Search Results (14)

This article examines a hybrid generation of cryptographic keys, whose novelty lies in the fusion of a multiscale subkey generation with prime sieve and subkeys inspired by quantum mechanics. It combines number theory with techniques emulated and inspired by quantum mechanics, also based on two demons capable of dynamically modifying the cryptographic model. The integration is structured through the JDL. In fact, a specific information fusion model is used to improve security. As a result, the resulting key depends not only on the individual components, but also on the fusion path itself, allowing for dynamic and cryptographically agile configurations that remain consistent with quantum mechanics-inspired logic. The proposed approach, called quantum and prime information fusion (QPIF), couples a simulated quantum entropy source, derived from the numerical solution of the Schrödinger equation, with a multiscale prime number sieve to construct multilevel cryptographic keys. The multiscale sieve, based on recent advances, is currently among the fastest available. Designed to be compatible with classical computing environments, the method aims to contribute to cryptography from a different perspective, particularly during the coexistence of classical and quantum computers. Among the five key generation algorithms implemented here, the ultra-optimised QRNG offers the most effective trade-off between performance and randomness. The results are validated using standard NIST statistical tests. This hybrid framework can also provide a conceptual and practical basis for future work on PQC aimed at addressing the challenges posed by the quantum computing paradigm. Full article

This study presents a systematic literature review on the mathematical modeling of resilient and viable supply chains, grounded in the PRISMA methodology and applied to a curated corpus of 235 peer-reviewed scientific articles published between 2011 and 2025. The search strategy was implemented across four major academic databases (Scopus and Web of Science) using Boolean operators to capture intersections among the core concepts of supply chains, resilience, viability, and advanced optimization techniques. The screening process involved a double manual assessment of titles, abstracts, and full texts, based on inclusion criteria centered on the presence of formal mathematical models, computational approaches, and thematic relevance. As a result of the selection process, six thematic categories were identified, clustering the literature according to their analytical objectives and methodological approaches: viability-oriented modeling, resilient supply chain optimization, agile and digitally enabled supply chains, logistics optimization and network configuration, uncertainty modeling, and immune system-inspired approaches. These categories were validated through a bibliometric analysis and a thematic map that visually represents the density and centrality of core research topics. Descriptive analysis revealed a significant increase in scientific output starting in 2020, driven by post-pandemic concerns and the accelerated digitalization of logistics operations. At the methodological level, a high degree of diversity in modeling techniques was observed, with an emphasis on mixed-integer linear programming (MILP), robust optimization, multi-objective modeling, and the increasing use of bio-inspired algorithms, artificial intelligence, and simulation frameworks. The results confirm a paradigm shift toward integrative frameworks that combine robustness, adaptability, and Industry 4.0 technologies, as well as a growing interest in biological metaphors applied to resilient system design. Finally, the review identifies research gaps related to the formal integration of viability under disruptive scenarios, the operationalization of immune-inspired models in logistics environments, and the need for hybrid approaches that jointly address resilience, agility, and sustainability. Full article

This paper discusses a new issue named domain generalization of fast motion planning in 3D environments, which benefits agility-required robot applications such as autonomous driving and uncrewed aerial vehicle obstacle avoidance flight. The existing work shows that conventional spatial search-based planning algorithms cannot meet the real-time requirement due to high time costs. The end-to-end neural network-based methods achieve an excellent balance between performance and planning speed in the seen environments, but are hard to transfer to new scenarios. To overcome this limitation, we propose a novel Robust Environment Encoder (Ro2En) approach to domain generalization of fast motion planning. Specifically, by demonstrating the reconstructed environment, we find that the previous environment encoder cannot encode the volume information properly, i.e., a volume collapse ensues, which leads to noisy environment modeling. Inspired by this observation, a dual-task auto-encoder is developed. It can not only reconstruct the point cloud of the obstacles, but also align their geometric centers. Experiment results showed that in the new scenarios, Ro2En outperformed previous state-of-the-art conventional and neural alternatives with a much smaller performance variation. Full article

This investigation into the aerodynamic efficiency and structural integrity of tubercle leading edges, inspired by the agile maneuverability of humpback whales, employs a multifaceted experimental and computational approach. By utilizing static load extensometer testing complemented by computational simulations, this study quantitatively assesses the impacts of unique wing geometries on aerodynamic forces and structural behavior. The experimental setup, involving a Wheatstone full-bridge circuit, measures the strain responses of tubercle-configured leading edges under static loads. These measured strains are converted into stress values through Hooke’s law, revealing a consistent linear relationship between the applied loads and induced strains, thereby validating the structural robustness. The experimental results indicate a linear strain increase with load application, demonstrating strain values ranging from 65 με under a load of 584 g to 249 με under a load of 2122 g. These findings confirm the structural integrity of the designs across varying load conditions. Discrepancies noted between the experimental data and simulation outputs, however, underscore the effects of 3D printing imperfections on the structural analysis. Despite these manufacturing challenges, the results endorse the tubercle leading edges’ capacity to enhance aerodynamic performance and structural resilience. This study enriches the understanding of bio-inspired aerodynamic designs and supports their potential in practical fluid mechanics applications, suggesting directions for future research on manufacturing optimizations. Full article

One of the significant challenges in scaling agile software development is organizing software development teams to ensure effective communication among members while equipping them with the capabilities to deliver business value independently. A formal approach to address this challenge involves modeling it as an optimization problem: given a professional staff, how can they be organized to optimize the number of communication channels, considering both intra-team and inter-team channels? In this article, we propose applying a set of bio-inspired algorithms to solve this problem. We introduce an enhancement that incorporates ensemble learning into the resolution process to achieve nearly optimal results. Ensemble learning integrates multiple machine-learning strategies with diverse characteristics to boost optimizer performance. Furthermore, the studied metaheuristics offer an excellent opportunity to explore their linear convergence, contingent on the exploration and exploitation phases. The results produce more precise definitions for team sizes, aligning with industry standards. Our approach demonstrates superior performance compared to the traditional versions of these algorithms. Full article

Robotic missions for solar farm inspection demand agile and precise object detection strategies. This paper introduces an innovative keypoint-based object detection framework specifically designed for real-time solar farm inspections with UAVs. Moving away from conventional bounding box or segmentation methods, our technique focuses on detecting the vertices of solar panels, which provides a richer granularity than traditional approaches. Drawing inspiration from CenterNet, our architecture is optimized for embedded platforms like the NVIDIA AGX Jetson Orin, achieving close to 60 FPS at a resolution of 1024 ×1376 pixels, thus outperforming the camera’s operational frequency. Such a real-time capability is essential for efficient robotic operations in time-critical industrial asset inspection environments. The design of our model emphasizes reduced computational demand, positioning it as a practical solution for real-world deployment. Additionally, the integration of active learning strategies promises a considerable reduction in annotation efforts and strengthens the model’s operational feasibility. In summary, our research emphasizes the advantages of keypoint-based object detection, offering a practical and effective approach for real-time solar farm inspections with UAVs. Full article

Soft continuum robots, inspired by the adaptability and agility of natural soft-bodied organisms like octopuses and elephant trunks, present a frontier in robotics research. However, exploiting their full potential necessitates precise modeling and control for specific motion and manipulation tasks. This study introduces an innovative approach using Deep Convolutional Neural Networks (CNN) for the inverse quasi-static modeling of these robots within the Absolute Nodal Coordinate Formulation (ANCF) framework. The ANCF effectively represents the complex non-linear behavior of soft continuum robots, while the CNN-based models are optimized for computational efficiency and precision. This combination is crucial for addressing the complex inverse statics problems associated with ANCF-modeled robots. Extensive numerical experiments were conducted to assess the performance of these Deep CNN-based models, demonstrating their suitability for real-time simulation and control in statics modeling. Additionally, this study includes a detailed cross-validation experiment to identify the most effective model architecture, taking into account factors such as the number of layers, activation functions, and unit configurations. The results highlight the significant benefits of integrating Deep CNN with ANCF models, paving the way for advanced statics modeling in soft continuum robotics. Full article

Agile ways of working have garnered recognition for their capacity to drive innovation, placing a strong emphasis on adaptability to change and a user-centric approach. Inspired by these proven principles, the authors envision that applying scaled agile—an extension of agile methodologies—can serve as a catalyst for revolutionary transformations in how buildings are redesigned, refurbished, and operated, ushering in a new era of practices within the industry. This paper conducts an in-depth literature review to explore the application of agile ways of working in building adaptation projects. Drawing on insights from the literature review and expert validations, the authors propose the development of the Agile Building Adaptation (AgiBuild) framework, delineating its core components and outlining the probable implementation process. Notably, the framework’s successful integration hinges on crucial factors, including effective leadership influence and comprehensive training. By embracing the AgiBuild framework, the building adaptation industry holds the potential to position itself as a highly innovative and user-centered sector, bolstering productivity and performance within the broader construction domain. By aligning with the framework’s principles, the industry can cultivate a culture of adaptability and collaboration, facilitating the delivery of sustainable and customer-focused building adaptation projects that cater to the evolving needs of the built environment. Full article

The main question proposed in this research is whether different types of organizational approaches could help in shortening the response time needed to analyze advanced design solutions in accordance with the changed circumstances. Approaches that we are considering have been adapted from rapidly changing disciplines—such as the IT industry, and software engineering. This paradigm allows for architectural programming to obtain different positions in the timeline of project planning and realization. We proposed a novel methodology of architectural design and project management as an instrument inspired by the Agile Manifesto and some of its instantiations, most notably by the Scrum framework. This research shows that application of the proposed framework significantly shortens the design process and facilitates the involvement of a larger number of authors within the same project team. This study focused on the specific case of architectural competitions. However, the results showed that the same framework could be applied in a broader design context, details of which have been left for future considerations. Full article

Particle Swarm Optimization is a metaheuristic optimization algorithm widely used across a broad range of applications. The algorithm has certain primary advantages such as its ease of implementation, high convergence accuracy, and fast convergence speed. Nevertheless, since its origin in 1995, Particle swarm optimization still suffers from two primary shortcomings, i.e., premature convergence and easy trapping in local optima. Therefore, this study proposes modified chaotic particle swarm optimization with uniform particle initialization to enhance the comprehensive performance of standard particle swarm optimization by introducing three additional schemes. Firstly, the initialized swarm is generated through a uniform approach. Secondly, replacing the linear inertia weight by introducing the nonlinear chaotic inertia weight map. Thirdly, by applying a personal learning strategy to enhance the global and local search to avoid trap in local optima. The proposed algorithm is examined and compared with standard particle swarm optimization, two recent particle swarm optimization variants, and a nature-inspired algorithm using three software effort estimation methods as benchmark functions: Use case points, COCOMO, and Agile. Detailed investigations prove that the proposed schemes work well to develop the proposed algorithm in an exploitative manner, which is created by a uniform particle initialization and avoids being trapped on the local optimum solution in an explorative manner and is generated by a personal learning strategy and chaotic-based inertia weight. Full article

A particular characteristic of disruptive products is in reengineering advanced technologies for addressing the needs of low-end consumers and/or non-consumers, to transform them into new consumers. This requires a lean co-creative analysis of requirements with all stakeholders involved. Even if a theory encourages the continuous connection of designers and users throughout the design lifecycle for agile adaptation of requirements to the new experiences of users by intersecting them with various versions of the prototype, the rigid budget and time allocated to the design project require novel approaches to clarify the right vectors of product-evolution from the very early design stages of the project lifecycle—allowing agile approaches to fine-tune the set of requirements. In this context, an analysis process of requirements that uses a constructor inspired by living systems is introduced in this paper. This constructor identifies gaps in requirement formulation and indicates areas where improvements must be undertaken. The method is applied in the case of a new cybersecurity software solution that targets micro and small companies. Full article

Introduced in 2013, the A.L.P.E.S. approach (AgiLe aPproaches in higher Education Studies) aims to apply agile practices to teaching. Agile approaches are project management practices for IT development. More pragmatic than traditional methods, they allow to be closer to the applicant and to involve him/her as much as possible. They offer a great reactivity and a good adaptation to best meet the needs. They are used today in a large part of IT companies. Largely inspired by agile approaches, the A.L.P.E.S. approach allows the teaching of project management in a transverse way to a main course. It makes teaching more flexible and more adapted to the students. In this article, we describe the approach. We describe the tools, the process of creating a course, and the process of running a course. Full article

Flexible, highly sensitive, easy fabricating process, low-cost pressure sensors are the trend for flexible electronic devices. Inspired by the softness, comfortable, environmental friendliness and harmless of natural latex mattress, herein, we report an agile approach of constructing a flexible 3D-architectured conductive network by dip-coating silver nanowires (AgNWs) on the natural rubber latex foam (NRLF) substrate that provide the 3D micro-network structure as the skeleton. The variation of the contact transformed into the electrical signal among the conductive three-dimensional random networks during compressive deformation is the piezoresistive effect of AgNWs/NRLF pressure sensors. The resulting AgNWs/NRLF pressure sensors exhibit desirable electrical conductivity (0.45–0.50 S/m), excellent flexibility (58.57 kPa at 80% strain), good hydrophobicity (~128° at 5th dip-coated times) and outstanding repeatability. The AgNWs/NRLF sensors can be assembled on a glove to detect hand motion sensitively such as bending, touching and holding, show potential application such as artificial skin, human prostheses and health monitoring in multifunctional pressure sensors. Full article

Climate change challenges mountain communities to prepare themselves via Community-Based Adaptation (CBA) plans that reduce vulnerability. This paper outlines the evaluation of a developed web-based information system to support CBA, referred to as a Mountain Community Adaptive System (MCAS). The web-based user interface visualizes collated data from data providers, integrating it with near real-time climate and weather datasets. The interface provides more up-to-date information than was previously available on the environment, particularly on land and climate. MCAS, a cloud-based Land Information System (LIS), was developed using an Agile-inspired approach offering system creation based on bare minimum system requirements and iterative development. The system was tested against Fit-For-Purpose Land Administration (FFP LA) criteria to assess the effectiveness in a case from Nepal. The results illustrate that an MCAS-style system can provide useful information such as land use status, adaptation options, near real-time rainfall and temperature details, amongst others, to enable services that can enhance CBA activities. The information can facilitate improved CBA planning and implementation at the mountain community level. Despite the mentioned benefits of MCAS, ensuring system access was identified as a key limitation: smartphones and mobile technologies still remain prohibitively expensive for members of mountain communities, and underlying information communication technology (ICT) infrastructures remain under-developed in the assessed mountain communities. The results of the evaluation further suggest that the land-related aspects of climate change should be added to CBA initiatives. Similarly, existing LIS could have functionalities extended to include climate-related variables that impact on land use, tenure, and development. Full article