Image Description using Deep Neural Networks

Current research in computer vision and machine learning has demonstrated some great abilities at detecting and recognizing objects in natural images. Current state-of-the-art results in object detection, classification and localization in ImageNet Challenges have the validation accuracy for top 5 predictions for classification to be at 3.08% while similar classification experiments run by trained humans report an accuracy of 5.1%. While some people might argue that human accuracy is a function of training time it can be said with great confidence that automated classification models are at least as good as trained humans in classification problems. The ability of these models to analyze and describe complex images, however, is still an active area of research. Image description is a good starting point for imparting artificial intelligence to machines by allowing them to analyze and describe complex visual scenes. This thesis work introduces a generic end-to-end trainable Fusion-based Recurrent Multi-Modal (FRMM) architecture to address multi-modal applications. FRMM allows each input modality to be independent in terms of architecture, parameters and length of input sequences. FRMM image description models seamlessly blend convolutional neural network feature descriptors with sequential language data in a recurrent framework. In addition to introducing FRMMs, this work also analyzes the impact of varying activation functions and vocabulary size. For training and testing Flickr8k, Flickr30K and MSCOCO datasets have been used, demonstrating state-of-the-art description results

Journey predictive energy management strategy for a plug-in hybrid electric vehicle

The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonisation of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialisation. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Further, recent studies suggest the use of \intelligent transport" infrastructure to include a predictive element to the energy management strategy to achieve reductions in emissions. The thesis addresses the problem of determining the links between component-sizing, real-world usage and energy management strategies for a PHEV. The objective is to develop an integrated framework in which the advantages of predictive energy management can be realised by component downsizing for a PHEV. The study is spilt into three sections. The first part presents the framework by which the predictive element can be included into the PHEV's energy management strategy. Second part describes the development of the PHEV component models and the various energy management strategies which control the split in energy used between the engine and the battery. In this section a new control strategy is presented which integrates the predictive element proposed in the first part. Finally, in the third section an optimisation framework is presented by which the size of the components within the PHEV are reduced due to the lower energy demands of the new proposed energy management strategy. The first part of the study presents a framework by which the energy consumption of a vehicle may be predicted over a route. The proposed energy prediction framework employs a neural network and was used o_-line for estimating the real-world energy consumption of the vehicle so that it can be later integrated within the vehicles energy management control system. Experimental results show an accuracy within 20%-30% when comparing predicted and measured energy consumptions for over 800 different real-world EV journeys … [cont.]

Methylxanthines induce structural and functional alterations of the cardiac system in zebrafish embryos

Background: Zebrafish embryos are emerging as a model for pharmacological and toxicological studies. We used zebrafish embryos to study the general toxicity and cardiovascular effects of eight methylxanthines: aminophylline, caffeine, diprophylline, doxofylline, etophylline, 3-isobutyl-1-methylxanthine (IBMX), pentoxifylline and theophylline. Methods: Microinjections of the eight methylxanthines were performed in 1-2 cell stage zebrafish embryos and the general toxicity and cardiovascular effects were analyzed at different time points. Embryotoxicity and teratogenicity were evaluated to understand the general toxicity of these compounds. Structural and functional alterations of the heart were evaluated to assess the cardiovascular effects. Results: Our results showed different activity patterns of the methylxanthines drugs. Caffeine, IBMX, pentoxifylline and theophylline were highly embryotoxic and teratogenic; aminophylline, doxofylline and etophylline were embryotoxic and teratogenic only at higher doses, and diprophylline showed a minimal (< 10%) embryotoxicity and teratogenicity. Most of these drugs induced structural alteration of the heart in 20-40% of the injected embryos with the maximum dose. This structural alteration was fatal with the embryos ultimately dying within 120 hpf. All the drugs induced a transient increase in heart rate at 48 hpf which returned to baseline within 96 hpf. This functional effect of methylxanthines showed similarity to the studies done in humans and other vertebrates. Conclusion: Our results indicate the potential toxicity and teratogenicity of different methylxanthines in the embryos during embryonic development, the most sensitive period of life. Although interspecies differences need to be considered before drawing any conclusion, our study elucidated that a single exposure of methylxanthines at therapeutic range could induce cardiac dysfunction besides causing embryotoxicity and teratogenicity. Of all the drugs, diprophylline appeared to be safer, with lower degree of embryotoxicity, teratogenicity and cardiac toxicity as compared to other methylxanthines

A Comparative Study of Modified and Unmodified Algae (Pediastrum boryanum) for Removal of Lead, Cadmium and Copper in Contaminated Water

The presence of heavy metals in water is of concern due to the risk toxicity. Thus there is need for their removal for the safety of consumers. Methods applied for removal of heavy metals include adsorption, membrane filtration and co-precipitation. However, studies have revealed adsorption is highly effective technique. Most adsorbents are expensive or require extensive processing before use and hence need to explore for possible sources of inexpensive adsorbents. This research work investigated the use an algal biomass (pediastrum boryanum) as an adsorbent for removal of Lead, Cadmium and Copper in waste water in its raw and modified forms. The samples were characterized with FTIR and was confirmed a successful modification with tetramethylethlynediamine (TMEDA). Sorption parameters were optimized and the material was finally applied on real water samples. It was found that the sorption was best at lower pH values (4.2-6.8). Sorption kinetics was very high as more that 90% of the metals were removed from the solution within 30 minutes. The adsorption of copper fitted into the Langmuir adsorption isotherm indicating a monolayer binding mechanism. Cadmium and lead fitted best the Freundlich adsorption mechanism. Sorption of lead and cadmium was of pseudo-second order kinetics, confirming a multisite interaction whereas copper was pseudo-first order indicating a single site adsorption. The adsorption capacity did not improve upon modification but the stability of the material was improved and secondary pollution of leaching colour was alleviated. This implies that the modified material is suitable for application on the removal of metals from water

Pandemic within a pandemic! Policy Implications of community-based Interventions to mitigate violence against women during COVID-19 in Urban Slums of Lucknow, India

Background: The COVID-19 pandemic has brought an unprecedented adverse impact on women's health. Evidence from the literature suggests that violence against women has increased multifold. Gender-based violence in urban slums has worsened due to a lack of water and sanitation services, overcrowding, deteriorating conditions and a lack of institutional frameworks to address gender inequities. Methods: The SAMBHAV (Synchronized Action for Marginalized to Improve Behaviors and Vulnerabilities) initiative was launched between June 2020 to December 2020 by collaborating with the Uttar Pradesh state government, UNICEF and UNDP. The program intended to reach 6000 families in 30 UPS (Urban Poor settlements) of 13 city wards. These 30 UPS were divided into 5 clusters. The survey was conducted in 760 households, 397 taken from randomly selected 15 interventions and 363 households from 15 control UPS. This paper utilized data from a baseline assessment of gender and decision-making from a household survey conducted in the selected UPS during July 03–15, 2020. A sample size of 360 completed interviews was calculated for intervention and control areas to measure changes attributable to the SAMBHAV intervention in the behaviours and service utilization (pre- and post-intervention). Results: The data analysis showed a significant difference (p-value < 0.001) between respondents regarding women's freedom to move alone in the control and intervention area. It also reflected a significant difference between control and intervention areas as the respondents in the intervention area chose to work for the cause of gender-based violence. Conclusion: The SAMBHAV initiative brought an intersectional lens to gender issues. The community volunteers were trained to approach issues based on gender-based violence with the local public, and various conferences and meetings were organized to sensitize the community. The initiative's overall impact was that it built momentum around the issue of applying the concept of intersectionality for gender issues and building resilience in the community. There is still a need to bring multi-layered and more aggressive approaches to reduce the prevalence of gender-based violence in the community