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This study aims to analyze the effect of cryptocurrency capitalization market development on bank deposits variability in the United Arab Emirates (UAE) spanning the period 2005M1–2020M4 using the novel nonlinear autoregressive distributive lag (NARDL).

The study employs the NARDL recently developed by Shin et al. (2014) to estimate the long and short-run relationships between the variables rather than the widely known ARDL (Pesaran et al., 2001), which suffers from a complex structure in the estimation equation that usually includes lags and differences in both short and long terms. The implementation of NARDL required several proceedings after plotting the descriptive data, commencing with unit root tests, selection of lag length, estimating the long-and-short variables coefficients, heteroscedasticity test and Wald test for symmetries.

The long-run estimations of the positive and negative asymmetric coefficients indicate that cryptocurrencies capitalization has a negative impact on bank deposits in the UAE. Further, the short-run estimations coefficients exhibit that both significant positive and negative partial sum squares of cryptocurrencies decrease bank deposits.

The study has applied to the UAE spanning the period 2005M1–2020M4 using the NARDL.

The short-run estimations coefficients exhibit that both significant positive and negative partial sum squares of cryptocurrencies decreases bank deposits, which means that the increase in the magnitude of cryptocurrencies capitalization stimulates depositors and speculators to adjust their portfolios towards contracting their deposits in banks to invest partially in cryptocurrencies, on the other hand, the decline in cryptocurrencies capitalization process spur depositors and speculators to reduce their deposits for purchasing cryptocurrencies at lower prices.

The study infers that individuals and businesses are cautious when investing in cryptocurrencies, and they need more certainty and trust to include these types of assets in their portfolios. The fluctuation in cryptocurrencies capitalization prompts speculators to change their deposits according to the cryptocurrencies' prices.

This study explores the short-and long-run asymmetric impacts of cryptocurrencies capitalization development on bank deposits volatility in the UAE, based on a NARDL, for providing a manifest depiction of whether the cryptocurrencies industry might be a threat to conventional banking system performance in the potential future.

Based on new data insulation resistance tests have little capacity to predict the life performance of double‐sided rigid printed wiring boards. This conclusion arises from an elaborate insulation resistance evaluation after 2300 hours of accelerated life testing. The study considers the effects of two sources of contamination and five material combinations on the initial insulation resistance at 23°C and 90% RH. Life test samples were exposed to four levels of humidity and three levels of temperature. Using 1 Megohm as failure criterion there are clear‐cut effects of humidity, temperature and material combination, but there is only a weak correlation between failures and the initial insulation resistance.

This paper aims to illustrate the strengths and weaknesses of experimental design and development in academic marketing since 1950.

The paper does so by taking one experimental design, Latin Square, and describing its history and development within academic marketing in detail.

The Latin Square is a powerful experimental technique that first rose to prominence in agriculture in the 1920s and has remained a key tool in this discipline ever since. The technique was introduced into marketing in 1953, and enjoyed a period of great influence and popularity until 1973, when it abruptly disappeared from the publications of the discipline. Careful investigation of the research record of this period revealed that its demise was due to increasingly poor application method that led to compromised results, combined with an erroneous assignation of superior capabilities to full and fractional factorials that occurred at approximately the same time.

Two major implications arise from these findings. First, the discipline has incorrectly retired a tool that is still unmatched in some key research situations. Second, the errors that led to the technique's demise led to the rise of other techniques that do not have the capabilities that many researchers appear to think they have.

This is the first longitudinal historical case study of a single research technique that has appeared in print in a major journal, and it reveals aspects of the discipline's approach to science that could not have been illustrated in any other way.

The purpose of this study is to build a high accuracy thrust model under various small turbojet engine shaft speeds by using robust, ordinary, linear and nonlinear least squares estimation methods for target drone applications.

The dynamic shaft speeds from the test experiment of a target drone engine is conducted. Then, thrust values are calculated. Based on these, the engine thrust is modeled by robust linear and nonlinear equations. The models are benefited from quadratic, power and various series expansion functions with several coefficients to optimize the model parameters.

The error terms and accuracy of the model are given using sum of squared errors, root mean square error (RMSE) and R-squared (R²) error definitions. Based on the multiple analyses, it is seen that the RMSE values are no more than 17.7539 and the best obtained result for robust least squares estimation is 15.0086 for linear at all cases. Furthermore, the R² value is found to be 0.9996 as the highest with the nonlinear Fourier series expansion model.

The motivation behind this paper is to propose robust nonlinear thrust models based on power, Fourier and various series expansion functions for dynamic shaft speeds from the test experiments.

The purpose of the study is to analyse the feasibility of using the potential and exponential curve models to assess the learning of a group of welders, when welding stainless steel piping with the tungsten inert gas process.

The welding productivity data grouped according to the requirements of the ASME SECTION IX code is organised into two groups: average productivity and baseline productivity. When processing the adjustment to the two models, the Excel software Solver tool was used. The criteria for assessing the quality of the fit were: least squared method, Spearman's correlation coefficient and graphical method. The impact of the variation coefficient on the average productivity and the amplitude (difference between the minimum and maximum productivity) was also evaluated on the baseline productivity.

The curves elaborated based on the average productivity presented better quality of adjustment than those constructed from the baseline productivity. The potential and exponential models presented similar adjustment conditions, with the second having a slightly superior performance. There were no productivity gains due to learning in the studied time interval. The grouping of the average daily productivity data based on the diameter range established in the ASME code section IX presented satisfactory results, enabling its use by the industry.

There is no news of work on piping welding with this focus. The proposal to group the productivity data according to the degree of difficulty of execution established by the ASME code section IX, widely used in the industry, is a significant contribution to monitoring the evolution of learning. In the same way, the results allow to adopt the average productivity determined from the first 20 days of realisation of a project, as a reasonable indicator to estimate the future performance of the work, helping to correct deadlines during the realisation of a project.

The purpose of this paper is to provide a practical method to be used in team decision making when allocating resources.

The paper proposes the following hypothesis: the selected team consensus strategy from among all available strategies must have minimum sum of squares of monetary regrets. A general algebraic representation of the above hypothesis is developed.

This hypothesis can be interpreted as a Nash equilibrium involving mixed strategies when the entire problem is viewed in game theoretic framework. The paper provides an explanation in quantitative terms of the reasoning process pursued by five business college department chairs faced with three strategies, in an actual consensus decision making to illustrate the above hypothesis. By making observations of the behavior of decision makers in the selection of a budget allocation formula, the paper shows that the hypothesis holds true for the specific reasoning process pursued by the chairs in arriving at the consensus solution. However, the chairs' consensus solution is found to be a local solution vis‐à‐vis the global optimal solution found by solving the game theoretic model.

The authors plan to conduct further empirical testing of the hypothesis using allocation strategies found in diverse decision‐making environments involving diverse decision makers such as business executives, government officers, education administrators, and others.

If this hypothesis can be validated to be true, decision makers should propose for consideration only those rational strategies that have minimal or low variance in monetary regrets since these are the strategies that would most likely be selected in team decision making.

Team decision making involving resource allocation abounds in all organizations, at all levels and in diverse applications. The practical procedure proposed in this paper, based on analytical foundation of game theory, provides decision makers a viable tool for allocating resources that results in consensus of all rational parties involved.

The aim of this paper is to circumvent the multi‐distribution effects and small sample constraints that may arise in unreplicated‐saturated fractional factorial designs during construction blueprint screening.

A simple additive ranking scheme is devised based on converting the responses of interest to rank variables regardless of the nature of each response and the optimization direction that may be issued for each of them. Collapsing all ranked responses to a single rank response, appropriately referred to as “Super‐Ranking”, allows simultaneous optimization for all factor settings considered.

The Super‐Rank response is treated by Wilcoxon's rank sum test or Mann‐Whitney's test, aiming to establish possible factor‐setting differences by exploring their statistical significance. An optimal value for each response is predicted.

It is stressed, by example, that the model may handle simultaneously any number of quality characteristics. A case study based on a real geotechnical engineering project is used to illustrate how this method may be applied for optimizing simultaneously three quality characteristics that belong to each of the three possible cases, i.e. “nominal‐is‐best”, “larger‐is‐better”, and “smaller‐is‐better” respectively. For this reason, a screening set of experiments is performed on a professional CAD/CAE software package making use of an L₈(2⁷) orthogonal array where all seven factor columns are saturated by group excavation controls.

The statistical nature of this method is discussed in comparison with results produced by the desirability method for the case of exhausted degrees of freedom for the error. The case study itself is a unique paradigm from the area of construction operations management.