Revistas UTB ( Universidad Tecnológica de Bolívar)
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Identifying disruptive innovation in the IT sector: a framework for evaluating intercompany impact
Full text linkIn the dynamic Information Technology (IT) sector, accurately assessing disruptive potential is crucial for companies aiming to maintain competitive advantages and preempt emerging threats. This study introduces a robust framework for evaluating the disruptive potential of IT companies, with a specific focus on company-to-company impacts. Our approach diverges from traditional models by integrating a holistic, multi-dimensional perspective that includes business model innovation, market dynamics, network effects, and customer adoption trends. Our approach incorporates a situational model that contextualizes disruptive dynamics within specific industry conditions, alongside a scoring model that systematically quantifies the potential impact of innovations. The framework was developed through an extensive literature review, expert interviews, and an analysis of both historical and contemporary case studies. Specifically, the historical case study examines Apple's disruption of Nokia in the smartphone market, while the contemporary case study analyzes the competitive dynamics between Mondoo and Lacework in the cybersecurity domain. These case studies provide an in-depth application of the framework, demonstrating its utility in both retrospective analysis and real-time market evaluation. This development process was significantly enriched by the primary author’s direct industry experience in a Silicon Valley cybersecurity startup, ensuring that the framework addresses real-world complexities and needs. Our research contributes a practical tool adapted to the IT sector's unique characteristics, offering strategic insights for IT professionals, strategists, and policymakers to effectively navigate and leverage disruption opportunities. The practical applications of this framework extend beyond academic discussion, providing actionable guidance for identifying and addressing potential disruptions in the IT landscape
Triboelectric nanogenerator to harness energy from low-frequency and low-amplitude vibrating sources
Full text linkDielectric Elastomer Generator (DEG) stands out as a promising electromechanical device to harness energy from non-conventional sources owing to its ability to convert mechanical energy into electrical power. DEG with no rotating part demonstrates a high performance-to-weight ratio with ease in fabrication and compactness that sets it apart from traditional energy harvesting techniques. Triboelectric nanogenerators (TENGs) belong to a self-powered class of DEG that capitalizes on low-frequency and amplitude mechanical sources. Existing models for predicting the performance of TENGs often assume parameters such as frequency, amplitude, and relative permittivity are constant. However, these parameters can vary depending on the specific application. In this study, a modified model is proposed to comprehensively investigate the performance of TENG in real-world conditions considering fluctuations in frequency, amplitude, and varying relative permittivity of elastomer layers. Results indicate that at a higher frequency of 55 Hz, there is a significant increase in output voltage, attributed to the higher energy release rate due to increased velocity. The study also emphasizes the role of the relative permittivity of TENG layers, revealing that elastomer layers with higher dielectric constants generate more voltage and power (151\%) compared to those with lower values, particularly at a separation distance of 0.1mm. The findings of this study exhibit notable concurrence with previously reported values and offer a valuable framework for researchers seeking to tailor energy generators for enhanced performance and precision for harnessing energy from low-frequency and low-amplitude sources
Thermal conductivity determination in Fe78Si9B13/GNP/Epoxy composites by observation of samples and use of ad-hoc software: a new approximation methodology
Full text linkThis study investigated the thermal conductivity (k) of composites composed of Fe78Si9B13 microparticles (weight fractions: 10%, 15%, and 25%) and graphene nanoplatelets (GNP) (weight fractions: 0%, 1.0%, and 1.5%) embedded in a transparent epoxy matrix. Nine cylindrical samples (7 mm diameter and 2 mm length) were prepared. Thermal conductivity was determined by measuring the thermal diffusivity using the flash technique and applying the relevant relationship between the two parameters. Because some samples contained pores, the measured diffusivity was corrected for porosity by using a novel method developed by the authors. This method allowed the estimation of the composite percentage porosity based on the Young's modulus (E) of the sample. This correction eliminates the influence of porosity on the calculated diffusivity value, allowing determination of the intrinsic diffusivity of the composite material. Finally, each sample's thermal conductivity was calculated using the diffusivity values. The values of the estimated parameters were compared with those determined by other well-known and established methods, and practically the same results were obtained. These comparative calculations demonstrated the efficiency of the proposed method. The results demonstrate the effectiveness of this method in correcting the effects of porosity on the thermal conductivity measurements in the studied samples
Experimental spray characterization of pyrolysis oil-diesel blend by effervescent atomization using high speed imaging
Full text linkThe utilization of plastic in everyday life has significantly increased due to its affordability, durability, versatility, lightweight, and hardness. However, the non-biodegradable nature of plastic has led to environmental pollution. Hence, this research focused on converting plastic waste into useful renewable energy through an environmentally friendly process. Specifically, the decomposition of polypropylene waste from PPE kits, which contains a high proportion of polypropylene plastic, was investigated. Proximate, ultimate and TGA analyses were conducted to understand the chemical composition and the decomposition temperature of polypropylene. The activation energy and kinetic parameters of decomposition were calculated using three different methods: Kissinger-Akahira-Sunose (KAS), Ozawa-Flynn-Wall (OFW), and Starink model, yielding values in the range of 175 to 190 kJ/mol. The pyrolysis of polypropylene resulted in an oil yield of 31.1 percentage. The collected oil was analyzed using FTIR Spectroscopy. Furthermore, blending of pyrolyzed oil with diesel was carried out and assessed for fuel properties, which were compared to diesel properties. To characterize the spray of the biodiesel blend, an effervescent atomizer was fabricated, and stability variables were extracted from flow visualization using a high-speed camera. Therefore, it was concluded the decomposition of polypropylene plastic waste offers an opportunity to extract pyrolyzed oil, which can be blended with diesel for combustion by achieving fine sprays using an atomizer. This study can be further extended to investigate the development of new kind of atomizers to disintegrate the pyrolytic blend diesel oil. This study will also assist to examine breakup morphology and spray characterization using high speed imaging techniques
Business intelligence for decision-making in royalties project management
Full text linkThis article highlights the critical role of specialized digital tools in enhancing project management and monitoring in Colombia's public sector, it showcases how Microsoft Office 365 and Power Platform have been effectively utilized to optimize royalty-funded initiatives, automating processes and improving decision-making through advanced data análisis. These tools enable process optimization, task automation, and enhanced data analysis, significantly improving the administration of resources and facilitating timely, well-informed decision-making. The methodology is quantitative and complementary information is taken from the technical and legal opinions of the participating professionals. For its execution, a combined approach had to be followed: a PMBOK methodology that gave a clear roadmap and an agile SCRUM methodology capable of highly prioritizing schedule management to provide effective results. In this process, the tools used are Power BI, Power Apps, and Power Automate; these are used to automate tasks and improve operational efficiency by addressing specific issues and contributing to project management and project optimization. Through this project, a proper technological infrastructure has been built for senior management strategic management, planning team tactical management, and operative monitoring to implement BI systems successfully. The project is structured in several phases: initial preparation and planning; implementation; and personnel training, emphasizing continuous training and personnel adaptation to manage resistance to change. Implementing BI and digital tools facilitates teams to work closely together, with noticeable improvements in coordination and operational efficiency. This paper deals with the optimization of monitoring project management of the SGR in the Office of the Attorney General of the Nation, and this experience seeks to be an example to other entities and to inspire them to walk these paths toward a culture of innovation and permanent improvement within the public sector in Colombia. These Microsoft tools are available to most national public servants and contractors, so generating the solution does not imply additional costs. The experience is well documented in the present work and provides a replicable model that can adapt to multiple contexts, promoting greater efficacy and efficiency in public administration
Evaluating the generalization capability of MobileNet-based CNNs for temperature prediction in simulated specklegram fiber optic sensors with combined synthetic datasets and data augmentation
Full text linkThe development of machine learning algorithms applied to specklegram-based sensors has facilitated the development of novel approaches for measuring several physical variables; however, most of these methods evaluate a single Fiber Specklegram Sensor (FSS) on a limited dataset. This paper assesses the generalization capability of applying these algorithms, in particular, Convolutional Neural Networks (CNN), to the prediction of temperature in simulated FSSs with different characteristics and conditions. This is achieved through the use of multiple combined synthetic datasets and data augmentation. The application of the Finite Element Method (FEM) enables the generation of datasets within the COMSOL Multi-physics software. The datasets are simulated with varying optical parameters, representing different optical fibers. Following the simulation of the datasets and training of selected models by combining them, data augmentation tests are conducted as though they were real fiber optic disturbances. Ultimately, a model is generated incorporating all the combined datasets and data augmentation, demonstrating the capacity of the model for generalization. This showcases the versatility of the computational methodology for evaluating, designing, and adjusting sensors without the need for experimental data. Additionally, it illustrates that a relatively simple model can be adapted to a variety of sensing system scenarios and configurations
Extraction of 4-HBA utilizing renewable and conventional solvents
Full text linkThe valuable chemical 4-hydroxybenzoic acid (4-HBA), the phenolic compound of carboxylic acid possesses promise as an antioxidant, antifungal, anticancer, antidiabetic, and cardioprotective properties. 4-HBA has promising uses in the pharmaceutical, cosmetic, and plastic sectors, making it worthwhile to recover. Using renewable solvent like Karanja oil and traditional solvents like n-Butanol, and Di-chlorobenzene, the experimental tests were carried out to separate 4-hydroxybenzoic acid from solute mixed aqueous solution. Parameters including the distribution coefficient KD, the percentage extraction efficiency %E, the partition coefficient P, and the dimerization constant D were discovered and associated with several solvent physicochemical features while evaluating the equilibrium for this physical extraction experiment. The following parametric values were obtained for KD and %E: n-Butanol (1.704, 63.02%)> Karanja oil (0.810, 44.75%) > Di-chlorobenzene (0.555, 35.69%)
Inversión en educación y resultados en pruebas estandarizadas: El desempeño en las pruebas Saber 11 de los estudiantes del Caribe colombiano
Full text linkThis study analyzes the evolution of investment in education, its sources, and its uses across the 196 municipalities of the Colombian Caribbean from 2014 to 2021, including capital cities. Using spatial analysis of the data, we identify clusters based on students' results in the Saber 11 tests, investment spending on education, the municipality's category, and multidimensional poverty levels. The findings indicate that, between 2014 and 2018, the overall scores of municipalities in the region on the Saber 11 tests decreased and. Additionally, we observed a positive spatial autocorrelation between investment in educational quality and multidimensional poverty in the Colombian Caribbean region. Finally, the spatial autoregression model revealed that the fiscal category of the municipality does not account for the results obtained in the Saber 11 tests. In contrast, poverty—measured by the multidimensional poverty index—exhibits an inverse relationship with students' scores on the Saber 11 test: as multidimensional poverty increases, overall scores decrease.En el presente estudio se analiza la evolución de la inversión en educación, sus fuentes y usos en los 196 municipios del Caribe colombiano en el período 2014 – 2021, incluyendo las ciudades capitales. Mediante el análisis espacial de datos se identificaron clústeres a partir de los resultados en las pruebas Saber 11, el gasto de inversión en educación, la categoría del municipio y la pobreza multidimensional. Se encontró que, entre 2014 y 2018, el puntaje general de los municipios de la región en las pruebas Saber 11 se redujo y que existe autocorrelación espacial positiva de la inversión en calidad educativa y la pobreza multidimensional en la región Caribe colombiana. Por último, el modelo de autorregresión espacial muestra que la categoría fiscal del municipio no explica los resultados obtenidos en las pruebas Saber 11, mientras que la pobreza, medida con el índice de pobreza multidimensional, tiene relación inversa con el resultado obtenido por los estudiantes en esta prueba
Closed loop battery current controlled zeta converter for improved power quality in electric vehicle charging stations
Full text linkTo encourage an eco-friendly environment and pollution-free transportation, most of the automobile industries are promoting electric vehicles. However, with the adoption of electric vehicles, various power quality problems are encountered mainly during vehicle battery charging. Thus, this research work focuses on power quality improvement in electric vehicle battery charging stations. In this article, a closed-loop battery current-controlled zeta converter with a PI controller is introduced to achieve quality power to charge electric vehicles. The proposed converter enhances the overall performance of the system by reducing voltage fluctuations, harmonic content, and frequency variations. Besides, this suggested closed-loop battery-controlled zeta converter improves the power factor and overall efficiency of the system. The converter provides a wide range of output with ripple-free current. In the proposed scheme, the vehicle battery current feedback to the PI controller generates the switching pulses, thereby generating the desired duty ratio to operate the converter to maintain a constant current. The entire system is implemented in MATLAB/Simulink and various power quality parameters namely voltage and current characteristics, active and reactive power characteristics, frequency, total harmonic distortion (THD), power factor, and efficiency are measured. To validate the usefulness of the proposed scheme, it is compared with conventional buck converter-based charging station and conventional zeta converter-based charging station. From the results, it is found that the proposed closed-loop battery current-controlled zeta converters charging station produce improved power quality characteristics over conventional methods. It achieved a voltage THD of 4.93%, current THD of 1.9%, power factor of 0.96, and efficiency of 91.8%, which are far better than the conventional buck and zeta models
Enhancing the solar water pumping efficiency through Beta MPPT method-controlled drive
Full text linkThis paper presents an innovative approach to achieve efficient solar water pumping through the integration of a Photovoltaic (PV) array and a Brushless Direct Current (BLDC) motor water pumping system. The system incorporates a Voltage Source Converter (VSC) with six switches, utilized to facilitate commutation. The inherent solar radiation is harnessed by the PV array, capitalizing on its renewable nature to generate electricity. By dynamically adjusting the switching states of the six VSC switches, the speed of the BLDC motor is modulated in response to the varying levels of available solar radiation. The BLDC motor's hall sensor signals play a crucial for determining the rotor's position and they are employed to generate precise commutation signals. The control strategy integrates the Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) algorithm, which initially governs the commutation signals. To enhance adaptability to rapidly changing solar irradiation conditions, the control strategy dynamically updates the commutation signals using the innovative Beta MPPT algorithm. To assess the efficiency of the proposed control strategy, a comprehensive comparison between the INC and Beta MPPT algorithms is conducted using MATLAB Simulink. The performance of the BLDC motor under these algorithms was evaluated in terms of its ability to optimize energy extraction. The graphical analysis of these algorithms, considering the temporal aspect, substantiates the identification of the superior MPPT algorithm for BLDC motor control in solar water pumping applications. This study contributes to the advancement of solar water pumping systems by introducing a novel control approach that combines PV array utilization, VSC-based commutation, and a dual-step MPPT algorithm. The results demonstrate the effectiveness of the Beta MPPT algorithm by enabling the system to respond promptly to fluctuating solar irradiation conditions, thereby enhancing the overall efficiency of the solar water pumping process