The Evolution of Active Learning in Response to the Pandemic: The Role of Technology.

Active learning has consistently played a significant role in education. Through interactive tasks, group projects, and a variety of engaging activities, students are encouraged to forge connections with the subject matter. However, the pandemic has necessitated that educators adapt and refine their active learning techniques to accommodate the online environment. This has resulted in stimulating innovations in the field, encompassing virtual simulations, online collaboration tools, and interactive multimedia. The COVID-19 pandemic has rapidly transformed the landscape of teaching and learning, particularly in higher education. One of the most prominent shifts has been the widespread adoption of active learning techniques, which have increased student engagement and fostered deeper learning experiences. In this chapter, we examine the evolution of active learning during the pandemic, emphasizing its advantages and challenges. Furthermore, we delve into the role of advances in artificial intelligence and their potential to enhance the effectiveness of active learning approaches. As we once focused on leveraging the opportunities of remote teaching, we must now shift our attention to harnessing the power of AI responsibly and ethically to benefit our students. Drawing from our expertise in educational innovation, we provide insights and recommendations for educators aiming to maximize the benefits of active learning in the post-pandemic era.

Building bio-innovation systems through advanced biotechnology education.

We discuss the role of advanced biotechnology education in fostering sustainable bio-innovation systems. As a case study, we focus on Paraguay's Graduate Diploma in Innovation Management and Biotechnological Projects, which emphasizes interdisciplinary collaboration, stakeholder integration, and professionals skilled in the interplay between biotechnology, society, and governance. We highlight the relevance of educational programs in addressing the gap between academic research and industrial needs, thereby contributing to sustainable growth in the biotechnology sector.

Pooled Cohort Profile: ReCoDID Consortium's Harmonized Acute Febrile Illness Arbovirus Meta-Cohort.

Infectious disease (ID) cohorts are key to advancing public health surveillance, public policies, and pandemic responses. Unfortunately, ID cohorts often lack funding to store and share clinical-epidemiological (CE) data and high-dimensional laboratory (HDL) data long term, which is evident when the link between these data elements is not kept up to date. This becomes particularly apparent when smaller cohorts fail to successfully address the initial scientific objectives due to limited case numbers, which also limits the potential to pool these studies to monitor long-term cross-disease interactions within and across populations. CE data from 9 arbovirus (arthropod-borne viruses) cohorts in Latin America were retrospectively harmonized using the Maelstrom Research methodology and standardized to Clinical Data Interchange Standards Consortium (CDISC). We created a harmonized and standardized meta-cohort that contains CE and HDL data from 9 arbovirus studies from Latin America. To facilitate advancements in cross-population inference and reuse of cohort data, the Reconciliation of Cohort Data for Infectious Diseases (ReCoDID) Consortium harmonized and standardized CE and HDL from 9 arbovirus cohorts into 1 meta-cohort. Interested parties will be able to access data dictionaries that include information on variables across the data sets via Bio Studies. After consultation with each cohort, linked harmonized and curated human cohort data (CE and HDL) will be made accessible through the European Genome-phenome Archive platform to data users after their requests are evaluated by the ReCoDID Data Access Committee. This meta-cohort can facilitate various joint research projects (eg, on immunological interactions between sequential flavivirus infections and for the evaluation of potential biomarkers for severe arboviral disease).

Complex artificial intelligence models for energy sustainability in educational buildings.

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.

Enhancing Buoyant force learning through a visuo-haptic environment: a case study.

Introduction: This study aimed to develop, implement, and test a visuo-haptic simulator designed to explore the buoyancy phenomenon for freshman engineering students enrolled in physics courses. The primary goal was to enhance students' understanding of physical concepts through an immersive learning tool. Methods: The visuo-haptic simulator was created using the VIS-HAPT methodology, which provides high-quality visualization and reduces development time. A total of 182 undergraduate students were randomly assigned to either an experimental group that used the simulator or a control group that received an equivalent learning experience in terms of duration and content. Data were collected through pre- and post-tests and an exit-perception questionnaire. Results: Data analysis revealed that the experimental group achieved higher learning gains than the control group (p = 0.079). Additionally, students in the experimental group expressed strong enthusiasm for the simulator, noting its positive impact on their understanding of physical concepts. The VIS-HAPT methodology also reduced the average development time compared to similar visuo-haptic simulators. Discussion: The results demonstrate the efficacy of the buoyancy visuo-haptic simulator in improving students' learning experiences and validate the utility of the VIS-HAPT method for creating immersive educational tools in physics.

Pharmaceutical innovativeness index: methodological approach for assessing the value of medicines - a case study of oncology drugs.

BACKGROUND: We propose a framework to assess the value of pharmaceutical innovations, with explicit clinical and methodological parameters, based on the therapeutic value and health needs. RESEARCH DESIGN AND METHODS: The study was based on the adaptation of health technology assessment methods documented in the literature, which was applied to a sample of oncological drugs. Difficulties and issues during the application of those tools were identified and addressed to develop a new framework with new and revised domains and clear classification criterion for each domain. Scores were assigned to each level and domain according to their relevance to generate the final score of innovativeness. RESULTS: The Pharmaceutical Innovation Index (PII) includes four domains, two related to clinical and social dimensions - Therapeutic Need and Added Therapeutic Value - and other two about methodological features - Study Design and Quality (risk of bias). The scores combined after assigned to each domain results Index of the Innovativeness of the medicines represents the degree of pharmaceutical innovation. CONCLUSION: This work proposes a transparent methodology with well-defined criteria and script; the algorithm developed with authors' weightings and criteria may be switched to best adjust to other applications, perspective or clinical indications, while keeping the transparency and objectiveness.

Learning manufacturing computer vision systems using tiny YOLOv4.

Implementing and deploying advanced technologies are principal in improving manufacturing processes, signifying a transformative stride in the industrial sector. Computer vision plays a crucial innovation role during this technological advancement, demonstrating broad applicability and profound impact across various industrial operations. This pivotal technology is not merely an additive enhancement but a revolutionary approach that redefines quality control, automation, and operational efficiency parameters in manufacturing landscapes. By integrating computer vision, industries are positioned to optimize their current processes significantly and spearhead innovations that could set new standards for future industrial endeavors. However, the integration of computer vision in these contexts necessitates comprehensive training programs for operators, given this advanced system's complexity and abstract nature. Historically, training modalities have grappled with the complexities of understanding concepts as advanced as computer vision. Despite these challenges, computer vision has recently surged to the forefront across various disciplines, attributed to its versatility and superior performance, often matching or exceeding the capabilities of other established technologies. Nonetheless, there is a noticeable knowledge gap among students, particularly in comprehending the application of Artificial Intelligence (AI) within Computer Vision. This disconnect underscores the need for an educational paradigm transcending traditional theoretical instruction. Cultivating a more practical understanding of the symbiotic relationship between AI and computer vision is essential. To address this, the current work proposes a project-based instructional approach to bridge the educational divide. This methodology will enable students to engage directly with the practical aspects of computer vision applications within AI. By guiding students through a hands-on project, they will learn how to effectively utilize a dataset, train an object detection model, and implement it within a microcomputer infrastructure. This immersive experience is intended to bolster theoretical knowledge and provide a practical understanding of deploying AI techniques within computer vision. The main goal is to equip students with a robust skill set that translates into practical acumen, preparing a competent workforce to navigate and innovate in the complex landscape of Industry 4.0. This approach emphasizes the criticality of adapting educational strategies to meet the evolving demands of advanced technological infrastructures. It ensures that emerging professionals are adept at harnessing the potential of transformative tools like computer vision in industrial settings.

Science, technology, and innovation in health for the next twenty years: A survey analysis in Colombia.

Background: Colombia is a Latin American country with a very complex social and political context that has not allowed the allocation of sufficient resources to the fields of science, technology, and innovation (STI). This is particularly worrying for the area of health since not enough resources are allocated for public health, research, or education. Methods: The so-called "Great Survey in Health 2019" was administered online through the SurveyMonkey platform to 5298 people in different regions of the country, representing the public, private, and academic sectors. The questionnaire consisted of 46 open and closed questions, including demographic inquiries. Data analysis involved textual analytics and sentiment analysis. Results: Overall, 56% of those surveyed were women within the adult life cycle. Most respondents had a postgraduate education. Greater participation was observed in the Oriental, Bogotá, and Antioquia regions, which also concentrate the largest number of resources for STI. Among the main recommendations derived from the results, priorities include investing in research, personalised medicine, promoting the social appropriation of knowledge, addressing mental health, regulating research through a statute, promoting undergraduate research, and establishing recertification exams to pursue excellence. Conclusion: The results of this original study serve as a fundamental input to promote and strengthen the STI processes in life sciences and health. They serve as a guide to generate public policies and actions that guarantee better health and well-being for the Colombian population, strategically proposing a clear roadmap for the next 20 years.

Development of a sustainability-oriented university laboratory: Insight into adsorption kinetics models for the removal of pollutants from aqueous solution.

The aim of the present research is to show the development of a sustainability-oriented lab that teaches adsorption concepts in a virtual environment based on the premise "learning-through-play". Kinetic results in the virtual environment are contrasted to those obtained experimentally when diverse adsorbents prepared from Agave Bagasse (Raw Fibers, Hydrothermal Fibers, and Activated Fibers) were synthesized. Comparison between virtual and real-life experiments involving removal of methylene blue in solution showed that a pseudo-first-order model could describe adsorption kinetics satisfactorily. The study is complemented with a characterization of the adsorbents through SEM, nitrogen adsorption isotherms, FTIR and Raman. In addition, the environmental impact of the synthesis of adsorbents was evaluated through well-known methodologies (GAPI, NEMI, and Eco-Scale), which agree that raw fibers are the most eco-friendly material. This research provides an exciting opportunity to advance our knowledge on developing new technologies for teaching in engineering and to compliment real-life practices that consider environmental impacts with virtual experiments.

Perception of hypotheticality in technology-based business ideas: effects on Opportunity Beliefs from a Construal Level Theory perspective.

This research investigates how entrepreneurs perceive the hypothetical nature of technologies (based on situations that are often imagined or theoretical) as a foundation for entrepreneurial endeavors and how this perception influences the formation of business Opportunity Beliefs. Drawing on the Construal Level Theory, we explore the relationship between the perceived hypotheticality of technologies and Opportunity Beliefs. Two experimental studies are conducted to examine these relationships, with Study 1 (n = 177 entrepreneurs) focusing on the perception of innovative technologies as more distant or hypothetical, and Study 2 (n = 404 entrepreneurs) delving into how the perceived distance to technology influences Opportunity Beliefs. The results indicate that entrepreneurs view more innovative technologies as more hypothetical and that hypotheticality mediates the relationship between the perceived degree of innovation and Opportunity Beliefs. We find evidence that Entrepreneurs tend to view the feasibility and fit/alignment of business opportunities more favorably when they perceive the psychological distance (hypotheticality) of the opportunity as closer rather than more distant. However, the difference this difference is nonsignificant in how they evaluate the desirability of the opportunity in any psychological distance. These results provide insight into the cognitive processes of entrepreneurs and offer implications for understanding how entrepreneurs perceive and evaluate business opportunities.

Open Innovation or Traditional Patenting Strategies to Efficiently Address Health Emergencies? How Patent Information Was Used Towards Effective Treatments for COVID-19.

INTRODUCTION/OBJECTIVE: During the 1150 days of COVID-19 pandemic there were great efforts to develop efficient treatments for the disease. After this long time, some drugs emerged as treatment for COVID-19. Some of them are new drugs, most of them, known drugs. These developments were triggered by information already available in patent documents. Pharmaceutical companies, therefore, rushed to conduct drugs evaluations and trials in order to deliver to the world a reasonable treatment that could reach the majority of its population. However, it is not immediately clear how companies operated to reach their goals. The ability of open innovation to achieve results assertively and faster than closed innovation strategies is questioned and therefore, it is questioned whether pharmaceutical companies use open innovation to face COVID-19. METHODS: In this work, data available on patent databases were mined to inform about the scientific and technological panorama of selected drugs tested for COVID-19 treatment and to understand the perspectives of such developments during the pandemic. RESULTS: This study evidenced that most treatments were based on known drugs, that some of the initially promising drugs were abandoned during the pandemic, and that it was able to inform if open innovation and collaborations were explored strategies. CONCLUSION: This study evidenced that the developments during COVID-19 were not based on open innovation by revealing a patent race towards the treatment development, but with practically no collaborations or information exchange between companies, universities, and research facilities.

The S20 Brazilian Mental Health Report for Building a Just World and a Sustainable Planet: Part II.

This is the second part of the Brazilian S20 mental health report. The mental health working group is dedicated to leveraging scientific insights to foster innovation and propose actionable recommendations for implementation in Brazil and participating countries. In addressing the heightened mental health challenges in a post-pandemic world, strategies should encompass several key elements. This second part of the S20 Brazilian Mental Health Report will delve into some of these elements, including: the impact of climate change on mental health, the influence of environmental factors on neurodevelopmental disorders, the intersection of serious mental illness and precision psychiatry, the co-occurrence of physical and mental disorders, advancements in biomarkers for mental disorders, the utilization of digital health in mental healthcare, the implementation of interventional psychiatry, and the design of innovative mental health systems integrating principles of innovation and human rights. Reassessing the treatment settings for psychiatric patients within general hospitals, where their mental health and physical needs are addressed should be prioritized in mental health policy. As the S20 countries prepare for the future, we need principles that stand to advance innovation, uphold human rights, and strive for the highest standards in mental health care.

GDP-mannose pyrophosphorylase is an efficient target in Xanthomonas citri for citrus canker control.

Xanthomonas citri subsp. citri (Xcc) is a bacterium that causes citrus canker, an economically important disease that results in premature fruit drop and reduced yield of fresh fruit. In this study, we demonstrated the involvement of XanB, an enzyme with phosphomannose isomerase (PMI) and guanosine diphosphate-mannose pyrophosphorylase (GMP) activities, in Xcc pathogenicity. Additionally, we found that XanB inhibitors protect the host against Xcc infection. Besides being deficient in motility, biofilm production, and ultraviolet resistance, the xanB deletion mutant was unable to cause disease, whereas xanB complementation restored wild-type phenotypes. XanB homology modeling allowed in silico virtual screening of inhibitors from databases, three of them being suitable in terms of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, which inhibited GMP (but not PMI) activity of the Xcc recombinant XanB protein in more than 50%. Inhibitors reduced citrus canker severity up to 95%, similarly to copper-based treatment. xanB is essential for Xcc pathogenicity, and XanB inhibitors can be used for the citrus canker control. IMPORTANCE: Xcc causes citrus canker, a threat to citrus production, which has been managed with copper, being required a more sustainable alternative for the disease control. XanB was previously found on the surface of Xcc, interacting with the host and displaying PMI and GMP activities. We demonstrated by xanB deletion and complementation that GMP activity plays a critical role in Xcc pathogenicity, particularly in biofilm formation. XanB homology modeling was performed, and in silico virtual screening led to carbohydrate-derived compounds able to inhibit XanB activity and reduce disease symptoms by 95%. XanB emerges as a promising target for drug design for control of citrus canker and other economically important diseases caused by Xanthomonas sp.

The impact of the integration of digital platforms and active teaching strategies (Kahoot!) on the performance of Brazilian medical course students in the discipline of histology.

Teaching human histology is part of understanding the tissues of the human body and, therefore, it is part of the training curriculum of all health courses. The increase in technologies and active teaching methodologies has a positive impact on student learning, as it reduces the challenges present in the subject. Therefore, this work aimed to compare the performance of students in the histology discipline, when compared to traditional teaching methodology and its association with game-based learning and a basic histology teaching platform. Three classes of the medical course were selected between the years 2022 and 2023, each of which was separated into a group. One group did not have access to any platform, being called the Traditional Group (TG), a group that used Kahoot!, being called the Kahoot Group (KG), and a group that used the teaching platform, being called the Histoatlas Group (HG). Both KG and HG groups showed greater effectiveness in learning and improved performance, when compared to TG. These improvements in KG and HG were also highlighted as learning aids and easy to use. KG students performed better in the practical test when comparing groups. However, this difference was not observed in the students' averages. However, students pointed out the relevance of trying to improve the traditional teaching methodology. Therefore, this study points out that, even though the traditional teaching methodology is efficient in the student's teaching and learning process, there is a need on the part of students to make the subject more dynamic.

Dynamic measurements of geographical accessibility considering traffic congestion using open data: a cross-sectional assessment for haemodialysis services in Cali, Colombia.

Background: Many cities with traffic congestion lack accessibility assessments accounting for traffic congestion and equity considerations but have disaggregated georeferenced municipal-level open data on health services, populations, and travel times big data. We convened a multistakeholder intersectoral collaborative group that developed a digital, web-based platform integrating open and big data to derive dynamic spatial-temporal accessibility measurements (DSTAM) for haemodialysis services. We worked with stakeholders and data scientists and considered people's places of residence, service locations, and travel time to the service with the shortest travel time. Additionally, we predicted the impacts of strategically introducing haemodialysis services where they optimise accessibility. Methods: Cross-sectional analyses of DSTAM, accounting for traffic congestion, were conducted using a web-based platform. This platform integrated traffic analysis zones, public census and health services datasets, and Google Distance Matrix API travel-time data. Predictive and prescriptive analytics identified optimal locations for new haemodialysis services and estimated improvements. Primary outcomes included the percentage of residents within a 20-min car drive of a haemodialysis service during peak and free-flow traffic congestion. Secondary outcomes focused on optimal locations to maximise accessibility with new services and potential improvements. Findings were disaggregated by sociodemographic characteristics, providing an equity perspective. The study in Cali, Colombia, used geographic and disaggregated sociodemographic data from the adjusted 2018 Colombian census. Predicted travel times were obtained for two weeks in 2020. Findings: There were substantial traffic variations. Congestion reduced accessibility, especially among marginalised groups. For 6-12 July, free-flow and peak-traffic accessibility rates were 95.2% and 45.0%, respectively. For 23-29 November, free-flow and peak traffic accessibility rates were 89.1% and 69.7%. The locations where new services would optimise accessibility had slight variation and would notably enhance accessibility and health equity. Interpretation: Establishing haemodialysis services in targeted areas has significant potential benefits. By increasing accessibility, it would enhance urban health and equity. Funding: No external or institutional funding was received.

Sorption Behavior of Azo Dye Congo Red onto Activated Biochar from Haematoxylum campechianum Waste: Gradient Boosting Machine Learning-Assisted Bayesian Optimization for Improved Adsorption Process.

This work aimed to describe the adsorption behavior of Congo red (CR) onto activated biochar material prepared from Haematoxylum campechianum waste (ABHC). The carbon precursor was soaked with phosphoric acid, followed by pyrolysis to convert the precursor into activated biochar. The surface morphology of the adsorbent (before and after dye adsorption) was characterized by scanning electron microscopy (SEM/EDS), BET method, X-ray powder diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) and, lastly, pHpzc was also determined. Batch studies were carried out in the following intervals of pH = 4-10, temperature = 300.15-330.15 K, the dose of adsorbent = 1-10 g/L, and isotherms evaluated the adsorption process to determine the maximum adsorption capacity (Qmax, mg/g). Kinetic studies were performed starting from two different initial concentrations (25 and 50 mg/L) and at a maximum contact time of 48 h. The reusability potential of activated biochar was evaluated by adsorption-desorption cycles. The maximum adsorption capacity obtained with the Langmuir adsorption isotherm model was 114.8 mg/g at 300.15 K, pH = 5.4, and a dose of activated biochar of 1.0 g/L. This study also highlights the application of advanced machine learning techniques to optimize a chemical removal process. Leveraging a comprehensive dataset, a Gradient Boosting regression model was developed and fine-tuned using Bayesian optimization within a Python programming environment. The optimization algorithm efficiently navigated the input space to maximize the removal percentage, resulting in a predicted efficiency of approximately 90.47% under optimal conditions. These findings offer promising insights for enhancing efficiency in similar removal processes, showcasing the potential of machine learning in process optimization and environmental remediation.

Implementing a challenge-based learning experience in a bioinstrumentation blended course.

BACKGROUND: Bioinstrumentation is essential to biomedical engineering (BME) undergraduate education and professional practice. Several strategies have been suggested to provide BME students with hands-on experiences throughout the curriculum, promoting their preparedness to pursue careers in industry and academia while increasing their learning and engagement. This paper describes the implementation of challenge-based learning (CBL) in an undergraduate bioinstrumentation blended course over the COVID-19 pandemic. METHODS: The CBL experience was implemented in a third-year bioinstrumentation course from the BME program at Tecnologico de Monterrey. Thirty-nine students enrolled in two sections formed fourteen teams that tackled blended learning activities, including online communication, lab experiments, and in-person CBL activities. Regarding the latter, students were challenged to design, prototype, and test a respiratory or cardiac gating device for radiotherapy. An institutional student opinion survey was used to assess the success of our CBL implementation. RESULTS: Student responses to the end-of-term survey showed that they strongly agreed that this course challenged them to learn new concepts and develop new skills. Furthermore, they rated the student-lecturer interaction very positively despite the blended format. Overall, students assessed their learning experience positively. However, implementing this CBL experience required a substantial time increase in planning, student tutoring, and constant communication between lecturers and the industry partner. CONCLUSION: This work provides an effective instance of CBL for BME education to improve students' learning experience despite decreased resource efficiency. Our claim is supported by the student's performance and the positive feedback from our industrial partner.

Lessons from a Neo-Aristotelian Theory Based on Evolutionary Science to the Field of Organizational Innovation.

This article's goal is to understand innovation factors (e.g., job autonomy and knowledge sharing) through the lens of a neo-Aristotelian theory based on evolutionary science in order to show that this paradigm of analysis provides a richer understanding of this organizational phenomenon, and consequently better support for the deliberation on what measures to implement when the objective is to make the organization prone to innovate.

A Systematic Patent Review (2008-2023) for Treatment in Pregnancy.

INTRODUCTION: During pregnancy, the woman's body undergoes anatomical and physiological changes, making this period susceptible to maternal-fetal diseases and complications. The consequences of not treating pregnant women include premature birth, low birth weight fetuses, and postnatal behavior disorders. Developing new therapies can accelerate the discovery of safe and effective drugs, contributing to designing novel natural and synthetic products to treat complications the pregnancy. OBJECTIVE: This study aimed to carry out a patent review to identify and explore trends in innovation and therapeutic strategies for treating pregnant women. METHODS: The Espacenet and WIPO databases were used, with the inclusion criteria being the keywords "pregnancy and drug" and code A61k, from 2008 to 2023, and as exclusion were the access to the patent and focus on human pregnant women. RESULTS: After the final screening, 32 patents were selected, with strategies for the treatment of diseases in pregnant women. Of these, 20 patents are on preclinical studies on animals and 12 on pregnant women. It was observed that universities lead the ranking of applications (17/32), and China has the highest number of patents (18/32). Most findings contain herbal medicines and/or the association of natural extracts with synthetic drugs. CONCLUSION: From this perspective, new drug administration systems were also developed, which can be a promising source for obtaining new medicines for the treatment of pregnant women; however, research is still limited and shows a gap in stimulating the rapid development of safe drugs that improve the health of pregnant women.