Enhancing Search User Interface of an Assets Management Solution

In today's time, information is of paramount importance and affects human life in conspicuous ways. Different companies, solutions and tools are developed to harness this information and retrieve is efficiently. One such tool is 'FA Platform', an Asset Management Solutions developed by FA Solutions Oy, which allows users to manage and search their assets and funds. This thesis contemplates over the issues faced by the search user interface of the 'FA Platform'. The thesis then proposes a new design implementation to improve the user experience of using the search functionality from the user's point of view. This study explores the concepts of search user interface in-depth to understand the industry standards and research previously done in these fields that could assist this study. Interviews were then conducted with users of the software to identify problems with the current interface. To further identify the possibilities, client's search-usage history was collected and analysed for patterns. The problems identified were that the existing solution lacked features to add new search criteria, option to select multiple values, and the general user interface was cluttered and difficult to comprehend. This thesis proposed a design prototype of the search user interface and tested ii with users against the existing implementation. Through testing, the new design prototype was found to be easy to use and provided a nice overall user-experience. The proposed design improved over the current implementation in three key areas: Redesigning the layout of search user interfaces to improve legibility; Adding an option to dynamically add more filter-fields as per requirement; and Providing an option to select multiple values for filter-fields. Having a good search user interface design is an important factor while developing a database management tool as it will increase user’s productivity, allowing them to find exactly what they want, how they want and when they want it. Even more so, a good design can also be very profitable for the company as it causes less distress to users

In situ and satellite-based estimates of aerosol-cloud interactions between biomass burning aerosols and marine stratocumulus clouds over the southeast Atlantic Ocean

Ubiquitous low-level, marine stratocumulus clouds provide the largest contribution of all cloud types to the shortwave cloud radiative forcing. A cooling effect from small changes in low-level cloud properties due to aerosol-cloud interactions (ACIs) could partially offset the global warming due to increasing greenhouse gas concentrations in the atmosphere. A large marine stratocumulus cloud deck exists over the southeast Atlantic Ocean where the clouds are overlaid by biomass burning aerosols with instances of contact and separation between the aerosol and cloud layers. Biases in satellite retrievals of aerosol and cloud properties and the vertical distance between the aerosol and cloud layers have led to uncertainties in the regional estimates of ACIs and the effective radiative forcing due to ACIs (ERFaci). ERFaci remains the largest source of uncertainty in climate model estimates of Earth’s energy budget in future climate scenarios. In this study, in situ data are used to quantify aerosol-induced changes in stratocumulus cloud properties and to evaluate satellite-based estimates of the aerosol-induced changes. Size distributions of aerosols and cloud droplets were sampled during the three phases of the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign using in situ probes onboard the NASA P-3B aircraft. Size distributions from vertical profiles of aerosol and cloud layers over the southeast Atlantic were used to estimate aerosol concentration (Na) along with cloud microphysical properties like droplet concentration (Nc), effective radius (Re), and liquid water content (LWC), optical properties like cloud optical thickness (), and macrophysical properties like liquid water path (LWP), cloud geometric thickness (H) and precipitation rate (Rp). Across the ORACLES campaigns in September 2016, August 2017, and October 2018, 173 “contact” profiles had Na > 500 cm-3 within 100 m above cloud tops and 156 “separated” profiles had Na < 500 cm-3 up to 100 m above cloud tops. The average Nc, LWC, and for contact profiles were 87 cm-3, 0.02 g m-3, and 1.8 higher and Re was 1.5 m lower compared to separated profiles. These differences were associated with higher below-cloud Na and weaker droplet evaporation near cloud top in the presence of high Na immediately above cloud tops. Larger differences were observed between Nc and Re for contact and separated profiles in high Na boundary layers (108 cm-3 and 1.8 m) compared to low Na boundary layers (31 cm-3 and 0.5 m). A smaller decrease in humidity across cloud top during contact profiles led to a smaller decrease in median Nc and LWC near cloud top (25% and 12%) compared to separated profiles (33% and 18%). Higher Nc and lower Re for contact profiles resulted in precipitation suppression with 50% lower Rp compared to separated profiles along with 20% lower precipitation susceptibility to aerosols (So). So depends on both Nc and Rp, and differences between So for contact and separated profiles varied with H due to the co-variability between changes in Nc and Rp due to droplet growth with height and increasing Na. Based on reanalysis data, contact and separated profiles had statistically similar meteorological conditions like surface temperature (To), lower tropospheric stability (LTS), and estimated inversion strength (EIS), on average. For 67 contact and 82 separated profiles, in situ data were co-located with a retrieval from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra or Aqua satellite with a time gap of less than 1 hour. On average, the MODIS Re, , and Nc (11.4 m, 11.7, and 150.3 cm-3) were 1.7 m, 2.4, and less than 1 cm-3 higher than the in situ Re, , and Nc with Pearson’s correlation coefficient (R) = 0.78, 0.72, and 0.90, respectively. The 67 contact profiles had 103 cm-3 and 2.8 higher in situ Nc and with 2.2 m lower in situ Re compared to the 82 separated profiles. MODIS estimates of the differences in Re, , and Nc between contact and separated profiles were within 0.5 m, 0.7, and 5 cm-3 of the in situ estimates when profiles with MODIS Re > 15 m and MODIS > 25 were removed. Agreement between MODIS and in situ estimates of Re, , and Nc and the aerosol-induced changes in Re, , and Nc was observed due to low biases in MODIS retrievals which were consistent for contact and separated profiles. The aerosol-induced changes in cloud properties quantified in this study could impact the stratocumulus-to-cumulus or closed-to-open cell transitions in the region. Future work should examine in-cloud aerosol samples from the counterflow virtual impactor inlet to examine the extent of entrainment mixing of aerosols into the cloud layer. Modeling studies should examine the impact of precipitation suppression on cloud lifetime and boundary layer dynamics. Model parameterizations of Rp should be adjusted to account for changes in the relationship between Nc, Rp, and H under different aerosol conditions. Future work should also be aimed at improving satellite-based estimates of the vertical displacement between the aerosol and cloud layers. Combined with MODIS retrievals, this would allow studies of ACIs in marine stratocumulus over longer timescales and larger domains than possible using in situ data alone

Observations of aerosol-cloud interactions with varying vertical separation between biomass-burning aerosols and stratocumulus clouds over the South-East Atlantic

Cloud and aerosol data collected above, within and below clouds from six research flights during the ObseRvations of Aerosols above Clouds and their intEractionS (ORACLES) field campaign in September 2016 were used to determine how the microphysical properties of marine stratocumulus clouds over the South-East Atlantic varied depending on whether overlying biomass-burning aerosols were present immediately above, or separated from cloud tops. Forty vertical cloud profiles were classified into two regimes, Mixing and Separated, according to whether the plume of densest aerosols was mixing into or separated from cloud tops. A statistical analysis showed that more numerous and smaller cloud droplets were sampled in the mixing regime with the median Nc 100 to 150 cm-3 higher and the median Re 1.5 to 2 µm lower than the separated regime. In addition, the median liquid water content (LWC) near cloud base was 0.06 g m-3 lower while similar LWC was sampled within cloud and near cloud top the LWC was 0.08 g m-3 higher. Inhomogeneous mixing near cloud top led to a decrease in the total number concentration and LWC by up to 28% and 20% respectively and led to an increase in the median volume diameter up to 29 µm for the separated regime. These patterns were observed regardless of the large-scale meteorological conditions. Precipitation suppression was observed during the mixing regime as a lower probability of high drizzle (D > 50 µm) concentration (>0.1 cm-3) was observed during the mixing regime (0.2) compared to the separated regime (0.24)

Influence of Vermiwash, Panchagavya and Weed Extract on Growth, Yield and Seed Quality Parameters of Cluster Bean (Cyamopsistetragonoloba (L).)

Cluster bean (Cyamopsistetragonoloba L.) popularly known as guar is a drought tolerant, deep-rooted, annual legume is grown for Vegetable, Food, Fodder, Green manure, Gum and as a seed. The fortification of seeds for better Growth and Yield has become important and emphasized. The study was conducted to determine the ― Effect of Vermiwash, Panchagavya and Weed Extract on Growth, Yield and Seed Quality Parameters of Cluster bean (Cyamopsistetragonoloba (L).).The experiment was carried out at Field Experimentation Centre of the Department of Genetics and Plant Breeding, Sam Higginbottom University of Agriculture, Technology &amp; Sciences. Prayagraj (UP) during Kharif-2019. The experiment was laid out in Randomised Blocked Design and comprised of 13 treatments and 3 replications. The treatments were T0 (Control),T1 – vermiwash 5% @ 12hrs, T2 - vermin wash 10% @ 12hrs, T3 - vermiwash 15% @ 12hrs, T4 -&nbsp; vermiwash 20% @ 12hrs, T5 – panchagavya 5% @ 12hrs, T6 - panchagavya 10% @ 12hrs, T7 - panchagavya 15% @ 12hrs, T8 -&nbsp; panchagavya 20% @ 12hrs, T9 – weed seed extract 5% @ 12 hrs, T10 - weed seed extract 10% @ 12 hrs, T11 – weed seed extract 15% @ 12 hrs, T12 - weed seed extract 20% @ 12 hrs. View Article DOI: 10.47856/ijaast.2021.v08i10.00

Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments

Smart homes require every device inside them to be connected with each other at all times, which leads to a lot of power wastage on a daily basis. As the devices inside a smart home increase, it becomes difficult for the user to control or operate every individual device optimally. Therefore, users generally rely on power management systems for such optimization but often are not satisfied with the results. In this paper, we present a novel multi-objective reinforcement learning framework with two-fold objectives of minimizing power consumption and maximizing user satisfaction. The framework explores the trade-off between the two objectives and converges to a better power management policy when both objectives are considered while finding an optimal policy. We experiment on real-world smart home data, and show that the multi-objective approaches: i) establish trade-off between the two objectives, ii) achieve better combined user satisfaction and power consumption than single-objective approaches. We also show that the devices that are used regularly and have several fluctuations in device modes at regular intervals should be targeted for optimization, and the experiments on data from other smart homes fetch similar results, hence ensuring transfer-ability of the proposed framework.Comment: 8 pages, 7 figures, Accepted at IEEE SMDS'202

Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe From China, the disease started spreading to the rest of the world After China, Italy became the next epicentre of the virus and witnessed a very high death toll Soon nations like the USA became severely hit by SARS-CoV-2 virus The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine of SARS-CoV-2 To identify the impact of various policies implemented by the affected countries on the pandemic spread, a myriad of AI-based models have been presented to analyse and predict the epidemiological trends of COVID-19 In this work, the authors present a detailed study of different artificial intelligence frameworks applied for predictive analysis of COVID-19 patient record The forecasting models acquire information from records to detect the pandemic spreading and thus enabling an opportunity to take immediate actions to reduce the spread of the virus This paper addresses the research issues and corresponding solutions associated with the prediction and detection of infectious diseases like COVID-19 It further focuses on the study of vaccinations to cope with the pandemic Finally, the research challenges in terms of data availability, reliability, the accuracy of the existing prediction models and other open issues are discussed to outline the future course of this stud

Evaluation and Multivariate Analysis of Cowpea [Vigna unguiculata (L.) Walp] Germplasm for Selected Nutrients—Mining for Nutri-Dense Accessions

A total of 120 highly diverse cowpea [Vigna unguiculata (L.) Walp] genotypes, including indigenous and exotic lines, were evaluated for different biochemical traits using AOAC official methods of analysis and other standard methods. The results exhibited wide variability in the content of proteins (ranging from 19.4 to 27.9%), starch (from 27.5 to 42.7 g 100 g−1), amylose (from 9.65 to 21.7 g 100 g−1), TDF (from 13.7 to 21.1 g 100 g−1), and TSS (from 1.30 to 8.73 g 100 g−1). The concentration of anti-nutritional compounds like phenols and phytic acid ranged from 0.026 to 0.832 g 100 g−1 and 0.690 to 1.88 g 100 g−1, respectively. The correlation coefficient between the traits was calculated to understand the inter-trait relationship. Multivariate analysis (PCA and HCA) was performed to identify the major traits contributing to variability and group accessions with a similar profile. The first three principal components, i.e., PC1, PC2, and PC3, contributed to 62.7% of the variation, where maximum loadings were from starch, followed by protein, phytic acid, and dietary fiber. HCA formed six distinct clusters at a squared Euclidean distance of 5. Accessions in cluster I had high TDF and low TSS content, while cluster II was characterized by low amylose content. Accessions in cluster III had high starch, low protein, and phytic acid, whereas accessions in cluster IV contained high TSS, phenol, and low phytic acid. Cluster V was characterized by high protein, phytic acid, TSS, and phenol content and low starch content, and cluster VI had a high amount of amylose and low phenol content. Some nutri-dense accessions were identified from the above-mentioned clusters, such as EC169879 and IC201086 with high protein (&gt;27%), TSS, amylose, and TDF content. These compositions are promising to provide practical support for developing high-value food and feed varieties using effective breeding strategies with a higher economic value