Ultrasonicated synthesis of 1-(2-hydroxyaryl)-3-(pyrrolidin-1-yl)-prop-2-en-1-ones and their antimicrobial screening

Abstract: A facile synthesis of title compounds has been carried out under ultrasound irradiation. The main advantages of the present procedure are shorter reaction time and higher yield. Products have been characterized by IR, PMR, CMR, GCMS study and screened for their antimicrobial activity

TOPIC CLASSIFICATION USING HYBRID OF UNSUPERVISED AND SUPERVISED LEARNING

There has been research around the idea of representing words in text as vectors and many models proposed that vary in performance as well as applications. Text processing is used for content recommendation, sentiment analysis, plagiarism detection, content creation, language translation, etc. to name a few. Specifically, we want to look at the problem of topic detection in text content of articles/blogs/summaries. With the humungous amount of text content published each and every minute on the internet, it is imperative that we have very good algorithms and approaches to analyze all the content and be able to classify most of it with high confidence for further use. The project aims to work with unsupervised and supervised machine learning algorithms in an effort to tackle the topic detection problem. The project will target various unsupervised learning algorithms like Word2vec, doc2vec and LDA for corpus and language dictionary learning to have a trained model which understand the semantic of texts. The objective of the project is to combine this unsupervised learning with supervised learning algorithms like Support Vector Machine and deep learning methods to analyze and hopefully better the performance in terms of accuracy of topic detection. The project also aims at performing user interest-based modelling, which is orthogonal to topics modeling. The idea is to make sure the model is free of predefined categories. The project results show that hybrid models are comfortably accurate when classifying text in a particular topic category. The project also concludes that user interest modelling can also be accurately achieved along with topic detection. The project successfully determines these results without any meta information about the input text and purely based on the corpus of the input text. This makes the project framework really robust as it has no dependency on source of text, length of text or any other meta information about the text content

Advance Authentication Technique: 3D Password

Providing more security to any system requires providing any authentication strategy to that system. There are many authentication strategies, such as textual password, graphical password, etc. But these techniques have some limitation and drawback like they can easily hacked or cracked by using various tools. One of the tools is brute-force algorithm. So, to overcome the drawbacks of existing authentication technique, a new improved authentication strategy is proposed. This strategy is multi-password and multi-factor authentication system as it combines a various authentication techniques such as textual password, graphical password etc. Most important part of 3d password scheme is inclusion of 3d virtual environment. This authentication Strategy is more advanced than any other schemes as we can combine existing schemes. Also this Strategy is tough to break & easy to use. This paper introduced contribution towards 3D Password to make it more secure & more user-friendly to users of all categories

Improvement of agronomicaly desirable genotypes for downy mildew disease resistance in Pearl millet [Pennisetum glaucum (L.) R. Br.] By recombination breeding

In the present study gene action assessed by hybridizing the five female lines (one resistant and four susceptible) and eight male lines (six resistant and two susceptible) in Line x tester design was studied. The study indicated additive gene action playing predominant role in the control of yield and four yield contributing traits in addition to downy mildew resistance while fodder yield and days to 50 per cent flowering were controlled by non-additive gene effects. Among the 40 crosses, APMB 89 x APMR 70 and APMB 89 had non-significant sca and highly significant gca for yield and yield contributing traits along with downy mildew resistance and it is therefore, expected to throw superior segregants for the development of lines with desirable traits

Receptivity of injured and aged coconut petiole for oviposition by red palm weevil Rhynchophorus ferrugineus (Coleoptera: Curculionidae)

Red palm weevil, Rhynchophorus ferrugineus (Coleoptera: Curculionidae) is a key pest of palm-based ecosystem. Globally R.  ferrugineus is reported from 50 countries infecting 40 palm species. R. ferrugineus are attracted to wounded, damaged, dying palms or apparently healthy palms. R. ferrugineus gains entry into a palm when female weevils are drawn to palm tissue volatiles to lay eggs. The females use the rostrum to bore into palm tissue to form a hole for oviposition. Because of its cryptic feeding habit management of R. ferrugineus is difficult leading to death of palms. The laboratory study was conducted with the aim to assess the ovipositional preference of injured and aged coconut petiole (var. Benaulim) to red palm weevil R. ferrugineus (Coleoptera: Curculionidae). Results reveals that freshly injured coconut petiole was most preferred for egg laying by R. ferrugineus (mean egg lay: 4.11) and was statistically at par with one and two-day old, injured coconut petiole, indicating that injuries and wounds on coconut petiole between 0-2 days after damage emit palm volatiles that are most attractive to female R. ferrugineus adults for egg laying. It concludes that injured part should be treated with effective insecticides immediately after damage to prevent further losses

NASLMRP: Design of a Negotiation Aware Service Level Agreement Model for Resource Provisioning in Cloud Environments

Cloud resource provisioning requires examining tasks, dependencies, deadlines, and capacity distribution. Scalability is hindered by incomplete or complex models. Comprehensive models with low-to-moderate QoS are unsuitable for real-time scenarios. This research proposes a Negotiation Aware SLA Model for Resource Provisioning in cloud deployments to address these challenges. In the proposed model, a task-level SLA maximizes resource allocation fairness and incorporates task dependency for correlated task types. This process's new tasks are processed by an efficient hierarchical task clustering process. Priority is assigned to each task. For efficient provisioning, an Elephant Herding Optimization (EHO) model allocates resources to these clusters based on task deadline and make-span levels. The EHO Model suggests a fitness function that shortens the make-span and raises deadline awareness. Q-Learning is used in the VM-aware negotiation framework for capacity tuning and task-shifting to post-process allocated tasks for faster task execution with minimal overhead. Because of these operations, the proposed model outperforms state-of-the-art models in heterogeneous cloud configurations and across multiple task types. The proposed model outperformed existing models in terms of make-span, deadline hit ratio, 9.2% lower computational cycles, 4.9% lower energy consumption, and 5.4% lower computational complexity, making it suitable for large-scale, real-time task scheduling

Role of Artificial Intelligence in Cardiovascular Risk Prediction and Prevention

Globally, cardiovascular diseases (CVDs) continue to be the leading cause of death, making precise risk assessment and efficacious preventative measures imperative. Although essential, traditional cardiovascular risk assessment instruments like the Framingham Risk Score have shortcomings when it comes to precisely identifying individual risks. The use of Artificial Intelligence (AI) into the prediction of cardiovascular risk presents a revolutionary strategy to overcome these constraints. Artificial Intelligence (AI), which includes deep neural networks and machine learning algorithms, improves risk assessment through the analysis of large datasets, allowing for personalised risk forecasts that go beyond traditional risk indicators. The transition from population-based risk assessment to individualised profile is signalled by this integration, which will increase accuracy and facilitate prompt actions. AI-powered models outperform conventional approaches in detecting complex risk variables and trends, providing higher forecasting accuracy. These models provide personalised risk profiles by utilising a variety of data sources, such as lifestyle, medical imaging, and genetic information. This allows for more focused preventative actions. In addition, AI applications in preventive cardiology include risk assessment, customised care plans, and early diagnosis via sophisticated imaging analysis. Widespread adoption is hampered, nevertheless, by issues with data quality, AI model interpretability, generalizability across different populations, and ethical issues. In order to fully utilise AI to transform preventive cardiology and emphasise openness, morality, and ongoing technological breakthroughs, it will be essential to overcome these obstacles

Folic Acid Supplementation for Women of Childbearing Age versus Supplementation for the General Population: A Review of the Known Advantages and Risks

This paper focuses on the current best-evidence-based clinical practices and controversies surrounding folic acid supplementation/fortification for the prevention of neural tube defects (NTDs) during early pregnancy. The paper also discusses the controversies surrounding the effect of folic acid on the prevention as well as the promotion of cancer. Sufficient data is available to safely conclude that folic acid reduces the risk of NTDs during pregnancy; however, a safe dosage has not yet been calculated for the rest of the population. More research is necessary to study the complete role of folic acid in human growth and development

Grain Size Induced Metal-insulator Transition in La0.7Sr0.3MnO3 Compounds

The effect of different synthesis techniques on structural, microstructural and electrical properties of La0.7Sr0.3MnO3 (LSMO) was investigated. Two different techniques viz solid state reaction (SSR) and sol-gel (SG) method were used to prepare the samples. X-ray studies have confirmed the single phase formation of LSMO by both the techniques. The average grain size was 2 m for solid state reaction sample and 22 nm for sol-gel sample. A substantial enhancement in electrical resistivity was observed in the sample with nanosized grains. The micro grain size sample exhibited metallic behaviour whereas nanoparticle sample showed metal-insulator transition around 250 K. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3554