Islamic feminism is characterised by a debate, a practice enunciated within the Islamic values and frame. Muslim women brought their experiences to the forefront and challenged the traditional and post-classical interpretation of the Qurʾan and Sunna. They claimed interpretations of the religious text as totally biased and based on men’s experience, questions that are male-centric, and the overall influence of the patriarchal society and culture. According to Islamic feminists, Islam has guaranteed women’s rights since its inception, confirming the notion of egalitarian ethics within Islam. However, the original message of Islam has been hindered by the hegemonic interpretation of Islamic jurisprudence; a product of existing patriarchy in the long passage of Islamic history for over several centuries. The rights of women as prescribed in Islam are not in practice anymore, even the demand for women’s rights is seen by many as going against the basic principle of Islam. Islamic feminists give their justifications from the Qurʾan and Hadith, and they called for re-opening the door of ijtihād (reasoning). This paper captures the significant works of feminist discourses and analyses different perspectives by the Islamic feminists who challenged the dominant discourses in Islam. It deals with the dominant discourse of Islamic feminists such as feminist hermeneutics of the Qurʾan, and includes a discussion on how feminist hermeneutics or new gender-sensitive interpretation of the Qurʾan tries to assert gender equality in the Qurʾan. There are two ways in which Muslims read patriarchy in the Qurʾan: first from the verses and the other from the different treatment of the Qurʾan on issues including marriages, divorce, inheritances, and witness. Islamic feminists reject anti-women elements, present in the Muslim umma and consider them as unethical and against Islam.

Data on the effects of selenium (Se) supplementation on clinical outcomes, metabolic profiles, and pulsatility index (PI) in high-risk mothers in terms of preeclampsia (PE) screening with quadruple tests are scarce. This study evaluated the effects of Se supplementation on clinical outcomes, metabolic profiles, and uterine artery PI on Doppler ultrasound in high-risk mothers in terms of PE screening with quad marker. The current randomized, double-blind, placebo-controlled trial was conducted among 60 high-risk pregnant women screening for PE with quad tests. Participants were randomly allocated into two groups (30 participants each group), received either 200 µg/day Se supplements (as Se amino acid chelate) or placebo from 16 to 18 weeks of pregnancy for 12 weeks. Clinical outcomes, metabolic profiles, and uterine artery PI were assessed at baseline and at the end of trial. Se supplementation resulted in a significant elevation in serum Se levels (β 22.25 µg/dl; 95% CI, 18.3, 26.1; P < 0.001) compared with the placebo. Also, Se supplementation resulted in a significant elevation in total antioxidant capacity (β 82.88 mmol/L; 95% CI, 3.03, 162.73; P = 0.04), and total glutathione (β 71.35 µmol/L; 95% CI, 5.76, 136.94; P = 0.03), and a significant reduction in high-sensitivity C-reactive protein levels (β − 1.52; 95% CI, − 2.91, − 0.14; P = 0.03) compared with the placebo. Additionally, Se supplementation significantly decreased PI of the uterine artery in Doppler ultrasound (β − 0.09; 95% CI, − 0.14, − 0.04; P = 0.04), and a significant improvement in depression (β − 5.63; 95% CI, − 6.97, − 4.28; P < 0.001), anxiety (β − 1.99; 95% CI, − 2.56, − 1.42; P < 0.001), and sleep quality (β − 1.97; 95% CI, − 2.47, − 1.46; P < 0.001). Se supplementation for 12 weeks in high-risk pregnant women in terms of PE screening with quad marker had beneficial effects on serum Se level, some metabolic profiles, uterine artery PI, and mental health. IRCT Registration: htpp:// www.irct.ir ; identifier IRCT20200608047701N1.

PurposeTo avoid the high cost of poor quality (COPQ), there is a constant need for minimizing the formation of defects during manufacturing through defect detection and process parameters optimization. This research aims to develop, design and test a smart system that detects defects, categorizes them and uses this knowledge to enhance the quality of subsequent parts.Design/methodology/approachThe proposed system integrates data collected from the deep learning module with the machine learning module to develop and improve two regression models. One determines if set process parameters would yield a defective product while the second model optimizes them. The deep learning model utilizes final product images to categorize the part as defective or not and determines the type of defect based on image analysis. The developed framework of the system was applied to the forging process to determine its feasibility during actual manufacturing.FindingsResults reveal that implementation of such a smart process would lead to significant contributions in enhancing manufacturing processes through higher production rates of acceptable products and lower scrap rates or rework. The role of machine learning is evident due to numerous benefits which include improving the accuracy of the regression model prediction. This artificial intelligent system enhances itself by learning which process parameters could lead to a defective product and uses this knowledge to adjust the process parameters accordingly overriding any manual setting.Research limitations/implicationsThe proposed system was applied only to the forging process but could be extended to other manufacturing processes.Originality/valueThis paper studies how an artificial intelligent (AI) system can be developed and used to enhance the yield of good products.

No abstract