Search Results (610)

Background/Objectives: Artificial teeth in complete dentures are classified according to the materials used: porcelain (PO) or composite resin (CR). However, these materials’ effects on function, patient satisfaction, and quality of life (QOL), as well as occlusal wear, remain unclear. We compared PO and CR complete dentures in edentulous patients by assessing masticatory function, patient satisfaction, and oral health-related QOL at 3, 6, and 12 months post-insertion, as well as occlusal surface morphology owing to material differences. Methods: In this open-label, randomized, single-center, parallel-group study, participants were edentulous patients who visited our hospital and underwent treatment with new complete dentures. The outcomes were oral health-related QOL; subjective satisfaction, assessed using a visual analog scale; and masticatory performance, evaluated with gummy jelly and were assessed at baseline and 3, 6, and 12 months post-denture insertion. Occlusal surface impressions were taken twice, digitized as STL models, superimposed, and analyzed for wear. The Wilcoxon rank-sum test was used to compare between groups. Results: All evaluated items showed improvement. However, no significant differences were observed between the PO and CR groups, including between the amount of wear observed in the two groups. However, the PO group showed a tendency toward less wear. Extended observation may be required to clarify the long-term effects of artificial tooth materials. Conclusions: In the short term, the artificial tooth material did not influence masticatory function, oral health-related QOL, or patient satisfaction. Full article

We explore how easy it is to enforce the advent of exceptional points starting from random matrices of non-Hermitian nature. We use the Petermann factor, whose mathematical version is called “overlap”, for guidance, as well as simple pseudo-spectral tools. We attempt to proceed in the most agnostic way, by adding random perturbation and checking basic metrics such as the sum of all vectors’ Petermann factors, equivalently the sum of diagonal overlaps. Issues such as the location of high Petermann factors vs. the modulus of eigenvalue are addressed. We contrast the fate of exploratory approaches in the Ginibre set (real matrices) and complex matrices, noting the special role of exceptional points on the real axis for the Ginibre matrices, completely absent in complex matrices. Full article

In recent years, the installed capacity of renewable energy sources, such as wind power and photovoltaic generation, has been steadily increasing in power systems. However, the inherent randomness and volatility of renewable energy generation pose greater challenges to grid frequency stability. To address this issue, this paper first introduces the Minkowski sum algorithm to map the feasible regions of dispersed individual units into a high-dimensional hypercube space, achieving efficient aggregation of large-scale schedulable capacity. Compared with conventional geometric or convex-hull aggregation methods, the proposed approach better captures spatio-temporal coupling characteristics and reduces computational complexity while preserving accuracy. Subsequently, aiming at the coordination challenge between day-ahead planning and real-time dispatch, a “hierarchical coordination and dynamic optimization” control framework is proposed. This three-layer architecture, comprising “day-ahead pre-dispatch, intraday rolling optimization, and terminal execution,” combined with PID feedback correction technology, stabilizes the output deviation within ±15%. This performance is significantly superior to the market assessment threshold. The research results provide theoretical support and practical reference for the engineering promotion of vehicle–grid interaction technology and the construction of new power systems. Full article

Background/Objectives: This study evaluated whether yoghurt containing Apilactobacillus kunkeei DSM 12361 protects rats against non-alcoholic fatty liver disease (NAFLD). We hypothesized that this fructophilic probiotic, with anti-inflammatory properties, may affect NAFLD progression by improving the gut microbiome, lowering intestinal ethanol production, and modulating inflammatory and metabolic pathways linked to hepatic fat accumulation. Methods: Wister rats were randomized into three groups; rats in the control group (HFrD) were fed a high-fructose (70%) diet while rats in experimental groups were fed the same diet mixed with 10% of yoghurt containing YC-180 starter culture (HFrD-Y) or yoghurt containing YC-180 and Apilactobacillus kunkeei DSM 12361 (HFrD-Y-A). Results: After six weeks of intervention, levels of plasma triglycerides, cholesterol, glucose, liver enzymes (ALT and AST), interleukin (IL)-6, fecal ethanol, Enterobacteriaceae, and hepatic index were significantly increased (p < 0.05) in the HFrD group as compared to rats in both experimental groups. Moreover, plasma levels of liver enzymes, lipid profile, glucose, and IL-6 were significantly lower (p < 0.05) in rats of the HFrD-Y-A group than those in the HFrD-Y group. Furthermore, plasma levels of IL-10 and fecal Lactobacilli and Bifidobacteria were significantly increased (p < 0.05) in the experimental groups when compared to rats in the control group. Conclusions: In sum, the obtained results indicated that yoghurt containing Apilactobacillus kunkeei could decrease the risk of non-alcoholic fatty liver disease (NAFLD) through (a) blocking the inflammation process associated with NAFLD, (b) enhancing the lipid profile, (c) lowering fecal ethanol, and (III) decreasing the levels of fecal Enterobacteriaceae in comparison with levels of fecal Lactobacilli and Bifidobacteria in rats. More research on molecular mechanisms of the potential effects of the Apilactobacillus kunkeei strain against NAFLD is still required. Full article

With the growing penetration of renewable energy, automatic generation control (AGC) faces challenges like frequent frequency fluctuations and tie-line power deviations. Traditional proportional (PROP) allocation algorithms, limited by fixed weights, struggle to adapt to dynamic system changes. To address this, this study proposes a coordinated AGC allocation framework fusing a dual-flow Gate Recurrent Unit (GRU) with residual Multilayer Perceptron (MLP) based on PROP, preserving physical prior knowledge while learning adaptive correction terms. Validated on a provincial power grid, the proposed method reduces the cumulative absolute ACE (Sum) by about 0.3–0.9% compared with PROP under 10–100 MW step disturbances. Under random disturbances, it achieves larger reductions of about 3.2% (vs. PROP) and 4.8% (vs. MLP), while maintaining interpretability and deployment feasibility, improving the relevant performance indicators of AGC unit allocation while maintaining interpretability and deployment feasibility, providing an effective solution for AGC under high renewable energy penetration. Full article

We prove that any positive random variable may be the limit of the random sums of independent identically distributed random variables. Thus, the limit distribution in the case of summing a random number of random variables may not be stable. The lack of stability in the limit distribution significantly distinguishes the summation scheme for a random number of random variables from the classical summation scheme. Moreover, the analogs of stable distributions in the summation of a random number of terms also turn out to be limit distributions in a suitable summation scheme. Full article

Accurate measurement of plant height in leafy vegetables is challenging due to their short stature, high planting density, and severe canopy occlusion during later growth stages. These factors often limit the reliability of single-plant monitoring across the full growth cycle in open-field environments. To address this, we propose a multi-temporal point cloud alignment method for accurate plant height measurement, focusing on Choy Sum (Brassica rapa var. parachinensis). The method estimates plant height by calculating the vertical distance between the canopy and the ground. Multi-temporal point cloud maps are reconstructed using an enhanced Oriented FAST and Rotated BRIEF–Simultaneous Localization and Mapping (ORB-SLAM3) algorithm. A fixed checkerboard calibration board, leveled using a spirit level, ensures proper vertical alignment of the Z-axis and unifies coordinate systems across growth stages. Ground and plant points are separated using the Excess Green (ExG) index. During early growth stages, when the soil is minimally occluded, ground point clouds are extracted and used to construct a high-precision reference ground model through Cloth Simulation Filtering (CSF) and Kriging interpolation, compensating for canopy occlusion and noise. In later growth stages, plant point cloud data are spatially aligned with this reconstructed ground surface. Individual plants are identified using an improved Euclidean clustering algorithm, and consistent measurement regions are defined. Within each region, a ground plane is fitted using the Random Sample Consensus (RANSAC) algorithm to ensure alignment with the X–Y plane. Plant height is then determined by the elevation difference between the canopy and the interpolated ground surface. Experimental results show mean absolute errors (MAEs) of 7.19 mm and 18.45 mm for early and late growth stages, respectively, with coefficients of determination (R²) exceeding 0.85. These findings demonstrate that the proposed method provides reliable and continuous plant height monitoring across the full growth cycle, offering a robust solution for high-throughput phenotyping of leafy vegetables in field environments. Full article

Background/Objectives: Food is Medicine (FIM) programs have been shown to be effective at addressing food and nutrition insecurity among individuals. However, more evidence is needed to determine effective interventions at the household level and their impact on child health outcomes. Feeding the Family is a randomized controlled trial which aims to determine whether the amount of food provided and the ability to select foods in FIM interventions have an incremental effect on child and caregiver clinical outcomes relative to nutrition counseling alone. The objective of this paper is to describe the population at baseline among those enrolled in Feeding the Family, an FIM family intervention. Methods: A pragmatic randomized controlled trial (pRCT) with a 2 × 2 factorial study design was used at an urban primary care clinic. Participants were randomized into one of four arms for a 3-month intervention: (1) medically tailored meals (MTMs), (2) grocery prescription (GP), (3) combined MTMs + GP, and (4) delayed control. Primary outcomes consisted of child and caregiver biomarkers (BMI, blood pressure, A1c, LDL, and HDL). Secondary outcomes included child and caregiver dietary behaviors, nutrition security, and food security. Spearman correlations and Kruskal–Wallis rank sum tests determined correlations between caregiver and child biomarkers, as well as correlations between caregiver socioeconomic factors and child outcomes, respectively. Results: Thirty-one caregivers and fifty-one children were enrolled. Nearly 90% of caregivers reported low–very low household food security; 93.6% experienced ongoing financial strain. Several caregiver–child biomarker correlations were observed, including caregiver and child BMI (r = 0.59, p = 0.043), caregiver LDL and child A1c (r = −0.79, p = 0.004), and caregiver total cholesterol and child BMI (r = −0.62, p = 0.032). In addition, food assistance status was associated with child vegetable intake (H = 6.16, df = 2, p = 0.046), and caregiver food security score was associated with child food security score (H = 18.31, df = 9, p = 0.032). Conclusions: There are robust correlations between caregiver and child clinical outcomes at baseline. These findings underscore the need for FIM research to examine how a tailored program can improve the clinical outcomes of entire households to address health disparities effectively. Full article

Fruit growing is a traditional component of Bulgarian agricultural production. According to the latest statistical data, the share of areas planted with cherries is 10.5% of the total orchard area, and with apples, 7.2%, totaling 67,800 ha. This article presents the results of ground and remote (satellite) measurements and observations of cherry and apple orchards, along with the methods for their processing and interpretation, to define the current state and forecast their expected development. This research aims to combine the capabilities of the two approaches by improving and expanding observation and forecasting activities. Ground-based measurements and observations consider the dates of a permanent transition in air temperature above 5 °C and several cardinal phenological stages, based on the idea that a certain temperature sum (CU, GDH, GDD) must accumulate to move from one phenological stage to another. The obtained data were statistically analyzed, and by means of classification with the Random Forest algorithm, the dates for the occurrence of the stages of bud break, flowering, and fruit ripening in the development of cherry and apple orchards were predicted with an accuracy of −6 to +2 days. Satellite studies include creating a database of Sentinel-2 digital images across different spectral bands for the studied orchards, investigating various post-processing approaches, and deriving indicators of developmental phenostages. Ground data from the 2021–2023 experiment in Kyustendil and Plovdiv were used to determine the phases of fruit bursting, flowering, and ripening through satellite images. An assessment of the two approaches to predicting the development of the accuracy of the models was carried out by comparing their predictions for bud swelling and bursting (BBCH 57), flowering (BBCH 65), and fruit ripening (BBCH 87/89) of the observed phenological events in the two selected orchard types, representatives of stone and pome fruit species. Full article

Background. Acute radiation injury to the small-intestinal mucosa and the hematopoietic system is a key determinant of early mortality after high-dose total-body irradiation. ε-Aminocaproic acid (EACA), a lysine analogue with antifibrinolytic properties, has been proposed as a potential radioprotective agent, but its effects on intestinal and hematologic injury remain insufficiently characterized. Methods. In this experimental study, 240 male Wistar rats were subjected to single-dose total-body γ-irradiation at 10.6 Gy and randomized into six groups: two non-irradiated controls (CG-1, CG-2), an irradiated control without treatment (CG-3), and three experimental groups receiving EACA (EG-1: 3 h before irradiation; EG-2: 3 h after irradiation; EG-3: both 3 h before and 3 h after irradiation). Pain behavior was assessed using the Rat Grimace Scale. Intestinal damage was evaluated by a modified Radiation Injury Intestinal Mucosal Damage Score (RIIMS_sum), villus and crypt morphometry, and qualitative histology of the ileum. Hemoglobin, leukocytes, and platelets were measured serially, and 30-day survival was analyzed using Kaplan–Meier curves with log-rank tests. Results. Across all EACA regimens, the odds of being in a higher Rat Grimace Scale pain category were reduced compared with CG-3, with the strongest effect in EG-3 (OR 0.42; 95% CI 0.31–0.58). At 72 h after irradiation, the cumulative RIIMS score was lower in EACA-treated groups by approximately 17–36% versus CG-3, with the lowest injury in EG-3 (18.5 vs. 29.0 points). EACA attenuated shortening and blunting of villi, preserved crypt architecture, and mitigated anemia, leukopenia, and thrombocytopenia. Thirty-day survival was 20% in CG-3 and 60%, 65%, and 80% in EG-1, EG-2, and EG-3, respectively (all p < 0.05 vs. CG-3). Conclusions. ε-Aminocaproic acid exerts a pronounced, timing-dependent radioprotective effect in a rat model of acute total-body γ-irradiation, concurrently reducing the severity of radiation enteritis, hematologic toxicity, and early mortality. These findings support further investigation of EACA as a candidate adjunct in the prevention of acute radiation injury. Full article

Islamic Microfinance Institutions (IMFIs) encounter distinct difficulties with credit scoring because they need to follow Shariah principles that combine riba bans with fair financial dealings regulations. Conventional credit scoring models exhibit two shortcomings: a poor capability to incorporate non-financial behavioral data and inadequate support for Islamic Microfinance Institutions’ requirements. Researchers use machine learning coupled with blockchain technology to create an adaptive Shariah-compliant credit scoring method that solves problems found in standard evaluation systems. Using a dataset of 1275 farmers with 52 weeks of transaction data, we implemented and compared three ML models: Linear Regression, Random Forest, and Gradient Boosting. Data preparation involved addressing 53% missing transaction data, followed by summing weekly financial activity to prepare it for predictive evaluations. Our analysis shows that the Random Forest model produced the best results with an R-squared value of 0.87 and a Mean Squared Error (MSE) of 12.4. In creditworthiness binary classification tasks, Gradient Boosting delivered an F1 score of 0.91 while maintaining precision at 0.89 and recall at 0.93. Blockchain integration exists to protect data through secure mechanisms that also conserve Islamic financial integrity and promote transparency. The research shows how ML and Blockchain technology enable fundamental changes in IMFIs by delivering elevated predictive accuracy, operational enhancements, and complete transparency. The conceptual framework guides ethical financial inclusion strategy by offering a solution for marginalized communities, but remains consistent with global sustainability objectives. The research established foundational elements for implementing cutting-edge technologies within IMFIs, which will promote new economic growth and build confidence in Shariah-compliant financial systems. Full article

The Haezendonck–Goovaerts (HG) risk measure defined on Orlicz spaces via the so-called normalised Young function is a direct generalisation of the Expected Shortfall risk measure. The HG measure is known to be a coherent one, thus making it more robust than some of the alternatives, such as Value-at-Risk, for aggregating and comparing risks, and at the same time more flexible for capital allocation problems, risk premium estimation, solvency assessment, and stress testing in insurance and finance. As random risk in practical applications is often assessed in a portfolio setting—a collection of insurance policies or financial assets, like stocks or bonds—we examine the situation in which the total portfolio risk is expressed as the sum of individual random risks. For this, we consider the sum $S_n^{\left(\xi \right)}={\xi }_1+\dots +{\xi }_n$ of possibly dependent and non-identically distributed real-valued random variables ${\xi }_1,\dots ,{\xi }_n$ with consistently varying distributions. Assuming that the collection $\{{\xi }_1,\dots ,{\xi }_n\}$ follows the dependence structure, similar to the asymptotic independence, we obtain the asymptotic estimations of the HG risk measure for the sum $S_n^{\left(\xi \right)}$ when the confidence level tends to 1. The formulas presented in our work show that in the case where a portfolio of random losses contains consistently varying losses and the others are asymptotically negligible, it is sufficient for risk assessment to consider only the tails of those dominant losses. Full article

Accurate estimation of commercial tree height is essential for volumetric predictions in forest management plans, particularly in Amazonian forests with high species diversity. We assessed two predictive approaches for estimating commercial height, using the sum of actual commercial log lengths as the reference metric. The dataset comprised 1745 harvested trees from Annual Production Unit 8 in the Tapajós National Forest. Three commercial volume groups dominated the structural gradient: 46.1% of the trees Group 1 (<6 m³), 36.7% into Group 2 (6–10 m³), and 17.2% into Group 3 (≥10 m³). The Linear Mixed-Effects Model included diameter at breast height (DBH) as a fixed effect and species as a random effect, whereas the Random Forest model used DBH and species as predictors. The mixed-effects model achieved higher accuracy (r = 0.77; RMSE = 2.95 m), while the Random Forest model performed slightly worse (r = 0.73; RMSE = 3.10 m). Species with greater commercial heights exerted a strong influence on both modelling approaches. Principal Component Analysis revealed structural separation among the three volume groups, driven by DBH, commercial height, number of logs, and log volume. The mixed-effects model provided effective framework for predicting commercial height in heterogeneous tropical forests. Full article

Suppose $\overrightarrow{x}$ and $\overrightarrow{y}$ are two arbitrary fixed points in $d$-dimensional space and $\overrightarrow{Z}$ is a random vector with a known probability density. It is desired in some applications to find the joint probability distribution function for the distance between $\overrightarrow{x}$ and $\overrightarrow{Z}$ and the distance between $\overrightarrow{y}$ and $\overrightarrow{Z}$. This calculation has applications in signal processing, goodness-of-fit testing and two-sample testing. In this article, the efficient numerical calculation of the probability distribution is illustrated. The calculation reduces to the sum of two separate integrals where each integral is over a spherical cap. This is achieved by a transformation of the complex spherical intersection region into a sum of integrals over hypercubes via a carefully constructed variable change. The general approach applies to any application where integrals over a region defined by a spherical cap need to be evaluated. Full article

Chickpeas are a pulse crop that originated in Eurasia and are a source of protein for many people. The objective of this research is to select stable, high-yielding chickpea genotypes using uni- and multivariate methods of adaptability and stability analysis. Fifteen genotypes were tested in the 2020 and 2021 agricultural years. The experimental design was a completely randomized block design with three replications. The collected data were yield (kg/ha) values, and the stability analyses were performed using Eberhart and Russell’s, Lin and Binns’s modified by Carneiro’s, additive main effects and multiplicative interaction (AMMI), and weighted average of absolute scores (WAASB) methods. The average sum of ranks (ASR) was then calculated by ranking genotypes according to their yield and stability indices. The AMMI analysis of variance showed significant effects (p < 0.05) for environments, genotypes, and the interaction between genotypes and environments. From AMMI, the first three principal components (PCs) had significant effects, and the cumulative variance on the PC1 and PC2 axes was 86%. FLIP02-23C, FLIP03-109C, and Jamu 96 had the lowest ASR, indicating that these genotypes are the most stable and productive chickpea genotypes. According to AMMI2, genotypes FLIP03-109C, FLIP03-35C, FLIP02-23C, and FLIP06-155C could be adapted to irrigated environments. Full article