Exploring status of library management technical aspects in Indian schools

PurposeSchool library is regarded as the heart and soul of the school. It acts as learning resource centre and support the school curriculum in all possible manners. The main aim of this paper is to document the factors associated with library management in selected school libraries in Varanasi, India.Design/methodology/approachSurvey method is used to conduct this research in which close-ended questions were provided to the school librarians. Simple random sampling was used to collect samples from secondary schools of Varanasi.FindingsIt was found that 34% school libraries affiliated to Central Board of Secondary Education are managed properly and they are termed as first class library. Rest of the schools are not managed as per rules and regulations of school librarianship. Some librarians themselves are not properly aware of the significance of the library and hence the management is suffered accordingly.Practical implicationsThis research will help in exploring the existing status of library management in school libraries. The work is designed specifically for school libraries. Simple ranking system for school libraries will be helpful to make an exhaustive school ranking based on library management.Originality/valueThis research will bring on record the ground reality of school librarianship in India and the way they are managed. Simple ranking system for school library is given in this paper that will help to find out which school library is functioning properly or not.

AI-Based Well Ranking System for Hydraulic Fracturing Events

We are presenting a new, highly intelligent AI-based ranking system for selecting the most appropriate candidates for well treatment. The system is trained to predict flow rates after hydraulic fracturing (HF) and rank wells by the expected effect of the event with machine learning techniques. We demonstrate a significant effort for preprocessing the available field data to create a dataset for training machine learning (ML) models. The dataset included information about geology, transport and storage properties, depths, oil/liquid rates before fracturing for target and neighboring wells. Each ML model has been trained to predict monthly production of oil and liquid right after fracturing and after flow stabilization. Also, confidence intervals of the prediction have been provided. To study the dynamics of future oil rate decline after HF on a stable regime, we have trained several regression models to make predictions at each future point (6 next months after fracturing). To estimate the effect due to HF, we defined expected production "without fracturing." Typically, wells behave with a stable decline trend of production that is approximated by Arps function. The function is defined before HF, then extrapolated to the period after the event where it shows expected production without fracturing. One may conclude about the effectiveness of HF by calculating areas difference under the extrapolated curve (cumulative production without HF), and ML predicted cumulative production for future six months. Reservoir engineers could calculate these differences for each well and create a ranking list from the highest effect to the lowest. The developed system does this automatically for the required oilfield or its part. Therefore, one may easily define the list of best candidates for HF. Gradient Boosting algorithm has been applied to obtain results. Feature selection and tuning of hyperparameters have been provided with the application of cross-validation technique. To test the developed approach, we have divided the dataset from 8 conventional oil fields at a ratio of four to one. The total dataset included 700+ well interventions. Then we have trained and validated models for flow rate prediction on the major part and tested on the holdout part. For different oil field determination coefficients (R2) and normalized root mean square errors (n-RMSE) for oil rate predictions were around R2=0.8 and n-RMSE=0.35 correspondently. The proposed technique is a new approach for fast, accurate, and objective selection of the candidates for hydraulic fracturing based on real-time state of a field. Such AI-based system could become very handy assistant for reservoir engineer in addition to hydraulic fracturing and hydrodynamic simulators. The presented solution computationally efficient and does not require detailed information about HF design.

Ranking of Legal Periodicals and Prerequisites for Academic Promotion in Serbian, Croatian, and Slovenian Law

Scholars need to obtain a certain level of international recognition for academic progression. This is usually achieved by publishing articles in internationally recognized journals, books, and conference papers. The question is which journals should be considered of international relevance and how they should be ranked. For this purpose, a ranking system based on the Journal Citation Reports (JCR), combined with the leading research engine, the Web of Science (WoS), is used. While a ranking system based on the JCR is considered most suitable for natural and technical sciences, it has many shortcomings when considering social sciences and humanities, including legal science. This is observed when such a system is applied in countries that cannot claim to have a profound impact on the global development of legal thought and where scholarly legal production is almost exclusively conducted in the national language, such as in Central and Eastern European (CEE) countries. This study analyzes the general laws and rules regarding the qualification of journals in Serbia, Croatia, and Slovenia, and special laws pertaining to social sciences, especially legal science. Although there are many points of interest regarding different situations in which the national laws on the qualification of journals gain importance, this study focuses on the relevance of these laws in terms of the promotion of legal scholars to positions of university lecturers. It analyzes the requirements for the promotion to a full professor of law. It concludes that the laws of the three countries, through different forms, managed to find a delicate balance between the requirement of publishing articles in internationally recognized journals and the characteristics of legal science as it is predominantly conducted in the national language and addressed to a domestic audience.

Exploring the built-environment: heritage trails, values and perceptions

Understanding visitor preferences to heritage areas is essential in informing management planning and interpretive strategies for these places. This paper uses a quantitative method approach to investigate local Malaysian visitor preferences to heritage trails in the Old Town of central Kuala Lumpur, in Malaysia, to understand what values and qualities visitors are experiencing that informs their preferences. The findings of this research offers a ranking system of heritage trails and buildings based upon visitors’ preferences, that can aid in understanding of visitor preferences of heritage trails and the places and values along such trails.

Global money laundering appeal index: application of principal component analysis

Purpose The paper aims at developing a global ranking system determining a country's appeal as a destination for money laundering. Design/methodology/approach This paper uses principal component analysis (PCA), with a mix of standardised and unstandardised components relating to attractiveness, economic freedom and money laundering risk to come up with an index of money laundering appeal. Findings Four components relating to economic feasibility, financial liberty, government spending and tax regime are critical in influencing a country's money laundering appeal. Research limitations/implications This paper attempts to use a standardised and replicable methodology to condense into a single measure the complex and multifaceted phenomenon of a country's appeal as a destination for money laundering, thus avoiding the difficulty associated with precisely calculating illicit financial flows. Practical implications The ranking system could be used to determine the destinations attractive for laundering money. Such information can be used to come up with more effective preventative strategies to combat phenomena responsible for the stagnation of economic growth through tax evasion, corruption and creation of non-competitive markets. Originality/value It is the first attempt to use a statistical technique to understand the underlying components of a country's money laundering appeal.

Does the Food Safety Ranking System Act as a Nudge?

Public health safety is of concern to authorities across the globe, and inspector's food hygiene ranking system has been introduced in many countries. Mandatory disclosure of hygiene ranking information was introduced to empower consumers to make informed decisions regarding foodborne risks. Evaluating available research on public perception and attitude towards such rankings, it seems that the food safety rankings may prompt hygiene compliance by the food business operators and may act as a nudge for consumers to avoid outlets, though with certain caveats. Significant assessment of the scheme remains to be carried out for the ranking system to be an effective nudge for sustainable consumer protection. The public health authorities and organisations need to consider several real-world cognitive and behavioural constraints.

Modern stimulations

This chapter looks at how modern developments have made the pilgrimage more accessible and given rise to a new cohort of ‘pensioner pilgrims’ who make numerous circuits, often sleeping in customised cars and supported by their pensions. The chapter also looks at other developments that encourage repeated performance, from status symbols that indicate one has done 100 pilgrimages, to a ranking system among pilgrims. It explores themes of status and examines how every pilgrim can determine their own ways of performance and thereby create a sense of personalised autonomy and authority. The chapter also indicates how issues of competition also play a part in this process. It introduces various pilgrims met during fieldwork, showing why and how they perform numerous pilgrimages in Shikoku and how they talk about addiction, ‘Shikoku illness’, and faith.

A Robust Reputation-Based Group Ranking System and Its Resistance to Bribery

The spread of online reviews and opinions and its growing influence on people’s behavior and decisions boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings of products and services gained a critical role in business and governments. Current state-of-the-art solutions rank the items with an average of the ratings expressed for an item, with a consequent lack of personalization for the users, and the exposure to attacks and spamming/spurious users. Using these ratings to group users with similar preferences might be useful to present users with items that reflect their preferences and overcome those vulnerabilities. In this article, we propose a new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters users by their similarities using three measures, two of them based on Kolmogorov complexity. We also study its resistance to bribery and how to design optimal bribing strategies. Our system is novel in that it reflects the diversity of preferences by (possibly) assigning distinct rankings to the same item, for different groups of users. We prove the convergence and efficiency of the system. By testing it on synthetic and real data, we see that it copes better with spamming/spurious users, being more robust to attacks than state-of-the-art approaches. Also, by clustering users, the effect of bribery in the proposed multipartite ranking system is dimmed, comparing to the bipartite case.