Evaluating the Nature of Distractive Driving Factors towards Road Traffic Accident

For past two decades many researchers have been working on quantitative as well as qualitative study of distractive driving using different approaches. Road traffic accidents have been identified as the main source of human casualties and cause of damages to the economy and society, as millions of humans is killed every year in these accidents around the world. National-level studies in Pakistan reveal that a higher percentage of males in the age group from twenty to forty years lose their lives in road traffic accidents when compared with that of females. Due to these factors, it is alarming for a society, which is highly dependent on males such as Pakistan, as these losses put numerous families into the financial crisis that lead to poverty. This study envisaged identifying whether moods and emotions play any role in road traffic accidents of young drivers. The study reviews have shown various gaps in our understanding. For this purpose, qualitative interviews of young drivers who are university going and have met some road accidents in recent years in Pakistan had been conducted. Data from the interviews had been transcribed for analysis while maintaining the anonymity of the participants for confidentiality. Analysis of the transcribed data reveals various factors that contribute to road traffic accidents where major causes are distractions, different weather conditions, sleep deprivation, unsafe lane changes, night-time driving, and these factors are triggered by the behavior when youthful drivers engage in driving for sensation seeking and self-esteem. We conclude that it is just through the appropriation of a systems approach that coordinated countermeasures can be proposed and actualized to relieve driver mistakes caused by distraction

Mobility management in multi-RAT multiI-band heterogeneous networks

Support for user mobility is the raison d'etre of mobile cellular networks. However, mounting pressure for more capacity is leading to adaption of multi-band multi-RAT ultra-dense network design, particularly with the increased use of mmWave based small cells. While such design for emerging cellular networks is expected to offer manyfold more capacity, it gives rise to a new set of challenges in user mobility management. Among others, frequent handovers (HO) and thus higher impact of poor mobility management on quality of user experience (QoE) as well as link capacity, lack of an intelligent solution to manage dual connectivity (of user with both 4G and 5G cells) activation/deactivation, and mmWave cell discovery are the most critical challenges. In this dissertation, I propose and evaluate a set of solutions to address the aforementioned challenges. The beginning outcome of our investigations into the aforementioned problems is the first ever taxonomy of mobility related 3GPP defined network parameters and Key Performance Indicators (KPIs) followed by a tutorial on 3GPP-based 5G mobility management procedures. The first major contribution of the thesis here is a novel framework to characterize the relationship between the 28 critical mobility-related network parameters and 8 most vital KPIs. A critical hurdle in addressing all mobility related challenges in emerging networks is the complexity of modeling realistic mobility and HO process. Mathematical models are not suitable here as they cannot capture the dynamics as well as the myriad parameters and KPIs involved. Existing simulators also mostly either omit or overly abstract the HO and user mobility, chiefly because the problems caused by poor HO management had relatively less impact on overall performance in legacy networks as they were not multi-RAT multi-band and therefore incurred much smaller number of HOs compared to emerging networks. The second key contribution of this dissertation is development of a first of its kind system level simulator, called SyntheticNET that can help the research community in overcoming the hurdle of realistic mobility and HO process modeling. SyntheticNET is the very first python-based simulator that fully conforms to 3GPP Release 15 5G standard. Compared to the existing simulators, SyntheticNET includes a modular structure, flexible propagation modeling, adaptive numerology, realistic mobility patterns, and detailed HO evaluation criteria. SyntheticNET’s python-based platform allows the effective application of Artificial Intelligence (AI) to various network functionalities. Another key challenge in emerging multi-RAT technologies is the lack of an intelligent solution to manage dual connectivity with 4G as well 5G cell needed by a user to access 5G infrastructure. The 3rd contribution of this thesis is a solution to address this challenge. I present a QoE-aware E-UTRAN New Radio-Dual Connectivity (EN-DC) activation scheme where AI is leveraged to develop a model that can accurately predict radio link failure (RLF) and voice muting using the low-level measurements collected from a real network. The insights from the AI based RLF and mute prediction models are then leveraged to configure sets of 3GPP parameters to maximize EN-DC activation while keeping the QoE-affecting RLF and mute anomalies to minimum. The last contribution of this dissertation is a novel solution to address mmWave cell discovery problem. This problem stems from the highly directional nature of mmWave transmission. The proposed mmWave cell discovery scheme builds upon a joint search method where mmWave cells exploit an overlay coverage layer from macro cells sharing the UE location to the mmWave cell. The proposed scheme is made more practical by investigating and developing solutions for the data sparsity issue in model training. Ability to work with sparse data makes the proposed scheme feasible in realistic scenarios where user density is often not high enough to provide coverage reports from each bin of the coverage area. Simulation results show that the proposed scheme, efficiently activates EN-DC to a nearby mmWave 5G cell and thus substantially reduces the mmWave cell discovery failures compared to the state of the art cell discovery methods

Factor VII deficiency and pregnancy: a case report and review of literature

Factor VII deficiency is one of the \u27rare inherited disorders of coagulation.\u27 Few cases of Factor VII deficiency have been reported during pregnancy, a state which could potentially cause fatal haemorrhage. Here we report a case of a pregnant lady with a history of heavy menorrhagia and multiple first pregnancy failures. Delivery was carried out via Caesarean section due to non-reassuring foetal heart monitoring. Patient was treated with Fresh Frozen Plasma (FFPs) and Factor VII concentrates, however, the patient developed bleeding postoperatively. Literature indicates that whilst Factor VII levels rise during pregnancy in normal women, no increase is seen in homozygous cases, whereas there is a moderate rise in heterozygous individuals. History of heavy menorrhagia, multiple first pregnancy failures and a positive family history for bleeding disorders necessitate investigation and monitoring of Factor VII levels during pregnancy. Factor VII concentrates achieve adequate homeostasis in most cases. Recombinant Factor VIIa, however, is the treatment of choice and does not carry a risk of infection transmission or thrombus formation

Optimisation of Microwave Pretreatment for Biogas Enhancement through Anaerobic Digestion of Microalgal Biomass

In this study, optimization of microwave (MW) pretreatment conditions for anaerobic digestion of green microalgae (Enteromorpha) is carried out by using response surface methodology (RSM). MW power, pretreatment time and liquid-solid ratio were selected as independent variables for optimization. The optimum conditions were achieved at MW power, pretreatment time and liquid-solid ratio of 656.92 W, 5.10 min and 33.63:1, respectively. From these optimum conditions, it was found that MW pretreatment power of about 600 W had better effect. An anaerobic digestion was carried out batch-wise with working volume, operating temperature and mixing rate as 250 ml, 37 °C and 150 rpm, respectively. Optimum conditions provide highest amount of COD and reducing sugar increase of 10,420 mg/L and 0.77-0.79 g/L respectively. The increase in COD and reducing sugar showed that the pretreatment has improved anaerobic digestion of microalgae. The peak biogas production amount of MW pretreated 20:1, 6 min group reached 244 mL whereas the control group only reached 188 mL in total

Reducing overall delay in MULTI-RADIO WOBAN with least per node processing overhead on data packet

Wireless-Optical Broadband Access Network (WOBAN) is a hybrid network technology. The back-End of the WOBAN being optical has very high performance both in terms of speed and bandwidth. There has been lot of research and numbers of protocols were designed and numerous algorithms have been proposed to bring the performance of the Front-End at par with that of the optical part. So in this paper too, we propose a technique to upgrade the performance of the wireless part so that there may be lesser processing on the actual data packet and may move smoothly across the nodes in the wireless part of WOBAN. Also when the data packet reaches the Optical Network Unit (ONU), it may be forwarded as soon as it reaches the ONU without having to wait for the designated time slot. In this way, there will be no time slot synchronization delay at ONU

Effect of Autoclave Pretreatment on Biogas Production through Anaerobic Digestion of Green Algae

Anaerobic Digestion (AD) is one of the most widely used methods in the field of sustainable bioenergy production from various feedstock. One such feedstock is algae waste which has become an increasingly serious environmental problem. AD of algal biomass is hindered by the presence of resistant cell walls; hence a pretreatment step is usually required to decompose the cell wall structure. This study uses green algae (Enteromorpha) and anaerobic sludge as raw materials to explore the impact of autoclave (AC) pretreatment on biogas production. AC pretreatment was performed at 120 °C and 80 °C. The cumulative biogas production of the 120 °C AC pretreatment, 80 °C AC pretreatment and control group were 600 mL, 450 mL and 400 mL, respectively. The results showed that AC pretreatment improved the biodegradability of biomass as 120 °C AC pretreatment group achieved higher degradation rate of cells (95.99 %). The energy evaluation showed that the net energy ratio of the 120 °C AC pretreatment group was 1.07, indicating high overall energy gain via AD process. The experimental data is further modeled by using Modified Gompertz Model (MGM) and Logistic Function Model (LFM). To check the applicability of better model for this AD process, an Akaike Information Criteria (AIC) test was performed. AIC showed that the MGM is basically consistent with the experimental data and more reliable for prediction modeling of Enteromorpha AD