Autonomous Navigation of Flying Quadcopter

The goal of the project is to design a semi-autonomous Quadcopter, capable of self-controlled flight with the help of wireless communications. It utilises an Arducopter Version 2.6 having an in-built microcontroller. The size of Quadcopter is designed small enough to take care of expenses, therefore small scale motors and propellers are used in its construction. With the help of APM 2.6, Gyroscope, Accelerometer the Quadcopter maintains control. Raspberry Pie 2 is used as a development board which provides IDE for Python scripts. The design of Quadcopter is in plus ‘+’ configuration. The Quadcopter’s movement is controlled by varying the relative thrusts of each rotor. To roll or pitch, one rotor’s thrust is decreased and the opposite rotor’s thrust is increased by the same amount. DOI: 10.17762/ijritcc2321-8169.15063

Light Weight Location Verification Algorithm in Wireless Sensors for Checking the Reliability of Data

Wireless sensors can be deployed in any environment, even if that is hazardous and they send back the data gathered to the verification center which is placed at some safe location. Since the data collected by these are very vital so any compromisation may lead to undesirable results. Sensors can be easily compromised by changing its actual position to some false position so there is need for some algorithm to verify the position and ensure that the data is unblemished. Since in previous scheme, heavy and expensive equipments were used along with the deployment knowledge required, it becomes inefficient for all cost range. Therefore, we have proposed a verification system which utilizes the concept of on-spot and in-region location verification. In on-spot verification, we calculate the distance of the wireless sensor from its actual deployed position. In-region verification depends upon neighbouring sensors. Along with that, once a sensors gets out of its tolerable region, even for once, its data gets discarded. Putting the sensors back to its original position after the discarding of the data won’t make it trusted and the sensor will still be considered compromised. This additional feature ensures that the data received in the verification center is from a trusted device and is true. DOI: 10.17762/ijritcc2321-8169.150512

An Approach to Convert Grayscale Images to Color

This paper investigates the coloring problems that occur while transforming a grayscale image into a color image. Earlier works on colorization techniques involve approaches to choose colors from a palette of RGB and transferring them on to the gray image. In other methods it either requires a lot of color scribbling on the black and white image or a huge dataset of color reference for the image in particular to the era it belongs to. We use convolutional neural networks along with a feature extractor and the Inception-ResNet-v2 pre-trained classifier model for higher efficiency in coloring. Our neural network is combined with the classifier that increases the performance of similar images. We train our neural network on images from Unsplash, an image collection website, that are available as a public datase

Paclobutrazol treatment as a potential strategy for higher seed and oil yield in field-grown camelina sativa L. Crantz

<p>Abstract</p> <p>Background</p> <p><it>Camelina (Camelina sativa </it>L. Crantz) is a non-food oilseed crop which holds promise as an alternative biofuel energy resource. Its ability to grow in a variety of climatic and soil conditions and minimal requirements of agronomical inputs than other oilseed crops makes it economically viable for advanced biofuel production. We designed a study to investigate the effect of paclobutrazol [2RS, 3RS)-1-(4-Chlorophenyl)-4,4-dimethyl-2-(1H-1,2,4-triazol-1-yl)pentan-3-ol] (PBZ), a popular plant growth regulator, on the seed and oil yield of <it>Camelina sativa </it>(cv. Celine).</p> <p>Results</p> <p>A field-based micro-trial setup was established in a randomized block design and the study was performed twice within a span of five months (October 2010 to February 2011) and five different PBZ treatments (Control: T₀; 25 mg l^-1: T₁; 50 mg l^-1: T₂; 75 mg l^-1: T₃; 100 mg l^-1: T₄; 125 mg l^-1: T₅) were applied (soil application) at the time of initiation of flowering. PBZ at 100 mg l^-1concentration (T₄) resulted in highest seed and oil yield by 80% and 15%, respectively. The seed yield increment was mainly due to enhanced number of siliques per plant when compared to control. The PBZ - treated plants displayed better photosynthetic leaf gas exchange characteristics, higher chlorophyll contents and possessed dark green leaves which were photosynthetically active for a longer period and facilitated higher photoassimilation.</p> <p>Conclusion</p> <p>We report for the first time that application of optimized PBZ dose can be a potential strategy to achieve higher seed and oil yield from <it>Camelina sativa </it>that holds great promise as a biofuel crop in future.</p

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study

Background: Many causes of vision impairment can be prevented or treated. With an ageing global population, the demands for eye health services are increasing. We estimated the prevalence and relative contribution of avoidable causes of blindness and vision impairment globally from 1990 to 2020. We aimed to compare the results with the World Health Assembly Global Action Plan (WHA GAP) target of a 25% global reduction from 2010 to 2019 in avoidable vision impairment, defined as cataract and undercorrected refractive error. Methods: We did a systematic review and meta-analysis of population-based surveys of eye disease from January, 1980, to October, 2018. We fitted hierarchical models to estimate prevalence (with 95% uncertainty intervals [UIs]) of moderate and severe vision impairment (MSVI; presenting visual acuity from <6/18 to 3/60) and blindness (<3/60 or less than 10° visual field around central fixation) by cause, age, region, and year. Because of data sparsity at younger ages, our analysis focused on adults aged 50 years and older. Findings: Global crude prevalence of avoidable vision impairment and blindness in adults aged 50 years and older did not change between 2010 and 2019 (percentage change −0·2% [95% UI −1·5 to 1·0]; 2019 prevalence 9·58 cases per 1000 people [95% IU 8·51 to 10·8], 2010 prevalence 96·0 cases per 1000 people [86·0 to 107·0]). Age-standardised prevalence of avoidable blindness decreased by −15·4% [–16·8 to −14·3], while avoidable MSVI showed no change (0·5% [–0·8 to 1·6]). However, the number of cases increased for both avoidable blindness (10·8% [8·9 to 12·4]) and MSVI (31·5% [30·0 to 33·1]). The leading global causes of blindness in those aged 50 years and older in 2020 were cataract (15·2 million cases [9% IU 12·7–18·0]), followed by glaucoma (3·6 million cases [2·8–4·4]), undercorrected refractive error (2·3 million cases [1·8–2·8]), age-related macular degeneration (1·8 million cases [1·3–2·4]), and diabetic retinopathy (0·86 million cases [0·59–1·23]). Leading causes of MSVI were undercorrected refractive error (86·1 million cases [74·2–101·0]) and cataract (78·8 million cases [67·2–91·4]). Interpretation: Results suggest eye care services contributed to the observed reduction of age-standardised rates of avoidable blindness but not of MSVI, and that the target in an ageing global population was not reached. Funding: Brien Holden Vision Institute, Fondation Théa, The Fred Hollows Foundation, Bill & Melinda Gates Foundation, Lions Clubs International Foundation, Sightsavers International, and University of Heidelberg