Binocular Functions

Binocular single vision is the ability to use both eyes simultaneously so that each eye contributes to a common single perception. Normal binocular single vision occurs with bifoveal fixation and normal retinal correspondence in everyday sight. There are various anatomical and physiological factors concerned in the development of Binocular vision. The development of binocular function starts at 6 weeks and is completed by 6 months. Any obstacles, sensory, motor, or central, in the flex pathway is likely to hamper the development of binocular vision. The presence of these obstacles gives rise to various sensory adaptations to binocular dysfunction. Clinically the tests used can be based on either of the two principles: (A) assessment of relationship between the fovea of the fixing eye and the retinal area stimulated in the squinting eye, viz. Bagolini striated glasses test, red filter test, synoptophore using SMP slides for measuring the objective and subjective angles, and Worth 4-dot test; and (B) Assessment of the visual directions of the two foveae, viz. after image test (Hering Bielschowsky); and Cuppers binocular visuoscopy test (foveo-foveal test of Cuppers). Anomalies of binocular vision results in confusion, diplopia, which leads to suppression, eccentric fixation, anomalous retinal correspondence, and amblyopia

Improvement of both performance and stability of photovoltaic devices by in situ formation of a sulfur-based 2D perovskite

Perovskite solar cells (PSCs) with superior performance have been recognized as a potential candidate in photovoltaic technologies. However, the defects in active perovskite layer induce non-radiative recombination which restricts the performance and stability of the PSCs. The construction of thiophene-based 2D structure is one of the significant approaches for surface passivation of hybrid PSCs that may combine the benefits of the stability of 2D perovskite with the high performance of 3D perovskite. Here, a sulfur-rich spacer cation 2-thiopheneethylamine iodide (TEAI) is synthesized as a passivation agent for the construction of three-dimensional/two-dimensional (3D/2D) perovskite bilayer structure. TEAI-treated PSCs possess a much higher efficiency (20.06%) compared to the 3D perovskite (MAFAPbI3) devices (17.42%). Time-resolved photoluminescence (TRPL) and femtosecond transient absorption (TA) spectroscopy are employed to investigate the effect of surface passivation on the charge carrier dynamics of the 3D perovskite. Additionally, the stability test of TEAI-treated perovskite devices reveals significant improvement in humid (RH ~ 56%) and thermal stability as the sulfur-based 2D (TEA)2PbI4 material self-assembles on the 3D surface making the perovskite surface hydrophobic. Our findings provide a reliable approach to improve device stability and performance successively, paving the way for industrialization of PSCs

Persistence of Primary and Secondary Pollutants in Delhi : Concentrations and Composition from 2017 through the COVID Pandemic

We assess impacts of the 2020 COVID-19 lockdown on ambient air quality in Delhi, building on over three years of real-time measurements of black carbon (BC) and nonrefractory submicrometer aerosol (NR-PM1) composition from the Delhi Aerosol Supersite and public data from the regulatory monitoring network. We performed source apportionment of organic aerosol (OA) and robust statistical analyses to differentiate lockdown-related impacts from baseline seasonal and interannual variability. The primary pollutants NOx, CO, and BC were most reduced, primarily due to lower transportation emissions. Local and regional emissions such as agricultural burning decreased during the lockdown. PM2.5 declined but remained well above WHO guidelines. Despite the lockdown, NR-PM1 changed only moderately compared to prior years. Differences in the trends of hydrocarbon-like OA and BC suggest that some sources of primary aerosol may have increased. Despite notable reductions in some primary pollutants, the lockdown restrictions led to rather small perturbations in the primary fraction of NR-PM1, with secondary aerosol continuing to dominate. Overall, our results demonstrate the impact of secondary and primary pollution on Delhi's air quality and show that large changes in emissions within Delhi alone are insufficient to bring about needed improvements in air quality.Peer reviewe

Persistence of Primary and Secondary Pollutants in Delhi : Concentrations and Composition from 2017 through the COVID Pandemic

We assess impacts of the 2020 COVID-19 lockdown on ambient air quality in Delhi, building on over three years of real-time measurements of black carbon (BC) and nonrefractory submicrometer aerosol (NR-PM1) composition from the Delhi Aerosol Supersite and public data from the regulatory monitoring network. We performed source apportionment of organic aerosol (OA) and robust statistical analyses to differentiate lockdown-related impacts from baseline seasonal and interannual variability. The primary pollutants NOx, CO, and BC were most reduced, primarily due to lower transportation emissions. Local and regional emissions such as agricultural burning decreased during the lockdown. PM2.5 declined but remained well above WHO guidelines. Despite the lockdown, NR-PM1 changed only moderately compared to prior years. Differences in the trends of hydrocarbon-like OA and BC suggest that some sources of primary aerosol may have increased. Despite notable reductions in some primary pollutants, the lockdown restrictions led to rather small perturbations in the primary fraction of NR-PM1, with secondary aerosol continuing to dominate. Overall, our results demonstrate the impact of secondary and primary pollution on Delhi's air quality and show that large changes in emissions within Delhi alone are insufficient to bring about needed improvements in air quality.Peer reviewe

Advances and application of positive matrix factorization for source attribution of air pollution in megacities

Air pollution is considered the greatest current environmental health threat to humanity, with an estimated mortality burden of 7 million per year. More than half the world’s population is exposed to increasing air pollution. Reduction of air pollution is essential to global health and can be expected to generate long-term societal benefits. Receptor models are efficient mathematical tools for identification of sources of air pollution. A popular receptor modeling technique is Positive Matrix Factorization (PMF). However, PMF is limited by the assumption of constant source profiles throughout the modeling period—while the contribution of each source is modeled to change over time, its profile (e.g., mass spectrum, when PMF is applied to mass spectrometer data) stays constant. PMF is frequently applied to data on air pollution from fine particulate matter (PM), particularly in megacities. Megacities are centers of economic activity, harbor very large populations, and have high PM levels, especially in the developing world, posing acute challenges to public health. One such city is Delhi, India. Delhi is the second most populated city in the world and routinely experiences some of the highest particulate matter concentrations of any megacity on the planet. However, the current understanding of the sources and dynamics of PM pollution in Delhi is limited. Measurements at the Delhi Aerosol Supersite (DAS) provide long-term chemical characterization of ambient submicron aerosol in Delhi, with near-continuous online measurements of aerosol composition. In this dissertation, I apply PMF on data collected in the DAS study to characterize sources and atmospheric dynamics of submicron aerosols in Delhi. In study 1 (chapter 2), I report on source apportionment based on unsupervised (unconstrained) positive matrix factorization (PMF), conducted on 15 months of highly time-resolved speciated submicron non-refractory PM₁ (NR-PM₁) between January 2017 and March 2018. This dataset was collected in the DAS study. I report on seasonal variability across four seasons of 2017 and interannual variability using data from the two winters and springs of 2017 and 2018. I also show that a modified tracer-based organic component analysis provides an opportunity for a real-time source apportionment approach for organics in Delhi. Phase equilibrium modeling of aerosols using the extended aerosol inorganics model (E-AIM) predicts equilibrium gas-phase concentrations and allows evaluation of the importance of the ventilation coefficient (VC) and temperature in controlling primary and secondary organic aerosol. I also find that primary aerosol dominates severe air pollution episodes, and secondary aerosol dominates seasonal averages. An edited version of this chapter has been published in Atmospheric Chemistry and Physics. In study 2 (chapter 3), we develop the approach of conducting supervised (constrained) PMF on long-term datasets separated into 4 hour periods with limited variability in emissions and meteorology and statistically demonstrate its viability. I apply this time-of-day PMF approach on two seasons of highly time-resolved NR-PM₁ organics. This approach improves upon the seasonal source apportionment previously employed in Delhi by capturing the diurnal variability in source mass spectral profiles and retaining low computational intensity. Use of the EPA PMF tool allows application of constraints and quantifies random errors and rotational ambiguity in PMF solutions. Results in this study demonstrate that time-of-day PMF approach gives a greater number of more appropriate PMF factors compared to the traditional seasonal PMF approach. The time-of-day PMF approach fits data better, improving fits at specific time points, and at key m/zs. Portions of this chapter will be submitted to Atmospheric Measurement Techniques. Previous receptor modeling studies have identified vehicular emissions and fossil fuel combustion as prevalent factors contributing to fine PM pollution in Delhi. However, cooking and biomass burning have not been consistently identified in ambient studies. Bottom-up (source-oriented) studies have recognized the high exposure to residential energy emissions from cooking and heating and associated biomass burning emissions. In study 3 (chapter 4), I address these limitations of receptor modeling studies by applying PMF on two seasons of highly time-resolved NR-PM₁ organics. I utilize the time-of-day PMF approach (chapter 3) to separate primary organics into component primary factors. Hydrocarbon-like organic aerosol, or HOA, the fuel combustion and traffic primary organic aerosol surrogate, occurs in every season, and shows strong diurnal patterns. Biomass burning organic aerosol, or BBOA, separates only in winter, and exhibits time series peaks associated with space heating and solid-fuel combustion. Cooking organic aerosol, or COA, separates only in monsoon and reports stable diurnal patterns, suggesting the presence of cooking sources all-day. Equilibrium modeling of organic aerosols using volatility basis sets (VBS) suggests that differences in ventilation coefficient and temperature can explain the differences in factor separation between winter and monsoon. Overall, I show that traffic, and cooking and biomass burning contribute almost equally to the primary organic aerosol burden in Delhi, in broad agreement with several bottom-up studies. Portions of this chapter will be submitted to Atmospheric Chemistry and PhysicsChemical Engineerin

Causation, historiographic approaches and the investigation of serious adverse incidents in mental health settings

From Crossref journal articles via Jisc Publications RouterHistory: epub 2022-05-03, issued 2022-05-03Publication status: PublishedTo improve the safety of healthcare systems, it is necessary to understand harm-related events that occur in these systems. In mental health services, particular attention is paid to harm arising from the actions of patients against themselves or others. The primary intention of examining these adverse events is to inform changes to care provision so as to reduce the likelihood of the recurrence of such events. The predominant approach to investigating adverse incidents has relied on the cause-and-effect conceptualisation of past events. Whilst the merits of approaches which are reliant on cause-and-effect narratives have been questioned, alternatives models to explain adverse incidents in health settings have not been theoretically or empirically tested. This novel article (i) examines the notion of causation (and the related notion of omission) in the context of explaining adverse events in mental health settings, and (ii) draws on a long-established discipline devoted to the study of how the past is interpreted (namely historiography) to theoretically investigate the innovative application of two historiographical approaches (i.e. counterfactual analysis and historical materialism) to understanding adverse events in mental health settings