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To demonstrate corrosion inhibition capabilities of new cyclic nitrones, containing hydrophobic substituents.

A number of new cyclic nitrones were synthesized. Corrosion inhibition efficiencies of these organic inhibitors were determined by gravimetric and electrochemical methods, using carbon steel as the substrate metal and 1 M HCl at 60°C as the corrosive environment. Concentration of inhibitor was varied between 50 and 400 ppm.

All compounds exhibited excellent corrosion efficiencies that ranged between 90.0 and 98.3 percent in 1 M HCl at 60°C. Tafel tests corroborated these results.

The inhibitors were tested in acidic medium. It is unknown how these inhibitors will function in the presence of other ions that are typically present in natural corrosive environment.

All organic compounds presented in this work are new and this is the first time their corrosion inhibition characteristics have been evaluated.

Hundreds of thousands of deaths each year in the world are caused by breast cancer (BC). An early-stage diagnosis of this disease can positively reduce the morbidity and mortality rate by helping to select the most appropriate treatment options, especially by using histological BC images for the diagnosis.

The present study proposes and evaluates a novel approach which consists of 24 deep hybrid heterogenous ensembles that combine the strength of seven deep learning techniques (DenseNet 201, Inception V3, VGG16, VGG19, Inception-ResNet-V3, MobileNet V2 and ResNet 50) for feature extraction and four well-known classifiers (multi-layer perceptron, support vector machines, K-nearest neighbors and decision tree) by means of hard and weighted voting combination methods for histological classification of BC medical image. Furthermore, the best deep hybrid heterogenous ensembles were compared to the deep stacked ensembles to determine the best strategy to design the deep ensemble methods. The empirical evaluations used four classification performance criteria (accuracy, sensitivity, precision and F1-score), fivefold cross-validation, Scott–Knott (SK) statistical test and Borda count voting method. All empirical evaluations were assessed using four performance measures, including accuracy, precision, recall and F1-score, and were over the histological BreakHis public dataset with four magnification factors (40×, 100×, 200× and 400×). SK statistical test and Borda count were also used to cluster the designed techniques and rank the techniques belonging to the best SK cluster, respectively.

Results showed that the deep hybrid heterogenous ensembles outperformed both their singles and the deep stacked ensembles and reached the accuracy values of 96.3, 95.6, 96.3 and 94 per cent across the four magnification factors 40×, 100×, 200× and 400×, respectively.

The proposed deep hybrid heterogenous ensembles can be applied for the BC diagnosis to assist pathologists in reducing the missed diagnoses and proposing adequate treatments for the patients.

The research aims to identify the impacts of strategic knowledge (SK) and information technology capabilities (ITC) on innovation ambidexterity (IAM) through business process performance (BPP).

The research framework is developed based on the theoretical grounding of resource orchestration (RO) (SK and ITC) impacts on IAM. The structural equation modeling (SEM) technique was used to test the research framework on a sample of 441 responses from Brazilian firms.

The results suggest that SK and ITC facilitate BPP, resulting in IAM. The findings also suggested differences in path coefficients in the SK and ITC of the business value generation process framework under environmental turbulence (ET). Finally, a strong SK of ITC is especially important in enabling BPP and IAM in large firms. Another case of most manufacturing and service firms demonstrated that both SK and ITC are essential to impacting IAM through BPP mediation.

The findings provide insight into how professionals can think and plan carefully to align SK and ITC for achieving balanced innovation and improving BPP in the dynamic business environment.

The study establishes a relationship between SK, ITC, BPP and IAM. The study developed novel constructs of SK and ITC and tested them, which gives new insight and links among the constructs.

The purpose of this paper is investigate the inhibition efficiency of three similar bi-cyclic organic compounds, namely, benzimidazole (BI), benzotriazole (BTAH) and benzothiazole (BTH) on carbon steel in 1 M hydrochloric acid (HCl) solution. Organic inhibitors are widely used to protect metals in acidic media. Among abundant suggestions for acid corrosion inhibitors, azole compounds have gained attention.

The inhibition efficiency of the three organic compounds was investigated using potentiodynamic polarization and electrochemical impedance spectroscopy (EIS).

Superiorities of BTH and BTAH corrosion inhibitors were shown by EIS data and polarization curves. Moreover, the results revealed that BTAH and BTH can function as effective mixed-type adsorptive inhibitors, whereas no inhibition behavior was observed for BI. Both BTAH and BTH obeyed Longmuir adsorption isotherm. The results obtained from this isotherm showed that both inhibitors adsorbed on the specimen surface physically and chemically. The difference in inhibition efficiencies of BTAH, BTH and BI was related to the presence of nitrogen and sulfur hetero atoms on their molecular structures.

This study evaluated inhibition efficiency of BI, BTAH and BTH using electrochemical methods. In addition, the study attempted to find inhibition mechanism of the inhibitors and to find modes of adsorption of the inhibitors, correlating effects of heteroatoms and inhibition efficiency.

COVID-19 continues to spread, and cause increasing deaths. Physicians diagnose COVID-19 using not only real-time polymerase chain reaction but also the computed tomography (CT) and chest x-ray (CXR) modalities, depending on the stage of infection. However, with so many patients and so few doctors, it has become difficult to keep abreast of the disease. Deep learning models have been developed in order to assist in this respect, and vision transformers are currently state-of-the-art methods, but most techniques currently focus only on one modality (CXR).

This work aims to leverage the benefits of both CT and CXR to improve COVID-19 diagnosis. This paper studies the differences between using convolutional MobileNetV2, ViT DeiT and Swin Transformer models when training from scratch and pretraining on the MedNIST medical dataset rather than the ImageNet dataset of natural images. The comparison is made by reporting six performance metrics, the Scott–Knott Effect Size Difference, Wilcoxon statistical test and the Borda Count method. We also use the Grad-CAM algorithm to study the model's interpretability. Finally, the model's robustness is tested by evaluating it on Gaussian noised images.

Although pretrained MobileNetV2 was the best model in terms of performance, the best model in terms of performance, interpretability, and robustness to noise is the trained from scratch Swin Transformer using the CXR (accuracy = 93.21 per cent) and CT (accuracy = 94.14 per cent) modalities.

Models compared are pretrained on MedNIST and leverage both the CT and CXR modalities.

It has been demonstrated in the US market that expected market excess returns can be predicted using the average higher-order moments of all firms. This study aims to empirically test this theory in emerging markets.

Two measures of average higher moments have been used (equal-weighted and value-weighted) along with the market moments to predict subsequent aggregate excess returns using the linear as well as the quantile regression model.

The authors report that both equal-weighted skewness and kurtosis significantly predict subsequent market returns in two countries, while value-weighted average skewness and kurtosis are significant in predicting returns in four out of nine sample markets. The results for quantile regression show that the relationship between the risk variable and aggregate returns varies along the spectrum of conditional quantiles.

This is the first study that investigates the impact of third and fourth higher-order average realized moments on the predictability of subsequent aggregate excess returns in the MSCI Asian emerging stock markets. This study is also the first to analyze the sensitivity of future market returns over various quantiles.

Histopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides.

Three pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system.

This study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately.

The best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F1-score of 99.20 per cent.

Lean manufacturing is an operational strategy oriented toward achieving the shortest possible cycle time by eliminating waste. It is derived from the Toyota Production System and its key thrust is to increase the value-added work by eliminating waste and reducing incidental work. In today's competitive global marketplace, the concept of lean manufacturing has gained vital consciousness to all manufacturing sectors, their supply chains, and hence a logical measurement index system is indeed required in implementing leanness in practice. Such leanness estimation can help the enterprises to assess their existing leanness level and can compare different industries who are adapting this lean concept. The paper aims to discuss these issues.

The present work exhibits an efficient fuzzy-based leanness assessment system using generalized interval-valued (IV) trapezoidal fuzzy numbers set. The concept of “degree of similarity” between two IV fuzzy numbers has been explored here to identify ill-performing areas towards lean achievement.

The methodology described here has been found fruitful while applying for a particular industry, in India, as a case study. Apart from estimating overall lean performance metric, the model presented here can identify ill-performing areas towards lean achievement.

The major contributions of this work have been summarized as follows: development and implementation of an efficient decision-making procedural hierarchy to support leanness extent evaluation. An overall lean performance index evaluation platform has been introduced. Concept of generalized IV trapezoidal fuzzy numbers has been efficiently explored to facilitate this decision-making. The appraisement index system has been extended with the capability to search ill-performing areas which require future progress.

The purpose of this study is to provide an efficient index system for evaluating leanness extent of the organizational supply chain. In today’s competitive global marketplace, the concept of lean manufacturing has gained vital consciousness to all manufacturing sectors, their supply chains and, hence, a logical measurement index system is indeed required in implementing leanness in practice. Such leanness estimation can help the enterprises to assess their existing leanness level and can compare different industries that are adapting this lean concept.

The present work exhibits an efficient fuzzy-based leanness assessment system using trapezoidal fuzzy numbers set.

The methodology described here has been found fruitful while applying for a particular industry, in India, as a case study. Apart from estimating overall lean performance metric, the model presented here can identify ill-performing areas toward lean achievement.

The major contributions of this work have been summarized as follows: development and implementation of an efficient decision-making procedural hierarchy to support leanness extent evaluation; an overall lean performance index evaluation platform has been introduced; concept of generalized trapezoidal fuzzy numbers has been efficiently explored to facilitate this decision-making; and the appraisement index system has been extended with the capability to search ill-performing areas which require future progress.

Lean manufacturing is a production practice that considers the expenditure of resources for any goal other than the creation of value for the end customer to be wasteful, and thus a target for elimination. In today's competitive global marketplace the concept of lean manufacturing has gained vital consciousness to all manufacturing sectors, their supply chains and hence a logical measurement index system is indeed required in implementing leanness in practice. The paper aims to discuss these issues.

The present work exhibits an efficient grey-based leanness assessment system using concept of grey numbers theory.

Such leanness estimation can help the enterprises to assess their existing leanness level; can compare different industries who are adapting this lean concept. The methodology described here has been found fruitful while applying for a particular industry, in India, as a case study.

Lean extent evaluation module (lean index appraisement system) exploring grey numbers theory is quite new and not documented in literature before.