Search Results (7)

Geochemical and isotopic analysis of bitumen lining potsherds from the al-Qusur monastery (second half of the 7th c. CE and the middle of the 9th c. CE), at the central part of Failaka Island (Kuwait Bay), confirms the presence of two distinct compositional categories that can be matched to contemporary sources from two different areas of Iran: the Kermanshah province on one side, and the Khuzestan–Fars–Busher provinces on the other side. Potsherds comprise different types: TORP-S amphorae, TORP-C amphorae, SPORC storage jar, turquoise alkaline-glazed jar (TURQ.T), and CREAC jar. There is no relationship between the type of potsherd and the origin of bitumen. The bitumen coating SPORC jar, first identified as a kind of juice strainer to filter the «garum-like juice», was examined in greater details to try to identify traces of fish sauce mentioned in the Arabic kitchen books as ‘murri’, and quite similar to the Roman garum. The mineralogical analysis exhibits the classical minerals of archaeological mixtures (quartz, calcite, dolomite) and no halite. Hydrocarbons, alcohols, and methyl esters show a typical biodegraded bitumen signature but no fatty acids and terpenoids. It seems that the bitumen matrix has not adsorbed any molecules from the presumed «garum» filtered in the basin. Full article

Recent migration and globalization trends have led to the emergence of ethnically, religiously, and linguistically diverse countries. Understanding the unfolding of social dynamics in multicultural contexts becomes a matter of common interest to promote national harmony and social cohesion among groups. The current functional magnetic resonance imaging (fMRI) study aimed to (i) explore the neural signature of the in-group bias in the multicultural context; and (ii) assess the relationship between the brain activity and people’s system-justifying ideologies. A sample of 43 (22 females) Chinese Singaporeans (M = 23.36; SD = 1.41) was recruited. All participants completed the Right Wing Authoritarianism Scale and Social Dominance Orientation Scale to assess their system-justifying ideologies. Subsequently, four types of visual stimuli were presented in an fMRI task: Chinese (in-group), Indian (typical out-group), Arabic (non-typical out-group), and Caucasian (non-typical out-group) faces. The right middle occipital gyrus and the right postcentral gyrus showed enhanced activity when participants were exposed to in-group (Chinese) rather than out-group (Arabic, Indian, and Caucasian) faces. Regions having a role in mentalization, empathetic resonance, and social cognition showed enhanced activity to Chinese (in-group) rather than Indian (typical out-group) faces. Similarly, regions typically involved in socioemotional and reward-related processing showed increased activation when participants were shown Chinese (in-group) rather than Arabic (non-typical out-group) faces. The neural activations in the right postcentral gyrus for in-group rather than out-group faces and in the right caudate in response to Chinese rather than Arabic faces were in a significant positive correlation with participants’ Right Wing Authoritarianism scores (p < 0.05). Furthermore, the activity in the right middle occipital gyrus for Chinese rather than out-group faces was in a significant negative correlation with participants’ Social Dominance Orientation scores (p < 0.05). Results are discussed by considering the typical role played by the activated brain regions in socioemotional processes as well as the role of familiarity to out-group faces. Full article

Recently, signature verification systems have been widely adopted for verifying individuals based on their handwritten signatures, especially in forensic and commercial transactions. Generally, feature extraction and classification tremendously impact the accuracy of system authentication. Feature extraction is challenging for signature verification systems due to the diverse forms of signatures and sample circumstances. Current signature verification techniques demonstrate promising results in identifying genuine and forged signatures. However, the overall performance of skilled forgery detection remains rigid to deliver high contentment. Furthermore, most of the current signature verification techniques demand a large number of learning samples to increase verification accuracy. This is the primary disadvantage of using deep learning, as the figure of signature samples is mainly restricted to the functional application of the signature verification system. In addition, the system inputs are scanned signatures that comprise noisy pixels, a complicated background, blurriness, and contrast decay. The main challenge has been attaining a balance between noise and data loss, since some essential information is lost during preprocessing, probably influencing the subsequent stages of the system. This paper tackles the aforementioned issues by presenting four main steps: preprocessing, multifeature fusion, discriminant feature selection using a genetic algorithm based on one class support vector machine (OCSVM-GA), and a one-class learning strategy to address imbalanced signature data in the practical application of a signature verification system. The suggested method employs three databases of signatures: SID-Arabic handwritten signatures, CEDAR, and UTSIG. Experimental results depict that the proposed approach outperforms current systems in terms of false acceptance rate (FAR), false rejection rate (FRR), and equal error rate (EER). Full article

This paper proposes a high-performance embedded system for offline Urdu handwritten signature verification. Though many signature datasets are publicly available in languages such as English, Latin, Chinese, Persian, Arabic, Hindi, and Bengali, no Urdu handwritten datasets were available in the literature. So, in this work, an Urdu handwritten signature dataset is created. The proposed embedded system is then used to distinguish genuine and forged signatures based on various features, such as length, pattern, and edges. The system consists of five steps: data acquisition, pre-processing, feature extraction, signature registration, and signature verification. A majority voting (MV) algorithm is used for improved performance and accuracy of the proposed embedded system. In feature extraction, an improved sinusoidal signal multiplied by a Gaussian function at a specific frequency and orientation is used as a 2D Gabor filter. The proposed framework is tested and compared with existing handwritten signature verification methods. Our test results show accuracies of 66.8% for ensemble, 86.34% for k-nearest neighbor (KNN), 93.31% for support vector machine (SVM), and 95.05% for convolutional neural network (CNN). After applying the majority voting algorithm, the overall accuracy can be improved to 95.13%, with a false acceptance rate (FAR) of 0.2% and a false rejection rate (FRR) of 41.29% on private dataset. To test the generalization ability of the proposed model, we also test it on a public dataset of English handwritten signatures and achieve an overall accuracy of 97.46%. Full article

Obesity causes gut dysbiosis; nevertheless, little is known about the oral microbiome. We aimed to identify differences in the subgingival microbiota influenced by body weight and periodontal status. Patients (n = 75) recruited at the University Dental Hospital Sharjah, United Arab Emirates, were distributed into three equal groups (healthy weight, overweight, and obese) sub-divided into having either no-mild (NM) or moderate-severe (MS) periodontitis. Subgingival plaques were collected. Microbiota were identified by 16S rRNA sequencing using nanopore technology. Linear discriminant analysis demonstrated significant bacterial biomarkers for body weight and periodontal health. Unique microbiota signatures were identified, with enrichment of periopathogens in patients with MS periodontitis (Aggregatibacter actinomycetemcomitans in obese, Tannerella forsythia and Treponema denticola in overweight, Porphyromonas gingivalis and Fusobacterium nucleatum in healthy weight), thus reflecting differences in the microbiota affected by body weight. Other pathogenic bacteria, such as Salmonella enterica and Klebsiella pneumoniae, were enriched in overweight subjects with NM periodontitis, suggesting an increase in the relative abundance of pathogens even in patients with good periodontal health if they were overweight. Alpha and beta diversities were significantly different among the groups. Dysbiosis of the subgingival microbiota in obese and overweight individuals was associated with increased prevalence and severity of periodontal disease, which was correlated with the body mass index. This study highlights the immense importance of the oral microbiome and the need for lifestyle and dental interventions to resolve oral dysbiosis and restore normal homeostasis. Full article

Early-onset colorectal cancer (EOCRC) incidence is increasing worldwide. Efforts are directed to understand the biological and clinical signatures of EOCRC compared to late-onset colorectal cancer (LOCRC). EOCRC is thought to present differently across different ethnic groups and geographical regions. This study was an attempt to contribute with data from the Arab world toward the understanding of the clinicopathological parameters of EOCRC compared to LOCRC. Data from 254 CRC patients diagnosed at Sultan Qaboos University Hospital from the period 2015–2020 were studied. About 32.6% of all diagnosed CRC patients are below 50 years old, with no differences in gender distribution between EOCRC and LOCRC (p-value 0.417). Rectal involvement and tumor laterality were comparable among the two groups. Adenocarcinoma accounts for 83.3% and 94.2% of EOCRC and LOCRC, respectively. More mucinous and signet ring adenocarcinoma (8.3% each) were reported in EOCRC than LOCRC (2.9% and 2.2%, respectively). MLH1 and PMS2 loss are more common among LOCRC, but MSH6 loss is more frequent in EOCRC. The overall survival of EOCRC and LOCRC was comparable (median survival 64.88 and 67.24 months, respectively). This study showed comparable clinicopathological parameters between EOCRC and LOCRC from Arabs, which adds to the bigger picture of understand the disease. Full article

This work presents the design and analysis of newly developed reconfigurable, flexible, inexpensive, optically-controlled, and fully printable chipless Arabic alphabet-based radio frequency identification (RFID) tags. The etching of the metallic copper tag strip is performed on a flexible simple thin paper substrate (ϵ_r = 2.31) backed by a metallic ground plane. The analysis of investigated tags is performed in CST MWS in the frequency range of 1–12 GHz for the determination of the unique signature resonance characteristics of each tag in terms of its back-scattered horizontal and vertical mono-static radar cross section (RCS). The analysis reflects that each tag has its own unique electromagnetic signature (EMS) due to the changing current distribution of metallic resonator. This EMS of each tag could be used for the robust detection and recognition of all realized 28 Arabic alphabet tags. The study also discusses, for the first time, the effect of the change in font type and size of realized tags on their EMS. The robustness and reliability of the obtained EMS of letter tags is confirmed by comparing the RCS results for selective letter tags using FDTD and MoM numerical methods, which shows very good agreement. The proposed tags could be used for smart internet of things (IoT) and product marketing applications. Full article