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THIS article deals with the cold starting and associated problems of reciprocating engines as distinct from jet engines. The associated problems are those which limit the satisfactory operation of the engine after the initial start, and are included because they form part of the functioning of the unit. The general problem of cold starting may be split up under the following headings and each one will be dealt with separately:

The purpose of this paper is to present results of practical experience of cold starting a gasoline engine on low volatility fuel suitable for unmanned aerial vehicle (UAV) deployment.

Experimental research and development is carried out via dynamometer testing of systems capable of achieving cold start of a spark ignition UAV engine on kerosene JET A-1 fuel.

Repeatable cold starts have been satisfactorily achieved at ambient temperatures of 5°C. The approximate threshold for warm engine restart has also been established.

For safety and supply logistical reasons, the elimination of the use of gasoline fuel offers major advantages not only for UAVs but also for other internal combustion engine-powered equipment to be operated in military theatres of operation. For gasoline crankcase-scavenged two-stroke cycle engines, this presents development challenges in terms of modification of the lubrication strategy, achieving acceptable performance characteristics and the ability to successfully secure repeatable engine cold start.

The majority of UAVs still operate on gasoline-based fuels. Successful modification to allow low volatility fuel operation would address single fuel policy objectives.

This paper aims to improve the recommendations performance for cold-start users and controversial items. Collaborative filtering (CF) generates recommendations on the basis of similarity between users. It uses the opinions of similar users to generate the recommendation for an active user. As a similarity model or a neighbor selection function is the key element for effectiveness of CF, many variations of CF are proposed. However, these methods are not very effective, especially for users who provide few ratings (i.e. cold-start users).

A new user similarity model is proposed that focuses on improving recommendations performance for cold-start users and controversial items. To show the validity of the authors’ similarity model, they conducted some experiments and showed the effectiveness of this model in calculating similarity values between users even when only few ratings are available. In addition, the authors applied their user similarity model to a recommender system and analyzed its results.

Experiments on two real-world data sets are implemented and compared with some other CF techniques. The results show that the authors’ approach outperforms previous CF techniques in coverage metric while preserves accuracy for cold-start users and controversial items.

In the proposed approach, the conditions in which CF is unable to generate accurate recommendations are addressed. These conditions affect CF performance adversely, especially in the cold-start users’ condition. The authors show that their similarity model overcomes CF weaknesses effectively and improve its performance even in the cold users’ condition.

The modern motorist rightly expects to use his car under all weather conditions. In fact, its utility is greatest in bad weather. It is, therefore, essential that he—and his wife—should be able to start the engine promptly and drive away in cold weather with the minimum of trouble.

IN HIS CHAIRMAN'S ADDRESS to the Automobile Division of The Institution of Mechanical Engineers, on the 6th October, L. H. Dawtrey, M.I.Mech.E. (Chief Engineer, Research and Development, The Standard Motor Co., Ltd.) presented a paper entitled “Some Problems We Meet,” in which he outlined the problems with which he had been confronted during his engineering career and more recently, within the motor industry. He had the following to say concerning the causes of high oil consumption, and also included some remarks concerning cold starting.

The purpose of this paper is to explore the effects of online recommender systems (RS) on three types of diversity: algorithmic recommendation diversity, perceived recommendation diversity and sales diversity. The analysis distinguishes different recommendation algorithms and shows whether user perceptions match the actual effects of RS on sales.

An online experiment was conducted using a realistic shop design, various recommendation algorithms and a representative consumer sample to ensure the generalizability of the findings.

Recommendation algorithms show a differential impact on sales diversity, but only collaborative filtering can lead to higher sales diversity. However, some of these effects are subject to how much information firms have about users’ preferences. The level of recommendation diversity perceived by users does not always reflect the factual diversity effects.

Recommendation and consumption patterns might differ for other types of products; future studies should replicate the study with search or credence goods. The authors also recommend that future research should move from taking a unidimensional measure for the assessment of diversity and employ multidimensional measures instead.

Online shops need to conduct a more comprehensive assessment of their RS’ effect on diversity, taking into account not only the effects on their sales distribution, but also on users’ perceptions and faith in the recommendation algorithm.

This study offers a framework for assessing different forms of diversity in online RS. It employs various recommendation algorithms and compares their impact using not just one but three different types of diversity measures. This helps explaining some of the contradictious findings from the previous literature.

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.

Parts One and Two (in our March and April issues) dealt respectively with the Theory of Cooling and Oil Coolers in General. Part Three (in May) dealt with Control Valves and Air Cooled Oil Coolers.

To many facilities managers the chance of a major disaster — fire, say, or flood — may seem so remote that the time and expense entailed in planning for it couldn't possibly be justified.

A comprehensive series of articles covering the duties and properties required of oil engine lubricants and circulating systems.