The aim of the present work is to guarantee the safety of the Tajoura Nuclear Research Centre's reactor (TNRC) as a result of a hypothetical Loss of Coolant Accident (LOCA). Transient evaluation of the postulated LOCA in primary coolant system is executed using PARET/ANL Code. A large break (LB-LOCA) is assumed and the location of the rupture is supposed to be 4.32m above the core. Therefore, the core is covered with water and natural convection mainly provides the cooling after the accident. The simulations are performed at the operating power of the reactor equals to 9 MW and for 180 s after SCRAM. The results show that the reactor is safe since the clad surface temperature (95.23 °C) at the hottest channel is lower than the maximum permitted value this means the existence of sufficient water above the core to cool it by the natural convection.

In recent years, many studies target anti-social behaviour such as offensive language and cyberbullying in online communication. Typically, these studies collect data from various reachable sources, the majority of the datasets being in English. However, to the best of our knowledge, there is no dataset collected from the YouTube platform targeting Arabic text and overall there are only a few datasets of Arabic text, collected from other social platforms for the purpose of offensive language detection. Therefore, in this paper we contribute to this field by presenting a dataset of YouTube comments in Arabic, specifically designed to be used for the detection of offensive language in a machine learning scenario. Our dataset contains a range of offensive language and flaming in the form of YouTube comments. We document the labelling process we have conducted, taking into account the difference in the Arab dialects and the diversity of perception of offensive language throughout the Arab world. Furthermore, statistical analysis of the dataset is presented, in order to make it ready for use as a training dataset for predictive modelling.

We present the results of predictive modelling for the detection of anti-social behaviour in online communication in Arabic, such as comments which contain obscene or offensive words and phrases. We collected and labelled a large dataset of YouTube comments in Arabic which contains a broad range of both offensive and inoffensive comments. We used this dataset to train a Support Vector Machine classifier and experimented with combinations of word-level features, N-gram features and a variety of pre-processing techniques. We summarise the pre-processing steps and features that allow training a classifier which is more precise, with 90.05% accuracy, than classifiers reported by previous studies on Arabic text.

A B S T R A C T

Swirl stabilised combustion is one of the most successful technologies for flame stabilisation in gas turbine combustors. Lean premixed combustion systems allow the reduction of NOx coupled with fair flame stability. The swirl mechanism produces an aerodynamic region known as central recirculation zone (CRZ) providing a low velocity

region where the flame speed matches the flow velocity, thus anchoring the flame whilst serving to recycle heat and active chemical species to the root of the former. Another beneficial feature of the CRZ is the enhancement of the mixing in and around this region. However, the mixing and stabilisation processes inside of this zone have shown to be extremely complex. The level of swirl, burner outlet configuration and combustor expansion are very important variables that define the features of the CRZ. The complex fluid dynamics and lean conditions pose a problem for stabilization of the flame. The

problem is even more acute when alternative fuels are used for flexible operation.

Therefore, in this paper swirling flame dynamics are investigated using computational fluid dynamics (CFD) with commercial software (ANSYS). A new generic swirl burner operated under lean-premixed conditions was modelled. A variety of nozzles were analysed using isothermal case to recognize the the behavers of swirl.

The investigation was based on recognising the size and strength of the central recirculation zones. The dimensions and turbulence of the Central Recirculation Zone were measured and correlated to previous experiments. The results show how the strength and size of the recirculation zone are highly influenced by both the shear layer surrounding the Central

Recirculation Zones (CRZ) and outlet configurations.

Neurodegenerative disorders results in inflammatory processes, including inflammatory cytokine secretion and concomitant superoxide production. Acetylcholinesterase inhibitors (AChEIs) influence neuro degeneration through anti-inflammatory effects. Donepezil as an AChEIs also provide neuro protection. This study aimed to evaluate the effect of donepezil (DZ) as a potent acetyl-cholinesterase (AChE) inhibitor, L-NG-Nitroarginine methyl ester (L.NAME) as non-selective nitric oxide synthase inhibitor and 7-nitroindazole (7-NI) as a selective neuronal nitric oxide synthase (NOS) inhibitor against Amyloid beta-peptide (1-42) (Aβ(1-42)) induced neurological disorder. Rats were divided into six groups including control, Aβ(1-42) ,Aβ(1-42) +L-arginine, Aβ(1-42) +L-NAME ,Aβ(1-42) +7-NI, and Aβ(1-42) +DZ. Brain AChE, malondialdehyde, nitric oxide, super oxidedismutase, catalase, reduced glutathione, interleukin 1-beta (IL-1β) and tumor necrosis factor alpha (TNF-α) were measured. Also, brain fatty acids fractions were estimated by high performance liquid chromatography (HPLC). Aβ(1-42) significantly alter levels of brain antioxidant, inflammatory markers and fatty acids content compared to the control group. Treatment with L.NAME, 7-NI and especially DZ improve these parameters. Administration of acetylcholinesterase inhibitors such as DZ attenuates neurodegenerative disorders through decreasing oxidation and inflammation.

Keywords: Neurotoxicity, Aβ (1-42), Donepezil, Nitric oxide, 7-nitroindazole, Fatty acids, HPLC