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Early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is crucial to prevent their progression. In this study, we proposed the analysis of magnetic resonance imaging (MRI) based on features including; hippocampus (HC) area size, HC grayscale statistics and texture features (mean, standard deviation, skewness, kurtosis, contrast, correlation, energy, homogeneity, entropy), lateral ventricle (LV) area size, gray matter area size, white matter area size, cerebrospinal fluid area size, patient age, weight, and cognitive score. Five machine learning classifiers; K-nearest neighborhood (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), and multi-layer perception (MLP) were used to distinguish between groups: cognitively normal (CN) vs AD, early MCI (EMCI) vs late MCI (LMCI), CN vs EMCI, CN vs LMCI, AD vs EMCI, and AD vs LMCI. Additionally, the correlation and dependence were calculated to examine the strength and direction of association between each extracted feature and each classification of the group. The average classification accuracies in 20 trials were 95% (SVM), 71.50% (RF), 82.58% (RF), 84.91% (SVM), 85.83% (RF), and 85.08% (RF), respectively, with the best accuracies being 100% (SVM, RF, and MLP), 83.33% (RF), 91.66% (RF), 95% (SVM, and MLP), 96.66% (RF), and 93.33% (DT). Cognitive scores, HC and LV area sizes, and HC texture features demonstrated significant potential for diagnosing AD and its subtypes for all groups. RF and SVM showed better performance in distinguishing between groups. These findings highlight the importance of using 2D-MRI to identify key features containing critical information for early diagnosis of AD.

Supplementary information: The online version contains supplementary material available at 10.1007/s42452-024-06440-w.

The Canadian prairies are renowned for their substantial agricultural contributions to the global food market. Harrow tines are indispensable in farming equipment, especially for soil preparation and weed control before planting crops. During operation, these tines are exposed to repetitive cyclic loading, which eventually causes fatigue failure. Commercially available three different harrow tines named 0.562HT, 0.625HT, and 0.500HT undergo an experimental fatigue evaluation and are validated through Finite Element Analysis (FEA). Fatigue life estimation for different deflections under various real-field deflections was carried out where 0.562HT showed groundbreaking life compared with others. The study results showed that the fatigue life is highly dependent on geometry, number of coils, pitch angle, leg length, and coil diameter. The 0.354HT model, developed to investigate the effect of wire diameter, closely resembles the 0.500HT model. The harrowing ability of the four different harrow tine models against identical deflections has been analyzed. Experimental fractured surfaces went through morphological investigation. This research has an impeccable impact on prairies' agricultural acceleration by saving time and mitigating unpredictable fatigue failure often faced by farmers. Even the observed failure phenomena can serve as motivation to develop more reliable and durable harrow tines, which could increase agricultural efficiency.

Supplementary information: The online version contains supplementary material available at 10.1007/s42452-024-06310-5.