Unidades Tecnológicas de Santander

This article presents a computational model for transmission and generation expansion planning considering the impact of virtual power lines, which consists of the investment in energy storage in the transmission system as well as being able to determine the reduction and postponement of investments in transmission lines. The flexibility from the TSO-DSO interconnection is also modeled, analyzing its impact on system expansion investments. Flexibility is provided to the AC power flow transmission network model by distribution systems connected at the transmission system nodes. The transmission system flexibility requirements are provided by expansion planning performed by the connected DSOs. The objective of the model is to minimize the overall cost of system operation and investments in transmission, generation and flexibility requirements. A data-driven distributionally robust optimization-DDDRO approach is proposed to consider uncertainties of demand and variable renewable energy generation. The column and constraint generation algorithm and duality-free decomposition method are adopted. Case studies using a Garver 6-node system and the IEEE RTS-GMLC were carried out to validate the model and evaluate the values and impacts of local flexibility on transmission system expansion. The results obtained demonstrate a reduction in total costs, an improvement in the efficient use of the transmission system and an improvement in the locational marginal price indicator of the transmission system.

The field of ophthalmology relies on digital image processing techniques, such as Optical Coherence Tomography (OCT), for diagnosing retinal diseases. However, manual interpretation of OCT images is time-consuming and prone to human error. This study developed a deep learning-based model to assist in diagnosing retinal pathologies from OCT images. A modified VGG16 architecture was trained on a dataset of OCT images to classify four retinal conditions: choroidal neovascularization, diabetic macular edema, drusen, and normal. Rigorous evaluation, including cross-validation and independent testing, demonstrated the model’s ability to achieve an accuracy of 95.19% and high precision (95.29%), recall (95.19%), and F1-score (95.20%). In addition, gradient-weighted class activation mapping was employed to visualize network decisions, and a graphical user interface was developed to enhance user interaction with the diagnostic tool. The developed model can potentially improve the early detection and diagnosis of retinal diseases, ultimately enhancing patient care.

Background: In volleyball, the upper limb dimensions and grip strength greatly influence offensive and defensive movements during a match. However, the relationship between these parameters remains underexplored in elite female volleyball players. Objective: This study aimed to contrast the upper limb anthropometric characteristics and handgrip strength (HGS) of female elite volleyball players against a control group. Methods: Selected upper limb anthropometric parameters and maximal HGS of 42 female volleyball players and 40 non-athletes were measured. Results: Players exhibited higher values in almost all variables studied than non-athletes. The differences were statistically significant (p < 0.001) except for body mass index and elbow and wrist diameters. Players showed a moderate correlation between dominant HGS and hand parameters (length r = 0.43 and breadth r = 0.63; p < 0.05). Weak correlations were identified with height, upper arm length, elbow diameter, and hand shape index (r = 0.32 to 0.38; p < 0.05). In the non-dominant hand, a moderate correlation with handbreadth (r = 0.55, p ≤ 0.01) and weak correlations with upper arm length, wrist diameter, hand length, and hand shape index (r = 0.32 to 0.35; p ≤ 0.05) was found. Conclusions: These findings underscore the importance of the upper limb anthropometric parameters as predictors of HGS and their utility in athlete selection. Future research should investigate biomechanical factors influencing HGS and injury prevention.

University campus networks need wired (ethernet) and dense wireless fidelity networks that have devices like access points, switches, and routers that are always turned on. Consequently, they generate two important problems: the energy bill and the influence of carbon dioxide into the atmosphere. Energy savings are the solution to those problems. There are several proposals to augment the energy savings separately in ethernet and wireless fidelity, but there is no integrated method to simultaneously reduce them in both parts of the networks. Our novel method combines idle cycling and machine learning techniques to efficiently obtain energy savings in both parts of the network simultaneously. We categorize network devices into two groups: (a) those that are always turned on and (b) those that can be dynamically turned on or off based on network performance. We formulated two algorithms that decide when to turn on and off access points. We use Ward's machine learning hierarchical clustering technique to optimize the energy savings of our model in the network of the Unidades Tecnológicas de Santander (Bucaramanga, Colombia). We showed that energy savings of 21.5 kWh per day are possible. The success of the model in this context highlights its potential to achieve substantial energy savings.

The smart transformer (ST) is a multiport and multi-stage converter that allows for the formation of meshed hybrid microgrids (MHMs) by enabling AC-DC ports in medium and low voltage. This type of microgrid has advantages over the performance of conventional hybrid AC-DC microgrids (HMGs); however, the number of degrees of freedom of the ST increases the complexity of the energy management systems (EMSs), which require adequate and accurate modeling of the power flow of the converters and the MG to find the feasible solution of optimal power flow (OPF) problems in the MHM. An ST’s equivalent power flow model is proposed for formulating the MHM OPF problem and developing low-frequency equivalent models integrated with a decoupled hierarchical control architecture under a real-time simulation approach to the ST-based MHM. A simulation model of the MHM in the Simulink® environment of Matlab® 9.12 is developed and implemented under a digital real-time simulation (DRTS) approach on the OPAL-RT® platform. This model allows for determining the accuracy of the developed equivalent models, both low-frequency and power flow, and determining the MHM performance based on optimal day-ahead scheduling. Simulation test results demonstrated the ST equivalent model’s accuracy and the MHM’s accuracy for OPF problems with an optimal day-ahead scheduling horizon based on the model-in-the-loop (MIL) and DRTS approach.

El objetivo del artículo es fortalecer las competencias matemáticas en estudiantes de séptimo grado de la Institución Educativa Santa María Goretti de Bucaramanga, Colombia a través de secuencias didácticas mediadas por las Tecnologías de la Información y Comunicación (TIC) y de la modelación de situaciones problema. La metodología empleada en este trabajo consiste en un estudio cuantitativo, descriptivo donde participaron 98 estudiantes. Inicialmente, se realizó una prueba diagnóstica construida con preguntas de la prueba Evaluar para Avanzar, los resultados evidenciaron un bajo desempeño en saberes relacionados con Introducción a la geometría. Para fortalecer las temáticas, se diseñaron e implementaron dos secuencias didácticas mediadas por TIC. Finalmente, se realizó una valoración del impacto de la propuesta. Los resultados obtenidos demostraron el impacto positivo de la propuesta porque los estudiantes pasaron de un nivel de desempeño Básico a un nivel Superior.

This paper presents the advances in forecasting mechanical properties by applying machine learning regressors in advanced manufacturing processes. Computational intelligence is advancing by leaps and bounds in artificial intelligence and data science, which is how data is currently considered the new gold. In addition to advances in information technology, manufacturing processes have also evolved. In that sense, additive manufacturing has several advantages over conventional manufacturing processes. On the one hand, it offers the possibility of processing any material: metals, ceramics, polymers, and composite materials. On the other hand, these technologies make it possible to optimize the use of the material, reducing its impact on the environment. Due to its versatility, it is possible to improve design to save material and reduce assembly complexity. This work presents a series of cases of applying machine learning regressors in forecasting mechanical properties in advanced manufacturing processes, from friction welding, laser welding, selective laser melting, wire arc additive manufacturing, and fused filament fabrication. The results show that the gradient-boosting regressors have greater precision in predicting several mechanical properties than conventional artificial neural networks. These algorithms are more effective than using the traditional design of experiments, reducing costs and allowing information about the physical phenomenon involved in each manufacturing process.

Climate change is one of the most pressing challenges facing our planet today. To abort this problem, the importance of reducing greenhouse gas emissions, particularly carbon dioxide (CO2), has been considered. Applying carbon capture, transport, and storage systems is vital to the mitigation process in this context. However, evaluating and selecting the most influential and appropriate technologies for a specific environment can be a complex challenge due to the many variables involved. In this regard, a critical review of carbon capture, transport, and storage technologies is presented, applying the formal concept analysis (FCA) method to evaluate the compliance of a series of attributes or characteristics with the technologies to some extent. The review process is subdivided into CO2 capture, CO2 transport, and CO2 storage. For the treatment of the information, the free access software Concept Explorer is implemented to obtain a series of diagrams that simplify the treatment of the information and facilitate the understanding of the results. Finally, among the results, it is highlighted that traditional carbon sequestration technologies have partially overcome some limitations presented in decades: efficient application at a low scale. At the same time, traditional carbon transport methods prevail over emerging alternatives. Finally, traditional technologies stand out notoriously for storage at large-scale levels concerning the new proposals. However, there are emerging applications with potential storage capacity for low-scale systems.

This article outlines the creation of a model designed to capture energy from staircases. The setup revolves around a hydraulic mechanism meant to retain the energy produced by individuals of varying weights (ranging from 72 to 85 kg) as they step on an tilting step. The design process considered the system's operational modes, followed by experimental assessments aiming to measure the potential energy by vertically moving a 12 kg load. Findings indicate variations in the device's performance concerning alterations in the angles of hydraulic actuators and shifts in the contact point with the stairs, thereby emphasizing the dependence on the contact area. The average values for inclination angles and contact area approximately yield 0.082 J per step. Challenges in subsequent phases include adjusting the angle across more than three positions and accurately quantifying pressure and volumetric flow within the circuit.

This chapter looks at land inequality from a systemic perspective and explores the potential of using cooperation to address it, understanding that the effects of unequal land ownership on quality of life are mixed and that land ownership can have negative effects on the quality of life of the poorest. people. However, the possibility of analyzing the actors involved in the problem of inequity and the role of cooperation between governments, communities, and individuals to generate equitable public policies and solutions tailored to the needs of vulnerable populations allows joint responsibility and accountability among local governments, communities, and individuals. Similarly, cooperation allows for more comprehensive and efficient land reform initiatives and better stakeholder communication and understanding. This understanding can lead to better bargaining and fairer and more equitable regulations, ensuring that vulnerable populations are not left behind. In conclusion, cooperation is an important tool for addressing complex issues of land inequality. Cooperation among governments, communities, and individuals is key to creating public policies and community-led solutions tailored to the specific needs of vulnerable populations. Economic interests and international initiatives remain relevant throughout the process. Finally, this chapter examines other relevant factors, such as economic interests and international initiatives, and emphasizes the importance of a systemic analysis to combat land inequality.

There is a growing need to foster problem-solving skills and creativity in Latin America among primary-school students. This chapter highlights the importance of using system dynamics to achieve these goals. Educators can create an integrated learning environment that helps students develop analytical and creative capabilities to tackle complex issues by incorporating collaborative teaching strategies like visualization, structured dialogue, problem-solving activities, and simulations. Developing systems thinking skills is crucial for making informed decisions and taking responsible action in today’s complex world. Educators can use system dynamics models and instructional materials to create a dynamic and interactive learning experience that promotes critical thinking, problem-solving, and teamwork. This approach requires clear communication, problem-understanding, creative and critical thinking, and evaluation of solutions. To achieve success, educators must foster an environment that emphasizes collaboration, creativity, openness to new ideas, and interaction. By empowering students to tackle localized problems, educators can create leaders in problem-solving and promote better learning and development outcomes for Latin American students and communities. Overall, this chapter provides insights into the benefits of using system dynamics to foster problem-solving skills and creativity among primary school students in Latin America. The chapter also offers practical guidance for educators on creating an integrated learning environment that promotes critical thinking, problem-solving, and teamwork and ultimately drives better learning outcomes for students.

The digital divide remains a significant challenge for many communities worldwide. However, recent advancements in AI technology offer a unique opportunity to address this divide by enhancing digital literacy, promoting access to information and resources, and facilitating personalized learning experiences. This chapter explores how machine learning algorithms, natural language processing, and other AI-driven technologies can be leveraged to provide more effective and efficient training tailored to individualneeds and learning styles. Additionally, AI tools can provide more accessible and relevant information to individuals regardless of their level of digital competence. By utilizing AI tools to improve digital literacy, promote access to information and resources, and facilitate personalized learning experiences, we can create a dynamic feedback loop that contributes to closing the digital divide. This chapterproposes that this feedback loop can lead to a cycle of empowerment, where the accumulation of knowledge and resources reinforces the ability to learn and acquire more resources. Finally, this chapter provides recommendations on how to effectively and responsibly use AI tools to address the digital divide.

Despite progress towards gender equality in science and education, women remain underrepresented in decision-making roles, and unconscious bias in recruitment and promotion processes remains a persistent challenge. Systems thinking provides a helpful framework for identifying the complex causes of gender inequality and developing realistic strategies and solutions. The dynamic hypothesis of gender inequality proposes the existence of four feedback loops, each with four cycles of reinforcement, that contribute to structural inequality between men and women in science. To effectively address gender inequality, a multifaceted approach is needed that targets the various feedback loops reinforcing gender disparities and addresses the structural factors that shape social inequalities. This could include combating unconscious bias, reducing gender-based stereotypes, and increasing access to resources. Continued efforts toward gender equality and equality of opportunity are crucial for creating a more equitable and inclusive scientific community. By applying systems thinking and taking a comprehensive approach to address gender inequality, we can progress towards a more just and equitable society.

Background: There is growing interest in the quality of manual ventilation during cardiopulmonary resuscitation (CPR), but accurate assessment of ventilation parameters remains a challenge. Waveform capnography is currently the reference for monitoring ventilation rate in intubated patients, but fails to provide information on tidal volumes and inspiration–expiration timing. Moreover, the capnogram is often distorted when chest compressions (CCs) are performed during ventilation compromising its reliability during CPR. Our main purpose was to characterize manual ventilation during CPR and to assess how CCs may impact on ventilation quality. Methods: Retrospective analysis were performed of CPR recordings fromtwo databases of adult patients in cardiac arrest including capnogram, compression depth, and airway flow, pressure and volume signals. Using automated signal processing techniques followed by manual revision, individual ventilations were identified and ventilation parameters were measured. Oscillations on the capnogram plateau during CCs were characterized, and its correlation with compression depth and airway volume was assessed. Finally, we identified events of reversed airflow caused by CCs and their effect on volume and capnogram waveform. Results: Ventilation rates were higher than the recommended 10 breaths/min in 66.7% of the cases. Variability in ventilation rates correlated with the variability in tidal volumes and other ventilatory parameters. Oscillations caused by CCs on capnograms were of high amplitude (median above 74%) and were associated with low pseudo-volumes (median 26 mL). Correlation between the amplitude of those oscillations with either the CCs depth or the generated passive volumes was low, with correlation coefficients of −0.24 and 0.40, respectively. During inspiration and expiration, reversed airflow events caused opposed movement of gases in 80% of ventilations. Conclusions: Our study confirmed lack of adherence between measured ventilation rates and the guideline recommendations, and a substantial dispersion in manual ventilation parameters during CPR. Oscillations on the capnogram plateau caused by CCs did not correlate with compression depth or associated small tidal volumes. CCs caused reversed flow during inspiration, expiration and in the interval between ventilations, sufficient to generate volume changes and causing oscillations on capnogram. Further research is warranted to assess the impact of these findings on ventilation quality during CPR.

Networks and social media are important tools for the development of technology in society, where their ease of use and adoption is an important indicator for the competitiveness of countries. On the other hand, these digital media have an impact on society through the communication that is generated through the publications in the profiles of social networks. Precisely, one of the key elements for start-ups and companies is the use of new technologies and the inclusion of networks and social media in their marketing processes. However, their use has been limited to recreational use, which reduces their importance, since their advantages are only evidenced as elements of organizational communication. This chapter provides theoretical content on global development trends by considering start-ups as engines of competitiveness based on social technology and demonstrating that influencers are people who have a positive impact on successful transaction through social media. These two factors are important for any organization that intends to conduct successful transactions through the use of social media and networks.

On the occasion of the 20th anniversary of the discovery of acrylamide in food, an analysis of patents related to the mitigation of this compound in food products obtained through immersion frying was carried out. For this purpose, a comprehensive search, compilation, and information analysis were conducted using free online databases such as Google Patents, Patenscope, and Lens. The search yielded a total of 79 patents within the considered time period (2002-2022). The countries with the highest number of granted patents were the United States, the European Union, and South Korea. The patents were classified into four main approaches: raw material modification (49%), application of pre-treatments (27%), process modification (16%), and measurement techniques (8%). Among the results, Frito-Lay, an American company, stands out as the food industry company with the highest number of granted patents, totaling 15. Based on this review, it is concluded that while a significant number of patents have been granted in recent years, there is still a lag in developing countries. Furthermore, more studies are needed to determine acrylamide in starchy food matrices subjected to immersion frying different from potatoes.