Search Results (619)

The objective of this study was to compare subjective manifest refraction with wavefront-based automated refraction using iTrace (ray tracing) and LadarWave (Hartmann–Shack) in eyes implanted with two enhanced monofocal intraocular lenses (IOLs) and a standard aspheric monofocal IOL, emphasizing agreement and refractive variability across optical designs. This retrospective cohort included 84 eyes from 42 patients implanted with Tecnis Eyhance (DIB00), RayOne EMV (RAO200E), or Tecnis ZCB00 IOLs. Postoperative evaluation (1–3 months) included uncorrected and corrected distance visual acuity and subjective manifest refraction, followed by automated refraction with iTrace and LadarWave. Outcomes were sphere, cylinder, and spherical equivalent (SE). Agreement was assessed using mean signed difference, mean absolute error, root mean square error, Bland–Altman limits of agreement, proportions within clinically relevant thresholds, and vector astigmatism (J0, J45). Linear mixed-effect modeling evaluated SE differences across methods and IOL types while accounting for within-subject correlation. Subjective SE differed among IOLs (p = 0.027), with RAO200E more myopic than ZCB00 (−0.20 ± 0.32 D vs. −0.08 ± 0.44 D, p = 0.035). Automated refraction showed greater variability and poorer agreement in enhanced monofocal IOLs, particularly for cylinder and SE, with wider limits of agreement and fewer eyes within ±0.50 D compared with ZCB00. In mixed-effect contrasts (three-method repeated-measures model), iTrace and LadarWave showed a consistent myopic bias versus manifest refraction in DIB00 and RAO200E, whereas in ZCB00 the iTrace–manifest difference was not significant and LadarWave retained a significant myopic bias. Enhanced monofocal IOLs exhibit reduced agreement between wavefront-based automated and subjective manifest refraction compared with a standard aspheric monofocal IOL. Manifest refraction remains essential for postoperative assessment, and automated measurements should be interpreted as complementary, particularly in IOL designs that modify aberrations. Full article

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

Water quality monitoring faces increasing challenges due to accelerating industrialization and urbanization, demanding accurate, real-time, and reliable prediction technologies. This study presents a novel ensemble learning framework integrating Gaussian Process Regression, Support Vector Regression, and Random Forest algorithms for high-precision water quality pH prediction. The research utilized a comprehensive spatiotemporal dataset, comprising 11 water quality parameters from 37 monitoring stations across Georgia, USA, spanning 705 days from January 2016 to January 2018. The ensemble model employed a dynamic weight allocation strategy based on cross-validation error performance, assigning optimal weights of 34.27% to Random Forest, 33.26% to Support Vector Regression, and 32.47% to Gaussian Process Regression. The integrated approach achieved superior predictive performance, with a mean absolute error of 0.0062 and coefficient of determination of 0.8533, outperforming individual base learners across multiple evaluation metrics. Statistical significance testing using Wilcoxon signed-rank tests with a Bonferroni correction confirmed that the ensemble significantly outperforms all individual models (p < 0.001). Comparison with state-of-the-art models (LightGBM, XGBoost, TabNet) demonstrated competitive or superior ensemble performance. Comprehensive ablation experiments revealed that Random Forest removal causes the largest performance degradation (+4.43% MAE increase). Feature importance analysis revealed the dissolved oxygen maximum and conductance mean as the most influential predictors, contributing 22.1% and 17.5%, respectively. Cross-validation results demonstrated robust model stability with a mean absolute error of 0.0053 ± 0.0002, while bootstrap confidence intervals confirmed narrow uncertainty bounds of 0.0060 to 0.0066. Spatiotemporal analysis identified station-specific performance variations ranging from 0.0036 to 0.0150 MAE. High-error stations (12, 29, 33) were analyzed to distinguish characteristics, including higher pH variability and potential upstream pollution influences. An integrated software platform was developed featuring intuitive interface, real-time prediction, and comprehensive visualization tools for environmental monitoring applications. Full article

This paper examines the nexus between transportation infrastructure, economic growth, and carbon dioxide (CO₂) emissions in the United States, with particular emphasis on the moderating role of green energy consumption (GEC). The United States is an economically advanced country with a well-developed transport infrastructure and sustained economic growth; however, this development has been accompanied by increasing environmental pressures, notably rising CO₂ emissions from the transport sector. Drawing on the Environmental Kuznets Curve (EKC) framework, the study investigates whether renewable energy sources—specifically wind, solar, and hydropower—can decouple economic growth from environmental degradation. A Vector Error Correction Model (VECM) was employed to analyze both short-run dynamics and long-run cointegrating relationships among transport infrastructure, economic activity, CO₂ emissions, and green energy consumption. The results indicate that relative to fossil-based energy, green energy significantly mitigates the emission-enhancing effects of transport infrastructure expansion and economic growth. These findings underscore the pivotal role of renewable energy in achieving sustainable development. From a policy perspective, the results highlight the importance of integrating green energy into national transport and infrastructure planning. Overall, the study demonstrates that in transport-intensive economies, the expansion of renewable energy does not constrain economic growth but is essential for ensuring its long-term environmental sustainability. Full article

The increasing penetration of solar photovoltaic (PV) generation into power systems poses significant operational and planning integration challenges due to the high variability in solar irradiance, which makes PV power forecasting difficult—particularly in the short term. These fluctuations originate from atmospheric dynamics that are only partially captured by numerical weather prediction (NWP) models. In this context, probabilistic forecasting has emerged as a state-of-the-art approach, providing central estimates and additional quantification of uncertainty for decision-making under risk conditions. This work proposes a novel hybrid methodology for day-ahead, hourly resolution point, and probabilistic PV power forecasting. The approach integrates a multiple linear regression (LM) model to predict global tilted irradiance (GTI) from NWP-derived variables, followed by support vector quantile regression (SVQR) applied to the residuals to correct systematic errors and derive GTI quantile forecasts and a linear mapping to PV power quantiles. Robust data preprocessing procedures—including outlier filtering, smoothing, gap filling, and clustering—ensured consistency. The hybrid model was applied to a 960 kWp PV plant in southern Italy and outperformed benchmarks in terms of interval coverage and sharpness while maintaining accurate central estimates. The results confirm the effectiveness of hybrid risk-informed modeling in capturing forecast uncertainty and supporting reliable, data-driven operational planning in renewable energy systems. Full article

This study analyzes the relationship between macroeconomic distress and financial dollarization in Türkiye using annual regional panel data for 26 Nomenclature of Territorial Units for Statistics 2 regions over the period 2005–2021. Macroeconomic distress is captured using the misery index, computed as the compound of inflation and unemployment rates, while the share of foreign-currency-denominated deposits in total deposits measures financial dollarization. Applying second-generation panel econometric models that account for regional heterogeneity, we investigate both long-run equilibrium relationships and short-run interactions. Panel cointegration tests show a long-run connection between macroeconomic distress and dollarization. Short-run effects estimated using a Panel Vector Error Correction Model and a Cross-Sectionally Augmented ARDL framework point to bidirectional causality. Long-run coefficient estimates obtained via Dynamic Ordinary Least Squares indicate an apparent asymmetry. Increases in dollarization exert a substantial and economically significant effect on macroeconomic distress, whereas the long-run impact of distress on dollarization is comparatively modest. The findings suggest that dollarization functions not only as a response to macroeconomic instability but also as a structural element that intensifies inflationary pressures and labor market distortions over time. Focusing on regional patterns rather than national aggregates, the paper provides new evidence on the spatial dimension of the dollarization–instability link. Full article

Pairs trading via futures calendar spreads offers a robust market-neutral approach to exploiting transient mispricings, yet real-time implementation is hindered by asynchronous trading. This paper introduces a Cointegrated Ising Spin Model, CISM, for real-time signal generation in high-frequency spread trading. The model links the macro-level equilibrium of cointegration with micro-level agent interactions, representing prices as magnetizations in an agent-based system. A novel $\Delta$-weighted arbitrage force dynamically adjusts agents’ corrective behavior to account for information staleness. Calibrated on tick-by-tick Brent crude oil futures, the model produces a time-varying probability of spread reversion, enabling probabilistic trading decisions. Backtesting demonstrates a 74.65% success rate, confirming the CISM’s ability to generate stable, data-driven arbitrage signals in asynchronous environments. The model bridges macro-level cointegration with micro-level agent interactions, representing prices as magnetizations within an agent-based Ising system. A novel feature is a $\Delta$-weighted arbitrage force, where the corrective pressure applied by agents in response to the standard Error Correction Term is dynamically amplified based on information staleness. The model is calibrated on historical tick data and designed to operate in real time, continuously updating its probability-based trading signals as new quotes arrive. The model is framed within the context of Discrete Choice Theory, treating agent transitions as utility-maximizing decisions within a Vector Logistic Autoregressive (VLAR) framework. Full article

Two-dimensional (2D) powders constitute an important class of molecular thin films where a specific close-packed plane forms parallel to the substrate surface, while there is no preferred lateral ordering. Using results from classic lattice reduction theory, a systematic scheme is proposed in order to determine the 3D surface unit cell for 2D powders in reciprocal space. The approach is based on a sorted set of lengths $q_1,q_2,q_3,\dots$ of the in-plane components of the scattering vector, which is directly obtained from the scattering pattern. After a first match is established, a refinement procedure is presented that makes full use of the complete set of scattering vectors and, as such, corrects for small experimental errors and ensures a good overall match with the observed reflections. After identifying the in-plane components, the full 3D surface unit cell can be found in a straightforward way. 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

Accurate colorimetric quantification presents a significant challenge, as traditional imaging technologies fail to resolve metamerism and even hyperspectral imaging (HSI) is compromised by nonlinearities and specular reflections. This study introduces a high-fidelity colorimetric system using cross-polarized HSI to suppress specular reflections, integrated with a Support Vector Regression (SVR) model to correct the system’s nonlinear response. The system’s performance was rigorously validated, demonstrating exceptional stability and repeatability (average $\Delta E_{00}<0.1$). The SVR calibration significantly enhanced accuracy, reducing the mean color error from $\Delta E_{00}=4.36$ to $0.43$. Furthermore, when coupled with a Random Forest classifier, the system achieved 99.0% accuracy in discriminating visually indistinguishable (metameric) samples. In application-specific validation, it successfully quantified cosmetic color shifts and achieved high-precision skin-tone matching with a fidelity as low as $\Delta E_{00}=0.82$. This study demonstrates that the proposed system, by synergistically combining cross-polarization and machine learning, constitutes a robust tool for high-precision colorimetry, addressing long-standing challenges and showing significant potential in fields like cosmetic science. Full article

While domestic green finance is widely recognized for its role in fostering green innovation and supporting climate change mitigation, the impact of international green finance (IGF) remains critical, particularly for developing economies where external finance inflows can catalyse transitions toward low-carbon development. This study investigates the long-run and short-run effects of IGF on green innovation and further examines the influence of green innovation on carbon dioxide (CO₂) emissions across a panel of 76 developing countries from 2000 to 2019. Using second-generation panel cointegration and the vector error correction mechanism, our findings reveal a nonlinear long-run relationship between IGF and total innovation, indicating that IGF must exceed a threshold before significantly boosting total innovation in developing economies. We also identify an inverted U-shaped relationship between IGF and green innovation, in which the positive effects of IGF diminish beyond a certain point. Crucially, IGF emerges as a significant driver of CO₂ emissions reduction in both the short- and long-run. While total innovation is associated with increased emissions over the long term, green innovation contributes to a substantial and sustained decrease in CO₂ emissions. These results emphasize the need to design targeted policies that prioritize green innovation and scale up IGF to support sustainable growth in developing countries. Full article

Construction price indices play a critical role in shaping construction activity and determining the economic success of building projects in Germany, where they can serve as central inputs to cost planning and to updating trade-level project budgets over the planning and construction horizon. This paper develops a forecasting framework for 35 sub-construction price indices for office buildings, providing granular inputs for cost escalation and risk assessment. We employ regularized vector autoregressive models with exogenous variables (VARX) implemented via the BigVAR package and estimate them in a model-vintage design for an unbalanced panel. These high-dimensional models are benchmarked against compact VARX and vector error-correction models (VECM) that jointly forecast each target index with a small macroeconomic block consisting of the gross domestic product (GDP) and the three-month interbank rate. Candidate specifications are evaluated using mean absolute percentage error (MAPE) and out-of-sample root mean square error (RMSE), and the final forecasting model for each index is selected based on ex post MAPE. The results show that regularized VARX models capture dynamic interdependencies among the sub-indices and, for most series, outperform the VARX and VECM benchmarks. The resulting forecasts provide practitioners with trade-specific escalation factors that can support budgeting, contract design, and the mitigation of cost risk in office-building projects. Full article

In recent years, Peru’s agricultural sector has expanded steadily despite recurrent external shocks and persistent volatility in global commodity markets. This sustained performance reflects the sector’s exposure to international price dynamics, a connection with direct implications for structural sustainability in a small, open and commodity-dependent economy. In this context, the study examines whether the terms of trade (TOT) sustain a stable long-run relationship with Peru’s agricultural GDP and assesses how this linkage shapes structural sustainability. The analysis applies Johansen’s cointegration method combined with a bivariate Vector Error Correction Model (VECM), enabling the identification of common long-run trends and the estimation of adjustment speeds following external shocks. The results reveal a single cointegrating vector and a negative, highly significant error-correction term in the agricultural equation, indicating that the sector gradually corrects deviations from its long-run equilibrium. In contrast, the TOT display no meaningful adjustment mechanism, behaving as a weakly exogenous driver. Short-run effects of external shocks are small and statistically fragile, suggesting that quarterly disturbances are overshadowed by the longer-run correction process. Beyond quantifying these dynamics, the study offers a structural reading of how volatile imported inputs—fertilisers, fuels and agricultural machinery—influence agricultural performance, even when export prices are favourable. Overall, the findings underscore that long-term sustainability depends not only on global price trajectories but also on domestic productive capacities and gradual technological improvement, highlighting the need for adaptive strategies in an environment of persistent global volatility. Full article

This paper introduces an adaptive impedance control strategy for robotic manipulators, developed through the extremum seeking technique. A model-based disturbance observer (DOB) is employed to estimate contact forces, removing the dependency on torque sensors. An impedance vector is constructed to correct the errors arising from motor uncertainties and unknown couplings, without considering the threshold value of the control parameters. Joint tracking errors and fluctuations in contact force are incorporated into the cost function. For various tasks, suitable control parameters are adaptively optimized in real time using an extremum seeking approach, which continuously evaluates the cost function. A rigorous analysis is conducted on the stability of the proposed controller. Compared to conventional approaches, the proposed adaptive impedance control offers a more streamlined design for adjusting the manipulator’s contact impedance. Experimental results confirm that the extremum seeking strategy successfully tuned the controller parameters online according to variations in the cost function. Full article

This study examines the macroeconomic impacts of major earthquakes in Türkiye using annual data from 1990 to 2023. Despite growing global interest in disaster economics, evidence on how large seismic events shape national economic performance over extended periods remains limited, particularly in emerging economies. Using data from the World Bank, the Central Bank of the Republic of Türkiye, and the Disaster and Emergency Management Authority, the analysis incorporates real gross domestic product, gross fixed capital formation, consumer prices, and export capacity. A dummy variable identifies years with high-fatality earthquakes. After confirming stationarity, Johansen cointegration and a Vector Error Correction Model were applied. Results indicate that earthquakes exert a statistically significant negative influence on long-term economic growth. Based on the log-level specification, the long-run equilibrium level of real gross domestic product in earthquake years is approximately 45 percent lower than in non-earthquake years. Investment, price stability, and trade capacity support long-term growth. Model diagnostics confirm stability, normality, and no autocorrelation. These findings highlight the structural economic vulnerabilities created by major earthquakes and underscore that disaster risk reduction and resilient infrastructure policies must be integral components of sustainable growth strategies. The study contributes updated national time-series evidence from a structurally fragile context. Full article