Search Results (1,342)

This paper presents a systematic application of the Homotopy Perturbation Method (HPM) to the nonlinear static analysis of cantilever beams subjected simultaneously to three coplanar follower loads: an axial force H, a transverse force V, and a bending moment M₁. The studied configuration introduces complex mathematical self-coupling, as the bending moment depends on the solution of the differential equation even in its boundary conditions (γ₁), transforming the problem into a nonlinear one that is resistant to standard analytical methods. The primary methodological contribution of this work is the successful extension of the HPM framework to treat, within a unified mathematical formalism, this complete loading case, which has practical applications in compliant mechanisms, micro-electromechanical systems (MEMSs), and auxetic structures. The paper provides a complete mathematical formulation and explicit derivation of the HPM solution terms up to the third order and a rigorous demonstration of the method’s convergence, with quantitative error estimates and the establishment of a practical domain of validity, γ₁ < 30°, for an accuracy below 0.5%. As a direct consequence of this analytical advancement, we derive a series of practical engineering tools: nomograms, simplified empirical formulas, interaction diagrams, and a systematic six-step design procedure, which includes an adaptive algorithm for selecting the auxiliary parameter η to optimize convergence. The solution’s structure also lends itself to AI-based optimization frameworks, demonstrating how HPM solutions can serve as a foundation for machine learning surrogates and automated multi-objective optimizations. HPM proves to be a robust and efficient alternative, providing semi-analytical solutions in the form of convergent series without requiring an explicitly small physical parameter. This enables a direct parametric understanding of the structural response and offers rapid tools for the conceptual and preliminary sizing phases, thereby complementing the intensive numerical methods used in the final design stages. Full article

Ensuring space environment security through the detection of space debris and non-cooperative threat objects has become a critical mission for next-generation spaceborne surveillance systems. Frequency diversity array (FDA) radar, with its unique range angle-dependent beampattern, offers a transformative capability to distinguish closely-spaced space threats from intense background clutter. However, the operational deployment of spaceborne FDA is inherently hindered by stringent platform resource constraints, including limited power supply, high hardware complexity, and restricted data transmission bandwidth. These physical limitations inevitably lead to incomplete signal observations, resulting in elevated sidelobes that can obscure small, high-speed space debris. To bridge the gap between hardware constraints and high-fidelity surveillance, this paper proposes an AI-enabled data recovery framework based on deep matrix factorization. Specifically designed to process the complex-valued nature of radar echoes, the proposed framework introduces two specialized architectures: a real-valued representation-based method (DMF-Rr) and a native complex-valued deep matrix factorization (CDMF) network that preserves vital phase coherence. By leveraging deep learning to “enable” sparse-sampled systems, the proposed method effectively reconstructs missing observations without requiring prior knowledge of the signal rank. Numerical results demonstrate that the AI-powered CDMF significantly suppresses the high sidelobes induced by resource-limited sampling, enabling the reliable identification and localization of weak threat objects. This study demonstrates the power of AI in overcoming the physical bottlenecks of spaceborne hardware, providing a robust solution for enhancing space situational awareness in an increasingly crowded orbital environment. Full article

The rapid growth of wind energy has increased the need for advanced condition monitoring (CM), predictive maintenance, and remaining useful life (RUL) estimation strategies for wind turbines. In this context, digital twins (DTs) have emerged as a key tool for improving reliability, availability, and operational efficiency by integrating physical models, operational data, and artificial intelligence (AI). This paper presents a systematic literature review (SLR) aimed at analyzing the state of the art, classifying the main applications, and identifying research gaps. A rigorous search protocol was applied across scientific databases, considering inclusion and exclusion criteria and analysis categories aligned with four research questions. The results show a high concentration of studies on critical wind turbine components, a predominance of hybrid physics-based and data-driven approaches, and an increasing use of deep learning (DL) models. However, several research gaps remain, including the predominance of component-level digital twin implementations rather than system-level architectures, the lack of standardized datasets and benchmarking frameworks, and challenges related to SCADA data heterogeneity and real-time scalability. It is concluded that DTs are evolving toward more autonomous and prescriptive systems; however, they still require further maturation for widespread industrial adoption. Full article

Power system control rooms are undergoing a profound transformation as renewable integration, distributed energy resources, sector coupling, and increasing operational uncertainty reshape the technical, organisational, and cognitive demands of grid operation. At the same time, Digital Twins and Agentic Artificial Intelligence offer new possibilities for monitoring, forecasting, reasoning, and decision support. However, existing control room architectures remain fragmented and insufficiently structured to support the coherent integration of digital models, intelligent reasoning systems, human operators, and regulatory accountability mechanisms in safety-critical power system environments. This article addresses that gap through a PRISMA ScR-informed scoping review combined with a structured architectural synthesis process. The study develops Infostructure as a reference architectural framework for situation awareness in future power system control rooms. The framework is derived from a synthesis of operational challenges, regulatory constraints, and human AI collaboration requirements identified across the scientific and regulatory literature. Infostructure formalises four interrelated architectural layers, Physical, Semantic, Orchestration, and Cognitive, constrained by cross cutting governance and compliance principles. The architectural coverage and internal coherence of the framework are illustrated through representative transmission and distribution system use cases, including wide area disturbance anticipation, distribution level congestion management, and cross organisational coordination during extreme events. A structured research and validation agenda is further outlined to support empirical evaluation and phased implementation. By transforming review-based synthesis into a coherent architectural formalisation, Infostructure contributes a rigorous foundation for the evolution of transparent, accountable, and resilient power system control rooms. Full article

This paper presents an integrated multi-modal framework for detecting hyperthyroidism-associated abnormalities, namely exophthalmos and thyroid-related neck swelling, through the joint analysis of frontal facial and neck images using a deep learning-based approach. The objective of this research is to develop an integrated AI framework that improves hyperthyroid-related abnormality detection by simultaneously analyzing facial images of both the eye and neck based on pattern clinical knowledge. The multi-modal framework mimics a biological visual mechanism by using a dual-pathway architecture that concurrently processes foveal-like details of the eyes and neck. It integrates these high-resolution visual embeddings with quantitative morphological measurements to simulate a clinician’s ability to fuse observation with physical assessment. The proposed system employs a multi-faceted decision-making process derived from three distinct data components: two from frontal face analysis and one from neck region analysis. Specifically, eye regions extracted from facial images are preprocessed using the YOLOv11s model. The proposed system leverages a dual-pathway processing architecture to extract comprehensive diagnostic features. For the eye dataset, the framework utilizes a face mesh-based eye landmark (FMEL) to extract both eye regions and perform eyes unfold processing. These regions are subsequently analyzed by the proposed sclera map unwrapping engine (SMUE) to derive quantitative sclera metrics from both the left and right eyes. To optimize classification, a dual-branch architecture is employed by integrating CNN visual embeddings with SMUE-derived statistical features through a feature fusion layer. Simultaneously, the neck processing path executes the neck region of interest (ROI) prediction {upper, lower} to segment critical regions for goiter assessment via the proposed neck $\mu -\sigma$ ensemble thresholding (NSET) algorithm. The experimental results demonstrate that the proposed algorithm for eye analysis achieved a mean average precision (mAP50) of 96.4%, with a specific mAP50 of 98.6% for the hyperthyroid class. Regarding quantitative scleral measurement, the SMUE process revealed distinct morphological differences, with the experimental data group exhibiting consistently higher pixel distances across the reference points compared with the normal group. Furthermore, the proposed NSET algorithm yielded the highest performance for swollen neck classification with an mAP50 of 92.0%, significantly outperforming the baseline deep learning models while maintaining lower computational complexity. Full article

The increasing penetration of renewable energy sources reduces system inertia and introduces significant challenges for maintaining frequency stability in modern power grids. Battery Energy Storage Systems (BESS) have emerged as an effective solution for mitigating frequency deviations; however, existing studies typically recommend relocating BESS to the bus that is electrically furthest from the Center of Inertia (COI) to maximize frequency support. This paper investigates an alternative operational strategy in which the BESS remains co-located with the renewable energy source. A methodology combining COI-based electrical distance analysis and an artificial intelligence (AI)-driven dispatch framework is proposed to evaluate optimal BESS utilization without physical relocation. The AI model generates generator dispatch scenarios that are evaluated through dynamic simulations to assess the resulting system frequency nadir following disturbances. The proposed approach is validated using a modified IEEE nine-bus power system model. Simulation results demonstrate that, under specific generator dispatch conditions, maintaining the BESS at the renewable energy bus can achieve frequency-nadir performance comparable to relocating the BESS to the furthest bus from the COI. The analysis further identifies critical generator output ranges that influence frequency stability under different BESS placement scenarios. These findings suggest that optimized dispatch strategies can reduce the need for costly infrastructure relocation while maintaining effective frequency support in low-inertia power systems. Full article

Given the global aging of the population and the rising retirement age, the development of cross-generational technologies is crucial for a sustainable workforce supply. While AI-powered smart glasses can provide continuous cognitive support, current industrial solutions often prioritize work efficiency at the expense of the physical, cognitive, and socio-emotional needs of older workers. This study employed a mixed-methods approach grounded in embodied cognition. First, semi-structured interviews with ten participants were analyzed using grounded theory to develop a four-dimensional model of embodied experience: Perceived Pressure, Action Feedback, Collaboration Embedding, and Belonging. Subsequently, four interaction strategies—Rhythm Control, Transparent Feedback, Non-intrusive Assistance, and Legible Privacy & Social Signaling—were formulated and implemented. A high-fidelity prototype was developed to embody these strategies. Finally, a team of eight multidisciplinary experts evaluated the device using the System Usability Scale (SUS) and a proprietary twelve-item questionnaire. The results showed that the device’s overall usability was borderline acceptable (SUS = 68.13 ± 8.94). While the devices received stronger ratings for Control & Safety, the ratings for dignity and social acceptance were comparatively low. These findings contribute to the development of wearable device operation strategies suitable for users of different generations, and underline the importance of social and emotional compatibility as a prerequisite for future practice tests. Full article

This study proposes a real-time data-based Digital Value Stream Mapping (Digital VSM) framework that integrates Artificial Intelligence (AI) feature selection and discrete-event simulation validation to enhance production system performance. Unlike conventional VSM approaches that rely on static, manually aggregated data, the proposed framework uses real-time operational data to dynamically quantify Value Added (VA), Non-Value Added (NVA), and Necessary Non-Value Added (NNVA) activities. To improve decision accuracy, an Artificial Neural Network (ANN) combined with Genetic Algorithm (GA) feature selection is employed to identify dominant production variables influencing lead time and line imbalance. Furthermore, Ranked Positional Weight (RPW) optimization results are validated through Tecnomatix Plant Simulation to ensure robustness before physical implementation. The proposed framework was applied to a discrete manufacturing line, resulting in a reduction of total lead time from 8755 s to 6400 s and an increase in process ratio from 33.64% to 45.91%, with line efficiency reaching 91.7%. The findings demonstrate that integrating Digital VSM with AI-driven feature selection and simulation validation transforms Lean analysis from a descriptive tool into a predictive and validated decision-support system suitable for Industry 4.0 environments. Full article

Active matter actively searches for models that allow them to connect the behavior of multiple agents to particle system with a physical law. However, the arsenal of models used to model active matter systems is very restricted. Modern differential equation discovery approaches allow one to extract governing equations from data for a single particle in the form of the ODE. However, there is still the question of how to model at the meso- and macroscales. This paper presents a data-driven framework for extracting the governing physical laws of a hardware-made swarm across multiple scales of organization. Using the EPDE framework, we transition from a discrete, chaotic trajectory of individual agents to a continuous, effective field theory of the collective. We show that augmenting the symbolic search space with interaction-aware tokens allowed for the derivation of stochastic partial differential equations (SDEs) that significantly outperformed baseline deterministic models (reducing CRPS by up to 10%). Additionally, we derive a system of SPDEs that governs the macroscale displacement field. Full article

Future cyber-physical systems (CPSs), integrating subsystems of the mechanical, electrical and software domains, are becoming increasingly interconnected and complex. As complexity grows, testing effort increases as well. This includes the test-case definition step, where the test targets and boundary conditions are specified. With rising system complexity, the effort required to ensure that all relevant conditions for each test target are identified increases. Manual test-case definition remains the norm, creating effort bottlenecks in ensuring systematic coverage and compliance with standards such as ISO 26262 and ISO 29119. This paper explores how large language models (LLMs) can support the identification of complex boundary conditions for CPS test cases through detailed requirement analysis. The impact of performing taxonomy-guided, structured requirement mapping prior to test-case generation was evaluated by comparing it with a version without this guidance. Furthermore, the influence of supplying a Model-Based Systems Engineering (MBSE) system model as context information via Graph RAG is examined. The results show that structured, stepwise reasoning significantly improves reliability and consistency over unguided generation, while system-model information provides valuable contextual insight but has a minor impact in the chosen example. These findings outline a scalable framework for AI-assisted test-case generation. Full article

Artificial intelligence (AI) is increasingly applied to the design and optimization of solar thermal collectors, particularly in the development of selective absorber coatings. This systematic review analyzes recent advances (2020–2026) in AI-driven modeling, optimization, and sustainability strategies for solar thermal technologies following the PRISMA 2020 methodology. The results indicate that current research is largely dominated by Artificial Neural Networks and metaheuristic algorithms, mainly focused on short-term performance prediction and system-level optimization. However, durability, degradation mechanisms, and life-cycle sustainability metrics remain significantly underrepresented in AI-assisted design frameworks. From a materials perspective, recent studies highlight the emergence of multifunctional absorber surfaces, including thermochromic, self-cleaning, and multilayer coatings, often combined with AI-enabled monitoring and digital twin approaches. In addition, sustainable processing routes such as green sol–gel synthesis and low-temperature deposition show strong potential for reducing environmental impact when integrated with AI-based optimization. Nevertheless, the holistic integration of AI with sustainability metrics at the early design stage remains limited. Future research should therefore focus on hybrid and physics-informed AI frameworks capable of simultaneously addressing performance, durability, economic viability, and environmental impact in solar thermal collector design. Full article

This review deals with the development and progress of micro-electromechanical systems (MEMS) actuators, which are needed in microfluidic applications, such as lab-on-a-chip and diagnostics. In the last 10 years, there have been tremendous advances in materials, microfabrication and computational modeling that have increased the functionality and scope of MEMS-based microfluidic actuation. This study classifies MEMS actuators on the basis of the physical method of actuation, including electrostatic, piezoelectric, and pneumatic actuation designs, in comparison with their application in pumping, valving, and droplet control. It examines the suitability of emerging structural and functional materials, such as piezoelectric thin-films and electroactive polymers, paying special attention to their reliability and biocompatibility. It also highlights the progress in multiphysics modeling that incorporates electrical, thermal, mechanical, and fluidic models, which facilitates the efficient design and performance optimization procedures. Other trends are multifunctional actuators with built-in sensing capability and the use of artificial intelligence (AI)-assisted design in production. With these developments, however, there exist issues of power efficiency, thermal control, fabrication uniformity and operational durability, and also the absence of standardized benchmarking. Finally, future research directions are outlined, including hybrid MEMS actuation, intelligent microfluidic operations, to improve the performance of the system and enable the transfer of the lab demonstrations to the large scale application of the system. Full article

Generative Artificial Intelligence (GenAI) has been a viable technology for decades, yet widespread adoption in healthcare and academic settings has remained limited to research. One possible explanation for this is limited understanding about the beliefs around GenAI use amongst faculty and students training in biomedical disciplines that frequently lead to non-physician healthcare careers, including physical therapy (PT), occupational therapy (OT), allied health (AH), and biomedical engineering (BME). Furthermore, no known studies exist assessing differences that may exist across those disciplines. Given the significant number of professionals in those disciplines and the outsized impact they have on the healthcare system, investigating their beliefs around GenAI use is vital before widespread adoption. Accordingly, we investigated the perceptions of GenAI among students and faculty in the aforementioned fields that frequently lead to careers in healthcare. We found that knowledge of GenAI significantly influences comfort with its use completing college coursework including whether respondents believed it contributed to the process of completing that coursework and whether use of GenAI enhances learning. Interestingly, however, there were no statistically significant differences in perceptions of GenAI across disciplines, roles, or institution sizes. Qualitative findings revealed concerns about plagiarism, decline of critical thinking skills, and ethical challenges, while also recognizing GenAI’s potential to enhance learning efficiency and idea generation. Critically, the study results emphasize the need for proper training and guidelines to ensure GenAI is integrated responsibly into healthcare-related education. Full article

This paper explores how semantic-space reasoning, traditionally used in computational linguistics, can be extended to tactical decision-making in team sports. Building on the analogy between texts and teams—where players act as words and collective play conveys meaning—the proposed methodology models tactical configurations as compositional semantic structures. Each player is represented as a multidimensional vector integrating technical, physical, and psychological attributes; team profiles are aggregated through contextual weighting into a higher-level semantic representation. Within this shared vector space, tactical templates such as high press, counterattack, or possession build-up are encoded analogously to linguistic concepts. Their alignment with team profiles is evaluated using vector-distance metrics, enabling the computation of tactical “fit” and opponent-exploitation potential. A Python-based prototype demonstrates how these methods can generate interpretable, dynamically adaptive strategy recommendations, accompanied by fine-grained diagnostic insights at the attribute level. Evaluation through synthetic scenarios and a pilot study with real match data establishes internal consistency and feasibility of the approach; operational validity in live coaching contexts remains an open question for future prospective validation. Beyond football, the framework offers a potentially generalizable approach for collective decision-making in team-based domains—ranging from basketball and hockey to cooperative robotics and human–AI coordination systems. The paper concludes by outlining future directions toward real-world data integration, predictive simulation, and the validation work required before operational deployment. Full article

Aircraft icing prediction is crucial for aerodynamic design and airworthiness assessment. Traditional physics-based models struggle with complex multi-physical processes, while existing AI methods (function-based characterization or direct image learning) face issues like multi-valued mapping, high data dependency, or lack of physical interpretability. This study proposes a deep learning framework based on point set displacement description, transforming the icing process into airfoil boundary point movements. PCA dimensionality reduction mitigates the curse of dimensionality while retaining physical meaning. A neural network is used to map environmental parameters to low-dimensional principal components. Comparative analysis shows the 64 × 64 network achieves optimal fitting; 2000 samples reproduce complex ice shapes, and 800 low samples characterize simple ones. Balancing efficiency, accuracy, and interpretability with reduced data dependency, this method provides a new approach for rapid engineering icing prediction. Full article