Profiling drivers based on driver dependent vehicle driving features

This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.     

A model to measure QoE for virtual personal assistant

Until now the virtual assistants (like Siri, Google Now and Cortana) have primarily been confined to voice input and output only. Is there a justification for voice only confinement or can we enhance the user experience by adding a visual output? We hypothesized that providing a higher level of visual/auditory immersion would enhance the quality of user experience. In order to test this hypothesis, we first developed 4 variants of virtual assistant, each with a different audio/visual level of immersion. Developed virtual assistant systems were the following; audio only, audio and 2D visual display, audio and 3D visual display and audio and immersive 3D visual display. We developed a plan for usability testing of all 4 variants. The usability testing was conducted with 30 subjects against eight (8) dependent variables included presence, involvement, attention, reliability, dependency, easiness, satisfaction and expectations. Each subject rated these dependent variables based on a scale of 1–5, 5 being the highest value. The raw data collected from usability testing was then analyzed through several tools in order to determine the factors contributing towards the quality of experience for each of the 4 variants. The significant factors were then used develop a model that measures the quality of user experience. It was found that each variant had a different set of significant variables. Hence, in order to rate each system there is a need to develop a scale that is dependent upon the unique set of variables for the respective variant. Furthermore, it was found that variant 4 scored the highest rate for Quality of Experience (QoE). Lastly several other qualitative conclusions were also drawn from this research that will guide future work in the field of virtual assistants.     

Simultaneously determination of methyl and inorganic mercury in fish species by cold vapour generation atomic absorption spectrometry

A simple and rapid non-chromatographic method was developed to determine methylmercury (MeHg) and inorganic mercury (iHg) levels in muscles tissues of 10 freshwater fish species. The MeHg and iHg were determined by cold vapour atomic absorption spectrometry after alkaline wet digestion of samples. The digested samples were reduced sequentially with stannous chloride and sodium tetrahydroborate for iHg and MeHg, respectively. Parameters such as carrier gas flow rate (argon), volume of oxidizing and potassium persulphate solutions were investigated in detail. The accuracy of the technique was evaluated by using certified reference material (DORM-2) and spiking the both Hg species in muscles tissue of a fish. The limits of detection were 0.117 and 0.133 μg kg⁻¹ for MeHg and iHg, respectively. The concentrations of MeHg and iHg in muscles tissues of ten fish species were found in the range of (28.4–56.3) and (3.01–8.11) μg kg⁻¹, respectively.     

Double Repositioning: Veterinary Antiparasitic to Human Anticancer

Drug repositioning, the approach of discovering different uses for existing drugs, has gained enormous popularity in recent years in the anticancer drug discovery field due to the increasing demand for anticancer drugs. Additionally, the repurposing of veterinary antiparasitic drugs for the treatment of cancer is gaining traction, as supported by existing literature. A prominent example is the proposal to implement the use of veterinary antiparasitics such as benzimidazole carbamates and halogenated salicylanilides as novel anticancer drugs. These agents have revealed pronounced anti-tumor activities and gained special attention for “double repositioning”, as they are repurposed for different species and diseases simultaneously, acting via different mechanisms depending on their target. As anticancer agents, these compounds employ several mechanisms, including the inhibition of oncogenic signal transduction pathways of mitochondrial respiration and the inhibition of cellular stress responses. In this review, we summarize and provide valuable information about the experimental, preclinical, and clinical trials of veterinary antiparasitic drugs available for the treatment of various cancers in humans. This review suggests the possibility of new treatment options that could improve the quality of life and outcomes for cancer patients in comparison to the currently used treatments.     

Comparison of electrothermal and hydride generation atomic absorption spectrometry for the determination of total arsenic in broiler chicken

The purpose of this study was to estimate total arsenic concentration in different tissues (leg, breast, liver and heart) of broiler chicken by hydride generation atomic absorption spectrometry (HGAAS) and graphite furnace atomic absorption spectrometry (GFAAS), prior to microwave assisted acid digestion. The accuracy of the techniques was evaluated by using certified reference material DORM-2. The percentage recoveries of total As were observed as 100.6% and 99.4% for HGAAS and GFAAS, respectively. The precision of the techniques, expressed as relative standard deviation, was observed as 1.71% and 4.18% for HGAAS and GFAAS measurements, respectively. The limits of detection for HGAAS and GFAAS were 0.025 μg/g and 0.052 μg/g, respectively. The concentrations of total arsenic in different tissues of broiler chicken were found in the range of 2.19–5.28, 2.15–5.22, 2.97–7.17 and 2.68–6.36 μg/g for leg, breast, liver and heart tissues, respectively. At a mean level of chicken consumption (60 g/person/day), people may ingest in the range of 72.0–85.1 μg arsenic/person/day from chicken alone.     

Prediction of warp and weft yarn crimp in cotton woven fabrics

The aim of this study was to develop statistical models for the prediction of warp and weft crimp percentage of cotton woven fabrics. The developed models are based on the empirical data obtained from carefully developed 60 fabric samples with different yarn linear densities, fabric densities, and weave designs. The predictability and accuracy of the developed models was assessed by correlation analysis of the predicted and actual crimp values of another set of eight fabric samples which was not used for the development of models. The results show fairly good capability and accuracy of the prediction models.     

Molecular Pathogenesis of Glioblastoma in Adults and Future Perspectives: A Systematic Review

Glioblastoma (GBM) is the most common and malignant tumour of the central nervous system. Recent appreciation of the heterogeneity amongst these tumours not only changed the WHO classification approach, but also created the need for developing novel and personalised therapies. This systematic review aims to highlight recent advancements in understanding the molecular pathogenesis of the GBM and discuss related novel treatment targets. A systematic search of the literature in the PubMed library was performed following the PRISMA guidelines for molecular pathogenesis and therapeutic advances. Original and meta-analyses studies from the last ten years were reviewed using pre-determined search terms. The results included articles relevant to GBM development focusing on the aberrancy in cell signaling pathways and intracellular events. Theragnostic targets and vaccination to treat GBM were also explored. The molecular pathophysiology of GBM is complex. Our systematic review suggests targeting therapy at the stemness, p53 mediated pathways and immune modulation. Exciting novel immune therapy involving dendritic cell vaccines, B-cell vaccines and viral vectors may be the future of treating GBM.     

The design of consumer packaging: Effects of manipulations of shape,orientation, and alignment of graphical forms on consumers’ assessments

On-package graphics have the potential to influence consumers’ product-related attitudes and behaviours. In the reported study graphics designs on the labels of two products (water and vodka) were manipulated with respect to shape angularity, orientation, and left–right alignment. Participants’ evaluations indicated a preference for rounded shapes that could not be accounted for by differences in design typicality; and preference for upward shape orientation. An interaction between these response variables for ratings of purchase likelihood suggested that congruence between graphical and product form (droplet shape) may be advantageous. Effects of alignment were not consistent with existing theories, with right-aligned graphics being preferred. An explanation that distinguishes processing efficiency and hemispheric efficiency is proposed. Finally, as predicted, a halo effect was apparent, such that effects of aesthetic manipulations extended to ratings of product attributes that were not experienced. Theoretical and practical implications of these results are discussed.     

Artificial Neural Network modeling of a hydrogen dual fueled diesel engine characteristics: An experiment approach

The rapid growth of vehicular pollution; mostly running on the diesel engine, emissions emerging are the concerns of the day. Owing to clean burn characteristics features, Hydrogen (H₂) as a fuel is the paradigm of the researcher. Extensive research presented in the literature on H₂ dual fueled diesel engine reveals, the significant role of H₂ in reducing emissions and enhancing the performance of a dual fueled diesel engine. With meager qualitative experiment data, the feasibility to develop an efficient Artificial Neural Network (ANN) model is investigated, the developed model can be utilized as a tool to investigate the H₂ dual fueled diesel engine further. In the process of developing an ANN model, engine load and H₂ flow rate are varied to register performance and emission characteristics. The creditability of the experiment is ascertained with uncertainty analysis of measurable and computed parameters. Leave-out-one method is adopted with 16 data sets; seven training algorithms are explored with eight transfer function combinations to evolve a competent ANN model. The efficacy of the developed model is adjudged with standard benchmark statistic indices. ANN model trained with Broyden, Fletcher, Goldfarb, & Shanno (BFGS) quasi-Newton backpropagation (trainbfg) stand out the best among other algorithms with regression coefficient ranging between 0.9869 and 0.9996.     

Laser sensor for detection of SF6 leaks in high powerinsulated switchgear systems

A novel photoacoustic spectrometer (PA) has been developed for in-situ detection of SF₆ leaks in low concentrations. The developed system is equipped with a sound alarm system and has been tested in the laboratory for very minute SF₆ leaks. This newly developed SF₆ detection device utilizes a high quality factor resonant photoacoustic cell and continuous wave (CW) line tunable CO₂ laser at 10.55 μm wavelength. Whenever SF₆ is detected an acoustic signal is generated and no signal appears from ambient air if there is no leakage of SF₆. An electret microphone is used for the detection of these acoustic signals. The system is capable of detecting leaks of the order of 3.5 ppbv (parts per billion by volume) concentration. This device has been proved to have significant applications to industrial organizations that have electric power gas insulated systems (GIS). It could be also applied for other applications such as monitoring of environmental pollutants with minimal adjustments