Search Results (19)

This study aimed to investigate the effect of high-amylose corn starch (HACS) addition on the physicochemical properties and in vitro digestibility of an ultrasound-treated waxy rice flour (UWRF)–HACS blend system. As the proportion of HACS increased, the amylose content in the blends significantly increased (p < 0.05), while their water solubility index (WSI) and swelling power (SP) significantly decreased (p < 0.05). Additionally, the average particle size of the blends increased, and the surface of starch granules became smoother. Compared to UWRF, the blends did not generate new functional groups, but increased the starch’s relative crystallinity and short-range ordered structure. Rheological results indicated that the HACS-UWRF blends were mainly elastic and exhibited a typical weak gel system. In vitro digestibility results showed that the addition of HACS significantly increased the resistant starch (RS) content in the rice cakes (p < 0.05), while substantially reducing the hydrolysis index (HI) and estimated glycemic index (eGI) (p < 0.05). This study revealed the processing characteristics and gelatinization behavior changes in the HACS-UWRF blends. It provides a theoretical basis for the development of specialized flour for slow-glycemic rice cakes. Full article

The present study provides a comprehensive characterization of soil elemental composition in the Nile Delta, Egypt. The soil samples were analyzed using Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES), highly appropriative for the major element determination and Inductively Coupled Plasma Mass Spectrometry (ICP–MS), outstanding for the trace element analysis. A total of 55 elements were measured across 53 soil samples. A variety of statistical and analytical techniques, including both descriptive and inferential methods, were employed to assess the elemental composition of the soil. Bivariate and multivariate statistical analyses, discriminative ternary diagrams, ratio biplots, and unsupervised machine learning algorithms—such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Hierarchical Agglomerative Clustering (HAC)—were utilized to explore the geochemical similarities between elements in the soil. The application of t-SNE for soil geochemistry is still emerging and is characterized by the fact that it preserves the local distribution of elements and reveals non-linear relationships in geochemical research compared to PCA. Geochemical background levels were estimated using Bayesian inference, and the impact of outliers was analyzed. Pollution indices were subsequently calculated to assess potential contamination. The findings suggest that the studied areas do not exhibit significant pollution. Variations in background levels were primarily attributed to the presence of outliers. The clustering results from PCA and t-SNE were consistent in terms of accuracy and the number of identified groups. Four distinct groups were identified, with soil samples in each group sharing similar geochemical properties. While PCA is effective for linear data, t-SNE proved more suitable for nonlinear dimensionality reduction. These results provide valuable baseline data for future research on the studied areas and for evaluating their environmental situation. Full article

This study investigates the interactive link between bank performance and financial innovation in Tunisian banking using a mixed-methods research framework that combines econometric approaches and institutional factors. The empirical analysis uses a panel data of 11 commercial banks from the period of 2000–2024 and employs an Autoregressive distributed lag (ARDL) model to estimate short- and long-run impacts of innovation on return on equity (ROE). A composite indicator of Fintech investment, digital service adoption, and innovation productivity characterizes financial innovation. Governance factors like the presence of risk management departments and executive compensation are taken into account. The results reveal a robust positive impact of financial innovation on bank performance in the long run, especially in more concentrated market settings. Risk management supports performance, while inefficient executive compensation is negatively associated with profitability. These findings are confirmed by robustness tests with HAC standard errors. This research contributes to the literature by situating financial innovation in the context of an emerging North African market and produces practitioner-relevant information for policymakers and bank executives interested in ensuring that performance results are consistent with innovation strategy. Full article

This study proposes a hierarchical Archimedean copula (HAC) framework to model the complex dependence structures in hydrodynamic landslide deformations, with a focus on the Donglinxin (DLX) landslide. Hierarchical Archimedean copulas, compared to elliptical copulas, offer greater flexibility by requiring fewer parameters while maintaining broader applicability. The HAC model, combined with pseudo-maximum likelihood estimation (PMLE), is applied to analyze the interdependencies among the landslide-related variables, such as monthly displacement increments, reservoir water level fluctuations, groundwater variations, and precipitation. A case study of the DLX landslide demonstrates the model’s ability to quantify the critical aspects of landslide deformation, including variable correlations, risk thresholds, conditional probabilities, and return periods. The analysis reveals a strong hierarchical dependence between monthly displacement increments and reservoir water level drops. The model also provides valuable insights into the potential risk factors, helping to optimize landslide monitoring and early-warning systems for more effective disaster mitigation. Full article

Humic acids (HAs) possess diverse functionalities, endowing them with multiple applications as bioactive compounds in agriculture. Alkaline extraction is key to obtaining HAs from their source material. The presence of oxygen during extraction can lead to oxidative changes in the humic structure. The extent of HA transformation depending on their origin remains poorly understood, and the effect of alkaline extraction on the HA biological activities is yet to be estimated. Here, we compare the physicochemical properties of HAs extracted from fresh organic material, compost, in air (HA-O₂) and under nitrogen (HA-N₂). We also assess the antioxidant properties of HAs-O₂ and HAs-N₂ from compost (HAC), Retisol (HAR), and Chernozem (HACh) and relate them to the HA biological activities. Changes in the HAC properties were analyzed using the following techniques: elemental composition, ultraviolet–visible and infrared spectroscopy, ¹³C nuclear magnetic resonance (¹³C-NMR), electron paramagnetic resonance (EPR), gel filtration using Sephadex G-75 gel, and potentiometric titration. The HA antioxidant properties were explored using the 2,2-diphenyl-1-picrylhydrazyl radical (DPPH) assay (antiradical activity) and phosphomolybdenum assay (total antioxidant capacity). The HA biological activity was estimated by priming radish and wheat seeds (0.5 g L⁻¹ HAs, 25 °C, 5 h for radish and 14 h for wheat), followed by germination tests. Alkaline extraction of HAC in air vs. nitrogen resulted in a 1.2-fold increase in the O/C ratio and optical density at E₄₆₅, oxidation of aliphatic fragments, a 2-fold increase in the contents of functional groups, and a 1.2-fold increase in the number of paramagnetic centers. All HA-O₂ preparations have demonstrated an enhanced antiradical activity (1.3–1.6 times) and total antioxidant capacity (1.1–1.3 times) compared to HA-N₂. The Vigor Index of seeds primed with HA-O₂ was 1.1-to-1.8-fold higher than those treated with HA-N₂, depending on the HA origin. We demonstrate that alkaline treatment in air benefits the antiradical and biological activities of HAs, making such preparations more attractive for use as natural antioxidants and priming agents. This opens up new perspectives for using O₂-modified HAs as innovative plant stimulants in agriculture. Full article

Accurate estimation of the mean fragment size is crucial for optimizing open-pit mining operations. This study presents an approach that combines clustering, hyperparameter optimization, and data augmentation to enhance prediction accuracy using the Xtreme Gradient Boosting (XGBoost) regression model. A dataset of 110 blasts was divided into 97 blasts for training and testing, whereas a separate set of 13 new, unseen blasts was used to evaluate the robustness and generalization of the model. Hierarchical Agglomerative (HA) and K-means clustering algorithms were used, with HA clustering providing a higher cluster quality. To address class imbalance and improve model generalization, a synthetic minority oversampling technique for regression with Gaussian noise (SMOGN) was employed. Hyperparameter tuning was conducted using HyperOpt by comparing Random Search (RS) with the Advanced Tree-structured Parzen Estimator (ATPE). The combination of ATPE with HA clustering and SMOGN in an expanded search space produced the best results, achieving superior prediction accuracy and reliability. The proposed HAC1-SMOGN model, which integrates HA clustering, ATPE tuning, and SMOGN augmentation, achieved a mean squared error (MSE) of 0.0002 and an R² of 0.98 on the test set. This study highlights the synergistic benefits of clustering, hyperparameter optimization, and data augmentation in enhancing machine learning models for regression tasks, particularly in scenarios with class imbalance or limited data. Full article

The temperature estimation within asphalt concrete (AC) overlaid on cement-stabilized bases (CSB) is necessary for pavement analysis and design. However, the impact of different CSB gradations and rubberized CSB on AC temperature has not been thoroughly investigated. This study aims to clarify this effect by examining two types of CSB with nominal particle aggregate sizes of 25 mm and 31.5 mm, as well as the substitution of 5%, 10%, and 20% graded aggregates with rubber aggregates (RA) in CSB Dmax 25 using Ansys-based numerical simulations. The modelling also investigated 11 scenarios with different AC thicknesses (h_AC) ranging from 6 to 26 cm. The results indicated that CSB Dmax 31.5 reduced the daily maximum temperature fluctuation at the bottom of the AC (∆T_bottomAC) by approximately 8% compared to CSB Dmax 25. The inclusion of 5% RA in CSB Dmax 25 decreased ∆T_bottomAC by up to 20%. Additionally, the rubberized CSB increased the maximum temperature gradient between the top and bottom of the AC (ΔT_maxAC) by 9.5% with 5% RA and a 6 cm AC thickness; however, this increase was insignificant when h_AC exceeded 12 cm. This study also proposed the use of artificial neural network (ANN) models to predict the AC’s temperature distribution based on depth, the time of day, surface paving temperatures, and h_AC. The proposed ANN model demonstrated high accuracy (R² = 0.996 and MSE = 0.000685),which was confirmed by the numerical simulations, with an acceptable RMSE ranging from 0.28 °C to 0.67 °C. Full article

This paper investigates whether renewable energy influences economic growth and happiness. Using panel data from 25 European Union countries for the period 2012–2022, this study employs a panel model for estimation with fixed and random effects, and robust HAC standard errors. According to the research results, in general, the growing share of renewable energy in the energy mix has a positive impact on economic growth and the happiness of citizens. However, detailed research has shown that this effect depends on the type of energy; a significant positive impact was recorded only in solar share energy, wind share energy and economic growth. However, almost all types of renewable energy were included, i.e., biofuel, hydro, solar and other renewable share energy, and all had a significantly positive impact on the level of happiness. The exception was wind share energy, which showed a significant negative impact. The research findings of this paper provide empirical support for promoting renewable energy, which is positive both for economies and the happiness of citizens. It is one of the main aspects of sustainable economic growth. Full article

In the current study, the production of microparticles released from fifteen commercial sugarcane pulp (SCP) take-out containers into different food simulants under different conditions was investigated, where deionized water (DI water), 4% acetic acid (4% HAc), and 95% ethanol (95% EtOH) were used to simulate aqueous, acidic, and fatty foods, respectively. Results showed that compared with DI water and 95% EtOH, 4% HAc caused the degradation of sugarcane fibers, thereby releasing the highest number of microparticles. The overall migration values of the sugarcane pulp take-out containers in 4% HAc were above the prescribed limit of 10 mg/dm². Furthermore, it was estimated that consumers may intake 36,400–231,700 microparticles in a take-out container at one time, of which the proportion of particles with a particle size between 10 and 500 μm was the highest, ranging from 26,470 to 216,060 items. Moreover, the Al and Fe are the main metals in these take-out containers, ranging between 35.16 and 1244.04 and 44.71 and 398.52 mg/kg, respectively, followed by Pb, Ti, and Sr. This study provides important information that the safety of both the production of microparticles and the metallic elements should be considered for SCP take-out containers when in contact with food. Full article

4-chlorophenol (4-CP), a hydroxylated aromatic compound (HAC), is a recalcitrant and toxic organic pollutant found in industrial wastewater and various environmental media. In this paper, visible-light-activated photocatalysis using graphitic carbon nitride (GCN) was used to treat 4-CP in an aqueous media. Graphitic carbon nitride from different precursors (dicyanamide, urea, and melamine), as well as GCN/silver nanocomposites (AgBr, Ag₃PO₄, Ag₂CrO₄, and Ag), were successfully synthesized and characterized by BET, XRD, SEM, EDS, and UV-Vis DRS. The band gaps of the photocatalysts were estimated using the UV-Vis DRS characterization results and Tauc plots. The evaluation of the efficacy of the GCN-based catalysts in degrading 4-CP was conducted with different photoreactors such as a royal blue light-emitting diode (LED), a UV-A LED, LUZCHEM cool white lamps, and a solar simulator. The results showed that GCNs with royal blue LED can effectively degrade 4-CP from aqueous media. Among the different precursors, urea-derived GCN showed the best performance in degrading 4-CP due to its large surface area. GCN/0.3Ag₂CrO₄ nanocomposite showed a synergistic effect for the enhanced photocatalytic degradation of 4-CP. The degradation of 4-CP with a rate constant of 2.64 × 10⁻² min⁻¹ was achieved with a GCN/0.3Ag₂CrO₄ nanocomposite under royal blue LED irradiation. Full article

Central sensitisation is assumed to be one of the underlying mechanisms for chronic low back pain. Because central sensitisation is not directly assessable in humans, the term ‘human assumed central sensitisation’ (HACS) is suggested. The objectives were to investigate what definitions for HACS have been used, to evaluate the methods to assess HACS, to assess the validity of those methods, and to estimate the prevalence of HACS. Database search resulted in 34 included studies. Forty different definition references were used to define HACS. This review uncovered twenty quantitative methods to assess HACS, including four questionnaires and sixteen quantitative sensory testing measures. The prevalence of HACS in patients with chronic low back pain was estimated in three studies. The current systematic review highlights that multiple definitions, assessment methods, and prevalence estimates are stated in the literature regarding HACS in patients with chronic low back pain. Most of the assessment methods of HACS are not validated but have been tested for reliability and repeatability. Given the lack of a gold standard to assess HACS, an initial grading system is proposed to standardize clinical and research assessments of HACS in patients with a chronic low back. Full article

We aim to develop a comprehensive tunnel lining detection method and clustering technique for semi-automatic rebar identification in order to investigate the ten tunnels along the South-link Line Railway of Taiwan (SLRT). We used the Ground Penetrating Radar (GPR) instrument with a 1000 MHz antenna frequency, which was placed on a versatile antenna holder that is flexible to the tunnel’s condition. We called it a Vehicle-mounted Ground Penetrating Radar (VMGPR) system. We detected the tunnel lining boundary according to the Fresnel Reflection Coefficient (FRC) in both A-scan and B-scan data, then estimated the thinning lining of the tunnels. By applying the Hilbert Transform (HT), we extracted the envelope to see the overview of the energy distribution in our data. Once we obtained the filtered radargram, we used it to estimate the Two-dimensional Forward Modeling (TDFM) simulation parameters. Specifically, we produced the TDFM model with different random noise (0–30%) for the rebar model. The rebar model and the field data were identified with the Hierarchical Agglomerative Clustering (HAC) in machine learning and evaluated using the Silhouette Index (SI). Taken together, these results suggest three boundaries of the tunnel lining i.e., the air–second lining boundary, the second–first lining boundary, and the first–wall rock boundary. Among the tunnels that we scanned, the Fangye 1 tunnel is the only one in category B, with the highest percentage of the thinning lining, i.e., 13.39%, whereas the other tunnels are in category A, with a percentage of the thinning lining of 0–1.71%. Based on the clustered radargram, the TDFM model for rebar identification is consistent with the field data, where k = 2 is the best choice to represent our data set. It is interesting to observe in the clustered radargram that the TDFM model can mimic the field data. The most striking result is that the TDFM model with 30% random noise seems to describe our data well, where the rebar response is rough due to the high noise level on the radargram. Full article

In this research, a new uncertainty method has been developed and applied to forecasting the hotel accommodation market. The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case. The Log-log BeTSUF method estimated by GMM-HAC-Newey-West is considered as a contribution for measuring uncertainty vs. other prognostic models in the literature. The results of our model present better indicators of the RMSE and Ratio Theil’s for the predictive evaluation period of twelve months. Furthermore, the straightforward interpretation of the model and the high descriptive capacity of the model allow economic agents to make efficient decisions. Full article

The features and appearance of the human face are affected greatly by aging. A human face is an important aspect for human age identification from childhood through adulthood. Although many traits are used in human age estimation, this article discusses age classification using salient texture and facial landmark feature vectors. We propose a novel human age classification (HAC) model that can localize landmark points of the face. A robust multi-perspective view-based Active Shape Model (ASM) is generated and age classification is achieved using Convolution Neural Network (CNN). The HAC model is subdivided into the following steps: (1) at first, a face is detected using aYCbCr color segmentation model; (2) landmark localization is done on the face using a connected components approach and a ridge contour method; (3) an Active Shape Model (ASM) is generated on the face using three-sided polygon meshes and perpendicular bisection of a triangle; (4) feature extraction is achieved using anthropometric model, carnio-facial development, interior angle formulation, wrinkle detection and heat maps; (5) Sequential Forward Selection (SFS) is used to select the most ideal set of features; and (6) finally, the Convolution Neural Network (CNN) model is used to classify according to age in the correct age group. The proposed system outperforms existing statistical state-of-the-art HAC methods in terms of classification accuracy, achieving 91.58% with The Images of Groups dataset, 92.62% with the OUI Adience dataset and 94.59% with the FG-NET dataset. The system is applicable to many research areas including access control, surveillance monitoring, human–machine interaction and self-identification. Full article

The usual t test, the t test based on heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators, and the heteroskedasticity and autocorrelation robust (HAR) test are three statistics that are widely used in applied econometric work. The use of these significance tests in trend regression is of particular interest given the potential for spurious relationships in trend formulations. Following a longstanding tradition in the spurious regression literature, this paper investigates the asymptotic and finite sample properties of these test statistics in several spurious regression contexts, including regression of stochastic trends on time polynomials and regressions among independent random walks. Concordant with existing theory (Phillips 1986, 1998; Sun 2004, 2014b) the usual t test and HAC standardized test fail to control size as the sample size $n\to \infty$ in these spurious formulations, whereas HAR tests converge to well-defined limit distributions in each case and therefore have the capacity to be consistent and control size. However, it is shown that when the number of trend regressors $K\to \infty ,$ all three statistics, including the HAR test, diverge and fail to control size as $n\to \infty$ . These findings are relevant to high-dimensional nonstationary time series regressions where machine learning methods may be employed. Full article