Baghdad Science Journal
College of Science for Women, University of Baghdad
About: Baghdad Science Journal is an academic journal published by College of Science for Women, University of Baghdad. The journal publishes majorly in the area(s): Mathematics & Chemistry. It has an ISSN identifier of 2411-7986. It is also open access. Over the lifetime, 1605 publications have been published receiving 2856 citations. The journal is also known as: Baghdad science journal.
Papers published on a yearly basis
TL;DR: In this article, the authors investigated the yield of cellulose produced in kombucha, where the tea broth was fermented for up to 20 days in the presence of different amounts of black tea and sucrose as nitrogen and carbon sources.
Abstract: Kombucha(Khubdat Humza) is composed of yeast and acetic acid bacteria especially, Acetobacter xylinum which forms a cellulose pellicle on tea broth. Kombucha(Khubdat Humza) produces bacterial cellulose pellicles, with unique purity and fine structure. It can be used in many forms, such as an emulsifier, stabilizer, dispersing agent, thickener and gelling agent but these are generally subsidiary to its most important use of holding on to water. Recently, bacterial cellulose is used in many special applications such as a scaffold for tissue engineering of cartilages and blood vessels, also for artificial skin for temporary covering of wounds, as well as its used in the clothing industry. The yield of cellulose produced were investigated in this study, the tea broth was fermented naturally over a period of up to 20 days in the presence of different amounts of black tea and sucrose as nitrogen and carbon sources. 10g/L black tea produced highest weight of bacterial cellulose (55.46g/L) and 100g/L sucrose also exhibited high amount of pellicle (63.58g/L). Temperature was essential factor on growth, where the pellicle was formed at range (20°C 50°C) and higher temperature over 50°C depressed the bacterial cellulose formation. The bacterial cellulose production increased with the increase of surface area and depth of the broth. Findings from this study suggest that the yield of cellulose depends on many factors that need to be optimized to achieve maximum yield.
TL;DR: In this paper, the effect of addition of some amine compounds on electrical properties of Graphene oxide has been studied by the preparation of graphene oxide amino containing compound, which could be classified under Nano carbon compounds containing nitrogen (N-doped carbon nanomaterials).
Abstract: Previously many properties of graphene oxide in the field of medicine, biological environment and in the field of energy have been studied. This diversity in properties is due to the possibility of modification on the composition of this Nano compound, where the Graphene oxide is capable of more modification via addition other functional groups on its surface or at the edges of the sheet. The reason for this modification possibility is that the Sp3 hybridization (tetrahedral structure) of the carbon atoms in graphene oxide, and it contains many oxygenic functional groups that are able to reac with other groups. In this research the effect of addition of some amine compounds on electrical properties of graphene oxide has been studied by the preparation of graphene oxide amino containing compound, which could be classified under Nano carbon compounds containing nitrogen (N-doped carbon nanomaterials). These amines are used as expanders for the distance between the layers of graphene oxide (spacers), and thus prevent agglomeration of graphene oxide layers in addition to enhanced electric properties of graphene oxide. The following amines (thiocarbohydrazide(TCH),o-phenylenediamine(oPD) and poly aniline(PAni)) were used for the preparation of the corresponding amino graphene oxide (GO-TCH, GO-containing Benzoimidazol & benzoxazole, and GO-PAni), and characterized by X-RAY diffraction (XRD) ,infra red spectrum (FTIR) and atomic force microscope (AFM) , also the electrical properties of these materials were studied using inductance, capacitance, and resistance ( LCR) measurements.
TL;DR: This paper presents a methodology and experimentation using social media as a tool to analyse the suicidal ideation in a better way, thus helping in preventing the chances of being the victim of this unfortunate mental disorder.
Abstract: يعد التفكير في الانتحار من أخطر مشكلات الصحة العقلية التي يواجهها الناس في جميع أنحاء العالم. هناك عوامل خطر مختلفة يمكن أن تؤدي إلى الانتحار. من أكثر عوامل الخطر شيوعا وأكثرها خطورة الاكتئاب والقلق والعزلة الاجتماعية واليأس. يمكن أن يساعد الاكتشاف المبكر لعوامل الخطر هذه في منع أو تقليل عدد حالات الانتحار. أصبحت منصات الشبكات الاجتماعية عبر الإنترنت مثل تويتر وريدت وفيس بوك طريقة جديدة للناس للتعبير عن أنفسهم بحرية دون القلق بشأن الوصمة الاجتماعية. تقدم هذه الورقة منهجية وتجربة باستخدام وسائل التواصل الاجتماعي كأداة لتحليل الأفكار الانتحارية بطريقة أفضل ، وبالتالي المساعدة في منع فرص الوقوع ضحية لهذا الاضطراب العقلي المؤسف. نجمع البيانات ذات الصلة عبر توترأحد مواقع الشبكات الاجتماعية الشهيرة (SNS) . ومن ثم تتم معالجة التغريدات يدويا وإضافة تعليقات توضيحية لها يدويا. وأخيرا ، يتم استخدام أساليب التعلم الآلي المختلفة والمجموعات لتمييز التغريدات الانتحارية وغير الانتحارية تلقائيا. ستساعد هذه الدراسة التجريبية الباحثين على معرفة وفهم كيفية استخدام الأشخاص للتعبير عن النفس في التعبير عن مشاعرهم وعواطفهم. وأكدت الدراسة أيضا أنه من الممكن تحليل وتمييز هذه التغريدات باستخدام التشفير البشري ثم تكرار الدقة حسب تصنيف الماكينة. ومع ذلك ، فإن قوة التنبؤ للكشف عن الانتحار الحقيقي لم يتم تأكيدها بعد ، وهذه الدراسة لا تتواصل بشكل مباشر وتتدخل مع الأشخاص الذين لديهم سلوك انتحاري..
TL;DR: This research develops a real-time cloth simulator for a virtual human character (VHC) with wearable clothing and perceives the proposed method can serve as a completion to the game assets clothing pipeline.
Abstract: Cloth simulation and animation has been the topic of research since the mid-80's in the field of computer graphics. Enforcing incompressible is very important in real time simulation. Although, there are great achievements in this regard, it still suffers from unnecessary time consumption in certain steps that is common in real time applications. This research develops a real-time cloth simulator for a virtual human character (VHC) with wearable clothing. This research achieves success in cloth simulation on the VHC through enhancing the position-based dynamics (PBD) framework by computing a series of positional constraints which implement constant densities. Also, the self-collision and collision with moving capsules is implemented to achieve realistic behavior cloth modelled on animated characters. This is to enable comparable incompressibility and convergence to raised cosine deformation (RCD) function solvers. On implementation, this research achieves optimized collision between clothes, syncing of the animation with the cloth simulation and setting the properties of the cloth to get the best results possible. Therefore, a real-time cloth simulation, with believable output, on animated VHC is achieved. This research perceives our proposed method can serve as a completion to the game assets clothing pipeline.
TL;DR: A Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the sink and reduces the energy consumption up to 93% compared to prefix frequency filtering (PFF) and ATP protocols.
Abstract: : The transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the sink. By utilizing Intel Berkeley Research Lab (IBRL) dataset, the efficiency of the proposed method was measured. The experimental findings illustrate the benefits of the proposed method as it reduces the overhead on the sensor node level up to 1.25% in remaining data and reduces the energy consumption up to 93% compared to prefix frequency filtering (PFF) and ATP protocols.