This paper presents on ionic conductivity of MG30-PEMA blend solid polymer electrolytes (SPEs) prepared by solution cast technique. The analysis has shown that conductivity increases with the increasing salt composition. It is observed via x-ray diffraction analysis that the crystallinity of the sample decreased with the amount of salt composition as expected. It is also observed that the dielectric value increases with increasing amount of LiCF3SO3 in the sample. Surface morphology revealed that ion aggregation occurred after optimum conductivity which has lowered the conductivity.
Mutations in the hepatitis B virus (HBV) genome can potentially lead to vaccination failure, diagnostic escape, and disease progression. However, there are no reports on viral gene expression and large hepatitis B surface antigen (HBsAg) antigenicity alterations due to mutations in HBV isolated from a Bangladeshi population. Here, we sequenced the full genome of the HBV isolated from a clinically infected patient in Bangladesh. The open reading frames (ORFs) (P, S, C, and X) of the isolated HBV strain were successfully amplified and cloned into a mammalian expression vector. The HBV isolate was identified as genotype C (sub-genotype C2), serotype adr, and evolutionarily related to strains isolated in Indonesia, Malaysia, and China. Clinically significant mutations, such as preS1 C2964A, reverse transcriptase domain I91L, and small HBsAg N3S, were identified. The viral P, S, C, and X genes were expressed in HEK-293T and HepG2 cells by transient transfection with a native subcellular distribution pattern analyzed by immunofluorescence assay. Western blotting of large HBsAg using preS1 antibody showed no staining, and preS1 ELISA showed a significant reduction in reactivity due to amino acid mutations. This mutated preS1 sequence has been identified in several Asian countries. To our knowledge, this is the first report investigating changes in large HBsAg antigenicity due to preS1 mutations.
Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Multilayer perceptron neural networks (MLPNN), adaptive neural fuzzy detection systems (ANFIS), and genetic programming are all used (GEP). From accessible literature data, a broad and accurate database of 283 compressive strengths and 149 split tensile strengths was created. The six most significant input parameters were cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume. Different statistical measures were used to evaluate models, including mean absolute error, root mean square error, root mean squared log error and the coefficient of determination. Both machine learning models, MLPNN and ANFIS, produced acceptable results with high prediction accuracy. Statistical analysis revealed that the ANFIS model outperformed the MLPNN model in terms of compressive and tensile strength prediction. The GEP models outperformed all other models. The predicted values for compressive strength and splitting tensile strength for GEP models were consistent with experimental values, with an R2 value of 0.97 for compressive strength and 0.93 for splitting tensile strength. Furthermore, sensitivity tests revealed that cement and water are the determining parameters in the growth of compressive strength but have the least effect on splitting tensile strength. Cross-validation was used to avoid overfitting and to confirm the output of the generalized modelling technique. GEP develops an empirical expression for each outcome to forecast future databases' features to promote the usage of green concrete.