The study was aimed to evaluate the marketing efficiency of dry Onion crop in Salah al-Deen, as estimate the impact of some quality and quantity factors in the efficiency of marketing process of crop using Tobit regression model. The average marketing efficiency of the research sample was 71.3686%. The marketing margins differed according to the marketing channel followed in marketing the crop. The qualitative and quantitative variables in the model are productivity, family size, distance from the market, educational level. The estimated model revealed that a variable productivity is the most important and influential in marketing efficiency, followed by the variable of the distance between the farm and the market, then the variable

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt

The study was aimed to evaluate the marketing efficiency of dry Onion crop in Salah al-Deen, as estimate the impact of some quality and quantity factors in the efficiency of marketing process of crop using Tobit regression model. The average marketing efficiency of the research sample was 71.3686%. The marketing margins differed according to the marketing channel followed in marketing the crop. The qualitative and quantitative variables in the model are productivity, family size, distance from the market, educational level. The estimated model revealed that a variable productivity is the most important and influential in marketing efficiency, followed by the variable of the distance between the farm and the market, then the variable

Spatial data analysis is performed in order to remove the skewness, a measure of the asymmetry of the probablitiy distribution. It also improve the normality, a key concept of statistics from the concept of normal distribution “bell shape”, of the properties like improving the normality porosity, permeability and saturation which can be are visualized by using histograms. Three steps of spatial analysis are involved here; exploratory data analysis, variogram analysis and finally distributing the properties by using geostatistical algorithms for the properties. Mishrif Formation (unit MB1) in Nasiriya Oil Field was chosen to analyze and model the data for the first eight wells. The field is an anticline structure with northwest- south

Explainable Artificial Intelligence (XAI) techniques enable transparency and trust in automated visual inspection systems by making black-box machine learning models understandable. While XAI has been widely applied, prior reviews have not addressed the specific demands of industrial and medical inspection tasks. This paper reviews studies applying XAI techniques to visual inspection across industrial and medical domains. A systematic search was conducted in IEEE Xplore, Scopus, PubMed, arXiv, and Web of Science for studies published between 2014 and 2025, with inclusion criteria requiring the application of XAI in inspection tasks using public or domain-specific datasets. From an initial pool of studies, 75 were included and categorized in

<span lang="EN-US">The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of e

This investigation proposed an identification system of offline signature by utilizing rotation compensation depending on the features that were saved in the database. The proposed system contains five principle stages, they are: (1) data acquisition, (2) signature data file loading, (3) signature preprocessing, (4) feature extraction, and (5) feature matching. The feature extraction includes determination of the center point coordinates, and the angle for rotation compensation (θ), implementation of rotation compensation, determination of discriminating features and statistical condition. During this work seven essential collections of features are utilized to acquire the characteristics: (i) density (D), (ii) average (A), (iii) s

This research aims to study the important of the effect of analysis of covariance manner for one of important of design for multifactor experiments, which called split-blocks experiments design (SBED) to deal the problem of extended measurements for a covariate variable or independent variable (X) with data of response variable or dependent variable Y in agricultural experiments that contribute to mislead the result when analyze data of Y only. Although analysis of covariance with discussed in experiments with common deign, but it is not found information that it is discussed with split-Blocks experiments design (SBED) to get rid of the impact a covariance variable. As part application actual field experiment conducted, begun at