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Any business that opts to adopt a recommender engine (RE) for various potential benefits must choose from the candidate solutions, by matching to the task of interest and domain. The purpose of this paper is to choose RE that fits best from a set of candidate solutions using rule-based automated machine learning (ML) approach. The objective is to draw trustworthy conclusion, which results in brand building, and establishing a reliable relation with customers and undeniably to grow the business.

An experimental quantitative research method was conducted in which the ML model was evaluated with diversified performance metrics and five RE algorithms by combining offline evaluation on historical and simulated movie data set, and the online evaluation on business-alike near-real-time data set to uncover the best-fitting RE.

The rule-based automated evaluation of RE has changed the testing landscape, with the removal of longer duration of manual testing and not being comprehensive. It leads to minimal manual effort with high-quality results and can possibly bring a new revolution in the testing practice to start a service line “Machine Learning Testing as a service” (MLTaaS) and the possibility of integrating with DevOps that can specifically help agile team to ship a fail-safe RE evaluation product targeting SaaS (software as a service) or cloud deployment.

A small data set was considered for A/B phase study and was captured for ten movies from three theaters operating in a single location in India, and simulation phase study was captured for two movies from three theaters operating from the same location in India. The research was limited to Bollywood and Ollywood movies for A/B phase, and Ollywood movies for simulation phase.

The best-fitting RE facilitates the business to make personalized recommendations, long-term customer loyalty forecasting, predicting the company's future performance, introducing customers to new products/services and shaping customer's future preferences and behaviors.

The proposed rule-based ML approach named “2-stage locking evaluation” is self-learned, automated by design and largely produces time-bound conclusive result and improved decision-making process. It is the first of a kind to examine the business domain and task of interest. In each stage of the evaluation, low-performer REs are excluded which leads to time-optimized and cost-optimized solution. Additionally, the combination of offline and online evaluation methods offer benefits, such as improved quality with self-learning algorithm, faster time to decision-making by significantly reducing manual efforts with end-to-end test coverage, cognitive aiding for early feedback and unattended evaluation and traceability by identifying the missing test metrics coverage.

This paper aims to propose a numerically efficient multi-objective optimization strategy, which can improve both the efficiency and performance during the optimization process.

This paper discusses the multi-objective optimization algorithm by combining multi-objective differential evolution (MODE) algorithm with an adaptive dynamic Taylor Kriging (ADTK) model.

The proposed approach is validated through application to an analytic example and applied to a shape optimal design of a multi-layered interior permanent magnet synchronous motor for torque ripple reduction while maintaining the average torque.

The ADTK model selects its basis functions adaptively and dynamically so that it may have better accuracy than any other Kriging models. Through adaptive insertion of new sampling data, it guarantees minimum required sampling data for a desired fitting accuracy.

THROUGHOUT recorded history metals have played an important role in the development of civilised life. In fact they have been used as markers for stages in that development — hence the terms bronze age and iron age. We would appear to be still living in what could be called the late steel age. In spite of changes in patterns of metal use we shall stay in this age at least well into the 21st century.

This paper analyses the impact of institutional proximity, cognitive proximity and geographical proximity (in the form of agglomeration economies) on the firm-level productivity of foreign-owned firms in Ireland. The analysis of agglomeration economies, consisting of internal economies of scale, localization economies, related variety and urbanization economies, has a strong pedigree in regional economics literature. Increasingly, however, alternative explanations of firm-level productivity performance have been explored with institutional and cognitive proximity often identified as other important determinants of performance. This paper presents an analysis of the importance of agglomeration economies (based on geographical proximity) versus institutional and cognitive proximity (which may be a-spatial).

A series of measures capturing regional level agglomeration economies are generated as well as measures of institutional and cognitive proximity. The impact of these effects on foreign-owned firm-level productivity is analysed using data from the Irish Census of Industrial Local Units 2009. The estimation method employed is general method of moments (GMM) which allows for the potential endogeneity of variables within the system of analysis.

The results reveal that institutional proximity has a positive impact on productivity. A possible reason for this result is that local units of the same nationality are sharing knowledge in relation to successfully conducting business in Ireland. However, cognitive proximity is found to be statistically insignificant. Agglomeration economies are also important with urbanization economies and the availability of skilled labour having a positive effect on productivity.

The key contributions of this paper are as follows; firstly, the paper provides the first test of the institutional and cognitive proximity hypotheses on productivity while also controlling for a series of internal and external agglomeration economies. Secondly, the analysis considers, firm level, regional level and national level indicators as determinants of firm's productivity. In combining micro and macro level indicators, the paper attempts to answer the call of Van Oort et al. (2012) for such analyses. Thirdly, the paper provides the first detailed examination of the role of ‘proximity’ on foreign-owned manufacturing firms in the Irish context.

THE Avery Connecting Rod Prover is designed for the balancing of both ends of a connecting rod simultaneously, the results being then compared with a standard rod previously tested. The standard rod is placed in the machine and balanced off by moving the poise weights until the automatic indicators remain at zero.

The modern workplace contains many physical and interpersonal hazards to employee physical and psychological health/well-being. This chapter integrates the literatures on occupational safety (i.e., accidents and injuries) and mistreatment (physical violence and psychological abuse). A model is provided linking environmental (climate and leadership), individual differences (demographics and personality), motivation, behavior, and outcomes. It notes that some of the same variables have been linked to both safety and mistreatment, such as safety climate, mistreatment climate, conscientiousness, and emotional stability.

Introduction Plastic deformation of steel causes crystalline imperfections such as increased dislocation density, vacancies, cracks and microvoids which, in turn, influence dissolution and transport of hydrogen in traps. The increased dislocation density and dislocation pile‐ups against cementile lamella or non‐metallic inclusions lead to microcrack formation. The dislocation pile‐ups are mobile under stress. Transport of hydrogen by dislocation under stress can be expected but the temperature should be neither so high as to force the hydrogen to leave the dislocation sites nor so low that the hydrogen cannot diffuse into the dislocation sites.

The widespread usage of magnetic nanoparticles (MNPs) requires their efficient synthesis during combustion process. This study aims to present a mathematical model for the oxidation of MNPs in a counter-flow non-premixed combustion system to produce MNPs, where the key sub-processes during the oxidation reaction are involved.

To accurately describe structure of flame and determine distributions of temperature and mass fractions of both reactants and products, equations of energy and mass conservations were solved based on the prevailing assumptions that three regions, i.e. preheating, reaction and oxidizer zones exist.

The numerical simulation was first validated against experimental data and characteristics of the combustion process are discussed. Eventually, the influences of crucial parameters such as reactant Lewis numbers, strain rate ratio, particle size, inert gas and thermophoretic force on structure of flame and combustion behavior were examined. The results show that maximum flame temperature can achieve 2,205 K. Replacing nitrogen with argon and helium as carrier gases can increase flame temperature by about 27% and 34%, respectively. Additionally, maximum absolute thermophoretic force was found at approximately 9.6 × 10–8 N.

To the best of authors’ knowledge, this is the first time to numerically model the preparation of MNPs in a counter-flow non-premixed combustion configuration, which can guide large-scale experimental work in a more effective way.

Participants needed for task group on corrosion testing. We have been asked to draw our readers attention to the fact that participants are needed for ASTM Task Group G1.05.04 on Corrosion Testing at Elevated Temperatures and Pressures, a new activity of standards‐writing Committee GF‐1 on Corrosion of Metals.

Dr. Hoar on Lecture Visit. DR. T. P. Hoar, Department of Metallurgy, Cambridge University, will make a British Council‐sponsored visit from August 19 to September 22 to lecture on the corrosion of metals, at the University of Chile's Post‐graduate Engineering Winter School in Santiago. During his stay as guest of the university, Dr. Hoar will also make advisory visits to industrial plants and laboratories. Before then he will be on a private visit to the U.S.A.