispe Peña, Edgar Carlos
Se evaluaron cinco características tecnológicas de la fibra de llama: diámetro medio de fibra (MDF), coeficiente de variación de MDF (CVMDF), factor de confort (FC), índice de curvatura (IC) y finura al hilado (FH) antes y después de descerdar. Se tomaron muestras de 10 g de fibra de vellones de 227 llamas Chaku de la región Apurímac, Perú. Las fibras sin descerdar y descerdadas fueron analizadas con el equipo OFDA 2000 (Optical Fibre Diameter Analyser). Se consideraron las variables sexo y edad (1-2,>2 años) en el análisis estadístico a través de un diseño completamente al azar con arreglo factorial. Los resultados indican que la fibra descerdada es de mejor calidad, disminuyendo la MDF (0.70 µm), el CVMDF (1.8%) y la FH (1.06 µm) e incrementando el FC (2.74%) y el IC (4.66°/mm). Asimismo, el sexo no tuvo un efecto significativo en las características tecnológicas de la fibra pero las llamas juveniles presentaron mejor calidad de fibra. Se concluye que el descerdado y la edad de la llama tienen efectos significativos sobre la finura y su variación, el factor de confort, el índice de curvatura y la finura al hilado.

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