University of Duhok

Human skin is continuously exposed to varying environmental conditions. While it is known that the skin's biophysical properties are influenced by seasonal changes, the impact of these conditions on its electrical characteristics-particularly skin surface susceptance-remains unclear. Therefore, this study aimed to investigate the effects of seasonal variations on skin surface susceptance using a low-frequency electrical technique. The investigation was performed on 46 (23 males and 23 females) apparently healthy volunteers. Readings of electrical skin surface susceptance were taken from the volar forearm in all four seasons between autumn 2024 and June 2025. Seasonal changes had a significant effect on skin surface susceptance, with the highest values recorded in summer and the lowest in winter. In addition, a highly significant (p< 0.05,r= 0.98) positive correlation was established between seasonal temperature and the skin surface susceptance, and a significant (p< 0.05,r= -0.97) negative correlation between seasonal temperature and the skin surface susceptance was obtained. No significant differences were observed between male and female groups in response to seasonal changes, indicating that gender is an unimportant factor in this context. Our results suggest that seasonal variations should be taken into consideration when using the skin electrical technique. In addition, this will be relevant in the applications of skin sensors and dermatology.

Background: Electrocardiography (ECG), particularly the 12-lead configuration, is a crucial method for identifying heart rhythm abnormalities. However, its effectiveness can be reduced by noise contamination. State-of-the-art denoising methods based on neural networks have demonstrated promising performance in denoising complex biosignals like ECG. However, most of these methods have focused on denoising single-lead ECG recordings. Methods: This research aims to leverage the inherent correlation among multi-lead ECG signals. Therefore, a multi-lead convolutional denoising autoencoder (ML-CDAE) model is proposed, to learn more effective representations, leading simultaneously to improved denoising performance and enhanced quality of 12-lead ECG recordings. Results: The findings indicate that ML-CDAE consistently outperforms a single-lead convolutional denoising autoencoder (SL-CDAE) and fully convolutional denoising autoencoder (FCN-DAE) model in denoising ECG signals corrupted by a mixture of physical noises. In particular, the mean squared error (MSE) and signal-to-noise ratio improvement (SNRimp) are used as evaluation metrics to assess the performance. Conclusions: The strong correlation among multi-lead ECG signals can be leveraged not only to enhance the denoising performance of the ML-CDAE model but also to simultaneously denoise 12-lead ECG signals more successfully compared to both the SL-CDAE and FCN-DAE models.

ئەڤ ڤەکۆلینە ب ناڤونیشانێت (كاريگه‌ريا ئاستێ ڕه‌وشه‌نبيريێ دایک و بابان د وه‌رارا په‌يڤێن زمانيدا ل ده‌ڤ بچویکان ل دویڤ (هارت و ڕیزلی) و مە ڤیایە د ئەڤێ ڤەکۆلینێدا دیاربکەین ڕۆلێ دایک و بابان و ب تایبەتی ئەڤێن ئاستەکێ ڕەوشەنبیری یێ بلند و ناڤنجی د وەرارا پەیڤێن زمانیدا هەین و مە چەند پرسیارێن ل سەر ئەڤی بابەتی ل دویڤ تیورا (هارت و ریزلی) وەکو ڕاوەرگرتن ل سەر (٥٠) سمپلان کو ژ (دکتور و ماستەر و بەکەلوریوس و دبن بەکەلوریوسێدا) بەلاڤەکرینە و ب رێکا ئامارێ ئەم گەهشتینە ئەنجامێن زانستی کو ژ گرنگترین ئەوان ئەنجامان ئەوبوو کو دایک و بابان ل دەڤ هەمی ئاستێن خواندنێ (دکتورا و ماستەر و بەکەلوریوس و دبن بەکەلوریوسێدا) کاریگەریەکا مەزن ل سەر وەرارا پەیڤێن زمانی ل دەڤ بچویکان هه‌بوويه‌. په‌يڤێن سه‌ره‌كى: زمانڤانيا پراكتيكى، زمانڤانيا ده‌روونى، وه‌رگرتنا زمانى، تيورێن وه‌رگرتنا زمانى، تيورا هارت و ڕێزلى.

This study provides empirical evidence on resource-related challenges in philosophy education and informs educational decision-makers about targeted support needs for philosophy teachers. This study aimed to identify the challenges of teaching philosophy in the absence of educational resources among eleventh grade teachers in Duhok Governorate preparatory schools according to the following variables: gender, years of teaching experience, academic qualification, and school location. The study sample included 132 male and female philosophy teachers. A questionnaire consisting of 27 items was developed based on relevant literature and distributed to the research sample during November 2025. The study found statistically significant differences at the 0.05 level between the means of sample responses according to gender, years of experience, and academic qualification variables, indicating that the absence of educational resources significantly impacts teaching effectiveness differently based on these demographic factors. Significant interaction effects were also found between gender and years of experience. However, no statistically significant differences were found regarding school location. Based on these findings, the researcher recommended several measures, most importantly the need for educational authorities to provide specialized philosophy resources, establish philosophy resource centers, allocate budgets for philosophical texts and materials, and provide professional development focused on teaching philosophy with limited resources.

RetinaFace is a multi-task and single-stage face detection model that detects faces and landmarks. However, it has limitations in detecting non-face content in output bounding boxes and mislocalizes facial landmarks for profile faces. To address these issues, Refined-RetinaFace (R-RetinaFace) is proposed. R-RetinaFace adds a post-optimization module that resizes bounding boxes and ensures all landmarks are within them. R-RetinaFace outperforms RetinaFace on SDUMLA-HMT and CASIA-3D-FaceV1 databases. On SDUMLA-HMT, R-RetinaFace achieves an ideal detection rate of 98.02%, a moderate detection rate of 1.32%, and a poor detection rate of 0.66%. On CASIA-3D-FaceV1, R-RetinaFace achieves ideal detection rates of 92.2%, moderate detection rates of 7%, and poor detection rates of 0.8%. In contrast, RetinaFace did not achieve ideal detection on both databases. It achieved only moderate and poor detection rates. On SDUMLA-HMT, RetinaFace achieves a moderate detection rate of 96.32% and a poor detection rate of 3.68%. On CASIA-3D-FaceV1, RetinaFace achieves a moderate detection rate of 83.9% and a poor detection rate of 16.1%. These results put R-RetinaFace a state-of-the-art method for face detection

Generally, Domestication and foreignization are important techniques in translation process. Pragmatically, the two techniques have been employed in literary and religious studies. Therefore, the new translation of the Glorious Qur’an is in need for those two techniques to render the divine content of Qur’an into the other languages. They should be employed in a path guarantees the semantic, phonetic and divine features in the target languages. This study focuses on deriving new two techniques of domestication and foreignization. However, they will be benefiting to render the divine content of the Glorious Qur’an into the other languages. Yet, they are: the emphasizing Qur’anic foreignization and the Arabic domestication. The first is adopting rendering the divine content of the Glorious Qur’an in a way preserves on the semantic and phonetic features. The second is focusing on adopting the Arabic domestication through rendering the divine content according to the culture and environment of Makkah and Al-Madinah.[1] The suggested Theory of the study will assist in obtaining many advantages. The Most important are: revelation Concentrating the divine discourse in the translation of the target languages. Strengthening and the wide spreading of the Arabic language through the employing of the two techniques. Works as resistance against the traditional theories and strategies which have approved its weakness in rendering the Qur’anic discourse into the other languages. Preserving on the Islamic Arabic culture and making it seen by the world. [1] Makkah and Al-Madinah, two Arabic Holy cities which witnessed the prophethood of Mohammed peace be upon him, and witnessed the revelation of the Holy Qur’an.