Search Results (217)

To mitigate the position divergence resulting from attitude error amplification in conventional fully coupled architectures, this study proposes a decoupled navigation architecture based on the Versatile Quaternion-based Filter (VQF). This architecture removes attitude estimation from the state vector, forming a two-layer structure comprising an independent attitude module and a navigation filter. The VQF is integrated as a standalone attitude module via a standardized interface. An uncertainty quantification model is developed by extracting the VQF’s internal correction states, which maps deviations among intermediate quaternion values to a measurable uncertainty metric. To compensate for the loss of cross-covariance induced by decoupling, a dual-layer compensation mechanism is introduced: a base layer adjusts the overall uncertainty using innovation statistics, while a compensation layer explicitly propagates attitude uncertainty through parameterized noise matrices. Experimental results demonstrate that the proposed method achieves notable improvements in positioning accuracy and significantly suppresses extreme errors in high-curvature scenarios. The approach is particularly effective for high-curvature, high-dynamic applications where process noise modeling is inherently difficult. Compared to traditional fully coupled architectures, the decoupled architecture offers enhanced robustness. The complementary characteristics identified between the two architectures provide valuable insights for expanding the operational envelope of underwater navigation systems. Full article

Accurate mapping of localization error distribution is essential for assessing passive sensor systems and guiding sensor placement. However, conventional analytical methods like the Geometrical Dilution of Precision (GDOP) rely on idealized error models, failing to capture the complex, heterogeneous error distributions typical of real-world environments. To overcome this challenge, we propose a novel data-driven framework that reconstructs high-fidelity localization error maps from sparse observations in TDOA-based systems. Specifically, we model the error distribution as a tensor and formulate the reconstruction as a tensor completion problem. A key innovation is our physics-informed regularization strategy, which incorporates prior knowledge from the analytical error covariance matrix into the tensor factorization process. This allows for robust recovery of the complete error map even from highly incomplete data. Experiments on a real-world dataset validate the superiority of our approach, showing an accuracy improvement of at least 27.96% over state-of-the-art methods. Full article

The primary objective of this research is to develop a probabilistic nonlinear model predictive control structure (NMPC) that efficiently operates uncrewed surface vehicles (USVs) in an environment that has probabilistic disturbances, such as wind, waves, and currents of the water, while simultaneously maneuvering the vehicle in a way that avoids stationary or moving stochastic obstacles in its path. The proposed controller structure considers the mean and covariances of the inputs or state variables of the vehicle in the cost function to handle probabilistic disturbances, where an extended Kalman filter (EKF) is utilized to calculate the mean, and the covariances are calculated dynamically via a linear matrix equality based on this mean and obtained system matrices with successive linearization for every sampling instance. The proposed control structure deals with non-zero-mean probabilistic disturbances such as water current via an innovative approach that treats the mean of the disturbance as a deterministic part, which is estimated by a disturbance observer and eliminated by a control term in the controller in addition to the control signal obtained via MPC optimization; the effect of the remaining zero-mean part is handled over its covariance during the probabilistic MPC optimization. The probabilistic constraints are also dealt with by converting them to deterministic constraints, as in linear probabilistic MPC. However, unlike the linear MPC, these constraints updated each sampling instance with the information obtained via successive linearization. The control structure incorporates the velocity obstacle (VO) method for collision avoidance. In order to ensure stability, the proposed NMPC adopts a dual-mode strategy, and a stability analysis is presented. In the second mode, an LQG design that ensures stability in the existence of non-zero mean disturbance is also provided. The simulation results demonstrate that the proposed probabilistic NMPC framework effectively handles probabilistic disturbances as well as both stationary and moving obstacles, ensuring collision avoidance while reaching the desired position and orientation through optimal path tracking, outperforming the conventional NMPC. Full article

To address the challenges to driving stability caused by large-curvature steering of wire-controlled mining vehicles in narrow tunnels, a fused four-wheel steering (4WS) control strategy based on real-time estimation of vehicle state parameters is proposed. A comprehensive longitudinal–lateral–yaw dynamics model for 4WS is established, and a comparative study is conducted on three control methods: proportional feedforward control, yaw rate feedback control, and fused control. Expressions for steady-state yaw rate gain under different control modes are derived, and the stability differences in 4WS characteristics among these strategies are thoroughly analyzed. To overcome the difficulty in directly acquiring state information for chassis steering control, a vehicle state parameter estimator based on the unscented Kalman filter (UKF) is designed. To enhance the robustness to noise and computational real-time performance of vehicle state estimation in complex environments, a method for real-time estimation of noise covariance matrices using innovative sequences is adopted, improving the estimation accuracy of the algorithm. To validate the effectiveness of the control strategies, a co-simulation platform integrating Carsim and Matlab/Simulink is developed to simulate the performance of the three 4WS control methods under step steering and sinusoidal steering input conditions. The results show that, under low-speed conditions, 4WS strategies increase the yaw rate by approximately 50% and reduce the turning radius by over 45%, significantly enhancing steering maneuverability. Under medium-high speed conditions, 4WS strategies decrease the yaw rate by up to 68% and increase the turning radius by 17–29%, effectively suppressing oversteering tendencies to comprehensively improve stability, with the integrated control strategy demonstrating the best performance. Under both test conditions, the fused feedforward and feedback control strategy reduces the steady-state yaw rate by approximately 12.7% and 48.7%, respectively, compared to other control strategies, demonstrating superior stability. Full article

This study examines the impact of Green Entrepreneurial Orientation (GEO), Managerial Environmental Concerns (MECs), and Green Absorptive Capacity (GAC) on the environmental performance of Pakistani SMEs. The Dynamic Capabilities View (DCV) and Natural Resource-Based View (NRBV) demonstrate that innovation focused on sustainability and competitive advantage is enhanced by managerial engagement, knowledge capability, and environmental awareness. A cross-sectional survey involving 350 managers of SMEs in Pakistan utilized covariance-based structural equation modeling (CB-SEM). The MEC-to-GEO direction was insignificant, and this implies that the issue of managerial concern is not a driving force towards the initiation of green entrepreneurial endeavors. The confirmatory factor analysis model for the 350 SMEs demonstrates a satisfactory fit (CFI = 0.947; RMSEA = 0.073), along with reliability and validity. GEO and EP are positively influenced by GAC and MECs, with R² values of 0.204 and 0.526, respectively. The findings indicate that the absorptive and managerial capabilities of SMEs can integrate environmental responsibility into strategic decision-making, exceeding regulatory criteria to foster proactive environmental innovation. The study emphasizes ethical leadership, environmental competitiveness, and social responsibility through green information management and cooperative networks. The sustainability ideas and GEO are enhanced in developing nations by linking global green initiatives with local institutions and cultural contexts. Organizational management and policymakers should promote environmental education, ecological innovations, and sustainable practices within sectors. The limitations of the study include the use of self-reported data and cross-sectoral replication utilizing objective environmental indicators. Full article

Effectively managing multi-source heterogeneous data remains a critical challenge in distributed cyber-physical systems (CPS). To address this, we present a novel and edge-centric computing framework integrating four key technological innovations. Firstly, a hybrid OPC communication stack seamlessly combines Client/Server, Publish/Subscribe, and P2P paradigms, enabling scalable interoperability across devices, edge nodes, and the cloud. Secondly, an event-triggered adaptive Kalman filter is introduced; it incorporates online noise-covariance estimation and multi-threshold triggering mechanisms. This approach significantly reduces state-estimation error by 46.7% and computational load by 41% compared to conventional fixed-rate sampling. Thirdly, temporal asynchrony among edge sensors is resolved by a Dynamic Time Warping (DTW)-based data-fusion module, which employs optimization constrained by Mahalanobis distance. Ultimately, a content-aware deterministic message queue data distribution mechanism is designed to ensure an end-to-end latency of less than 10 ms for critical control commands. This mechanism, which utilizes a “rules first” scheduling strategy and a dynamic resource allocation mechanism, guarantees low latency for key instructions even under the response loads of multiple data messages. The core contribution of this study is the proposal and empirical validation of an architecture co-design methodology aimed at ultra-high-performance industrial systems. This approach moves beyond the conventional paradigm of independently optimizing individual components, and instead prioritizes system-level synergy as the foundation for performance enhancement. Experimental evaluations were conducted under industrial-grade workloads, which involve over 100 heterogeneous data sources. These evaluations reveal that systems designed with this methodology can simultaneously achieve millimeter-level accuracy in field data acquisition and millisecond-level latency in the execution of critical control commands. These results highlight a promising pathway toward the development of real-time intelligent systems capable of meeting the stringent demands of next-generation industrial applications, and demonstrate immediate applicability in smart manufacturing domains. Full article

The impact of the COVID-19 pandemic on the number of international tourist arrivals in the Republic of South Africa (RSA) is studied in this paper using the seasonal autoregressive integrated moving average (SARIMA) model comprising a pulse function covariate vector evaluated via trial and error as an exogenous variable (SARIMAX). This paper provides a methodological innovation that combines outlier detection with intervention quantification so that tourism academics and practitioners can correctly capture estimated economic losses caused by the COVID-19 pandemic and the response to it. In the pre-intervention modelling, four additive outliers and innovative outliers were detected and incorporated into the SARIMAX$(1,1,1)({0,1,2)}_{12}$ model, which significantly lowered the model’s evaluation metrics, making it the best fitting pre-intervention model. Next, from March 2020 to June 2025 (end of dataset), it is shown that the estimated total losses amount to 7,328,919 tourists compared to if there been no pandemic. This means that the number of tourist arrivals in the RSA has not yet returned to the pre-COVID-19 forecasted path as of June 2025, indicating that the COVID-19 pandemic continues to have long-term negative effects on the RSA’s number of tourist arrivals. Therefore, more efforts must be focused on developing innovative and advanced statistical models to assist the RSA government and private entities in creating incentives for investment, planning more effectively, providing societies reliant on tourism with more resources, and creating suitable regulations that boost the economy through the tourism sector. Full article

Accurate estimation of flow discharge, Q, through hydraulic structures such as spillways and gates is of great importance in water resources engineering. Each hydraulic structure, due to its unique characteristics, requires a specific and comprehensive study. In this regard, the present study innovatively focuses on predicting Q through Rectangular Top-Hinged Gates (RTHGs) using advanced Gradient Boosting (GB) models. The GB models evaluated in this study include Categorical Boosting (CatBoost), Histogram-based Gradient Boosting (HistGBoost), Light Gradient Boosting Machine (LightGBoost), Natural Gradient Boosting (NGBoost), and Extreme Gradient Boosting (XGBoost). One of the essential factors in developing artificial intelligence models is the accurate and proper tuning of their hyperparameters. Therefore, four powerful metaheuristic algorithms—Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Sparrow Search Algorithm (SSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA)—were evaluated and compared for hyperparameter tuning, using LightGBoost as the baseline model. An assessment of error metrics, convergence speed, stability, and computational cost revealed that SSA achieved the best performance for the hyperparameter optimization of GB models. Consequently, hybrid models combining GB algorithms with SSA were developed to predict Q through RTHGs. Random split was used to divide the dataset into two sets, with 70% for training and 30% for testing. Prediction uncertainty was quantified via Confidence Intervals (CI) and the R-Factor index. CatBoost-SSA produced the most accurate prediction performance among the models (R² = 0.999 training, 0.984 testing), and NGBoost-SSA provided the lowest uncertainty (CI = 0.616, R-Factor = 3.596). The SHapley Additive exPlanations (SHAP) method identified h/B (upstream water depth to channel width ratio) and channel slope, S, as the most influential predictors. Overall, this study confirms the effectiveness of SSA-optimized boosting models for reliable and interpretable hydraulic modeling, offering a robust tool for the design and operation of gated flow control systems. Full article

In the United States, although the gaps in health insurance coverage by sexual orientation have been closing since the implementation of the Affordable Care Act and legalization of same-sex marriage, the LGB group (i.e., lesbian, gay, bisexual) continues to report healthcare utilization and access inequities such as more delayed or unmet care. The extant research has often examined healthcare utilization and access inequities due to affordability (e.g., out-of-pocket costs). However, healthcare utilization and access inequities are only partially explained by cost reasons; there are non-cost reasons that have not been adequately empirically examined. The present study innovatively includes discrimination structural variables to understand how social structure is associated with healthcare utilization and access inequities of LGB people. It focuses on two routine health services—regular check-ups and prescription medications—for LGB people who have chronic diseases. Additionally, sexual orientation may intersect with sex assigned at birth (sex, hereafter, i.e., male, female) to impact healthcare utilization and access inequities. The current study applies quantitative intersectional analysis to understand healthcare utilization and access inequities from a sexual orientation and sex intersectional lens and for easier and clearer interpretations of intersectional results and more actionable policy implications for inter-categorical groups. Using the 2023 National Health Interview Survey (weighted N = 136,231,053), we conducted quantitative intersectional analysis for logistic regression using complex survey data. First, we fit a series of logistic regression models with sexual orientation–sex interactions for routine healthcare utilization and access outcomes, adjusting for covariates. Second, we calculated average marginal predictions for inter-categorical groups by interacting sexual orientation and sex and other covariates. Third, we computed risk ratios of average marginal predictions for all the covariates. Lastly, we examined the interaction of inter-categorical groups/sexual orientation and structural variables. Our results show that experiencing a higher level of discrimination is positively associated with underutilization of regular check-ups and lower access to prescription medications, and this effect is stronger for LGB people. Further, LGB women are least likely to utilize regular check-ups and LGB men are least likely to access prescription medications among the inter-categorical groups. Highlighting structural issues of healthcare utilization and access offers new evidence on healthcare utilization and access inequities that can inform policies for raising awareness of and addressing structural issues. The intersectional analyses suggest that relevant policies target LGB women and LGB men. Full article

The design parameters of the sucker rod pumping unit (SRPU) are influenced by multiple factors. Traditional methods based on oil production engineering theories involve numerous simplifications, making it difficult to effectively address the complex realities of oilfields, thereby requiring improvement in the reliability of pumping system design solutions. This paper, based on the massive design schemes and corresponding operational performance data accumulated during the long-term development of oilfields, innovatively proposes an intelligent optimization model combining Random Forest and Covariance Matrix Adaptation Evolution Strategy algorithm (CMA-ES). This model overcomes the shortcomings of insufficient data and incomplete design indicators in the establishment of lifting design models. By standardizing and processing the data from 5000 historical lifting scheme sets, a sample database of SRPU lifting system designs was created, covering dimensions such as well geology, fluid, and production. Based on this, aiming at system efficiency and pump life expectancy, geological development characteristic parameters and lifting design parameters were taken as variables to establish a predictive model for the operation effect of the lifting system. The dataset was divided into 8:1:1 subsets for training, hyperparameter tuning and performance testing. Subsequently, an optimization model was established to jointly optimize the lifting system design parameters. Case studies show that the intelligent optimization method can simultaneously optimize parameters such as pump setting depth, pump diameter, stroke, and frequency, with expected improvements in system efficiency of 6.75% and pump life expectancy of 29%. Full article

Soil organic matter (SOM) is a key indicator of cropland quality and carbon cycling. Accurate SOM mapping is essential for sustainable soil management and carbon sink assessment. This study investigated the effects of interannual climatic variability on SOM prediction using remote sensing and machine learning. Youyi Farm in the Sanjiang Plain, Heilongjiang Province, was selected as the study area, covering three representative years: 2019 (flood), 2020 (normal), and 2021 (drought). Based on multi-temporal Sentinel-2 imagery and environmental covariates, Random Forest models were used to evaluate single- and dual-period combinations. Results showed that combining bare-soil and crop-season images consistently improved accuracy, with optimal combinations varying by year (R² = 0.544–0.609). Incorporating temperature, precipitation, and elevation enhanced model performance, particularly temperature, which contributed most to prediction accuracy. Feature selection further improved model stability and generalization. Spatially, SOM showed a pattern of higher values in the northeast and lower in the central region, shaped by topography and cultivation. This study innovatively integrates interannual climatic variability with remote sensing temporal combination and feature selection, constructing a climate-adaptive SOM mapping framework and providing new insights for accurate inversion of cropland SOM under extreme climates, highlights the importance of multi-temporal imagery, environmental factors, and feature selection for robust SOM mapping under different climatic conditions, providing technical support for long-term cropland quality monitoring. Full article

Efficient fusion of navigation sensor data with different output frequencies and data types is critical for ensuring that vehicle-mounted integrated navigation systems consistently provide stable, reliable navigation solutions in complex dynamic operational environments. To address the degradation of estimation accuracy caused by the noise characteristics mismatch of sensor measurement, an information fusion framework based on federated Kalman filter (FKF) framework is designed by incorporating an improved variational Bayesian-based adaptive Kalman filter (IVBAKF) as the core estimation module of local filters. IVBAKF mitigates the impact of uncertain measurement noise from navigation sensors through effectively estimating the measurement noise covariance matrix (MNCM) by leveraging an adaptive forgetting factor. The adjustment strategy for the forgetting factor employs a predefined mapping function derived from the squared Mahalanobis distance (SMD) of the measurement innovation, which serves as an indicator for detecting anomalies in measurement noise within the FKF, thereby enhancing the tracking capability for the MNCMs. The effectiveness of the proposed algorithm is validated through Monte Carlo simulation-based comparative experiments. The simulation results demonstrate that compared to the FKF-based baseline algorithm with nominal covariance matrices, the proposed algorithm achieves an average reduction of 43.21% in the Root Mean Square Errors (RMSEs) of the estimated navigation parameters in scenarios characterized by uncertain and time-varying measurement noise. Thus, the robustness of the proposed algorithm against complex measurement noise conditions is verified. Full article

This study examines how Zero-Waste Management (ZWM) and Green Innovative Supply Chain Management (GISCM) influence environmental and operational performance through the mediating role of Green Dynamic Capabilities (GDCs). Building on the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), the study develops a model that explains how firms translate green practices into superior performance outcomes. Using survey data from 132 manufacturing firms in Oman and covariance-based structural equation modeling (CB-SEM), the results demonstrate that both ZWM and GISCM significantly enhance sustainable performance, with GISCM exerting the stronger overall effect. Moreover, GDC fully mediates the relationship between ZWM and operational performance and partially mediates the GISCM–performance links. These findings highlight that sustainability-driven outcomes depend on firms’ abilities to sense, seize, and reconfigure resources for continuous environmental innovation. The study advances theory by integrating RBV and DCT within the green operations context and provides practical insights for managers in emerging economies on developing dynamic and learning-based capabilities to achieve sustainable manufacturing competitiveness. Full article

With the widespread use of sustainable building materials and the rise of emotional design, the use of wooden elements in public large-scale architecture has garnered significant attention. In public cultural spaces, especially music halls, although previous research has explored the aesthetic value and functional applications of wood in architecture, the micro-level exploration of how wooden design influences user perception and satisfaction has not been fully addressed. Therefore, this study uses a sample of 965 offline users of wooden music halls and applies Covariance-Based Structural Equation Modeling (CB-SEM) to investigate the pathways through which wooden design perception shapes user satisfaction. The results indicate: (1) Wooden design perception positively influences user satisfaction in wooden music halls; (2) Perceived restorativeness and musical resonance independently mediate the relationship between wooden design perception and satisfaction; (3) Wooden design perception positively influences user satisfaction through the chain mediation effect of perceived restorativeness and musical resonance. This study highlights how wooden design, through visual and tactile design, creates a profound immersive experience and emotional resonance, thereby optimizing the user experience and enhancing satisfaction in music halls. This research fills the gap in emotional and sensory experience studies in the design of wooden architecture in cultural venues, innovatively combining Emotional Design Theory and Immersion Theory, and proposes a new theoretical framework for how wooden design influences user satisfaction through perceived restorativeness and musical resonance, providing a fresh perspective for the field of architectural design. This study also provides theoretical support and actionable recommendations for the design practice of wooden music halls, helping designers better integrate cultural symbolism, perceived restorativeness, and multisensory experiences in space planning, material selection, and overall design. Full article

As urban areas in developing countries, including South Africa, continue to grapple with the adverse challenges of climate change and rapid population growth, there is an increasing call for nature-inspired solutions. This is because nature-based solutions (NbSs) can significantly enhance urban resilience by managing stormwater, reducing flooding and creating livable spaces within urban centers. One such NbS is permeable pavement, which has gained attention for its ability to allow water to infiltrate rather than run off. However, while its use is growing in developed nations, the story is not the same in South Africa, where the literature is silent on its usage and issues of flooding and other associated disasters have persisted. Therefore, this study adopts a post-positivist approach to investigate the application and challenges of permeable pavements as an NbS in South African urban areas. The study reveals a low level of permeable pavement use, albeit an encouraging level of awareness among built environment professionals. Covariance-based structural equation modelling further revealed the significant causes of this poor application. The findings provide valuable insights for policymakers to create incentives and frameworks that promote permeable pavement adoption in urban areas facing environmental challenges. Moreover, this research contributes to the limited literature on NbSs in South Africa, offering a foundation for future studies and addressing the pressing need for innovative solutions to flooding and urban resilience. Full article