المبحث الخامس: زواجها
بعدما انتھت نازک الملائکۃ دراستھا في أمریکا عادت إلی بغداد وعینت أستاذۃ مساعدۃ في جامعتھا۔ فأصدرت عدۃ دواوین شعریۃ ونقدیۃ، وبعدہ فترۃ انتقلت من جامعۃ بغداد إلی جامعۃ البصرۃ، ثم تزوجت بالدکتور ’’عبدالھادي محبوبۃ في سنۃ 1962م۔ وزوجھا ھذا کان زمیلھا في جامعۃ بغداد في قسم اللغۃ العربیۃ في کلیۃ التربیۃ وکان عالماً لغویاً وأستاذاً سابقاً بجامعۃ الکویت، تزوجتہ نازک وھي تقرب الاربعین سنۃ من عمرھا۔
وعندما توفي زوجھا كانت ھي أیضاً مریضۃ وکانت مقیمۃ في القاھرۃ ولکنھا لا تعلم بوفاتہ المفاجئة، لأنھا کانت مریضۃ۔ لذلک لم یخبرھا أحد ولم یشأ أحداً نقل (النعي) إلیھا خوفًا من أن یضر بصحتھا، وبعد فترۃ من وفاتہ أخبروھا بأنّہ سافر إلی بغداد، وھي لا تعلم بأنھا الآن وحیدۃ وقد فارقھا رفیق حیاتھا، ویقول إبنھا البراق بأننا أخبرناھا بعد أسابیع من وفاتہ ودفنہ في القاھرۃ ورثتہ في قصیدۃ عنوانھا ’’أنا وحدي‘‘، ویبدو ھذہ آخر قصیدۃ قدمتھا نازک وتم نشرھا قبل وفاتھا۔
Tujuan penelitian ini adalah untuk meningkatkan kemampuan siswa membaca intensif dengan menerapkan model Cooperative Script. Penelitian ini menggunakan metode Penelitian Tindakan Kelas dengan prosedur pelaksanaannya adalah (1). Perencanaan, (2). Tindakan, (3). Observasi, dan (4). Refleksi. Kegiatan Penelitian Tindakan Kelas ini dilaksanakan dua siklus dengan menggunakan teknik pengumpulan data, lembar observasi dan lembar penilaian. Subjek penelitian ini adalah siswa kelas VIII-D yang berjumlah 33 orang dengan jumlah laki-laki 18 orang dan jumlah perempuan 15 orang. Berdasarkan hasil penelitian, peningkatan hasil kemampuan siswa membaca intensif dengan menggunakan model cooperative script pada siklus I dengan nilai terendah 49 dan nilai tertinggi 75, 69 dengan nilai rata-rata 59, 03. Sedangkan, siklus II nilai terendah 76, 69 dan nilai tertinggi 90 dengan nilai rata-rata 81, 74. Hasil observasi peneliti siklus I pertemuan pertama 56, 94% dan pertemuan kedua sebesar 70, 83%. Sedangkan hasil observasi peneliti siklus II pertemuan pertama sebesar 77, 33% dan pertemuan kedua sebesar 90, 27%. Hasil observasi siswa siklus I pertemuan pertama sebesar 51, 51% dan pertemuan kedua 70, 83% sedangkan pada siklus II pertemuan pertama sebesar 91, 28% dan pertemuan kedua 94, 69%.
Simulation of scientific problems is an important aspect of natural and engineering sciences. Simulations demanding higher accuracy or involving larger data sets require higher computing power. Complex mathematical models involving partial differential equations (PDEs) from computational fluid dynamics (CFD) are some examples of these simulations. The conventional serial computers are not able to meet the increasing demand of computation power for such applications and the only rescue is parallel or high-performance computing. This study presents research regarding parallel numerical solution of PDEs. Message Passing Interface (MPI) clusters and Graphic Processor Units (GPUs) being the leading platforms for parallel computing were used for simulation of results. The research begins with the unified analysis of the existing parallel iterative algorithms using MPI. A set of diverse PDEs was solved using the MPI cluster. After getting an insight of iterative methods for MPI platform, the parallel system with shared memory architecture was experimented. The most advent platform in this regard is GPUs having thousands of concurrent running cores along with many Giga bytes (GBs) of memory. 3D Laplace equation was solved using twelve different kernels to exploit the memory hierarchy of GPU and an efficient technique involving surfacing pointer’s capability of GPU was materialized. The GPU kernel exhibiting said features gained a speedup of 70 as compared to serial version of same program running on Intel core i5 processor. The derived technique was further extended to simulate the compressible, high-speed flows modeled by Navier Stokes equations using GPU. Four different structured geometries were modeled; the governing equations were solved using modified RK4 method and TVD scheme was used for shockwave capturing. The derived technique was also used to simulate the flow in micro channel using Lattice Boltzmann Method. The GPU results show a speedup of 23 and 77 as compared with serial variants of codes running on conventional core i5®CPU for both cases respectively. It is evident from obtained results that the performance of CFD and other compute intensive application can be enhanced many folds by using the devised technique involving surface pointers in GPU computation.