The development of technology brings the computerization of everyday life. The most common device used to communicate with a computer is the keyboard. Many papers have been devoted to the study of computer keyboards and virtual keyboards. The authors of this study propose a new concept of a single line keyboard named One Row Keyboard (ORK) which can be applied for both computer and virtual keyboards. Applying the proposed concept enables the number of keys on the keyboards to be reduced significantly. This allows for the development of keyboards even on really small mobile devices. The article also presents a new method of assigning characters to keys for the ORK and shows the use of this method to create exemplary ORK layouts. One Row Keyboard is also the first concept that makes it possible to create a fully alternating keyboard. Noteworthy is that the criterion of maximizing typing alternation is the most popular criterion for keyboard optimization used in the studies devoted to computer keyboards as it leads to an increase in typing speed and decrease in the number of errors.

Despite the growing share of renewable energy sources, most of the world energy supply is still based on hydrocarbons and the vast majority of world transport is fuelled by oil products. Thus, the profitability of many companies may depend on the effective management of oil price risk. In this article, we analysed the effectiveness of artificial neural networks in hedging against the risk of WTI crude oil prices increase. This was reformulated from a regressive problem to a classification problem. The effectiveness of our approach, using artificial neural networks to classify observations, was verified for over ten years of WTI futures quotes, starting from 2009. The data analysis presented in this paper confirmed that the buyer of a call option was more often likely to incur a loss as a result of its purchase than make a profit after the final payoff from the call option. The results of the conducted research confirm that neural networks can be an effective form of protection against the risk of price fluctuations. The effectiveness of a network’s operation depends on the choice of assessment indicators, but analyses show that the networks which, for the indicator that was selected, gave the best results for the training set, also resulted in positive rates of return for the test set. Significantly, we also showed interdependence between seemingly unrelated indicators: percentage of the best possible results achieved in the analysed period of time by the proposed method and percentage of all available call options that were purchased based on the results from the networks that were used.

Modern IT systems collect detailed data on each activity, transaction, forum entry, conversation and many other areas. The availability of large data volumes in the business, industry and research fields opens up new opportunities for the empirical verification of various economic theories and laws. The analysis of big datasets in turn allows us to look at many issues from a new point of view and see the dependencies that are otherwise difficult to derive. In this paper, we propose a new measure for dependencies between goods in market basket data. The introduced measure was inspired by the well-known microeconomic concept of complementarity. Due to its similar properties to those of complementarity, the new measure was called basket complementarity (b-complementarity). B-complementarity not only measures the strength of dependencies between goods but also measures the direction of these dependencies. The values of the proposed measure can be relatively easily calculated using market basket data. This paper also presents a simple example illustrating this new concept, areas of possible application (e.g., in e-commerce) and preliminary results of searching for goods that meet the criteria of basket complementarity in real market basket data.

Scheduling of production jobs falls into the area of planning, which, according to Henri Fayol’s conception, is one of the basic functions of management. The permutation flow-shop scheduling problem (PFSP) with makespan criterion is one of the most studied scheduling problems in the area of scheduling theory and applications. The most-known polynomial complexity method for solving this complex problem is the Nawaz-Enscore-Ham (NEH) deterministic constructive algorithm. The subject literature shows that the results of NEH strongly depend on the input sequence of jobs. In this paper, we propose a new method to build the input sequence of jobs for NEH-based heuristics. The proposed Turning-off-Machine (ToM) method and its generalized version ToM+ (having the feature to produce a set of input sequences that can be used in population-based optimization methods) compute the total processing time of jobs by virtually “turning off” one machine. The ToM+ method is one of a few deterministic methods for modifying the input sequence, and is the first one that modifies the input sequence based on individual machine processing times. Extensive numerical experiments on standard Taillard and VRF benchmarks show good efficiency of the proposed method in solving PSFP with makespan criterion. The method improved the performance (measured using ARPD) of the NEH-based algorithms by up to nearly 35%. Moreover, by combining ToM+ method, SMαP+, N -list, and vN -list technique, it was possible to improve the results of the original NEH algorithm by up to nearly 50% (the method outperformed most of the NEH-based methods). This confirms that creating an adequate input sequence is of great importance for the performance of NEH-based algorithms.