Search Results (316,733)

Bio-oil produced via fast pyrolysis, irrespective of the biomass source, faces several limitations, such as high water content, significant oxygenated compound concentration (35–40 wt.%), a low heating value (13–20 MJ/kg), and poor miscibility with fossil fuels. These inherent drawbacks hinder the bio-oil’s desirable properties and usability, highlighting the necessity for advanced processing techniques to overcome these challenges and improve the bio-oil’s overall quality and applicability in energy and industrial sectors. To address the limitations of bio-oil, a magnetic bimetallic oxide catalyst supported on activated rice straw biochar (ZrO₂-Fe₃O₄/AcB), which has not been previously employed for this purpose, was developed and characterized for upgrading rice straw bio-oil in supercritical butanol via esterification. Furthermore, the silica in the biochar, combined with the Lewis acid sites provided by ZrO₂ and Fe₃O₄, offers Brønsted acid sites. This synergistic combination enhances the bio-oil’s quality by facilitating esterification, deoxygenation, and mild hydrogenation, thereby reducing oxygen content and increasing carbon and hydrogen levels. The effects of variables, including time, temperature, and catalyst load, were optimized using response surface methodology (RSM). The optimal reaction conditions were determined using a three-factor, one-response, and three-level Box-Behnken design (BBD). The ANOVA results at a 95% confidence level indicate that the results are statistically significant due to a high Fisher’s test (F-value = 37.07) and a low probability (p-value = 0.001). The minimal difference between the predicted R² and adjusted R² for the ester yield (0.0092) suggests a better fit. The results confirm that the optimal reaction conditions are a catalyst concentration of 1.8 g, a reaction time of 2 h, and a reaction temperature of 300 °C. Additionally, the catalyst can be easily recycled for four reaction cycles. Moreover, the catalyst demonstrated remarkable reusability, maintaining its activity through four consecutive reaction cycles. Its magnetic properties allow for easy separation from the reaction mixture using an external magnet. Full article

Per- and polyfluoroalkyl substances (PFAS) are found in a variety of places including cosmetics, rain jackets, dust, and water. PFAS have also been applied to occupational gear to protect against water and oils. However, PFAS have been identified as immunosuppressants and perfluorooctanoic acid (PFOA), a specific PFAS, has been identified as carcinogenic. Since there is a risk for dermal exposure to these compounds, there is a need to characterize their dermal absorption. Using in vitro flow-through diffusion, skin permeabilities were determined for ¹⁴C-labeled perfluorooctanoic acid (PFOA), perfluorohexanoic acid (PFHxA), and perfluorobutanoic acid (PFBA) using porcine skin. Tests were conducted over 8 h with either acetone or artificial perspirant as the vehicle. PFBA was found to have greater permeability than PFHxA, likely due to having a smaller molecular weight. The dosing vehicle did not appear to impact permeability rates but impacted the disposition through the skin model. While these PFAS compounds showed a low permeability rate through the skin membranes, they can stay in the skin, acting as a reservoir. Full article

In this paper, we propose a Bayesian approach to estimate the curve of a function $f(·)$ that models the solar power generated at k moments per day for n days and to forecast the curve for the $(n+1)$th day by using the history of recorded values. We assume that $f(·)$ is an unknown function and adopt a Bayesian model with a Gaussian-process prior on the vector of values $\mathbf{f}\left(\mathbf{t}\right)=\left(f\left(1\right),\dots , f\left(k\right)\right)$. An advantage of this approach is that we may estimate the curves of $f(·)$ and $f_{n+1}(·)$ as “smooth functions” obtained by interpolating between the points generated from a k-variate normal distribution with appropriate mean vector and covariance matrix. Since the joint posterior distribution for the parameters of interest does not have a known mathematical form, we describe how to implement a Gibbs sampling algorithm to obtain estimates for the parameters. The good performance of the proposed approach is illustrated using two simulation studies and an application to a real dataset. As performance measures, we calculate the absolute percentage error, the mean absolute percentage error (MAPE), and the root-mean-square error (RMSE). In all simulated cases and in the application to real-world data, the MAPE and RMSE values were all near 0, indicating the very good performance of the proposed approach. Full article

Abstract: The urine albumin (Alb)-to-creatinine (Crn) ratio (UACR) is a sensitive and early indicator of chronic kidney disease (CKD) and cardiorenal syndrome. This study developed a portable and wireless electrochemical-sensing platform for the sensitive and accurate determination of UACR. The developed platform consists of a carbon nanotube (CNT)-2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid)(ABTS)-based modified UACR sensor, a miniaturised potentiostat, a cup holder embedded with a magnetic stirrer and a smartphone app. The UACR sensing electrode is composed of two screen-printed carbon working electrodes, one screen-printed carbon counter electrode and a screen-printed AgCl reference electrode. The miniaturised potentiostat, which is controlled by the developed app, performs cyclic voltammetry and amperometry to detect Alb and Crn, respectively. Clinical trials of the proposed system by using spot urine samples from 30 diabetic patients indicate that it can accurately classify all three CKD risk statuses within 30 min. The high accuracy of our proposed sensing system exhibits satisfactory agreement with the commercial biochemical analyser TBA-25FR (Y = 0.999X, R² = 0.995). The proposed UACR sensing system offers a convenient, reliable and affordable solution for personal mobile health monitoring and point-of-care urinalysis. Full article

Tomato is an economically and nutritionally important crop and is vulnerable to drought. Under drought, soil microbes provide beneficial effects to plants and alleviate stress. We suggest a reconstruction of the soil microbiome using biofumigation, an organic farming method, to protect tomatoes. In this study, we treated soil in four ways with varied concentrations: biofumigation (BF0.5, BF1.0, and BF1.5), green manure treatment (GM0.5, GM1.0, and GM1.5), autoclaving (AT), and non-treatment (NT). Tomatoes were grown in each treated soil, subjected to water shortages, and were rewatered. We investigated plant phenotypes and soil properties, focused on microbial communities using the Illumina MiSeq^® System. Relative Water Content and malondialdehyde were measured as plant stress. The results showed that the 1% biofumigation treatment had 105% and 108.8% RWC during drought and after rewatering, compared to the non-treated soil. The highest concentration, the 1.5% treatment, lowered RWC due to an excess of NO₃⁻, K⁺, Ca²⁺, and decreased alpha diversity. Through PLS-PM, bacterial alpha diversity was found to be the largest factor in the increase in RWC (coefficient = 0.3397), and both biofumigant and green manure significantly increased the Shannon index and observed species. In addition, biofumigation increased beneficial functional genes (purine metabolism, pyrimidine metabolism, carbon fixation pathways, and zeatin bio-synthesis) of soil microorganisms (p value < 0.05, <0.01, >0.05, and <0.05, respectively). The 1% biofumigation treatment enriched the core five genera of the fungal network (Enterocarpus, Aspergillus, Leucothecium, Peniophora, and Wallemia) of the fungal network which might suppress the most dominant pathogen, Plectosphaerella. In conclusion, biofumigation-derived soil microbiome alterations have the potential to lower plant stress under drought. Full article

We explore the mathematical behavior of van der Waals gases at temperatures where classical descriptions are inadequate due to emerging quantum effects. Specifically, we focus on temperatures $T_2$ at which the thermal de Broglie wavelength becomes comparable to the interparticle spacing, signifying the onset of quantum mechanical influences. At such temperatures, we find that the isothermal compressibility of the gas becomes negative, indicating mechanical instability. In the pressure–density diagrams, we note that the pressure can become negative at small densities, illustrating the limitations of classical models and the necessity for quantum mechanical approaches. These phenomena serve as clear indicators of the transition from classical thermodynamics to quantum statistical mechanics. The observed mechanical instability and negative pressures represent rare macroscopic manifestations of quantum effects, demonstrating their profound impact on gas behavior. Our study highlights the significant role of emerging quantum properties on observable macroscopic scales, particularly for van der Waals gases at low temperatures and small densities. Additionally, we discuss the theoretical implications of our findings, underlining the limitations of the van der Waals model under extreme conditions and emphasizing the critical need to include quantum corrections in thermodynamic frameworks. Full article

The advancement of 3D printing technology has been remarkable, yet the quality of printed prototypes heavily relies on the rheological behavior of the materials used. This study focuses on optimizing geopolymer-based composite formulas to achieve high-quality 3D printing, with particular attention given to rheological analysis. Three metakaolins, Argical M1200s, Metamax, and Tempozz M88, were used as alumino-silicate precursors for the preparation of the geopolymer binders. Rheological studies were conducted on viscosity, shear stress, and responses to oscillations in amplitude and frequency. The Tempozz M88-based binder was identified as the most effective for the extrusion due to its optimal rheological properties. Subsequently, the study investigated the influence of the amount, up to 55%, and morphology of the fillers, comprising feldspar and wollastonite, on the rheology of the pastes. Also, the addition of Xanthan gum, a gelling agent in the geopolymer paste, was analyzed, revealing improved extrusion quality and more stable bead structures. Finally, a comprehensive comparison was carried out between two formulations chosen according to rheological observations, utilizing image sequences captured during 3D printing. This comparison highlighted the formulation that ensures structural stability, design accuracy, and minimized sagging. This study underscores the significance of geopolymer formula optimization, leveraging rheology as a pivotal tool to enhance 3D printing quality, thereby facilitating more precise and reliable applications of additive manufacturing. Full article

RNA editing is increasingly recognized as a post-transcriptional modification that directly affects viral infection by regulating RNA stability and recoding proteins. the duck hepatitis A virus genotype 3 (DHAV-3) infection is seriously detrimental to the Asian duck industry. However, the landscape and roles of RNA editing in the susceptibility and resistance of Pekin ducks to DHAV-3 remain unclear. Here, we profiled dynamic RNA editing events in liver tissue and investigated their potential functions during DHAV-3 infection in Pekin ducks. We identified 11,067 informative RNA editing sites in liver tissue from DHAV-3-susceptible and -resistant ducklings at three time points during virus infection. Differential RNA editing sites (DRESs) between S and R ducks were dynamically changed during infection, which were enriched in genes associated with vesicle-mediated transport and immune-related pathways. Moreover, we predicted and experimentally verified that RNA editing events in 3′-UTR could result in loss or gain of miRNA–mRNA interactions, thereby changing the expression of target genes. We also found a few DRESs in coding sequences (CDSs) that altered the amino acid sequences of several proteins that were vital for viral infection. Taken together, these data suggest that dynamic RNA editing has significant potential to tune physiological processes in response to virus infection in Pekin ducks, thus contributing to host differential susceptibility to DHAV-3. Full article

This paper is concerned with an end-to-end electrocardiogram (ECG) signal transformation from a continuous-wave (CW) radar signal using a specialized deep learning model. For this purpose, the presented deep learning model is designed using convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) with a maximum-overlap discrete wavelet transform (MODWT) layer and an adaptive neuro-fuzzy network (ANFN) layer. The proposed method has the advantage of developing existing deep networks and machine learning to reconstruct signals through CW radars to acquire ECG biological information in a non-contact manner. The fully connected (FC) layer of the CNN is replaced by an ANFN layer suitable for resolving black boxes and handling complex nonlinear data. The MODWT layer is activated via discrete wavelet transform frequency decomposition with maximum-overlap to extract ECG-related frequency components from radar signals to generate essential information. In order to evaluate the performance of the proposed model, we use a dataset of clinically recorded vital signs with a synchronized reference sensor signal measured simultaneously. As a result of the experiment, the performance is evaluated by the mean squared error (MSE) between the measured and reconstructed ECG signals. The experimental results reveal that the proposed model shows good performance in comparison to the existing deep learning model. From the performance comparison, we confirm that the ANFN layer preserves the nonlinearity of information received from the model by replacing the fully connected layer used in the conventional deep learning model. Full article

Our current understanding of the relationship between personality traits and subjective well-being, or happiness, is limited to the conceptualization of subjective well-being as being life satisfaction and a positive affective experience (i.e., the presence of positive emotions and the absence of negative ones), thus lacking the sense of acceptance, balance, adaptation, and self-transcendent unity (i.e., harmony in life) that is appreciated as part of the good life in many ancient and modern cultures. Moreover, most studies use the Big Five Model to understand which personality traits predict subjective well-being. Here, I examine the predictive power of personality on harmony in life using the Big Five Model, the Dark Triad, and Cloninger’s Biopsychosocial Model. The present study utilized past published data from three cross-sectional studies. In each separate sample, participants self-reported personality by answering the Big Five Inventory (N₁ = 297), the Short Dark Triad (N₂ = 1876), or the Temperament and Character Inventory (N₃ = 436). All participants (N_Total = 3698) answered to the Harmony in Life Scale. The traits in the Biopsychosocial Model explained the highest variance in harmony in life (R² = 0.435, F(7, 428) = 47.136, p < 0.001), followed by the Big Five (R² = 0.341, F(5, 291) = 30.110, p < 0.001) and the Dark Triad (R² = 0.096, F(3, 1872) = 66.055, p < 0.001). The key significant predictors were Self-Directedness, Self-Transcendence, and Harm Avoidance from the Biopsychosocial Model and Agreeableness, Conscientiousness, and Neuroticism from the Big Five. Narcissism was the only predictor from the Dark Triad, although this relationship was very small. The findings underscore the importance of a multidimensional approach for understanding subjective well-being and the inclusion of harmony in life as its third component. The Biopsychosocial Model’s inclusion of both temperament and character dimensions provided the most comprehensive understanding of harmony in life. While positive traits like Agreeableness, Self-Directedness, and Self-Transcendence enhance harmony, negative traits like Neuroticism and Harm Avoidance diminish it. Moreover, research only including “dark traits” might give the impression that an inflated sense of self-importance, a deep need for admiration, and a lack of empathy for others (i.e., Narcissism) is predictive of balance in life. However, this association was not only extremely low but can be interpreted as misguided since the results using the other models showed that helpful, empathetic, kind, and self-transcendent behavior predicted harmony. These results suggest that interventions aimed at enhancing well-being should consider a broad range of personality traits, especially those that are not present in the Big Five Model, thus advocating for a biopsychosocial approach to well-being interventions. Full article

A convolution neural network (CNN) is developed in this work to detect damage in pylons by measuring their vibratory response. More specifically, damage detection through testing relies on the development of damage-sensitive indicators, which are then used to reach a decision regarding the existence/absence of damage, provided they have been retrieved from at least two distinct structural states. Damage indicators, however, exhibit a relatively low sensitivity regarding the onset of structural damage, further exacerbated by the low amplitude response to a variety of environmentally induced loads. To this end, a mathematical model is developed to interpret the experimental data recovered from a fixed-base pylon with a top mass attachment to transverse motion. Damage is introduced in the mathematical model in the form of springs corresponding to the cracking of the beam’s lower end. Families of numerically generated acceleration records are produced at select stations along the beam’s height, which are then used for training a CNN. Once trained, it is used to identify damage from acceleration records produced from a series of experiments. Difficulties faced by CNN in correctly identifying the presence/absence of damage in the pylon are discussed, and steps taken to improve the quality of the results are proposed. Full article

Tartary buckwheat (Fagopyrum tataricum Gaertn.) is a coarse grain crop rich in flavonoids that are beneficial to human health because they function as anti-inflammatories and provide protection against cardiovascular disease and diabetes. Flavonoid biosynthesis is a complex process, and relatively little is known about the regulatory pathways involved in Tartary buckwheat. Here, we cloned and characterized the FtMYB163 gene from Tartary buckwheat, which encodes a member of the R2R3-MYB transcription factor family. Amino acid sequence and phylogenetic analysis indicate that FtMYB163 is a member of subgroup 7 (SG7) and closely related to FeMYBF1, which regulates flavonol synthesis in common buckwheat (F. esculentum). We demonstrated that FtMYB163 localizes to the nucleus and has transcriptional activity. Expression levels of FtMYB163 in the roots, stems, leaves, flowers, and seeds of F. tataricum were positively correlated with the total flavonoid contents of these tissues. Overexpression of FtMYB163 in transgenic Arabidopsis enhanced the expression of several genes involved in early flavonoid biosynthesis (AtCHS, AtCHI, AtF3H, and AtFLS) and significantly increased the accumulation of several flavonoids, including naringenin chalcone, naringenin-7-O-glucoside, eriodictyol, and eight flavonol compounds. Our findings demonstrate that FtMYB163 positively regulates flavonol biosynthesis by changing the expression of several key genes in flavonoid biosynthetic pathways. Full article

The gas sweetening process is essential for removing harmful acid gases, such as hydrogen sulfide (H₂S) and carbon dioxide (CO₂), from natural gas before delivery to end-users. Consequently, chemical absorption-based acid gas removal units (AGRUs) are widely implemented due to their high efficiency and reliability. The most common solvent used in AGRU is monodiethanolamine (MDEA), often mixed with piperazine (PZ) as an additive to accelerate acid gas capture. The absorption performance, however, is significantly influenced by the solvent mixture composition. Despite this, solvent composition is often determined through trial and error in experiments or simulations, with limited studies focusing on predictive methods for optimizing solvent mixtures. Therefore, this paper aims to develop a predictive technique for determining optimal solvent compositions under varying sour gas conditions. An ensemble algorithm, Extreme Gradient Boosting (XGBoost), is selected to develop two predictive models. The first model predicts H₂S and CO₂ concentrations, while the second model predicts the MDEA and PZ compositions. The results demonstrate that XGBoost outperforms other algorithms in both models. It achieves R² values above 0.99 in most scenarios, and the lowest RMSE and MAE values of less than 1, indicating robust and consistent predictions. The predicted acid gas concentrations and solvent compositions were further analyzed to study the effects of solvent composition on acid gas absorption across different scenarios. The proposed models offer valuable insights for optimizing solvent compositions to enhance AGRU performance in industrial applications. Full article

This paper presents the design of a strain-sensitive, dual ball-shaped tunable zone (DBT) taper structure for light intensity modulation. Unlike conventional tapered optical fibers, the DBT incorporates a central light field modulation zone within the taper. By precisely controlling the fusion parameters between single-mode fiber (SMF) and polarization-maintaining fiber (PMF), the ellipticity of the modulation zone can be finely adjusted, thereby optimizing spectral characteristics. Theoretical analysis based on polarization mode interference (PMI) coupling confirms that the DBT structure achieves a more uniform spectral response. In cantilever beam strain tests, the DBT exhibits high sensitivity and a highly linear intensity–strain response (R² = 0.99), with orthogonal linear polarization mode interference yielding sensitivities of 0.049 dB/με and 0.023 dB/με over the 0–244.33 με strain range. Leveraging the DBT’s light intensity sensitivity, a temperature-compensated intensity difference and ratio calculation method is proposed, effectively minimizing the influence of light source fluctuations on sensor performance and enabling high-precision strain measurements with errors as low as ±6 με under minor temperature variations. The DBT fiber device, combined with this innovative demodulation technique, is particularly suitable for precision optical sensing applications. The DBT structure, combined with the novel demodulation method, is particularly well-suited for high-precision and stable measurements in industrial monitoring, aerospace, civil engineering, and precision instruments for micro-deformation sensing. Full article

The release of greenhouse gases (GHGs) is a major contributor to global warming, endangering both human and nonhuman well-being, environmental integrity, economic development, and the planet’s long-term survival. This study delves into the interplay between crop production, livestock production, fertilizer utilization, and agricultural land usage on CO₂ emissions in four Asian economies: China, India, Pakistan, and Bangladesh. Employing panel data analysis techniques, the research uncovers the significant impacts of various agricultural activities on environmental degradation. The findings derived from the panel autoregressive distributed lag (PARDL) estimation reveal that crop production in these emerging economies contributes to CO₂ emissions, as evidenced by the positive coefficients and statistically significant results. Similarly, livestock production and agricultural land used for crop production exhibit a substantial impact on CO₂ emissions, further highlighting their role in environmental degradation. While fertilizer usage also displays a positive coefficient, its impact on CO₂ emissions is not statistically significant. The results of our study highlight the critical importance of addressing the environmental impacts of agricultural practices, particularly in emerging economies. Crop and livestock production, along with the expansion of agricultural land, significantly contribute to CO₂ emissions, which underscores the urgent need for sustainable agricultural practices. These findings suggest that policymakers should prioritize the development and implementation of strategies that mitigate the environmental impacts of agriculture. This could include promoting sustainable land management practices, investing in technology that reduces emissions from crop and livestock production, and encouraging the adoption of eco-friendly fertilizers. Full article