Search Results (3,643)

CO₂ is a promising fracturing fluid for tight reservoirs because it avoids water-phase damage and offers low viscosity, high diffusivity, and strong penetration into fine pore throats, but its pore-scale flow in pore-fracture systems remains difficult to evaluate because thermodynamic state, fractures, and mass transfer act together. In this study, a radial microfluidic model containing randomly distributed microfractures was used with a temperature- and pressure-controlled visualization platform to compare CO₂–oil and water–oil flow. Image segmentation and areal-fraction statistics quantified swept area and final fluid distribution. Gaseous CO₂ at ambient pressure and compressed-liquid CO₂ below the critical temperature differ substantially in density and viscosity, but both retain a discernible CO₂–oil interface and exhibit pressure-driven preferential-path flow. The gaseous case shows strong fracture guidance and fingering, whereas the compressed-liquid velocity series demonstrates increasingly rapid advancement and stronger channeling at excessive velocity. Under near-critical supercritical conditions (35 °C, 8 MPa), progressive oil-color fading ahead of the displacement front shows that dissolution participates while flow expands through matrix pores. Under higher-temperature supercritical conditions, disappearance of the sharp interface and continuous color attenuation identify dissolution-assisted diffusion as a significant transport mechanism and produce diffuse redistribution across the pore space. Water undergoes immiscible channelized displacement and remains capillary-trapped in small throats and low-permeability regions. The results identify three flow regimes: distinct-interface pressure-driven displacement, near-critical convection–dissolution coupling, and higher-temperature supercritical dissolution-assisted diffuse redistribution. Full article

Background: Spinocerebellar ataxia type 2 (SCA2) is an inherited neurodegenerative disorder characterized by progressive cerebellar degeneration. One difficulty in treating this disease lies in identifying preclinical carriers: individuals who carry the pathogenic ATXN2 mutation but remain asymptomatic with respect to motor manifestations. Though magnetic resonance imaging (MRI) has proven valuable in supporting the diagnosis of ataxia, traditional univariate approaches using linear measurements have shown limited ability to capture the complex anatomical changes that occur across the disease spectrum, particularly during the preclinical phase. Methods: This study employed a comprehensive multivariate approach to improve the classification of individuals across the SCA2 spectrum. We developed a multinomial logistic regression model incorporating multiple linear measurements derived from magnetic resonance imaging to discriminate between healthy controls (n = 72), preclinical carriers (n = 17), and patients with manifest SCA2 (n = 61). To mitigate inherent class imbalance, particularly in the smaller preclinical subgroup, we implemented the Synthetic Minority Over-sampling Technique (SMOTE), generating a balanced dataset that enhances the model’s ability to discern the distinctive anatomical features. This was compared to the model applied to the unbalanced data. An improvement was observed when applying SMOTE. Results: The multivariate model demonstrated discriminatory performance, achieving an overall accuracy of 80.7%. The ability to identify healthy controls (AUC: 0.96), preclinical individuals (AUC: 0.75), and clinical individuals (AUC: 95%). This represents an advance over previous univariate approaches, which have had difficulty capturing the neurodegenerative changes characteristic of the preclinical stage. Conclusions: By integrating multiple neuroimaging biomarkers into a multivariable model, this study provides a tool for early identification of preclinical SCA2 carriers. The ability to accurately classify these individuals opens an opportunity for early therapeutic intervention before irreversible neurological deterioration occurs. This approach shows promise for optimizing clinical trial design and personalized care in SCA2. Full article

The safety profile for bio-derived phenols post-oxidation and their related antioxidant/redox potential remain largely under-explored. Oxidation by fungi, in terms of environmental impacts via fungal oxidation by enzymes, remains an attractive strategy under milder conditions, since it is one route by which many naturally occurring lignocellulosic phenols are modified; thus, an immediate need still exists for characterizing the effects that these modified phenolic compounds may have. Methodology: We examined four different biomass-derived phenolics—vanillin, ferulic acid, syringaldehyde and guaiacol—that were oxidized with fungal laccase and characterized their effects on normal human lung fibroblasts and levels of cellular oxidative stress. Laccase activity was evaluated via the ABTS method and through simple observation and UV-Vis spectroscopic scanning of the phenolics in question, and compared with the untreated version of each phenolic. In addition to assessing the cytotoxic effect and oxidative stress generated by the phenols alone, an ELISA-based measurement assay was used to investigate the relative abundance of malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx) and reduced glutathione (GSH) in the human normal lung fibroblast cell line under varying treatment regimes, complemented by phase-contrast microscopy. Scores integrating the biomarkers were analyzed via clustering, PCA, radar and Pearson correlation analyses, to discern distinct trends in antioxidant potential after laccase conversion. Observations: Each of the four tested phenolics demonstrated the presence of laccase activity, leading to substantial differences in visible appearance compared with the control and characteristic absorbance shifts at differing wavelengths from the original molecule. Cell viability dropped dramatically as phenol concentration was increased and the untreated phenolics resulted in diminished confluence and induced greater levels of oxidative damage, from guaiacol and syringaldehyde. Laccase treatment resulted in higher MTT reduction activity and improved cellular morphology compared with the corresponding untreated phenolic compounds. Untreated phenols induced the highest levels of MDA, while decreasing SOD, CAT, GPx and GSH levels. Post-oxidation with laccase, there were lower amounts of lipid peroxidation, along with improved levels of antioxidant activity compared with the control phenol. Multi-technique analyses show clear distinctness between the untreated and laccase-converted phenolic groups. Clustering with multivariate techniques separated all cell groups in line with control samples, grouping the laccase-converted treatments towards the middle and displaying an inverse relationship between MDA and the antioxidant markers. Conclusions: Laccase conversion markedly decreases the adverse effects that bio-derived phenols have on normal cell viability and induces fewer detrimental effects on the cellular redox balance. This is a critical discovery in terms of finding greener methods by which to upgrade bio-derived substances as we research these lignocellulosic phenols. By employing ELISA-based measurements along with multiple analysis techniques, we present a suitable paradigm for studying biological effects in all bio-based goods intended for pharmaceuticals, packaging materials, nutraceuticals or a host of different applications. Full article

This study investigates the mechanical performance of a pulsed laser butt-welded IN792 DS joint and its relationship to its microstructure by means of grid nanoindentation. A new ISE-free (rate-derived) hardness parameter (H_R) has been introduced to account for the local bulk elastoplastic behavior of the material in combination with the stable contribution of residual stress, thus overcoming the limitations of the current standard codes. It allows performance comparability between different welding experiments, materials, and joint configurations. It offers an alternate means to mechanically determine the HAZ width when microscopic and metallurgical methods fail to detect it. Moreover, the spectra of two independent indentation parameters have been utilized as an input within an iterative statistical deconvolution scheme to estimate the composition of the relevant phases present within the fused zone. While one parameter spectrum acted as a predictor in the first stage, the second one served as a corrector for the final estimation of the four detected phases, thereby self-validating the iteration procedure with 5% tolerance. The validity of phase estimation was first determined over the entire FZ and then at three levels of the weald seam (top, neck and bottom) for further validation. The results indicate that the γ-matrix and ultrafine fine/hard second phases in the fused zone amounted to 54% and 43% volume fractions, respectively. The associated deconvoluted mechanical performance, expressed in terms of E_IT, H_IT, and H_R, corresponded to approximately 209 ± 4.5, 6.3 ± 0.2, 4.4 ± 0.1 and 224 ± 7.0, 6.7 ± 0.1, and 4.6 ± 0.1 GPa, respectively. A correlation between the estimated phases and the local mechanical performance via the conventional indentation parameter (H_IT and E_IT) and the new H_R parameter in the three relevant regions of the fused zone was discussed while discerning the effect of cooling rate on precipitate size, heterogeneity, porosity, residual stresses, and grain orientation. Further validation studies on different sample geometries, materials and joint configurations are needed to confirm the generality of the proposed methodology. Full article

This study investigated balance control during the half squat by analyzing the relationship between the center of mass (CoM) and the center of pressure (CoP) in five experienced male weightlifters performing segmented squats at five load levels (20–80% 1 RM) across four Power-Based Training (PBT) exercises. The area of the 95% confidence ellipse was quantified using the Vicon motion capture system in conjunction with AMTI force plates. Given the small sample size (n = 5), a dual inference approach was implemented—frequentist repeated-measures analysis of variance (ANOVA) complemented by a unified adaptive Bayesian hierarchical model—to mitigate Type II error in low-power scenarios. Regarding the movement phase, a marked effect on center of pressure (CoP) stability was observed, as evidenced by both statistical approaches (frequentist: F(1.65, 6.59) = 19.44, p = 0.002, ηp² = 0.829; Bayesian: P(β_phase < 0) > 0.999). Although external load did not reach statistical significance in the frequentist analysis (p = 0.177, achieved power = 0.27), the Bayesian model provided moderate evidence of a positive impact (β_load = 0.059, 95% HDI [0.005, 0.115], p = 0.981). The area of the center of mass (CoM) ellipse showed no effects of interest. Limb asymmetries were significant and consistent throughout the experiment (frequentist: 48.01 ± 30.13%; Bayesian: 69.48%, 95% HDI [55.86%, 81.44%], P(AI > 20%) = 1.000) and were not modulated by the experimental condition. CoP-CoM coupling was stronger in the mediolateral direction than in the anteroposterior direction. The findings reveal that phase is the primary factor in postural stability, exerting a modest positive influence discernible only through low-powered probabilistic inference, and that the dual framework strengthens inferential robustness in small-sample biomechanical studies. Confirmatory studies with larger samples are recommended. Full article

In artificial breeding of forest musk deer (Moschus berezovskii), common diseases such as abscess, enteritis, pneumonia, and parasitic infections exhibit persistently high morbidity rates. The early symptoms of certain diseases are often insidious and difficult to discern. Conventional manual inspection routines not only fail to achieve accurate diagnosis but also frequently disturb the animals, induce stress responses, and consequently delay optimal treatment windows. To address this practical challenge, this study employs an improved BRW-GPLinker joint entity-relationship extraction approach to perform integrated extraction and structural organization of disease entities, symptom manifestations, etiological associations, and preventive and therapeutic measures from farming literature and clinical records, thereby constructing a disease knowledge graph for forest musk deer. Through the introduction of a Boundary-Aware Module for refined entity boundary detection, a Relative Distance Bias Module to mitigate pairing errors in dense contexts, and a Weighted Sparse Multi-label Cross-Entropy loss function to enhance recall for infrequent relations, the proposed model achieves an F1 score of 0.887 on a self-constructed dataset and demonstrates favorable generalization capability on medical-domain datasets. By transforming fragmented clinical logs and manuals into structured medical associations, this knowledge graph facilitates rapid retrieval of forest musk deer disease information, thereby enhancing veterinary decision-making efficiency and assisting forest musk deer health management. Full article

Pre-Cenomanian Nubia sandstone is recognized one of the most prolific reservoirs in the Gulf of Suez, Egypt. Accurately determining its reservoir rock type (RRT) is crucial for reservoir characterization and modeling, especially when the reservoir is extremely heterogeneous. This study addresses the critical challenge of characterization in extremely heterogeneous reservoirs by introducing a novel integrated workflow that bridges the gap between traditional sedimentological geology, traditional x-y approaches, and advanced machine learning methods. To achieve this, this study utilizes sedimentological core description, routine core analysis, and conventional well log data from two wells (well A and well B) located in the southern Gulf of Suez, Egypt. The results demonstrate that the complete Nubia interval in the southern Gulf of Suez can be separated into seven distinct lithofacies (LF1–LF7). The first six lithofacies comprise various types of sandstone, while the seventh is composed of shale. The traditional techniques used to predict the RRTs show that the normalized reservoir quality index (NRQI) was the most effective method for predicting the Nubia rock types. The machine learning K–means clustering and self-organizing map (SOM) techniques utilizing raw log data and principal component analysis (PCA) can properly predict the Nubia reservoir rock types. The reservoir quality ranges from poor to very good; well A is dominated by moderate reservoir quality, while well B exhibits predominantly very good reservoir quality. This discernible difference in reservoir quality between the two wells is probably attributed to post-depositional diagenetic processes and variations in sandstone texture. Full article

Background/Objectives: Lung cancer is a leading cause of cancer-related mortality worldwide. As patients increasingly utilize large language models (LLMs) for health information, evaluating the readability and patient-centeredness of these tools is critical. This study aims to compare the performance of ChatGPT-4o mini, Microsoft Copilot, and Google Gemini in providing lung cancer information, focusing on their utility for individuals with limited health literacy. Methods: In this cross-sectional study (March 2026), 30 responses to ten standardized lung cancer-related queries were analyzed. Outputs were assessed using JAMA benchmarks and mDISCERN for quality, the SMOG index for readability, and PEMAT-P for understandability and actionability. Inter-rater reliability was analyzed using intraclass correlation coefficients (ICCs). Results: ChatGPT-4o mini demonstrated superior readability, achieving a sixth-grade level (SMOG: 6.23 ± 0.72, p < 0.001). Gemini achieved higher JAMA scores, indicating stronger academic rigour. While PEMAT-P scores were highest for ChatGPT (63.7%), all models exhibited moderate mDISCERN quality. Inter-rater reliability was excellent for JAMA (ICC = 1.000) and PEMAT-P (ICC = 0.883), though moderate for mDISCERN (ICC = 0.365), reflecting inherent interpretative subjectivity in qualitative assessment. No hallucinations were observed. Conclusions: Current LLMs exhibit a trade-off between accessibility and academic rigour: ChatGPT favours patient-friendly readability, while Gemini emphasizes structured content. The observed inter-rater variability in mDISCERN underscores the complexity of standardizing qualitative AI evaluation. These findings suggest that LLMs function best as complementary aids rather than substitutes for physician-led communication. Full article

Orthosis compliance monitoring provides insights into effective orthosis design and user wear time. Frequently, patient reports of orthosis use are subjective and often result in overestimation of compliance. Therefore, a tool to objectively observe whether patients wear their orthoses as instructed is vital. This study assessed the real-world practicality of using an objective compliance monitoring device with a hand orthosis. A device consisting of a pressure sensor and accelerometer was tested by ten healthy volunteers who wore a hand orthosis daily and completed a diary of their wear time and activities for a week. Sensor data obtained from the compliance monitoring device were analysed to discern each user’s orthosis wear time. Differences between estimated wear time and actual wear time were insignificant. Pressure-based wear time estimations had a specificity of 99.3 ± 0.7% and a sensitivity of 80.3 ± 19.2%, whilst acceleration-derived estimations had a specificity of 94.5 ± 6.4% and a sensitivity of 73.2 ± 15.8%. This study demonstrated that orthosis compliance can be monitored outside the laboratory, and, furthermore, this device offers insights into the intensity and frequency of a user’s activities and has the future potential to monitor orthosis fit and forces applied to affected joints using pressure. Full article

This article examines the transformation of the concepts of contingency and providence from Aristotle to Augustine. For Aristotle, contingency defines the sublunary world: singular future events are neither determined nor already true, as he argues in De Interpretatione 9, 19a7–19b4, and action takes place among particulars that could be otherwise, τὸ ἐνδεχόμενον ἄλλως ἔχειν (“that which could be otherwise”). Φρόνησις (“practical wisdom”) enables deliberation in this realm by discerning the means to εὐδαιμονία (“happiness,” “the good life”), where rules do not exhaust judgment and outcomes remain exposed to risk. For Augustine, apparent contingency is encompassed within divine providence; casus (“chance,” “case”) or fortuitum (“the fortuitous”) expresses human ignorance, not the absence of an ordo causarum (“order of causes”). In De Civitate Dei V.9, nothing occurs without a cause known to God, and chance occurs occulto quodam ordine (“by a certain hidden order”). The relationship between the two is not one of direct influence, given that Augustine had limited and indirect access to Aristotle. The comparison is thematic: it analyzes how problems initially formulated by Aristotle—the open future, deliberation, particulars—are reconfigured through creatio ex nihilo (“creation out of nothing”), praescientia (“foreknowledge”), and gratia (“grace”). Both affirm human responsibility, though within distinct horizons: Aristotle, in an open field measured ὡς ἐπὶ τὸ πολύ (“for the most part”); Augustine, in a created order in which the will itself is foreknown and sustained by God. Prudence thus becomes twofold: navigating what might be otherwise without a guarantee of success and ordering temporal goods toward the unchanging Good, trusting that no risk escapes providence. Full article

This study identified δ¹⁵N stable isotope ratios as a robust tracer for fishmeal inclusion in aquaculture feeds. δ¹⁵N and δ¹³C values from fish muscle samples derived from feeding trials with seven species (n = 3–5 fish per diet group) were evaluated (+/−0.1‰ for both δ¹⁵N and δ¹³C; ~1% relative to % N and % C) to verify whether the presence or absence of fishmeal (FM) in feeds could be detected. C and N isotopic data were also analyzed for feed in two of the trials. δ¹³C signatures did not differ consistently across diet groups for each species examined, with mean δ¹³C values for all species investigated being −20.2‰ ± 1.3. In contrast, a strong δ¹⁵N distinction was discerned between FM- and non-FM-fed fish for both muscle and feed samples, with FM-fed groups presenting higher values (p < 0.01) than non-FM-fed groups (range 0.8 to 9.5‰). Dietary ingredients other than FM (e.g., fish oil and algal oil) did not impact the δ¹⁵N isotopic fingerprint, although the addition of poultry byproduct meal to plant-based salmon diets caused an average 0.3‰ difference in δ¹⁵N values. The findings are not absolute as CN isotopes can be used to detect large but not small differences in feed components. Additional research on threshold levels, ingredient sourcing, and species differences is warranted to refine the method to enhance industry transparency and seafood consumer confidence. Full article

We demonstrate a mechanism-oriented Surface-Enhanced Raman Scattering (SERS) platform based on a hybrid structure integrating monolayer molybdenum disulfide (MoS₂) and gold nanospheres (AuNSs) on an optical microfiber (MF). The microfiber serves as a whispering-gallery-mode (WGM) microcavity. Monolayer MoS₂, grown directly on the microfiber surface via chemical vapor deposition (CVD), provides a chemically active interface for molecular adsorption and charge-transfer-related chemical enhancement. Subsequently deposited AuNSs couple with the microfiber-supported WGM, leading to the formation of hybrid photonic–plasmonic modes. This coupling results in a narrowed scattering resonance and a localized electromagnetic hotspot near the AuNS–microfiber interface. The combined contribution of electromagnetic enhancement from the microfiber–AuNS hybrid cavity and chemical enhancement from the MoS₂ layer produces discernible Raman enhancement for Rhodamine 6G (R6G) molecules under proof-of-concept measurement conditions. This work provides a useful platform for studying SERS enhancement mediated by hybrid photonic–plasmonic modes and offers guidance for the future development of optimized fiber-based SERS sensors. Full article

This article examines the impact of the integration of artificial intelligence (AI) on human formation from the perspective of Christian theological anthropology. Although recent scholarship highlights the advantages of AI for personalised learning and educational efficiency t frequently neglects the ontological and spiritual dimensions of human development. This study argues that the widespread use of AI in education risks externalising interior processes such as reflection, discernment, and memory. In contrast, the Christian theological tradition—as articulated by Augustine of Hippo (Confessions), Dumitru Stăniloae (Orthodox Dogmatic Theology), John Zizioulas (Being as Communion), and Christos Yannaras (The Freedom of Morality)—conceives of education as an inner transformation rooted in communion and participation in divine life. Drawing on interdisciplinary dialogue among theology, the philosophy of technology, and AI studies, this article introduces the Integrative Theological Formation Model (ITFM), comprising three dimensions: functional, reflexive, and contemplative–relational. The model seeks to integrate technology into education while safeguarding interiority and the spiritual dimension of the person. The article concludes that, while AI can support educational processes, it cannot generate communion, interiority, or ontological transformation. Full article

Liquid bridge formation between wet granular governs a wide range of industrial processes. In experiments aimed at observing the volume and evolution of liquid bridges, the ability to form stable and uniform liquid films on particle surfaces is an essential prerequisite. However, existing experimental setups are incapable of maintaining such uniform coating, thereby precluding a complete characterization of the bridge evolution dynamics. To address this gap, a new experimental setup is developed in this work. Uniform liquid film coating on spherical particles is achieved for the first time. The formation process is captured by high-speed imaging, and the control variable method systematically quantifies the effects of liquid film thickness, distance between two particle surfaces, and particle radius ratio on the dimensionless liquid bridge volume. Quantitatively, increasing the dimensionless liquid film thickness by 0.01 raises the maximum dimensionless liquid bridge volume by 0.2; enlarging the dimensionless initial particle spacing from 0.067 to 0.133 and 0.200 reduces the maximum dimensionless liquid bridge volume by 3.0% and 4.9%, respectively; and a radius ratio of 6:4 lowers the maximum dimensionless liquid bridge volume by 10.9% compared to 6:6. The Reynolds number exhibits no discernible effect within the viscous-dominated regime investigated. Full article

Binding affinity prediction is about estimating the degree to which a drug binds to a protein. Predicting the binding affinity between a drug and a protein in a computational process helps researchers filter huge libraries of compounds before performing expensive biochemical lab experiments. Currently, there is interest in predicting binding affinity through computational pattern recognition or machine learning methods instead of the classical physics-inspired methods, which are computationally intractable except for tiny chemical compounds. In the last five years, several machine learning-based methods have been presented, whose experimental validations have achieved increasing Pearson coefficients while trained and tested in the PDBBind 2016 and CASF 2016 databases, respectively. These methods have an important diversity of architectures that provide different properties. The aim of this paper is to discern which binary properties (existence or absence) of these methods make them return higher Pearson coefficients. Basically, the properties introduced are related to the level of structural knowledge, the presence of 3D information, and the introduction of the relationship between the drug and the protein in the input of the model. The t-test confirms that the important binary properties for having a high Pearson coefficient are the protein (or part of the protein) being represented and introduced into the computational model as a graph, the pocket and the drug–protein interaction being part of the input, and incorporating the distance between atoms and the type of chemical bonds into the model. Full article