Search Results (485)

Sintered porous mullite-alumina ceramics are obtained from the concentrated suspension of powdered raw materials such as kaolin, gamma and alpha Al₂O₃, and amorphous SiO₂, mainly by a solid-state reaction with the presence of a liquid phase. The modification of mullite ceramic is achieved by the use of micro- and nanosize TiO₂ powders. The phase compositions were measured using an X-ray powder diffraction (XRD) Rigaku Ultima+ (Tokyo, Japan) and microstructures of the sintered specimens were analysed using scanning electron microscopy (SEM) Hitachi TM3000-TableTop (Tokyo, Japan). The shrinkage, bulk density, apparent porosity, and water uptake of the specimens was determined after firing using Archimedes’ principle. The apparent porosity of the modified mullite ceramic is 52–69 ± 1%, water uptake is 33–40 ± 1%, pore size distributions are 0.05–0.8 μm, 0.8–10 μm and 10–1000 μm, and bulk density are variated from 1.15 ± 0.05 to 1.4 ± 0.05 g/cm³. The microsize TiO₂ and nanosize TiO₂ speed up the mullitisation process and allow the decrease in the quantity used as raw material amorphous SiO₂, which was the purpose of the study. The use of nanosize TiO₂ additive increases the porosity of such a ceramic, decreasing the bulk density and linear thermal expansion. Full article

The deep coal seams in the southern Sichuan region contain abundant coalbed methane resources. Determining the characteristics and distribution patterns of coal structures in this study area, and analyzing their impact on pore and fracture structures within coal reservoirs, holds substantial theoretical and practical significance for advancing coal structure characterization methods and the efficient development of deep coalbed methane resources. This paper quantitatively characterizes coal structures through coal core observations utilizing the Geological Strength Index (GSI) and integrates logging responses from different coal structures to develop a quantitative coal structure characterization model based on logging curves. This model predicts the spatial distribution of coal structures, while nitrogen adsorption data are used to analyze the development of pores and fractures in different coal structures, providing a quantitative theoretical basis for accurately characterizing deep coal seam features. Results indicate that density, gamma, acoustic, and caliper logging are particularly sensitive to coal structure variations and that performing multiple linear regression on logging data significantly enhances the accuracy of coal structure identification. According to the model proposed in this paper, primary-fragmented structures dominate the main coal seams in the study area, followed by fragmented structures. Micropores and small pores predominantly contribute to the volume and specific surface area of the coal samples, with both pore volume and specific surface area increasing alongside the degree of coal fragmentation. Additionally, the fragmentation of coal structures generates more micropores, enhancing pore volume and suggesting that tectonic coal has a greater adsorption capacity. This study combines theoretical analysis with experimental findings to construct a coal structure characterization model for deep coal seams, refining the limitations of logging techniques in accurately representing deep coal structures. This research provides theoretical and practical value for coal seam drilling, fracturing, and reservoir evaluation in the southern Sichuan region. Full article

Metabolic Syndrome (MetS) is a complex, multifactorial condition characterized by risk factors such as abdominal obesity, insulin resistance, dyslipidemia and hypertension, which significantly contribute to the development of cardiovascular disease (CVD), the leading cause of death worldwide. Early identification and effective monitoring of MetS is crucial for preventing serious cardiovascular complications. This article provides a comprehensive overview of various biomarkers associated with MetS, including lipid profile markers (triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio and apolipoprotein B/apolipoprotein A1 (ApoB/ApoA1) ratio), inflammatory markers (interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α), plasminogen activator inhibitor type 1 (PAI-1), C-reactive protein (CRP), leptin/adiponectin ratio, omentin and fetuin-A/adiponectin ratio), oxidative stress markers (lipid peroxides, protein and nucleic acid oxidation, gamma-glutamyl transferase (GGT), uric acid) and microRNAs (miRNAs) such as miR-15a-5p, miR5-17-5p and miR-24-3p. Additionally, this review highlights the importance of biomarkers in MetS and the need for advancements in their identification and use for improving prevention and treatment. Seaweed therapy is also discussed as a significant intervention for MetS due to its rich content of fiber, antioxidants, minerals and bioactive compounds, which help improve cardiovascular health, reduce inflammation, increase insulin sensitivity and promote weight loss, making it a promising nutritional strategy for managing metabolic and cardiovascular health. Full article

This paper investigates statistical methods for estimating unknown lifetime parameters using a progressive first-failure censoring dataset. The failure mode’s lifetime distribution is modeled by the odd-generalized-exponential–inverse-Weibull distribution. Maximum-likelihood estimators for the model parameters, including the survival, hazard, and inverse hazard rate functions, are obtained, though they lack closed-form expressions. The Newton–Raphson method is used to compute these estimations. Confidence intervals for the parameters are approximated via the normal distribution of the maximum-likelihood estimation. The Fisher information matrix is derived using the missing information principle, and the delta method is applied to approximate the confidence intervals for the survival, hazard rate, and inverse hazard rate functions. Bayes estimators were calculated with the squared error, linear exponential, and general entropy loss functions, utilizing independent gamma distributions for informative priors. Markov-chain Monte Carlo sampling provides the highest-posterior-density credible intervals and Bayesian point estimates for the parameters and reliability characteristics. This study evaluates these methods through Monte Carlo simulations, comparing Bayes and maximum-likelihood estimates based on mean squared errors for point estimates, average interval widths, and coverage probabilities for interval estimators. A real dataset is also analyzed to illustrate the proposed methods. Full article

This paper proposes and implements a methodology to fit a seven-parameter Generalized Tempered Stable (GTS) distribution to financial data. The nonexistence of the mathematical expression of the GTS probability density function makes maximum-likelihood estimation (MLE) inadequate for providing parameter estimations. Based on the function characteristic and the fractional Fourier transform (FRFT), we provide a comprehensive approach to circumvent the problem and yield a good parameter estimation of the GTS probability. The methodology was applied to fit two heavy-tailed data (Bitcoin and Ethereum returns) and two peaked data (S&P 500 and SPY ETF returns). For each historical data, the estimation results show that six-parameter estimations are statistically significant except for the local parameter, $\mu$. The goodness of fit was assessed through Kolmogorov–Smirnov, Anderson–Darling, and Pearson’s chi-squared statistics. While the two-parameter geometric Brownian motion (GBM) hypothesis is always rejected, the GTS distribution fits significantly with a very high p-value and outperforms the Kobol, Carr–Geman–Madan–Yor, and bilateral Gamma distributions. Full article

Prolactin (PRL) has recently been found to play a role in lipid metabolism in addition to its traditional roles in lactation and reproduction. However, the effects of PRL on lipid metabolism in liver and adipose tissues are unclear. Therefore, we aimed to study the role of PRL on lipid metabolism in goats. Twenty healthy eleven-month-old Yanshan cashmere goats with similar body weights (BWs) were selected and randomly divided into a control (CON) group and a bromocriptine (BCR, a PRL inhibitor, 0.06 mg/kg, BW) group. The experiment lasted for 30 days. Blood was collected on the day before BCR treatment (day 0) and on the 15th and 30th days after BCR treatment (days 15 and 30). On day 30 of treatment, all goats were slaughtered to collect their liver, subcutaneous adipose, and perirenal adipose tissues. A portion of all collected tissues was stored in 4% paraformaldehyde for histological observation, and another portion was immediately stored in liquid nitrogen for RNA extraction. The PRL inhibition had inconclusive effects found on BW and average daily feed intake (ADFI) in goats (p > 0.05). PRL inhibition decreased the hormone-sensitive lipase (HSL) levels on day 30 (p < 0.05), but the effects were inconclusive on days 0 and 15. PRL inhibition had inconclusive effects found on total cholesterol (TCH), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fatty acid synthase (FAS), 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR), and acetyl-CoA carboxylase (ACC) on days 0, 15, and 30 (p > 0.05). Furthermore, hematoxylin–eosin (HE) staining of the liver, subcutaneous adipose, and perirenal adipose sections showed that PRL inhibition had inconclusive effects on the pathological changes in their histomorphology (p > 0.05), but measuring adipocytes showed that the area of perirenal adipocytes decreased in the BCR group (p < 0.05). The qPCR results showed that PRL inhibition increased the expression of PRL, long-form PRL receptor (LPRLR), and short-form PRL receptor (SPRLR) genes, as well as the expression of genes related to lipid metabolism, including sterol regulatory element binding transcription factor 1 (SREBF1); sterol regulatory element binding transcription factor 2 (SREBF2); acetyl-CoA carboxylase alpha (ACACA); fatty acid synthase (FASN); 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR); 7-dehydrocholesterol reductase (DHCR7); peroxisome proliferator-activated receptor gamma (PPARG); and lipase E, hormone-sensitive type (LIPE) in the liver (p < 0.05). In the subcutaneous adipose tissue, PRL inhibition increased SPRLR gene expression (p < 0.05) and decreased the expression of genes related to lipid metabolism, including SREBF1, SREBF2, ACACA, PPARG, and LIPE (p < 0.05). In the perirenal adipose tissue, the inhibition of PRL decreased the expression of the PRL, SREBF2, and HMGCR genes (p < 0.05). In conclusion, the inhibition of PRL decreases the serum HSL levels in cashmere goats; the effects of PRL on lipid metabolism are different in different tissues; and PRL affects lipid metabolic activity by regulating different PRLRs in liver and subcutaneous adipose tissues, as well as by decreasing the expression of the PRL, SREBF2, and HMGCR genes in perirenal adipose tissue. Full article

Low-density foreign objects (LDFOs) in foods pose significant safety risks to consumers. Existing detection methods, such as metal and X-ray detectors, have limitations in identifying low-density and nonmetallic contaminants. To address these challenges, our research group constructed and optimized a continuous-wave sub-terahertz (THz) imaging system for the real-time, on-site detection of LDFOs in infant snacks. The system was optimized by adjusting the attenuation value from 0 to 9 dB and image processing parameters [White (W), Black (B), and Gamma (G)] from 0 to 100. Its detectability was evaluated across eight LDFOs underneath snacks with scanning at 30 cm/s. The optimal settings for puffed snacks and freeze-dried chips were found to be 3 dB attenuation with W, B, and G values of 100, 50, and 80, respectively, while others required 0 dB attenuation with W, B, and G set to 100, 0, and 100, respectively. Additionally, the moisture content of infant snacks was measured using a modified AOAC-based drying method at 105 °C, ensuring the removal of all free moisture. Using these optimized settings, the system successfully detected a housefly and a cockroach underneath puffed snacks and freeze-dried chips. It also detected LDFOs as small as 3 mm in size in a single layer of snacks, including polyurethane, polyvinyl chloride, ethylene–propylene–diene–monomer, and silicone, while in two layers of infant snacks, they were detected up to 7.5 mm. The constructed system can rapidly and effectively detect LDFOs in foods, offering a promising approach to enhance safety in the food industry. Full article

This work presents an improved modelling approach for wind turbine power curves (WTPCs) using fractional differential equations (FDE). Nine novel FDE-based models are presented for mathematically modelling commercial wind turbine modules’ power–velocity (P-V) characteristics. These models utilize Weibull and Gamma probability density functions to estimate the capacity factor (CF), where accuracy is measured using relative error (RE). Comparative analysis is performed for the WTPC mathematical models with a varying order of differentiation ($\alpha$) from 0.5 to 1.5, utilizing the manufacturer data for 36 wind turbines with capacities ranging from 150 to 3400 kW. The shortcomings of conventional mathematical models in various meteorological scenarios can be overcome by applying the Riemann–Liouville fractional integral instead of the classical integer-order integrals. By altering the sequence of differentiation and comparing accuracy, the suggested model uses fractional derivatives to increase flexibility. By contrasting the model output with actual data obtained from the wind turbine datasheet and the historical data of a specific location, the models are validated. Their accuracy is assessed using the correlation coefficient (R) and the Mean Absolute Percentage Error (MAPE). The results demonstrate that the exponential model at $\alpha =0.9$ gives the best accuracy of WTPCs, while the original linear model was the least accurate. Full article

Gamma-aminobutyric acid (GABA) from plants has several bioactivities, such as neurotransmission, anti-cancer cell proliferation, and blood pressure control. Its bioactivities vary when exposed to pH, heat, and ultraviolet. This study analyzed the protective effect of the GABA encapsulation technique using gum arabic (GA) and maltodextrin (MD) and the freeze-drying method. The impact of different ratios of the wall material GA and MD on morphology, GABA content, antioxidant activity, encapsulation efficiency, process yield, and physical properties were analyzed. Results showed that the structure of encapsulated GABA powder was similar to broken glass pieces of various sizes and irregular shapes. The highest GABA content and encapsulation efficiency were, respectively, 90.77 mg/g and 84.36% when using the wall material GA:MD ratio of 2:2. The encapsulated powder’s antioxidant activity was 1.09–1.80 g of encapsulation powder for each formula, which showed no significant difference. GA and MD as the wall material in a 2:2 (w/w) ratio showed the lowest bulk density. The high amount of MD showed the highest Hausner ratio (HR), and Carr’s index (CI) showed high encapsulation efficiency and process yield. The stability of encapsulated GABA powder can be kept in clear glass with a screw cap at 35 °C for 42 days compared to the non-encapsulated one, which can be preserved for only 18 days under the same condition. In conclusion, this study demonstrated that the freeze-drying process for GABA encapsulation preserved GABA component extracts from brown rice while increasing its potential beneficial properties. Using a wall material GA:MD ratio of 2:2 resulted in the maximum GABA content, solubility, and encapsulation efficiency while having the lowest bulk density. Full article

Background/Objectives: Hypnosis shows great potential for managing patients suffering from fibromyalgia and chronic pain. Several studies have highlighted its efficacy in improving pain, quality of life, and reducing psychological distress. Despite its known feasibility and efficacy, the mechanisms of action remain poorly understood. Building on these insights, this innovative study aims to assess neural activity during hypnosis in fibromyalgia patients using high-density electroencephalography (EEG) and self-reported measures. Methods: Thirteen participants with fibromyalgia were included in this study. EEG recordings were done during resting state and hypnosis conditions. After both conditions, levels of pain, comfort, absorption, and dissociation were assessed using a numerical rating scale. Time perception was collected via an open-ended question. The study was prospectively registered in the ClinicalTrials.gov public registry (NCT04263324). Results: Neural oscillations showed increased theta power during hypnosis in the left parietal and occipital electrodes, increased beta power in the frontal and left temporal electrodes, and increased slow-gamma power in the frontal and left parietal electrodes. Functional connectivity using pairwise-phase consistency measures showed decreased connectivity in the frontal electrodes during hypnosis. Graph-based measures, the node strength, and the cluster coefficient were lower in frontal electrodes in the slow-gamma bands during hypnosis compared to resting state. Key findings indicate significant changes in neural oscillations and brain functional connectivity, suggesting potential electrophysiological markers of hypnosis in this patient population. Full article

The design of a sampling unit, whether a simple plot or a subplot within a clustered structure, including shape and size, has received little attention in inferential forestry research. The use of auxiliary variables from remote sensing impacts the precision of estimators from both model-assisted and model-based inference perspectives. In both cases, model parameters are estimated from a sample of field plots and information from pixels corresponding to these units. In studies assisted by remote sensing, the shape of the plot used to fit regression models (typically circular) often differs from the shape of the population elements for prediction, where the area of interest is divided into equal tessellated parts. This raises interest in understanding the effect of the sampling unit shape on the mean of variables in forest stands of interest. Therefore, the objective of this study was to evaluate the effect of circular and square subplots, concentrically overlapped and arranged in an inverted Y cluster structure, over tree density, basal area, and aboveground biomass in a managed temperate forest in central Mexico. We used a Multivariate Generalised Linear Mixed Model, which considers the Gamma distribution of the variables and accounts for spatial correlation between Secondary Sampling Units nested within the Primary Sampling Unit. The main findings of this study indicate that the type of secondary sampling unit of the same area and centroid, whether circular or square, does not significantly affect the mean tree density (trees), basal area (m²), and aerial biomass. Full article

With advancements in gravitational wave detection technology, an increasing number of binary neutron star (BNS) merger events are expected to be detected. Due to the narrow opening angle of jet cores, many BNS merger events occur off-axis, resulting in numerous gamma-ray bursts (GRBs) going undetected. Models suggest that kilonovae, which can be observed off-axis, offer more opportunities to be detected in the optical/near-infrared band as electromagnetic counterparts of BNS merger events. In this study, we calculate kilonova emission using a three-dimensional semi-analytical code and model the GRB afterglow emission with the open-source Python package afterglowpy at various inclination angles. Our results show that it is possible to identify the kilonova signal from the observed color evolution of BNS merger events. We also deduce the optimal observing window for SVOM/VT and JWST/NIRCam, which depends on the viewing angle, jet opening angle, and circumburst density. These parameters can be cross-checked with the multi-band afterglow fitting. We suggest that kilonovae are more likely to be identified at larger inclination angles, which can also help determine whether the observed signals without accompanying GRBs originate from BNS mergers. Full article

To investigate the mechanism and influence of applying gamma-polyglutamic acid (γ-PGA) on soil water infiltration, laboratory experiments and numerical simulations were conducted using Hydrus-1D. These studies assessed the impact of various application rates of γ-PGA on soil water characteristic parameters. Orthogonal simulation experiments on soil bulk density, γ-PGA application rates, and burial depths were performed utilizing predefined soil water characteristic values (twelve groups: nine groups of numerical simulation experiments and three groups of laboratory verification tests), and the soil infiltration characteristics were analyzed. Concurrently, an empirical model was developed to elucidate the relationships between the empirical model parameters and influencing factors, as well as to examine the sensitivity of these factors to changes in soil infiltration rate. The relationship between cumulative infiltration and the distance of wetting front movement, based on the water balance equation, was refined. The results indicated that γ-PGA significantly affected soil water characteristic parameters, where the saturated water content and the reciprocal of soil intake suction increased with rising γ-PGA applications (p < 0.01), while the saturated hydraulic conductivity and the parameter n decreased (p < 0.01), with no notable changes in the retained water content (p > 0.05). The trend in cumulative infiltration influenced by various factors could be modeled by a capacitive charging model function, which yielded a superior fit. A negative correlation existed between the sensitivity index and all the influencing factors (p < 0.05), with the order of influence being soil bulk density, γ-PGA application rate, and γ-PGA burial depth, respectively. Utilizing the modified water balance equation, the ratio of cumulative infiltration to wetting front migration distance corresponded more closely with a power function. These findings provide a theoretical foundation for further studies on the effects of γ-PGA on crop growth characteristics in fields and the optimization of γ-PGA technical element combinations. Full article

The goal of neuroaesthetic research is to understand the neural mechanisms underpinning the perception and appreciation of art. The human brain has the remarkable ability to rapidly recognize different artistic styles. Using functional connectivity, this study investigates whether there are differences in connectivity networks formed during the processing of abstract and figurative paintings. Eighty paintings (forty abstract and forty figurative) were presented in a random order for eight seconds to each of the 29 participants. High-density EEG recordings were taken, from which functional connectivity networks were extracted at several time points (−300, 100, 300 and 500 ms). The debiased weighted phase lag index (dwPLI) was used to extract the connectivity networks for the abstract and figurative conditions across multiple frequency bands. Significant connectivity differences were detected for both conditions at each time point and in each frequency band: delta (p < 0.0273), theta (p < 0.0292), alpha (p < 0.0299), beta (p < 0.0275) and gamma (p < 0.0266). The topology of the connectivity networks also varied over time and frequency, indicating the multi-scale dynamics of art style perception. The method used in this study has the ability to identify not only brain regions but their interaction (communication) patterns and their dynamics at distinct time points, in contrast to average ERP waveforms and potential distributions. Our findings suggest that the early perception stage of visual art involves complex, distributed networks that vary with the style of the artwork. The difference between the abstract and figurative connectivity network patterns indicates the difference between the underlying style-related perceptual and cognitive processes. Full article

The Pareto–Feller distribution has been widely used across various disciplines to model “heavy-tailed” phenomena, where extreme events such as high incomes or large losses are of interest. In this paper, we present a new bivariate distribution based on the Appell hypergeometric function with marginal Pareto–Feller distributions obtained from two independent gamma random variables. The proposed distribution has the beta prime marginal distributions as special case, which were obtained using a Kibble-type bivariate gamma distribution, and the stochastic representation was obtained by the quotient of a scale mixture of two gamma random variables. This result can be viewed as a generalization of the standard bivariate beta I (or inverted bivariate beta distribution). Moreover, the obtained bivariate density is based on two confluent hypergeometric functions. Then, we derive the probability distribution function, the cumulative distribution function, the moment-generating function, the characteristic function, the approximated differential entropy, and the approximated mutual information index. Based on numerical examples, the exact and approximated expressions are shown. Full article