Search Results (1,007)

Addressing the difficulty of intuitively identifying and effectively correcting induced heave error in multibeam measurements, this paper proposes a two-stage methodology comprising error identification and correction. This scheme includes an error discrimination method based on regression diagnostics and an error correction method based on Partial Least Squares Regression (PLSR). By establishing a mathematical model between bathymetric discrepancies and attitude parameters, statistical diagnosis and effective identification of the error are achieved. To further mitigate the impact of induced heave error on bathymetric data, an elimination model based on PLSR is developed, enabling high-precision prediction and compensation of the induced heave error. Validation using field survey data demonstrates that this method can effectively estimate the installation offset parameters of the attitude sensor. After correction, the root mean square of bathymetric discrepancies between adjacent survey lines is reduced by approximately 78.8%, periodic stripe-shaped distortions along the track direction are essentially eliminated, and the quality of terrain mosaicking is significantly improved. This provides an effective solution for controlling induced heave error under complex topographic conditions. Full article

Sustainable Urban Drainage Systems (SUDSs) are essential for stormwater management in urban areas, with varying hydrological, social, ecological, and economic benefits. Nevertheless, choosing the SUDS most appropriate for public spaces poses a challenge when balancing details/specifications against community decisions, primarily social implications and perceptions. Building on the SUDS design pillar of the amenity, this study outlines a three-phase methodological framework for selecting SUDS based on social facilitation. The first phase introduces the application of the Partial Least Squares Structural Equation Modeling (PLS-SEM) and Classificatory Expectation–Maximization (CEM) techniques by modeling complex social interdependencies to find critical components related to urban planning. A Likert scale survey was also conducted with 440 urban dwellers in Tunja (Colombia), which identified three dimensions: Residential Satisfaction (RS), Resilience and Adaptation to Climate Change (RACC), and Community Participation (CP). In the second phase, the factors identified above were transformed into eight operational criteria, which were weighted using the Analytic Hierarchy Process (AHP) with the collaboration of 35 international experts in SUDS planning and implementation. In the third phase, these weighted criteria were used to evaluate and classify 13 types of SUDSs based on the experts’ assessments of their sub-criteria. The results deliver a clear message: cities must concentrate on solutions that will guarantee that water is managed to the best of their ability, not just safely, and that also enhance climate resilience, energy efficiency, and the ways in which public space is used. Among those options considered, infiltration ponds, green roofs, rain gardens, wetlands, and the like were the best-performing options, providing real and concrete uses in promoting a more resilient and sustainable urban water system. The methodology was also used in a real case in Tunja, Colombia. In its results, this approach proved not only pragmatic but also useful for all concerned, showing that the socio-cultural dimensions can be truly integrated into planning SUDSs and ensuring success. Full article

This study examines the longitudinal shortening of clay blanks during extrusion and introduces a hybrid soft sensor framework for early prediction of ceramic roof tile performance. Targeted properties include shrinkage, water absorption, and saturation. The models integrate real-time process data collected after vacuum extrusion and pressing with clay-specific descriptors such as carbonate content and granulometry, alongside additional variables including moisture, firing temperature, and length reduction. Partial Least Squares (PLS) regression was adopted as the core method due to robustness against multicollinearity and ease of industrial integration. In contrast to complex machine learning pipelines, PLS-based soft sensors enable lightweight edge deployment without reliance on IoT infrastructure. Complementary regression and machine learning models were used to benchmark predictive accuracy and explore nonlinear effects. The results confirm reliable prediction of key performance indicators and reveal mechanistic links between extrusion-induced deformation and downstream behavior. Although developed for clay systems, the framework is generalizable and can be adapted to other traditional ceramic processes or industries seeking interpretable, locally deployable solutions for process control. Full article

Although green housing is widely regarded as an effective solution to energy and environmental challenges, its actual rate of adoption remains lower than expected. In the context of increasingly prominent sustainable development goals, promoting residents’ adoption of green housing has become a key issue in advancing sustainable transformation within the housing sector. Consequently, enhancing residents’ willingness to adopt green housing is critical to its broader diffusion. Drawing on diffusion of innovation theory, attitude theory, and perceived value theory, this study develops a multidimensional integrated model to identify factors influencing the adoption of green housing. The model examines how the innovation attributes of green housing and residents’ psychological evaluations jointly shape adoption intention. A questionnaire survey was conducted among 387 residents in Nanjing, China, and the data were analysed using partial least squares modelling. The results indicate that the five attributes derived from diffusion of innovation theory are significant antecedents of residents’ attitudes. Relative advantage, compatibility, trialability, and observability exert significant positive effects on residents’ attitudes toward adopting green housing, with relative advantage emerging as the most influential factor. Complexity has a negative, though comparatively weaker, effect on residents’ attitudes toward green housing adoption. Residents’ attitudes and perceived value are identified as significant predictors of green housing adoption intention. These findings contribute to a clearer understanding of residents’ green housing adoption intentions for both researchers and practitioners. More importantly, the study offers general policy and managerial implications for governments and developers seeking to enhance the uptake of green housing. Full article

Autonomous experimental platforms increasingly rely on robust, vision-guided robotic manipulation to support reliable and repeatable laboratory operations. This work presents a modular motion-execution subsystem designed for integration into self-driving laboratory (SDL) workflows, focusing on the coupling of real-time visual perception with smooth and stable manipulator control. The framework enables autonomous detection, tracking, and interaction with textured objects through a hybrid scheme that couples advanced motion planning algorithms with real-time visual feedback. Kinematic analysis of the manipulator is performed using the screw theory formulations, which provide a rigorous foundation for deriving forward kinematics and the space Jacobian. These formulations are further employed to compute inverse kinematic solutions via the Damped Least Squares (DLS) method, ensuring stable and continuous joint trajectories even in the presence of redundancy and singularities. Motion trajectories toward target objects are generated using the RRT* algorithm, offering optimal path planning under dynamic constraints. Object pose estimation is achieved through a a vision workflow integrating feature-driven detection and homography-guided depth analysis, enabling adaptive tracking and dynamic grasping of textured objects. The manipulator’s performance is quantitatively evaluated using smoothness metrics, RMSE pose errors, and joint motion profiles, including velocity continuity, acceleration, jerk, and snap. Simulation results demonstrate that the proposed subsystem delivers stable, smooth, and reproducible motion execution, establishing a validated baseline for the manipulation layer of next-generation SDL architectures. Full article

This paper proposes an efficient and accurate numerical framework for solving fractional Bagley–Torvik equations, which model viscoelastic and memory-dependent dynamic systems. The method combines the Hermite polynomial approximation with a least-squares optimization scheme to achieve high-accuracy solutions. By leveraging the analytical properties of Hermite polynomials, Caputo fractional derivatives were computed efficiently, avoiding the complexities of direct fractional differentiation. The resulting weighted least-squares formulation transforms the problem into a stable algebraic system. Numerical results confirm the method’s superior accuracy, rapid convergence, and robustness compared with existing techniques, demonstrating its potential for broader applications in fractional-order boundary value problems. Full article

Managing risk in drifting complex systems is hindered by the weak integration of unstructured incident narratives into quantitative, decision-ready models. We present a phenomena-centric semantic factor framework that closes the data–model–decision gap by transforming free-text incident reports into transparent, traceable drivers of risk and actionable interventions. The pipeline normalizes and encodes narratives, extracts domain-invariant phenomena, couples them to risk outcomes through calibrated partial least squares factors, and applies scenario optimization to recommend portfolios of measures aligned with EAM/CMMS taxonomies. Applied to a large corpus of incident notifications, the method yields stable, interpretable phenomena, improves out-of-sample risk estimation against strong text-only baselines, and delivers prescriptive recommendations whose composition and cost–risk trade-offs remain robust under concept drift. Sensitivity and ablation analyses identify semantic factorization and PLS coupling as the principal contributors to performance and explainability. The resulting end-to-end process is traceable—from tokens through phenomena and factors to actions—supporting auditability and operational adoption in critical infrastructure. Overall, the study demonstrates that phenomenological semantic factorization combined with scenario optimization provides an effective and transferable solution for integrating incident text into the proactive risk management of complex, drifting systems. Full article

Small and medium enterprises (SMEs) represent a fundamental pillar of economic development in Eastern Europe. Yet, they frequently encounter significant obstacles in accessing financing, stemming from informational asymmetries, elevated risks, the absence of collateral, and adverse regulatory environments. This research examines the primary determinants of adopting blockchain-based supply chain financing platforms, an alternative financing solution that streamlines processes, reduces costs, and enhances transparency and security. The study develops and validates an innovative conceptual model grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to a sample of 200 respondents across seven Eastern European countries, and the model’s hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The research findings demonstrate that supply chain partner readiness constitutes the most influential factor affecting behavioral intention to use blockchain-based supply chain financing platforms. Additionally, performance expectancy, effort expectancy, and perceived trust were identified as significant positive determinants. Furthermore, the study highlights blockchain readiness as a crucial factor influencing actual usage behavior. These findings provide valuable insights and contribute to advancing knowledge through the utilization of an extended UTAUT framework and validation of obtained results through comparison with other relevant studies in the field. Full article

The extensive use of sulfathiazole in poultry farming has raised growing concerns regarding its residues in poultry-derived products, posing risks to human health and food safety. To overcome the limitations of conventional detection methods and address the analytical challenges posed by inherent complexity of chicken blood matrix for the detection of sulfathiazole residues in chicken blood, a rapid and sensitive surface-enhanced Raman spectroscopy (SERS) method was developed for detecting sulfathiazole residues in chicken blood. Four colloidal substrates, i.e., gold colloid A, gold colloid B, gold colloid C, and silver colloids, were synthesized and evaluated for their SERS enhancement capabilities. Key parameters, including electrolyte type (NaCl solution), colloidal substrate type (gold colloid A), volume of gold colloid A (550 μL), volume of NaCl solution (60 μL), and adsorption time (14 min), were systematically optimized to maximize SERS intensities at 1157 cm⁻¹. Furthermore, a genetic algorithm-support vector regression (GA-SVR) model integrated with adaptive iteratively reweighted penalized least squares (air-PLS) and multiplicative scatter correction (MSC) preprocessing demonstrated superior predictive performance with a prediction set coefficient of determination (R²_p) value of 0.9278 and a root mean square error of prediction (RMSEP) of 3.1552. The proposed method demonstrated high specificity, minimal matrix interference, and robustness, making it suitable for reliable detection of sulfathiazole residues in chicken blood and compliant with global food safety requirements. Full article

To address clock drift arising from the absence of GPS synchronization during ocean-bottom seismic observations, we propose a time-offset correction and quality-control scheme that uses the correlation of P-wave empirical Green’s functions (EGFs) as the metric, and we demonstrate its efficacy in mitigating cross-correlation asymmetry caused by azimuthal noise in shallow-water environments. The method unifies the time delays of the four components into a single objective function, estimates per-node offsets via sparse weighted least squares with component-specific weights, applies spatial second-difference smoothing to suppress high-frequency oscillations, and performs spatiotemporally constrained regularized iterative optimization initialized by the previous day’s inversion to achieve a robust solution. Tests on a real four-component ocean-bottom node (4C-OBN) hydrocarbon exploration dataset show that, after conventional linear clock-drift correction of the OBN system, the proposed method can effectively detect millisecond-scale time jumps on individual nodes; compared with traditional noise cross-correlation time-shift calibration based on surface-wave symmetry, our four-component fusion approach achieves superior robustness and accuracy. The results demonstrate a marked increase in the coherence of the four-component cross-correlations after correction, providing a reliable temporal reference for subsequent multicomponent seismic processing and quality control. Full article

In lifetime testing, reliably assessing the life performance index of the Weibull distribution under Type I interval-censored data is a critical task. Although maximum likelihood estimation (MLE) is a conventional approach for parameter estimation, closed-form solutions are unavailable for this data type. To address this limitation, four least-squares estimation methods based on data transformation are developed. The proposed estimations can provide closed-form solutions for the Weibull distribution and life performance index. The asymptotic unbiasedness and normality of the proposed estimators are rigorously established. Their effectiveness is further supported by simulation studies. Moreover, the practical relevance of the methods is illustrated with two real-data applications. Full article

The solvability conditions, symmetric solutions, and antisymmetric solutions of matrix equations $AX=B$ are important research topics in matrix theory. As a higher-order generalization of matrices, tensors have made the research on solving tensor equations a hot topic in recent years. This paper focuses on the representation, properties, and computational methods of tensor generalized inverses under the C-product, and systematically explores their applications in solving the tensor equation $\mathcal{A}{\ast }_c\mathcal{X}=\mathcal{B}$. Firstly, the definition, existence conditions, analytical expressions, and computational algorithms of tensor generalized inverses under the C-product are discussed. By applying tensor generalized inverses under the C-product, the solvability conditions of tensor equation $\mathcal{A}{\ast }_c\mathcal{X}=\mathcal{B}$$\mathcal{A}{\ast }_c$ are derived. The minimum norm solution method for consistent equation $AX=B$ and the minimal norm least squares solution inconsistent equation $\mathcal{A}{\ast }_c\mathcal{X}=\mathcal{B}$ are presented, respectively. Finally, numerical experiments were provided to verify the correctness of the theoretical analysis and algorithm implementation through numerical experiments, demonstrating the effectiveness of solving tensor equations under the C-product. Full article

This study examines how consumers’ perceptions of threat severity and anxiety during public health crises influence their motivation to protect themselves and, subsequently, their intentions to purchase protection insurance products. Drawing on Protection Motivation Theory (PMT), we develop an integrated framework that links cognitive risk assessments and emotional responses to financial protection decisions. Using survey data collected from 437 respondents in Taiwan during the COVID-19 pandemic, the research model is tested through partial least squares structural equation modeling (PLS-SEM). The empirical results indicate that both perceived severity and anxiety significantly enhance protection motivation, with perceived severity exerting a stronger effect. These two antecedents also directly strengthen consumers’ intentions to purchase protection insurance. Furthermore, protection motivation partially mediates the effects of perceived severity and anxiety on purchase intention. These findings extend the application of PMT to the financial and insurance domains by demonstrating how cognitive and affective factors jointly shape demand for protection insurance in high-risk environments. The practical implications of these results for insurers include risk communication strategies, product positioning, and the development of crisis-responsive insurance solutions. Full article

Accurate, scalable, and outlier-robust state estimation (SE) is critical for large AC power systems with mixed SCADA and PMU measurements. This paper proposes D-BSE-L1, a distributed robust state estimator for the bilinear AC model. The method combines the bilinear state estimation framework with a convex weighted least absolute value (WLAV) loss so that all area subproblems become convex linear or quadratic programs coordinated by ADMM, and a cache-enabled Cholesky factorization is used to accelerate the third-stage linear solves. Simulations on the IEEE 14-, 118-, and 1062-bus systems show that D-BSE-L1 achieves estimation accuracy comparable to its centralized bilinear counterpart. Under severe bad-data conditions, its advantage over weighted least squares with the largest normalized residual test (WLS + LNRT) is pronounced: with 10% 1.5× bad data, the voltage magnitude and angle MAEs are about 62% and 54% of those of WLS + LNRT, and with 5% 5× bad data, they further drop to roughly 43% and 51%, while requiring only about one-tenth of the CPU time. On the 1062-bus system, D-BSE-L1 maintains the MAE of the centralized estimator but reduces runtime from 2.46 s to 0.72 s, providing a scalable, hyperparameter-free, and robust solution for partitioned state estimation in large-scale power grids. Full article

The accurate and rapid determination of olefin content in gasoline is crucial for fuel quality control. While near-infrared spectroscopy (NIR) offers a rapid analytical solution, multiple parameters in the conventional partial least squares regression (PLSR) modeling process rely on the modeler’s subjective judgment. Consequently, the quantitative accuracy of the model is often influenced by the modeler’s experience. To address this limitation, this study developed an integrated adaptive PLSR framework. The methodology incorporates four core adaptive components: automated selection of latent variables based on the rate of decrease in PRESS values, dynamic formation of calibration subsets using Spectral Angle Distance and sample number thresholds, optimization of informative wavelength regions via correlation coefficients, and systematic database cleaning through iterative residual analysis. Applied to 248 gasoline samples, this strategy dramatically enhanced model performance, increasing the coefficient of determination (R²) from 0.7391 to 0.9102 and reducing the root mean square error (RMSE) from 1.51% to 0.866% compared to the global PLSR model. This work demonstrates that the adaptive PLSR framework effectively mitigates spectral nonlinearity and improves predictive robustness, thereby providing a reliable and practical solution for the on-site, rapid monitoring of gasoline quality using handheld NIR spectrometers. Full article