Search Results (433)

Renewable energy efficiency (REE) remains critically low across many Sub-Saharan African (SSA) countries, yet the existing literature provides limited empirical clarity on how governance quality shapes efficiency outcomes and through which mechanisms these effects operate. This study addresses this gap by examining the influence of governance quality on REE in 23 SSA countries from 2005 to 2023, drawing on institutional theory and innovation diffusion theory. The analysis investigates three mediating channels, renewable investment, green policy, and green technology, using a multidimensional empirical framework that integrates the Malmquist Productivity Index (MPI), Two-Step System GMM, Generalized Estimating Equations (GEE), Generalized Least Squares (GLS), and Panel-Corrected Standard Errors (PCSE). Results consistently show that governance quality significantly enhances REE through investment, policy, and technological pathways. To capture nonlinearities and heterogeneous responses often overlooked in traditional models, we complement the econometric estimations with causal machine-learning simulations (Double Machine Learning and Causal Forests). These counterfactual analyses reveal that governance improvements and renewable-policy adoption produce the highest efficiency gains in mid-governance countries with stronger absorptive capacity. While the study offers policy-relevant insights, limitations remain, due to data constraints, unobserved institutional dynamics, and the uneven maturity of green-technology systems across the region. Nevertheless, the findings underscore that strengthening governance and fostering innovation are fundamental to accelerating a sustainable and inclusive green-energy transition in Sub-Saharan Africa. Full article

Polyphenol oxidase (PPO) is the primary biochemical driver of browning and the subsequent decline of market quality in harvested fruit. In this work, a fully non-invasive analytical framework was built using Visible/Near-Infrared (VIS/NIR) spectroscopy coupled with chemometric modeling in order to estimate PPO activity in two commercially relevant plum cultivars (Khormaei and Khoni). A comprehensive comparative study was conducted utilizing multiple machine learning and linear regression techniques, including Support Vector Regression (SVR), Decision Tree (DT), and Partial Least Squares Regression (PLSR). After acquiring the full VIS/NIR spectra, a suite of metaheuristic feature selection strategies was applied to compress the spectral space to roughly 10–15 highly informative wavelengths. SVR, DT, and PLSR models were then trained and benchmarked using (a) the complete spectral domain and (b) the reduced wavelength subsets. The results consistently demonstrated that non-linear models (DT and SVR) significantly outperformed the linear PLSR method, confirming the inherent complexity and non-linearity of the relationship between the spectra and PPO activity. Across all tests, DT consistently produced the strongest generalization. Under full spectra inputs, DT reached RPD values of 4.93 for Khormaei and 5.41 for Khoni. Even more importantly, the wavelength-reduced configuration further enhanced performance while substantially lowering computational cost, yielding RPDs of 3.32 (Khormaei) and 5.69 (Khoni). The results show that VIS/NIR combined with optimized key-wavelength DT modeling provides a robust, fast, and field-realistic route for quantifying PPO activity in plums without physical destruction of the product. Full article

This study introduces a systematic methodology for modelling the radius of curvature of the arc-shaped section $\stackrel{⌢}{\mathrm{BC}}$ in a Michell–Banki cross-flow turbine blade. The method combines geometric modeling in polar coordinates with nonlinear regression, using both two- and three-parameter formulations estimated through the Ordinary Least Squares (OLS) method. Model performance is assessed through two complementary criteria: the coefficient of determination ($R^2$) and the computed arc length, ensuring that statistical accuracy aligns with geometric fidelity. The methodology was validated on digital measurements obtained from CATIA, using datasets with $N=187$ and a reduced subset of $N=48$ points. Results demonstrate that even with fewer data points, the regression model maintains high predictive accuracy and geometric consistency. The best-performing three-parameter model achieved $R^2=0.958$, with a five-point Gauss–Legendre quadrature yielding an arc length of approximately $145\mathrm{mm}$, representing 98.8% agreement with the reference value of $146.78\mathrm{mm}$. By representing the arc as a single smooth exponential function rather than a piecewise mapping, the approach simplifies analysis and enhances reproducibility. Coupling regression precision with arc-length verification provides a robust and reproducible basis for curvature modeling. This methodology supports turbine blade design, manufacturing, and quality control by ensuring that the blade geometry is validated with high statistical confidence and physical accuracy. Future research will focus on deriving analytical arc-length integrals and integrating the procedure into automated design and inspection workflows. Full article

This paper proposes a simplified adaptive filtering approach using a Hammerstein function and the spline interpolation based on a Fair cost function for denoising electrocardiogram (ECG) signals. The use of linear filters in real-world applications has many limitations. Adaptive nonlinear filtering is a key development in tackling the challenge of discovering the specific characteristics of biomimetic systems for each person in order to eliminate unwanted signals. A biomimetic system refers to a system that mimics certain biological processes or characteristics of the human body, in this case, the individual features of a person’s cardiac signals (ECG). Here, the adaptive nonlinear filter is designed to cope with ECG variations and remove unwanted noise more effectively. The objective of this paper is to explore an individual biomedical filter based on adaptive nonlinear filtering for denoising the corrupted ECG signal. The Hammerstein spline adaptive filter (HSAF) architecture consists of two structural blocks: a nonlinear block connected to a linear one. In order to make a smooth convergence, the Fair cost function is introduced for convergence enhancement. The affine projection algorithm (APA) based on the Fair cost function is used to denoise the contaminated ECG signals, and also provides fast convergence. The MIT-BIH 12-lead database is used as the source of ECG biomedical signals contaminated by random noises modelled by Cauchy distribution. Experimental results show that the estimation error of the proposed HSAF–APA–Fair algorithm, based on the Fair cost function, can be reduced when compared with the conventional least mean square-based algorithm for denoising ECG signals. Full article

Accurate estimation of branch-level traffic flows at urban Y-intersections from limited mainline measurements remains a critical challenge in intelligent transportation systems. Y-intersections, with their symmetric geometric configuration where multiple branches converge, pose unique challenges from flow coupling, signal-induced periodicity, and merging delays. This study develops a hybrid mathematical modeling framework that integrates piecewise linear segments with periodic components for each branch flow. The model enforces physical constraints including flow conservation, non-negativity, and segment continuity, while incorporating operational features such as signal timing and merging delays. Parameter estimation employs a two-stage optimization approach combining least-squares fitting with constrained nonlinear programming, utilizing sparse mainline detector data and minimal historical priors. Experimental validation across five progressive problem formulations demonstrates robust performance, achieving RMSE values of 3.3432 and 5.4467 for complex multi-branch scenarios while accurately capturing 10-min green/8-min red signal cycles and 2-min merging delays. The method successfully reconstructs branch flow profiles at required time points (07:30 and 08:30), reducing observation requirements by 60–80% while maintaining estimation accuracy. The proposed framework provides a practical and interpretable solution for branch flow estimation under sparse sensing conditions, bridging physics-based modeling with data-driven techniques and offering transportation agencies a deployable tool for intersection monitoring without extensive instrumentation. Full article

Low-cost and real-time remote sensing of moving targets is increasingly required in civilian applications. Micro unmanned aerial vehicles (UAVs) provide a promising platform for such missions because of their small size and flexible deployment, but they are constrained by payload capacity and energy budget. Consequently, they typically carry lightweight monocular cameras only. These cameras cannot directly measure distance and suffer from scale ambiguity, which makes accurate geolocation difficult. This paper tackles geolocation and short-term trajectory prediction of moving targets over uneven terrain using bearing-only measurements from a monocular camera. We present a two-stage estimation framework in which a pseudo-linear Kalman filter (PLKF) provides real-time state estimates, while a sliding-window nonlinear least-squares (NLS) back end refines them. Future target positions are obtained by extrapolating the estimated trajectory. To improve localization accuracy, we analyze the relationship between the UAV path and the Cramér–Rao lower bound (CRLB) using the Fisher Information Matrix (FIM) and derive an observability-enhanced trajectory planning method. Real-flight experiments validate the framework, showing that accurate geolocation can be achieved in real time using only low-cost monocular bearing measurements. Full article

Polymethoxy butyl ether (BTPOM_n), a novel diesel additive developed for suppressing incomplete combustion emissions, was synthesized via an optimized batch slurry method employing n-butanol and trioxane (TOX) over NKC-9 acid cation-exchange resin (90–110 °C). A comprehensive kinetic model elucidated the reaction mechanism, addressing competitive pathways governing both main product formation and key side reactions—specifically polyoxymethylene hemiformals (HD_n) and polyoxymethylene glycols (MG) generation. As the first detailed kinetic investigation of BTPOM_n synthesis, this work provides a fundamental dataset and a robust predictive model that are crucial for process intensification and reactor design. Hybrid optimization integrating genetic algorithms with nonlinear least-squares regression achieved robust parameter estimation, with model predictions showing excellent agreement with experimental data. Thermal effects significantly influenced reaction rates, enhancing decomposition and propagation processes with increasing temperature. Optimal catalyst loading was identified at 3 and 6 wt.%, balancing reaction acceleration and byproduct suppression. Temperature-dependent equilibrium revealed chain length regulation through growth and depolymerization processes. This mechanistic understanding enables predictive reactor design for cleaner fuel additive synthesis. It provides critical insights for developing emission-control technologies in diesel engine systems. Full article

Background/Objectives: Evidence regarding the associations between coffee and tea consumption and bone mineral density (BMD) in postmenopausal women remains inconclusive. Prior studies have not examined these relationships using repeated measures of both beverage intake and BMD over an extended follow-up. This study aimed to evaluate the longitudinal associations of coffee and tea consumption with BMD in older women. Methods: Data were drawn from the Study of Osteoporotic Fractures (SOF), a prospective cohort of 9704 women aged ≥65 years. Coffee and tea intake were repeatedly assessed via self-administered questionnaires at visits 2, 4, 5, and 6, spanning approximately 10 years. Femoral neck and total hip BMD were repeatedly measured by dual-energy X-ray absorptiometry. Linear mixed-effects models with random intercepts were used to estimate associations, adjusting for demographic, physical activity, comorbidities, and medication use. Nonlinear relationships were assessed using natural splines, and subgroup analyses were conducted using exposure-by-covariate interaction terms. Results: During the 10-year follow-up, tea consumption was positively associated with total hip BMD (least squares mean: 0.718 vs. 0.715 g/cm²; mean difference: 0.003; 95% CI: 0.000–0.005; p = 0.026). No significant overall association was observed on coffee consumption with femoral neck or total hip BMD. However, spline analyses suggested that consuming more than five cups of coffee per day may be associated with lower BMD. Interaction analyses indicated significant interactions between coffee and alcohol intake (p = 0.0147) and between tea consumption and BMI (p = 0.0175). Conclusions: Tea consumption was associated with higher total hip BMD in postmenopausal women, whereas excessive coffee intake (>5 cups/day) may adversely affect BMD. Coffee consumption was negatively associated with femoral neck BMD in women with higher alcohol intake, while tea consumption appeared particularly beneficial for those with obesity. Full article

Pipeline transportation of petroleum products remains one of the safest and most efficient methods of bulk energy delivery, yet overpressure events continue to pose serious operational and regulatory challenges. Traditional fixed-gain PI controllers, commonly used with centrifugal pump drives, cannot adapt to varying product densities or transient disturbances such as valve closures that generate water hammer. This paper proposes a self-tuning adaptive controller based on Recursive Least Squares (RLS) parameter estimation to improve safety and efficiency in pipeline pump operations. A nonlinear simulation model of a centrifugal pump driven by an induction motor is developed, incorporating pipeline friction losses via the Darcy–Weisbach relation and pressure transients induced by rapid valve closures. The RLS algorithm continuously estimates effective loop dynamics, enabling online adjustment of proportional and integral gains under changing fluid and operating conditions. Simulation results demonstrate that the proposed RLS-based adaptive controller maintains discharge pressure within ±2% of the target setpoint under density variations from 710 to 900 kg/m³ and during severe transient events. Compared to a fixed-gain PI controller, the adaptive strategy reduced pressure overshoot by approximately 31.9% and settling time by 6%. Model validation using SCADA field data yielded an $R^2$ = 0.957, RMSE = 3.95 m³/h, and normalized NRMSE of 12.6% (by range), confirming strong agreement with measured system behavior. The findings indicate that RLS-based self-tuning provides a practical enhancement to existing pipeline control architectures, offering both improved robustness to abnormal transients and greater efficiency during steady-state operation. This work establishes a foundation for higher-level supervisory and game-theoretic coordination strategies to be explored in subsequent studies. Full article

Understanding heat and mass transfer phenomena is fundamental to successful roasting practices. These phenomena can be quantified via an energy balance over the roaster, whereby heat and mass transfer equations can be formulated. Through rigorous calibration of the simulation with experimentally derived data obtained using a spouted bed roaster, a zero-dimensional, batch-scale model of coffee roasting was developed to predict time–temperature roasting profiles. Calibration involved implementation of (i) an airflow calibration to determine the air mass flow rate and velocity of air input to the roaster, (ii) kinetic models and empirical correlations to describe coffee’s physicochemical development during roasting and (iii) a non-linear least squares fitting procedure to estimate system-dependent parameters—such as the thermal response coefficient and heat transfer effectiveness—that are otherwise difficult to determine. In this way, user inputs of roasting parameters relevant for spouted bed roasters—batch size, airflow and inlet air temperature—were probed to capture the full kinetics of coffee roasting under various process conditions, from which rate constants for mass loss kinetics were determined. In this study, development of the zero-dimensional, batch-scale simulation is described, alongside rigorous calibration with pilot-scale roasting trials. These simulations are application-ready and can be used by product and process developers to roast coffee in silico, providing not just an informative tool, but one that can be instructive and predict requirements for raw material (green coffee) properties, roasting process conditions, or roasted coffee properties. Full article

The growing use of electrical and electronic appliances, coupled with shorter product lifespans, has accelerated the rise in waste electrical and electronic equipment (WEEE). Accurate forecasting is essential for addressing environmental challenges, conserving resources, and advancing the circular economy (CE). This research employs a Trigonometry-Based Discrete Grey Model (TBDGM(1,1)) that integrates the Jaya algorithm and Least Squares Estimation (LSE) for parameter estimation. By leveraging Jaya’s parameter-free robustness and LSE’s computational efficiency, the model enhances prediction accuracy for small-sample and nonlinear datasets. WEEE data from Washington State (WA) in the USA and Türkiye are utilized to validate the model, demonstrating cross-context adaptability. To evaluate performance, the model is benchmarked against five state-of-the-art discrete grey models. For the WA dataset, additional benchmarking against methods used in prior e-waste forecasting literature enables a dual-layer comparative analysis, which strengthens the validity and practical relevance of the approach. Across evaluations and multiple performance metrics, TBDGM(1,1) attains satisfactory and competitive prediction performance on the WA and Türkiye datasets relative to comparator models. Using TBDGM(1,1), Türkiye’s e-waste is forecast for 2021–2030, with the 2030 amount projected at approximately 489 kilotones. The findings provide valuable insights for policymakers and researchers, offering a standardized and reliable forecasting tool that supports CE-driven strategies in e-waste management. Full article

This paper proposes a two-layer adaptive proportional–integral–derivative (PID) controller for precise pose control of a six-degree-of-freedom cable-driven parallel robot with eight cables, specifically designed to handle dynamic changes caused by the movement of attachment points. The positions of the attachment points on the base are adjusted to avoid collisions between humans and cables, where humans and robots are working in a shared workspace. The inherent nonlinearity of the robot system was addressed using model identification based on the recursive least squares (RLS) algorithm equipped with an adaptive forgetting factor. This method enables real-time updates to the dynamic model of the robot, thereby ensuring accurate parameter estimation as the attachment points move. The combination of the PID controller and RLS algorithm enhances the system’s ability to respond effectively to changing dynamics. Simulation results highlight the superior accuracy, robustness, and adaptability of the proposed approach, making it well suited for applications requiring a reliable performance in dynamic and unpredictable environments. The proposed method can guarantee human safety, while the end effector tracks the desired trajectory. Full article

In mineral processing and metallurgy, total iron grade serves as a critical indicator guiding the entire production chain from crushing to smelting, directly influencing the quality and yield of steel products. To address the limitations of conventional matrix effect correction methods in X-ray fluorescence (XRF) analysis—such as low accuracy, high time consumption, and labor-intensive procedures—this study proposes a novel hybrid model (DSCN-LS) integrating least squares (LS) with dynamically regularized stochastic configuration networks (DSCNs) for total iron ore grade quantification. Through feature analysis, we decompose the grade modeling problem into a linear structural component and nonlinear residual terms. The linear component is resolved by means of LS, while the nonlinear terms are processed by the DSCN with a dynamic regularization strategy. This strategy implements node-specific weighted regularization: weak constraints preserve salient features in high-weight-norm nodes, while strong regularization suppresses redundant information in low-weight-norm nodes, collectively enhancing model generalizability and robustness. Notably, the model was trained and validated using datasets collected directly from industrial sites, ensuring that the results reflect real-world production scenarios. Industrial validation demonstrates that the proposed method achieves an average absolute error of 0.3092, a root mean square error of 0.5561, and a coefficient of determination (R²) of 99.91% in total iron grade estimation. All metrics surpass existing benchmarks, confirming significant improvements in accuracy and operational practicality for XRF detection under complex industrial conditions. Full article

In-band full-duplex (IBFD) communication systems offer a promising means of improving spectral efficiency by enabling simultaneous transmission and reception on the same frequency channel. Despite this advantage, self-interference (SI) remains a major challenge to their practical deployment. Among the different SI cancellation (SIC) techniques, this paper focuses on digital SIC methodologies tailored for multiple-input multiple-output (MIMO) wireless transceivers operating under digital beamforming architectures. Two distinct digital SIC approaches are evaluated, employing a generalized memory polynomial (GMP) model augmented with Itô–Hermite polynomial basis functions and a phase-normalized neural network (PNN) to effectively model the nonlinearities and memory effects introduced by transmitter and receiver hardware impairments. The robustness of the SIC is further evaluated under both single off-line training and closed-loop real-time adaptation, employing estimation techniques such as least squares (LS), least mean squares (LMS), and fast Kalman (FK) for model coefficient estimation. The performance of the proposed digital SIC techniques is evaluated through detailed simulations that incorporate realistic power amplifier (PA) characteristics, channel conditions, and high-order modulation schemes. Metrics such as error vector magnitude (EVM) and total bit error rate (BER) are used to assess the quality of the received signal after SIC under different signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR) conditions. The results show that, for time-variant scenarios, a low-complexity adaptive SIC can be realized using a GMP model with FK parameter estimation. However, in time-invariant scenarios, an open-loop SIC approach based on PNN offers superior performance and maintains robustness across various modulation schemes. Full article

The paper addresses the problem of attitude determination using Global Navigation Satellite System (GNSS) measurements from multiple antennas mounted on a navigation platform. To achieve attitude determination by GNSS with typical accuracy down to tenths of a degree for one-meter baselines, GNSS phase measurements are employed. A key challenge with phase measurements is the presence of unknown integer ambiguities. Consequently, the attitude determination problem traditionally reduces to a nonlinear, non-convex optimization problem with integer constraints. No closed-form solution to this problem is known, and its real-time calculation is computationally intensive. Given an a priori initial attitude approximation, we propose a new algorithm for attitude tracking based on the reduction of the nonlinear orthogonality-constrained attitude estimation problem to a linear integer least squares problem, for which numerical methods are well known and computationally much less demanding. Additionally, a simple a priori model for GNSS measurement error variance is introduced, grounded on the geometry of satellite signal propagation through vacuum and the Earth’s atmosphere, providing a clear physical interpretation. Applying the algorithm to a real dataset collected from a quasi-static multi-antenna, multi-GNSS system with sub-meter baselines, we obtain promising results. Full article