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The purpose of this paper is to present a critical analysis of the concept of “change of circumstances” as a justification for judicial revision of contracts.

The study analyses international legal texts on the subject in the light of a decision of the Inner House of the Court of Session in Scotland, Lloyds TSB Foundation for Scotland v. Lloyds Banking Group plc [2011] CSIH 87 (currently subject to appeal to the UK Supreme Court).

Whatever the merits of a change of circumstances doctrine, the Lloyds case does not provide a good example for its application.

The scope of a change of circumstances doctrine should be tested by further comparative study.

This is the first consideration of the Lloyds case in an international and comparative context.

Although risk culture is a key determinant for an effective risk management, identifying the risk culture of a firm can be challenging due to the abstract concept of culture. This paper proposes a novel approach that uses unsupervised machine learning techniques to identify significant features needed to assess and differentiate between different forms of risk culture.

To convert the unstructured text in our sample of banks' 10K reports into structured data, a two-dimensional dictionary for text mining is built to capture risk culture characteristics and the bank's attitude towards the risk culture characteristics. A principal component analysis (PCA) reduction technique is applied to extract the significant features that define risk culture, before using a K-means unsupervised learning to cluster the reports into distinct risk culture groups.

The PCA identifies uncertainty, litigious and constraining sentiments among risk culture features to be significant in defining the risk culture of banks. Cluster analysis on the PCA factors proposes three distinct risk culture clusters: good, fair and poor. Consistent with regulatory expectations, a good or fair risk culture in banks is characterized by high profitability ratios, bank stability, lower default risk and good governance.

The relationship between culture and risk management can be difficult to study given that it is hard to measure culture from traditional data sources that are messy and diverse. This study offers a better understanding of risk culture using an unsupervised machine learning approach.

Though originality is a requirement for copyright protection, the term is not defined, leaving room for differing interpretations. Over the years, there has been a shift in the yardstick for determining originality. The lack of definition gives room for ambiguity and creates hurdle for protection. The difficulty in assessment of originality is more felt in certain forms of work like music, which has its own theories and limitations. Absence of determining tests creates uncertainty for the authors to draw the line between inspiration and infringement. This paper aims to define originality as applicable to each subject matter.

The methodology used is a qualitative analytical approach and draws from theories and relevant case laws.

The requirement of “originality” as laid down in the Copyright Act has to be defined with precision. The requirement of originality for different categories of subject matter encompassed within copyright law will differ and so the term should be redefined with respect to each subject matter.

This paper is an original work and canvasses for a definition of the term originality in the Copyright Act with reference to the various subject matter entitled to protection.

When large samples are used to estimate airport efficiency, clustering is a necessary step before carrying out any benchmarking analysis. However, the existing literature has paid little attention to developing a robust methodology for airport classification, instead relying on ad hoc techniques. In order to address this issue, this paper aims to develop a new airport clustering procedure.

A frontier-based hierarchical clustering procedure is developed. An application to cost-efficiency benchmarking is presented using the cost function parameters available in the literature. A cross-section of worldwide airports is clustered according to the relevant outputs and input prices, with cost elasticities and factor shares serving as optimal variable weights.

The authors found 17 distinct airport clusters without any ad hoc input. Factors like the use of larger aircraft or the dominance of low-cost carriers are shown to improve cost performance in the airport industry.

The proposed method allows for a more precise identification of the efficiency benchmarks, which are characterized by a set of cophenetic distances to their “peers”. Furthermore, the resulting classification can also be used to benchmark other indicators linked to airport costs, such as aeronautical charges or service quality.

This paper contributed to airport clustering by providing the first discussion and application of optimal variable weighting. In regard to efficiency benchmarking, the paper aims to overcome the limitations of previous papers by defining a method that is not dependent on performance, but on technology, and that can be easily adapted to large airport datasets.

In India, agriculture is considered as the major source of income for a major sector of people. Our country's GDP (Gross Domestic Product) can increase only if we focus on agriculture and its growth toward global economy. There have been several attempts to improve the agricultural sector since decades.

This work describes about the design of a device which continuously monitors the plant growth and sends the data to a centralized database, where data is dynamically analyzed based on base references using various machine learning algorithms like regression, gradient descent, clustering etc.

This paper aims at analyzing the plant growth in of our country and focuses on the improvement of plant growth based on factors such as temperature, air moisture, radiant energy, carbon dioxide levels, soil pH& temperature through the design of a device.

It is anticipated to provide a solution by analyzing the plant growth percentage in different regions over a period of time. Based on the inferences, we will be able to suggest an optimum environment for the plant species to grow best. Various sensors like temperature and humidity sensors, light sensors and pH electrodes can be used in collecting data from the plant environment.