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Background/Objectives: Nuclear pleomorphism, a crucial determinant of breast cancer grading under the Nottingham Histopathology Grading (NHG) system, remains inadequately quantified in the existing literature. Motivated by this gap, our study seeks to investigate and establish correlations among morphological features across various scores of nuclear pleomorphism, as per the NHG system. We aim to quantify nuclear pleomorphism across these scores and validate our proposed measurement method against ground-truth data. Methods: Initially, we deconstruct the descriptions of nuclear pleomorphism into three core elements: size, shape, and appearance. These elements are subsequently mathematically modeled into equations, termed ESize, EShape, and EAppearance. These equations are then integrated into a unified model termed Harmonic Mean (HM). The HM equation yields a value approaching 1 for nuclei demonstrating characteristics of score-3 nuclear pleomorphism and near 0 for those exhibiting features of score-1 nuclear pleomorphism. Results: The proposed HM model demonstrates promising performance metrics, including Accuracy, Recall, Specificity, Precision, and F1-score, with values of 0.97, 0.96, 0.97, 0.94, and 0.95, respectively. Conclusions: In summary, this study proposes the HM equation as a novel feature for the precise quantification of nuclear pleomorphism in breast cancer.

Oil palm (Elaeis guineensis Jacq.) is of the most profitable and widespread commercial high tree crops in the tropical world, typically in Southeastern Asia. The present study aims to provide a brief but broad overview of different applications of expert systems (ESs) in oil palm precision agriculture (PA), focusing on the three main generic categories: crop, water, and soil management. This study is meant to review research articles from the past decade: 2011–2020. Based on the search strategy alongside the inclusion criteria, 108 articles were included for synthesis activity. The findings of the study reveal patterns, networks, relationships, and trends in the application of ESs in oil palm PA in the past decade. The broad insight obtained from the synthesis activity was used to identify the possible roads ahead in oil palm PA. The findings of this study could be useful and beneficial to the research community and stakeholders in identifying the progress and trends of ESs in oil palm PA in the past decade, help to gain a holistic view on research gaps, potential markets, relevant advantages, the roads ahead, and contributing to further systematic research (deepen or broaden) in this topic.

This paper presents the fault tolerance of model predictive direct torque control for a permanent magnet synchronous generator under a faulty Vienna rectifier. The fault applied includes open-switch and short-switch fault in a particular active switching device of the Vienna rectifier. The measured input current is used in the proposed fault diagnosis approach to detect the switch fault’s position without any additional hardware being implemented. Whenever a switching fault occurs at any phase of the Vienna rectifier, the available switching vectors for prediction control are reduced from five to four. The feasibility and effectiveness of the proposed fault tolerance model predictive direct torque control under a faulty Vienna rectifier are demonstrated and investigated through MATLAB/Simulink. The results show that it is feasible for the proposed method to be operated under a short-switch fault with slightly higher total harmonics distortion of the input current but out of control under an open-switch fault.

Based on the Nottingham Histopathology Grading (NHG) system, mitosis cells detection is one of the important criteria to determine the grade of breast carcinoma. Mitosis cells detection is a challenging task due to the heterogeneous microenvironment of breast histopathology images. Recognition of complex and inconsistent objects in the medical images could be achieved by incorporating domain knowledge in the field of interest. In this study, the strategies of the histopathologist and domain knowledge approach were used to guide the development of the image processing framework for automated mitosis cells detection in breast histopathology images. The detection framework starts with color normalization and hyperchromatic nucleus segmentation. Then, a knowledge-assisted false positive reduction method is proposed to eliminate the false positive (i.e., non-mitosis cells). This stage aims to minimize the percentage of false positive and thus increase the F1-score. Next, features extraction was performed. The mitosis candidates were classified using a Support Vector Machine (SVM) classifier. For evaluation purposes, the knowledge-assisted detection framework was tested using two datasets: a custom dataset and a publicly available dataset (i.e., MITOS dataset). The proposed knowledge-assisted false positive reduction method was found promising by eliminating at least 87.1% of false positive in both the dataset producing promising results in the F1-score. Experimental results demonstrate that the knowledge-assisted detection framework can achieve promising results in F1-score (custom dataset: 89.1%; MITOS dataset: 88.9%) and outperforms the recent works.

Tubule detection is very crucial and is one of the important criterions for tubule formation assessment in breast carcinoma. The output of tubule formation is essential to determine the grade of breast carcinoma based on the Nottingham Histological Grading (NHG) system. Very few studies have been done on the tubule quantification in breast carcinoma. In this study, the geometrical pattern and regularity measurement in tubule and non-tubule were investigated. Spatial Angle (SA), Distribution (D), and Harmonic Mean (HMκ) indices were proposed to quantify the tubule and non-tubule. The information of the spatial angle and distribution of neighborhood nuclei surrounding the central lumen were considered in the proposed measurements. The main advantage of the proposed measurements over the other existing findings is that the proposed measurements provide a clear geometrical meaning for the irregular tubule. The proposed Harmonic Mean Index (HMκ) was found significant in discriminating the tubule and non-tubule (p value

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a “one-stop center” synthesis and provide a holistic bird’s eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

Research has been conducted on the chiral allylic alcohol epoxidation. The study of this epoxidation has been carried out via the use of the density functional theory method and the role of the metal-alcoholate bonding in the allylic alcohol diastereoselective epoxidation has been investigated.

DFT/B3LYP calculations are carried out on complexes formed by N(H)4(super + ) with aromatics to characterize the forces involved in such interactions and to gain further insight in to the nature and diversity of cation-aromatic interactions. The calculated results indicate that the protanated heterocyclic N(H)3 hydrogen-bond complexes produce the largest enthalpies.

The interaction properties if tetramethylammonium(TMA) with aromatic compounds such as benzene and pyrrole are theoretically analysed, based on B3LYP density-functional theory (DFT) and ab initio second order Moller-Plesset perturbation theory (MP2) calculations. The TMA-aromatic interactions are mainly due to typical cation-pi interaction and the dispersion interaction.

The quantum chemical calculations were performed on the complexes formed by silicane and ammonium in order to investigate the binding characteristics and nature of the dihydrogen bond. The calculation results reveal that the dihydrogen-bond angle is ~180.0degrees and that the bond length is ~1.60A.

Highlights a nonepisodic autosomal dominant (AD) spinocerebellar ataxia (SCA), the first type of SCA that is not caused by nucleotide repeat expansion. Common symptoms of autosomal dominant spinocerebellar ataxia; Reasons for amplification of isolated DNA from peripheral blood and exons; Sequencing of coding regions of candidate gene PRKCG in genomic DNA extracted from patients with ataxia.