As they are the smallest functional parts of the muscle, motor units (MUs) are considered as the basic building blocks of the neuromuscular system. Monitoring MU recruitment, de-recruitment, and firing rate (by either invasive or surface techniques) leads to the understanding of motor control strategies and of their pathological alterations. EMG signal decomposition is the process of identification and classification of individual motor unit action potentials (MUAPs) in the interference pattern detected with either intramuscular or surface electrodes. Signal processing techniques were used in EMG signal decomposition to understand fundamental and physiological issues. Many techniques have been developed to decompose intramuscularly detec

In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor

Cancer is in general not a result of an abnormality of a single gene but a consequence of changes in many genes, it is therefore of great importance to understand the roles of different oncogenic and tumor suppressor pathways in tumorigenesis. In recent years, there have been many computational models developed to study the genetic alterations of different pathways in the evolutionary process of cancer. However, most of the methods are knowledge-based enrichment analyses and inflexible to analyze user-defined pathways or gene sets. In this paper, we develop a nonparametric and data-driven approach to testing for the dynamic changes of pathways over the cancer progression. Our method is based on an expansion and refinement of the pathway bei

بسمة الأوقاتي/ أكاديمية وباحثة في العلاقات الدولية تولي الرئيس الأمريكي دونالد ترامب منصبه لولاية ثانية في العشرين من كانون الثاني / يناير من هذا العام (2025) وهو أكثر تمسكاً بعقيدته السياسية التي كان عنوانها في ولايته الأولى (أمريكا أولا / America First) وحاملا لتوجهات جيوسياسية جديدة ومثيرة للجدل

The focus of this research lies in the definition of an important aspect of financial development, which is reflected on the alleviation of poverty in Iraq, namely financial inclusion and then taking the path of achieving a sustainable economy, certainly after reviewing one of the important international experiences in this regard and finally measuring the level of financial inclusion in Iraq and its impact on poverty reduction through the absolute poverty line indicator.

 This paper adapted the neural network for the estimating of the direction of arrival (DOA). It uses an unsupervised adaptive neural network with GHA algorithm to extract the principal components that in turn, are used by Capon method to estimate the DOA, where by the PCA neural network we take signal subspace only and use it in Capon (i.e. we will ignore the noise subspace, and take the signal subspace only).

Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

This research is a theoretical study that deals with the presentation of the literature of statistical analysis from the perspective of gender or what is called Engendering Statistics. The researcher relied on a number of UN reports as well as some foreign sources to conduct the current study. Gender statistics are defined as statistics that reflect the differences and inequality of the status of women and men overall domains of life, and their importance stems from the fact that it is an important tool in promoting equality as a necessity for the process of sustainable development and the formulation of national and effective development policies and programs. The empowerment of women and the achievement of equality between men and wome

Historical cultural environments are a repository of values and symbols that pass down across generations through spatial experiences. Despite their intellectual and cultural potential, their role in fostering belonging and identity has declined; they are often viewed as silent landmarks, isolated from lived experiences. This highlights the need for an integrated model that makes spatial experience a stimulating process for reinvigorating the meaning inherent in historical contexts and reconnecting the new generation with their cultural roots. This research aims to explore how cultural meaning in historical contexts can be reactivated through spatial experience. To achieve this, the study proposes a triadic model – physical encoun

This paper offers a systemic review of the deep learning methods to detect violence on campus, which is a critical issue in intelligent surveillance to improve the student safety and prompt cut off of violent accidents. The review reviews studies published 2018-2025, concentrating on model structure to detect fights, bullying, vandalism, and aggressive behavior on problematic campuses due to occlusion and light variations and complicated human interactions. The research design includes a comparative study of different deep learning networks, such as CNNs, RNNs, 3D CNNs, attention-based networks, transformers, graph neural networks, neuro-fuzzy, and multimodal systems and federated learning methods. The paper also assesses benchmark