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.

AI chatbots (AICs) have the potential to increase the sustainability of a manufacturing supply chain (SC) through sales engagement and customer engagement to accomplish various activities related to logistics and SC in real time. Industry 4.0 (I4.0) has opened up several opportunities with internet-based technologies, along with challenges for small and medium enterprises (SMEs). SMEs are beginning to adopt such technologies for their competitive advantages and the required sustainability in the manufacturing supply chain. AICs may help in accomplishing supply chain visibility (SCV) to enhance sustainable supply chain performance (SSCP). Innovation capability (IC) is also due to disruptive technologies being adopted by SMEs. The present research investigates the role of AICs in SCV and IC, which lead to SSCP, by employing structural equation modeling (SEM). An empirical study based on dynamic capability (DC) theory was carried out using 246 responses, and later Smart PLS-4.0 was used for SEM. The analysis revealed that AICs positively influence SCV and IC to support SSCP. SCV and IC also partially mediate the relationship between the adoption of AICs and SSCP.

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 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.

This study used a hybrid combination of kenaf and hemp fibers and the multi-walled carbon nanotube (MWCNT) reinforcements in the matrix phase to synthesize the composites. A kenaf/hemp fiber blend with MWCNTs in epoxy was used for the specific concentration. The procedure used three composite materials chosen from pilot trials. The ratio of MWCNT filler particles was altered up to the agglomeration limit based on initial trials. Two specimens (2 and 3) were supplemented with MWCNTs in a concentration range of 0.5 wt. % to 1 wt. %, with the fiber concentration being maintained in equilibrium with the epoxy resin, all of the materials were tested under the same conditions. The hybrid nanocomposite was characterized for its morphological and mechanical properties; the tensile properties were higher for 1% MWCNTs concentration (specimen 2), while the flexural properties were higher for 0.5% MWCNTs, with values of 43.24 MPa and 55.63 MPa, correspondingly. Once the MWCNT concentration was increased to 1 wt. %, the maximum impact strength was achieved (specimen 3). In the limits of the Shore-D scale, the kenaf fiber and hemp fiber matrix composite (specimen 1) gained a hardness index of 84. Scanning electron microscopy was carried out to analyze the morphological features of the fractured samples and to assess the adhesion between the fiber, matrix, and surface. Among the various fillers tested, the kenaf fiber/hemp/MWCNT composite (specimen 3) demonstrated superior binding and reduced the incidence of fiber pull-out, breakage, and voids. In addition to the comparative analysis, the addition of 0.5 wt. % MWCNTs resulted in better mechanical properties compared to the other two combinations.

Past research reveals that many lean implementation barriers hinder lean implementation in small and medium-sized enterprises (SMEs). Among many sectors, the manufacturing sector suffers more as it generates more waste while carrying out manufacturing processes. Many manufacturing units make unsuccessful attempts to implement lean principles in their manufacturing systems. Hence, such units must eliminate the prevailing lean barriers to accomplish successful lean implementation. Moreover, the contextual relationship of lean barriers must be studied to understand the effect of such barriers. This paper uses interpretive structural modeling (ISM) to explore lean barriers, their relationships, and their influence on other lean barriers. The present research also reveals the most significant classification of lean barriers into various categories of independent, dependent, autonomous, and linkage using the (MICMAC) Matrice d’Impacts Croisés Multiplication Appliquée á un Classement analysis. ISM and MICMAC together provide relationship modeling and reveal the interrelationship between each lean implementation barrier and its categories, respectively. The ISM model is validated using the Delphi technique. The interpretative ranking process (IRP) is used to rank the barriers. The three significant lean implementation barriers revealed through the IRP include “lack of lean understanding”, “lack of strong quality policy”, and “risk of sustainable practice implementation”. The present research will help practicing managers of SMEs in the manufacturing sector to understand the mutual influence of lean barriers before introducing lean implementation. It is suggested that SMEs work on independent barriers so that dependent barriers can also be overcome with the least amount of resources and effort.