Surface electromyography (sEMG) and accelerometer (Acc) signals play crucial roles in controlling prosthetic and upper limb orthotic devices, as well as in assessing electrical muscle activity for various biomedical engineering and rehabilitation applications. In this study, an advanced discrimination system is proposed for the identification of seven distinct shoulder girdle motions, aimed at improving prosthesis control. Feature extraction from Time-Dependent Power Spectrum Descriptors (TDPSD) is employed to enhance motion recognition. Subsequently, the Spectral Regression (SR) method is utilized to reduce the dimensionality of the extracted features. A comparative analysis is conducted between the Linear Discriminant Analysis (LDA) class

ABSTRACT This study presents an efficient approach for the separation and preconcentration of norepinephrine (NOR) from pharmaceutical formulations, environmental water, and human urine samples using a dispersive micro – solid phase extraction (DμSPE) technique employing magnetic nanoadsorbents. Two adsorbents, Fe3O4@TTAB and Fe3 O4@SiO2@TTAB, were prepared by functionalising iron oxide and silicacoated iron oxide nanoparticles with the cationic surfactant tetradecyltrimethylammonium bromide (TTAB). NOR was first converted into a sensitive diazonium dye via reaction with diazotised sulphamethazine and then extracted using mixed ademicelle – hemimicelle magnetic solid-phase extraction, followed by spectrophotometric quantification. Key

in this paper the collocation method will be solve ordinary differential equations of retarted arguments also some examples are presented in order to illustrate this approach

Reliable estimation of critical parameters such as hydrocarbon pore volume, water saturation, and recovery factor are essential for accurate reserve assessment. The inherent uncertainties associated with these parameters encompass a reasonable range of estimated recoverable volumes for single accumulations or projects. Incorporating this uncertainty range allows for a comprehensive understanding of potential outcomes and associated risks. In this study, we focus on the oil field located in the northern part of Iraq and employ a Monte Carlo based petrophysical uncertainty modeling approach. This method systematically considers various sources of error and utilizes effective interpretation techniques. Leveraging the current state of a

This research dealt with the analysis of murder crime data in Iraq in its temporal and spatial dimensions, then it focused on building a new model with an algorithm that combines the characteristics associated with time and spatial series so that this model can predict more accurately than other models by comparing them with this model, which we called the Combined Regression model (CR), which consists of merging two models, the time series regression model with the spatial regression model, and making them one model that can analyze data in its temporal and spatial dimensions. Several models were used for comparison with the integrated model, namely Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Random Forest Reg