A New Approach for Mining Correlated Frequent Subgraphs

Nowadays graphical datasets are having a vast amount of applications. As a result, graph mining—mining graph datasets to extract frequent subgraphs—has proven to be crucial in numerous aspects. It is important to perform correlation analysis among the subparts (i.e., elements) of the frequent subgraphs generated using graph mining to observe interesting information. However, the majority of existing works focuses on complexities in dealing with graphical structures, and not much work aims to perform correlation analysis. For instance, a previous work realized in this regard, operated with a very naive raw approach to fulfill the objective, but dealt only on a small subset of the problem. Hence, in this article, a new measure is proposed to aid in the analysis for large subgraphs, mined from various types of graph transactions in the dataset. These subgraphs are immense in terms of their structural composition, and thus parallel the entire set of graphs in real-world. A complete framework for discovering the relations among parts of a frequent subgraph is proposed using our new method. Evaluation results show the usefulness and accuracy of the newly defined measure on real-life graphical datasets.

Machine Learning Approach

Ayurveda medicines uses herbs for curing many ailments without side effects. The biggest concern related to Ayurveda medicine is extinction of many important medicinal herbs, which may be due to insufficient knowledge, weather conditions, and urbanization. Another reason consists of lack of online facts on Indian herbs because it is dependent on books and experts. This concern has motivated in utilizing the machine learning techniques to identify and reveal few details of Indian medicinal herbs because, until now, it is identified manually, which is cumbersome and may lead to errors. Many researchers have shown decent results in identifying and classifying plants with good accuracy and robustness. But no complete framework and strong evidence is projected on Indian medicinal herbs. Accordingly, the chapter aims to provide an outline on how machine learning techniques can be adopted to enrich the knowledge of Indian herbs, which advantages both common man and the domain experts with wide information on traditional herbs.

Toolkit for Conservation of Urban Biodiversity

Cities present multiple opportunities to create a more sustainable future by way of enhancing resource-efficiency and fostering innovation and political and social responsibility. The imperative for biodiversity in cities therefore goes beyond simple conservation to considerations of internalizing provisions of ecosystem services, which would otherwise be sought from outside the city. By presenting a practical approach to biodiversity planning and management, this toolkit seeks to help local governments harness available resources and opportunities to address global biodiversity loss by providing them a baseline of biodiversity, which would further help them to prepare local biodiversity strategy and action plan under the mandate of Biological Diversity Act 2002 providing the scope to municipal corporations to perform all activities relevant to overall biodiversity management. The study proposes a complete framework for formulating LBSAP using the existing tools for biodiversity assessment and how it can be incorporated into the city development plan for effective implementation.

Conducting Interviews during the COVID-19 Pandemic and Beyond

The use of telephone and VoIP (Voice over Internet Protocol) interviews has become necessary owing to the legal restrictions and safety measures introduced during the COVID-19 pandemic. This has furthered scholarly dialogue surrounding the choice of interview mode, and the assumption that face-to-face interviews are the "gold standard" [Novick, 2008: 397]. The general public has also become more accustomed to utilising VoIP in their day-to-day lives. In this article, I discuss this change in communication methods and the impact it could have on interview modes. Nevertheless, the researcher's and participant's contexts remain paramount when deciding which interview mode to employ. For this reason, Oltmann's model [Oltmann, 2016] has been extended to include VoIP, providing a more complete framework for researchers to make an informed decision when contemplating interview modes.

Evaluating cloud interactions with costs and SLAs

In this paper, we investigate how to improve the profits in cloud infrastructures by using price schemes and analyzing the user interactions with the cloud provider. For this purpose, we consider two different types of client behavior, namely regular and high-priority users. Regular users do not require a continuous service, and they can wait to be attended to. In contrast, high-priority users require a continuous service, e.g., a 24/7 service, and usually need an immediate answer to any request. A complete framework has been implemented, which includes a UML profile that allows us to define specific cloud scenarios and the automatic transformations to produce the code for the cloud simulations in the Simcan2Cloud simulator. The engine of Simcan2Cloud has also been modified by adding specific SLAs and price schemes. Finally, we present a thorough experimental study to analyze the performance results obtained from the simulations, thus making it possible to draw conclusions about how to improve the cloud profit for the cloud studied by adjusting the different parameters and resource configuration.

Polynomial inflation and dark matter

We present a minimal UV complete framework to embed inflation and dark matter by extending the standard model with a singlet real scalar field (the inflaton) and a singlet fermionic field acting as dark matter. The inflaton features the most general renormalizable polynomial up to quartic order, which is flat due to the existence of a perturbed inflection-point, comfortably fitting CMB measurements. We also analyze (p)reheating by considering the Higgs production via inflaton decay. In the early universe, dark matter can be generated by the mediation of gravitons or inflatons. However, the production via the direct decay of the inflatons dominates, making viable a large range of dark matter masses, from $${\mathcal {O}}(10^{-5})$$ O ( 10 - 5 ) GeV to $${\mathcal {O}}(10^{11})$$ O ( 10 11 ) GeV.

EFFECT OF EDUCATIONAL BACKGROUND AND WORK EXPERIENCE ON COUNSELOR’S WORK ENGAGEMENT

Work engagement is one of the dimensions that contribute to the development of professional identity. This article aims to describe 1) the level of work involvement of the counselor, and 2) the influence of work experience and educational background on counselors' work engagement. The research method used is comparative. A total of 211 school counselors participated as online-incidental sampling respondents. The research respondent was asked to be involved by sharing the invitation link to several counselor social media groups in Indonesia. The instrument used is a work engagement scale adapted from the Utrecht Work Engagement Scale. The results of the instrument reliability test showed a score of 0.88. The data were analyzed by inferential statistics using a two-way analysis of variance (two-way ANOVA). The conclusions of this study are the average work engagement of 211 respondents in this study is in the high category and there is no effect of work experience and educational background on counselor work engagement. The results of this study serve as preliminary findings of a complete framework for further research on the professional identity of school counselors in Indonesia.

Taxonomy Analysis in Bacteria Kingdom based on Protein Domain: A Comparison Study

It is important to conduct taxonomy research on the bacteria kingdom for deeper understanding, which can utilize the conserved genes, 16s rRNA, protein domain, and so on. Among them, the methods based on the protein domain has a direct relationship with phenotype. However, these methods still lack analysis of their biological significance, models evaluation and the comparison of taxonomy results. To this end, we propose a complete framework to standardize the process for taxonomy problem based on the protein functional domain. By applying it to bacteria kingdom and comparing the results with the NCBI taxonomy, we point out the most appropriate method in each step of the framework and evaluate models according to the biological significance. Finally, taxonomy suggestions and recommendations are proposed based on the phylogenetic tree generated by the framework with the most appropriate combination.

How Much Can Small-Scale Wind Energy Production Contribute to Energy Supply in Cities? A Case Study of Berlin

In light of the global effort to limit the temperature rise, many cities have undertaken initiatives to become climate-neutral, making decentralized urban energy production more relevant. This paper addresses the potential of urban wind energy production with small wind turbines, using Berlin as an example. A complete framework from data selection to economic feasibility is constructed to enable the empirical assessment of wind energy for individual buildings and Berlin as a whole. Based on a detailed dataset of all buildings and hourly wind speed on a 1 km² grid, the results show that multiple turbines on suitable buildings can significantly contribute to households’ energy consumption but fall short of covering the full demand. For individual households, our economic evaluation strongly recommends the self-consumption of the produced electricity. The findings suggest that while the use of small wind turbines should be continuously encouraged, exploring other renewable resources or combination of wind and photovoltaic energy in the urban environment remains important.