Digital lawmaking: evolving landscape of digital lawmaking and the integration of digital twins

Scrutinizing environmental governance in a digital age: New ways of seeing, participating, and intervening

Agencies as Legislators: An Empirical Study of the Role of Agencies in the Legislative Process

While the field of hydrography is crucial for maritime navigation and other maritime applications, oceanography is the field that provides the relevant data and knowledge for predicting climate change, monitoring marine resources, and exploring marine life. Digital ocean twins combine these two exciting fields and combine ocean observations and ocean models to establish virtual representations of a real world system, in this case the ocean or an ocean area, as well as assets in the ocean and processes within ocean industries or the natural environment. They have the potential to play a critical role in optimising and supporting sustainable ocean development. Digital Twins are synchronised with their real-world counterparts at a specific frequency and fidelity. They can provide valuable insights into the ocean's state and its evolution over time, which can be used to support decision-making in ocean governance and various ocean-related industries. Digital ocean twins can transform human ocean interactions by accelerating holistic understanding, optimal decision-making, and effective interventions. Digital twins of the ocean use ocean observations, historical and forecast data to represent the past and present and simulate possible future scenarios. They are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT systems. In this article, we explore the benefits of digital twins for the ocean, the challenges in developing them, and the current state of the art in ocean digital twin technology. One of the main benefits of digital ocean twins is their ability to provide accurate predictions of ocean conditions under expected interventions. Their information can be used to support decision- making in various applications including ocean-related industries, such as fishing, shipping, and offshore energy production. Additionally, digital twins can help to improve our understanding of the ocean's complex processes and their interactions with human activities, such as climate change, pollution, resource extraction and overfishing. Researchers and IT companies are combining various technologies and data sources, such as the Internet of Things for ocean observations, state of the art data science, artificial intelligence and machine learning, data spaces and vocabularies into digital ocean twins to contextualise data, improve the accuracy of ocean models and make ocean knowledge more accessible to a wide range of users.

Background: The growing availability of high-resolution imaging, biosensors, molecular profiling, and artificial intelligence has enabled the development of digital patient twins—computational models that reproduce individual physiological and pathological processes in silico. While digital twins have been widely proposed as tools for personalised medicine, their clinical and translational value across major disease domains has not yet been systematically synthesised. Methods: A narrative review was conducted of full-text publications from 2020–2025 addressing digital patient twins in cardiology, oncology, chronic disease management, and rehabilitation. The analysed literature included translational and clinical studies, mechanistic modelling papers, and healthcare system implementations. Evidence was prioritised from studies reporting patient-specific simulations, comparisons with real clinical or imaging data, and therapy-support scenarios. Results: In cardiology, electrophysiological and haemodynamic digital twins demonstrated high concordance with invasive mapping and imaging data and were associated with improved ablation planning, device optimisation, and reduced arrhythmia recurrence. In oncology, tumour digital twins integrating imaging and molecular data predicted tumour growth and treatment response with clinically meaningful accuracy, supporting personalised and adaptive cancer therapy. In chronic diseases, sensor-driven digital twins enabled early detection of physiological deterioration and supported proactive intervention, reducing exacerbations and hospitalisations. In rehabilitation, biomechanical and neurophysiological digital twins improved functional recovery by guiding personalised and robot-assisted therapy. Conclusions: Digital patient twins are transitioning from experimental computational tools to clinically relevant systems capable of influencing diagnosis, therapy selection, monitoring, and patient outcomes. By enabling in silico testing of therapeutic strategies on a virtual representation of the patient, digital twins reduce uncertainty in clinical decision-making and support truly personalised care. Continued progress in data integration, model validation, and regulatory governance will be essential for their safe and widespread adoption in clinical practice.

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

Digital twins are virtual models of physical artefacts that may or may not be synchronously connected, and that can be used to simulate their behavior. They are widely used in several domains such as manufacturing and automotive to enable achieving specific quality goals. In the health domain, so-called digital patient twins have been understood as virtual models of patients generated from population data and/or patient data, including, for example, real-time feedback from wearables. Along with the growing impact of data science technologies like artificial intelligence, novel health data ecosystems centered around digital patient twins could be developed. This paves the way for improved health monitoring and facilitation of personalized therapeutics based on management, analysis, and interpretation of medical data via digital patient twins. The utility and feasibility of digital patient twins in routine medical processes are still limited, despite practical endeavors to create digital twins of physiological functions, single organs, or holistic models. Moreover, reliable simulations for the prediction of individual drug responses are still missing. However, these simulations would be one important milestone for truly personalized therapeutics. Another prerequisite for this would be individualized pharmaceutical manufacturing with subsequent obstacles, such as low automation, scalability, and therefore high costs. Additionally, regulatory challenges must be met thus calling for more digitalization in this area. Therefore, this narrative mini-review provides a discussion on the potentials and limitations of digital patient twins, focusing on their potential bridging function for personalized therapeutics and an individualized pharmaceutical manufacturing while also looking at the regulatory impacts.

In this article, the author analyzes the variety of lists of subjects of the right of legislative initiative in the Russian regions. The author conducts an analysis of the constitutions (charters) of all regions of the Russian Federation, uses formal legal and comparative methods. According to the results of the study, the author notes that at the regional level, the right of legislative initiative is granted to 46 categories of subjects, which are classified into 4 groups: 1) state authorities and officials; 2) local self-government bodies and their associations; 3) judicial authorities and prosecutor's offices; 4) citizens and public associations. The latter group reflects representatives of the civil and expert community and includes 13 categories of subjects. Based on the results of the analysis of scientific works, generalization of the emerging legal practice, the author proposed the concepts of "civil participation in the law-making (legislative) process" and "expert participation in the law-making (legislative) process". Summarizing the Russian and foreign experience of civil and expert participation in the legislative process, the author confirms that citizens easily support ideas for changing legislation (vote for them), but at the same time have difficulties in converting such ideas into draft laws. With this in mind, in order to increase the professionalism of the preparation of the bill and the openness of its discussion, the author proposed a two-stage model of civil and expert participation in the legislative process, which involves: 1) at the first stage, the development, taking into account the real needs of the draft law, carried out on the principle of professionalism, by representatives of the expert community and (or) public associations; 2) at the second stage, a public discussion of the draft law developed in order to obtain the support of citizens. The author focuses on the need to implement a public discussion of the bill using digital technologies.

Purpose: Technological advancements are transforming the field of sports science and medicine, leading to a new era of performance improvement and injury prevention. Digital twins and artificial intelligence (AI) are at the forefront of these innovations, working together to redefine athletic training and monitoring. This editorial offers a comprehensive overview of the integration of digital twins and AI in sports science, with a focus on their potential applications, challenges, and future developments. By utilizing sensor data, AI algorithms, and biometrics, digital twins create virtual replicas of athletes, enabling precise performance monitoring and personalized training programs. Conclusions: Large datasets generated by AI can be used to predict and prevent injuries, as well as to enhance communication among stakeholders. Despite the promises, challenges such as privacy concerns and data accuracy need to be addressed. Future advancements will concentrate on sensor accuracy, AI algorithm refinement, and broader applications. The editorial highlights exciting research opportunities, including predictive injury models, real-time performance monitoring, and longitudinal health studies. Ultimately, the collaboration between digital twins and AI represents a paradigm shift in sports science, with the potential to revolutionize athlete well-being and performance optimization.

A pipeline for developing digital cardiac twins integrating cardiovascular magnetic resonance and electrocardiographic imaging: results from the MyoFit46 study

A digital twin is an artificial intelligence (AI)- or human-controlled avatar that replicates a specific person’s identity. While organizations have used digital twins of employees (e.g. digital doctors answering patients’ questions), a more common use is celebrity-as-a-service, where digital twins of celebrities interact with customers. Unlike traditional celebrity endorsements in marketing videos, these digital-twin agents interact directly with consumers. Although digital twins do not enhance the underlying AI or human agents’ capabilities, their appearance as a specific person can influence users’ perception. Across two experiments, we found that when a digital twin resembled a celebrity, regardless of whether it was AI- or human-controlled, it was perceived to be more capable, benevolent, and trustworthy, leading to greater user engagement. Even when errors occurred, the celebrity’s likeness reduced negative reactions, suggesting that celebrity effects can buffer against algorithm aversion. Practically, digital celebrity twins offer business value comparable to traditional celebrity endorsements, influencing user trust and service adoption despite identical underlying performance.

Artificial intelligence and uncertainty

This article examines the transformative impact of Digital Transformation (DX), Artificial Intelligence (AI), and Big Data on modern Quality Management (QM). It explores how traditional frameworks like PDCA and DMAIC are evolving to meet the demands of Industry 4.0, while also acknowledging their limitations in today’s fast-paced, data-rich environments. Emerging technologies – including IoT, Machine Learning, and Digital Twins – are enabling real-time monitoring, predictive analytics, and autonomous decision-making. This technological shift is driving the emergence of ‘Open Quality’, a more dynamic, agile, and integrated paradigm for quality management. To address the shortcomings of conventional methods, new frameworks such as PEARL (Plan, Execute, Assess, Results, Learn) and 3DQIF (Domain, Data, Discovery) are introduced, emphasizing adaptability and data-driven insights. While applications of Industrial Big Data demonstrate significant potential for quality improvements, they also present challenges related to data governance, system integration, and workforce skills. Case studies illustrate how AI and analytics can deliver substantial cost reductions and quality enhancements. Ultimately, this paper advocates for a hybrid approach, integrating timeless quality principles with advanced digital technologies to navigate modern complexities and achieve sustainable quality excellence.

Biopharma Is Going Digital … Bit by Bit

Despite the increasing affordability of data processing and storage and the enhancement of artificial intelligence (AI) and digital technologies in recent years, scalability and adoption continue to be a challenge when it comes to digital twins (DTs). Common challenges that are often cited include the effort of designing and building DTs, high customisation, the cost to operate and maintain DTs, interoperability between DT components and DTs, and the extensive analysis and effort required to turn DT outputs into useful insights. AI has seen significant advancements and growth lately, driven by the release of popular AI products such as ChatGPT, Google Gemini and DeepSeek's R1. Many of the recent developments have the potential to address the challenges of scaling and adopting DTs. This paper examines the intersection of AI and DTs and explores how AI can be used to address some of the challenges of scaling and adopting DTs. It concludes with a set of principles that aim to apply to most DT applications, regardless of use case or industry, and proposes AI methods and techniques that can potentially be used for each principle. These principles are (1) reduce effort, cost and/or time; (2) optimise resource and system efficiency; (3) improve interaction and outcome and (4) improve interoperability, reusability and maintainability.

In today’s rapidly changing work environment, the introduction of digital employee twins marks an important modification regarding the way we embrace and engage fresh faces to our team. These virtual avatars, backed by artificial intelligence, are poised to change onboarding and training by providing employees with a tailored and engaging learning experience. Their applications are wide-ranging, from onboarding and training to offering assessment of achievement and mentoring. Digital twins (DTs) have emerged as an increasingly vital resource in the age of digital innovation, as the need to adjust to the shifting dynamics of building production grows. Many industries, like production, power generation, transportation, medical treatment, and many more, have shown a great deal of interest in and adoption of DT. As a result of rapid advances in communication and technology, electronic devices have woven themselves into the fabric of our daily lives. The variety and capabilities of these electronic products and services are expanding at an unprecedented pace. Today’s consumers no longer settle for “standard" solutions—they expect every good or service to be tailored to their unique needs. Continuous worker opinion collection is made simpler by DTs, which provide administrators with insightful data on satisfaction with work. Using an environmental approach like this might influence important choices that could have gone unnoticed or unnoticed. Similarly, flexibly reacting to actual-time information can be used to simulate upcoming occurrences (Piras et al., 2024).