Search Results (1,610)

Inertial Measurement Units (IMUs), which embed several sensors (accelerometers, gyroscopes, magnetometers) are employed by musicians and performers to control sound, music, or lighting on stage. In particular, wireless IMU systems in the performing arts require particular attention due to strict requirements regarding streaming sample rate, latency, power consumption, and programmability. This article presents a review of systems developed in this context at IRCAM as well as in other laboratories and companies, highlighting specificities in terms of sensing, communication, performance, digital processing, and usage. Although basic IMUs are now widely integrated into IoT systems and smartphones, the availability of complete commercial wireless systems that meet the constraints of the performing arts remains limited. For this reason, a review of systems used in performing Arts provides exemplary use cases that may also be relevant to other applications. Full article

This paper charts AI’s transformative path toward advancing sustainability within art museums, introducing a Human–AI compass as a conceptual framework for navigating its integration. It advocates for human-centric AI that optimizes operations, modernizes collection management, and deepens visitor engagement—anchored in meaningful human–technology synergy and thoughtful human oversight. Drawing on extensive literature review and real-world museum case studies, the paper explores AI’s multifaceted impact across three domains. Firstly, it examines how AI improves operations, from audience forecasting and resource optimization to refining marketing, supporting conservation, and reshaping curatorial practices. Secondly, it investigates AI’s influence on digital collection management, highlighting its ability to improve organization, searchability, analysis, and interpretation through automated metadata and advanced pattern recognition. Thirdly, the study analyzes how AI elevates the visitor experience via chatbots, audio guides, and interactive applications, leveraging personalization, recommendation systems, and co-creation opportunities. Crucially, this exploration acknowledges AI’s complex challenges—technical-operational, ethical-governance, socioeconomic-cultural, and environmental—underscoring the indispensable role of human judgment in steering its implementation. The Human-AI compass offers a balanced, strategic approach for aligning innovation with human values, ethical principles, museum mission, and sustainability. The study provides valuable insights for researchers, practitioners and policymakers, enriching the broader discourse on AI’s growing role in the art and cultural sector. Full article

Cyber-physical infrastructures such as hospitals and smart campuses face hybrid threats that target both digital and physical domains. Traditional security solutions separate surveillance from network monitoring, leaving blind spots when attackers combine these vectors. This paper introduces ARGUS, an autonomous robotic platform designed to close this gap by correlating cyber and physical anomalies in real time. ARGUS integrates computer vision for facial and weapon detection with intrusion detection systems (Snort, Suricata) for monitoring malicious network activity. Operating through an edge-first microservice architecture, it ensures low latency and resilience without reliance on cloud services. Our evaluation covered five scenarios—access control, unauthorized entry, weapon detection, port scanning, and denial-of-service attacks—with each repeated ten times under varied conditions such as low light, occlusion, and crowding. Results show face recognition accuracy of 92.7% (500 samples), weapon detection accuracy of 89.3% (450 samples), and intrusion detection latency below one second, with minimal false positives. Audio analysis of high-risk sounds further enhanced situational awareness. Beyond performance, ARGUS addresses GDPR and ISO 27001 compliance and anticipates adversarial robustness. By unifying cyber and physical detection, ARGUS advances beyond state-of-the-art patrol robots, delivering comprehensive situational awareness and a practical path toward resilient, ethical robotic security. Full article

The increasing adoption of digital technologies, robotic systems, and IoT applications in sectors such as medicine, agriculture, and industry drives a surge in data generation and necessitates secure and efficient encryption. For resource-constrained systems, lightweight yet robust cryptographic algorithms are critical. This study addresses the security demands of IoRT systems by proposing an enhanced chaos-based encryption method. The approach integrates the lightweight structure of NIST-standardized Ascon-AEAD128 with the randomness of the Zaslavsky map. Ascon-AEAD128 is widely used on many hardware platforms; therefore, it must robustly resist both passive and active attacks. To overcome these challenges and enhance Ascon’s security, we integrate into Ascon the keys and nonces generated by the Zaslavsky chaotic map, which is deterministic, nonperiodic, and highly sensitive to initial conditions and parameter variations.This integration yields a chaos-based Ascon variant with a higher encryption security relative to the standard Ascon. In addition, we introduce exploratory variants that inject non-repeating chaotic values into the initialization vectors (IVs), the round constants (RCs), and the linear diffusion constants (LCs), while preserving the core permutation. Real-time tests are conducted using Raspberry Pi 3B devices and ROS 2–based IoRT robots. The algorithm’s performance is evaluated over 100 encryption runs on 12 grayscale/color images and variable-length text transmitted via MQTT. Statistical and differential analyses—including histogram, entropy, correlation, chi-square, NPCR, UACI, MSE, MAE, PSNR, and NIST SP 800-22 randomness tests—assess the encryption strength. The results indicate that the proposed method delivers consistent improvements in randomness and uniformity over standard Ascon-AEAD128, while remaining comparable to state-of-the-art chaotic encryption schemes across standard security metrics. These findings suggest that the algorithm is a promising option for resource-constrained IoRT applications. Full article

The arts sector—museums, theaters, and orchestras—in the United States and Europe faces increasing financial and operational challenges, from declining attendance to reduced public and private funding. While these organizations have historically pursued their goals independently, their future may depend on fostering collaboration across these traditionally siloed institutions. By pooling resources, expertise, and, most importantly, audiences, cross-disciplinary partnerships can amplify the impact of cultural institutions while addressing shared challenges. For instance, museums and performing arts organizations could collaborate on immersive experiences that integrate visual and performing arts, attracting a broader and more diverse audience base. Similarly, joint programming and shared digital platforms could reduce overhead costs and expand outreach. These partnerships also enable the arts to present a unified case for public and philanthropic support, leading to greater collective societal impact. Drawing on successful examples of cross-sector collaboration, this paper explores practical strategies for fostering synergies among arts institutions. By working together, the arts can not only enhance their resilience in a challenging environment but also redefine how cultural experiences are created and consumed, ensuring their relevance and vibrancy for future generations. Full article

With the rapid advancement of image processing techniques, digital image forgery detection has emerged as a critical research area in information forensics. This paper proposes a novel deep learning model based on Multi-view Multi-dimensional Forgery Detection Networks (MMFD-Net), designed to simultaneously determine whether an image has been tampered with and precisely localize the forged regions. By integrating a Multi-stream Edge Feature Learning module with a Multi-dimensional Information Fusion module, MMFD-Net employs joint supervised learning to extract semantics-agnostic forgery features, thereby enhancing both detection performance and model generalization. Extensive experiments demonstrate that MMFD-Net achieves state-of-the-art results on multiple public datasets, excelling in both pixel-level localization and image-level classification tasks, while maintaining robust performance in complex scenarios. Full article

Codex 462 of the Fondo Hortus Pisanus of the Biblioteca Universitaria of Pisa is a precious example of a 16th century illustrated herbal, Icones variarum plantarum, containing 35 tempera paintings by the German soldier Georg Dyckman, an amateur but highly talented artist. The manuscript was recently restored on the occasion of an international exhibition; the necessary preliminary studies for the restoration included a series of in situ diagnostic studies using contactless techniques (digital microscope, multispectral imaging, XRF, Raman and ER-FTIR). These analyses proved useful in deepening the knowledge of the materials and the execution technique of this type of illustrated herbals and in choosing the most appropriate solutions during the restoration phase. In view of the growing interest in this type of historical evidence, which involves both art history and the history of science, this study offers an interesting new perspective on the subject, useful both from a technical point of view for future conservation and for analytical and historical artistic studies. Full article

Climate change and urbanization are intensifying the urban heat island effect and negatively impacting outdoor thermal comfort in cities. Innovative planning strategies are required to design more livable and resilient urban spaces. Building on a state of the art of current Urban Digital Twins (UDTs) for outdoor thermal comfort analysis, this paper presents the design and implementation of a functional UDT prototype. Developed for a pilot area in Huelva, Spain, the system integrates real-time environmental data, spatial modeling, and simulation tools within an open-source architecture. The literature reveals that while UDTs are increasingly used in urban management, their application to outdoor thermal comfort remains limited and technically challenging, especially in terms of real-time data, modeling accuracy, and user interaction. The case study demonstrates the feasibility of a modular, open-source UDT capable of simulating mean radiant temperature and outdoor thermal comfort indexes at high resolution and visualizing the results in a 3D interactive environment. UDTs have strong potential for supporting microclimate-sensitive planning and improving outdoor thermal comfort. However, important challenges remain, particularly in simulation efficiency, model detail, and stakeholder accessibility. The proposed prototype addresses several of these gaps and provides a basis for future improvements. Full article

With the rapid development of Industry 5.0, smart manufacturing has become a key focus in production systems. Hence, achieving efficient planning and scheduling on the shop floor is important, especially in job shop environments, which are widely encountered in manufacturing. However, traditional job shop scheduling problems (JSP) assume fixed operation sequences, whereas in modern production, some operations exhibit sequence flexibility, referred to as sequence-free operations. To mitigate this gap, this paper studies the JSP with discrete operation sequence flexibility (JSPDS), aiming to minimize the makespan. To effectively solve the JSPDS, a mixed-integer linear programming model is formulated to solve small-scale instances, verifying multiple optimal solutions. To enhance solution quality for larger instances, a digital twin (DT)–enhanced initialization method is proposed, which captures expert knowledge from a high-fidelity virtual workshop to generate high-quality initial population. In addition, a statistical learning-assisted local search method is developed, employing six tailored search operators and Thompson sampling to adaptively select promising operators during the evolutionary algorithm (EA) process. Extensive experiments demonstrate that the proposed DT-statistical learning EA (DT-SLEA) significantly improves scheduling performance compared with state-of-the-art algorithms, highlighting the effectiveness of integrating digital twin and statistical learning techniques for shop scheduling problems. Specifically, in the Wilcoxon test, pairwise comparisons with the other algorithms show that DT-SLEA has p-values below 0.05. Meanwhile, the proposed framework provides guidance on utilizing symmetry to improve optimization in complex manufacturing systems. Full article

Molecular quantum-dot cellular automata (QCA) are attracting much attention as an alternative that can improve the problems of digital circuit design technology represented by existing CMOS technology. In particular, they are well suited to the upcoming nanoquantum environment era with their small size, fast switching speed, and low power consumption. In this study, we propose a 5 × 5 × 1 ultra-slim vertical panel type multi-layer 2-to-1 multiplexer (Mux) using molecular QCA, departing from conventional multi-layer formats, and show its expansion to 4-to-1 Mux and application to vertical panel type D-latch and RAM cells. In addition, the polarization phenomenon of cells is physically proven using the potential energy, distance among electrons, and the relative positions of cells, and the secure RAM design takes noise elimination and polarization of the output signal into consideration. The circuits are simulated in terms of operation and performance using QCADesigner 2.0.3 and QCADesignerE, and the proposed multi-layer 2-to-1 Mux shows a significant improvement of at least 1473% and 277% in two representative standard design costs compared to the state-of-the-art multi-layer Muxes. Full article

This work presents the design and simulation of a time-interleaved successive approximation register (SAR) analog-to-digital converter (ADC) implemented in GlobalFoundries’ 22 nm Fully Depleted Silicon-on-Insulator (FD-SOI) CMOS process. Motivated by the increasing demand for high-speed electrical links in data center and AI/ML applications, the proposed ADC architecture targets medium-resolution, high-throughput conversion with optimized power and area efficiency. The design leverages asynchronous SAR operation, bootstrapped sampling switches, and a hybrid binary/non-binary capacitive digital-to-analog converter (DAC) to achieve robust performance across process, voltage, and temperature (PVT) variations. System-level modeling using channel operating margin (COM) methodology guided the specification of key circuit blocks, enabling efficient trade-offs between resolution, speed, and power. Post-layout simulations demonstrated effective number of bits (ENOB) performance consistent with system requirements, while Monte Carlo analysis confirmed the statistical yield. The converter achieved competitive figures of merit compared to state-of-the-art designs, as benchmarked against the Murmann ADC survey. This work highlights critical design considerations for scalable mixed-signal architectures in advanced CMOS nodes and lays the foundation for future integration in high-speed SerDes systems. Full article

In recent years, the construction sector has experienced a significant technological transition, driven by the introduction of innovative digital tools and the evolution of the legislative environment. This article presents a Systematic Scoping Review conducted in accordance with the PRISMA-ScR guidelines, aiming to examine the role of Public Administration (PA) regarding the adoption of innovative technologies, such as Building Information Modelling (BIM) and Digital Twin (DT), to improve the management of construction and public procurement processes. The review analyses the state of the art in the implementation of digitalised procedures for project management in the construction phase, according to PA organisational purposes and national and international standard requirements. The data obtained was used to structure the analysis in order to provide a useful framework for understanding the level of convergence between the academic world and public administration in the use of digital technologies and their combined applications. The review results are organised in a thematic matrix classifying contributions according to key topics, building process phases, and operational aims. This approach highlights adopted strategies and emerging best practices, aiming to support both PAs and professionals in overcoming digitalisation challenges. A specific focus has been dedicated to the need for continuous training and legislative adaptation, which are essential for integrating digital technologies into building processes. The analysis and verification of the results of the systematic scoping review on the digitalisation process in the construction sector, conducted between academia and the public administration, is supported by a comparison with an Italian case study from the Emilia-Romagna region, which illustrates the specific application of the strategies identified in the digital management of public construction processes. Full article

Rapid access to defibrillators, blood products, and time-critical medicines can improve survival, yet urban congestion and fragmented infrastructure delay deliveries. We present and evaluate an end-to-end framework for beyond-visual-line-of-sight (BVLOS) UAV logistics in Essex (UK), integrating (I) strategic depot placement, (II) a hybrid obstacle-aware route planner, and (III) a time-window-aware (TWA) Mixed-Integer Linear Programming (MILP) scheduler coupled to a battery/temperature feasibility model. Four global planners—Ant Colony Optimisation (ACO), Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Rapidly Exploring Random Tree* (RRT*)—are paired with lightweight local refiners, Simulated Annealing (SA) and Adaptive Large-Neighbourhood Search (ALNS). Benchmarks over 12 destinations used real Civil Aviation Authority no-fly zones and energy constraints. RRT*-based hybrids delivered the shortest mean paths: RRT* + SA and RRT* + ALNS tied for the best average length, while RRT* + SA also achieved the co-lowest runtime at $v=60\mathrm{km}{\mathrm{h}}^{-1}$. The TWA-MILP reached proven optimality in 0.11 s, showing that a minimum of seven UAVs are required to satisfy all 20–30 min delivery windows in a single wave; a rolling demand of one request every 15 min can be sustained with three UAVs if each sortie (including service/recharge) completes within 45 min. To validate against a state-of-the-art operations-research baseline, we also implemented a Vehicle Routing Problem with Time Windows (VRPTW) in Google OR-Tools, confirming that our hybrid planners generate competitive or shorter NFZ-aware routes in complex corridors. Digital-twin validation in AirborneSIM confirmed CAP 722-compliant, flyable trajectories under wind and sensor noise. By hybridising a fast, probabilistically complete sampler (RRT*) with a sub-second refiner (SA/ALNS) and embedding energy-aware scheduling, the framework offers an actionable blueprint for emergency medical UAV networks. Full article

Bridges play an increasingly indispensable role in endorsing the economic and social development of societies by linking highways and facilitating the mobility of people and goods. Concurrently, they are susceptible to high traffic volumes and an intricate service environment over their lifespans, resulting in undergoing a progressive deterioration process. Hence, efficient measures of maintenance, repair, and rehabilitation planning are critical to boost the performance condition, safety, and structural integrity of bridges while evading less costly interventions. To this end, this research paper furnishes a mixed review method, comprising systematic literature and scientometric reviews, for the meticulous examination and analysis of the existing research work in relation with maintenance fund allocation models of bridges (BriMai_all). With that in mind, Scopus and Web of Science databases are harnessed collectively to retrieve peer-reviewed journal articles on the subject, culminating in 380 indexed journal articles over the study period (1990–2025). In this respect, VOSviewer and Bibliometrix R package are utilized to create a visualization network of the literature database, covering keyword co-occurrence analysis, country co-authorship analysis, institution co-authorship analysis, journal co-citation analysis, journal co-citation, core journal analysis, and temporal trends. Subsequently, a rigorous systematic literature review is rendered to synthesize the adopted tools and prominent trends of the relevant state of the art. Particularly, the conducted multi-dimensional review examines the six dominant methodical paradigms of bridge maintenance management: (1) multi-criteria decision making, (2) life cycle assessment, (3) digital twins, (4) inspection planning, (5) artificial intelligence, and (6) optimization. It can be argued that this research paper could assist asset managers with a practical guide and a protocol to plan maintenance expenditures and implement sustainable practices for bridges under deterioration. Full article

Digitalization has become a driving force for significant advancements in property/facility management (PFM). It is necessary to identify the research gaps and future research directions, which could enable the effective development of digital technologies (DTs) in the context of PFM. This paper aims to review how DTs emerge to drive digitalization in PFM and identify gaps that need to be addressed in future research. The findings reveal that research on integrating BIM, IoT, AR, AI, and big data in sustainable transformations, real-time data, and energy optimization is limited, with challenges in data security, privacy, and system interoperability. Future research should focus on BIM for sustainability, real-time data, and AR applications, alongside IoT and blockchain integration for security. Investigating VR in maintenance, AI for energy optimization, improved prediction accuracy, and enhanced NLP for chatbots are also critical areas for exploration. This state-of-the-art review summarized the gaps from the existing literature of property management digitalization and provides an update on research gaps and directions for the digitalization in PFM. Full article