Digital twins

Our research at the Generative AI-SynBio Infrastructures Design Studio, supported by the National Science Foundation (NSF US), Intelligent Europe, and the EU Belmont/Horizon 2020 programs, proposes a pioneering investigation into applying bio-digital intelligent systems in design. Over six years of research, we have targeted low-lying coastal regions, creating bio-inspired code-driven growth and adaptation scenarios with an open-access integrated decision support system app for scenarios from 2018 to 2100. This research interlaces advanced tools and methodologies, including Generative AI, Machine Learning, and Generative Adversarial Networks, merged with natural and synthetic biology data ecosystems. This amalgamation fosters evolutionary growth, design algorithms, and techniques pivotal for developing resilient, adaptive, and carbon-positive urban landscapes, specifically addressing challenges like sea-level rise, soil subsidence, hurricane-driven storm surges, and heatwaves in low-lying areas of Miami,

Focus groups have evolved from traditional market research tools into dynamic platforms that now drive innovation in industrial and product design. This paper revisits their historical role, examines contemporary updates such as remote collaboration, AI integration, and inclusivity practices, and explores a futuristic outlook where immersive VR/AR, AI co-moderators, and biometric integration redefine user engagement. By connecting past insights with future scenarios, this paper positions focus groups as central to user-centered innovation and design research ecosystems.

Modern healthcare infrastructures and services are dependent on advanced data analytics, sensing and communication technologies that include 5G/6G networks, Artificial intelligence (AI), Internet of Medical Things (IoMT), Information Technology (IT), and Operational Technology (OT). This integration incurs a large vulnerability surface and cyber attackers can exploit those vulnerabilities to wage successful attacks against modern healthcare infrastructures and services. Therefore, identifying potential vulnerabilities in healthcare infrastructures and services and securing end-to-end monitoring of sensitive healthcare infrastructures and services are crucial for achieving resilient healthcare infrastructures and services. In this paper, we propose an architecture designed to enhance the resilience of healthcare infrastructures and services. This architecture is centered around the concept of Adaptive Cognitive Digital Twins (ACDTs), which are capable of orchestrating adaptive defenses to proactively respond to anticipated cyber attacks. Our architecture has seven layers. We detail the functions at each layer of the architecture to guide the design and development of mechanisms that can be employed at each layer.

The healthcare sector has a profound impact on humankind, society, and the global economy because each individual requires healthcare services at some point in their lifetime. This demand is particularly pronounced as the population of elderly individuals increases, given advancements in medical science and technology. However, cyber attacks against healthcare infrastructures and services are on the rise, partly because attackers can gain substantial financial benefits from successful attacks on these systems. This calls for research on investigating how to achieve the new paradigm of resilient healthcare infrastructures and services. Along this direction, an innovative architecture was introduced at SUN-RISE'2024, dubbed the Adaptive Cognitive Digital Twins (ACDT) architecture for achieving resilient healthcare infrastructures and services. In this paper, we investigate how to model the ACDT architecture, what mechanisms can be incorporated into the architecture to implement healthcare infrastructures and services, and what metrics can be used to analyze the resilience of the resulting healthcare infrastructures and services. Thus, this paper represents a significant step towards instantiating the abstract ACDT architecture into real-life systems.

The healthcare sector has a profound impact on humankind, society, and the global economy because each individual requires healthcare services at some point in their lifetime. This demand is particularly pronounced as the population of elderly individuals increases, given advancements in medical science and technology. However, cyber attacks against healthcare infrastructures and services are on the rise, partly because attackers can gain substantial financial benefits from successful attacks on these systems. This calls for research on investigating how to achieve the new paradigm of resilient healthcare infrastructures and services. Along this direction, an innovative architecture was introduced at SUN-RISE'2024, dubbed the Adaptive Cognitive Digital Twins (ACDT) architecture for achieving resilient healthcare infrastructures and services. In this paper, we investigate how to model the ACDT architecture, what mechanisms can be incorporated into the architecture to implement healthcare infrastructures and services, and what metrics can be used to analyze the resilience of the resulting healthcare infrastructures and services. Thus, this paper represents a significant step towards instantiating the abstract ACDT architecture into real-life systems.

Crisis management within residential life has become an increasingly vital concern in higher education, where residential environments are both centers of student life and critical extensions of institutional support systems. Broader crises, including public health emergencies, natural disasters, and campus-specific incidents, often test the capacity of residential life programs to respond effectively and safeguard the academic mission. At a systemic level, residential life crisis management practices function not only as immediate interventions but also as frameworks that shape institutional preparedness, student well-being, and long-term resilience. Student well-being is a central outcome of effective crisis management. Residential programs that integrate proactive communication, mental health support, and culturally responsive interventions can mitigate stress and foster a sense of safety during uncertain times. These practices also influence student retention, as students who feel supported and secure in their living environments are more likely to persist academically and remain engaged in campus life. Narrowing further, residential life crisis management contributes directly to institutional resilience by creating adaptive structures that bridge student affairs, health services, and academic continuity planning. Effective models often employ layered strategies, including staff training, peer leadership, and collaborations with emergency management offices to ensure both immediate response and sustainable recovery. Ultimately, the impact of residential life crisis management extends beyond immediate crisis response to reinforcing the broader educational mission. By aligning student-centered support with institutional priorities, higher education systems can cultivate resilient communities capable of thriving even in times of disruption.

The Digital Twin technology is currently becoming an innovative instrument in Chemistry, Manufacturing, and
Controls (CMC), allowing to simulate specific variability of the process precisely and make essential program
decisions based on these simulations. This paper shows that digital twins are useful in predicting interactions
between Critical Process Parameters (CPPs) and Critical Quality Attributes (CQAs) throughout pharmaceutical
production. Enabling real-time performance of processes under different conditions, digital twins can be used to
simulate processes and, thus, guide sound decision-making by creating a virtual replica of manufacturing
processes. The method increases the understanding of processes because it measures how variability influences
product quality to determine the best control measures. In addition, digital twins help in making informed
decisions in make-or-buy analyses, by modeling in-house manufacturing and outsourcing situations. They are also
able to offer critical insights on site-readiness assessments, where it can be determined in advance whether
manufacturing sites can deliver process requirements before actual deployment. Computer simulations and highquality models are used to combine experimental data with sophisticated modeling methods to improve predictive
quality of digital twins, including mechanistic models and data-driven models. Case studies describe effective
uses in the case of continuous biologics production and the purification of monoclonal antibodies, where digital
twins can reduce the time to development, without compromising regulatory standards. Finally, the methodology
underpins Quality by Design (QbD) and Process Analytical Technology (PAT) approaches by giving a dataoriented basis of risk-based decisions. Simulations on digital twin decrease uncertainty, build efficiency, and
facilitate sustainable production practices within the pharmaceutical industry. The findings emphasize the
importance of digital twins to streamline CMC strategies, enhance the quality of products assurance.

The Combining techno-economic analysis (TEA) with technical performance management (TPM) establishes a systematic approach for analyzing the financial impact of Chemistry, Manufacturing, and Controls (CMC) risks in program business cases. Standard TEA approaches typically record deterministic cost and revenue forecasts but do not account for the uncertainty of CMC activities like process scale-up, regulatory adherence, and manufacturing reliability. We suggest an approach that infuses probabilistic risk assessment with TEA to analyze the sensitivity of the net present value (NPV) of CMC uncertainties. Using stochastic modeling and scenarios of simulation, the framework helps decision-makers determine key drivers of cost, benchmark CMC variability, and integrate risk-adjusted forecasts into biopharmaceutical R&D and manufacturing programs' portfolios. The outcomes indicate the benefit of integrating TEA with probabilistic risk models in raising the precision of capital budgeting, making investment decisions consistent with risk tolerance, and boosting the robustness of biopharmaceutical R&D and manufacturing programs. Ideally, the integrated approach bolsters business case validity and facilitates more open communication with stakeholders about the trade-offs in terms of cost, schedule, and performance.

In today's digital landscape, safeguarding data against cyberattacks has become increasingly crucial. While traditional methods and algorithms have focused on securing networks, software, and hardware, the rapidly evolving threat landscape has surpassed the effectiveness of these conventional approaches. Consequently, artificial intelligence (AI) has emerged as a key tool in strengthening cybersecurity. The following research explores the applications of AI in cybersecurity, assessing the impact of AI-driven threat detection systems on modern cybersecurity practices, while addressing both their advancements and challenges. This paper also investigates how AI shapes cyberspace and its potential influence on future malware and cyber threats, emphasizing its dual role as both an asset and a potential risk.

This study explores the relevance, evolution and application of major Software Revolution Laws (SRLs) including Moore's Law, Metcalfe's Law, Bell's Law, Conway's Law and Amara's Law in the development and transformation of Information and Communication Technologies (ICTs). Through a systematic literature review involving 55 peer reviewed sources and supported by thematic content analysis, citation frequency studies, cross tabulation and visual analytical modeling. The research examines how these laws continue to shape ICT design, scalability and policy in contexts such as cloud computing, mobile systems, enterprise software and the Internet of Things (IoT). Key findings revealed that Moore's Law remains instrumental in driving expectations around performance scaling and cost efficiency, especially in cloud computing and hardware virtualization. Metcalfe's Law underpinned the value creation logic of networked applications, notably in mobile ecosystems, social platforms and IoT architectures. Bell's Law captured the constant emergence of new computing classes reinforcing the need for adaptive software architectures across device evolution cycles. Conway's Law highlighted the interplay between organizational design and software modularity, becoming increasingly relevant in DevOps and microservice oriented development. Amara's Law served as a strategic lens for evaluating the long-term implications of disruptive technologies and avoiding both over and underestimations in ICT planning. Recommendations are offered for educational curricula, policy development and further research in localized and emerging technological contexts.

Industry 4.0 has transformed modern manufacturing by incorporating internet-connected devices, artificial intelligence, cyber-physical platforms, and advanced data analysis. These innovations have raised automation levels, improved operational performance, and enabled predictive maintenance. Industry 5.0 builds on these foundations by emphasizing collaboration between humans and intelligent machines, promoting environmentally responsible practices, and supporting personalized production models. In parallel, affordable sensor technologies are gaining traction due to their low cost and practical ability to support essential monitoring across diverse applications. This study offers a comparative evaluation of sensor technologies in Industry 4.0, Industry 5.0, and budget-conscious systems, outlining their advantages, limitations, and areas of application. The findings suggest that sensor integration remains central to industrial progress, particularly when linked with IoT, artificial intelligence, and edge-computing solutions that enhance adaptability and real-time decision-making.

This paper investigates the ability of utilizing City Information Modeling (CIM) and Internet of Things (IoT) technologies in the Egyptian context to develop and enhance smart city management to keep pace with the development of Egypt's Vision 2030 by leveraging Building Information Modeling (BIM), Geographic Information Systems (GIS), and advanced communication networks as foundational elements for CIM-IoT in smart cities. It explores how these technologies can be applied to tackle local critical urban issues such as traffic congestion, energy consumption, and public safety. The research methodology involves a comprehensive literature review, through conducting a comparative analysis for twenty-six international and regional smart cities case studies, based on which a proposed framework "City Planning Information Model (CPIModel)" has been inducted. The research conducted a local field survey, as results and findings provide the requirement adjustment for the model to suit the Egyptian planning context. The findings of this research highlight the significant potential for assessing Egyptian cities and prioritizing their development into sustainable, resilient, and efficient environments for citizens by leveraging the power of data and technology, Egyptian smart cities can improve the quality of life for their residents and contribute to a more sustainable and smarter future.

Virtual reality (VR) cloud gaming faces significant challenges in terms of latency and bandwidth consumption, particularly in softwarized 5G and beyond (5G&B) networks. Ensuring an immersive experience on low-end devices, such as smartphones and cardboard-like headsets, requires innovative solutions to mitigate delay effects and optimize resource usage. This research focuses on enhancing VR cloud gaming performance by leveraging Digital Twins (DTs) and hierarchical compression techniques. The proposed system adopts a client-server architecture, where computationally intensive tasks are offloaded to the server, minimizing processing requirements on the client side. A key contribution is a novel delay estimation and management framework that employs DTs to synchronize video streaming and user input, reducing the impact of latency. Additionally, viewport-based hierarchical compression is applied to optimize bandwidth consumption while maintaining high perceived quality. The proposed architecture is evaluated through extensive experiments analyzing the effects of input delay, delay fluctuations, and compression methods on user performance and perceived quality. Results demonstrate that the system effectively mitigates adverse network conditions, ensuring a seamless and immersive VR gaming experience even in suboptimal environments.

The rapid evolution of telecommunication networks is poised to have a profound impact on the complexity of future networks, promising a revolutionary leap in the field of communications. This rapid evolution necessitates the exploration of novel techniques that enable adaptive, flexible, and intelligent decision-making in resource management, orchestration and access control. One promising approach to implementing the network intelligence paradigm is through the adoption of the Network Digital Twin (NDT) paradigm, aiming at modeling the behavior of devices, communication links, operating environments, and applications within complex networking systems. The main objective of this article is to define IDLE, an Intent-based Digital twin LEarning framework that enables the possibility of performing simultaneous What-if Scenario analyses to support decision-making for real-time network management and orchestration. A proof-of-concept (POC) of the proposed architecture is described, targeted to manage horizontal offload inside a Flying Ad-Hoc Network (FANET) providing edge computing to ground devices in remote areas. Some numerical results are presented to demonstrate the gain achieved thanks to the proposed framework.

This article explores the role of Artificial Intelligence (AI) in enhancing the security of .NET applications, focusing on the detection of threats and anomalies. With a dynamic cyber threat, the security measures used in the past tend to overlook certain vulnerabilities arising in newly developed software systems. The paper examines the role of AI, more precisely machine learning and anomaly detection in enhancing security measures in the environment of .NET. Combined with a set of case studies, the use of real-life examples, as well as a comprehensive analysis of AI techniques, the research proves the power of AI to realize and prevent potential threats in real-time. Important conclusions indicate that AIbased systems can help greatly narrow the response time to security breaches, enhance the accuracy of detection, and be more effective tracking new threats compared to traditional approaches to security. False positives and the necessity to perpetually train the models are also noted among the issues in the article. To summarize, not only is the incorporation of AI in the security of the .NET applications beneficial, but this also ensures that the complications of cybersecurity today are addressed with a proactive approach to counter the threats that evolve in the environment.

This study provides a structured review and synthesis of existing Life Cycle Assessment (LCA) studies on six battery chemistries-PbA, NMC, LFP, NCA, LTO, and LMO-commonly used in transportation applications. Rather than conducting a new LCA, this work compiles and analyzes data from multiple sources to establish a comparative framework for assessing the global warming potential (GWP) of these technologies. The analysis focuses on emissions generated across key life cycle stages, including material extraction, battery production, transportation, recycling, and end-of-life, while integrating uncertainty through a data quality indicator. Results indicate that the average GWP for lithium-based batteries is 110.3 kgCO 2 eq/kWh, with recycling processes reducing emissions by approximately 39.7 %. Lead-acid batteries exhibit a lower GWP (67.7 kgCO 2 eq/ kWh) but are technically outperformed by lithium-based chemistries in energy density and cycle life. The findings highlight the need for a holistic approach in battery selection, emphasizing the importance of integrating technical performance indicators such as energy density, charge/discharge cycles, and recyclability when assessing environmental impact. This study supports the development of eco-efficient energy storage solutions and informed decision-making for sustainable transportation applications.

This paper explores the adaptation of Federated Learning (FL) to enhance cybersecurity in industrial control systems, which are vital for critical infrastructures. Integrate ICS into digital networks to better control distant or remote places, which increases sophistication in cyber-attacks, thereby threatening traditional centralized IDS solutions for ICS. Centralized systems are inefficient for ICS's needs as they adopt a decentralized and privacy-preserving aspect. FL is an approach to decentralized machine learning that promises solutions in the realm of multiple entities training security models on their data while hiding sensitive operational information. This study evaluates the potential applicability of FL in solving some of the issues ICS faces under increasing cyber-attacks and draws a comparative analysis with traditional IDS versus FL-based approaches. Findings will assist in the betterment of ICS security, thereby ensuring the smooth operation of critical infrastructures against increasingly sophisticated cyber threats.

Introduction. In the context of economic globalization, enterprises face the need to enhance their innovative potential, which serves as the foundation for ensuring longterm competitiveness. One of the factors influencing these processes is corporate social policy, which acts as a tool for shaping the intellectual potential of organizations. Social initiatives implemented within the framework of corporate policy can significantly impact innovation activity and the development of a company's scientific potential. The study is aimed at investigating the relationship between corporate social policy and the level of intellectual potential in enterprises. Materials and methods. In order to address this task, a comprehensive approach was employed, incorporating theoretical and empirical analysis. The theoretical foundation of the study is based on a review of publications in the fields of corporate social responsibility and innovation activity. Empirical analysis was conducted using multi-criteria evaluation methods, including the analytic hierarchy process. Data from seven machine-building enterprises were collected, enabling a comparative analysis, identification of key trends, and calculation of intellectual potential indices for each enterprise. Results. The study identified criteria reflecting the level of intellectual potential in enterprises, such as staff qualifications, access to training, implementation of innovative technologies, the number of scientific developments, and employee social support. The obtained data revealed disparities in the level of intellectual resource development, highlighting the importance of corporate social policy in shaping these resources. Using the analytic hierarchy process, final intellectual potential indices were calculated for all enterprises, allowing for the identification of organizations that most effectively leverage social policy. Conclusion. The research results confirm the importance of a comprehensive approach to corporate social policy for enhancing the intellectual potential of enterprises. The findings can be used to develop recommendations for improving social policy in machine-building enterprises to increase their competitiveness and innovation activity.

The increasing complexity and sophistication of cyber threats have made traditional cybersecurity tools less effective in protecting organizations against modern attacks. As a result, the need for nextgeneration cybersecurity tools has become more critical to detect, respond to, and mitigate advanced threats in real time. This paper explores cutting-edge cybersecurity technologies, focusing on their application in advanced threat detection and incident response. We delve into the role of Artificial Intelligence (AI), machine learning, blockchain, and automation in transforming threat detection
mechanisms and enhancing incident response systems. The paper also discusses the challenges associated with implementing these advanced tools, such as cost, integration with legacy systems, and data privacy concerns. Additionally, real-world case studies of successful implementations are presented to highlight the effectiveness of these tools in combating cyber threats. The study concludes
with recommendations for improving cybersecurity infrastructures and offers insights into the future direction of cybersecurity technologies, emphasizing the integration of emerging technologies like quantum computing and 5G networks.

This study is based on the Define, Measure, Analysis, Improvement, and Control (DMAIC) methodology to reduce the rework ratio in the workwear garments sector of Sapphire Finishing Mill Lahore. The researcher first selected one line of the floor, observed the line for one month, and defined the line's problems based on the daily quality reports. The defects are then measured in terms of defects per hundred companies (DHU). Furthermore, the data analysis is done using different Six Sigma tools, such as a fishbone diagram for root cause investigation and 5WHY analysis. Corrective measures are taken on the basis of findings. Once the system is improved, the control phase of the methodology is implemented. After compiling the result, the author realized that 14% efficiency is increased, while on average 8% defect rate is reduced. Moreover, a little improvement is also indicated in the productivity of the line, which is 13%.

Keywords: DHU, efficiency, productivity, and DMAIC methodology.

Digital transformation is reshaping the construction sector through technologies that promote collaboration, streamline processes, and support data-informed decision-making. The combination of Building Information Modelling (BIM) and Internet of Things (IoT) technologies marks a major leap in construction practices, providing unparalleled capabilities for project management, monitoring, and optimization. This research investigates how BIM applications are changing construction workflows and collaborative efforts, while also examining how IoT and cloud-based systems facilitate realtime monitoring of construction activities. The results indicate that integrated BIM-IoT solutions bring notable enhancements in efficiency, safety, and sustainability within the construction field, driven by improved information sharing, predictive analytics, and collaborative decision-making systems. As construction companies increasingly embrace these digital tools, the industry is poised for benefits, including shorter project timelines, reduced costs, and enhanced quality and performance of buildings.

Il presente contributo descrive il progetto di digitalizzazione gigapixel
e 3D delle cinque lastre che componevano la Tomba del Tuffatore
(480/70 a.C.) di Paestum. Verranno esposte le potenzialità del primo
digital twin che riprende fedelmente l’aspetto originale della Tomba e
analizzati casi di utilizzo di questo modello.

Lessons learnt on “Digital Twins” technologies and activities in the Green Deal area.
The presentation discusses experiences and results related to various analysis, design, and development activities (including operational activities) within the ISDE, the WMO GEOSS initiative, the European Commission's JRC, and numerous European projects concerning the conceptualization of geospatial digital ecosystems.

In the era of shifting toward greener and zero-emission energy production and transportation, Electrical Vehicles (EVs) gained substantial attention worldwide owing to their potentiality of the least carbon footprints on the environment. Nowadays, climate change is considered as the principal side effects of using fossil fuels and using conventional transportation systems. Considering the replacement of conventional plants with Renewable Energy (RE), the electrification of energy consumption is one of the key elements of the energy transition, due to the variability of Renewable Energy Sources (RESs), and EVs are one of the main ways to increase it. Meanwhile, limited infrastructure for charging and maintenance has made us step forward in battery management, Battery Thermal Management Systems (BTMSs), and predictive maintenance for EVs to optimize energy efficiency, to have a range prediction over the distance these smart vehicles will commute. In this study, we had a comprehensive review of integrating Artificial Intelligence (AI), Digital Twins (DTs), and Metaverse into the EVs sector to anticipate the energy consumption behavior of electric machines and vital factors that affect their distance navigation. Having energy-related insights and also developing a road map for project owners to commence replacing traditional methods with cutting-edge optimizing technologies distinguishes this paper from other studies.

We propose a Byzantine Fault-Tolerant (BFT) Bio-Parts Metadata Validation System, a decentralized framework designed to standardize synthetic biology part metadata while dynamically adhering to regional regulatory constraints. The system merges a permissioned blockchain with smart contracts that encode compliance rules, thereby guaranteeing metadata integrity across jurisdictions in the absence of centralized oversight. Our method adopts a Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, with nodes acting as research institutions or manufacturers verifying metadata blocks against regulatory smart contracts, which enforce rules specific to jurisdictions, including EU Directive 2001/18/EC or NIH standards for CRISPR-Cas9 alterations. Furthermore, the framework introduces a tokenized incentivization mechanism to reward compliant submissions, which encourages participation and preserves auditability. The proposed method connects legacy bioparts registries (for instance, iGEM's Registry) and contemporary AI-driven design tools by means of REST APIs, which results in uninterrupted metadata submission and pre-validation. Validated blocks are then propagated to distributed bioproduction hubs, where biosafety levels and containment protocols are auto-configured based on on-chain metadata. Additionally, the system employs Hyperledger Fabric for rapid consensus and Chainlink oracles to obtain real-time regulatory updates, thereby achieving adaptability to changing GMO legislation. This work distinctively harmonizes technical agreement, regulatory coordination, and financial motivations while tackling essential interoperability obstacles in worldwide synthetic biology partnerships. The framework's importance stems from its capacity to align metadata standardization with legal adherence, thus supporting scalable, auditable, and regulation-conscious bioparts exchange internationally.

This article explores modern approaches to forecasting technological risks using simulation modeling and machine learning algorithms. It analyzes typical failure scenarios, methods for constructing digital twins, and the architecture of interaction between simulation environments and intelligent modules. The effectiveness of hybrid systems is emphasized in the context of unstable technological regimes, including aggressive environments and equipment failure prevention. The study presents simulation results of a manufacturing process using the SimPy environment and embedded ML classifiers, which demonstrated a reduction in emergency shutdowns and production losses. The findings confirm the practical applicability of intelligent systems for adaptive control and enhanced resilience of industrial processes under uncertainty.

This article formulates digital twins as planetary media-an assemblage of human and nonhuman elements that emerges from planetary-scale computation to observe and predict changes in the Earth's human, geophysical, and socioeconomic spheres. It aims to revise Gabrys' original concept of planetary media by revealing the collapse of the globe (as capitalist and human-centered) and planet (as geological and ecological) in digital twins applied to both Earth System Science and socioeconomic domains. Their collapse is also evident in scientific papers related to EU's Destination Earth and China's Digital Earth, policy reports on EU's green/digital transition and China's Sustainable Development Goals (SDG), and documents on the smart cities in both regions. All of these share optimization and resilience as not only digital twins' technical requirements but also key values for establishing them as sociotechnical imaginaries of transforming the Earth's human activities and natural environments in the service of its sustainable future.

As climate change intensifies the frequency and severity of extreme weather events, the urgency for resilient and sustainable urban development becomes increasingly critical. This study investigates the role of digital twins in advancing climate-responsive urban strategies, with a focus on their integration into zero-energy buildings (ZEBs) and smart city frameworks. A systematic literature review was conducted following PRISMA guidelines, covering 1000 articles initially retrieved from Scopus and Web of Science between 2014 and 2024. After applying inclusion and exclusion criteria, 70 full-text articles were analyzed. Bibliometric analysis using VOSviewer revealed five key application areas of digital twins: energy efficiency optimization, renewable energy integration, design and retrofitting, realtime monitoring and control, and predictive maintenance. The findings suggest that digital twins can contribute to up to 30-40% improvement in building energy efficiency through enhanced performance monitoring and predictive modeling. This review synthesizes trends, identifies research gaps, and contextualizes the findings within the Middle Eastern urban landscape, where climate action and smart infrastructure development are strategic priorities. While offering strategic guidance for urban planners and policymakers, the study also acknowledges limitations, including the regional focus, lack of primary field data, and potential publication bias. Overall, this work contributes to advancing digital twin applications in climate-resilient, zero-energy urban development.

Introduction. The relevance of studying consumer behavior characteristics in the restaurant
business segment stems from economic factors, including the population’s income level and
inflation, which impact consumer behavior in the restaurant industry.
This study aims to identify the peculiarities of the restaurant market and consumer behavior in
the Moscow restaurant services market.
Materials and methods. The research materials consisted of scientific articles, books, and
publications on consumer behavior in the restaurant business and marketing strategies in this area.
Results. Within the framework of the study, three stages were implemented. At the first stage,
an analysis of the macroenvironment of the Moscow restaurant market was conducted to
identify external factors influencing the modern restaurant business. External factors that have
both positive and negative impacts on the development of the restaurant industry were
structured and described. At the second stage, to identify the main trends in the Moscow
restaurant market, a content analysis method was used to examine the websites of Moscow
restaurant business establishments with high rating indicators, based on data from the Yandex
neural network. The main trends in the Moscow restaurant market include the orientation of
restaurants towards preparing healthy meals and vegetarian cuisine, the use of farm products
and local ingredients in prepared dishes, the introduction of technological innovations that
simplify the consumer experience, a focus on sustainability in the business model, and a variety
of restaurant formats. At the third stage of the study, a survey was conducted among visitors to
restaurants and cafes in Moscow to identify the characteristics of their consumer behavior. The
identified consumer practices in the restaurant market indicate, on the one hand, the active
involvement of the audience in visiting restaurants, cafes, and bars in Moscow; on the other
hand, the use of restaurant delivery services through restaurant websites or mobile applications.
The dominant influence of rational factors was revealed when consumers choose a restaurant
to visit, changing the structure of consumer motivation when visiting restaurants and cafes.
Conclusion. As areas of further research corresponding to the subject area , we
designate: the study of changes in consumer practices under the influence of the
VUCA environment; exploring the impact of digital and standard communication tools
on restaurant decision-making; and examining the phenomenon of customers visiting
restaurant establishments on a budget.

Introduction. The Russian tourism market, like the global one, is facing serious
challenges due to the COVID-19 pandemic and other factors. Additionally, in the
current geopolitical context and amid travel restrictions, domestic tourism in Russia is
of particular importance. The economic crisis, the level of income of the population,
the exchange rate, and other financial indicators have a significant impact on
tourist activity.
Aim of the article: to analyze the current state of the Russian tourism market.
Materials and methods. The materials included official statistics, specifically
information from the Federal State Statistics Service, Rostourism, and other state
bodies, which contained data on the number of tourists, income from tourism
activities, and infrastructure. Methods: statistical analysis, including descriptive
statistics and time series analysis, are employed to study the dynamics of tourism
market indicators over time, identifying trends and predicting future changes.
Results. The analysis of statistical data by years allowed identifying and describing the
main trends in the development of the tourism sector, the volume and cost of selling
tourist packages. The structure of collective accommodation facilities showed their
percentage during the study period and the change in their shares.
The leading destination for 2019-2021 was Turkey, accounting for 56.1% of the total
Russian tourist flow. At the same time, domestic tourism is on the rise – the most
popular regions of the Russian Federation for tourist trips in 2021, as in 2019 and 2020,
are Krasnodar Krai, the Republic of Crimea, Moscow, and St. Petersburg.
Conclusion. The modern tourism sector in the Russian Federation is a highly
effective means of generating revenue. It is a successful direction that combines
interconnected and complementary sets of events, services, and products, which,
together, create a whole experience and value for their visitors. Still, it is subject to
external factors and is associated with numerous costs, depending on the tourist
market served.

This exploratory study investigates the potential trajectories of education through a unique thought experiment conducted using three artificial intelligence (AI)-powered chatbots, namely GPT-4o, Gemini 2.0 Flash, and DeepSeek R1. The chatbots were tasked with generating both utopian and dystopian scenarios for the future of education, providing a glimpse into how these systems conceptualize key themes such as equity, creativity, and technology integration. A comparative analysis of the scenarios reveals shared trends and critical differences. Utopian visions commonly emphasize equity, collaboration, and sustainability, while dystopian scenarios caution against inequity, control, and the erosion of individuality. Remarkably, AI frequently appears as a central driver of change within these scenarios, reflecting the underlying assumptions embedded in these chatbot systems. This experiment not only highlights recurring patterns in AI-generated futures but also raises critical questions about the potential blind spots, omissions, and biases embedded in such tools. The findings prompt reflection on the value of AI in speculative future thinking and its limitations in capturing the complexities of education. Implications for policymakers, educators, and technologists include the need to critically assess AI’s role in shaping discourse about education and ensure that human values, equity, and sustainability guide future educational frameworks.

Climate-vulnerable megacities like Dhaka, Bangladesh, face escalating challenges in managing mounting volumes of municipal solid waste (MSW), exacerbated by rapid urbanization, climate shocks, and inadequate resource recovery systems. This research proposes an advanced AI-driven Smart Waste-to-Energy (AI-CIR-WtE) framework designed to transform linear waste systems into adaptive, circular, and climate-resilient urban infrastructure. Integrating artificial intelligence, life cycle modeling, digital twins, and blockchain, the framework offers a comprehensive pathway to optimize waste valorization, emissions reduction, and sustainable energy generation in resource-constrained settings. The proposed system leverages Long Short-Term Memory (LSTM) networks for forecasting waste generation by ward and season, coupled with Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization of waste routing, energy efficiency, and environmental impact. An AI-LCA engine, developed using OpenLCA and TensorFlow, dynamically quantifies GHG emissions, carbon offsets, and energy returns under multiple WtE configurations. Simulations are embedded within a 3D digital twin of Dhaka, constructed in Unity/Unreal Engine, enabling real-time modeling of disaster impacts (e.g., monsoon flooding, urban heatwaves) on infrastructure and service delivery. To ensure transparency and verifiability in carbon credit mechanisms, a blockchain-enabled MRV (Monitoring, Reporting, and Verification) layer tracks waste origin, conversion outputs, and emission reductions across the value chain. The framework incorporates climate equity through a gender and social inclusion lens, offering AI-based training modules and digital participation platforms for women, youth, and informal waste workers. Results show a projected 27–35% increase in circular material recovery, up to 41% reduction in lifecycle emissions, and 18% rise in decentralized energy yields under optimized conditions. The AI-CIR-WtE model demonstrates strong alignment with UN SDGs, Verra’s Verified Carbon Standard, and investment criteria from the Green Climate Fund (GCF) and World Bank climate finance facilities. By converging data-driven optimization, immersive simulation, and climate-just governance, this research offers a scalable blueprint for circular economy transition in megacities under climate threat. The framework is replicable in other Global South contexts and serves as a digital, equitable infrastructure roadmap toward net-zero urban futures.

Исследование посвящено современным подходам к обеспечению маркшейдерско-геодезической информации в условиях цифровизации и интеллектуализации. Анализ показывает, что автоматизация процессов, таких как лазерное сканирование и аэрофотосъемка, достигла значительных успехов, однако некоторые задачи требуют участия человека. Исторический анализ демонстрирует эволюцию маркшейдерско-геодезической сферы: начиная от простых моделей месторождений до внедрения трехмерного моделирования и использования алгоритмов искусственного интеллекта. В статье программное обеспечение для обработки маркшейдерско-геодезической информации разделено на несколько категорий. Эти инструменты могут быть усовершенствованы с применением искусственного интеллекта и цифровых двойников для улучшения управления и автоматизации процессов. Цифровые технологии играют ключевую роль в дальнейшем развитии геопространственного обеспечения маркшейдерско-геодезической информации.

The integration of Digital Twin (DT), Open Brain Computing (OBC), Machine Learning (ML), and Artificial Intelligence (AI) with Computational Fluid Dynamics (CFD) and experimental facilities, such as the Wall of Wind, holds enormous potential for revolutionizing various engineering and computational science fields. This integration promises advancements in realtime monitoring, predictive maintenance, and optimized design, particularly in industries like wind energy, aerospace, and process engineering. However, to fully realize the potential of these technologies, we must address several research gaps and challenges. The current use of integration shows that digital twins (DT) are successfully applied in computational fluid dynamics (CFD) for predicting outcomes and optimizing systems, and that machine learning (ML) and artificial intelligence (AI) are used to make CFD simulations cheaper and more accurate. The novel inclusion of OBC in CFD offers promising directions for efficient data processing, while experimental facilities provide vital data for validating CFD models. Important areas that need more research include better hybrid modeling methods, faster data processing, understanding uncertainties, making integrated systems more scalable, and looking into how OBC can improve interactions between humans and machines. Future research should aim to create better hybrid models, make real-time data transfer faster, improve how we measure uncertainty, and look into using OBC to make interactions in real-time CFD simulations easier and more effective. These advances will drive innovation in wind energy systems, aerospace technologies, and other critical applications.

Threat modeling is a pivotal analytical procedure employed to specify the potential threats and select the appropriate security measures. It helps reduce the risk of cyber-attacks that may target several components of the cyber-physical power systems. Accordingly, identifying potential cyber threats and assessing their consequences is an imperative aspect of the supervision and monitoring of power systems. This paper presents a threat analysis scheme of a power system case study utilizing the STRIDE threat model methodology. The developed model addresses several attack and threat scenarios combined into an attack graph model. In terms of security measures, this paper introduces a Secure, Encrypted, Authenticated Communication Channel (SEAC2), which is a two-level encryption method to secure the communication layer. Further, this study introduces an open-source Digital Twin (DT) platform that enables a real-time comprehensive assessment of the system's energy dynamics. It also prioritizes threat detection as another crucial aspect. A real case study from the Jordanian electrical network has been utilized in this study to validate the proposed platform.

Electronic waste (e-waste) is the world's most rapidly growing domestic waste stream and is accelerated by obsolescence. A contemporary manifestation is 'intended performance throttling,' in which manufacturers degrade device performance via software updates to drive premature upgrades. However, the empirical detection of this practice has been elusive. This study empirically investigated the intended performance throttling and algorithmic obsolescence driving e-waste via software updates. We introduced the Performance Degradation-to-Upgrade (PDU) score, a novel metric that integrates longitudinal benchmarking (Geekbench/AnTuTu), natural language processing (NLP) sentiment analysis (500,000+ comments), and marketing synchronization. Surveying 2,000+ users, we found that the PDU Score explained 62% of the upgrade intent variance (significantly predicted upgrade intent (β = 0.47, p<0.01)), and performance drops peak near product launches (LaunchWindow β = 0.29, p<0.05). Regulatory applications include PDU Ratings for consumers, and evidence-based right-torepair policies. We introduce and validate the Performance Degradation-to-Upgrade (PDU) Correlation Score to detect "intended performance throttling," software-mediated device slowdowns that drive premature upgrades and e-waste. A stratified survey of more than 2,000 mobile users generated a Perceived Performance Degradation Score (PPDS). Objective benchmarking data over 36 months from Geekbench and AnTuTu yielded an Actual Performance Degradation Score (APDS). The natural language processing of 500,000+ user comments measured negative sentiment shifts (). Manufacturer marketing spending and launch timelines produce a market alignment coefficient (). Exploratory factor analysis and cross-validated ridge regression were used to define the weights (w₁-w₃) for the composite PDU Score. Multivariate regression and structural equation modeling assessed throttling, with "LaunchWindow" interactions testing synchronization with new-model promotions. The PDU Score explained 62% of the variance in upgrade intention (it significantly predicted upgrade intention (β = 0.47, p<0.01)). APDS (significantly predicted upgrade intent (β = 0.47, p<0.01)), (β = 0.35, p<0.01), and (β = 0.23, p<0.05) predicted purchase likelihood. A significant LaunchWindow effect (β = 0.29, p<0.05) confirms that performance drops are strategically timed with product launch. Model fit was robust (CFI = 0.94, RMSEA = 0.06). Regulators can use the PDU framework for right-to-repair enforcement, consumers gain transparency via "PDU Ratings" and manufacturers are incentivized to extend device lifespans. This is the first empirical method to integrate technical, behavioral, and market data to unmask software-mediated planned obsolescence and its environmental impact.

Structures in the ever-changing construction market. HBD includes adaptability, environmental sustainability, maintenance, and disaster preparation. It is a key idea in the present boom in resilient and sustainable building techniques. A surprising dearth of quantitative empirical research on HBD's impact on LMBS considering the field's growing importance. This study uses a quantitative method to explore the relationship between essential HBD components and LMBS outcomes especially Extended Service Life (ESL) and Optimized Performance and Value (OPV) are critical factors to address this information gap. The quantitative study polled 171 construction industry professionals and utilized Structural Equation Modelling to examine the data. This approach was selected for its ability to explore complex relationships between variables and constructs. HBD components Adaptability and Retrofitting (AAR), Environmental Considerations and Sustainability (ECS), Maintenance and Inspection Programs (MIP), and Resilience and Disaster Preparedness (RDP) profoundly influenced the Enhanced Service Life (ESL) and Optimized Performance and Value (OPV) elements of LMBS. Building adaptation and retrofitting, maintenance and inspection programs, and other activities increased building value and lifespan. These findings have major theoretical and practical implications. The work potentially fills a research gap by providing actual evidence on HBD-LMBS linkages. Practically, the results advise construction industry specialists to use HBD ideas for sustainable building management. Finally, our work illuminates the vital roles of HBD components in LMBS and paves the way for ecologically friendly building approaches. The ever-changing demands of society, technology, and the environment have had a profound impact on the architectural and construction industries in recent years 1 . Forecasts show that construction activity throughout the globe is set to soar dramatically, with the global construction industry expected to reach over USD 15.5 trillion by 2030 2 . This is based on a compounded annual growth rate of 9.25% since 2022 3 . This quantitative increase reflects a shift towards more sustainable, strong, and flexible building structures 4 . Public awareness of the building industry's environmental impact is driving this change. Building activities account for 28% of worldwide CO₂ emissions, while building processes generate 11% 5 . The definitions and scope of Holistic Building Design (HBD) and Lifecycle Management of Building Structures (LMBS) have been clarified. HBD encompasses the integration of Adaptability and Retrofitting (AAR), Environmental Considerations and Sustainability (ECS), Maintenance and Inspection Programs (MIP), and Resilience and Disaster Preparedness (RDP) into both traditional and performance-based design processes . These principles aim to enhance the overall performance and value of buildings throughout their lifecycle. LMBS focuses on extending the Enhanced Service Life (ESL)

Additive Manufacturing (AM), a transformative production method, is gaining momentum across industries due to its ability to fabricate complex geometries with minimal waste. However, challenges in optimizing process parameters and ensuring quality control hinder its full potential. Digital Twins (DTs), virtual replicas of physical systems, have emerged as a solution to enhance AM processes by enabling real-time monitoring, simulation, and predictive analysis. The paper highlights key advancements in combining DTs with Industry 4.0 and 5.0 technologies, such as machine learning, augmented reality, and high-performance computing, to improve efficiency, scalability, and sustainability in manufacturing. Challenges including data quality, system integration, and computational demands are discussed, with a vision for adaptive and intelligent DT systems.

Cyberattacks pose significant risks to the safety and functionality of industrial control systems (ICS), particularly in critical sectors such as water. Understanding the implications of these cyberattacks is essential for evaluating their environmental and economic repercussions to develop defensive strategies and implement security mechanisms. This study seeks to create a controlled and reproducible virtualised ICS testbed, enabling a risk-free environment for cybersecurity testing. Based on the IEC 62264 Industrial Automation Pyramid (IAP), the proposed virtual ICS testbed features a scalable multi-layered network architecture that supports multiple ICS protocols. This case study features an Operational Technology (OT) environment with a digital twin of a Wastewater Treatment Plant (WWTP) developed using MATLAB/Simulink, OpenPLC and SCADA-LTS. The MITRE ATT&CK adversary emulation technique was utilised to assess the potential impacts of Modbus/TCP protocol attacks on a WWTP industrial process by targeting levels 1 and 2 of the IAP. The findings highlight the necessity for threat emulation to simulate real-world attack scenarios, vulnerability assessments, and operational impact analysis. Furthermore, establishing robust detection mechanisms guarantees that threats are identified early, minimising potential risks to operational integrity and environmental safety. In conclusion, the significance of creating ICS testbeds for cybersecurity testing is emphasised, ultimately suggesting directions for future research.

It is an empirical study examining how AI can be the driving force for cultural heritage and humanities conservation. As the AI tools that digitize, analyze, and conserve cultural resources have become in recent years, they have led to digital transformations in machine learning, digital twinning, and natural language processing. These technologies answer the industry's most challenging problems, such as the loss of historical material, access restrictions to heritage sites, and the labor-intensive nature of traditional conservation techniques. In implementing AI in heritage, institutions can document more accurately, keep records predictively, and offer more interesting public experiences via rich digital media. Nevertheless, the effects of AI on cultural heritage do not stop there. It is an article about AI's shift to humanities research practices. Humanities researchers have long been guided by interpretive, qualitative methods of historical analysis: texts, artifacts, and cultural histories. Artificial intelligence tools, in contrast, bring discipline into the data-looking for patterns and relationships humans cannot. Natural language processing systems, for example, can rapidly and accurately transcribe and interpret vast archive texts-unearthing unseen truths and forgotten voices from long ago. Just as radically, AI-powered image recognition software allows scientists to pick out finer features in paintings, excavations, and manuscripts, allowing for novel interpretations and insights. The study also points out that interdisciplinary cooperation leads to meaningful AI integration. The best uses of AI for heritage conservation emerge from collaborations between technologists, humanities scholars, and cultural experts. These collaborations also promote openness to the cultural landscape in which AI operates so that technological advances are culturally sensitive and ethically justified. The case studies in this study show how such partnerships have brought about creative approaches to preserve the dignity of heritage while tapping the power of AI to be revolutionary. Besides, the research considers global inequalities in AI technology access. In tech-enabled parts of the world, big institutions have already been leading AI-enabled heritage initiatives, but many marginalized communities do not have the resources to do so. This digital divide threatens to make a warped account of world culture, favoring histories at the expense of others. The research calling for open-access AI tools, international funding networks, and knowledge-sharing networks that democratize AI-based heritage conservation must overcome this asymmetry. This paper provides a systematic plan for an interdisciplinary AI in cultural heritage and humanities research. It is a paradigm that emphasizes the marriage of technological progress and humanism, in which AI does not take over from human interpretation but augments it. By linking AI devices to cultural conservation standards, organizations can save heritage stories with sensitivity to their historical, social, and emotional resonance. In the long run, this research suggests that AI is unsurpassable and promising as a means of merging tradition and technology, providing new means to access the past and prepare for the future. It offers theory and practice for a more sustainable, inclusive, and innovative heritage management. It aims to encourage a more dynamic exchange between digital Creativity and human history so that cultural narratives are both visible and alive forever.

The rapid expansion of urban areas combined with an increasing population poses challenges for urban planners (Roberts and Mukim, 2023). This could include a reduction in green spaces and vegetation within urban areas (Rioux et al., 2019), destroying habitats, pollution (Mohammed et al., 2021), pressure on infrastructure demands, such as transportation (Doheim et al., 2020b), loss of permeable surfaces, and increasing the risk of flooding in localized events (Thodesen et al., 2022), and many other challenges, which undermine cities' sustainability and human well-being. Urban planners can minimize cities' environmental footprint, enhance sustainability, and promote human well-being by prioritizing sustainable urban development practices (Przywojska et al., 2019). However, it does not appear to be a simple undertaking. To achieve urban sustainability goals in cities, urban planners should work with a wide range of interconnected elements such as transportation networks, land use patterns, socioeconomic and demographic data, economic activities, and environmental conditions. These components have complex interactions, mutually influencing one another and defining the overall city structure and function (Tadi and Manesh, 2014). Moreover, maintaining a balance between the cities' different goals poses a significant challenge to urban planners (Singh and Jain, 2022). For example, planners must effectively manage the competing demands of population growth while also achieving sustainability goals and maintaining climate resiliency practices. Urban planners should employ smart strategies to optimize intricate urban issues, including traffic flow, energy consumption, population management, and environmental impact (Bibri, 2020, 2021; Farag, 2024). They must design cities that prioritize walkability, minimize reliance on cars, and provide accessible public transportation in the present and future (Doheim et al., 2020a), while enhancing the coordination and integration of various transportation modalities (Doheim et al., 2019). Furthermore, they should design cities with the capacity to attain a state of net-zero carbon emissions. They must integrate equity into their plans to ensure equal distribution of city services while mitigating the disparate impacts of climate change. Therefore, they experience growing pressure to ensure that their projects are sustainable. In addition, a significant challenge is using cities as experimental environments to examine the strategies used to achieve sustainability goals (ESRI, 2024), which presents a series of consequences. A change in one area of the city can have unintended consequences in another area, making it difficult

Cities face multiple challenges due to climate change, worsened by rapid urbanization. These challenges include higher levels of greenhouse gas emissions and increased vulnerability to severe weather events such as extreme heat, flooding, and sea-level rise (Fernández and Angel, 2020), resulting in significant risks to human health, safety, and economic prosperity. The concept of "Climate Resilient City" is increasingly gaining attention, referring to cities that exhibit the capacity to swiftly adapt to climateinduced changes (Hossain and Fernández-Güell, 2023). This concept is in line with the growing number of cities engaging in global climate change mitigation efforts through collaborations and integrated sustainability policies. The local governments worldwide and the C40 Cities, a climate leadership organization, play an essential role in promoting city-level actions to address climate change (Hubacek et al., 2023). Furthermore, the adoption of "climate urbanism" as a model for urban development highlights the significance of cities as key locations for addressing climate issues and prioritizes the protection of urban areas from the risks posed by climate change (Long and Rice, 2019). Accordingly, urban policymakers worldwide are actively seeking efficient and innovative strategies to address climate change risks and impacts on cities (Farag, 2018; Bibri, 2020; Molina and Abadal, 2021; Mariano and Marino, 2023). However, they face challenges in effectively implementing climatic policy-making processes due to limitations while employing planning approaches such as reactive, inactive, and proactive approaches. These limitations include real-time data, future scenario development, and holistic system optimization (Storbjörk and Uggla, 2015). City digital twin technology has emerged as a proactive tool for assessing urban systems for its capacity to evaluate proposed strategies and predict their future implications (Pan et al., 2024). By creating virtual replicas of physical assets and integrating real-time data sources, city digital twins can improve risk management in urban environments (Astarita et al., 2024). The integration of real-time data from diverse sources like sensors, traffic, and weather information into digital twin models allows the visualization, simulation, and analysis of urban systems and the prediction of potential issues, aiding in implementing early warning systems to mitigate climate change impacts on urban infrastructure (Zhu et al., 2024). City digital twins enable policymakers to identify deficiencies, evaluate proposed strategies, and improve urban infrastructure resiliency while simulating threat scenarios in controlled environments (Vempati, 2024). Therefore, this technology could provide a unique opportunity for policymakers to manage the complexities of urban development and climate change mitigation effectively.

The global drinking water crisis- driven by climate change, rapid urbanization, and infrastructure gaps- demands intelligent, adaptive, and decentralized management systems. This study explores how artificial intelligence (AI) can transform conventional water governance into predictive, equitable, and sustainable frameworks. A simulation-based case study using a UCI-inspired dataset demonstrates the viability of AI models in potable water prediction: Support Vector Machines (SVM) achieved 51.4% accuracy and a 55.3% F1-score, outperforming Random Forests (accuracy 47.3%), thereby validating AI’s capacity to handle complex physicochemical classification problems even under data constraints. Building on this, the paper proposes an integrated AI framework combining federated learning for privacy-preserving collaboration, edge AI for low-connectivity environments, AutoML for usability by non-experts, and blockchain for tamper-proof water quality certification. Digital twins are incorporated to simulate water infrastructure behavior under real-time operational scenarios. The framework further extends to advanced AI paradigms- including adaptive, graph-based, and multimodal learning systems- used to optimize distribution networks, detect anomalies, and support climate-sensitive water forecasting. To guide policy and track development goals, an AI-driven SDG metrics dashboard is proposed for real-time assessment of progress toward SDG 6.1 (safe and affordable drinking water) and SDG 6.3 (improved water quality). Ethical dimensions such as data privacy, transparency, and explainable AI (XAI) are emphasized to ensure trustworthy deployment, especially in climate-vulnerable or under-resourced regions. While the results are based on synthetic data modeled after open-source benchmarks, the approach presents a scalable template for future field integration. This research demonstrates that AI introduces a new affordance in global water governance, enabling systems that are not only smarter and faster but also more inclusive, resilient, and accountable in addressing 21st-century water challenges.

The rapid digitalization of healthcare through the adoption of Medical Internet of Things (MIoT) technologies has given rise to smart hospital ecosystems that are highly efficient yet increasingly vulnerable to cybersecurity threats. As MIoT devices become integral to patient monitoring, diagnostics, and treatment, the risk of cyberattacks-ranging from ransomware and data breaches to insider threats and Distributed Denial of Service (DDoS) attacks-has grown substantially. In response, this study conducts a structured meta-analysis to evaluate the effectiveness of neural network-based risk prediction and simulation frameworks in securing smart hospital environments. Using the PRISMA 2020 methodology, the review systematically screened and synthesized findings from 112 peer-reviewed studies published between 2010 and 2024, encompassing various experimental setups, real-world hospital case studies, and benchmark datasets. The meta-analysis focused on comparing performance metrics such as detection accuracy, false positive rates, real-time responsiveness, and attack versatility between traditional cybersecurity systems and advanced neural network architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and hybrid deep learning models. The findings indicate that neural network-based intrusion detection systems (NN-IDS) consistently outperform rule-based and statistical models, achieving higher accuracy in identifying both known and novel cyber threats. Additionally, these models demonstrate significant reductions in false positive rates and enhanced responsiveness under real-time operational constraints, which are critical for patient safety in clinical environments. These simulation tools support data-driven decisionmaking and engineering management by forecasting breach impacts, operational disruptions, and compliance risks. Moreover, the adaptability and scalability of NN-IDS across different hospital sizes and digital maturity levels position them as suitable for wide-scale deployment in healthcare systems globally. Overall, this research offers a comprehensive evaluation of neural network-enabled cybersecurity solutions and establishes their practical and strategic value in developing resilient, intelligent, and secure smart hospital infrastructures.

Digitalization initiatives and technology are essential for risk management and for maintaining a sustainable manufacturing system during disruptions and failures. This research has identified and analyzed the impact of digital twins on the manufacturing supply chain (MSC) in enhancing the resilient-sustainable capabilities of the production systems. Both sustainability and resilience are interrelated concepts and have recently gotten attention from researchers because of the rising complexity in the supply chain. This research study has identified the seventeen resilient sustainable factors in the digitally driven MSC through extant literature and experts' opinions. This research work has used a novel technique, grey influence analysis (GINA), for causal analysis of the factors. After analysis, based on total influence score, visibility (VIS) and coordination (COO) ranked first and second, respectively. These are the most influencing factors because of the adoption of digital twins (DTs) in the MSC. The DT in MSC for the resilient-sustainable systems will improve the visibility, and supply chain players must collaborate and connect to effectively manage risks and ensure the sustainability of all links in the digital supply chain. This research study is helpful for practitioners, researchers, and managers working in digitally driven MSC.

As IoT continues on its path to mass adoption, energy efficiency and environmental sustainability are growing concerns. Research on energy efficient high-performance computing (HPC) paradigms and green computing strategies For IoT There energy sustainable applications The review also touches upon optimization strategies such as AI-based power management, adaptive power scaling, federated learning, and blockchainbased energy trading. Datasets from the real world, including Google, IBM, and NREL, were used to model energy usage of IoT devices, the usage of renewable energy, and the efficiency of the communication protocol. It illustrates the considerable potential of IoT-enabled smart grids, artificial intelligence-based optimization, and green computing methods in lowering the carbon footprint and boosting energy efficiency. Our results yield practical guidance for the design of sustainable IoT ecosystems that optimize for computational performance while minimizing energy expenditure and environmental footprint.

The integration of robotics and artificial intelligence (AI) in surgery represents a transformative advancement in modern healthcare, promising enhanced precision, efficiency, and patient outcomes. Recent studies indicate a rapid adoption of AIassisted robotic surgery across various surgical specialties, driven by improvements in accuracy and reduced complication rates. The research synthesizes findings from 25 recent peer-reviewed studies (2024-2025) on AI-driven robotic surgery. Systematic review and meta-analyses were conducted focusing on clinical efficacy, surgical precision, complication rates, and economic impacts. Quantitative data were extracted from retrospective trials, cohort studies, and systematic reviews to evaluate outcomes compared to manual surgical techniques. AI-assisted robotic surgeries demonstrated a 25% reduction in operative time and a 30% decrease in intraoperative complications compared to manual methods. Surgical precision improved by 40%, reflected in enhanced targeting accuracy during tumor resections and implant placements. Patient recovery times were shortened by an average of 15%, with lower postoperative pain scores. Additionally, studies reported an average 20% increase in surgeon workflow efficiency and a 10% reduction in healthcare costs over the conventional procedures. AI-enhanced robotic surgery significantly improves surgical outcomes through higher precision and efficiency, supporting widespread clinical adoption. Despite upfront costs and ethical concerns, continued innovation and integration promise substantial benefits for patient safety and healthcare resource optimization. Future research should focus on long-term patient outcomes and addressing ethical and training challenges.