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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.
Analysis of the value of infrared thermal imaging technology in the diagnosis of emergency pulmonary infections
Surface renewal driven copper recovery by cementation in a stirred reactor with a rotating wiper mechanism
Afatinib amplifies cAMP-induced fluid secretion in a mouse mini-gut model via TMEM16A-mediated fluid secretion and secretory cell differentiation
Siamese change detection based on information interaction and fusion network
Large-scale transformer-based topic graphs identify thematic links between engineering and biology
Effect of systematic nursing on scar appearance following radical thyroidectomy: a retrospective cohort analysis
AI enhanced model predictive control for optimizing LPG recovery through integrated computational modeling design of experiments and multivariate regression
Abstract Liquefied Petroleum Gas (LPG) recovery in debutanizer columns presents challenges in balancing operational efficiency and process stability under varying conditions. Conventional control strategies often fail to sustain optimal recovery. This study integrates process modeling and control, using Aspen HYSYS for steady-state simulation and dynamic implementation of model predictive control (MPC). Response surface methodology (RSM) was applied to steady-state simulation results to analyze key process variables. Feed molar flow rate was the most influential factor, while pressure-related variables showed minor but statistically significant effects. The quadratic model and 3D response surfaces confirmed key interactions. A regression decision tree model was developed in MATLAB to support deployment of artificial intelligence-enhanced MPC (AI-enhanced MPC). MPC improved LPG recovery from 99.73 to 99.85%, reduced reboiler duty from 1,557,000 to 1,550,000 kcal/h, and reflux flow from 281.2 to 271 kgmole/h. AI-enhanced MPC further increased recovery to 99.9%, reduced reboiler duty to 1,501,956 kcal/h, condenser duty to 2,415,726 kcal/h, and reflux flow to 262.6 kgmole/h, indicating superior energy efficiency and control precision. Although feed molar flow remained dominant, both control systems regulated its impact via pressure, temperature, and reflux. Product temperature dropped from 49.88 °C to 49.24 °C, and pressure from 12.39 to 11.95 bar, indicating enhanced thermal stability. The novelty of this study lies in integrating RSM with both conventional and AI-enhanced model predictive control, forming a hybrid framework enabling steady-state optimization and dynamic control for improved LPG recovery. The proposed framework supports industrial LPG recovery by improving energy efficiency, product quality, and dynamic stability.
High risk of hepatic complications in kidney transplantation with chronic hepatitis C virus infection
Abstract Data on liver issues including liver cirrhosis, hepatocellular carcinoma, and hepatic failure in renal transplant patients with HCV infection are scarce. In the present study, we conducted a large-scale population-based analysis to investigate the long-term outcomes of renal recipients with HCV infection. Propensity score matching with a ratio of 1:1 was applied. A total of 6,473renal recipients with HCV infection in case group were enrolled after PSM. Our findings showed that subjects with HCV infection in kidney transplant had significantly higher risk of hepatoma, cirrhosis, hepatic failure, and overall hepatic disease than those without HCV infection. (hepatoma: HR: 8.957; 95% CI: 5.324–15.069; cirrhosis: HR: 5.378; 95% CI: 4.363–6.631; hepatic failure: HR: 3.258; 95% CI: 2.527-4.200; overall hepatic disease: HR: 4.128; 95% CI: 3.428–4.971). In the present study, our findings show that renal recipients with HCV infection is significantly associated with a remarkably high risk of hepatic complications post-kidney transplantation.