The Gender-Biased Wording of Recruitment Advertisements in Iraq

This research studies the nature and the scope of gender discrimination in job advertisements across Iraq’s various business sectors, segments, and hierarchical levels. It also attempts to understand the correlation between the language of the vacancy announcements and the level of the gender discriminatory content in them. This study is conducted with the hope of contributing to gender equality at the workplace in Iraq and the wider region. The current study adopts a content coding and analysis method that depends on the analysis of job advertisements (n=1015) by organizations operating in Iraq published in the leading recruitment websites and social media pages for a period of about four consecutive months from June to October 2017. The analysis is conducted using descriptive statistics and tested using simple cross tabulation method. Although the topic has been studied in various countries and contexts, it lacks academic attention in the Middle East, which can be seen as a unique area for research. Also, this research is the first attempt, as far as we are aware of, to comprehend the correlation between the choice of language (English, Arabic or Kurdish) and gender-biased wording of vacancy announcements. Understanding the relationship between language and gender discrimination in job advertisements might as well unveil a new area of study and aid in the quest for gender equality in the Iraqi workplace. This paper provides scientific evidence that more than 41% of all job advertisements in Iraq commit gender discrimination. The majority of them indirectly favor male candidates. Additionally, the nature of the vacancies for which women are preferred is different from the ones that target men. The adverts generally try to segregate women into non-managerial and administrative jobs. The study also infers a statistically significant correlation between the language of the advertisement and gender discrimination rate.

Fingerprint Approaches Coupled with Chemometrics to Discriminate Geographic Origin of Imported Salmon in China’s Consumer Market

Of the salmon sold in China’s consumer market, 92% was labelled as Norwegian salmon, but was in fact was mainly imported from Chile. The aim of this study was to establish an effective method for discriminating the geographic origin of imported salmon using two fingerprint approaches, Near-infrared (NIR) spectroscopy and mineral element fingerprint (MEF). In total, 80 salmon (40 from Norway and 40 from Chile) were tested, and data generated by NIR and MEF were analysed via various chemometrics. Four spectral preprocessing methods, including vector normalization (VN), Savitzky Golay (SG) smoothing, first derivative (FD) and second derivative (SD), were employed on the raw NIR data, and a partial least squares (PLS) model based on the FD + SG9 pretreatment could successfully differentiate Norwegian salmons from Chilean salmons, with a R2 value of 98.5%. Analysis of variance (ANOVA) and multiple comparative analysis were employed on the contents of 16 mineral elements including Pb, Fe, Cu, Zn, Al, Sr, Ni, As, Cr, V, Se, Mn, K, Ca, Na and Mg. The results showed that Fe, Zn, Al, Ni, As, Cr, V, Se, Ca and Na could be used as characteristic elements to discriminate the geographical origin of the imported salmon, and the discrimination rate of the linear discriminant analysis (LDA) model, trained on the above 10 elements, could reach up to 98.8%. The results demonstrate that both NIR and MEF could be effective tools for the rapid discrimination of geographic origin of imported salmon in China’s consumer market.

Traceability of the geographical origin of Siraitia grosvenorii based on multielement contents coupled with chemometric techniques

Siraitia grosvenorii (LHG) is widely used as a medicinal and edible material around the world. The objective of this study was to develop an effective method for the authentication of the geographical origin of LHG in its main producing area Guangxi, China, which is identified as Chinese Protected Designation of Origin product, against other producing regions in China. The content of 14 elements (K, Na, Ca, P, Mg, Al, B, Ba, Cu, Fe, Mn, Ni, Zn, and Sr) of 114 LHG samples was determined by inductively coupled plasma optical emission spectrometry. Multivariate analysis was then performed to classify the geographical origin of LHG samples. The contents of multielement display an obvious trend of clustering according to the geographical origin of LHG samples based on radar plot and principal component analysis. Finally, three supervised statistical techniques, including linear discriminant analysis (LDA), k-nearest neighbours (k-NN), and support vector machine (SVM), were applied to develop classification models. Finally, 40 unknown LHG samples were used to evaluate the predictive ability of model and discrimination rate of 100%, 97.5% and 100% were obtained for LDA, k-NN, and SVM, respectively. This study indicated that it is feasible to attribute unknown LHG samples to its geographical origin based on its multielement content coupled with chemometric techniques.

Discrimination between schools and submerged trees in reservoirs: A preliminary approach using narrowband and broadband acoustics

With a growing demand for hydroelectric energy, the number of reservoirs is dramatically increasing worldwide. These new water bodies also present an opportunity for the development of fishing activities. However, these reservoirs are commonly impounded on uncut forests, resulting in many immersed trees. These trees hinder fish assessments by disrupting both gill netting and acoustic sampling. Immersed trees can easily be confused with fish schools on echograms. To overcome this issue, we developed a method to discriminate fish schools from immersed trees. A random forest algorithm was used to classify echo-traces at 120 and 200 kHz, recorded by an EK80 (SIMRAD) in narrowband (Continuous Wave) and in broadband mode (Frequency Modulated). We obtained a good discrimination rate between trees and schools, especially in broadband (90 % ratio of good classification). We demonstrate that it is possible to discriminate fish schools from immersed trees and thus facilitate the use of fisheries acoustics in reservoirs.

Dynamic Shadow Detection and Removal for Vehicle Tracking System

Shadow leads to failure of moving target positioning, segmentation, tracking, and classification in the video surveillance system thus shadow detection and removal is essential for further computer vision process. The existing state-of-the-art methods for dynamic shadow detection have produced a high discrimination rate but a poor detection rate (foreground pixels are classified as shadow pixels). This paper proposes an effective method for dynamic shadow detection and removal based on intensity ratio along with frame difference, gamma correction, and morphology operations. The performance of the proposed method has been tested on two outdoor ATON datasets, namely, highway-I and highway-III for vehicle tracking systems. The proposed method has produced a discrimination rate of 89.07% and a detection rate of 80.79% for highway-I video sequences. Similarly, for a highway-III video sequence, the discrimination rate of 85.60% and detection rate of 84.05% have been obtained. Investigational outcomes show that the proposed method is the simple, steadiest, and robust for dynamic shadow detection on the dataset used in this work.

Spectral Discrimination of Six Dominant Seaweed Species in the Intertidal Zone of GouQi Island

Probing the coverage and biomass of seaweed is the basis for achieving sustainable utilization of nearshore seaweed resources. Unlike traditional sample surveys, remote sensing technology can realize dynamic monitoring on a large scale and for a long time. In this paper, we measured the spectral data of six dominant seaweed species in different dry and wet conditions in the intertidal zone of Gouqi Island: Ulva pertusa, Sargassum thunbergii, Chondrus ocellatus, Chondria crassiaulis Harv., Grateloupia filicina C. Ag., and Hizikia fusifarme. The different seaweed species were identified and analyzed by a combination of ANOVA and support vector machine (SVM). Fourteen common spectral parameters were used as input variables, and the input parameters were filtered by ANOVA. The samples were divided into a training set (266 samples) and a test set (116 samples) at a 3:1 ratio for input into the SVM model. The results showed that when the input parameters were NDVI (Rg,Rr), RVI (Rg,Rr), Vre, Abe, Lbe, Lg, Lre, and Rr and the model parameters g=1.30 and c=2.85, the maximum discrimination rate of the six different wet and dry states of seaweed was 74.96%, and the highest accuracy was 93.94% when distinguishing different phyla of seaweed (g=6.85 and c=2.55). In addition, SVM is fused with XGBoost (eXtreme Gradient Boosting) by vote and further classified in combination with the selected parameters. The accuracy of the six seaweeds was 73.98% (vote mean score = 0.972). In this study, the spectral data of intertidal seaweed in different dry and wet states were classified for the first time to provide technical support for remote sensing monitoring of coastal zones and seaweed resource statistics.

Synthetic Case Study: Discrimination of Unexploded Ordnance (UXO) and non-UXO Sources with varying Remanent Magnetization Strength using Magnetic Data

Summary We investigate if it is theoretically possible to discriminate between unexploded ordnance (UXO) and non-UXO sources by modelling the magnetic dipole moment for ferrous objects of different shapes and sizes. This is carried out by approximating the volumetric demagnetization factors of rectangular prisms, representing shapes similar to a long rod or flat steel plate. By modelling different UXO as prolate spheroids the demagnetization factors can be determined which can be compared with the magnetic response of a prism. The inversion is carried out in a probabilistic framework, where the UXO forward model and the non-UXO forward model are assigned individual prior models in terms of shape, size, orientation and remanent magnetization of the object. 95 independent realizations of the prism prior model are generated to make 95 synthetic anomalies exemplifying non-UXO objects, which are inverted for using the UXO model. It is investigated if an identical magnetic moment can be produced between the two models and how well resolved the magnetic moment is in terms of the measured anomaly. The case study is carried out in two steps where we first have little prior information of expected UXO properties and another where a UXO prior is introduced with expected values of aspect ratio and size of 24 different UXO, that are often encountered in the North Sea. With no prior information of expected UXO, discrimination is at many times implausible, unless elongated rod prism objects are considered, where the magnetic moment often can not be reproduced by a spheroid. Introducing the UXO prior we achieve a much better discrimination rate when using the list of expected UXO properties. By using the UXO prior we can account for a much higher remanent magnetization allowed in the prior, and still achieve high discrimination capabilities in comparison to a case with no UXO prior.

Remote Judgment Method of Painting Image Style Plagiarism Based on Wireless Network Multitask Learning

Since the artistry of the work cannot be accurately described, the identification of reproducible plagiarism is more difficult. The identification of reproducible plagiarism of digital image works requires in-depth research on the artistry of artistic works. In this paper, a remote judgment method for plagiarism of painting image style based on wireless network multitask learning is proposed. According to this new method, the uncertainty of painting image samples is removed based on multitask learning algorithm edge sampling. The deep-level details of the painting image are extracted through the multitask classification kernel function, and most of the pixels in the image are eliminated. When the clustering density is greater than the judgment threshold, it can be considered that the two images have spatial consistency. It can also be judged based on this that the two images are similar, that is, there is plagiarism in the painting. The experimental results show that the discrimination rate is always close to 100%, the misjudgment rate of plagiarism of painting images has been reduced, and the various indicators in the discrimination process are the lowest, which fully shows that a very satisfactory discrimination result can be obtained.

Towards Inclusion in Spanish Higher Education: Understanding the Relationship between Identification and Discrimination

It is more and more evident that there is diversity among university students, but this diversity encompasses a wide variety of personal characteristics that, on occasion, may be subject to rejection or discrimination. The feeling of inequality is the result of one stand‐alone characteristic or an intersection of many. To widen our knowledge of this diversity and to be able to design actions with an inclusive approach, we have set out to explore the relationship between students’ feelings of discrimination, their group identification and their intersections. Participants for the study are selected from protected groups which fall into the following criteria: ethnic minority, illness, migrant minority, disability, linguistic minority, sexual orientation, income, political ideology, gender, age and religion. We will refer to this relationship as the ‘discrimination rate.’ To fulfil our objective, we have given a questionnaire to a sample of 2,553 students from eight Spanish universities. The results indicate that the characteristics with which they most identify are religion, age, sex and political ideology. However, the highest rate of discrimination is linked to linguistic minority, ideology and migration. Regarding intersectionality, it is worth noting that 16.6% of students feel discriminated against for more than one characteristic, with the most frequent relationships being the following: (1) ethnic or migrant minorities (2) sexual orientation, sex, being under 30, leftist ideology, low income, linguistic minority and (3) Christian Catholic, right‐wing and upper‐class ideology.

The analysis of sacrum and coccyx length measured with computerized tomography images depending on sex

Background Sex estimation is vital in establishing an accurate biological profile from the human skeleton, as sex influences the analysis of other elements in both Physical and Forensic Anthropology and Legal Medicine. The present study was conducted to analyze the sex differences between the sacrum and coccyx length based on the measurements calculated with computed tomography (CT) images. One hundred case images (50 females, 50 males) who were between the ages of 25 and 50 and admitted by the emergency department between September 2018 and June 2019 and underwent CT were included in the study. Eighteen lengths, 4 curvature lengths, and 2 regions were measured in sagittal, coronal and transverse planes with orthogonal adjustment for three times. Results It was stated that the mean anterior and posterior sacral length, anterior and posterior sacrococcygeal length, anterior and posterior sacral curvature length, anterior coccygeal curvature length, sacral area, lengths of transverse lines 1, 2, 3 and 4, sacral first vertebra transverse and sagittal length measurements were longer in males when compared to females (p < 0.05). It was noted that the parameter with the highest discrimination value in the receiver operating characteristic (ROC) analysis was the sacral area (AUC = 0.88/Acc = 0.82). Based on Fisher’s linear discriminant analysis findings, the discrimination rate was 96% for males, 92% for females and the overall discrimination rate was 94%. Conclusions It was concluded that the fourteen parameters that were indicated as significant in the present study could be used in anthropology, Forensic Medicine and Anatomy to predict sex.