Search Results (51,833)

Trajectory optimization during the terminal area energy management (TAEM) phase is pivotal for achieving accurate runway alignment and enhancing landing safety in autonomous aircraft operations. In the presence of initial state uncertainties in TAEM phase, conventional pseudo-spectral methods still suffer from robustness limitations and exhibit a strong dependence on the quality of the initial guess. Therefore, this paper proposes the composite African vulture optimization algorithm (CAVOA), a meta-heuristic framework designed to automate trajectory optimization. An in-depth examination of the heading alignment cone (HAC) trajectory model enables effective heading adjustments prior to landing, augmented by a tailored dynamic pressure profile to ensure safe touchdown velocities. By incorporating dynamic opposition learning, intelligent boundary processing, and composite exploration, CAVOA enhances global search efficiency. These enhancements are substantiated through comparisons with benchmark function optimization, Wilcoxon rank sum tests, and convergence analysis. Numerical simulations validate that CAVOA reliably directs autonomous aircraft to predefined touchdown states, demonstrating superior performance in complex aerial environments. Full article

This article proposes a gas-only continuation framework for steady-state loadability analysis in natural gas transmission networks based on a direction-free reformulation of the General Flow Equation (GFE). The proposed formulation introduces signed pipe flows directly as state variables, thereby representing bidirectionality intrinsically. As a result, flow reversals are handled without switching logic, while the branch geometry and criticality mechanism of the underlying gas-network equilibrium map are preserved. On this basis, a Gas Continuation Method (GCM) is developed to trace equilibrium branches directly in native gas-load space under specified gas-load stress. The method distinguishes the last admissible operating point from the mathematical critical point and incorporates a formal diagnosis to determine whether the detected limiting condition is consistent with a Saddle-Node Bifurcation (SNB). The proposed framework is validated on a three-node benchmark, a realistic Belgian gas transmission network, and a 40-node test system. The results show accurate agreement with Newton–Raphson (NR) solutions in the regular operating regime, robust branch tracing near limiting conditions where standalone NR loses convergence, and consistent handling of signed pipe flows under load-induced flow reversal and under algebraic orientations assigned a priori opposite to the solved physical flow. The Belgian and 40-node cases further show that the operational admissibility limit may precede the mathematical critical point, so pressure-based feasibility and branch-level criticality emerge as related but distinct notions. These features make the proposed methodology a rigorous and practical tool for identifying admissibility limits, interpreting critical behavior, and assessing loadability margins in gas transmission networks. Full article

Decarbonization of existing buildings is obstructed by the performance gap between intended and operational energy consumption. Smart energy management and monitoring of existing buildings through digital twins pose significant attributes towards decarbonization efforts. However, there is limited research that transforms digital twins’ monitored performance into actionable retrofitting strategies. Hence, this research develops a framework that bridges the digital twin concept with standards-based IEQ analytics, guiding retrofit decision-making in existing buildings. The framework offers a low-code workflow that uses Autodesk Tandem to develop a digital twin integrating indoor environmental quality (IEQ) data, including thermal comfort and air quality. IEQ is monitored since inefficient management of its parameters often results in excessive HVAC demand, contributing to the performance gap. The framework structures IEQ parameter evaluations against benchmarks guided by ASHRAE to identify deviations indicative of operational inefficiencies in energy consumption. The digital twin model positions live IEQ tracking and analysis as diagnostic measures, leading to targeted fabric-oriented retrofit prioritization. The framework was tested on a case study in a hot arid climate, where its results indicate that the integration of digital twin-based IEQ analysis with building characteristics effectively identified the need for targeted envelope improvements, including high-performance glazing, external shading elements, and sound isolation, as key factors for eliminating overheating and high noise levels. Validating the proposed retrofits’ effectiveness, energy simulations examines the whole building to find an 11.52% annual reduction in energy use intensity from 145.61 kWh/m²·year to 128.84 kWh/m²·year through shading elements and low-E films for glazing. Full article

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

This study examines the impact of ESG disclosure, leverage, and profitability on firm value, measured by Tobin’s Q, among 67 non-financial Tadawul-listed companies in Saudi Arabia over the period 2015–2024. ESG disclosure is captured through a manual content-analysis index that scores the proportion of expected environmental, social, and governance items reported by each firm. The study further investigates whether board independence moderates these relationships while controlling for liquidity, firm size, current ratio, capital expenditure, and board size. Methodologically, the study employs the two-step system generalized method of moments (system GMM) estimator, which addresses dynamic persistence, endogeneity, and unobserved heterogeneity. The findings reveal that ESG disclosure has a positive and significant effect on firm value, indicating that the Saudi market increasingly rewards firms that provide broader sustainability-related information. Profitability also exerts a positive influence on Tobin’s Q, while leverage has a negative and significant effect, suggesting that higher debt weakens market valuation. Among the moderating effects, board independence significantly reduces the negative impact of leverage on firm value, although it does not significantly strengthen the positive ESG disclosure–firm value relationship. The results also show that liquidity, firm size, capital expenditure, and board size positively influence firm value. The study’s novelty lies in being the first, to our knowledge, to integrate ESG disclosure, financial structure, profitability, and board independence within a single dynamic firm-value framework over a decade-long panel that brackets the Saudi Exchange’s 2021 ESG disclosure guideline. In doing so, it advances emerging-market ESG research by showing that, under Saudi Arabia’s largely voluntary disclosure regime and concentrated-ownership structure, board independence operates primarily as a risk-monitoring mechanism rather than as an amplifier of disclosure value. The findings imply that regulators should strengthen and progressively mandate ESG reporting frameworks, that investors should treat ESG transparency as value-relevant information, and that firms should view ESG transparency and prudent governance as strategic tools for enhancing market value in line with Vision 2030. Full article

Decommissioned airfields are increasingly recognized as strategic sites for ecological regeneration, climate adaptation, and the creation of new public spaces. However, research on their transformation has predominantly focused on the environmental performance of Nature-based Solutions (NBS), often overlooking the role of inherited spatial morphology in structuring regeneration processes and outcomes. This paper proposes an AI-assisted, morphology-based proto-ontological framework for analyzing and designing post-airfield architecture. The framework was developed through the inductive and comparative analysis of a corpus of 32 urban post-airfield regeneration projects, from which recurrent inherited morphologies, transformation actions, spatial devices, and NBS were identified and structured into a relational sequence. The framework was then applied to two contrasting case studies: Maurice Rose Airfield Park (Frankfurt) and Xuhui Runway Park (Shanghai); these were selected for their different transformation logics. The results show that similar airfield morphologies can generate markedly different climatic, ecological, social, and memory-related outcomes depending on how they are transformed and linked to NBS. The study demonstrates that inherited airfield morphologies are not passive remnants but operative spatial structures, and that NBS should be understood as spatially embedded and form-generating design components. The proposed proto-ontology offers a transferable analytical model and a basis for future computational and generative design applications. Full article

Nanostructured catalysts have become a core component of energy conversion in electrocatalysis and photocatalysis; however, successfully translating their performance from laboratory scale to industrial applications remains a long-standing challenge. This paper provides a critical assessment of the field, systematically tracing the entire development trajectory from catalyst design to practical application. We focus on five major classes of catalysts—monometallic catalysts, bimetallic/multimetallic alloy catalysts, metal compound catalysts, carbon-based composite catalysts, and single-atom catalysts—and explore synthetic strategies for achieving precise structural control, including hydrothermal/solvothermal methods, electrodeposition, template-assisted and MOF-derived syntheses, high-temperature pyrolysis, and post-treatment defect engineering. This paper delves into the mechanisms and performance descriptors governing the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), urea oxidation, photocatalytic water splitting, and CO₂ reduction. Based on the above analysis, this paper lays the mechanistic foundation for five core strategies to improve catalyst performance: morphology control, elemental doping, heterostructure and interface engineering, defect and vacancy engineering, and support modification. Furthermore, this paper provides an in-depth evaluation of the applications of these catalysts in water splitting, CO₂ valorization, fuel cells, metal–air batteries, and energy-saving electrolysis, with a particular focus on earth-abundant alternatives to precious metals. We argue that in many well-studied reactions, intrinsic activity may no longer be the primary bottleneck restricting their development; instead, the core challenge now lies in maintaining excellent catalytic performance under harsh and industrially relevant conditions, especially under high-current densities, impurity-containing feed systems, and long-term operating conditions. In response to this shift in research focus, this paper clearly identifies the key obstacles hindering the industrial application of catalysts and proposes practical directions for future research. Full article

This paper provides a comprehensive review of recent studies on event-triggered control schemes for DC microgrids. Several event-triggered mechanisms (ETMs) are thoroughly discussed, including static, dynamic, self-triggered, and edge-based algorithms. Considering the strengths and weaknesses of these algorithms, it is found that although such ETMs can decrease communication burden in the system, they are also susceptible to communication delays, Zeno behaviour, sensitivity to control parameter changes in triggering conditions, and inability to adapt to the fluctuating nature of renewable energy sources (RESs). Furthermore, this article examines implementation challenges, including data packet loss, quantisation effects, actuator faults, and a lack of cybersecurity measures, to provide readers with a clear vision of future trends in this field. Based on the main findings of the investigation, this review paper proposes possible areas for future research, highlighting the need for event-triggered control schemes that operate in discrete time, handle delays, and adapt to varying operating conditions. Other concepts, including adaptive control parameters for triggering conditions based on machine learning, the adoption of advanced cybersecurity measures, and data-aware transmission approaches that consider both communication frequency and total data volume, are also discussed. To conduct a comprehensive review of all the above-mentioned ETMs, several databases, including IEEE Xplore, Elsevier, and MDPI, were searched using the main keywords in this field, such as event-triggered, self-triggered, and edge-based ETMs, in conjunction with DC microgrids. This facilitated an in-depth analysis of such control schemes, including their strengths and weaknesses, providing readers with a strong basis for selecting a proper control scheme suited to their future research. Full article

Objective: Minimally invasive surgery in Hirschsprung disease (HSCR) management was introduced in the mid-1990s. Despite decades of clinical application of various laparoscopic approaches, there remains a paucity of high-powered prospective studies and comprehensive systematic reviews in the literature. This study aimed to systematically review and summarize published techniques and outcomes of laparoscopic- and robotic-assisted surgery in HSCR. Methods: A systematic literature review was conducted using PubMed and the Cochrane Library. Studies reporting technical and outcome data of laparoscopic- or robotic-assisted surgery for HSCR were included. Data extraction and analysis were performed in accordance with the PRISMA 2020 guidelines. Parameters of interest included surgical technique, age at primary pull-through (PT), operative time, and functional outcomes. Outcomes of laparoscopic- versus robotic-assisted Yancey–Soave PT were compared. Results: A total of 700 publications were screened, of which seven studies met the inclusion criteria. Data from 556 patients were analyzed. A total of 338 underwent laparoscopic-assisted, and 218 underwent robotic-assisted pull-through. Large variability of the reported transanal resection technique (modified Yancey–Soave PT) was reported. Four studies reported functional outcomes in patients aged over four years. Three studies directly compared laparoscopic- and robotic-assisted PT; two reported no difference in the incidence of postoperative Hirschsprung-associated enterocolitis (HAEC). Functional outcomes were assessed using the Krickenbeck classification in three studies and the bowel function score in one study, with no significant differences reported in patients aged >4 years. Conclusions: Laparoscopic- and robotic-assisted Yancey–Soave PT appears to be safe for HSCR. Large variability in the applied surgical technique—despite being commonly classified as modified Yancey–Soave PT—as well as heterogeneity in the bowel function assessment, limit direct comparability between studies. To date, no single minimally invasive approach has demonstrated clear superiority over others. Prospective, randomized controlled studies are required to enable robust comparative evaluation of techniques, overall costs, and outcomes. Full article

This paper presents a time-domain analysis of an N-bit pulse-width-modulated digital-to-analog converter (PWM DAC) employing a first-order passive resistor-capacitor (RC) filter. Exact analytical expressions are derived for the output settling time in both rising and decreasing digital-code transition modes. The worst-case condition is identified, and the settling-time criterion is expressed as a function of the DAC resolution N, tolerance ε, and normalized filter parameter k = T/RC. The derived criterion is compared with a commonly used first-order RC settling approximation. For N = 8 and ε = 1/4, the proposed worst-case criterion gives a discrete settling interval of 823 PWM periods, whereas the literature-based estimate gives 355 periods. The analytical results are confirmed by numerical evaluation and LTspice transient simulations and are further supported by experimental measurements obtained using a microcontroller-based PWM generator and a passive RC filter. The results confirm the duty-cycle dependence of the steady-state ripple and demonstrate that the proposed criterion provides a conservative design rule for selecting PWM DAC parameters while balancing accuracy, ripple, and settling speed. Full article

This study investigates the readiness of future teachers in Romania to meet the requirements of inclusive education, with a specific focus on curricular deficits and student teachers’ perceptions of competence. Respecting the right to education for students with Special Educational Needs (SEN) is a central policy commitment. Yet, the capacity of initial teacher education (ITE) programs to operationalize this mandate remains uncertain. Using a convergent parallel mixed-methods design, the research combines a systematic documentary analysis of national regulations and psycho-pedagogical curricula (Orders No. 4139/2022 and 4524/2020; Law No. 199/2023) with a survey of 327 student teachers across eight universities. Systematic Content Analysis, based on a three-level depth protocol, reveals a structural curricular deficit: Level 1 outcomes (theoretical awareness of SEN and inclusion) appear in approximately 40% of compulsory subjects, whereas Level 2 outcomes (operational competence, such as designing adapted lessons or differentiated assessments) are almost completely absent from the mandatory core and are confined to electives. Survey results mirror this gap: although 81% of respondents anticipate working with pupils with SEN, 29.9% feel poorly or very poorly prepared, 25.5% report a lack of basic knowledge of SEN, and only 14.6% report high confidence in designing adapted activities. Analysis of Covariance (ANCOVA) shows that training level has a statistically significant but small effect on technical inclusive skills (p = 0.043; η²_p = 0.013), while inclusive attitudes are mainly associated with age. The study concludes with a roadmap for reforming ITE through mandatory SEN-focused practicum placements and transversal integration of inclusive pedagogy. Full article

As power systems continue to expand and grow in complexity, power flow calculation remains a fundamental task in power system analysis and operation. Conventional methods rely on iterative solvers and detailed grid models, yet are often hindered by non-convergence and unreliable modeling assumptions. To address these limitations, this paper introduces a deep learning-based approach that integrates graph neural networks (GNNs) and recurrent neural networks (RNNs) for power flow calculation. The proposed model captures spatial dependencies through graph convolutional layers and temporal dynamics through recurrent layers, enabling accurate prediction of node voltage magnitudes, phase angles, and branch power flows. To enhance transparency, SHAP (Shapley Additive exPlanations)-based feature attribution and multi-modal visualizations are employed to interpret the model’s predictions. Experimental results on the IEEE 9-bus, 39-bus, and 118-bus systems demonstrate prediction errors within 4% and a computational speedup of approximately 40-fold over traditional Newton–Raphson methods. Beyond technical performance, these results suggest that the proposed method can support more efficient and reliable grid operation, thereby contributing to the integration of renewable energy, enhancement of grid resilience, and advancement of sustainable energy systems. Full article

When the magnetic field of a magnetic levitation bearing is lost, the cage-less ball bearing acts as a backup bearing to support the falling spindle. To ensure uniform distribution of the rolling elements in the cage-less ball bearing, researchers have designed local functional grooves on the outer ring raceway. However, the periodic motion of the rolling elements causes damage to these grooves, leading to discrete failure of the rolling elements and resulting in vibration during bearing operation. Therefore, this paper investigates the dynamic characteristics of the rolling elements and the factors influencing bearing vibration following damage to the local functional grooves in caged ball bearings. A vibration model for bearings with damaged functional grooves is established, and the research is conducted through theoretical analysis, numerical simulation, and experimental validation. Full article

This narrative review examines how big data analytics supports geospatial decision-making in smart cities through the combined roles of spatial data foundations, GeoAI methods, and urban digital twins. Methodologically, the article follows a structured narrative and critical review design rather than a PRISMA-based systematic review, bibliometric analysis, or meta-analysis. The paper responds to fragmentation across GIScience, smart-city studies, urban analytics, geospatial data engineering, and digital twin research, where related contributions often remain technically rich but weakly integrated from a decision-oriented perspective. Rather than treating geospatial decision-making as an extension of GIS or as a general expression of data-driven governance, the review frames it as a layered socio-technical process through which heterogeneous urban data are transformed into decision-relevant knowledge. The analysis first clarifies the conceptual evolution from GIS to spatial decision support and urban governance, and then examines the spatial data sources, integration problems, and representational limits that shape smart-city evidence. It also reviews GeoAI and geospatial analytics methods, including spatial statistics, machine learning, spatiotemporal forecasting, graph-based modeling, optimization, and explainable GeoAI. Urban digital twins are then analyzed as decision infrastructures that connect sensing, data integration, synchronization, semantic modeling, simulation, visualization, user interaction, and feedback into planning or operations. The review further maps these capabilities across mobility, land use, utilities, risk management, environmental resilience, public health, and cross-domain decision contexts. Overall, the paper argues that the value of smart-city geoinformation systems depends not on data abundance or model sophistication alone, but on their capacity to support interpretable, accountable, and context-sensitive urban decisions. Full article

Infrared cameras used in radio telescopes often suffer image degradation in complex optical and thermal environments. Solar radiation, convergent reflected light, and thermal emission from support structures can substantially impair imaging performance. To address this problem, this paper proposes a split reflective ellipsoidal baffle for suppressing infrared imaging degradation. Unlike conventional baffles, which mainly rely on structural occlusion and surface absorption, the proposed design functions as an upstream stray light regulation unit. It also establishes a computational framework integrating ellipsoidal vane geometry, realistic edge microtopography modelling, ray-tracing simulation, and detector plane irradiance response analysis. First, the reflective properties of the ellipsoidal surface are used to construct an off-axis stray light propagation constraint model. Under this model, incident stray radiation is redirected away from the effective imaging path or guided into light-trapping regions between adjacent vanes. Second, a laser confocal microscope is used to capture the true three-dimensional edge morphology of vanes with different materials and machining angles. This strategy addresses the limitations of the conventional 0.02 mm rounded edge approximation, which cannot accurately represent real scattering behaviour. The measured morphologies are then converted into high-fidelity computational models compatible with ray-tracing analysis. Furthermore, stray light suppression performance is evaluated using point source transmittance, detector plane irradiance distribution, and grey scale response in experimental images. Simulation and darkroom experiments show that the proposed baffle suppresses residual stray light more effectively than conventional absorptive baffles. The results demonstrate a computable, manufacturable, and experimentally verifiable strategy for front-end stray light control and baffle optimisation. This strategy can also support image quality enhancement in infrared imaging systems operating under complex optical and thermal environments. Full article