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This study presents a framework to estimate throughput and cost of additive manufacturing (AM) as related to process parameters, material thermodynamic properties and machine specifications. Taking a 3D model of the part design as input, the model uses a parametrization of the rate-limiting physics of the AM build process – herein focusing on laser powder bed fusion (LPBF) and scaling of LPBF melt pool geometry – to estimate part- and material-specific build time. From this estimate, per-part cost is calculated using a quantity-dependent activity-based production model.

Analysis tools that assess how design variables and process parameters influence production cost increase our understanding of the economics of AM, thereby supporting its practical adoption. To this aim, our framework produces a representative scaling among process parameters, build rate and production cost.

For exemplary alloys and LPBF system specifications, predictions reveal the underlying tradeoff between production cost and machine capability, and look beyond the capability of currently commercially available equipment. As a proxy for build quality, the number of times each point in the build is re-melted is derived analytically as a function of process parameters, showcasing the tradeoff between print quality due to increased melting cycles, and throughput.

Typical cost models for AM only assess single operating points and are not coupled to models of the representative rate-limiting process physics. The present analysis of LPBF elucidates this important coupling, revealing tradeoffs between equipment capability and production cost, and looking beyond the limits of current commercially available equipment.

The great difficulties which attach to the fixing of legal standards of composition for food products have now to be grappled with by the Departmental Committee appointed by the Board of Agriculture to consider and determine what regulations should be made by the Board, under Section 4 of the Sale of Food and Drugs Act, 1899, with respect to the composition of butter. As we predicted in regard to the labours of the Milk and Cream Standards Committee, so we predict now that the Butter Committee will be unable to do more than to recommend standards and limits, which, while they will make for the protection of the public against the sale of grossly adulterated articles, will certainly not in any way insure the sale of butter of really satisfactory, or even of fair, composition. Standards and limits established by law for the purposes of the administration of criminal Acts of Parliament must of necessity be such as to legalise the sale of products of a most inferior character, to which the term “genuine” may still by law be applied as well as to legalise the sale of adulterated and sophisticated products so prepared as to come within the four corners of the law. It is, of course, an obvious necessity that official standards and limits should be established, and the Board of Agriculture are to be congratulated upon the manner in which they are endeavouring to deal with these extremely knotty problems; but it is important that misconception on the part of the public and the trade with respect to the effect of the regulations to be made should be as far as possible prevented. All that can be hoped for is that the conclusions at which the Committee may find themselves compelled to arrive will not be such as to place too high and too obvious a premium upon the sale of those inferior and scientifically‐adulterated products which are placed in such enormous quantities on the food market.

The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques.

Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of students’ performance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used.

The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way.

Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate.

Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course.

This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.

The unfair prejudice remedy as contained in s.290 of the Companies Ordinance 1984 entitles a member with a shareholding of twenty percent or more to petition to the court for suitable and appropriate court orders in circumstances where the member has been unfairly prejudiced. The major difficulties and complexities emerging from the examination of s.290 relates to (but not limited to) locus standi, high cost of litigation due to the length and complexity of the unfair prejudice litigations, lacunas in share valuation, cumbersome court procedures, low quality of pleadings, unethical conduct of lawyers, etc. The purpose of this paper is to shed light on these topical questions. It is contended that the legislature and the courts will have a strong role to play in providing clarity and certainty to the law.

The first part provides a brief overview of the statutory unfair prejudice remedy contained in s.290. The second part discusses the concept of unfair prejudice in the United Kingdom and its difficulties. The third part provides a framework of the unfair prejudice remedies available under s.290 and discusses the inefficiencies and shortcomings of the remedy.

This article concludes that the statutory unfair prejudice remedy in Pakistan is inefficient and inadequate to redress personal and corporate wrongs in an unfair prejudice petition. The deficiencies of the statutory unfair prejudice remedy pose a challenge to the minority shareholders and the overall corporate governance and corporate law regime in Pakistan.

This article sheds light on the complexity and difficulty of the statutory unfair prejudice remedy, as contained in s.290 of the Companies Ordinance 1984 from a comparative law perspective.