Present work describes the optimization of reaction parameters for biodiesel production using chemical and enzymatic transesterification of conventional as well as nonconventional feedstocks. Response Surface Methodology based upon central composite response surface design was used to optimize reaction parameters for biodiesel production. Different reaction parameters including catalyst/enzyme type, catalyst/enzyme concentration, reaction temperature, reaction time and methanol to oil molar ratio were optimized and comprehensive protocols were developed to achieve highest biodiesel yields using both chemical and enzymatic transesterification. During chemical transesterification, NaOCH3 catalyzed transesterification was depicted to give highest biodiesel yields comparative to KOH, NaOH catalyzed transesterification whereas, during enzymatic transesterification, NOVOZYME-435 catalyzed transesterification resulted into higher biodiesel yields comparative to A.n.Lipase catalyzed transesterification reactions. Among different feedstocks studied, linseed oil exhibited highest biodiesel yield (96.5%) during chemical transesterification, while during enzymatic transesterification Eruca sativa oil exhibited best biodiesel yield (98.3%). For optimized chemical transesterification, 0.5 to 0.75% catalyst concentrations, 6:1 to 7.5:1 methanol to oil molar ratio, 45 to 52.5oC and 60 min reaction time, whereas, for enzymatic transesterification 1.0 to 1.25% enzyme concentrations, 6:1 to 9:1 methanol to oil molar ratio, 30 to 32.5oC reaction temperature and 60 to 96 hrs were depicted to be optimized reaction parameters to obtain highest product yields. Monitoring of transesterification reactions was carried out using FTIR spectroscopy and High Performance Liquid Chromatography (HPLC), while compositional analysis of synthesized biodiesels was performed with Gas Chromatography equipped with Mass Spectrometric detector (GC-MS). Fuel characteristics including flash point, fire point, pour point, cloud point, density, ash content, kinematic viscosity, cetane number, higher heating value and oxidative stability were evaluated and found technically compatible and comparable with EN 14214 and ASTM D 6751 specifications. Furthermore, exhaust emission levels of CO, NOx and PM from the engine exhaust operated on biodiesel and its blends (B-5, B-20, B-40, B-50, B-80 and B-100) were also estimated. Prominent reduction in both CO and PM whereas, irregular trends in NOx emissions were depicted from engine exhaust operated on biodiesel and its blends comparative to engine exhaust emissions based on petro diesel. Based upon the above described results, it can be depicted that biodiesel is an environment friendly alternative to the conventional petrodiesel.
(پی ایچ۔ڈی سکالر شعبہ اُردو، منہاج یونیورسٹی لاہور)
عرب ممالک میں اُردو سے محبت کرنی والی ایک ایسی شخصیت موجود ہے جنہوں نے ایم۔اے اُردو، پھر ڈاکٹریٹ کی ڈگری بھی اُردو میں حاصل کی۔مصر سے تعلق رکھنے والی عرب نژاد اُردو شاعرہ، افسانہ نگار ،کالم نگار، سفر نامہ نگار ڈاکٹر ولا جمال العسلی ہیں ۔
ڈاکٹر ولا جمال نے پی ایچ۔ڈی کی ڈگری کے حصول کے لیے مقالہ بہ عنوان "فہمیدہ ریاض کی شاعری "لکھا۔اس وقت عین شمس یونی ورسٹی مصر کے شعبہ اُردو میں تدریسی خدمات سرانجام دے رہی ہیں ۔
مادری زبان عربی بولنے والی ڈاکٹر ولا جمال کی اُردو سے والہانہ محبت ہم اُردو والوں کے لیے ایک قابل فخر بات ہے۔وہ اُردو سے پیار کا اظہار کچھ یوں کرتی ہیں :
اس کے ہر لفظ میں ہے اس لیے سمٹی خوشبو
دختر نیل کی سانسوں میں گھلی ہے اُردو
یہ شعر ان کی کتاب "دخترِ نیل " میں بہ طور انتساب شامل ہے۔اسی کتاب کے پیش لفظ میں وہ اُردو سے پیار کا اقرار کچھ یوں بھی کرتی ہیں:
"میں اُردو سے عقیدت کی حد تک محبت رکھتی ہوں ۔اُردو کے لیے خود کو وقف کر دیتی ہوں ۔اپنی زندگی میں اسے سب سے زیادہ اہمیت دیتی ہوں۔"(پیش لفظ:ص14)
نے اُردو سے اپنی محبت کا ثبوت کچھ اس طرح دیا ہے کہ متنوع اور فکر انگیز موضوعات کو مختلف اصناف میں طبع آزمائی کرکے پیش کیا ہے۔لیکن ان کی وجہ شہرت شاعری بن چکی ہے۔ان کا شاعری کا پہلا مجموعہ "سمندر ہے درمیان" کے نام سے شائع ہوا، یہ...
Sistem Pendukung Keputusan adalah bagian dari sistem informasi berbasis komputer termasuk sistem berbasis pengetahuan atau manajemen pengetahuan yang di pakai untuk mendukung pengambilan keputusan di dalam suatu organisasi atau perusahaan. Saat ini pengelolaan data penilaian karyawan perusahaan masih dilakukan dengan manual, sehingga semakin besar resiko kesalahan dalam mengelola data dan membutuhkan waktu yang relatif lama. Untuk mempermudah perhitungan penentuan kinerja karyawan terbaik maka penulis menggunakan metode Simple Additive Weighting (SAW). Metode simple additive weighting ini di pilih karena metode ini menentukan nilai bobot untuk setiap atribut, kemudian dilanjutkan dengan proses perangkingan yang akan menyeleksi alternatif-alternatif yang sudah di tentukan seperti etika atau kepribadian, kedisplinan, absensi, tanggung jawab, kerja sama, kemampuan memimpin, kecepatan kerja, ketelitian kerja dan kualitas hasil kerja. Dengan metode perangkingan tersebut, diharapkan penilaian akan lebih tepat karena didasarkan pada nilai kriteria dan bobot yang sudah ditentukan sehingga akan mendapatkan hasil yang lebih akurat terhadap siapa yang akan menerima reward/penghargaan tersebut.
A variety of dynamic objects, such as faces, bodies, and cloth, are represented in computer vision and computer graphics as a collection of moving spatial landmarks. A number of tasks are performed on this type of data such as character animation, motion editing, and nonrigid structure from motion. In theory, many of these tasks are highly under-constrained and the estimation algorithms exploit the natural regularity that exists as a cloud of points moves over time. In this thesis, we present compact and generalizable models of non- rigid objects by exploiting spatial and temporal regularities of time-varying point data. We demonstrate that several theoretically ill-posed tasks can be made well-posed with the help of these models. Our first contribution is to propose and demonstrate the effectiveness of the linear trajectory model for representing time-varying point clouds. Traditionally, a linear shape model has been used to represent time-varying point data; the 3D shape of a nonrigid object is modeled as a linear combination of a small number of basis shapes. In contrast, we represent point trajectories as a linear combination of basis trajectories. We show that the linear trajectory and the linear shape models are dual to each other and have equal representation power. In contrast to the shape basis, however, we demonstrate that the trajectory basis can be predefined by exploiting the inherent smoothness of trajectories. In fact, we show that the Discrete Cosine Transform (DCT) is a good choice for a predefined basis and empirically demonstrate its compactness by showing that it approaches Principal Component Analysis (PCA) for natural motions. This linear trajectory model is applied to the problem of nonrigid structure from motion. Analogous to the formulation under the shape model, the estimation of nonrigid struc- ture from motion under the trajectory model results in an optimization problem based on orthonormality constraints. Prior work asserted that structure recovery through orthonor- mality constraints alone is inherently ambiguous and cannot result in a unique solution. This assertion was accepted as a conventional wisdom and was the justification of several remedial heuristics in literature. In contrast, we prove that orthonormality constraints are, in fact, sufficient to recover the 3D structure in both the linear trajectory and the shape models. Moreover, we show that the primary advantage of the trajectory model over the shape model in nonrigid structure from motion is the possibility of predefining the basis.This results in a significant reduction in unknowns and corresponding stability in estima- tion. We demonstrate significant improvement in reconstruction results over the state of the art. After demonstrating the effectiveness of the linear trajectory model over linear shape model in nonrigid structure from motion, we also show how both the models can be synergisti- cally combined. We present the bilinear spatiotemporal basis as a model to simultaneously exploit spatial and temporal regularities, while maintaining the ability to generalize well to new sequences. The model can be interpreted as representing the data as a linear com- bination of spatiotemporal sequences consisting of shape modes oscillating over time at key frequencies. We apply the model to natural spatiotemporal phenomena, including face, body, and cloth motion data, and demonstrate its effectiveness in terms of compaction, gen- eralization ability, predictive precision, and efficiency against existing models. We demon- strate the application of the model in motion capture clean-up. We present an expectation- maximization algorithm for motion capture labeling, gap-filling, and denoising. The solu- tion provides drastic reduction in the clean-up time in comparison to the current industry standards.