Tecnura

Aboveground Biomass Estimation of Colombian Cocoa Agroforestry Systems using Multispectral Images and Regression Methods

Abstract Context: Aboveground biomass (AGB) estimation for agricultural and environmental applications traditionally relies on time-consuming manual methods at ground level. Emerging approaches use remote sensing data evaluating spectral responses and vegetation indices in order to estimate the AGB more efficiently. Nonetheless, such techniques face various challenges in accounting for complex spatial distributions, especially when dealing with agroforestry systems (AFS). Objective: This work aimed to estimate AGB in a Colombian cocoa AFS from multispectral images acquired with an un-manned aerial vehicle (UAV). Methodology: In this work, the AGB was estimated by computing different vegetation indices from the measured spectral reflectances and evaluating two linear regression models, i.e., principal component regression (PCR) and partial least squares regression (PLSR), as well as a nonlinear regression model, a neural network composed by a perceptron with a single layer. Control points were obtained via on-ground biomass manual acquisition. Results: Numerical experiments resulted in a coefficient of determination of R 2 = 0.58 for the best linear model, while the nonlinear model reached R 2 = 0.86. Conclusions: AGB estimation in a cocoa AFS can be effectively performed using multispectral data acquired with a UAV and a simple nonlinear regression model.

Resumen Contexto: La estimación de la biomasa aérea (AGB) para aplicaciones agrícolas y ambientales tradicionalmente se basa en métodos manuales a nivel del suelo que requieren mucho tiempo. Los enfoques emergentes utilizan datos de teledetección que evalúan respuestas espectrales e índices de vegetación para estimar la AGB de una manera más eficiente. No obstante, estas técnicas enfrentan varios desafíos a la hora de tener en cuenta distribuciones espaciales complejas, especialmente en sistemas agroforestales (SAF). Objetivo: Este trabajo buscó estimar la AGB en un SAF de cacao colombiano a partir de imágenes multiespectrales adquiridas con un vehículo aéreo no tripulado (UAV). Metodología: En este trabajo se estimó la AGB calculando diferentes índices de vegetación a partir de reflectancias espectrales medidas y evaluando dos modelos de regresión lineal, i.e., regresión de componentes principales (PCR) y regresión de mínimos cuadrados parciales (PLSR), y un modelo de regresión no lineal, una red neuronal de un solo perceptrón. Los puntos de control se obtuvieron mediante una adquisición manual de biomasa en tierra. Resultados: Los experimentos numéricos dieron como resultado un coeficiente de determinación de R 2 = 0.58 para el mejor modelo lineal, mientras que el modelo no lineal alcanzó un R 2 = 0.86. Conclusiones: Es posible realizar una estimación eficaz de la AGB en un sistema agroforestal de cacao a partir de datos multiespectrales adquiridos con un UAV y un modelo de regresión no lineal simple.

Use of whey for the development of a functional product using spray drying technology

Resumen Objetivo: desarrollar un producto en polvo a base de suero lácteo dulce, pulpa de mora (Rubus glaucus), maltodextrina e inulina, mediante tecnología spray drying. Metodología: se caracterizó fisicoquímicamente la materia prima. Se optimizó la formulación de una suspensión, mediante un diseño experimental de superficie respuesta tipo Box-Behnken. Las variables independientes fueron; pulpa de mora (0,30 - 0,50), suero lácteo dulce (0,30 - 0,50), maltodextrina (0,10 - 0,20), todas en unidades de fracción másica, la inulina fue una variable fija al 6 %. Las variables dependientes fueron; pH, sólidos solubles (°Brix) y viscosidad (cP). La suspensión óptima se secó mediante spray drying y se evaluó el factor temperatura del aire de entrada (130 °C, 140 °C y 150 °C) con un diseño unifactorial y las variables dependientes fueron: Humedad ( %), actividad de agua (aw), solubilidad ( %), humectabilidad (g/min), capacidad antioxidante (ug/g) y color (L*a*b*). Con el mejor tratamiento se realizó análisis sensorial (sabor, olor, color y aceptación general). Resultados: la formulación óptima de la suspensión resultó con pulpa de mora (0,419); suero lácteo (0,319); maltodextrina (0,202) y (0,060) inulina. La viscosidad fue de 1.631 cP, pH 3,64 y 31,40 °Brix. El mejor tratamiento en el proceso de secado se obtuvo a 130 °C, con aw = 0,247±0,037, solubilidad = 60,44 ±0,53 %, humectabilidad = 0.082±0.006 (g/min) y capacidad antioxidante de 8,39±0,07 mg ácido gálico/g. En el análisis sensorial se obtuvo una aceptación general de 4,4±0,6 en escala de 1 a 5. Conclusiones: la formulación óptima de la suspensión permitió obtener un producto en polvo con potencial aporte funcional para el consumidor.

Abstract Objective: To develop a powdered product based on sweet whey, blackberry pulp (Rubus glaucus), maltodextrin and inulin, using spray drying technology. Methodology: the raw material was characterized physicochemically. The formulation of a suspension was optimized using a Box-Behnken type response surface experimental design. The independent variables were: blackberry pulp (0,30 - 0,50), sweet whey (0,30 - 0,50), maltodextrin (0,10 - 0,20), all in units of mass fraction; inulin was a fixed variable at 6 %. The dependent variables were; pH, soluble solids (°Brix) and viscosity (cP). The optimum suspension was dried by spray drying, the inlet air temperature factor was evaluated (130°C, 140°C and 150°C) with a unifactorial design and the dependent variables were: humidity ( %), water activity (aw), solubility ( %), wettability (g/min), antioxidant capacity (ug/g) and color (L*a*b*). Sensory analysis (taste, odor, color and general acceptance) was performed with the best treatment. Results: the optimal formulation of the suspension was blackberry pulp (0,419); whey (0,319); maltodextrin (0,202) and (0,060) inulin. The viscosity was 1.631 cP, pH 3,64 and 31,40 °Brix. The best treatment in the drying process was obtained at 130 °C, with aw = 0,247±0.037, solubility = 60,44 ±0.53 %, wettability = 0,082±0.006 (g/min) and antioxidant capacity of 8,39±0.07 mg gallic acid/g. In the sensory analysis, a general acceptance of 4,4±0.6 on a scale of 1 to 5 was obtained. Conclusions: the optimal formulation of the suspension allowed obtaining a powdered product with potential functional benefits for the consumer

Treatment of meat wastewater using a dispersed air flotation system with medium-sized bubles

Resumen Contexto: las industrias cárnicas generan efluentes con elevadas cargas contaminantes, lo cual exige la adopción de tecnologías de tratamiento eficientes que cumplan con la normativa ambiental. Objetivo: se investigó la eficacia de un sistema de flotación por aire disperso (Dispersed Air Flotation, DAF) en la remoción de contaminantes de aguas residuales cárnicas, con un tamaño mediano de burbujas generadas por aire. Metodología: se recolectaron muestras representativas de la planta procesadora de cárnicos del municipio de Agustín Codazzi, ubicado en el departamento del Cesar, Colombia. Las muestras fueron caracterizadas mediante análisis fisicoquímicos de acuerdo con los lineamientos del Standard Methods, y se evaluaron parámetros como pH, demanda química de oxígeno (DQO), Aceites y Grasas (AyG), turbidez y temperatura. Se implementó un sistema piloto de DAF a escala laboratorio, con el empleo de un inyector Venturi de diámetro ½" para inducir la formación de burbujas de tamaño mediano (0.69 mm). Resultados: los resultados revelaron una dependencia del tamaño de burbuja con el tiempo de retención. Se encontró que la configuración óptima para la remoción de AyG (77.9 %) y DQO (74.2 %) correspondió a un tiempo de retención de veinte minutos y un tiempo de cinco minutos para la turbidez con una eficiencia de 38.8 %. Conclusiones: los hallazgos demuestran que el sistema DAF, con la optimización del tamaño de burbuja y del tiempo de retención, constituye una alternativa eficiente para el tratamiento de efluentes industriales cárnicos y para maximizar la remoción de AyG, DQO y turbidez, lo que hace viable su potencial aplicación a mayor escala. Financiamiento: Universidad Popular del Cesar (UPC).

Abstract Background: Meat processing industries generate effluents with high pollutant loads, requiring the adoption of efficient treatment technologies that comply with environmental regulations. Objective: The efficacy of a Dispersed Air Flotation (DAF) system in removing contaminants from meat processing wastewater was investigated, using medium-sized bubbles generated by air. Methodology: Representative samples were collected from the meat processing plant of the municipality of Agustín Codazzi, located in the department of Cesar, Colombia. Samples were characterized through physicochemical analyses following Standard Methods guidelines, evaluating parameters such as pH, chemical oxygen demand (COD), oils and greases (O&G), turbidity, and temperature. A laboratory-scale pilot DAF system was implemented using a ½-inch diameter Venturi injector to induce the formation of medium-sized bubbles (0.69 mm). Results: Results revealed a dependence of bubble size on retention time. The optimal configuration for O&G removal (77.9%) and COD removal (74.2%) corresponded to a retention time of twenty minutes, while turbidity removal efficiency of 38.8% was achieved at a retention time of five minutes. Conclusions: The findings demonstrate that the DAF system, with optimized bubble size and retention time, constitutes an efficient alternative for the treatment of meat industry effluents and for maximizing O&G, COD, and turbidity removal, making its potential application at a larger scale viable. Funding: Universidad Popular del Cesar (UPC).

Cluster analysis for the prevention of hospital readmission of diabetic patients

Abstract Objective: Hospital readmission in diabetic patients represents a significant challenge for both healthcare systems and patients’ quality of life. That is why the main objective of this work is to identify common patterns associated with a higher risk of readmission. Methodology: This study presents a clustering analysis applied to a clinical dataset of diabetic patients who were readmitted to hospitals across 130 medical institutions in the United States. The analysis employs unsupervised clustering algorithms, such as k-means and PAM, to segment patients based on their clinical and demographic characteristics. The study also evaluates different feature selection methods, identifying Simulated Annealing (SA)as the most effective for selecting optimal subsets of variables. Results: Recurring factors such as length of hospital stay, associated medical conditions, and types of treatment received, significantly influence the probability of readmission. Conclusions: The clustering results provide valuable insights to guide the development of personalized intervention strategies aimed at reducing hospital readmission rates among diabetic patients.

Resumen Objetivo: El reingreso hospitalario en pacientes con diabetes representa un reto significativo tanto para los sistemas de salud como para la calidad de vida del paciente. El objetivo principal de este trabajo es identificar patrones comunes asociados a un mayor riesgo de reingreso. Metodología: Este estudio presenta un análisis de conglomerados (clusters) aplicado a un conjunto de datos clínicos de pacientes con diabetes que fueron reingresados en hospitales de 130 instituciones médicas en Estados Unidos. El análisis emplea algoritmos de conglomerados no supervisados, como k-means y PAM, con el fin de segmentar a los pacientes según sus características clínicas y demográficas. Este estudio también evalúa diferentes métodos de selección de características, identificando el Recocido Simulado (Simulated Annealing) como el más efectivo para seleccionar subconjuntos óptimos de variables. Resultados: Factores recurrentes, tales como: duración de la estancia hospitalaria, afecciones médicas asociadas y los tipos de tratamiento recibido, influyen significativamente en la probabilidad de reingreso. Conclusiones: Los resultados del conglomerado proporcionan información valiosa para guiar el desarrollo de intervenciones personalizadas dirigidas a reducir las tasas de reingreso hospitalario en pacientes con diabetes.

Electrocoagulation as an Emerging Technology for Wastewater Treatment in Different Industries: A Review

Abstract The electrocoagulation (EC) technique has been considered a popular treatment alternative for the effective separation of organic and inorganic contaminants. This technique generates coagulating particles in situ through the electrolysis of a sacrificial anode that destabilizes suspended, dissolved, or emulsified contaminants in a liquid medium by inducing an electric field. EC has been widely studied due to its versatility, ease of installation, lower cost, and for being an environmentally friendly technology. For this article, a search and information-gathering phase was conducted in which Scopus, ScienceDirect, and Google Scholar were identified as the necessary tools for locating information on the electrocoagulation process as a wastewater treatment in various industries. This paper reviews studies on advances in EC applied to different types of industrial wastewater, focusing on evaluating the operating variables and optimal treatment conditions that are vital to the process. Likewise, the effect of the electrocoagulation on the quality properties of wastewater from different sectors is examined, considering factors such as current density, pH, reactor geometry, treatment time, and electrode spacing. Objective: To determine the effect of the electrocoagulation process on the quality properties of wastewater from various industries as a function of physicochemical and geometric parameters. Methodology: A search and information-gathering phase was carried out using Scopus, Sciencedirect, and Google Scholar as the main sources. Subsequently, a scientific monitoring study was conducted following a methodology structured in four stages: planning, research, analysis, and competitive intelligence. Additionally, a literature review was conducted to identify needs and select keywords focused on the electrocoagulation process as a wastewater treatment technology in various industries. Results: The review revealed that wastewater treatment removal efficiency is achieved by removing one or more of its components in whole or in part, depending on the initial or inlet organic load. The main parameters evaluated were Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total Suspended Solids (TSS), color, turbidity, Total Organic Carbon (TOC), and oil and grease content. Several studies have concluded that, under optimal treatment conditions, electrocoagulation can achieve approximately 90% removal of the pollutant loads in wastewater from diverse industrial sectors. Conclusions: The review showed that electrocoagulation technology is a highly effective alternative for treating a wide variety of industrial effluents containing pollutants that cannot be effectively removed by conventional treatment methods. In addition, more research is needed to study the impact of new organic coagulant-assisted processes, reactor designs, electrode configuration, electrocoagulation mechanisms, processing conditions and electrode dissolution phenomena, in order to obtain greater removal and improvement in the treatment process. Financing: the project was financed through the National Fund for Science, Technology, and Innovation of the General System of Royalties (Bank of National Investment Programs and Projects), the University of Sucre and the PADES group, within the framework of the project "Technological strengthening of the Colombian Caribbean region through the development of transformation processes of starchy raw materials (cassava, yams and sweet potatoes) in the department of Sucre", identified with the BPIN code 2020000100035.

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Emerging Trends in Business Intelligence: A systemic Mapping Study

ABSTRACT Objective: The study aims to conduct a systematic mapping of emerging trends in business intelligence (BI) by analyzing research published between 2017 and 2024, in order to identify patterns, influential authors, key sectors, and future research opportunities. Methodology: We used the systematic mapping methodology described by Kitchenham, including two iterations of searches in academic databases such as Scopus, IEEE Xplore, and Web of Science. The collected data (1504 studies) were analyzed using tools such as VOSviewer and Microsoft Excel, focusing on network maps, overlays, and density visualizations to interpret key patterns and relationships. Results: The analysis revealed dominant trends such as the use of big data, machine learning, predictive analytics, and real-time BI. Geographically, Asia and the Middle East lead in publications, with strong BI adoption in sectors such as healthcare, education, and retail. Less explored areas were also identified, such as the integration of BI with the Internet of Things, advanced social network analytics, and data ethics. Conclusions: This study concludes that BI continues to evolve towards more advanced and ethically responsible technologies, with a focus on personalization and real-time decision-making. Less explored areas represent opportunities for future research, particularly regarding data governance and the integration of BI in specific sectors. This work provides a comprehensive overview of the current state of BI research and suggests strategic directions for its development.

RESUMEN Objetivo: Este estudio tiene como objetivo realizar un mapeo sistemático de las tendencias emergentes en inteligencia de negocios (BI), analizando investigaciones publicadas entre 2017 y 2024, para identificar patrones, autores influyentes, sectores principales y oportunidades futuras de investigación. Metodología: Se utilizó la metodología de mapeo sistemático descrita por Kitchenham, incluyendo dos iteraciones de búsqueda en bases de datos académicas como Scopus, IEEE Xplore y Web of Science. Los datos recopilados (1504 estudios) fueron analizados mediante herramientas como VOSviewer y Microsoft Excel, con énfasis en mapas de red, superposición y densidad para interpretar patrones y relaciones clave. Resultados: El análisis reveló tendencias predominantes, como el uso de big data, machine learning, análisis predictivo y BI en tiempo real. En términos geográficos, Asia y Medio Oriente lideran en publicaciones, con una fuerte adopción de BI en sectores como salud, educación y comercio minorista. También se identificaron áreas menos exploradas, como la integración de BI con el Internet de las Cosas, la analítica avanzada de redes sociales y la ética en el manejo de datos. Conclusiones: El estudio concluye que BI sigue evolucionando hacia tecnologías más avanzadas y éticamente responsables, con un enfoque en la personalización y la toma de decisiones en tiempo real. Las áreas menos investigadas representan oportunidades para futuras investigaciones, particularmente en la gobernanza de datos y la integración de BI en sectores específicos. Este trabajo ofrece una visión integral del estado actual de la investigación en BI y sugiere direcciones estratégicas para su desarrollo.

Sustainable Supply Chain Management in the Context of Small and Medium-Sized Enterprises: A Literature Review and Bibliometric Analysis

Abstract Context: Organizations must strategically address sustainability pressures. In this sense, it is considered that their responses tend to be more effective when adopted at the supply chain level. This approach is framed within the topic of sustainable supply chain management (SSCM), which integrates the environmental and social objectives inherent to sustainability. Objective: To determine the current status of SSCM in small and medium-sized enterprises (SMEs), given its importance to sustainability and the economy Methodology: This work uses bibliometric analysis and the Tree of Science, which allows identifying topics, authors, and outstanding journals, as well as the seminal works and the perspectives of development in this area. Results: The results suggest that it has become a topic of growing interest for the academic community, mainly in Asian countries. Likewise, the considered articles are published in highly recognized journals, and the most cited topics refer to the environmental dimension. Conclusions: Delving into the particularities of SSCM in SMEs contributes to the academic debate to support the path towards sustainability in this type of organization, which face their own challenges, given their characteristics, resources, and capabilities.

Resumen Contexto: Las organizaciones deben abordar estratégicamente las presiones de la sostenibilidad. En este sentido, se considera que sus respuestas tienden a ser más efectivas cuando se adoptan a nivel de la cadena de suministro. Este enfoque se enmarca en la gestión sostenible de la cadena de suministro (SSCM), que integra los objetivos ambientales y sociales inherentes a la sostenibilidad. Objetivo: Determinar el estado actual del área de SSCM en las pequeñas y medianas empresas (pymes), dada su importancia para la sostenibilidad y la economía. Metodología: Este trabajo utiliza el análisis bibliométrico y Tree of Science, que permite identificar los temas, autores y revistas destacados, así como los trabajos seminales y las perspectivas de desarrollo de esta área. Resultados: Los resultados sugieren que este se ha convertido en un tema de creciente interés para la comunidad académica, principalmente en los países asiáticos. Asimismo, los artículos considerados se publican en revistas de gran reconocimiento, y los temas más citados se refieren a la dimensión ambiental. Conclusiones: Profundizar en las particularidades de SSCM en las pymes contribuye al debate académico para apoyar el camino hacia la sostenibilidad de este tipo de organizaciones, ya que ellas afrontan sus propios retos, dados sus características, recursos y capacidades.