Body politics of the Indian state in the COVID-19 era: from an Ambedkarite lens

A health pandemic is a complex phenomenon that can’t be merely understood in biomedical terms while ignoring the socio-cultural context of how it has spread, and the way countries have responded to it. For this reason, many scholars like Reyes (2020) and Horton (2020) have argued that we need to look at natural/health disasters in conjunction with the social interactions and institutional responses to make an accurate sense of the situation. The Covid-19 pandemic was initially touted as a great leveller as it didn’t distinguish between different social classes while spreading profusely among people, which created a false image that somehow every person, whether rich or poor, is equally affected by it. However, in the past one year we have seen anything but the pandemic having an equal impact on everyone. In fact, what happened was that different measures were taken for different groups to address the spread of the virus, which showed a great bias against vulnerable groups and communities in India. Instead of being a great leveller, the pandemic exacerbated the social cleavages and ended up increasing inequalities

STRUCTURAL PROPERTIES OF NITROGEN DOPED ANATASE AND RUTILE TiO2 THIN FILMS

Anatase and rutile TiO2 thin films have been doped by N ion implantation. The effect of N doping on the structural changes of TiO2 thin films and its correlation to the optical and chemical properties of the films is investigated. The depth and concentration of the implanted N atoms is found not to exhibit substantial difference for anatase and rutile phases. The energy loss of the implanted N atoms correlates well to the energy gained by O and Ti atoms in the TiO2 lattice. An increased number of O vacancies are found to be generated as compared to Ti for both anatase and rutile phases. The energy loss mechanisms of the implanted N atoms together with the O vacancy generation are found to be the major driving forces for facilitating enhanced optical and chemical properties of the TiO2 thin films

Effect of stator blades on the startup dynamics of a vertical axis wind turbine.

Vertical Axis Wind Turbines (VAWTs) are omni-directional, low-cost, low-efficiency wind power extractors. A conventional drag-based VAWT consists of multiple thin rotor blades with a typical peak Tip Speed Ratio (λ) of less than 1. Their lower cut-in speed and maintenance cost make them ideal for power generation in urban environments. Numerous studies have been carried out analysing steady operation of VAWTs and quantifying their performance characteristics; however, minimal attention has been paid to their start-up dynamics. There are a few recent studies in which start-up dynamics of lift-based VAWTs have been analysed, but such studies for drag-based VAWTs are severely limited. In this study, start-up dynamics of a conventional multi-blade drag-based VAWT have been numerically investigated using a time-dependant Computational Fluid Dynamics (CFD) solver. In order to enhance the start-up characteristics of the drag-based VAWT, a stator has been integrated in the design assembly. The numerical results obtained in this study indicate that an appropriately designed stator can significantly enhance the start-up of a VAWT by directing the flow towards the rotor blades, leading to higher rotational velocity (ω) and λ. With the addition of a stator, the flow fields downstream the VAWT becomes more uniform

Perturbation of Interaction Networks for Application to Cancer Therapy

We present a computational approach for studying the effect of potential drug combinations on the protein networks associated with tumor cells. The majority of therapeutics are designed to target single proteins, yet most diseased states are characterized by a combination of many interacting genes and proteins. Using the topology of protein-protein interaction networks, our methods can explicitly model the possible synergistic effect of targeting multiple proteins using drug combinations in different cancer types

Recollection of flame height and smoke volume in domestic fires

When a domestic fire occurs, how well do people perceive and recall the fire hazards they encounter? Although much research has been conducted on threat or risk perception and memory for threatening stimuli, to the authors' knowledge no studies have systematically tested how well people perceive and recall the threat stimulus in a fire context. This is an important topic given that domestic fires are usually the main source of fire-related injuries and deaths, and human behaviour is believed to play a significant role in such outcomes. Moreover, eyewitness testimonies play a significant role in subsequent fire, insurance and coroner investigations. The current study – part of wider research on human behaviour in domestic fires, called LIFEBID – sought to address the gap in knowledge by conducting an online experiment testing participants' (a) memories for the size of the flames and smoke witnessed in a mock kitchen fire, and (b) reported willingness to engage with the fire hazards. Participants' behaviours and attitudes in relation to other risky activities and control over events were also measured. The results revealed that accurate recollections of flame height and smoke volume can be obtained from members of the public, in certain cases. Accuracy was negatively impacted when the flames and smoke witnessed were larger in size. The size of the fire hazards also had an impact on participants' willingness to engage with the hazards, moderating the number who stated that they could have successfully extinguished the flames or would have entered the room with the smoke. Although there were signs that many participants recognised the risks posed by the larger hazards, a not inconsiderable number were still willing to engage with them. Being someone who takes greater risks in a health/safety domain and believing in one's ability to control what happens to oneself did not explain this finding. There is a clear need for a deeper investigation into people’s perceptions of fire hazards in a domestic fire context and their associated behaviours and fire outcomes

Numerical investigations on the transient aerodynamic performance characterization of a multibladed vertical axis wind turbine.

The use of vertical axis wind turbines (VAWTs) in urban environments is on the rise due to their relatively smaller size, simpler design, lower manufacturing and maintenance costs, and above all, due to their omnidirectionality. The multibladed drag-based VAWT has been identified as a design configuration with superior aerodynamic performance. Numerous studies have been carried out in order to better understand the complex aerodynamic performance of multibladed VAWTs employing steady-state or quasi-steady numerical methods. The transient aerodynamics associated with a multibladed VAWT, especially the time–history of the power coefficient of each blade, has not been reported in the published literature. This information is important for the identification of individual blade’s orientation when producing negative torque. The current study aims to bridge this gap in the literature through real-time tracking of the rotor blade's aerodynamic performance characteristics during one complete revolution. Numerical investigations were carried out using advanced computational fluid dynamics (CFD)-based techniques for a tip speed ratio of 0 to 1. The results indicate that transient aerodynamic characterization is 13% more accurate in predicting the power generation from the VAWT. While steady-state performance characterization indicates a negative power coefficient (Cp) at λ = 0.65, transient analysis suggests that this happens at λ = 0.75

Sequence biases in large scale gene expression profiling data

We present the results of a simple, statistical assay that measures the G+C content sensitivity bias of gene expression experiments without the requirement of a duplicate experiment. We analyse five gene expression profiling methods: Affymetrix GeneChip, Long Serial Analysis of Gene Expression (LongSAGE), LongSAGELite, ‘Classic’ Massively Parallel Signature Sequencing (MPSS) and ‘Signature’ MPSS. We demonstrate the methods have systematic and random errors leading to a different G+C content sensitivity. The relationship between this experimental error and the G+C content of the probe set or tag that identifies each gene influences whether the gene is detected and, if detected, the level of gene expression measured. LongSAGE has the least bias, while Signature MPSS shows a strong bias to G+C rich tags and Affymetrix data show different bias depending on the data processing method (MAS 5.0, RMA or GC-RMA). The bias in the Affymetrix data primarily impacts genes expressed at lower levels. Despite the larger sampling of the MPSS library, SAGE identifies significantly more genes (60% more RefSeq genes in a single comparison)

Using machine learning to study the effect of medication adherence in Opioid Use Disorder

Background: Opioid Use Disorder (OUD) and opioid overdose (OD) impose huge social and economic burdens on society and health care systems. Research suggests that Medication for Opioid Use Disorder (MOUD) is effective in the treatment of OUD. We use machine learning to investigate the association between patient’s adherence to prescribed MOUD along with other risk factors in patients diagnosed with OUD and potential OD following the treatment. Methods: We used longitudinal Medicaid claims for two selected US states to subset a total of 26,685 patients with OUD diagnosis and appropriate Medicaid coverage between 2015 and 2018. We considered patient age, sex, region level socio-economic data, past comorbidities, MOUD prescription type and other selected prescribed medications along with the Proportion of Days Covered (PDC) as a proxy for adherence to MOUD as predictive variables for our model, and overdose events as the dependent variable. We applied four different machine learning classifiers and compared their performance, focusing on the importance and effect of PDC as a variable. We also calculated results based on risk stratification, where our models separate high risk individuals from low risk, to assess usefulness in clinical decision-making. Results: Among the selected classifiers, the XGBoost classifier has the highest AUC (0.77) closely followed by the Logistic Regression (LR). The LR has the best stratification result: patients in the top 10% of risk scores account for 35.37% of overdose events over the next 12 month observation period. PDC score calculated over the treatment window is one of the most important features, with better PDC lowering risk of OD, as expected. In terms of risk stratification results, of the 35.37% of overdose events that the predictive model could detect within the top 10% of risk scores, 72.3% of these cases were non-adherent in terms of their medication (PDC <0.8). Targeting the top 10% outcome of the predictive model could decrease the total number of OD events by 10.4%. Conclusions: The best performing models allow identification of, and focus on, those at high risk of opioid overdose. With MOUD being included for the first time as a factor of interest, and being identified as a significant factor, outreach activities related to MOUD can be targeted at those at highest risk

DiscoverySpace: an interactive data analysis application

DiscoverySpace is a graphical application for bioinformatics data analysis. Users can seamlessly traverse references between biological databases and draw together annotations in an intuitive tabular interface. Datasets can be compared using a suite of novel tools to aid in the identification of significant patterns. DiscoverySpace is of broad utility and its particular strength is in the analysis of serial analysis of gene expression (SAGE) data. The application is freely available online

The integration of building information modelling and fire evacuation models

This paper presents a framework for integrating fire evacuation models into Building Information Modelling (BIM). The framework is intended to enhance coordination between stakeholders from diverse disciplines involved in the domain of fire evacuation design. It supports a full in/out data loop linking BIM and evacuation design tools, which enables professionals and authorities to review building design models coupled with evacuation data and perform comprehensive assessments more efficiently. The development of the framework is discussed along with the associated data exchange from a fire safety engineering perspective. Additionally, the benefits of two-way data flow between BIM and fire evacuation design tools are demonstrated by implementing a prototype system for coupling Revit, a popular BIM platform, and Pathfinder, a widely used evacuation simulator. This open source tool is named Evac4BIM and has been systematically tested to demonstrate its applicability in building design