The main objective of this review is to study some important nanomaterials and their impact on the performance of geopolymer concrete. This paper is an investigation into trends and technology in the development of different nanomaterials to develop higher structural performance geopolymer concrete. The effect of the alkaline to binder and sodium silicate to sodium hydroxide ratio on the performances of geopolymer performances is studied. The relationship between setting time and slump is evaluated through the ternary plot, the variation in compressive strength values is evaluated using the kernel density plot, and the relationship between split tensile and flexural strength is investigated using the scattering interval plot. Regression analysis is carried out among water absorption and bulk-density result values obtained from previous literature. As the molarity and alkaline to binder (A/B) ratios increase, the strength development of geopolymer concrete increases up to a specific limit. The addition of a small quantity of nanomaterials, namely, nano silica, nano alumina, carbon nano tubes, and nano clay, led to the maximum strength development of geopolymer concrete. Incorporating these nanomaterials into the geopolymer significantly refines the structural stability, improving its durability. The various products in GP composites emerging from the incorporation of highly reactive SEM, XRD, and FTIR analysis of nanomaterials reveal that the presence of nanomaterials, which enhances the rate of polymerization, leads to better performance of the geopolymer.

Business enterprises such as small and medium-sized enterprises (SMEs) play a significant role in economic development but struggle for sustainability. A business enterprise such as a manufacturing unit tries many technological innovations and strategic initiatives to accomplish sustainability in the manufacturing system. Lean manufacturing implementation is one such initiative that helps SMEs manufacture value-added products with increased profitability and waste minimization. However, lean implementation in SMEs is challenging. Hence, it is essential to follow a systematic framework and control the critical success factors (CSFs) in attempting lean implementation. The purpose of this research is to find, evaluate, and rank the CSFs of lean implementation of SMEs so that they may be controlled to accomplish successful lean implementation. The CSFs of lean implementation found by an in-depth assessment of the literature are modeled using the interpretative structural modeling (ISM) approach. MICMAC analysis is also used in classifying and understanding the significance of each lean implementation CSF. ISM and MICMAC provide the relationship modeling to reveal the inter-relationships of each lean implementation CSF. Subsequently, the ISM model is validated using the Delphi technique. The interpretative ranking process (IRP) has been applied to rank the CSFs of lean implementations. The results show that sustainability in a manufacturing system, financial capability, and employee involvement hold significant importance in lean implementations in manufacturing SMEs. Practicing managers may benefit from revisiting their lean implementation plans and respective aligned strategies. They will also be in a position to identify and focus on the scarce resources required for the subsequent lean implementations.

The influence of concrete mix properties on the shear strength of slender structured concrete beams without stirrups (SRCB-WS) is a widespread point of contention. Over the past six decades, the shear strength of SRCB-WS has been studied extensively in both experimental and theoretical contexts. The most recent version of the ACI 318-19 building code requirements updated the shear strength equation for SRCB-WS by factoring in the macroeconomic factors and the contribution of the longitudinal steel structural ratio. However, the updated equation still does not consider the effect of the shear span ratio (a/d) and the yield stress of longitudinal steel rebars (Fy). Therefore, this study investigates the importance of the most significant potential variables on the shear strength of SRCB-WS to help develop a gene expression-based model to estimate the shear strength of SRCB-WS. A database of 784 specimens was used from the literature for training and testing the proposed gene expression algorithm for forecasting the shear strength of SRCB-WS. The collected datasets are comprehensive, wherein all considered concrete properties were considered over the previous 68 years. The performance of the suggested algorithm versus the ACI 318-19 equation was statistically evaluated using various measures, such as root mean square error, mean absolute error, mean absolute percentage error, and the coefficient of determination. The evaluation results revealed the superior performance of the proposed model over the current ACI 318-19 equation. In addition, the proposed model is more comprehensive and considers additional variables, including the effect of the shear span ratio and the yield stress of longitudinal steel rebars. The developed model reflects the power of employing gene expression algorithms to design reinforced concrete elements with high accuracy.

In the current scenario, climatic adversities and a growing population are adding woes to the concerns of food safety and security. Furthermore, with the implementation of Sustainable Development Goal (SDG) 12 by the United Nations (UN), focusing on sustainable production–consumption, climatic vulnerabilities need to be addressed. Hence, in order to map the sustainable production–consumption avenues, agricultural practices need to be investigated for practices like Climate-Smart Agriculture (CSA). A need has arisen to align the existing agricultural practices in the developing nation towards the avenues of CSA, in order to counter the abrupt climatic changes. Addressing the same, a relation hierarchical model is developed which clusters the various governing criteria and their allied attributes dedicated towards the adoption of CSA practices. Furthermore, the developed model is contemplated for securing the primacies of promising practices for the enactment of CSA using the duo of the Analytical Hierarchical Process (AHP) and Fuzzy AHP (FAHP). The outcomes result in the substantial sequencing of the key attributes acting as a roadmap toward the CSA. This emphasizes the adoption of knowledge-based smart practices, which leaps from the current agricultural practices toward the CSA. Furthermore, by intensifying the utilization of the improved and resilient seed varieties and implying the fundamentals of agroforestry, we secure primacy to counter the adversities of the climate.

The current outbreak of monkeypox (mpox) has become a major public health concern because of the quick spread of this disease across multiple countries. Early detection and diagnosis of mpox is crucial for effective treatment and management. Considering this, the purpose of this research was to detect and validate the best performing model for detecting mpox using deep learning approaches and classification models. To achieve this goal, we evaluated the performance of five common pretrained deep learning models (VGG19, VGG16, ResNet50, MobileNetV2, and EfficientNetB3) and compared their accuracy levels when detecting mpox. The performance of the models was assessed with metrics (i.e., the accuracy, recall, precision, and F1-score). Our experimental results demonstrate that the MobileNetV2 model had the best classification performance with an accuracy level of 98.16%, a recall of 0.96, a precision of 0.99, and an F1-score of 0.98. Additionally, validation of the model with different datasets showed that the highest accuracy of 0.94% was achieved using the MobileNetV2 model. Our findings indicate that the MobileNetV2 method outperforms previous models described in the literature in mpox image classification. These results are promising, as they show that machine learning techniques could be used for the early detection of mpox. Our algorithm was able to achieve a high level of accuracy in classifying mpox in both the training and test sets, making it a potentially valuable tool for quick and accurate diagnosis in clinical settings.