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Home > Al-Idah > Volume 35 Issue 2 of Al-Idah

Higher Education for Women in Peshawar: Barriers and Issues |
Al-Idah
Al-Idah

Article Info
Authors

Volume

35

Issue

2

Year

2017

ARI Id

1682060034497_98

Pages

72-84

PDF URL

http://www.al-idah.pk/index.php/al-idah/article/download/59/53

Chapter URL

http://www.al-idah.pk/index.php/al-idah/article/view/59

Subjects

Women Education Higher Education Barriers Issues Women education Higher education Barriers Issues

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قصاص کا اصطلاحی مفہوم

علامہ زبیدی ؒکے بقول قصاص سے مراد
"القصاص الاسم منہ وھو ان یفعل بہ مثل فعلہ من قتل او قطع او ضرب اوجرح۔"155
"قصاص اس با ت کا نام ہے کہ اس شخص کے ساتھ وہی کیا جائے جو کچھ اس کے ساتھ کیا ہے جس طرح اس نے قتل کیا ، یا ٹکرے کرنا، یا مارنا یا زخم لگاناوغیرہ۔ "
راغب اصفہانی قصاص کا مفہوم ان الفاظ میں بیان کرتے ہیں
"القصاص تتبع الالم بالقود ، قال ولکم فی القصاص حیوۃوالجروح قصاص ویقال قص فلان فلانا،وضربہ ضربافاقصہایارناہمنالموت، والقص والجص۔" 156
"قصاص وہ ہے یعنی قاتل کو مقتول کے بدلے قتل کرنا ، قرآن مجید میں ہے کہ تمہارے لیے قصاص میں زندگی ہے اور زخموں میں قصاص ہے اور اسے مار ماری ، پس اس سے قصاص لیا یعنی موت کے قریب کر دیا۔ "
علامہ کاسانیؒ کے مطابق قصاص سے مراد
"الْقِصَاصِ فإنه وَإِنْ كان عُقُوبَةً مُقَدَّرَةً لَكِنَّهُ يَجِبُ حَقًّا لِلْعَبْدِ حتى يَجْرِيَ فيه الْعَفْوُ وَالصُّلْحُ۔ "157
"قصاص ایسی سزا کو کہتے ہیں جو مقرر ہے لیکن یہ حق العبد کے طور پر واجب ہوتی ہے اور اس میں معاف کرنے اور صلح کی گنجائش ہوتی ہے۔ "
ان تعریفوں سے ثابت ہوا کہ قصاص کا مفہوم قتل کے بدلے قاتل کو قتل کرنا ہے اور یہ مقتول کے ورثاء کا حق ہے، چاہے قتل کے بدلے قتل کریں یا دیت قبول کریں یا معاف کردیں ۔

STUDENT'S READINESS TO CARRY OUT FACE-TO-FACE LEARNING AT KENDARI VOCATIONAL HIGH SCHOOL

The slowing spread of Covid-19 infections has brought positive changes in the education sector. The issue of implementing limited face to face learning begins to excite students in gaining knowledge. Online learning effects for approximately two years was relatively difficult to keep students away from themselves. This study time to determine how the level of student learning readiness in facing offline learning. This was quantitative research with a survey type. The population were State vocatoonal high school 2 Kendari students, totaling 558 students. The sample was drawn randomly with a magnitude estimated using the Slovin formula at a significance of 5% so that the total sample size was 233 students. Data were collected by learning readiness scale. Data were analyzed descriptively and comparative statistics. The results showed that the learning readiness of State vocatoonal high school Negeri 2 Kendari students was in the high category and female students had a higher level of learning readiness than male students.

Protein Subcellular Classification Using Machine Learning Approaches

Subcellular localization of proteins is one of the most significant characteristics of living cells that may reveal plentiful information regarding the working of a cell. Subcellular localization property of proteins plays a key role in understanding numerous functions of proteins. The proteins, located in their respective compartments or localizations, are in- volved in their relevant cellular processes, which may include cell apoptosis, asymmetric cell division, cell cycle regulation, and spermatic morphogenesis. In fact, cells may not perform their regular operations well in case proteins are not found in their proper subcellular lo- cations. Improper localization of proteins may lead to primary human liver tumors, breast cancer, and Bartter syndrome. Protein sequencing has observed rapid expansion due to the advancement in genomic sequencing technologies. This led the research community to recognize the functionalities of different proteins. In this connection, microscopy imaging is providing protein images well in time with low cost compared to protein sequencing. However, automated systems are required for fast and reliable classification of these protein images. Comprehensive analysis of fluorescence microscopy images is required in order to develop efficient automated systems for accurate localization of various proteins. For this purpose, representation of microscopy images with discriminative numerical descriptors has always been a challenge. This thesis focuses on the identification of discriminative feature extraction strategies effective for protein subcellular localization, the recognition capability of the prediction sys- tems, and the reduction of classifier bias towards the majority class due to the imbalance present in data. The contributions of this thesis include (1) Analysis of different spatial and transform domain features, (2) Development of a novel idea for GLCM construction in DWT domain, (3) Analysis of SMOTE oversampling in the feature space, (4) Analysis of GLCM in the spatial domain for capturing discriminative information from fluorescence microscopy protein images along different orientations, (5) Exploitation of Texton images for their capability of extracting discriminative information along different orientations from fluorescence microscopy protein images, (6) Development of the web based prediction sys- tems that can be accessed freely by the academicians and researchers. Extensive simulations are performed in order to assess the efficiency of the proposed pre- dictions systems in discriminating different subcellular structures from various datasets.