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  • 1
    In: Nanomaterials, MDPI AG, Vol. 9, No. 2 ( 2019-02-10), p. 237-
    Abstract: Chloramphenicol (CAP) is commonly employed in veterinary clinics, but illegal and uncontrollable consumption can result in its potential contamination in environmental soil, and aquatic matrix, and thereby, regenerating microbial resistance, and antibiotic-resistant genes. Adsorption by efficient, and recyclable adsorbents such as mesoporous carbons (MPCs) is commonly regarded as a “green and sustainable” approach. Herein, the MPCs were facilely synthesized via the pyrolysis of the metal–organic framework Fe3O(BDC)3 with calcination temperatures (x °C) between 600 and 900 °C under nitrogen atmosphere. The characterization results pointed out mesoporous carbon matrix (MPC700) coating zero-valent iron particles with high surface area (~225 m2/g). Also, significant investigations including fabrication condition, CAP concentration, effect of pH, dosage, and ionic strength on the absorptive removal of CAP were systematically studied. The optimal conditions consisted of pH = 6, concentration 10 mg/L and dose 0.5 g/L for the highest chloramphenicol removal efficiency at nearly 100% after 4 h. Furthermore, the nonlinear kinetic and isotherm adsorption studies revealed the monolayer adsorption behavior of CAP onto MPC700 and Fe3O(BDC)3 materials via chemisorption, while the thermodynamic studies implied that the adsorption of CAP was a spontaneous process. Finally, adsorption mechanism including H-bonding, electrostatic attraction, π–π interaction, and metal–bridging interaction was proposed to elucidate how chloramphenicol molecules were adsorbed on the surface of materials. With excellent maximum adsorption capacity (96.3 mg/g), high stability, and good recyclability (4 cycles), the MPC700 nanocomposite could be utilized as a promising alternative for decontamination of chloramphenicol antibiotic from wastewater.
    Type of Medium: Online Resource
    ISSN: 2079-4991
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2662255-5
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  • 2
    In: Sensors, MDPI AG, Vol. 23, No. 1 ( 2023-01-03), p. 524-
    Abstract: Outage probability (OP) and potential throughput (PT) of multihop full-duplex (FD) nonorthogonal multiple access (NOMA) systems are addressed in the present paper. More precisely, two metrics are derived in the closed-form expressions under the impact of both imperfect successive interference cancellation (SIC) and imperfect self-interference cancellation. Moreover, to model short transmission distance from the transmit and receive antennae at relays, the near-field path-loss is taken into consideration. Additionally, the impact of the total transmit power on the performance of these metrics is rigorously derived. Furthermore, the mathematical framework of the baseline systems is provided too. Computer-based simulations via the Monte Carlo method are given to verify the accuracy of the proposed framework, confirm our findings, and highlight the benefits of the proposed systems compared with the baseline one.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2052857-7
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  • 3
    In: Journal of Clinical Medicine, MDPI AG, Vol. 12, No. 17 ( 2023-08-25), p. 5516-
    Abstract: Background: The prevalence and risk factors of atrial fibrillation (AF) in patients with transient ischemic attack (TIA) or ischemic stroke in Northern Vietnam are not well understood. This study aimed to estimate the prevalence and identify factors associated with AF in this population. Methods: A cross-sectional study was conducted on 2038 consecutive patients with TIA or ischemic stroke admitted to Bach Mai Hospital. AF was diagnosed using an electrocardiogram or Holter monitor. Logistic regression analyses were performed to determine the association between AF and risk factors. Results: Among the patients, 18.1% (95% CI: 16.46 to 19.85) had AF. Older age, renal dysfunction, valvular heart disease (VHD), and low ejection fraction were significantly associated with AF. Advanced age (per 10 years) (adjusted OR, aOR 1.39; 95% CI, 1.23 to 1.57), estimated glomerular filtration ratio decrease (per 10 mL/min/1.73 m2) (aOR 1.12; 95% CI, 1.06 to 1.17), VHD (aOR 9.59; 95% CI, 7.10 to 12.95), and low ejection fraction ( 〈 50%) (aOR 2.61; 95% CI, 1.62 to 4.21) had notable odds ratios for AF. Conclusions: Atrial fibrillation is prevalent among patients with TIA or ischemic stroke in Northern Vietnam, surpassing rates in other Southeast Asian countries. Age, renal dysfunction, VHD, and low ejection fraction were significant risk factors for AF in this population.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662592-1
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  • 4
    In: Buildings, MDPI AG, Vol. 12, No. 10 ( 2022-09-20), p. 1493-
    Abstract: The determination of shear strength and the identification of potential failure modes are the crucial steps in designing and evaluating the structural performance of reinforced concrete (RC) columns. However, the current design codes and guidelines do not clearly provide a detailed procedure for governing failure types of RC columns. This study predicted the shear strength and identified the failure modes of rectangular RC columns using various Machine Learning (ML) models. Six ML models, including Multivariate Adaptive Regression Splines (MARSs), Naïve Bayes (NBs), K-nearest Neighbors (KNNs), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were developed to calculate the shear strength and to classify the failure modes of rectangular RC columns. A total of 541 experimental data samples were collected from literature and utilized for developing the ML models. The results reveal that the ANN and KNNs models outperformed other ML models in predicting the shear strength of rectangular RC columns with the R2 value larger than 0.99. Additionally, the KNNs model achieved the highest accuracy, mostly 100%, for identifying the failure modes of rectangular RC columns. Based on the superior performance of the ANN and KNNs models, a graphical user interface was also developed to rapidly predict the shear strength and failure modes of rectangular RC columns.
    Type of Medium: Online Resource
    ISSN: 2075-5309
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2661539-3
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