Drought is an abiotic stress that decreases crop photosynthesis, growth, and yield. Ascorbic acid has been used as a seed preconditioning agent to help mitigate drought in some species, but not yet in broccoli (Brassica oleracea var. italica). The objective was to investigate the effect of ascorbic acid on growth, photosynthesis, and related parameters in watered and drought-stressed broccoli seedlings. A 2 × 4 factorial experiment was designed where stress (watered or drought) was the first factor and ascorbic acid preconditioning (untreated, 0 ppm, 1 ppm, or 10 ppm) was the second factor. Positioning within the greenhouse was included as a blocking factor and the experiment was replicated three times. All seedlings were watered for 8 weeks and then half had water withheld for 7 days to impose drought while the other half continued to be watered. Ascorbic acid preconditioning increased shoot dry mass, root dry mass, water use efficiency, and photosynthesis in all seedlings while also increasing chlorophyll, relative water content, and leaf area in droughted seedlings. Ascorbic acid preconditioning also decreased membrane injury in droughted seedlings to the point that membrane injury was not significantly different than the watered control. There was strong evidence to support ascorbic acid as a successful seed preconditioning agent in watered and droughted broccoli.

Social media is a great platform that contains a pool of information combined with people. This can be used to analyze data for better results for an organization. The objective of the paper is to study data analytics and how this concept can be used in social media text analysis. Natural language processing is used for text analysis, information extraction, etc. This survey paper mainly focuses on understanding the need for data analytics in text analysis through various domains like governance, politics, and rural development. Classification and clustering techniques derived from machine learning that helps to extract important information through various algorithms to help companies, organizations, governing bodies understand their audience properly and work according to their needs. The paper consists of a detailed analysis of clustering and classification algorithms over a wide variety of domains and compares the results of the performance metrics. According to the research done by the authors, it is found that Fine-tuning BERT classifier gives the highest accuracy among other classification algorithms which includes Naïve Bayes Classifier, Decision Tree, and Support Vector Machine (SVM). The study shows that clustering has been used for finding sets of similar words, sentences, word sense, etc. These concepts can be used for solving problems related to e-governance as these authorities can deploy such methods to understand their people and work according to their needs.

No abstract

In legal domain Name Entity Recognition serves as the basis for subsequent stages of legal artificial intelligence. In this paper, the authors have developed a dataset for training Name Entity Recognition (NER) in the Indian legal domain. As a first step of the research methodology study is done to identify and establish more legal entities than commonly used named entities such as person, organization, location, and so on. The annotators can make use of these entities to annotate different types of legal documents. Variety of text annotation tools are in existence finding the best one is a difficult task, so authors have experimented with various tools before settling on the best one for this research work. The resulting annotations from unstructured text can be stored into a JavaScript Object Notation (JSON) format which improves data readability and manipulation simple. After annotation, the resulting dataset contains approximately 30 documents and approximately 5000 sentences. This data further used to train a spacy pre-trained pipeline to predict accurate legal name entities. The accuracy of legal names can be increased further if the pre-trained models are fine-tuned using legal texts.

Using games for teaching-Learning include features that engage the learners. Gamified instruction builds learning for the better in many ways. First, it empowers students to be responsible for their learning. Well-designed games are particularly effective at keeping reluctant learners engaged because they keep the learner close to but not over their capacity threshold. Second, gamified methods help students maintain a skillful mindset when encountering new obstacles. Moreover finally, gamified instructional techniques build on the ways games boost a player to survive in the face of challenges to help students better overcome hurdles in their learning environment. Engagement of the students in teaching-learning activities plays an important role, but only engaging the students and fun are insufficient, or it could not be the aim. The accomplishment of learning objectives is significant. So games should also be associated with the skill to be evaluated, and evaluation patterns decided by the teacher. During or after the conduction of the game, the teacher should collect the pieces of evidence; must interpret them to get a value with a view to action.