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A systematic algorithm using 16S ribosomal RNA for accurate diagnosis of pneumonia pathogens
Perceived social support as a moderator of posttraumatic stress in parents of children with autism spectrum disorder
Prediction of speed of sound of deep eutectic solvents using artificial neural network coupled with group contribution approach
Abstract Predicting the physiochemical properties of deep eutectic solvents (DESs) is crucial for designing new solvents. Heat capacity and speed of sound are important thermodynamic properties in chemical processes. However, experimental data on the speed of sound in DESs is limited. Consequently, a thermodynamic model is needed to estimate the speed of sound in DESs over a wide range of pressures and temperatures. A key challenge in these models is accurately estimating the ideal gas heat capacity. Since the ideal gas heat capacity of DESs is often unavailable, a machine learning (ML) approach, using artificial neural networks (ANNs) coupled with a Group Contribution (GC) method, is a promising technique. The GC approach will be used to estimate critical temperature, volume, and acentric factor of DESs, which can then be input into the ANN model to predict the speed of sound. The results show that using a combination of a GC method and ANNs or CatBoost ML provides a highly accurate prediction of the speed of sound in DESs. Input parameters to the ANN + GC include temperature, acentric factor, molecular weight, and critical volume. The absolute relative deviation (ARD%) and R2 values of correlated speed of sound for the ANN + GC model have been obtained 0.032% and 0.998, respectively. The ARD% for both the ANN + GC and ML + GC approaches was substantially lower than that of the correlation-based models. Furthermore, cumulative frequency diagrams and the leverage approach were implemented to validate the quality and reliability of the proposed model. The leverage analysis confirmed the accuracy of the data used and the high reliability of the ANN + GC model for estimating the speed of sound in DESs. This analysis indicates that the ANN + GC and ML + GC methods can effectively estimate the speed of sound in DESs based on molecular structure. Therefore, these approaches offer a promising tool for predicting the speed of sound of newly designed DESs when experimental data is unavailable.
Distilling knowledge from graph neural networks trained on cell graphs to non-neural student models
From community to science to community, enhancing remote sensing of water quality in Chesapeake Bay tributaries through participatory science
Association of HLA-DRA expression with prognosis and tumor microenvironment in clear cell renal cell carcinoma
Identification of the AKR gene family in sweet cherry and its response to different abiotic stresses
Microbial degradation of flubendiamide in different types of soils at tropical region using lactic acid bacteria formulation
H3K27me3-mediated epigenetic regulation of TET1 in the eutopic endometrium of women with endometriosis and infertility
Time series analysis of ex-vivo ischemia-reperfused heart using Q-space imaging
Improving early detection of Alzheimer’s disease through MRI slice selection and deep learning techniques
Abstract Alzheimer’s disease is a progressive neurodegenerative disorder marked by cognitive decline, memory loss, and behavioral changes. Early diagnosis, particularly identifying Early Mild Cognitive Impairment (EMCI), is vital for managing the disease and improving patient outcomes. Detecting EMCI is challenging due to the subtle structural changes in the brain, making precise slice selection from MRI scans essential for accurate diagnosis. In this context, the careful selection of specific MRI slices that provide distinct anatomical details significantly enhances the ability to identify these early changes. The chief novelty of the study is that instead of selecting all slices, an approach for identifying the important slices is developed. The ADNI-3 dataset was used as the dataset when running the models for early detection of Alzheimer’s disease. Satisfactory results have been obtained by classifying with deep learning models, vision transformers (ViT) and by adding new structures to them, together with the model proposal. In the results obtained, while an accuracy of 99.45% was achieved with EfficientNetB2 + FPN in AD vs. LMCI classification from the slices selected with SSIM, an accuracy of 99.19% was achieved in AD vs. EMCI classification, in fact, the study significantly advances early detection by demonstrating improved diagnostic accuracy of the disease at the EMCI stage. The results obtained with these methods emphasize the importance of developing deep learning models with slice selection integrated with the Vision Transformers architecture. Focusing on accurate slice selection enables early detection of Alzheimer’s at the EMCI stage, allowing for timely interventions and preventive measures before the disease progresses to more advanced stages. This approach not only facilitates early and accurate diagnosis, but also lays the groundwork for timely intervention and treatment, offering hope for better patient outcomes in Alzheimer’s disease. The study is finally evaluated by a statistical significance test.
Study on passivation mechanism of HRB500E rebar in highly alkaline concrete pore solution
Comparative analysis of three bronchopulmonary dysplasia diagnostic criteria in predicting longterm neurodevelopmental outcomes in preterm infants
Rapid diagnosis of alprazolam poisoning by Fourier transform infrared spectroscopy on saliva samples
Enhanced aqueous phosphate removal using chitosan-modified zirconium-loaded cork biochar
Abstract The adsorption of phosphate (PO4–P) is essential for controlling and reducing eutrophication. This study reports the synthesis of a new adsorbent material: Zr(IV)-loaded chitosan-modified used cork stopper biochar composite (CS–CBC–Zr) beads. The suitability of CS–CBC–Zr beads for PO4–P removal was assessed using the batch method. The effects of various parameters were investigated, including zirconium loading level, adsorbent dosage, pH, co-anions, contact time, and the initial concentration of PO4–P. The experimental data were thoroughly analyzed using adsorption kinetic and isotherm models. Virgin beads and P-adsorbed beads were characterized using FTIR, SEM, and XPS analyses. The results demonstrated that the combination of Zr coating and CS–CBC beads exhibited superior adsorption performance compared to individual CS–CBC beads. The CS–CBC–Zr beads exhibited a maximum adsorption capacity of 33.89 mg/g, as predicted using the Langmuir–Freundlich (Sips) model. The CS–CBC–Zr beads removed PO4–P with an efficiency of 95% at an initial pollutant concentration of 50 mg/L and reached adsorption equilibrium within 120 min of contact time, outperforming some comparable adsorbents. Moreover, the beads achieved excellent PO4–P removal performance over a wide pH range of 4–10, making them highly versatile. The experiment on the effect of coexisting anions demonstrated the excellent selectivity of CS–CBC–Zr for PO4–P. Phosphate adsorption on CS–CBC–Zr fitted well with the pseudo-second-order kinetic and Sips models. Kinetic data closely fitted the pseudo-second-order model, suggesting that adsorption was primarily governed by chemisorption.
Human-robot interaction using retrieval-augmented generation and fine-tuning with transformer neural networks in industry 5.0
Laser power stabilization using conservation law in acoustic optic modulator
Abstract Laser power stabilization plays an important role in modern precision instruments based on atom-laser interactions. Here we demonstrate an alternative active control method of laser power utilizing the conservation law in an acoustic optic modulator (AOM). By adjusting the 1st order beam power to dynamically follow the fluctuation of the total power of all diffraction beams, the 0th order application beam as the difference term, is stabilized. Experimental result demonstrates that the relative power noise of the controlled application beam is reduced by a factor of 200, reaching $$4 \times 10^{-6}$$ $$\hbox {Hz}^{-1/2}$$ at $$10^{-4}$$ Hz compared with the uncontrolled total power. Allan deviation shows that the application beam reaches a relative power instability of 3.28 $$\times 10^{-6}$$ at 500 s averaging time. In addition, the method allows a high availability of total power source. The method opens a new way of laser power stabilization and shall be very useful in applications such as atomic clocks, laser interferometers and gyroscopes.
Green synthesis of ZnO-Zn-MOF/bacterial nanocellulose for ultra oxidative desulfurization of actual diesel fuel
Abstract The oxidative desulfurization (ODS) process is a promising approach to reduce sulfur content in fossil fuels using efficient oxidant catalysts such as metal-organic frameworks (MOFs) and its composites. In this study, we aimed to exploit the amazing physicochemical properties of the naturally green polymer bacterial nanocellulose (BNC) as a platform for the ODS of actual diesel fuel. The methodology relied on a green one-pot solvothermal route to synthesize a ZnO-Zn-MOF/BNC nanocomposite. Owing to its 3D porous structure and richness of hydroxyl group, BNC served as a dispersing scaffold for growing the ZnO-Zn-MOF on its nanofibers, improved metal dispersion, and reduced catalyst aggregation. The prepared catalysts were thoroughly characterized using XRD, FTIR, SEM, TEM, and EDX. Moreover, BET analysis has interestingly revealed a morphological shift toward larger pores and enhanced interconnectivity, improving mass transfer and accessibility to active sites. Afterward, the ODS performance of the resulting ZnO-Zn-MOF/BNC nanocomposite was compared to the ZnO-Zn-MOF and ZnO-Zn-MOF/reduced graphene oxide (rGO) counterparts. Additionally, the impact of catalyst dosage, oxidant-to-sulfur molar ratio, extractant-to-oil ratio, reaction time, and temperature on the ODS efficiency of real diesel fuel was systematically investigated. Under optimized conditions 70 °C; O/S molar ratio of 8; catalyst dosage of 200 mg; solvent-to-oil ratio of 1.5:1; and 60 min reaction time, the ZnO-Zn-MOF/BNC catalyst attained 98.5% sulfur removal efficiency. Additionally, the catalyst maintained most of its structural integrity and catalytic activity over five successive cycles, demonstrating high resilience and potential for further in-depth studies to assess its viability for large-scale use as a green desulfurization toolkit.