Opportunities For Transformation Research Articles

Enhancing mechanical ventilator reliability through machine learning based predictive maintenance

Background With the advancement of Artificial Intelligence (AI), clinical engineering has witnessed transformative opportunities, enabling predictive maintenance of medical devices, optimization of healthcare workflows, and personalized patient care. Respiratory equipment plays a vital role in modern healthcare, supporting patients with compromised or impaired respiratory capacities. However, ensuring the reliability and safety of these devices is crucial to prevent adverse events and ensure patient well-being. Objective This study aims to explore machine learning techniques to enhance predictive maintenance for mechanical ventilators. Method The dataset used for this study contains information about 1350 entries of mechanical ventilators, made by 15 different manufacturers and available in 30 distinct models. Different machine learning algorithms, including Logistic Regression, Decision Trees, Random Forest, K-nearest Neighbors, Support Vector Machines, Naive Bayes, and XG Boost are developed and tested in terms of their performance in predicting mechanical ventilator failures. Results The ensemble methods, particularly Random Forest and XGBoost, have proven to be more adept at handling the complexities of the dataset. The Decision Tree and Random Forest models both showed remarkable accuracies of approximately 0.993, while K-Nearest Neighbors (KNN) performed exceptionally with near perfect accuracy. Conclusion Adoption of automated systems based on artificial intelligence will help in overcoming challenges of ensuring quality of MDs that are already being used in healthcare institutions. Implementing machine learning-based predictive maintenance can significantly enhance the reliability of mechanical ventilators in healthcare settings.

Wind Bands as Cultural Tradition and Economic Opportunities in Transformation in Sincelejo (Sucre, Colombia)

Human interactions are permeated by multiple factors, including cultural practices, where music is a fundamental element. The objective of this study was to understand the cultural identity of traditional wind band music and its organisational capacity in Sincelejo, Sucre - Colombia. The methodology was framed under the qualitativeapproach with a case study design, and interviews were used to collect the information. The results indicate that traditional wind band music constitutes the cultural identity of the inhabitants of Sincelejo, reinforcing the sense of belonging and social cohesion; in terms of organisational capacity, the bands have been financially consolidated over the years, although it is still necessary to strengthen this aspect to guarantee their sustainability. In conclusion, traditional wind band music in Sincelejo plays a fundamental role in the preservation of cultural identity and social cohesion. However, greater structuring of its financial management is required to ensure the continuity and development of this cultural tradition.

Harnessing artificial intelligence for business optimization: Enhancing efficiency and sustainability in SMEs

Small and Medium Enterprises (SMEs) play a crucial role in driving economic growth and innovation, yet they often struggle with limited resources, operational inefficiencies, and sustainability challenges. In this context, artificial intelligence (AI) presents a transformative opportunity for optimizing business processes, improving decision-making, and promoting sustainable practices. This review explores the integration of AI technologies to enhance efficiency and sustainability in SMEs, focusing on practical applications such as automation, predictive analytics, and intelligent resource management. By leveraging AI, SMEs can automate repetitive tasks, optimize supply chains, and streamline financial operations, leading to cost reductions and productivity gains. Additionally, AI-powered customer engagement tools, such as chatbots and personalized marketing systems, allow SMEs to better understand customer behavior and enhance service delivery. On the sustainability front, AI enables real-time monitoring of energy consumption, waste management, and resource utilization, helping SMEs align with environmental and regulatory standards. However, the adoption of AI in SMEs faces significant challenges, including data quality issues, limited budgets, and a lack of technical expertise. This review proposes a strategic approach to implementing AI solutions, emphasizing the need for tailored tools, effective data management, and employee training. By examining successful case studies, we highlight how AI-driven optimization has empowered SMEs to overcome operational hurdles and achieve sustainable growth. As the business landscape becomes increasingly competitive and sustainability-focused, integrating AI is not only beneficial but essential for SMEs seeking long-term resilience. This study aims to provide SMEs with actionable insights into adopting AI technologies, ultimately fostering efficiency, innovation, and sustainability in their operations. Keywords: Artificial intelligence, Small and medium enterprises (SMEs), Business Optimization, Review.

AI: A transformative opportunity in cell biology.

The success of artificial intelligence (AI) algorithms in predicting protein structure and more recently, protein interactions, demonstrates the power and potential of machine learning and AI for advancing and accelerating biomedical research. As cells are the fundamental unit of life, applying these tools to understand and predict cellular function represents the next great challenge. However, given the complexity of cellular structure and function, the diversity of cell types and the dynamic plasticity of cell states, the task will not be easy. To accomplish this challenge, AI models must scale and grow in sophistication, fueled by quantitative, multimodal data linking cell structure (their molecular composition, architecture, and morphology) to cell function (cell type and state). As cell biologists embrace the potential of AI models focused on cell features and functions, they are well positioned to contribute to their development, validate their utility, and perhaps, most importantly, play a leading role in leveraging the powers and insight emerging from the coming wave of cell-scale AI models.

Assessing the Quality of Automatic-generated Short Answers using GPT-4

Open-ended assessments play a pivotal role in enabling instructors to evaluate student knowledge acquisition and provide constructive feedback. Integrating large language models (LLMs) such as GPT-4 in educational settings presents a transformative opportunity for assessment methodologies. However, existing literature on LLMs addressing open-ended questions lacks breadth, relying on limited data or overlooking question difficulty levels. This study evaluates GPT-4’s proficiency in responding to open-ended questions spanning diverse topics and cognitive complexities in comparison to human responses. To facilitate this assessment, we generated a dataset of 738 open-ended questions across Biology, Earth Sciences, and Physics and systematically categorized it based on Bloom’s Taxonomy. Each question included eight human-generated responses and two from GPT-4. The outcomes indicate GPT-4’s superior performance over humans, encompassing both native and non-native speakers, irrespective of gender. Nevertheless, this advantage was not sustained in ’remembering’ or ’creating’ questions aligned with Bloom’s Taxonomy. These results highlight GPT-4’s potential for underpinning advanced question-answering systems, its promising role in supporting non-native speakers, and its capacity to augment teacher assistance in assessments. However, limitations in nuanced argumentation and creativity underscore areas necessitating refinement in these models, guiding future research toward bolstering pedagogical support.

The Expected Contribution of Artificial Intelligence (AI) Adoption in Supply Chain Management

In today's rapidly evolving economy, the adoption of artificial intelligence (AI) within supply chain management (SCM) has emerged as a key area of study. The integration of AI technologies into SCM systems presents a transformative opportunity for organizations seeking to enhance operational efficiency, reduce costs, and establish a competitive advantage in a dynamic market. AI technologies—ranging from machine learning algorithms to predictive analytics—serve as pivotal tools in addressing these challenges. By automating routine tasks, forecasting demand with greater accuracy, and facilitating real-time decision-making, AI enhances responsiveness and agility within supply chains. The economic benefits of incorporating AI into SCM frameworks are substantial. Implementing AI-driven solutions can lead to significant cost savings through improved inventory management, reduced waste, and enhanced resource allocation. For instance, machine learning models can predict stock requirements more accurately, minimizing excessive inventory and associated holding costs. Additionally, AI enhances supplier relationship management by analyzing vendor performance data, leading to more informed selection processes and negotiation strategies. As the field continues to evolve, it is crucial for professionals to engage with emerging technologies, ensuring that they remain competitive and responsive to the demands of an ever-changing market landscape. This study reviews the literature to determine why supply chain management (SCM) needs to adopt artificial intelligence (AI) in terms of integrative tactical planning, resource utilization and cost reduction, risk management, data management and inventory management. The aim of this study was to encourage professionals to investigate the possibilities of AI technology to enhance several elements of the supply chain.

The role of artificial intelligence in enhancing healthcare for people with disabilities

The integration of artificial intelligence (AI) in healthcare delivery represents a transformative opportunity to enhance the lives of people living with disabilities. AI-driven technologies, such as assistive devices, conversational agents, and rehabilitation tools, can mitigate health disparities, improve diagnostic accuracy, and facilitate effective communication with healthcare providers, fostering more equitable healthcare environments. This commentary explores these applications while addressing the ethical challenges and limitations associated with AI deployment. Specific challenges, such as algorithmic bias, privacy risks with patient data, and the complexity of designing inclusive technologies, are discussed to provide a balanced perspective. For example, biased diagnostic tools may lead to inequitable care, and privacy breaches can compromise sensitive data. Key areas of focus include personalised care through AI-powered systems, the design of inclusive AI technologies incorporating continuous feedback loops and partnerships with advocacy groups, and the development of AI-enabled robotics for physical assistance. This commentary paper emphasises the importance of addressing these limitations alongside advancing ethical AI practices and ensuring continuous user involvement to meet the diverse needs of people living with disabilities, ultimately promoting greater independence and participation in society. Consequently, while AI holds transformative potential in advancing equitable and inclusive healthcare for people with disabilities, addressing ethical challenges, overcoming limitations, and fostering user-centred design are essential to fully realise its benefits and ensure these innovations promote autonomy, accessibility, and well-being.

EQUAL AI: A Framework for Enhancing Equity, Quality, Understanding and Accessibility in Liberal Arts through AI for Multilingual Learners

The integration of artificial intelligence (AI) into liberal arts education offers a transformative opportunity to address the diverse needs of multilingual and multicultural learners. Consequently, this study introduces the EQUAL AI framework (Enhancing Equity, Quality, Understanding, and Accessibility in Liberal Arts through AI), a structured approach to utilizing AI to foster inclusion and innovation in liberal arts pedagogy. The framework identifies five key domains: linguistic support, cultural representation, creative expression, critical thinking, and collaborative learning. Additionally, the study underscores the necessity of systemic support, particularly through professional development programs that equip educators with technical proficiency, ethical awareness, and the ability to critically assess AI tools. By tackling challenges such as algorithmic bias, data privacy, and the digital divide, the study advocates for culturally responsive policies and inclusive practices. The EQUAL AI framework envisions liberal arts education as a space for equitable participation and cultural understanding, positioning AI as a tool to enhance rather than replace humanistic pedagogy, ensuring its relevance in a technology-driven, interconnected world.

Internet of things (IoT) for safety and efficiency in construction building site operations

Internet of Things (IoT) technologies present transformative opportunities through connectivity of intelligent devices, environmental sensors, and integrated management systems. This study aims to investigate the benefits and impact of IoT implementation on construction sites by analyzing relationships between key factors and outcomes for safety and efficiency. Hypotheses were developed proposing positive correlations between each factor and effective IoT adoption on construction sites. Structural equation modeling analysis on survey data from construction professionals and site reports strongly validated the research hypotheses. Positive path coefficients and high statistical significance confirmed environmental monitoring (0.38), equipment management (0.343), predictive analytics and maintenance (0.222) and safety monitoring (0.369) as crucial enablers for successful IoT integration leading to safer and more productive construction operations. The findings highlight imperative focus areas and provide actionable insights for construction stakeholders on strategies to effectively leverage IoT capabilities.

Art Education in the Age of Crises: Opportunities for Hope and Transformation

The culture of post-industrial societies has proven unsustainable in many ways: climate change, overconsumption of natural resources, violent conflicts, and social inequality create a looming dystopia that leaves people hopeless and without a vision for the future. Amid complex eco-social crises, it is essential to consider the role of art education as an educational and societal resource. Art education is not isolated from other sectors of society but is always connected to broader cultural and political contexts. In other words, art education can never be neutral; it either strengthens or challenges and reshapes the values and practices prevailing in society through its choices. This thematic issue, Art and a Hopeful Future, brings together studies presented in the Arts Education Special Interest Group at the 2023 FERA (Finnish Educational Research Association) Conference, focusing on the significance of art education as a builder of hope for the future.

Industrial Policy and Green Growth in a Small Island Economy: The Case of Singapore

Industrial Policy and Green Growth in a Small Island Economy: The Case of Singapore

Are They Ready to Teach? Generative AI as a Means to Uncover Pre-Service Science Teachers’ PCK and Enhance Their Preparation Program

AbstractIntegrating generative artificial intelligence (GenAI) in pre-service teachers’ education programs offers a transformative opportunity to enhance the pedagogical development of future science educators. This conceptual paper suggests applying the GenAI tool to evaluate pedagogical content knowledge (PCK) among pre-service science teachers. By holding interactive dialogues with GenAI, pre-service teachers engage in lesson planning in a way that reveals their understanding of content, pedagogy, and PCK while facilitating the practical application of theoretical knowledge. Interpretation of these interactions provides insights into teachers-to-be knowledge and skills, enabling personalized learning experiences and targeted program adjustments. The paper underscores the need to equip pre-service teachers with the necessary competencies to utilize GenAI effectively in their future teaching practices. It contributes to the ongoing discourse on technology’s role in teacher preparation programs, highlighting the potential of addressing existing challenges in evaluating and developing teacher knowledge via GenAI. The suggested future research directions aim to further investigate the GenAI usage implications in educational contexts.

ARTIFICIAL INTELLIGENCE IN THE CLASSROOM: THE FUTURE OF EDUCATION

In the wake of the digital revolution, the integration of emerging technologies in education has emerged as a powerful catalyst for promoting inclusion and breaking down barriers in education. This study investigates the transformative impact of technology on inclusive education, exploring how innovations such as artificial intelligence, virtual and augmented reality, and assistive technologies are redefining learning possibilities for all students. We adopt a qualitative methodology, based on a systematic literature review, to critically analyze the current state of technology integration in inclusive education. Our findings reveal that, when implemented effectively, these technologies have the potential to personalize learning on an unprecedented scale, adapt to individual student needs, and overcome physical, cognitive, and geographic barriers. We identify significant challenges, including the need for ongoing training of educators, issues of equity in access to technology, and concerns about privacy and data security. The study also highlights the importance of a holistic approach that considers not only the technical, but also the pedagogical, ethical, and social aspects of technology implementation. We conclude that while technology offers transformative opportunities for inclusive education, its success depends on careful and contextualized integration, supported by progressive education policies and an ongoing commitment to equity and inclusion.

Conceptualizing Agrifood Systems for a Healthy, Sustainable, and Just Transformation: A Systematic Scoping Review

Human and planetary health are interconnected through food and agriculture. Food production and consumption patterns continue to drive the global burden of malnutrition, diet-related disease, climate change, and environmental degradation. There is an urgent need to identify pathways for transforming agrifood systems to be increasingly healthy, sustainable, and just, but conceptual frameworks necessary for visualizing these complex relationships are limited. This systematic scoping review identified existing frameworks for analyzing human and environmental outcomes of agrifood systems and evaluated their inclusion of policy and governance. Frameworks have evolved to increasingly consider the food supply chain activities and actors, the drivers that shape them, and the outcomes of these interactions. The findings of the review were used to develop a conceptual framework specific to modern industrialized agrifood systems where policy landscape is an explicit component. The framework is tailored to researchers and policymakers with the intention of providing a foundation for analyzing and communicating agrifood system issues, including identifying facilitators and barriers to effective policy, places to intervene in the system, and windows of opportunity for successful transformation.

DigFrag as a digital fragmentation method used for artificial intelligence-based drug design

Fragment-Based Drug Design (FBDD) plays a pivotal role in the field of drug discovery and development. The construction of high-quality fragment libraries is a critical step in FBDD. Conventional fragmentation approaches often rely on rigid rules and chemical intuition, limiting their adaptability to diverse molecular structures. The rapid development of Artificial Intelligence (AI) technology offers a transformative opportunity to rethink traditional methods. Here, we present DigFrag, a digital fragmentation method that highlights important substructures by focusing locally within the molecular graph. In addition, we feed the fragments segmented by machine intelligence and human expertise into the deep generative model to compare the preference for data from different sources. Experimental results show that the structural diversity of fragments segmented by DigFrag is higher, and more desirable compounds are generated based on these fragments. These results also demonstrate that data generated based on AI methods may be more suitable for AI models. Moreover, a user-friendly platform called MolFrag (https://dpai.ccnu.edu.cn/MolFrag/) is developed based on various fragmentation techniques to support molecular segmentation.

Blockchain in Enterprise Resource Planning Systems: A Comprehensive Review of Emerging Trends, Challenges, and Future Perspectives

Abstract This research paper conducts a systematic review of the integration of blockchain technology with Enterprise Resource Planning (ERP) systems, a burgeoning field at the intersection of advanced technology and business management. The primary objective is to bridge the knowledge gap by synthesizing existing literature, providing insights into the current state, challenges, and potential of blockchain in enhancing ERP systems. The study follows a structured methodology involving four key stages: Literature Retrieval, Literature Screening, Biblio-metric Analysis, and Content Analysis. A comprehensive literature search in the Scopus database resulted in the identification and analysis of 250 scholarly articles, narrowed down to 66 relevant publications. The findings highlight a significant trend towards decentralized ERP solutions, driven by blockchain’s promise of enhanced security, transparency, and efficiency. Key areas impacted by blockchain integration include supply chain management, accounting, security, traceability, purchasing, and process automation within ERP systems. The bibliometric analysis reveals a growing academic interest in this domain, with a focus on scalability, data security, and interoperability. However, the integration faces challenges such as technical complexity, cultural resistance, and regulatory uncertainties. The study concludes that while blockchain presents transformative opportunities for ERP systems, realizing this potential requires overcoming substantial technical and organizational hurdles. Future research should concentrate on developing scalable, interoperable blockchain solutions, tailored to specific industry needs and compliant with regulatory standards, thereby facilitating widespread adoption and maximizing the benefits of blockchain in ERP systems.

AI in Biomedical Imaging and Diagnostics

Advances in artificial intelligence (AI) and synthetic biology have profoundly influenced biomedical research, creating transformative opportunities in imaging, diagnostics, and therapeutic engineering. In biomedical imaging, AI-driven algorithms enhance precision and accuracy, enabling automated analysis of complex datasets, real-time imaging insights, and identification of disease biomarkers. Meanwhile, synthetic biology redefines cellular engineering, particularly in T-cell research, by enabling customized functionalities, such as precision-targeted antigen recognition and tunable immune responses. The integration of AI into T-cell engineering amplifies these capabilities, facilitating the design and optimization of synthetic circuits, predictive modeling of cellular behaviors, and dynamic monitoring of therapeutic outcomes. This interdisciplinary approach is revolutionizing diagnostics and immunotherapy by streamlining the identification of disease-specific markers, improving diagnostic accuracy, and enabling real-time modulation of T-cell functionality within the tumor microenvironment. By combining AI-powered insights with synthetic biology's ability to engineer living systems, this research aims to address critical challenges in disease treatment, including tumor heterogeneity and immune evasion. This work explores the synergistic application of AI and synthetic biology in biomedical imaging and T-cell engineering, highlighting state-of-the-art technologies, their therapeutic potential, and the future landscape of personalized medicine.

Innovating Building Refurbishments: Embracing Global Digital Transformation for Today's Construction Industry

The Uberization of the construction industry is a clear indication that the sector is gradually shifting towards the digital era. This transformation signifies a significant digitization of construction, incorporating smart technology in various phases, from design and planning to execution, operations, and management. This paper delves into the importance of embracing digital technologies in building refurbishments to meet the evolving demands of today's construction landscape. By integrating innovative digital solutions such as Building Information Modelling (BIM), Internet of Things (IoT), artificial intelligence, robotics, and other cutting-edge technologies, refurbishment projects can achieve cost-effectiveness, precision, and environmental sustainability. The benefits of this digital shift encompass improved project coordination, safety, cost efficiency, and environmental impact management. However, challenges like high initial costs, resistance to change, cybersecurity threats, and skill shortages persist. The digital transformation is also shaped by global trends such as urbanization, population growth, and the push for smart, sustainable cities. Governments and private sectors acknowledge the potential of digital construction in meeting these demands more effectively. Thus, the industry must adapt to these technological advancements to stay competitive and address future infrastructure needs. The primary goal of this paper is to underscore the importance of embracing global digital transformation in building refurbishments, offering insights and recommendations for stakeholders in the construction sector to leverage digital technologies effectively for enhanced project outcomes and long-term sustainability. This study aims to explore the global digitalization of building construction, examining how advanced technologies like Building Information Modelling (BIM), Artificial Intelligence (AI), and the Internet of Things (IoT) are revolutionizing construction processes. By identifying the main drivers of digitalization in the construction industry, assessing the role of emerging technologies, and evaluating the benefits such as enhanced efficiency, cost savings, improved safety, and sustainability, this paper aims to showcase the necessity of digitalization to meet modern demands effectively. These abstract outlines the key concepts discussed in the paper, highlighting the advantages of integrating digital tools in building refurbishments, the obstacles, and opportunities in digital transformation within construction, and the potential impact on sustainability and project outcomes. Through case studies and industry insights, the study will demonstrate how digitalization is vital for staying abreast of modern requirements. Additionally, it will analysis case studies and successful implementations of digital technologies in refurbishment projects to illustrate best practices and lessons learned.

Harnessing Big Data for Machine Learning (Strategies, Approaches, and Challenges)

This paper explores the dynamic relationship between big data and machine learning, highlighting the key strategies, methodologies, and challenges associated with their integration. The convergence of these technologies presents transformative opportunities across industries, but it also introduces complexities in terms of data management, infrastructure, and real-time processing. This study examines the role of big data in fueling machine learning models, discusses critical success factors, and identifies best practices for implementing machine learning at scale.

Switching the Stereopreference of TeSADH: Enabling Anti‐Prelog Asymmetric Reduction of Aryl‐Ring‐Containing Ketones

AbstractThe biocatalytic asymmetric reduction of prochiral ketones offers a promising approach for producing optically active secondary alcohols. Alcohol dehydrogenases (ADHs) are enzymes that facilitate the reversible conversion of prochiral ketones into their corresponding optically active secondary alcohols, and thus are pivotal for this process. Most ADHs adhere to Prelog's rule in determining the stereopreference for the asymmetric reduction of prochiral ketones. This study focuses on the ΔP84/A85G mutation of TeSADH, demonstrating its capability to reduce aryl‐ring‐containing ketones to their corresponding alcohols in anti‐Prelog mode with high stereoselectivities. The study also highlights the crucial role of P84 in switching the stereopreference of TeSADH in the asymmetric reduction of aryl‐ring‐containing ketones. Furthermore, this mutant exhibits a broad substrate scope, including substrates accepted by the previously reported W110 mutants of TeSADH with opposite stereopreference. The ability to create mutants of the same enzyme with opposite stereopreferences presents intriguing opportunities for fascinating transformations, such as bienzymatic racemization, which can be utilized in dynamic kinetic resolution, and stereoinversion using two enantiocomplementary mutants of the same enzyme.