Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R² and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R², RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management.     

One-pot,ligand-free,room-temperature synthesis of Au/Pd/ZnO nanoclusters with ultra-low noble metal loading and synergistically improved photocatalytic performances

Au/Pd/ZnO nanoclusters with ultra-low noble metal loadings were prepared by a one-step and ligand-free method at room temperature. HRTEM, ICP-MS, XPS, and elemental mapping analysis confirmed that the obtained Au/Pd/ZnO nanoclusters were composed of ZnO nanoclusters decorated with well-dispersed AuPd nanoparticles. Au/Pd/ZnO nanoclusters exhibited higher photocatalytic activity compared with those of pristine ZnO, Au/ZnO and Pd/ZnO. Moreover, the high catalytic activity of Au/Pd/ZnO nanoclusters could be maintained even after 5 cycles of photocatalytic reaction. A mechanism for the enhanced photocatalytic performance was also suggested, which was in light of the synergistic effects of the SPR effect from Au and the improved photogenerated charge carrier separation from Pd.

Au/Pd/ZnO nanoclusters with ultra-low noble metal loadings were prepared by a one-step and ligand-free method at room temperature. The obtained Au/Pd/ZnO nanoclusters were composed of ZnO nanoclusters decorated with well-dispersed AuPd NPs with ultra-low noble metal loading.     

Removal of toluene as a toxic VOC from methane gas using a non-thermal plasma dielectric barrier discharge reactor

Methane is the main component of biogas, which could be used as a renewable energy source for electricity, source of heat, and biofuel production after upgrading from biogas. It also contains toxic compounds which cause environmental and human health problems. Therefore, in this work, the removal of a toxic compound (toluene) from methane gas was studied using a dielectric barrier discharge (DBD) reactor. It was observed that the removal of the toxic compound could be achieved from methane carrier gas using a dielectric barrier discharge reactor, and it depends on plasma input power. The maximum removal of the toxic compound was 85.9% at 40 W and 2.86 s. The major gaseous products were H₂ and lower hydrocarbons (LHC) and the yield of these products also increases with input power. In the current study, the yield of gaseous products depends on the decomposition of toxic compounds and methane, because the decomposition of methane also produces H₂ and lower hydrocarbons. The percentage yield of H₂ increases from 0.43–4.74%. Similarly, the yield of LHC increases from 0.56–7.54% under the same reaction conditions. Hence, input power promoted the decomposition of the toxic compound and enhanced the yield of gaseous products.

The removal of toluene as a toxic volatile organic compound from methane gas, which is the major component of biogas, is carried out using a non-thermal plasma dielectric barrier discharge reactor.     

The SARS-CoV-2 B.1.618 variant slightly alters the spike RBD–ACE2 binding affinity and is an antibody escaping variant: a computational structural perspective

Continuing reports of new SARS-CoV-2 variants have caused worldwide concern and created a challenging situation for clinicians. The recently reported variant B.1.618, which possesses the E484K mutation specific to the receptor-binding domain (RBD), as well as two deletions of Tyr145 and His146 at the N-terminal binding domain (NTD) of the spike protein, must be studied in depth to devise new therapeutic options. Structural variants reported in the RBD and NTD may play essential roles in the increased pathogenicity of this SARS-CoV-2 new variant. We explored the binding differences and structural-dynamic features of the B.1.618 variant using structural and biomolecular simulation approaches. Our results revealed that the E484K mutation in the RBD slightly altered the binding affinity through affecting the hydrogen bonding network. We also observed that the flexibility of three important loops in the RBD required for binding was increased, which may improve the conformational optimization and consequently binding of the new variant. Furthermore, we found that deletions of Tyr145 and His146 at the NTD reduced the binding affinity of the monoclonal antibody (mAb) 4A8, and that the hydrogen bonding network was significantly affected consequently. This data show that the new B.1.618 variant is an antibody-escaping variant with slightly altered ACE2–RBD affinity. Moreover, we provide insights into the binding and structural-dynamics changes resulting from novel mutations in the RBD and NTD. Our results suggest the need for further in vitro and in vivo studies that will facilitate the development of possible therapies for new variants such as B.1.618.

This study explored the binding patterns of the wild type and B.1.618 variant using which revealed that the B.1.618 variant possess a stronger binding affinity for the host ACE2 and escape the neutralizing antibodies.     

Eco-Friendly Incorporation of Crumb Rubber and Waste Bagasse Ash in Bituminous Concrete Mix

The consumption of waste materials in the construction sector is a sustainable approach that helps in reducing the environmental pollution and decreases the construction cost. The present research work emphasizes the mechanical properties of bituminous concrete mix prepared with crumb rubber (CR) and waste sugarcane bagasse ash (SCBA). For the preparation of bituminous concrete mix specimens with CR and SCBA, the effective bitumen content was determined using the Marshall Mix design method. A total of 15 bituminous concrete mix specimens with 4%, 4.5%, 5%, 5.5% and 6% of bitumen content were prepared, and the effective bitumen content turned out to be 4.7%. The effect of five different CR samples of 2%, 4%, 6%, 8% and 10% by weight of total mix and SCBA samples of 25%, 50%, 75% and 100% by weight of filler were investigated on the performance of bituminous concrete. A total of 180 samples with different percentages of CR and SCBA were tested for indirect tensile strength (ITS) and Marshall Stability, and the results were compared with conventional bituminous concrete mix. It was observed that the stability values rose with an increase in CR percentage up to 6%, while the flow values rose as the percentage of SCBA increased in the mix. Maximum ITS results were observed at 4% CR and 25% SCBA replacement levels. However, a decrease in stability and ITS result was observed as the percentages of CR and SCBA increased beyond 4% and 25%, respectively. We concluded that the optimum CR and SCBA content of 4% and 25%, respectively, can be effectively used as a sustainable alternative in bituminous concrete mix.     

Two cases of meningococcal purpura fulminans: the ‘less is more’ approach

This study presents a case series of patients with meningococcal purpura fulminans who were treated at a tertiary referral centre within a few days of each other. Presenting with signs and symptoms of florid meningococcal sepsis, they were managed initially by physicians and intensivists, whereas the development of large purpuric areas and tissue necrosis was managed expectantly by plastic surgeons. When the patients were deemed to have recovered clinically and the necrosis delineated, surgical management was implemented with subsequent involvement of various rehabilitation services. This article highlights the cases of two patients, and their clinical presentation, management and rehabilitation together with a current literature review on this area.     

Response Surface Methodology and Artificial Neural Network Modelling of Membrane Rotating Biological Contactors for Wastewater Treatment

Membrane fouling is a major hindrance to widespread wastewater treatment applications. This study optimizes operating parameters in membrane rotating biological contactors (MRBC) for maximized membrane fouling through Response Surface Methodology (RSM) and an Artificial Neural Network (ANN). MRBC is an integrated system, embracing membrane filtration and conventional rotating biological contactor in one individual bioreactor. The filtration performance was optimized by exploiting the three parameters of disk rotational speed, membrane-to-disk gap, and organic loading rate. The results showed that both the RSM and ANN models were in good agreement with the experimental data and the modelled equation. The overall R² value was 0.9982 for the proposed network using ANN, higher than the RSM value (0.9762). The RSM model demonstrated the optimum operating parameter values of a 44 rpm disk rotational speed, a 1.07 membrane-to-disk gap, and a 10.2 g COD/m² d organic loading rate. The optimization of process parameters can eliminate unnecessary steps and automate steps in the process to save time, reduce errors and avoid duplicate work. This work demonstrates the effective use of statistical modeling to enhance MRBC system performance to obtain a sustainable and energy-efficient treatment process to prevent human health and the environment.