Search Results (28,350)

To address the issues of low convergence accuracy, poor population diversity, and susceptibility to local optima in the Red-billed Blue Magpie Optimization Algorithm (RBMO), this study proposes a multi-strategy improved Red-billed Blue Magpie Optimization Algorithm (SWRBMO). First, an adaptive T-distribution-based sinh–cosh search strategy is used to enhance global exploration and speed up convergence. Second, a neighborhood-guided reinforcement strategy helps the algorithm avoid local optima. Third, a crossover strategy is also introduced to improve convergence accuracy. SWRBMO is evaluated on 15 benchmark functions selected from the CEC2005 test suite, with ablation studies on 12 of them, and further validated on the CEC2019 and CEC2021 test suites. Across all test sets, its convergence behavior and statistical significance are analyzed using the Wilcoxon rank-sum test. Comparative experiments on CEC2019 and CEC2021 demonstrate that SWRBMO achieves faster convergence and higher accuracy than RBMO and other competitive algorithms. Finally, four engineering design problems further confirm its practicality, where SWRBMO outperforms other methods by up to 99%, 38.4%, 2.4%, and nearly 100% in the respective cases, highlighting its strong potential for real-world engineering applications. Full article

Agentic AI systems are a recently emerged and important approach that goes beyond traditional AI, generative AI, and autonomous systems by focusing on autonomy, adaptability, and goal-driven reasoning. This study provides a clear review of agentic AI systems by bringing together their definitions, frameworks, and architectures, and by comparing them with related areas like generative AI, autonomic computing, and multi-agent systems. To do this, we reviewed 143 primary studies on current LLM-based and non-LLM-driven agentic systems and examined how they support planning, memory, reflection, and goal pursuit. Furthermore, we classified architectural models, input–output mechanisms, and applications based on their task domains where agentic AI is applied, supported using tabular summaries that highlight real-world case studies. Evaluation metrics were classified as qualitative and quantitative measures, along with available testing methods of agentic AI systems to check the system’s performance and reliability. This study also highlights the main challenges and limitations of agentic AI, covering technical, architectural, coordination, ethical, and security issues. We organized the conceptual foundations, available tools, architectures, and evaluation metrics in this research, which defines a structured foundation for understanding and advancing agentic AI. These findings aim to help researchers and developers build better, clearer, and more adaptable systems that support responsible deployment in different domains. Full article

A Universal Numerical Integrator (UNI) is a computational framework that combines a classical numerical integration method, such as Euler, Runge–Kutta, or Adams–Bashforth, with a universal approximator of functions, such as a feed-forward neural network (including MLP, SVM, RBF, among others) or a fuzzy inference system. The Euler-Type Universal Numerical Integrator (E–TUNI) is a particular case of UNI based on the first-order Euler integrator and is designed to model non-linear dynamic systems observed in real-world scenarios accurately. The UNI framework can be organized into three primary methodologies: the NARMAX model (Non-linear AutoRegressive Moving Average with eXogenous input), the mean derivatives approach (which characterizes E–TUNI), and the instantaneous derivatives approach. The E–TUNI methodology relies exclusively on mean derivative functions, distinguishing it from techniques that employ instantaneous derivatives. Although it is based on a first-order scheme, the E–TUNI achieves an accuracy level comparable to that of higher-order integrators. This performance is made possible by the incorporation of a neural network acting as a universal approximator, which significantly reduces the approximation error. This article provides a comprehensive overview of the E–TUNI methodology, focusing on its application to the modeling of non-linear autonomous dynamic systems and its use in predictive control. Several computational experiments are presented to illustrate and validate the effectiveness of the proposed method. Full article

The selection of the most economically advantageous tender (MEAT) in public procurement procedures requires transparent evaluation systems capable of integrating heterogeneous criteria, including qualitative ones, to reconcile quality and cost. This systematic review analyzes 74 studies published between 1998 and 2023 to explore the application of multi-criteria decision analysis (MCDA) methods in public construction procurement. The vast majority of MCDA applications focus on the award phase, with constant growth over the last 10 years. However, applications in the prequalification and verification phases are much less frequent and remain under-represented. Geographically, Europe is the most active area in terms of publications, followed by China and some countries in the Asia-Pacific area. In these regions, MCDA has been employed more systematically over time, while in other areas (e.g., Africa, Latin America), applications are sporadic or absent. Analytic Hierarchy Process (AHP) is confirmed as the most widely used technique. Emerging techniques (such as BWM, MABAC, EDAS, VIKOR, advanced TOPSIS) show greater computational rigor and in some cases better theoretical properties, but are less used due to complexity, less practical familiarity and the lack of accessible software tools. The operationalization of environmental and social criteria is still poorly standardized: clear indications on metrics, measurement scales and data sources are often lacking. In most cases, the criteria are treated in a generic or qualitative way, without common standards. Furthermore, the use of sensitivity analyses and procedures for aggregating judgments between evaluators is limited, with a consequent risk of poor robustness and transparency in the evaluation. In order to consider proposing a framework or guidelines based on the review findings, a six-step operational framework that connects selection of criteria and their operationalization, choice of method based on the context, robustness checks and standard minimum reporting, with clear assignment of roles and deliverables, is provided. The framework summarizes and makes the review evidence applicable. Full article

Cerebral cavernous malformation (CCM) is a cluster of abnormal blood vessels in the brain that leads to severe neurological deficits, seizures, and fatal hemorrhagic stroke. Currently, there is no available drug treatment for CCM. Most CCMs are conservatively managed by observing change in appearance (MRI), recent hemorrhage, or any clinical symptoms. Neurosurgery is the only current treatment option, but it is only effective in a few cases. Since most CCM lesions are surgically inaccessible, when left untreated they lead to severe neurological deficits, seizures, and fatal hemorrhagic stroke. Hence, new non-invasive, safe, and effective treatment strategies are urgently needed. Recent research has identified gut microbiome dysbiosis and its innate immune response as the critical stimulus in experimental CCM pathogenesis, demonstrating the importance of the gut–brain axis in CCM. Importantly, CCM patients also manifest gut microbiome dysbiosis and gut barrier health can impact CCM disease course. This review highlights the emerging involvement of the gut microbiome in CCM pathogenesis and its potential as a therapeutic target. While preclinical data suggest mechanistic links, the lack of clinical intervention studies limits current applicability and underscores the need for translational research. Full article

The integration of large language models (LLMs) into structural engineering workflows presents both a transformative opportunity and a critical challenge. While LLMs enable intuitive, natural language interactions with complex data, their limited arithmetic reasoning, contextual fragility, and lack of verifiability constrain their application in safety-critical domains. This study introduces a novel automation pipeline that couples generative AI with finite element modelling through the Model Context Protocol (MCP)—a modular, context-aware architecture that complements language interpretation with structural computation. By interfacing GPT-4 with OpenSeesPy via MCP (JSON schemas, API interfaces, communication standards), the system allows engineers to specify and evaluate 3D frame structures using conversational prompts, while ensuring computational fidelity and code compliance. Across four case studies, the GPT+MCP framework demonstrated predictive accuracy for key structural parameters, with deviations under 1.5% compared to reference solutions produced using conventional finite element analysis workflows. In contrast, unconstrained LLM use produces deviations exceeding 400%. The architecture supports reproducibility, traceability, and rapid analysis cycles (6–12 s), enabling real-time feedback for both design and education. This work establishes a reproducible framework for trustworthy AI-assisted analysis in engineering, offering a scalable foundation for future developments in optimisation and regulatory automation. Full article

Oil refineries depend greatly on the estimation of crude oil properties in order to understand the oil’s behaviour and the product fractions expected from the refining process. In yield estimation, the crude oil source and variant can cause variability in prediction and lead to the need for repeatable analysis. The necessity for fast, accurate, and high-generalization yield estimation initiates the framework of this review. This paper aims to comprehensively review the available techniques for estimating the yield of petroleum products in the petroleum refining industry. The review provides a brief overview of petroleum refining processes and high-value products, followed by a description of the traditional method, which utilizes laboratory analysis to offer detailed findings, but requires a tedious methodology. The improvement of yield estimation leads to process simulation, modelling, and machine learning, enabling a fast response and better prediction with higher accuracy. Thorough case studies related to simulation software, models, and algorithms are presented to discover the process and model development, applications, advantages, and drawbacks. Enhancing petroleum product yield estimation provides reliable techniques for oil refiners that enable them to achieve optimized production aligned with sustainability and modernization goals. Full article

Wildfires in Portugal have become increasingly frequent and severe, driven by a combination of fuel accumulation, extreme meteorological conditions, and topographic complexity. This study assesses the applicability of the Hot–Dry–Windy (HDW) index in characterizing fire-weather conditions during five major wildfires: Chamusca (2003), Pedrógão Grande and Lousã (2017), Monchique (2018), and Covilhã (2022). HDW values were computed at sub-daily resolution and compared against a 1991–2020 climatology. This study also evaluates the HDW index as a high-resolution fire danger indicator in Portugal and compares it with the traditional FWI using percentile-based climatology. The findings indicate that during 12 and 15 UTC, HDW in the wildfires in Chamusca (2003) and Lousã (2017) exceeded 180–370 units, suggesting extreme air conditions driven by hot, dry, and windy weather patterns. These values denoted extremely flammable conditions since they were significantly higher than the 95th percentile. A distinct peak at 15 UTC for Pedrógão Grande (2017) topped 140 units (>P95), which is consistent with the ignition timing and a rapid beginning spread. A continuous HDW anomaly that peaked above 200 units between 2 August and 5 August preceded the Monchique (2018) event, suggesting extended heat stress and increased wind contribution. While not as severe as in previous instances, HDW at Covilhã (2022) was above the 75th percentile in the early afternoon (12–18 UTC). Results show that in all cases, HDW values exceeded the 90th and 95th percentiles during the hours of ignition and early fire spread, with the most critical anomalies occurring between 12 UTC and 18 UTC. Spatial analyses revealed regional-scale patterns of HDW exceedance, aligning with observed ignition zones. Comparisons with the Canadian Fire Weather Index (FWI) revealed that while the FWI captured seasonal fuel aridity, the HDW more effectively resolved short-term meteorological extremes, particularly wind and atmospheric dryness. The HDW index was found to identify high-risk conditions even when FWI values were moderate, highlighting its added diagnostic value. These results support the inclusion of HDW in operational fire danger rating systems for Portugal and other Mediterranean countries, where compound fire-weather extremes are becoming more frequent due to climate change. Full article

In the context of polymer-based composites, the knowledge of the correlations between the processing conditions, the microstructure, and the final properties is essential to tailor polymeric systems for specific applications. Specifically concerning the extrusion process, an accurate design of the screw profile allows for achieving composites with modulable microstructures, according to the specific properties required by the intended application. In this work, films of polylactic acid-based composites with 5 wt.% of talc were obtained by means of a single-screw extruder equipped with a flat die and a calender unit. Three different screw profiles, namely a general-purpose compression screw, a screw with a reverse flow zone, and a barrier screw, were employed for the production of films. The ability of the screw profile in varying the degree of filler dispersion and distribution was assessed through morphological and rheological analyses, demonstrating that the barrier screw is more able in disaggregating the talc lamellae. Due to the achieved microstructures, films produced using this screw profile exhibited superior barrier properties, with a decrease of about 27% in the oxygen permeability as compared to unfilled PLA. However, a concurrent decrease in material ductility as compared to the other films was observed. Finally, the thermoformability of the composites was assessed; also in this case, trays with more precise edges and corners were obtained for the film formulated through the barrier screw. Full article

Active clearance control (ACC) is an effective means of reducing engine fuel consumption. Recently, an innovative fuel-cooled ACC (FCACC) scheme has been developed to improve engine performance by utilizing fuel from the aircraft fuel thermal management system (AFTMS) to precool bleed air, creating a trade-off between fuel supply and thermal management capabilities. To maximize flight endurance through FCACC, this paper firstly elucidates its mechanism for conserving both fuel and fuel heat sink when the thermal management flow path (TMFP) operates in the full recirculation state (FRS), benefiting from the configuration of the recirculation fuel supply branch (RFSB). Calculation results indicate that flight endurance can be extended by 2.28% and 11.62% under the standard condition and extreme mission, respectively. Then, the impact of further utilizing fuel heat sink on flight endurance at the critical transition from FRS to partial recirculation state (PRS) is investigated. In this case, thermal failure, rather than fuel depletion, dominates and shortens flight endurance. Based on this, a novel dynamic regulation strategy for fuel/bleed air heat exchange is established, which is applicable across various operating conditions. Finally, a common mission demonstrates that FCACC can reduce takeoff weight by 20.33 kg, enabling the aircraft to carry additional devices. Full article

The use of drones for monitoring mammal populations has increased in recent years due to their relatively low cost, accessibility, and ability to survey large areas quickly and efficiently. The type of drone sensor used during surveys can significantly influence species detection probability. For arboreal mammals, thermal infrared (TIR) sensors are commonly used because they can detect heat signatures of canopy-dwelling species. However, drones equipped with TIR cameras are more expensive and thus less accessible to conservation practitioners who often work with limited funding compared to drones equipped exclusively with standard visual spectrum cameras (Red, Green, Blue; RGB drones). Although RGB drones may represent a viable low-cost alternative for wildlife monitoring, their effectiveness for monitoring arboreal mammals remains poorly understood. Our objective was to evaluate the use of RGB drones for monitoring arboreal mammals, focusing on Geoffroy’s spider monkeys (Ateles geoffroyi) and southern muriquis (Brachyteles arachnoides). We used pre-programmed flights for spider monkeys and manual flights for muriquis, selecting the most suitable method according to the landscape characteristics of each study site; flat terrain with relatively homogeneous forest canopy height and mountainous forests with highly variable canopy height, respectively. We detected spider monkeys in only 0.4% of the 232 flights, whereas we detected muriquis in 6.2% of the 113 flights. Considering that both species are highly arboreal, use the upper canopy, and share similar locomotion patterns and group size, differences in detectability are more likely related to the type of drone flights used in each case study than to species differences. Preprogrammed flights allow for systematic and efficient area coverage but limit real-time adjustments to environmental conditions such as wind, canopy structure, and visibility. In contrast, manual flights offer greater flexibility, with pilots being able to adjust speed, height, and flight path as needed and spend more time over specific areas to conduct a more exhaustive search. This flexibility likely contributed to the higher detection rate observed in the muriqui study, but detectability was still low. The findings of the two studies suggest that RGB drones are better suited as a complementary tool rather than a primary method for monitoring arboreal mammals in dense forest habitats. Nonetheless, RGB drones offer valuable opportunities for other applications, and we highlight several examples of their potential utility in arboreal mammal research and conservation. Full article

Background: Augmented-reality (AR) navigation is emerging as a means of turning pre-operative cone-beam CT data into intuitive, in situ guidance for difficult tooth removal, yet the scattered evidence has never been consolidated nor illustrated with a full clinical workflow. Aims: This study aims to narratively synthesise AR applications limited to dental extractions and to illustrate a full AR-guided clinical workflow. Methods: We performed a PRISMA-informed narrative search (PubMed + Cochrane, January 2015–June 2025) focused exclusively on AR applications in dental extractions and found nine eligible studies. Results: These pilot reports—covering impacted third molars, supernumerary incisors, canines, and cyst-associated teeth—all used marker-less registration on natural dental surfaces and achieved mean target-registration errors below 1 mm with headset set-up times under three minutes; the only translational series (six molars) recorded a mean surgical duration of 21 ± 6 min and a System Usability Scale score of 79. To translate these findings into practice, we describe a case of AR-guided mandibular third-molar extraction. A QR-referenced 3D-printed splint, intra-oral scan, and CBCT were fused to create a colour-coded hologram rendered on a Magic Leap 2 headset. The procedure took 19 min and required only a conservative osteotomy and accurate odontotomy that ended without neurosensory disturbance (VAS pain 2/10 at one week). Conclusions: Collectively, the literature synthesis and clinical demonstration suggest that current AR platforms deliver sub-millimetre accuracy, minimal workflow overhead, and high user acceptance in high-risk extractions while highlighting the need for larger, controlled trials to prove tangible patient benefit. Full article

The rapid evolution of eHealth technologies has revolutionized healthcare, enabling data-driven decision-making and personalized care. Central to this transformation is interoperability, which ensures seamless communication among heterogeneous systems. This paper explores the critical role of interoperability, data management processes, and the use of international standards in enabling integrated healthcare solutions. We present an overview of interoperability dimensions—technical, semantic, and organizational—and align them with data management phases in a concise eHealth architecture. Furthermore, we examine two practical European use cases to demonstrate the extend of the proposed eHealth architecture, involving patients, environments, third parties, and healthcare providers. Full article

Against the background of China’s ecological civilization construction and sustainable development strategies, how remote sensing courses adapt to the demands of the artificial intelligence era has become an urgent issue for undergraduate education in relevant disciplines at universities. This study proposed a trinity teaching reform path of “deep learning and remote sensing, and ecological sustainability”, aiming to cultivate interdisciplinary talents with capabilities in intelligent interpretation and practical application. The study established a three-stage curriculum objective system, integrating knowledge, ability, and literacy, designed a five-dimensional linkage teaching method combining case-driven teaching, modular training, and blended learning, and conducted teaching practices using mainstream deep learning frameworks and cloud platforms. Through hierarchical teaching practice cases and multi-dimensional evaluation data, it was shown that the reform effectively enhanced the experiment group students’ abilities in deep learning applications, complex remote sensing data processing, and ecological problem-solving. The achievement values for all five evaluation indicators exceeded 80%, with the highest improvement reaching 28% compared to the control group. The results indicate that this teaching reform not only enhances learning outcomes but also provides a valuable framework and practical pathway for remote sensing education empowered by artificial intelligence and the cultivation of professional talent in future sustainable development fields. Full article

The importance of managing goods delivery in urban areas has reached its peak in recent years, driven by the constant and rapid growth of online commerce. Under such conditions, where smaller quantities of goods are ordered, yet the number of shipments continues to rise, the question of last-mile delivery (LMD) efficiency becomes increasingly relevant. This paper addresses the issue of last-mile delivery zone efficiency through the application of a new methodological approach. First, the concept of measuring last-mile delivery productivity is defined using a specific example from an urban environment. Next, Key Performance Indicators (KPIs) are established to enable a proper assessment of urban zone efficiency in line with the LMD concept. The main contribution of this study is the development of the IRN OWCM (Interval Rough Number Opinion Weight Criteria Method), which is used to calculate the weights of the criteria. To assess suitable delivery zones in terms of efficiency based on the defined KPIs, the previously developed IRN OWCM method is integrated with IRN AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalization). The results identify delivery zones that are suitable in terms of meeting standardized user needs. The developed model demonstrated stability through additional verification tests and can be adequately applied in cases when it is needed to minimize subjectivity and uncertainties. Full article