Rice Crop Responses to Global Warming: An Overview

The mean temperature might rise up to range of 2.0–4.5 °C worldwide by the end of this century. Beside from this, a prediction has been made that rise in minimum night temperature will be at a quicker rate as compare to the maximum day temperature. Rising temperatures not only affect the crop growth process, but also lead to direct changes in other environmental factors and pose indirect effect on yield and quality of rice has been observed, so at the present stage, it aroused public attention. Breeds, including through breeding and biotechnology to improve high temperature tolerance of rice help to mitigate the negative effects of high temperature, however, progress in this area have been slow. By adopting different methods like sowing, water and nutrient management can also to some extent mitigate the effects of high temperature on rice performance, but in most cases, these techniques are influenced by many factors, such as crop rotation, irrigation and other constraints like their applications are hard to applied to large area. Therefore, this chapter addresses (1) empirical reduction of rice yield (2) highlights the key significant mechanisms that influence main grain quality attributes under high temperature stress (3) inducing stress resistance and adopting mitigation strategies for high performance of rice

What Explains Natives and Sojourners Preventive Health Behavior in a Pandemic: Role of Media and Scientific Self-Efficacy

The COVID-19 pandemic triggered a severe global public health emergency. The current research investigated and compared “Natives and Sojourners” health-protective behavior in Mainland China during the pandemic. We adopted a unified view to propose our theoretical model by adapting the Health Belief Model (HBM) and Institutional Theory (IT). The data obtained through an online survey questionnaire from 435 respondents during the second and third quarters of were analyzed. Structural equation modeling (SEM) was used to empirically analyze the proposed model. The media self-efficacy (MSE), scientific self-efficacy (SSE), perceived health risks (PHRs), and the perceived benefits of being protected have positive and significant effects on the definition of health-protective behavioral intentions among natives and sojourners in mainland China. Media and SSE can play a strategic role in formulating public health-protective behavior. The current research recommends an effective communication with sojourners during crisis for them to be a part of the national crisis management plan (i.e., infectious disease)

Entrepreneurs and Environmental Sustainability in the Digital Era: Regional and Institutional Perspectives

Climate change and environmental degradation have negatively affected the sustainable development of mankind. The “green” concept has been gradually accepted by the public, thereby strongly promoting “green” business forms and social innovation. This study adopts related information and technology knowledge and experience and warm glow (altruistic value) for business initiatives as push factors, market opportunity (MO) and personal innovativeness (PI) in technology as pull factors, and institutional theory (regulatory support and normative support) as mooring factors. These factors are employed to analyze the switching intentions of individuals toward green entrepreneurship, which is a new persuasive psychological model based on Push–Pull–Mooring model (PPM). The survey questionnaires are collected from a total of 1562 respondents through WeChat in mainland China. The study findings present all variables that significantly affect individuals’ switching intentions toward green entrepreneurs. PI exhibits the most significant impact on intention of individuals toward green entrepreneurship, while the interaction between the mooring factor and MO on switching intentions to green entrepreneurship is relatively weak. Finally, the study contributes theoretical and practical implications for increasing intentions toward green entrepreneurship

Structural Performance of GFRP Bars based High-Strength RC Columns: An Application of Advanced Decision-Making Mechanism for Experimental Profile Data

Several past studies have shown the use of glass fibre-reinforced polymer (GFRP) bars to alleviate the reinforced steel rusting issue in different concrete structures. However, the practise of GFRP bars in concrete columns has not yet achieved a sufficient confidence level due to the lack of a theoretical model found in the literature. The objective of the current study is to introduce a novel prediction model for the axial capability of concrete columns made with bars of GFRP. For this purpose, two different approaches, such as data envelopment analysis (DEA) and artificial neural networks (ANNs) modelling, are used on a collected dataset of 266 concrete column specimens made with GFRP bars from previous literature works. Eight parameters were used to predict the axial performance of GFRP-based RC columns. The proposed DEA and ANNs predictions demonstrated a good correlation with the testing dataset, having R2 values of 0.811 and 0.836, respectively. A comparative analysis of the DEA and ANNs models is undertaken, and it was found that the suggested models are capable of accurately forecasting the structural response of GFRP-made RC column structures. Then, a comprehensive parametric analysis of 266 GFRP-based columns was performed to study the effect of different materials and their geometrical shape.publishedVersio

COVID-19 Patient Count Prediction Using LSTM

IEEE In December 2019, a pandemic named COVID-19 broke out in Wuhan, China, and in a few weeks, it spread to more than 200 countries worldwide. Every country infected with the disease started taking necessary measures to stop the spread and provide the best possible medical facilities to infected patients and take precautionary measures to control the spread. As the infection spread was exponential, there arose a need to model infection spread patterns to estimate the patient volume computationally. Such patients\u27 estimation is the key to the necessary actions that local governments may take to counter the spread, control hospital load, and resource allocations. This article has used long short-term memory (LSTM) to predict the volume of COVID-19 patients in Pakistan. LSTM is a particular type of recurrent neural network (RNN) used for classification, prediction, and regression tasks. We have trained the RNN model on Covid-19 data (March 2020 to May 2020) of Pakistan and predict the Covid-19 Percentage of Positive Patients for June 2020. Finally, we have calculated the mean absolute percentage error (MAPE) to find the model\u27s prediction effectiveness on different LSTM units, batch size, and epochs. Predicted patients are also compared with a prediction model for the same duration, and results revealed that the predicted patients\u27 count of the proposed model is much closer to the actual patient count

GMO/GMF on Social Media in China: Jagged Landscape of Information Seeking and Sharing Behavior through a Valence View

The study examines the critical factors affecting Chinese social media (SM) users’ intentions and behavior to seek and share information on genetically modified organisms/ genetically modified food (GMO/GMF). The proposed framework was conceptualized through benefit-risk analysis and subsequently mapped SM users’ perceived benefits and risks to seeks and share information using Kurt Lewin’s valence view. Quantitative data was collected using survey questionnaires administered from 583 SM users. The results of the path analysis demonstrated two key findings related to SM users’ perceived benefits and risks to seek and share information on GMO/GMF. Among risks, the psychological risk is the strongest predictor of perceived risk to use SM for GMO/GMF, which consequently determines the intentions and behaviors to share information about GMO/GMF on SM in People’s Republic of China. Among benefits, the results showed that perceived usefulness, creditability of GMO/GMF information, and information support are positively related to perceived benefits to use SM for GMO/GMF, which subsequently, predicts the intentions and behaviors to seek information about GMO/GMF on SM. This study suggests scholars and practitioners explore and utilize the efficient communication strategy to fulfill the potential of the SM to increase GMO/GMF acceptance in Chinese society

Structured knowledge creation for Urdu language: A DBpedia approach

Wikipedia information is extracted by DBpedia and linked to other web resources as Linked Open Data, which is an important contribution to the field of semantics. As part of its internationalisation endeavour, DBpedia now has 20 language chapters that have been mapped to it; nonetheless, there have been very few attempts from Urdu. This article outlines the procedures and highlights the efforts put forward as the first contribution to the manual creation of Urdu mappings with DBpedia Ontology classes. Our approach led to an increase in the number of mapped infoboxes, thus enhancing the DBpedia. The mapping procedure is broken down into two parts. The infobox template is first mapped to the DBpedia ontology's relevant class, and then the attributes of the infobox are mapped to the properties of that class. In addition, alongside other mapped languages, Urdu labels are included to the description of Ontology classes. We have covered around a thousand properties and attributes of Urdu with English DBpedia Ontology on DBpedia mapping server