Technological Transformation Research Articles

Nano-resolutions for Environmental Salvation: Leaping to Sustainability

Nanoparticles have emerged as a transformative technology in environmental remediation, addressing the pressing challenges of pollution across air, water, and soil. Nanoparticles, particularly metal oxides, carbon-based materials, and polymers, demonstrate remarkable capabilities in addressing water, air, and soil contamination. Their high surface area to volume ratio enhances their efficiency in pollutant removal while minimizing toxicity, making them suitable alternatives to conventional methods. As traditional remediation methods often carry their environmental risks, there is a pressing need for innovative and sustainable solutions. This review delves into the mechanisms and applications of nanoparticles in various remediation techniques, including photocatalysis, Nanoadsorption, and nanomembranes for water treatment, as well as their effectiveness in soil and air purification. The findings underscore the potential of nanomaterials to enhance remediation efficiency while reducing environmental toxicity. By integrating these innovative solutions into existing environmental management frameworks, nanoparticles can play a crucial role in achieving sustainable environmental practices and mitigating contamination. This review advocates for continued research, development, and application of nanotechnology as a promising avenue for fostering a cleaner, healthier environment and contributing to global sustainability goals.

Teaching the science of life: A multidisciplinary educational approach to reproductive technology debates through the lens of developmental biology.

Teaching the science of life: A multidisciplinary educational approach to reproductive technology debates through the lens of developmental biology.

Functional study of an O-methyltransferase in eupatilin biosynthesis in Artemisia argyi by an efficient hairy roots transformation system.

Functional study of an O-methyltransferase in eupatilin biosynthesis in Artemisia argyi by an efficient hairy roots transformation system.

Public discourse on CO₂ transformation technologies: Lessons from a German study

Public discourse on CO₂ transformation technologies: Lessons from a German study

Costs and CO2 emissions of technological transformation in China's power industry: The impact of market regulation and assistive technologies

Costs and CO2 emissions of technological transformation in China's power industry: The impact of market regulation and assistive technologies

INFLUENCE OF NOZZLE TEMPERATURE, BED TEMPERATURE, AND PRINTING SPEED ON THE TENSILE STRENGTH OF 3D-PRINTED PLA SPECIMENS

Additive Manufacturing (AM), or 3D printing, revolutionises modern manufacturing by reducing material waste and enabling the creation of complex geometries through layer-by-layer construction from digital files. Its environmental efficiency and versatility, particularly with materials like biodegradable Polylactic Acid (PLA), align with global sustainability goals and cater to diverse industries, from healthcare to aerospace. As ongoing research enhances PLA's properties and sustainability, AM's adoption across various sectors is poised to expand, solidifying its role as a transformative technology. This investigation examined the impact of nozzle temperature on the tensile strength of PLA specimens produced by a 3D printer, considering different bed temperatures and printing speeds. The experiment involved varying the nozzle temperature (190°C, 200°C, 210°C), bed temperature (35°C, 45°C, 55°C, 65°C), and printing speed (50 mm/s, 60 mm/s, 70 mm/s, 80 mm/s), while maintaining all other variables constant. The specimens were printed using a raster angle of (90°, 0°, 0°) and a grid infill pattern. The tensile strength of the specimens was assessed using a tensile testing machine. The recorded tensile strength values of the PLA samples produced through 3D printing exhibited a decrease with increasing nozzle temperature, bed temperature, and printing speed. However, the measured tensile strength values remained approximately consistent. There was an elevation in tensile strength at a nozzle temperature of 200°C, bed temperature of 45°C, and printing speed of 60 mm/s. It may be deduced that the examined parameters do not exert a substantial influence on the tensile strength of the specimens. Consequently, it is advisable to undertake further investigation to scrutinise the implications of these parameters on other aspects of the material properties.

Firmware in Flight: A Deep Dive into OTA Update Mechanisms for IoT

The Internet of Things (IoT) quickly evolved into transformation technology across industries such as health, agriculture, intelligent cities and industrial automation. With billions of connected devices generating and data exchange, the ability to manage, maintain and secure these devices has remotely become a critical requirement. The Out-the Air update mechanisms have appeared as a key activator for trouble-free device management, allowing remote firmware, software and configuration updates to update without physical access. This research work examines the integration of OTA updates in IoT ecosystems, emphasizing their role in increasing operating efficiency, reducing maintenance costs and improving equipment security. The study examines various OTA updates, including wireless firmware and wireless firmware (SOTA), along with protocols and communication technologies used to provide these updates. It also analyzes common security challenges such as unauthorized access, data capture and manipulation updates, and suggests secure strategies such as end-to-end encryption, digital signatures and return mechanisms. In addition, the contribution represents optimization techniques to ensure efficient, light and reliable updates in limited environments, especially where the device has limited strength, memory or bandwidth. Case studies in the real world are reviewed that emphasize the successful implementation of OTA and lessons in various IoT applications. Finally, the article discusses the discovering trends, such as managing AI -controlled updates and OTA systems with 5G support that indicates the future direction of this critical technology. The aim of the finding is to provide a comprehensive understanding of OTA in IoT and lead the parties to the construction of scalable, safe and future prepared IoT systems.

The hermeneutical challenge of reading digital biblical texts in Africa

This article explores the hermeneutical challenges and possibilities of reading digital biblical texts (Biblia Digitalia) in Africa, building upon Mazamisa’s concept of dialectica reconciliae. The shift from printed Bibles to Biblia Digitalia is the third major transformation in media technology, following the transitions from orality to literacy and from scrolls to codices. This digital shift presents both opportunities and challenges, including the potential for a de facto digital canon, the influence of algorithmic curation and the blurring of boundaries between the text and the reader. The article proposes several avenues for African scholarship, including the creation of open-source online commentaries, increased online availability of African biblical scholarship, intentional engagement with international platforms from an African hermeneutical perspective and critical awareness of the algorithms that govern online content.Contribution: The article emphasises the need for a balanced and contextually relevant approach to interpreting digital biblical texts in Africa.

Machine Learning Models and Explainable Artificial Intelligence Approaches for Intrusion Detection in IoT Networks

The rapid spread of Internet of Things (IoT) technologies and the rapidly increasing use of IoT devices offer technological transformation and innovative solutions in many areas from daily life to industrial processes. However, the resource constraints, simple operating systems, non-standard protocols and embedded software of IoT devices make them vulnerable to cyber-attacks. This makes IoT networks risky against malicious attacks and increases the size of security threats. Moreover, the complexity and heterogeneity of IoT networks render traditional security approaches inadequate and increase the need for advanced solutions. In this context, the need for methods for detecting and preventing attacks on IoT networks that are not only reliable and effective, but also understandable by users and security experts has become increasingly critical. This need for network security necessitates the development of strategies that will both secure technical infrastructures and increase the trust of human elements interacting with these infrastructures. In this context, the need for more interpretable, explainable and transparent security approaches is increasing. In particular, machine learning (ML) and deep learning (DL) based intrusion detection systems offer effective solutions to security problems such as anomaly detection and attack classification. The comprehensibility of the decision mechanisms of the models used enables both security experts to manage the systems more effectively and users to have more confidence in the security measures taken. Explainable Artificial Intelligence (XAI) techniques make the decision processes of ML and DL models transparent, allowing to understand how and why attacks are detected. Accordingly, it has become a critical requirement for security systems not only to achieve high accuracy rates, but also to make the decisions taken interpretable. In this study, the effectiveness of artificial intelligence (ML and DL) techniques for the detection and classification of security threats in IoT networks is analysed. In addition, the applications of XAI methods such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) and Explain Like I'm 5 (ELI5) for IoT security are investigated. It is shown how these methods make the decision processes of ML and DL models used in IoT networks more transparent and provide a better analysis. As a result, this study presents an approach that combines both performance and explainability in IoT security. By demonstrating the effectiveness of XAI-supported ML and DL models, it aims to contribute to future research and innovative security solutions for enhancing security in IoT networks.

Telecardiology and Digital Health Innovations for Cardiovascular Care

Cardiovascular disease remains the leading global health concern,1 with remote populations often facing barriers to timely and specialized cardiac care.2 Every 36 seconds, a life is lost to cardiovascular disease (CVD)—yet for more than 6 billion people, accessing specialized cardiac care remains a distant hope. Telecardiology, a transformative technology, integrates telemedicine with wearable sensors and Al analytics to remotely diagnose and manage heart conditions.3 This approach aims to enhance medical excellence and operational readiness within cardiovascular healthcare systems.

SAP AI Applications Across Diverse Sectors: Education, Law Enforcement, and Art

This article examines the implementation and impact of SAP AI frameworks across three distinct sectors: education, law enforcement, and art. As artificial intelligence continues to mature as a transformative technology, SAP's solutions have demonstrated remarkable versatility through sector-specific adaptations while maintaining consistent underlying principles. In education, these technologies enhance personalized learning environments, streamline administrative functions, and accelerate research capabilities. Law enforcement agencies leverage SAP AI for predictive policing, biometric identification, and resource optimization, transforming how public safety is maintained. In artistic domains, SAP AI serves as a creative collaborator rather than a replacement, expanding creative possibilities while preserving human artistic direction. Cross-sector analysis reveals common patterns critical for successful implementation: comprehensive data integration, effective human-AI collaboration, continuous learning mechanisms, domain-specific customization, and robust ethical frameworks. It provides valuable insights into both the adaptability of SAP's AI architecture and the fundamental principles that drive successful AI adoption across diverse application contexts.

Blockchain and AI: Catalysts for transformation in education technology

Blockchain and artificial intelligence are reshaping education through enhanced security, efficiency, and personalization. This article explores how these technologies address challenges in credential verification, student records management, and administrative processes. Blockchain provides tamper-proof educational records while AI enables personalized learning and sophisticated data analysis. Despite implementation challenges including technical complexity and adoption barriers, case studies demonstrate viable integration pathways. As these technologies mature, they promise more accessible, equitable, and effective educational experiences globally.

AI-powered metadata management: Enhancing data quality for effective global financial crime detection and prevention

This article presents a comprehensive framework for enhancing data quality through AI-powered metadata management in global financial crime prevention. Financial institutions face mounting challenges with fragmented, inconsistent, and incomplete data across multiple jurisdictions, hampering the effective detection of sophisticated criminal activities. The article demonstrates how artificial intelligence transforms metadata management by automating classification, enrichment, and validation processes, thereby significantly improving screening product data quality. A detailed article of the foundational architecture reveals interconnected components that seamlessly integrate with existing systems. Advanced AI techniques for metadata enhancement—including supervised learning, entity resolution, and semantic understanding—offer substantial improvements in detection accuracy while reducing false positives. The article explores operational implementation strategies, performance metrics, and a compelling case study that validates the transformative potential of this approach. Emerging trends such as real-time adaptation systems, cross-institutional collaboration, explainable AI, and transformative technologies point toward a future where financial institutions can stay ahead of evolving criminal tactics through intelligent metadata management.

Securing the voice channel: Advanced voice biometric authentication for contact centers

Voice biometrics is emerging as a transformative technology for contact centers seeking enhanced security without compromising customer experience. This comprehensive article examines how voice authentication leverages the unique vocal characteristics of individuals to create secure identity verification processes. By exploring the fundamental components of voice biometric systems—from enrollment and matching to liveness detection and anti-spoofing measures—it provides insights into the technological underpinnings of this authentication method. The article details integration approaches through APIs and decisioning frameworks that enable seamless implementation within existing customer journeys. It addresses key challenges including environmental factors, voice variations, and aging effects on voice patterns. The discussion concludes with an examination of privacy considerations and regulatory compliance requirements, demonstrating how voice biometrics, when implemented thoughtfully, offers a balanced solution that strengthens security while eliminating the friction associated with traditional authentication methods.

The intelligent warehouse: Where AI and Human Intelligence Converge

The modern fulfillment center is experiencing a technological transformation at the intersection of artificial intelligence and human capabilities. This article explores how cutting-edge AI accelerators, neural networks, and humanoid robotics are revolutionizing warehouse operations. The emergence of flexible humanoid systems that learn through observation, the computing architecture enabling real-time decision-making, and the development of dynamic spatial intelligence that continuously optimizes operations. The piece investigates how these technologies enhance rather than replace human workers through predictive assistance and natural collaboration. Finally, future innovations, including autonomous reconfiguration, predictive supply chain management, and the integration of quantum computing, reveal a vision where warehouses become truly intelligent environments fostering unprecedented human-AI cooperation.

Technical innovations transforming the property and casualty insurance landscape

The property and casualty insurance sector is experiencing profound technological transformation driven by digital innovations that fundamentally restructure traditional insurance models. This technical review explores how cloud-native architectures, artificial intelligence, telematics, and regulatory technologies are reshaping core insurance functions. Cloud platforms with microservices architectures enable superior scalability while significantly reducing operational costs across the insurance value chain. AI-driven algorithmic underwriting systems transcend traditional actuarial models through ensemble learning techniques and deep neural networks, delivering enhanced risk assessment accuracy and processing efficiency. Telematics infrastructure with multiple data acquisition mechanisms has revolutionized auto insurance risk assessment, while RegTech solutions have transformed regulatory compliance through automated monitoring systems and blockchain frameworks. Autonomous vehicle insurance requires fundamentally different technical approaches including Bayesian networks and digital twin simulations, while advanced analytics architectures incorporate sophisticated security implementations such as homomorphic encryption and federated learning. Despite implementation challenges related to legacy system integration, data standardization, and model explainability, emerging directions including quantum computing, edge AI deployment, and decentralized insurance platforms promise to further evolve the technological landscape of property and casualty insurance.

Promoting students’ data literacy using technologies through lesson study

PurposeThis article examines a lesson study (LS) approach bringing teachers and university faculty together to develop a lesson in data literacy using transformative technologies, including Generative Artificial Intelligence (Gen-AI) such as ChatGPT.Design/methodology/approachAn LS team with four teachers from a private school, facilitated by three researchers, conducted two iterations of LS on teaching scatterplots using technologies. Multiple data were collected: Videos of research lessons, videos of lesson planning and post-lesson debriefings, and post-LS focus student group and teacher interviews. Based on an enriched LS framework (Lewis, 2016), this study investigates both students’ and teachers’ learning.FindingsThe students learned new concepts and skills to investigate contextual problems using technologies through the data literacy cycle. Teachers developed an understanding of relevant statistical concepts and pedagogical content knowledge needed for teaching the topic in a technology-rich environment. Teachers realized the potential of using Gen-AI for planning lessons and were eager to explore the effective use of Gen-AI further. Meanwhile, some challenges in using Gen-AI in LS were identified.Research limitations/implicationsThis study focuses on both teachers’ and students’ perceived learning based on interview data. However, the integration of classroom teaching data and debriefing data could provide a richer picture of their learning processes.Practical implicationsThis study demonstrates how data literacy could be taught through addressing contextual problems using various technologies, revealing both positive effects and associated challenges.Originality/valueThe study contributes to a better understanding of how transformative technology like Gen-AI could be incorporated into LS to strengthen teachers’ and students’ learning.

A Novel Microsteganography Technique Leveraging Engineered Relaxation Pathways of High-Energy Excitons in Monolayer WSe2.

In the age of information overload, protecting sensitive data demands reliable solutions, with multidimensional optical encryption emerging as a transformative technology. Wavelength, a critical parameter of light, holds significant potential for developing highly secure encryption systems that restrict information access to specific illumination conditions, further enhancing data security. However, the encryption technology related to wavelength is still in its nascent stages. Here, we report a breakthrough wavelength-dependent microsteganography technique based on a monolayer WSe2/CdSe quantum dots heterostructure. By precise modulation of the band alignment to engineer the relaxation pathways of high-energy excitons in WSe2, the heterostructure encodes information that is selectively activated by a specific wavelength of light. Furthermore, the incorporation of Morse code encryption seamlessly merges spectral selectivity with enhanced information complexity, creating a dual-layer security mechanism. This work not only advances the field of optical encryption but also opens new avenues for applications in secure data storage and transmission.

Current perspectives of telesurgery applications among different specialties: first multicentric and multispecialty retrospective study.

Telesurgery has recently emerged as a transformative technology with the potential to revolutionize surgical care with humanitarian and training opportunities. The advent of new robotic systems with telesurgical capabilities promoted the interest toward telesurgery; the Toumai (Microport, China) is among these new platforms and the only approved in Europe so far. The Toumai is a multiport robotic system with an immersive console and a single patient cart. The aim of the study is to report the largest series of telesurgical robotic procedures reported so far to prove the feasibility and safety of the technology. This is a multicenter cohort study on a series of patients who underwent robotic telesurgical procedures across China with the Toumai robotic system. The primary endpoint is to evaluate the safety of telesurgery measured as the need for conversion to local surgery or to different approaches. Furthermore, we reported the type of procedures performed and summarized the characteristics of connectivities. Overall, 66 overall surgeries-consisting of ten urological, 55 general, and one gynecological case-were performed with the Toumai across China, with an average distance of was 800, 220, and 300km, respectively. No conversions to local surgery were recorded. The average delay time was 65, 34, and 61ms for urological, general, and gynecological surgery, respectively. Apart from colecistectomies, gastric surgery (total gastrectomy, gastrowedge resection) and radical prostatectomies were the mostly performed interventions. Despite some issues needs to be addressed from a regulatory standpoint, technological and telecommunication advancements are willing to support the successful implementation of telesurgery; further series are expected to address the reproducibility of these outcomes in European countries.

Ethical Considerations in the Application of Artificial Intelligence in Health Systems: A Narrative Review

Artificial Intelligence (AI) has emerged as a transformative technology in healthcare, enabling the management of vast data volumes and predictive analysis of the future of issues to support decision-making. This narrative review examines ethical dimensions of AI integration in health systems, drawing from articles published from January 1, 2000 to November 30, 2023, across seven databases—Cochrane Library, PubMed, SCOPUS, Science Direct, BMJ Journals, ProQuest, and SAGE. Using Boolean operators such as “AI” paired with “health”, “health system”, or “hygiene”, the study identifies critical ethical concerns including the preservation of human dignity, confidentiality, informed consent, and the dual principles of beneficence and nonmaleficence. It further highlights systemic challenges like algorithmic bias, transparency gaps in decision-making processes, and disruptions to social justice, alongside legal complexities surrounding accountability for errors, fraud, and compensatory mechanisms. To address these challenges, the study advocates for multilayered solutions. These include establishing ethical audits, formulating policies to ensure equitable global access to AI benefits, and enforcing robust data protection frameworks. Designers are urged to develop comprehensive systems safeguarding patient confidentiality and extending privacy protections to healthcare personnel and affiliated individuals. International regulatory standards must align with social and ethical norms rooted in human dignity, while frameworks for error identification and damage compensation should be prioritized. Continuous adaptation of AI capabilities to evolving medical expertise, coupled with strict adherence to ethical guidelines, is emphasized as essential for sustainable integration of AI in healthcare systems.