Translation effects in fluorine doped tin oxide thin film properties by atmospheric pressure chemical vapor deposition

In this work, the impact of translation rates in fluorine doped tin oxide (FTO) thin films using atmospheric pressure chemical vapour deposition (APCVD) were studied. We demonstrated that by adjusting the translation speeds of the susceptor, the growth rates of the FTO films varied and hence many of the film properties were modified. X-ray powder diffraction showed an increased preferred orientation along the (200) plane at higher translation rates, although with no actual change in the particle sizes. A reduction in dopant level resulted in decreased particle sizes and a much greater degree of (200) preferred orientation. For low dopant concentration levels, atomic force microscope (AFM) studies showed a reduction in roughness (and lower optical haze) with increased translation rate and decreased growth rates. Electrical measurements concluded that the resistivity, carrier concentration, and mobility of films were dependent on the level of fluorine dopant, the translation rate and hence the growth rates of the deposited films

Non-cascaded short-term pumped-storage hydro-thermal scheduling using accelerated particle swarm optimization

© 2018 IEEE. This paper presents the implementation of a variant of the famous particle swarm optimization, known as Accelerated Particle Swarm Optimization (APSO), on a non-cascaded or a two-unit hydro-thermal system with consideration of hydal pumping in light loading intervals of hydro-thermal scheduling period. APSO is an easy to program and easy to implement variant of Particle Swarm Optimization (PSO) that has the ability to converge to a good approximate to global optimum within a few iterations. A standard pumped-storage hydrothermal scheduling problem, discussed in existing literature, is considered for the implementation of APSO. A comparison of this implementation is also given with the previously existing implementations of other algorithms

Emotion classification and crowd source sensing; a lexicon based approach

In today's world, social media provides a valuable platform for conveying expressions, thoughts, point-of-views, and communication between people, from diverse walks of life. There are currently approximately 2.62 billion active users' social networks, and this is expected to exceed 3 billion users by 2021. Social networks used to share ideas and information, allowing interaction across communities, organizations, and so forth. Recent studies have found that the typical individual uses these platforms between 2 and 3 h a day. This creates a vast and rich source of data that can play a critical role in decision-making for companies, political campaigns, and administrative management and welfare. Twitter is one of the important players in the social network arena. Every scale of companies, celebrities, different types of organizations, and leaders use Twitter as an instrument for communicating and engaging with their followers. In this paper, we build upon the idea that Twitter data can be analyzed for crowd source sensing and decision-making. In this paper, a new framework is presented that uses Twitter data and performs crowd source sensing. For the proposed framework, real-time data are obtained and then analyzed for emotion classification using a lexicon-based approach. Previous work has found that weather, understandably, has an impact on mood, and we consider these effects on crowd mood. For the experiments, weather data are collected through an application-programming-interface in R and the impact of weather on human sentiments is analyzed. Visualizations of the data are presented and their usefulness for policy/decision makers in different applications is discussed

Screening of systemic fungicides and biochemicals against seed borne mycoflora associated with Momordica charantia

Study of seed borne fungi associated with bitter gourd seeds were conducted under in vitro condition in Department of Plant Pathology, Bahauddin Zakariya University, Multan, Pakistan. Two hundred (200) seed samples of Momordica charantia (bitter gourd) were collected from southern regions of Punjab province (Multan, Khanewal and Bahawalpur). Six fungal species were isolated out of which Aspergillus flavus showed highest percentage that is, 27.3% followed by Rhizopus stolonifer 17.98%, Alternaria alternata 13.34%, Aspergillus niger 5.23%, Myrothecium roridum 7.37% and Fusarium solani 6.69%. More number of fungi was observed by using blotter paper technique when compared with agar plate method. Of the three systemic fungicides used include ridomil gold MZ, bavistin, and score; and two low cost chemicals such as salicylic acid and boric acid. Ridomil gold MZ gave good results at all concentrations (20, 30 and 40 mg/10 ml) against all the isolated fungi compared with other fungicides. Salicyclic acid gave the best results against isolated fungi compared to boric acid.Key words: Myrothecium roridum, bitter gourd, salicyclic acid, southern Punjab, bavistin, Pakistan

Field traffic-induced soil compaction under moderate machine-field conditions affects soil properties and maize yield on sandy loam soil

Soil compaction due to field trafficking involves a complex interplay of machine-soil properties. In contrast to previous studies simulating worst field scenarios, this two-year field experiment investigated the effects of traffic-induced compaction involving moderate machine operational specifications (axle load, 3.16 Mg; mean ground contact pressure, 77.5 kPa) and lower field moisture contents (< field capacity) at the time of trafficking on soil physical properties, spatial root distribution, and corresponding maize growth and grain yield in sandy loam soil. Two compaction levels, i.e. two (C2) and six (C6) vehicle passes, were compared with a control (C0). Two maize (Zea mays L.) cultivars, i.e. ZD-958 and XY-335, were used. Results showed topsoil (< 30 cm) compaction with increases in bulk density (BD) and penetration resistance (PR) up to 16.42% and 127.76%, respectively, in the 10-20 cm soil layer in 2017. Field trafficking resulted in a shallower and stronger hardpan. An increased number of traffic passes (C6) aggravated the effects, and the carryover effect was found. Higher BD and PR impaired root proliferation in deeper layers of topsoil (10-30 cm) and promoted shallow horizontal root distribution. However, XY-335, compared with ZD-958, showed deeper root distribution under compaction. Compaction-induced reductions in root biomass and length densities were respectively up to 41% and 36% in 10-20 cm and 58% and 42% in the 20-30 cm soil layer. Consequent yield penalties (7.6%-15.5%) underscore the detriments of compaction, even only in topsoil. In crux, despite their low magnitude, the negative impacts of field trafficking under moderate machine-field conditions after just two years of annual trafficking foreground the challenge of soil compaction

An Adaptive Distributed Averaging Integral Control Scheme for Micro-Grids with Renewable Intermittency and Varying Operating Cost

The increasing penetration of intermittent renewable energy resources in micro-grids poses several issues, such as stochastic power generation, demand and supply miss-match, frequency fluctuation, and economic dispatch problems. To address such critical issues, a distributed secondary control scheme based for micro-grids with varying operating cost and intermittent renewable energy resources is proposed for frequency regulation and economic load dispatch. The paper presents an adaptive distributed averaging integral control scheme with conditional uncertainties, namely varying operating costs, and renewable intermittency. The proposed control scheme adapts to the uncertainties by updating the control law parameters dynamically and can maintain overall network stability. The distributed control scheme employs communication channels for exchange of generation data from the neighboring power units for optimal power sharing and consensus among the power units. An additional controller at tertiary control layer of the hierarchical control architecture is also augmented in the control structure to economically dispatch the load and the consensus-based algorithm guarantees optimal load sharing. The proposed communication based control scheme reveals the best combination of performance and flexibility. A performance-based comparative analysis is also presented, validating the effectiveness of the proposed control scheme compared to the prior works. The robustness and performance of the proposed control scheme is illustrated through computer simulations

Dynamic Profiling of β-Coronavirus 3CL Mpro Protease Ligand-Binding Sites

β-coronavirus (CoVs) alone has been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a backup against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensable role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand-protein interactions. Herein, we provide a comprehensive comparison of all nonredundant ligand-binding sites available for SARS-CoV2, SARS-CoV, and MERS-CoV Mpro. Extensive adaptive sampling has been used to investigate structural conservation of ligand-binding sites using Markov state models (MSMs) and compare conformational dynamics employing convolutional variational auto-encoder-based deep learning. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across β-CoV homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor

Dynamic Profiling of β-Coronavirus 3CL M^proProtease Ligand-Binding Sites

Data availability statement: The trajectories of Mpro simulations and models of the metastable states can be downloaded from 10.5281/zenodo.4782284.β-coronavirus (CoVs) alone has been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a backup against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensable role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand–protein interactions. Herein, we provide a comprehensive comparison of all nonredundant ligand-binding sites available for SARS-CoV2, SARS-CoV, and MERS-CoV Mpro. Extensive adaptive sampling has been used to investigate structural conservation of ligand-binding sites using Markov state models (MSMs) and compare conformational dynamics employing convolutional variational auto-encoder-based deep learning. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across β-CoV homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor.There was no funding for this wor

Laboratory scale optimization of alkali pretreatment for improvingenzymatic hydrolysis of sweet sorghum bagasse

Sweet sorghum has been identified as a promising feedstock for biological conversion to fuels as wellas other chemicals. The lignocellulosic stalk of sweet sorghum, called sweet sorghum bagasse (SSB) isa potential source of lignocellulosic biofuel. The primary goal of this study was to determine optimalalkali (lime: Ca(OH)2and lye: NaOH) pretreatment conditions to obtain higher yield of total reducingsugar while reducing the lignin content for biofuel production from SSB. Biomass conversion and ligninremoval were simultaneously optimized through four quadratic models analyzed by response surfacemethodology (RSM). The optimal conditions for lime pretreatment was 1.7% (w/v) lime concentration,6.0% (w/v) SSB loading, 2.4 h pretreatment time with predicted yields of 85.6 total biomass conversionand 35.5% lignin reduction. For lye pretreatment, 2% (w/v) alkali, 6.8% SSB loading and 2.3 h durationwere the optimal levels with predicted biomass conversion and lignin reduction of 92.9% and 50.0%,respectively. More intensive pretreatment conditions removed higher amounts of hemicelluloses andcellulose. Fourier transform infrared spectroscopy (FTIR) spectrum and scanning electron microscope(SEM) image revealed compositional and microstructural changes caused by the alkali pretreatment