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Cs2AgBiBr6 and related Halide double perovskite porous single crystals
Expression profiles of miR-101-3p and miR-431-5p as potential diagnostic biomarkers for rheumatoid arthritis
AbstractRheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation of the synovial joints, leading to cartilage and bone destruction. This study aimed to evaluate the diagnostic utility of specific microRNAs (miRNAs) as potential biomarkers for RA. The study was conducted on 60 patients with RA disease along with 20 control participants. Comprehensive analysis of patient data, encompassing serological, hematological, and biochemical markers, revealed significantly elevated levels of miR-99b-5p, miR-101-3p, and miR-431-5p in RA patients compared to healthy controls. Among these, miR-101-3p demonstrated the highest diagnostic accuracy, with an area under the curve (AUC) of 0.873. These findings contribute to a deeper understanding of RA pathogenesis and suggest that miR-101-3p may serve as a valuable biomarker for early disease detection and potentially improved patient management. Further research is warranted to elucidate the precise mechanisms underlying miRNA involvement in RA and to explore their potential as therapeutic targets.
Microstructure and properties of joint of Al/Cu welded by resistance element welding with an auxiliary gasket of Ni
Preliminary study on the explosive performance of coal dust and prospects for engineering applications
AbstractThis study aims to evaluate the efficiency and energy release characteristics of different types of coal in pulse detonation engines (PDE) to advance the development of deep coal fluidization detonation technology, achieving more efficient and cleaner coal utilization. Using a custom PDE setup, experiments were conducted with four coal types at mass flow rates from 30 to 120 g/s. High-frequency pressure sensors assessed pressure dynamics and detonation wave propagation, complemented by numerical simulations for accuracy. Results show that the maximum detonation pressure (Pmax) increases linearly with coal mass flow, with anthracite reaching 1.52 MPa at 120 g/s. A consistent linear relationship between Pmax and maximum temperature (Tmax) was observed. Detonation combustion efficiency (ηDCE) improved with mass flow, with peat rising from 52.3% to 83.7%. These findings provide a foundation for advancing deep coal fluidized detonation technology, contributing to more efficient and cleaner coal utilization.
Advancements in microalgal biomass conversion for rubber composite applications
AbstractCarbon black (CB) as rubber reinforcement has raised environmental concerns regarding this traditional petroleum-based filler, which is less susceptible to biodegradability. Although it has great reinforcing properties, the production technique is no longer sustainable, and its cost increases regularly. For these reasons, it is wise to look for sustainable replacement materials. Microalgal biomass (MB) has demonstrated great potential for use as biodegradable nano fillers in rubber composites. Microalgal has a high biomass productivity compared to traditional crops. They can produce a large amount of biomass per unit of land area, making them highly efficient in terms of resource utilization. In the present research, microalgal biomass was blended with CB at different concentrations for preparing two different kinds of rubber composites: Nitrile rubber Acrylonitrile-butadiene rubber (NBR) and styrene-butadiene rubber (SBR) are two common synthetic rubbers. In this study, the researchers investigated using microalgal biomass as filler in rubber composites. They assessed the filler-matrix interaction by evaluating the processability, mechanical characteristics, Payne effect, and swelling properties of the MB/CB-filled composites and compared them to CB-filled composites. The results show that rubber composites incorporating dual fillers (microalgal biomass and carbon black) had faster cure times, increased torque, and improved mechanical properties. The results prove biomass helps to minimize bulk quantities of CB and may be used as a partial replacement while still improving the mechanical properties. According to the study, microalgal biomass can successfully replace up to 50% of the CB filler. This will reduce petroleum dependence and possibly costs, depending on current petroleum prices.
Machine learning techniques for non-destructive estimation of plum fruit weight
AbstractPlum fruit fresh weight (FW) estimation is crucial for various agricultural practices, including yield prediction, quality control, and market pricing. Traditional methods for estimating fruit weight are often destructive, time-consuming, and labor-intensive. In this study, we addressed the problem of predicting plum FW using artificial intelligence (AI) methods based on fruit dimensions. We aimed to evaluate various machine learning (ML) techniques for this purpose. Images of fruit samples were captured using a smartphone camera, processed to extract binary images, and used to calculate dimensions. We tested several ML methods, including Support Vector Regression (SVR), Multivariate Linear Regression (MLR), Multi-Layer Perceptron (MLP), and Decision Tree (DT). The SVR model with a Pearson-VII kernel (PUK) function and penalty value (c) of 0.1 was the most accurate, achieving an R2 of 0.9369 and root mean squared error (RMSE) of 0.4850 (gr) during training, and 0.9267 and 0.4863 (gr) during testing. This method is important for researchers and practitioners seeking efficient, quick, and non-destructive ways to estimate fruit weight. Future research can build on these findings by applying the model to other fruit types and conditions.
Prevalence of anhedonia in women with deep endometriosis
Unveiling nature’s antidiabetic potential in Berberis orthobotrys seeds for alloxan-induced diabetic rats
Glycosylation gene expression profiles enable prognosis prediction for colorectal cancer
ATRX loss inhibits DDR to strengthen radio-sensitization in p53-deficent HCT116 cells
Analysis of full length transcriptome and resistance characteristics of Atraphaxis bracteata under drought
AbstractDrought is one of the main environmental factors affecting plant survival and growth. Atraphaxis bracteata is a common desert plant mainly utilized in afforestation and desertification control. This study analyzed the morphological, physiological and molecular regulatory characteristics of different organs of A. bracteata under drought stress. The results showed that with the intensification of drought stress, the height, root length and leaf area of A. bracteata seedlings decreased, while the content of osmotic substances and antioxidant enzyme activity increased. Furthermore, a total of 63 907 non-redundant transcript sequences, of which 55 574 transcripts were annotated, 248 178 FLNC sequences, 107 112 high-quality consistent sequences and 291 314 CCSs were obtained from Iso-Seq. Meanwhile, a total of 2 039 AS events, 22 919 SSR, 40 404 CDS and 5 902 lncRNA were also obtained. The RNA-Seq analysis results revealed that a total of 2 821, 3 907 and 5 532 DETs were identified from roots, stems and leaves, respectively, and which had significantly enrichment in “circadian rhythm-plant” and “starch and sucrose metabolism” pathway. These results would be great significance for further research on the stress resistance of A. bracteata and these DETs function.
Enhancement of quantum synchronization in triple-cavity system
Enhancing security and efficiency in Mobile Ad Hoc Networks using a hybrid deep learning model for flooding attack detection
Effects of urban sprawl due to migration on spatiotemporal land use-land cover change: a case study of Bartın in Türkiye
Unveiling the radiation shielding efficacy of diorite, granodiorite, tonalite, and granite: experimental and simulation study
Abstract For the purpose of this study, four natural rock samples-namely, diorite, granodiorite, tonalite, and granite-are being investigated about their radiation attenuation. The elemental composition of the rocks was obtained through Energy dispersive X-ray spectroscopy (EDX) which examines the microstructural and localized area elemental analyses of the four rock samples. A Monte Carlo simulation (MCNP) was used to determine and evaluate the investigated samples. Additionally, the samples were validated by Phy-X software (within the energy range of 0.015 to 15 MeV), and experimental measurements were achieved through the utilization of an HPGe detector (0.060, 0.662, 1.173, and 1.332 MeV). The investigation was carried out using various parameters such as linear attenuation (µ) and others. Furthermore, the Fast Neutrons Removal Cross Sections (FNRCS) were calculated using theoretical methods. In the case of granodiorite, tonalite, diorite, and granite, the values of µ were found to range from 7.931 to 0.049, 31.922 to 0.061, 17.267 to 0.060, and 23.860 to 0.056 cm−1, respectively. The samples of tonalite and diorite have the highest µ values due to the presence of heavy elements and the high densities of these samples. Granodiorite is the substance that possesses the highest value of FCS (0.108 cm−1) due to the high content of light elements (O = 0.6802%, and C = 0.2286% wt). The results of the study demonstrated that the investigated natural rocks possessed a substantial potential for shielding γ-rays and neutrons from radiation and could be suitable for use in radiological protection applications.
Natural sampling and aliasing of marine geochemical signals
AbstractIt is well known that the sedimentary rock record is both incomplete and biased by spatially highly variable rates of sedimentation. Without absolute age constraints of sufficient resolution, the temporal correlation of spatially disjunct records is therefore problematic and uncertain, but these effects have rarely been analysed quantitatively using signal processing methods. Here we use a computational process model to illustrate and analyse how spatial and temporal geochemical records can be biased by the inherent, heterogenous processes of marine sedimentation and preservation. This confirms that sedimentary hiatuses can span a substantial proportion of geological time, caused by inherent geological processes. Moreover, even in marine geochemical records that are essentially spatially continuous and complete, the signal is irreversibly disguised in time as lower frequency signals by an aliasing effect. We demonstrate that Nyquist’s theorem correctly predicts these biased signatures, proving that aliasing can be caused by cyclical and multiscale relative sea-level changes - a natural sampling effect. Our combined results show that deeper marine records are significantly more likely to provide unaliased environmental signatures. Also, some proxy residence times will be sufficiently long that they average over aliased frequencies, cancelling these in the geochemical record. And field observations of changes in sedimentation rate (such as hiatuses, condensation, or lateral expansion) can be used to infer possible aliasing. Where aliasing by natural sampling occurs, this cannot be undone simply by increasing sample resolution (density): aliasing is caused by an absence of sedimentary record, which by definition cannot be sampled at all. To overcome these issues, we propose that spatially separated aliased records may still be correctly correlated in age, and true geochemical cycles inferred, if a paired-sampling strategy informed by local stratigraphy is adopted. In this, two (or more) closely-spaced samples are analysed at each sampling point instead of only one, after which aliased cycles are inferred from geochemical gradients.
Integrated approach for estimating climate change impacts on CO2 sink capacity of inland waterbodies using hydrodynamic modelling and GIS analysis
AbstractAs one of their key regulatory ecosystem functions, inland lakes serve as CO2 sinks. The CO2 sink capacity of inland lakes depends on their water temperature and salinity as well as their water volume which are all highly sensitive to climate conditions. This paper aims to quantitatively estimate the change in the CO2 sink capacity of Wadi El-Rayan Lakes under climate change scenarios. For this purpose, an integrated approach combining CO2 solubility modelling, hydrodynamic simulations (Delft3D-FLOW) and GIS analysis was employed. According to the developed approach, CO2 solubility under variable temperature and salinity is mathematically modelled and this model is further used with the developed hydrodynamic model data for Wadi El-Rayan Lakes (temperature, salinity and water depth) to estimate their CO2 sink capacities. CO2 sink capacity is estimated for 2014 and 2050 under two Representative Concentration Pathways (RCPs) 2.6 and 8.5. Afterwards, the alteration in CO2 sink capacities due to climate change is determined using the modified hydrodynamic model. The results revealed that by 2050, the lakes would lose about 23–25% of their capacities compared to that of 2014 according to RCP 2.6 and 8.5, respectively.