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.
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 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.
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.
Case study of autocatalysis reactions on tetra hybrid binary nanofluid flow via Riga wedge: Biofuel thermal application.
(Elsevier, 2023-05) Sajid, Tanveer; Gari, Abdullatif Abdulhadi; Jamshed, Wasim; Eid, Mohamed R.; Abdulhafiz, Nazrul Islam; Irshad, Kashif; Cieza Altamirano, Gilder; M Tag El Din, El Sayed
Ethanol and biodiesel, which both belong to the first generation of biofuel technology, are the two most popular forms of biofuels now in use. In order to create next-generation biofuels using waste, cellulosic biomass, and algae-based resources, the Bioenergy Technologies Office (BETO) is working with business. The present article is designed to study the effect of tetra hybrid nanoparticles with the utilization of the novel Hamilton and Crosser model and catalytic reaction on binary fluid with the consideration of ethanol biofuel as a base fluid. Ethanol comprising tetra hybrid nanoparticles and heterogeneous catalytic reaction amplifies thermal conductivity and significantly increases brake thermal efficiency and reduces brake-specific fuel consumption in diesel engines. Therefore, the aim of this article is a theoretical checkup that is related to heat and mass transport of biofuel flow considering binary fluid accompanied with autocatalysis reaction and novel tetra hybrid nanoparticles on biofluid flow subjected to a Riga wedge. Transportation of heat via nonuniform heat source/sink, thermal radiation, and thermal conductivity are in our consideration. The mathematics of the assumed problem generates the system of non-linear partial differential equations from basic equations of momentum, energy, and mass. The usage of non-dimensional variables gave a system of non-linear ODEs with boundary conditions which are further dealt with in the Lobatto111A scheme. The results are obtained for stretching/shrinking wedge with several parameters. From obtained results, it is observed that the velocity field diminishes owing to magnification in Weissenberg number and Casson fluid parameter. The temperature field diminishes by amplifying heat generation, thermal radiation, and variable thermal conductivity parameter. Concentration distribution escalates by rising homogeneous reaction parameters.
Catalysis reaction influence on 3D tetra hybrid nanofluid flow via oil rig solar panel sheet: Case study towards oil extraction.
(Elsevier, 2023-07) Sajid, Tanveer; Jamshed, Wasim; Algarni, Salem A.; Alqahtani, Talal; Mohamed R. Eid; Irshad, Kashif; Cieza Altamirano, Gilder; M Tag El Din, El Sayed; Nahar Tajer, Khadiga Wadi
The power that comes from renewable sources is made back faster than it is used. Infinitely renewable examples include solar and wind energy. There are many sources of clean energy all around us. Motive behind current study: A framework has now been developed throughout this research to investigate the impact of the sun’s rays on novel Xue tetra hybrid magnetized cross nano-liquid as it moves through an expanding sheet inside that PV sheet of a solar module installed on top of such an offshore solar oil field. A novel Xue tetra hybrid nanofluid model has been developed and utilized to explore the impact of tetrahybrid nanoparticles on liquid flowing in order to investigate heat transport analysis. By directing the liquid’s motion through a magnetic field, we may examine its velocity impact. The concentration aspect of nanofluid has been examined by including homogeneous and heterogeneous chemical processes, and indeed, the temperature accessibility of cross-nanofluid is obtained by taking into consideration effects like thermal conductivity and thermal radiation. Technique: The PDEs derived from the working prototype are then subjected to the resemblance factors, yielding the ODEs. The bvp4c scheme is used to get numerical results. The procedure is an ODE solver that belongs to the Runge-Kutta family. In order to provide precise answers in the fourth and fifth orders, it performs six function evaluations. The discrepancy between them is therefore interpreted as the solution’s (fourth-order) inaccuracy. Adaptive stepsize integration techniques greatly benefit from this error estimation. Fehlberg’s (RKF) integration strategy is quite similar to Cash and Carp technique. Significant finding: From obtained results, it is detected that the addition of novel tetrahybrid nanoparticles in the conventional liquid (Xue tetrahybrid nanofluid) enhances heat transfer rate and temperature profile more quickly in contrast to conventional ternary hybrid nanofluid and dihybrid nanofluid available in the existing literature. The thing which makes the present work novel and one step ahead in contrast to existing available literature is the development and implementation of a tetrahybrid nanofluid model in contrast to conventional ternary hybrid and dihybrid nanofluid models. Tetra m hybridity nanoliquid generates extra heat in distinction to already existing ternary hybridity and dihybrid nanofluid models and more heat is absorbed by PV sheets in the existence of tetrahybrid nanoparticles. Conclusions: When the nanomaterials are submerged inside this liquid, their temperature increases throughout both the shear thinning and shear thickening cases. This is because the homogeneous chemical reaction mimics the migration of nanoparticles within the liquid. The increased magnetic force, in addition to an enhancement of Lorentz’s force, slows down the nanofluid motion. The increased volume percentage of tetrahybrid nanoparticles, thermal radiation, and thermal conductivity boost the heat that’s captured by a PV sheet, and that heat is utilized for other functions, such as drilling or navigation, on that solar offshore rig. Cost analysis: Cost analysis has also been presented in terms of Pakistan offshore oil rigs in terms of solar panels and diesel generators and it is observed that 49% cost is reduced if we replaced offshore oil rigs with solar-powered energy.
Characterization and Evaluation of the Efficiency of Organic Amendments and Native Macrophytes for the Treatment of Acid Mine Drainage in Hualgayoc—A Case Study.
(Multidisciplinary Digital Publishing Institute, 2025-04) Nuñez-Bustamante, Ever; Césare-Cora, Mary Flor; Cuba Torre, Hector Ricardo; Nuñez-Bustamante, Nelve; Sempértegui-Rafael, Roxana Mabel; Cornejo-La Torre, Melitza; Cueva, Mario D.; Arribasplata-Vargas, Marco Antonio; Castro-Pantoja, Jhimy Brayam; Virú-Vásquez, Paul Hermes
Acid mine drainage (AMD) is a major environmental issue due to its high heavy metal concentrations and low pH, posing risks to ecosystems and human health. In Hualgayoc, Cajamarca, Peru, AMD contamination from mining activities necessitates effective remediation strategies. This study evaluated the removal efficiency of organic amendments, municipal waste compost (MWC), cattle manure compost (CMC), vermicompost (HMS), corn stalk biochar (CSB), sludge biochar (SLB), pine biochar (PBC), and native macrophytes Carex pichinchensis (CAX), Juncus ecuadoriensis (JSP), and Myriophyllum aquaticum (MYA) in removing As, Cd, Cu, Fe, and Zn. A physicochemical characterization of AMD, amendments, and plants was conducted, and sorption capacity was determined through triplicate analyses. The results showed that SLB achieved a 100% Zn removal, while CSB removed over 90% of Cu and Fe. Among the organic amendments, CMC demonstrated the highest As and Cd removal (~100%). Regarding macrophytes, CAX achieved a ~97% Fe removal, and MYA exhibited Cd affinity. CSB had the highest sorption capacity for Cu (0.139 mg/g), Fe (1.942 mg/g), and Zn (0.149 mg/g), highlighting its potential for metal remediation. Organic amendments enhanced metal adsorption and stabilization, outperforming macrophytes in heavy metal removal. Combining organic amendments with native plants is recommended to assess their synergistic potential for AMD remediation.
Chlorella sp. inoculum doses affect ethinylestradiol removal in a wastewater treatment plant in the Peruvian Andes.
(Eduem - Editora da Universidade Estadual de Maringa, 2024-06) Chávez Collantes, Azucena; Solórzano Acosta, Richard Andi; Velarde Apaza, Leslie Diana; Campos Ruiz, Joseph; Alva Plasencia, Edin Edgardo; Castillo Rojas, Eisner Will
The microalgae Chlorella sp. have demonstrated efficiency in the removal of pharmaceutical contaminants. However, there is no agreement on the inoculum dose since it depends on the contaminant concentration and other very specific parameters in each case. This study aims to evaluate the effect of Chlorella sp. inoculum doses on ethinylestradiol (EE) removal from wastewater treatment plant effluent in Celendín district of the province of Celendín, Cajamarca region, Peru. Four doses of inoculum (0, 100, 200, and 300 mL) were tested at a 2,1x106 cell∙mL-1 microalgae cell concentration and 4 mg∙L -1 of ethinylestradiol in photobioreactors. The final concentration of ethinylestradiol was determined after 20 days through High Performance Liquid Chromatography (HPLC). It was evidenced that a dose of 300 mL∙L -1 of Chlorella sp. could remove 96,49% of ethinylestradiol from wastewater, unlike the other tested concentrations, which were below 80,92% removal. It was concluded that at a higher dose of Chlorella sp. inoculum, a higher ethinylestradiol removal percentage was observed in a wastewater sample from a treatment plant in the Peruvian Andes.
Communication as a Philosophical-Cultural Problem Notes for your Understanding
(Universidad del Zulia, 2022-07) Oseda Gago, Dulio; Añaños-Bedriñana, Marco A.; Mendivel Geronimo, Ruth Katherine; Morales Bedoya, Marco Antonio
In the 21st century, the changes caused by globalization and by the social dynamics of the new post-COVID-19 normality pose problems when it comes to defining communication relationships between individuals. From this point of view, communication problems involve several edges, difficult to face. In most cases, communication failures occur, due to the complex variety of nuances present in communication, as well as the lack of understanding of its concepts and the great breadth of cultural expressions, which can be conceived as diverse, not find space in everyday scenarios. In this sense, analyzing the scope of communication embodies a problem for philosophy, especially for philosophical anthropology, since it values communication as a representative act of cultural identity, since it responds to the ways of expressing, orally or gesturally, the human interior. This cultural universe is wide and there are not enough analyzes to define how communication influences the processes of cultural transmission. Under the hermeneutic-documentary method, the article explores the communication process, as a means to express the ontological and epistemic reality of individuals, as a meeting space, a source that enriches culture, identity and social interaction.

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