Protagonist of Mineral Nutrients in Drought Stress Tolerance of Field Crops

The food demand is increasing hastily, that is inducing continuous pressure on agriculture sector and industries to fulfill rising dietary needs. To meet with increasing demand, the food production must be elevated up to 70% until the year 2050. On the other hand, changing climate is disturbing crop production around the World. Crops grown under field conditions are affected by more than one abiotic stress. It is continuous task and challenge for agronomists to make crops environment hardy to obtain maximum yield. It is considered that different agronomic managements, if done appropriately, could be beneficial for increasing crop production. The optimal provision of plant nutrients can assist the crops to fight in better way with environmental stress like drought; it can help them to continue their normal metabolism even under hostile abiotic circumstances. The regions that have reduced availability of water for crop production, a balanced nutrient management can assist crops to give adequate production. Some of nutrients have potential of not only maintaining plant metabolism but also to enhance the quality of product. This chapter highlights the protagonist of plant nutrients in alleviation of drought stress in field crops

Applying deep neural networks for user intention identification

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. The social media revolution has provided the online community an opportunity and facility to communicate their views, opinions and intentions about events, policies, services and products. The intent identification aims at detecting intents from user reviews, i.e., whether a given user review contains intention or not. The intent identification, also called intent mining, assists business organizations in identifying user’s purchase intentions. The prior works have focused on using only the CNN model to perform the feature extraction without retaining the sequence correlation. Moreover, many recent studies have applied classical feature representation techniques followed by a machine learning classifier. We examine the intention review identification problem using a deep learning model with an emphasis on maintaining the sequence correlation and also to retain information for a long time span. The proposed method consists of the convolutional neural network along with long short-term memory for efficient detection of intention in a given review, i.e., whether the review is an intent vs non-intent. The experimental results depict that the performance of the proposed system is better with respect to the baseline techniques with an accuracy of 92% for Dataset1 and 94% for Dataset2. Moreover, statistical analysis also depicts the effectiveness of the proposed method with respect to the comparing methods

Applying Deep Neural Networks for Predicting Dark Triad Personality Trait of Online Users

© 2020 IEEE. In the recent times, the social networking sites act as a rich source of information, which is shared among online users, who post comments and express their opinions in the form of likes and dislikes. Such content reflects important clues about the personality and behavior of the online community. The dark triad personality traits, such as the psychopathic behavior of individuals, can be detected using computational models. The earlier studies on the dark triad (psychopath) prediction exploit traditional machine learning techniques with limited dataset size. Therefore, it is required to develop an advanced deep neural network-based technique. In this work, we implement a deep neural network model, namely BILSTM for the efficient prediction of dark triad (psychopath) personality traits regarding online users. Experimental results depict that the proposed model attained an improved AUC (0.82) when compared to the baseline study

A survey on sentiment analysis in Urdu: A resource-poor language

© 2020 Background/introduction: The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. Methods: We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Results and conclusions: Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis

Pregnancy and neonatal outcomes of COVID-19: The PAN-COVID study

Objective To assess perinatal outcomes for pregnancies affected by suspected or confirmed SARS-CoV-2 infection. Methods Prospective, web-based registry. Pregnant women were invited to participate if they had suspected or confirmed SARS-CoV-2 infection between 1st January 2020 and 31st March 2021 to assess the impact of infection on maternal and perinatal outcomes including miscarriage, stillbirth, fetal growth restriction, pre-term birth and transmission to the infant. Results Between April 2020 and March 2021, the study recruited 8239 participants who had suspected or confirmed SARs-CoV-2 infection episodes in pregnancy between January 2020 and March 2021. Maternal death affected 14/8197 (0.2%) participants, 176/8187 (2.2%) of participants required ventilatory support. Pre-eclampsia affected 389/8189 (4.8%) participants, eclampsia was reported in 40/ 8024 (0.5%) of all participants. Stillbirth affected 35/8187 (0.4 %) participants. In participants delivering within 2 weeks of delivery 21/2686 (0.8 %) were affected by stillbirth compared with 8/4596 (0.2 %) delivering ≥ 2 weeks after infection (95 % CI 0.3–1.0). SGA affected 744/7696 (9.3 %) of livebirths, FGR affected 360/8175 (4.4 %) of all pregnancies. Pre-term birth occurred in 922/8066 (11.5%), the majority of these were indicated pre-term births, 220/7987 (2.8%) participants experienced spontaneous pre-term births. Early neonatal deaths affected 11/8050 livebirths. Of all neonates, 80/7993 (1.0%) tested positive for SARS-CoV-2. Conclusions Infection was associated with indicated pre-term birth, most commonly for fetal compromise. The overall proportions of women affected by SGA and FGR were not higher than expected, however there was the proportion affected by stillbirth in participants delivering within 2 weeks of infection was significantly higher than those delivering ≥ 2 weeks after infection. We suggest that clinicians’ threshold for delivery should be low if there are concerns with fetal movements or fetal heart rate monitoring in the time around infection

Co-Combination of Pregabalin and Withania coagulans-Extract-Loaded Topical Gel Alleviates Allodynia and Hyperalgesia in the Chronic Sciatic Nerve Constriction Injury for Neuropathic Pain in Animal Model

The current study reports the fabrication of co-combination gel using Pregabalin and Withania coagulans fruit extract to validate its effectiveness for neuropathic pain in chronic constriction injury (CCI) rat models. Three topical gels were prepared using Carbopol 934 through a pseudo-ternary phase diagram incorporating the Pregabalin (2.5%), Withania coagulans extract (2%), and co-combination of both Pregabalin (2.5%) and Withania coagulans extract (2%). Gels were characterized. FTIR showed a successful polymeric network of the gel without any interaction. The drug distribution at the molecular level was confirmed by XRD. The AFM images topographically indicated the rough surface of gels with a size range from 0.25 to 330 nm. DSC showed the disappearance of sharp peaks of the drug and extract, showing successful incorporation into the polymeric network of gels. The in vitro drug release of co-combination gel was 73% over 48 h. The mechanism of drug release by combination gel was Higuchi+ fickian with values of n (0.282) and R2 (0.947). An in vivo study for pain assessment via four methods: (i) heat hyperalgesia, (ii) cold allodynia, (iii) mechano-hyperalgesia, and (iv) dynamic mechano-allodynia, confirmed that topical treatment with co-combination gel reduced the pain significantly as indicated by the p value: R1 (p < 0.001), R2 (p < 0.001), R3 (p < 0.015), and R4 (p < 0.0344). The significance order was R2 (****) > R1 (***) > R3 (**) > R4 (*) > R5 (ns)

A Systematic Literature Review of Personality Trait Classification from Textual Content

The day-to-day use of digital devices with Internet access, such as tablets and smartphones, has increased exponentially in recent years and this has had a consequent effect on the usage of the Internet and social media networks. When using social networks, people share personal data that is broadcast between users, which provides useful information for organizations. This means that characterizing users through their social media activity is an emerging research area in the field of Natural Language Processing (NLP) and this paper will present a review of how personality can be detected using online content

A Comparison of Central Composite Design and Taguchi Method for Optimizing Fenton Process

In the present study, a comparison of central composite design (CCD) and Taguchi method was established for Fenton oxidation. Dyeini, Dye : Fe+2, H2O2 : Fe+2, and pH were identified control variables while COD and decolorization efficiency were selected responses. L9 orthogonal array and face-centered CCD were used for the experimental design. Maximum 99% decolorization and 80% COD removal efficiency were obtained under optimum conditions. R squared values of 0.97 and 0.95 for CCD and Taguchi method, respectively, indicate that both models are statistically significant and are in well agreement with each other. Furthermore, Prob > F less than 0.0500 and ANOVA results indicate the good fitting of selected model with experimental results. Nevertheless, possibility of ranking of input variables in terms of percent contribution to the response value has made Taguchi method a suitable approach for scrutinizing the operating parameters. For present case, pH with percent contribution of 87.62% and 66.2% was ranked as the most contributing and significant factor. This finding of Taguchi method was also verified by 3D contour plots of CCD. Therefore, from this comparative study, it is concluded that Taguchi method with 9 experimental runs and simple interaction plots is a suitable alternative to CCD for several chemical engineering applications

Why Severity Rate of COVID-19 is High in Patients with Diabetes Mellitus: A Brief Insight

Novel Coronavirus disease 2019 (nCOVID-19) a global pandemic is an ever-remaining threat for patients with Diabetic Mellites (DM). Herein, we have tried to provide brief insight to critically analyze the reasons causing the severity of Coronavirus disease (COVID-19) in patients diagnosed with DM. This, mini review highlights the key investigations starting from binding of COVID-19 at the cellular surface to create sever infection or even death in DM patients. The study further suggested to pay urgent attention towards stabilization of deadly immune response arises as a result of COVID-19. We hope the highlighted investigation will help the researchers to understand and develop a road map to deal DM patients infected with COVID-19 to minimize the severity rate.</p