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1D Convolutional Neural Network for Detecting Ventricular Heartbeats

Suárez-León, A. A.; Núñez, José R. (Universidad de la Costa, 2020)

This paper shows a novel approach for detecting ventricular heartbeats using a 1D Convolutional Neural Network (1D-CNN). The algorithm input is the raw ECG signal, i.e., no signal pre-processing nor feature extraction are ...
2′-Deoxyribosyltransferase from Leishmania mexicana, an efficient biocatalyst for one-pot, one-step synthesis of nucleosides from poorly soluble purine bases

Crespo, N.; Sánchez Murcia, Pedro Alejandro; Gago, Federico; Cejudo Sanches, J.; Galmes, M.A.; Fernandez Lucas, Jesus; Mancheño, José Miguel (Applied Microbiology and Biotechnology, 2017)

Processes catalyzed by enzymes offer numerous advantages over chemical methods although in many occasions the stability of the biocatalysts becomes a serious concern. Traditionally, synthesis of nucleosides using poorly ...
5G mobile phone network introduction in Colombia

Cama-Pinto, Dora; Damas, Miguel; Holgado-Terriza, Juan Antonio; Gómez Mula, Francisco; Calderin-Curtidor, Andrés Camilo; Martínez Lao, Juan; Cama-Pinto, Alejandro (Corporación Universidad de la Costa, 2021-04-13)

The deployment of the 5G mobile network is currently booming, offering commercially available services that improve network performance metrics by minimizing network latency in countries such as the USA, China, and Korea. ...
A 75 bus bars model to evaluate the steady state operation of a sub-transmission electrical power grid

Silva Ortega, Jorge I; Daza-Guzmán, J. D.; Candelo Becerra, John Edwin; Rueda-López, J. C.; Umaña Ibáñez, Samir; Salas-Navarro, Katherinne; Mercado Caruso, Nohora Nubia (Corporación Universidad de la Costa, 2017)

This paper presents a 75 bus bars system interconnected with generating units, transmission lines, distribution grids and other elements seeking to model a sub-transmission grid. The survey has been focused on estimate the ...
A biological classification for Alzheimer's disease - amyloid, tau and neurodegeneration (A/T/N): results from the Argentine-Alzheimer's disease neuroimaging initiative

Allegri, Ricardo F.; Pertierra, Lucía; Cohen, Gabriela; Chrem Méndez, Patricio; Russo, María Julieta; Calandri, Ismael Luis; Bagnati, Pablo M.; Tapajóz, Fernanda; Clarens, Florencia; Campos, Jorge A.; Nahas, Federico E.; Surace, Ezequiel I.; Vázquez, Silvia E.; Sevlever, G. E. (Universidad de la Costa, 2019)

The aim of this study was to describe the A/T/N research framework classiﬁcation for AD in the Argentine ADNI cohort and to explore the prediction to develop dementia in a prospective two-year follow-up.
A bump-hole strategy for increased stringency of cell-specific metabolic labeling of rna

Nguyen, Kim; Kubota, Miles; Del Arco, Jon; Feng, Chao; Singha, Monika; Beasley, Samantha; Sakr, Jasmine; P. Gandhi, Sunil; Blurton-Jones, Mathew; Fernández Lucas, Jesus; C. Spitale, Robert (Corporación Universidad de la Costa, 2020-11-21)

Profiling RNA expression in a cell-specific manner continues to be a grand challenge in biochemical research. Bioorthogonal nucleosides can be utilized to track RNA expression; however, these methods currently have limitations ...
A BWS application to identify factors affecting user preferences for parking choices at university campuses

Orozco-Fontalvo, Mauricio; Martínez, Sheila; Arellana, Julian; Vega, Laura (Corporación Universidad de la Costa, 2020)

Parking around university campuses has become a major issue in recent decades because of nearby congestion impacts. Objective: To determine the factors influencing parking lot selection, which is crucial to propose adequate ...
A case of food supply chain management with ahp, dematel, and topsis

Ortiz Barrios, Miguel Angel; Miranda‐De la Hoz, Carlos; Meza López, Pedro; Petrillo, Antonella; De Felice, Fabio (Universidad de la Costa, 2019-08-27)

The bullwhip effect leads to considerable inefficiencies along the food supply chain such as missed production schedules, poor customer service, excessive inventory, and misguided capacity plans. To tackle this problem, ...
A case of food supply chain management with AHP, DEMATEL, and TOPSIS

Ortiz Barrios, Miguel Angel; Miranda‐De la Hoz, Carlos; Meza López, Pedro; Petrillo, Antonella; De Felice, Fabio (Universidad de la Costa, 2019-08-27)

The bullwhip effect leads to considerable inefficiencies along the food supply chain such as missed production schedules, poor customer service, excessive inventory, and misguided capacity plans. To tackle this problem, ...
A collaborative EPQ inventory model for a three-echelon supply chain with multiple products considering the effect of marketing effort on demand

Salas-Navarro, Katherinne; Acevedo-Chedid, Jaime; Florez, Whady F; Ospina Mateus, Holman; Cárdenas-Barrón, Leopoldo Eduardo; Sankar Sana, Shib (Corporación Universidad de la Costa, 2020)

This paper presents an inventory model for a three-echelon supply chain with multiple products and multiple members considering the demand as an increasing function of the marketing effort. In the proposed inventory model, ...
A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images

Escorcia-Gutierrez, Jose; Torrents-Barrena, Jordina; Gamarra, Margarita; Romero-Aroca, Pedro; Valls, Aida; Puig, Domenec (Corporación Universidad de la Costa, 2021-07-15)

Retinal disorders are a severe health threat for older adults because they may lead to vision loss and blindness. Diabetic patients are particularly prone to suffer from Diabetic Retinopathy. Identifying relevant structural ...
A comparison study of MPC strategies based on minimum variance control index performance

BORRERO-SALAZAR, Alex A.; CARDENAS-CABRERA, Jorge M.; BARROS-GUTIERREZ, Daniel A.; JIMÉNEZ-CABAS, Javier A. (Espacios, 2019-07)

Model Predictive Control (MPC) is a useful tool when controlling processes that handle a large number of input and output variables. This study presents a comparison of different MPC strategies when they are subjected ...
A complete psychophysiological profile of a paralympic athlete in a ultraendurance. a case study

Belinchon-De-Miguel, Pedro; Ruisoto, Pablo; Clemente Suárez, Vicente Javier (Association for Computing Machinery, 2018-10-24)

Psychophysiological response of athletes with spinal cord injurie has not been reported yet in scientific literature. The aim of this study is to analyze the specific psychophysiological response in a Paralympic athlete ...
A comprehensive study of biofilms growing on the built heritage of a caribbean industrial city in correlation with construction materials

Gallego Cartagena, Euler; MORILLAS, HECTOR; Maguregui, Maite; Patiño Camelo, Karen Lisana; Marcaida, Iker; MORGADO GAMERO, WENDY BEATRIZ; O. Silva, Luis F.; Madariaga, Juan Manuel (Universidad de la Costa, 2019)

Biodeteriogens growing on constructions belonging to the built heritage is one of the leading natural pathologies that cause aesthetical and in some cases, physical-geochemical problems in the materials. In this study, for ...
A computer simulation approach to reduce appointment lead-time in outpatient perinatology departments: a case study in a maternal-child hospital

Ortiz Barrios, M.; Jiménez Delgado, G.; De Ávila Villalobos, J. (Universidad De La Costa, 2017-06-25)

A significant problem in outpatient perinatology departments is the long waiting time for pregnant women to receive an appointment. In this respect, appointment delays are related to patient dissatisfaction, no shows and ...
A Cost Estimating Method for Agile Software Development

Aziz Butt, Shariq; Misra, Sanjay; Piñeres-Espitia, Gabriel; Ariza-Colpas, Paola; Mohan Sharma, Mayank (2021)

In every software development project, the software effort estimating procedure is an important process in software engineering and always critical. The consistency of effort and timeline estimation, along with several ...
A deep learning-based hybrid model for recommendation generation and ranking

Sivaramakrishnan, N.; Subramaniyaswamy, V.; amelec, viloria; Vijayakumar, V.; Senthilselvan, N. (2020)

A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect ...
A dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 context

Ortíz-Barrios, Miguel; Petrillo, Antonella; De Felice, Fabio; Jaramillo-Rueda, Natalia; Jiménez-Delgado, Genett; Borrero-López, Luz (Corporación Universidad de la Costa, 2021-05-31)

Scheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key ...
A FAHP-VIKOR approach for supporting the selection of tomography equipment in LMIC hospitals: a case study

Ortíz-Barrios, Miguel; Jaramillo-Rueda, Natalia; Petrillo, Antonella; Fernández-Mendoza, Zaury; Vidal-Pacheco, Lucelys (Corporación Universidad de la Costa, 2021)

Computerized Tomography Scanners (CT-SCAN) provide detailed cross-sectional images of the human body which are employed for the easier detection and further analysis of abnormalities concerning the functionality and structure ...
A fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer

Fernández Blanco, Juan Carlos; Corrales Barrios, Luis Benigno; Hernández González, Félix Herminio; Benitez Pina, Israel Francisco; Núñez Alvarez, José Ricardo (Corporación Universidad de la Costa, 2021)

For the safety and continuity of service in industrial electrical systems, the availability of transformers is essential. For this reason, it is necessary to develop intelligent fault diagnosis techniques to reduce repair ...

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1D Convolutional Neural Network for Detecting Ventricular Heartbeats
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Suárez-León, A. A. | 2020

This paper shows a novel approach for detecting ventricular heartbeats using a 1D Convolutional Neural Network (1D-CNN). The algorithm input is the raw ECG signal, i.e., no signal pre-processing nor feature extraction are involved. The output of the 1D-CNN is filtered using a combination of linear and nonlinear filters to produce the final output. The MIT-BIH arrhythmia database was used for both algorithm training/tuning and evaluation. The assessment methodology followed the interpatient paradigm, where the algorithm was trained and evaluated using independent subsets. The performance of the proposed method was evaluated for two tasks; QRS detection, and heartbeat classification. QRS detection resulted in a sensitivity of 99.0% and a positive predictivity of 96.5%. The performance assessment of the ventricular ectopic beat detection resulted in a sensitivity of 85.8% and a positive predictivity of 64.5%. Although there is still room for improvement, the results suggest that convolutional neural networks are a promising approach for building heartbeat classifiers.

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2′-Deoxyribosyltransferase from Leishmania mexicana, an efficient biocatalyst for one-pot, one-step synthesis of nucleosides from poorly soluble purine bases
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Crespo, N. | 2017

Processes catalyzed by enzymes offer numerous advantages over chemical methods although in many occasions the stability of the biocatalysts becomes a serious concern. Traditionally, synthesis of nucleosides using poorly water-soluble purine bases, such as guanine, xanthine, or hypoxanthine, requires alkaline pH and/or high temperatures in order to solubilize the substrate. In this work, we demonstrate that the 2'-deoxyribosyltransferase from Leishmania mexicana (LmPDT) exhibits an unusually high activity and stability under alkaline conditions (pH 8-10) across a broad range of temperatures (30-70 °C) and ionic strengths (0-500 mM NaCl). Conversely, analysis of the crystal structure of LmPDT together with comparisons with hexameric, bacterial homologues revealed the importance of the relationships between the oligomeric state and the active site architecture within this family of enzymes. Moreover, molecular dynamics and docking approaches provided structural insights into the substrate-binding mode. Biochemical characterization of LmPDT identifies the enzyme as a type I NDT (PDT), exhibiting excellent activity, with specific activity values 100- and 4000-fold higher than the ones reported for other PDTs. Interestingly, LmPDT remained stable during 36 h at different pH values at 40 °C. In order to explore the potential of LmPDT as an industrial biocatalyst, enzymatic production of several natural and non-natural therapeutic nucleosides, such as vidarabine (ara A), didanosine (ddI), ddG, or 2'-fluoro-2'-deoxyguanosine, was carried out using poorly water-soluble purines. Noteworthy, this is the first time that the enzymatic synthesis of 2'-fluoro-2'-deoxyguanosine, ara G, and ara H by a 2'-deoxyribosyltransferase is reported.

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5G mobile phone network introduction in Colombia
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Cama-Pinto, Dora | 2021-04-13

The deployment of the 5G mobile network is currently booming, offering commercially available services that improve network performance metrics by minimizing network latency in countries such as the USA, China, and Korea. However, many countries around the world are still in the pilot phase promoted and regulated by government agencies. This is the case in Colombia, where the assignment of the first 5G band is planned for the third quarter of 2021. By analyzing the results of the pilot phase and the roadmap of the Colombian Ministry of Information and Communication Technologies (MinTIC), we can determine the main issues, which contribute to the deployment of 5G mobile technology as well as the plans to achieve a 5G stand-alone network from 4G networks. This is applicable to other countries in Latin America and the world. Then, our objective is to synthesize and share the most important concepts of 5G mobile technology such as the MIMO (multiple input/multiple output) antenna, RAN (Radio Access Network), C-RAN (Centralised-RAN), and frequency bands, and evaluate the current stage of its introduction in Colombia.

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A 75 bus bars model to evaluate the steady state operation of a sub-transmission electrical power grid
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Silva Ortega, Jorge I | 2017

This paper presents a 75 bus bars system interconnected with generating units, transmission lines, distribution grids and other elements seeking to model a sub-transmission grid. The survey has been focused on estimate the operating conditions of the transmission network during the steady state and transient using simulations tools. Results can be used to prevent, identify, and solve contingencies that affect the normal operation of the network in the short, medium and long term. They are also validated with real and updated information, demonstrating their reliability to evaluate power flow, short-circuits, reliability. Information can be used as reference by utilities, national operators in their usual activities of energy planning in the power grid. This applied survey contribute in validate operating ranges on forecasts of low, medium and high demand, control of active and reactive power; measure single and three phase levels of short-circuits in all buses; estimate the reliability of the power grid using the N-1 criteria; evaluate specific cases during steady state operation.

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A biological classification for Alzheimer's disease - amyloid, tau and neurodegeneration (A/T/N): results from the Argentine-Alzheimer's disease neuroimaging initiative
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Allegri, Ricardo F. | 2019

The aim of this study was to describe the A/T/N research framework classiﬁcation for AD in the Argentine ADNI cohort and to explore the prediction to develop dementia in a prospective two-year follow-up.

LEER
A bump-hole strategy for increased stringency of cell-specific metabolic labeling of rna
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Nguyen, Kim | 2020-11-21

Profiling RNA expression in a cell-specific manner continues to be a grand challenge in biochemical research. Bioorthogonal nucleosides can be utilized to track RNA expression; however, these methods currently have limitations due to background and incorporation of analogs into undesired cells. Herein, we design and demonstrate that uracil phosphoribosyltransferase can be engineered to match 5-vinyluracil for cell-specific metabolic labeling of RNA with exceptional specificity and stringency.

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A BWS application to identify factors affecting user preferences for parking choices at university campuses
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Orozco-Fontalvo, Mauricio | 2020

Parking around university campuses has become a major issue in recent decades because of nearby congestion impacts. Objective: To determine the factors influencing parking lot selection, which is crucial to propose adequate parking demand management strategies. Materials and Methods: We evaluate different attributes using a best-worst scaling survey applied at Universidad de la Costa (CUC), Colombia. Using discrete choice modeling techniques, we identified the extent to which selected infrastructure attributes influence parking behavior. Results: Security and cover (roof) availability are the most relevant attributes of parking choice in the case study. Conclusions: Based on our results, we strongly recommend implementing a dynamic pricing rate, roof pricing, removing “reserved spots” and investing in security.

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A case of food supply chain management with ahp, dematel, and topsis
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Ortiz Barrios, Miguel Angel | 2019-08-27

The bullwhip effect leads to considerable inefficiencies along the food supply chain such as missed production schedules, poor customer service, excessive inventory, and misguided capacity plans. To tackle this problem, it is necessary, apart from other interventions, to continuously monitor the performance of food suppliers so that the demand information flow, order batching, transportation planning, and inventory management can be substantially improved. Therefore, supplier assessment has then become critical decision‐making support for identifying and addressing inefficiencies of food providers, which ends up reducing the variation of several key logistics parameters for upstream members of the food supply chain. In addition, such assessment is of multicriteria nature given the presence of several criteria from different domains and various food suppliers. With these considerations in mind, this paper proposes a hybrid approach integrating the analytic hierarchy process (AHP), decision‐making trial and evaluation laboratory (DEMATEL), and the technique for order of preference by similarity to ideal solution (TOPSIS) for evaluating the performance of pork suppliers. Thereby, the economic and operational burden caused by the bullwhip effect throughout the pork supply chain can be alleviated. AHP was first used to determine the criteria and subcriteria weights. Then DEMATEL was applied to assess the interdependence and feedback between the decision elements. Finally, TOPSIS was implemented to discriminate between high‐performance and low‐performance pork suppliers. A case study from the Colombian pork supply chain is presented to validate the proposed approach. The results of this study evidenced that the most important criterion was the “service level” and the most influencing factor was the “financial profile.” In addition, based on the supplier assessment results, improvement plans, and new negotiation, strategies were established for each supplier in order to diminish the bullwhip effect along the pork supply chain.

LEER
A case of food supply chain management with AHP, DEMATEL, and TOPSIS
...
Ortiz Barrios, Miguel Angel | 2019-08-27

The bullwhip effect leads to considerable inefficiencies along the food supply chain such as missed production schedules, poor customer service, excessive inventory, and misguided capacity plans. To tackle this problem, it is necessary, apart from other interventions, to continuously monitor the performance of food suppliers so that the demand information flow, order batching, transportation planning, and inventory management can be substantially improved. Therefore, supplier assessment has then become critical decision‐making support for identifying and addressing inefficiencies of food providers, which ends up reducing the variation of several key logistics parameters for upstream members of the food supply chain. In addition, such assessment is of multicriteria nature given the presence of several criteria from different domains and various food suppliers. With these considerations in mind, this paper proposes a hybrid approach integrating the analytic hierarchy process (AHP), decision‐making trial and evaluation laboratory (DEMATEL), and the technique for order of preference by similarity to ideal solution (TOPSIS) for evaluating the performance of pork suppliers. Thereby, the economic and operational burden caused by the bullwhip effect throughout the pork supply chain can be alleviated. AHP was first used to determine the criteria and subcriteria weights. Then DEMATEL was applied to assess the interdependence and feedback between the decision elements. Finally, TOPSIS was implemented to discriminate between high‐performance and low‐performance pork suppliers. A case study from the Colombian pork supply chain is presented to validate the proposed approach. The results of this study evidenced that the most important criterion was the “service level” and the most influencing factor was the “financial profile.” In addition, based on the supplier assessment results, improvement plans, and new negotiation, strategies were established for each supplier in order to diminish the bullwhip effect along the pork supply chain.

LEER
A collaborative EPQ inventory model for a three-echelon supply chain with multiple products considering the effect of marketing effort on demand
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Salas-Navarro, Katherinne | 2020

This paper presents an inventory model for a three-echelon supply chain with multiple products and multiple members considering the demand as an increasing function of the marketing effort. In the proposed inventory model, a collaborative approach is studied and an analytical method is applied to obtain the optimal production lot size and the optimal marketing effort in order to achieve the maximum profits. Some numerical examples are illustrated to justify the model. Moreover, a sensitivity analysis is well done in order to analysis the effect of the changes of key parameters of inventory model on the the maximum benefits of all members of the chain.

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A color fusion model based on Markowitz portfolio optimization for optic disc segmentation in retinal images
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Escorcia-Gutierrez, Jose | 2021-07-15

Retinal disorders are a severe health threat for older adults because they may lead to vision loss and blindness. Diabetic patients are particularly prone to suffer from Diabetic Retinopathy. Identifying relevant structural components in color fundus images like the optic disc (OD) is crucial to diagnose retinal diseases. Automatic OD detection is complex because of its location in an area where blood vessels converge, and color distribution is uneven. Several image processing techniques have been developed for OD detection so far, but vessel segmentation is sometimes required, increasing computational complexity and time. Moreover, precise OD segmentation methods utilize complex algorithms that need special hardware or extensive labeled datasets. We propose an OD detection approach based on the Modern Portfolio Theory of Markowitz to generate an innovative color fusion model. Specifically, the training phase calculates the optimal weights for each color channel. A fusion of weighted color channels is then applied in the testing phase. This approach acts as a powerful and real-time preprocessing stage. We use four heterogeneous datasets to validate the presented methodology. Three out of four datasets are publicly available (i.e., DRIVE, Messidor, and HRF), and the last corresponds to an in–house dataset acquired from Hospital Universitari Sant Joan de Reus (Spain). Two different segmentation methods are presented and compared with state-of-the-art computer vision techniques to analyze the model performance. An outstanding accuracy and overlap above 0.9 and 80%, respectively, and a minimal execution time of 0.05 s are reached. Therefore, our model could be integrated into daily clinical practice to accelerate the diagnosis of Diabetic Retinopathy due to its simplicity, performance, and speed.

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A comparison study of MPC strategies based on minimum variance control index performance
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BORRERO-SALAZAR, Alex A. | 2019-07

Model Predictive Control (MPC) is a useful tool when controlling processes that handle a large number of input and output variables. This study presents a comparison of different MPC strategies when they are subjected to control process variables directly. The strategies studied are IMC, GPC, MPC-D, MPC-DR, and DMC. Evaluation of the performance of the controlled loop was performed with the filtering and correlation analysis algorithm (FCOR). The methodology proposed is validated in a Continuous Stirred-Tank Reactor (CSTR) case study. Discrete predictive control demonstrated the best results in this study.

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A complete psychophysiological profile of a paralympic athlete in a ultraendurance. a case study
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Belinchon-De-Miguel, Pedro | 2018-10-24

Psychophysiological response of athletes with spinal cord injurie has not been reported yet in scientific literature. The aim of this study is to analyze the specific psychophysiological response in a Paralympic athlete during competitive activities. We collected the following psychophysiological measurements: anxiety-trait, anxiety-state, locus of control, perceived psychological stress, stress-copying style, rate of perceived exertion, perceived muscle pain, body temperature, forced vital capacity, blood oxygen saturation, blood glucose and lactate concentrations, isometric hand strength, cortical arousal, heart rate variability, heart rate and velocities of a female Paralympic spinal cord injured athlete in a 11 hours and 44 minutes mountain ultraendurance event. An increase in sympathetic autonomous nervous system, heart rate, lactate, muscular pain and rated of perceived exertion and a decrease in cortical arousal and hand strength and inspiratory muscle fatigue. These results are consistent with the expected response during a highly stressful situation and consistent with previous findings in athletes without spinal cord injurie.

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A comprehensive study of biofilms growing on the built heritage of a caribbean industrial city in correlation with construction materials
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Gallego Cartagena, Euler | 2019

Biodeteriogens growing on constructions belonging to the built heritage is one of the leading natural pathologies that cause aesthetical and in some cases, physical-geochemical problems in the materials. In this study, for the first time, the chemical composition of construction materials of the built heritage from an industrialized city (Barranquilla) of Colombia have been evaluated in correlation with the algal, cyanobacterial and fungal biodeteriogens present in biofilms by applying a multianalytical methodology. To achieve this objective, samples of biofilms and construction material were taken from different historical and modern constructions. For the mineralogical characterization of the construction materials, X-ray diffraction, Raman microscopy and energy dispersive X-ray fluorescence spectrometry (EDXRF) were used. In addition, microscopic observations and cultures were employed for the microbiological characterization. Most of the construction materials analyzed belong to calcareous mortars, and others to different types of cement (portlandite, ettringite and larnite identification). The EDXRF analysis through single point and imaging strategies allowed to identify differences in the elemental composition of the external and internal parts of the materials. The role of certain elements in the mortars, which will assist the growth of specific microorganisms, is also discussed in this work. The main biodeteriogens identified in the biofilms were cyanobacteria Oscillatoria sp., Lyngbya sp., Leptolyngbya sp. and Ascomycota Aspergillus niger, Aspergillus fumigatus, Penicillium sp. and Fusarium sp fungi. These microorganisms promote biodeterioration processes causing aesthetic, physical and chemical damage to the façades of the studied buildings. The mineralogical composition of the construction material together with environmental conditions contributes to the development of biofilms on the constructions and monuments of Barranquilla city. The results obtained in this study will be used in the future to design and implement conservation protocols and strategies useful to preserve the built heritage of Caribbean cities with similar climate conditions, geographical position and kind of construction materials

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A computer simulation approach to reduce appointment lead-time in outpatient perinatology departments: a case study in a maternal-child hospital
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Ortiz Barrios, M. | 2017-06-25

A significant problem in outpatient perinatology departments is the long waiting time for pregnant women to receive an appointment. In this respect, appointment delays are related to patient dissatisfaction, no shows and sudden infant death syndrome. This paper aims to model and evaluate improvement proposals to outpatient care delivery by applying computer simulation approaches. First, suitable data is collected and analyzed. Then, a discrete-event simulation (DES) model is created and validated to determine whether it is statistically equivalent to the current system. Afterward, the average appointment lead-time is calculated and studied. Finally, improvement proposals are designed and pretested by simulation modelling and statistical comparison tests. A case study of an outpatient perinatology department from a maternal-child is shown to validate the effectiveness of DES to fully understand and manage healthcare systems. The results evidenced that changes to care delivery can be effectively assessed and appointment lead-times may be significantly reduced based on the proposed framework within this paper.

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A Cost Estimating Method for Agile Software Development
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Aziz Butt, Shariq | 2021

In every software development project, the software effort estimating procedure is an important process in software engineering and always critical. The consistency of effort and timeline estimation, along with several factors, determines whether a project succeeds or fails. Both academics and professionals worked on the estimation approaches in software engineering. But, all these approaches have many problems that need to be addressed. One of the most difficult aspects of software engineering is estimating effort in agile development. This study aims to provide an effort estimation method for agile software development projects. Because in software engineering, the agile method is widely used for the development of software applications. The development and usage of the agile method are described in depth in this study. The framework is configured with empirical data gathered by projects from the software industry. The test findings reveal that the estimation method has great estimation accuracy in respect of mean magnitude of relative error (MMRE) and Prediction of Error PRED (n). The suggested approach achieves more accuracy for effort estimation as compare to others.

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A deep learning-based hybrid model for recommendation generation and ranking
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Sivaramakrishnan, N. | 2020

A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastically improving the performance of recommender systems via unique methods, such as the traditional way of performing matrix factorization (MF) and also applying deep learning (DL) techniques in recent years. By using DL in the recommender system, we can overcome the difficulties of collaborative filtering. DL now focuses mainly on modeling content descriptions, but those models ignore the main factor of user–item interaction. In the proposed hybrid Bayesian stacked auto-denoising encoder (HBSADE) model, it recognizes the latent interests of the user and analyzes contextual reviews that are performed through the MF method. The objective of the model is to identify the user’s point of interest, recommending products/services based on the user’s latent interests. The proposed two-stage novel hybrid deep learning-based collaborative filtering method explores the user’s point of interest, captures the communications between items and users and provides better recommendations in a personalized way. We used a multilayer neural network to manipulate the nonlinearities between the user and item communication from data. Experiments were to prove that our HBSADE outperforms existing methodologies over Amazon-b and Book-Crossing datasets.

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A dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 context
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Ortíz-Barrios, Miguel | 2021-05-31

Scheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key aspect to be addressed in this particular aim is the adoption of a multi-criteria approach incorporating the current dynamics of smart FJSS. Thus, this paper proposes an integrated and enhanced method of a dispatching algorithm based on fuzzy AHP (FAHP) and TOPSIS. Initially, the two first steps of the dispatching algorithm (identification of eligible operations and machine selection) were implemented. The FAHP and TOPSIS methods were then integrated to underpin the multi-criteria operation selection process. In particular, FAHP was used to calculate the criteria weights under uncertainty, and TOPSIS was later applied to rank the eligible operations. As the fourth step of dispatching the algorithm, the operation with the highest priority was scheduled together with its initial and final time. A case study from the smart apparel industry was employed to validate the effectiveness of the proposed approach. The results evidenced that our approach outperformed the current company’s scheduling method by a median lateness of 3.86 days while prioritizing high-throughput products for earlier delivery. View Full-Text

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A FAHP-VIKOR approach for supporting the selection of tomography equipment in LMIC hospitals: a case study
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Ortíz-Barrios, Miguel | 2021

Computerized Tomography Scanners (CT-SCAN) provide detailed cross-sectional images of the human body which are employed for the easier detection and further analysis of abnormalities concerning the functionality and structure of the skeleton, tissues, and organs. However, the appropriate CT-SCAN selection is an arduous task considering the complexity and high cost of these medical devices. This decision is even more sharpener in hospitals from Low-and-Middle-Income-Countries (LMIC) where the available budget is usually restricted and correct resource allocation should be therefore ensured while granting the greatest impact on the timeliness and efficacy of healthcare services. In this framework, multiple criteria from diverse fields need to be taken into account to satisfy the intricate requirements of users. In this regard, it is necessary to fully elicit the expectations of stakeholders as well as identify their importance in an overall decision-making context. To address these gaps, this study proposes a novel integration between the Fuzzy Analytic Hierarchy Process (FAHP) and VIKOR methods for the CT-SCAN selection problem. Initially, a Multi-Criteria Decision-Making (MCDM) model will be designed for selecting the most suitable CT-SCAN option for a particular LMIC hospital. Then IF-AHP will be applied to calculate the relative priorities of criteria and sub-criteria under uncertainty. Ultimately, VIKOR will be implemented for obtaining an overall decision-making context. To address these gaps, this study proposes a novel integration between the Fuzzy Analytic Hierarchy Process (FAHP) and VIKOR methods for the CT-SCAN selection problem. Initially, a Multi-Criteria Decision-Making (MCDM) model will be designed for selecting the most suitable CT-SCAN option for a particular LMIC hospital. Then IF-AHP will be applied to calculate the relative priorities of criteria and sub-criteria under uncertainty. Ultimately, VIKOR will be implemented for obtaining an overall appropriateness index per CT-SCAN candidate and thereby identifying the most pertinent one (s) for a specific LMIC medical institution. © 2021, Springer Nature Switzerland AG.

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A fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer
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Fernández Blanco, Juan Carlos | 2021

For the safety and continuity of service in industrial electrical systems, the availability of transformers is essential. For this reason, it is necessary to develop intelligent fault diagnosis techniques to reduce repair and maintenance costs. Recently, several methods have been developed that use artificial intelligence techniques such as neural networks, support vector machines, hybrid techniques, etc., for the diagnosis of faults in power transformers using gas analysis. These methods, although they present very good results, encounter restrictions to determine the precise moment before the occurrence of multiple fault of small magnitude and are difficult to implement in practice. This document proposes a method to diagnose multiple incipient faults in a power transformer using fuzzy logic. The proposal, based on historical data from the composition of the gases dissolved in the oil, achieves a performance in the classification of multiple incipient fault of 98.3%. With reliable samples of dissolved gas, it guarantees an overall rate of accuracy in detecting incipient faults that is superior to that obtained by the most successful conventional methods in the industry. The proposal does not encounter generalization difficulties and constitutes a simple solution that allows determining the state of the transformer in service without affecting the continuity of the electricity supply.

LEER