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The purpose of this paper is to evaluate advanced mathematical electrochemical noise analysis (ENA) as a way of corrosion monitoring for carbon steel.

The electrochemical potential/current noise was recorded simultaneously with a working‐reference‐working electrode set up and the processing of data was performed through fast Fourier transformation (FFT) and wavelet transformation (WT) routes. The formation and rupture of carbonate films on St37 steel electrodes in a 0.5 M sodium bicarbonate electrolyte was studied for 20 h utilizing an electrochemical noise approach.

Although the slope of mid‐range of noise impedance exhibited a mechanistic style, and increased with film formation and decreased with film rupture, FFT of potential noise was more sensitive to film formation and rupture. WT of potential noise depicted that ν=1.41 × 10⁻² Hz was the boundary frequency in the film formation. At frequencies higher than the mentioned limit, the fraction of distributed potential decreased with time. However, the opposite behavior was observed during the rupture of the film.

The preliminary results show that the proposed novel electrochemical method, wavelet and FFT ENA, is very able to monitor the corrosion behavior of carbon steel corrosion in carbonate media.

This study aims to propose that the corrosion resistance of the neat epoxy coating can be further enhanced by incorporating reinforcing agents.

Chitosan, silica and their hybrid compound were used to study the subject of corrosion resistance of epoxy coating systems. This work used 3.5 Wt.% NaCl solution as the electrolyte, and electrochemical impedance spectroscopy (EIS) was used to investigate the electrochemical behaviour of the studied coating systems. Standard and accelerated states were used without and with scratch on the coating layer.

It was found that the impedance value of composite coating incorporated with the hybrid compound was significantly higher at 10¹⁰ Ω after 14 days of exposure in both testing states. The breakpoint frequency (f_b) determination also proves with large capacitive region at low-to-high frequency of impedance plots corresponding to the high corrosion resistance.

The hybrid compound consisting of chitosan as organic biopolymer and silica as inorganic material, respectively, served as a promising reinforcing agent for composite coating as a promising corrosion inhibitor. Different states of EIS measurement were used which are standard (without scratch) and accelerated (with scratch) states associated with the f_b values.

Substitution of zinc chromate or zinc yellow, traditionally used as anticorrosive pigment, for other phosphate‐based pigments that are not hazardous to health and have the same anticorrosive behaviour or even better, is studied in this paper. Four alkyd paints were specially prepared; two of them contained calcium acid phosphate or micronised zinc phosphate as anticorrosive pigments respectively. A paint containing zinc chromate was used as reference and a paint without anticorrosive pigments was used as a blank, in which the other ingredients were increased proportionally to attain the desired PVC relationship. The corrosion behaviour of low carbon steel panels coated with these paints in a 3 per cent NaCl solution was assessed by electrochemical impedance spectroscopy (EIS). In addition, other painted panels were evaluated by salt spray and humidity chamber tests. Results of all tests showed that the paint with calcium acid phosphate and especially that with micronised zinc phosphate exhibited better behaviour than paint with zinc chromate. Analysis of impedance parameters (ionic resistance and capacitance of the paint film) against immersion time allowed the paints to be ranked in the same order as that obtained with salt spray and humidity chamber tests.

Explains corrosion protection and how it works. Discusses the effect of environmental legislation on corrosion protective paints, which necessitates the removal of solvents and toxic additives, making the protection weaker. In order to remedy this one must determine how protection is provided, which involves the separation of barrier properties and electrochemical passivation. Describes methods and tests involved in this and discusses the results. Concludes with recommendations and a suggestion for further tests.

Understanding how dyadic negotiations and group decision processes evolve over time requires specifying the basic elements of process, modeling the configuration of those elements over time, and providing a theoretical explanation for that configuration. We propose a bead metaphor for conceptualizing the basic elements of the group negotiation process and then “string” the beads of behavior in a helix framework to model the process by which group negotiations evolve. Our theorizing draws on the group decision development literature (e.g. Bales, 1953; Poole, 1981, 1983a, b; Poole & Roth, 1989a, b) as well as on the negotiation process literature (e.g. Gulliver, 1979; Morley & Stephenson, 1977). Our examples are from our Towers Market studies of negotiating groups.

The paper aims to estimate the segment size and alphabet size of Symbolic Aggregate approXimation (SAX). In SAX, time series data are divided into a set of equal-sized segments. Each segment is represented by its mean value and mapped with an alphabet, where the number of adopted symbols is called alphabet size. Both parameters control data compression ratio and accuracy of time series mining tasks. Besides, optimal parameters selection highly depends on different application and data sets. In fact, these parameters are iteratively selected by analyzing entire data sets, which limits handling of the huge amount of time series and reduces the applicability of SAX.

The segment size is estimated based on Shannon sampling theorem (autoSAXSD_S) and adaptive hierarchical segmentation (autoSAXSD_M). As for the alphabet size, it is focused on how mean values of all the segments are distributed. The small number of alphabet size is set for large distribution to easily distinguish the difference among segments.

Experimental evaluation using University of California Riverside (UCR) data sets shows that the proposed schemes are able to select the parameters well with high classification accuracy and show comparable efficiency in comparison with state-of-the-art methods, SAX and auto_iSAX.

The originality of this paper is the way to find out the optimal parameters of SAX using the proposed estimation schemes. The first parameter segment size is automatically estimated on two approaches and the second parameter alphabet size is estimated on the most frequent average (mean) value among segments.

The paper describes an efficient method to extract the B‐H nonlinear characteristic from the experimental flux‐current Φ‐I data obtained using a non‐uniform magnetic field device. Both functions are monotonically piecewise linear approximated with the same number of breakpoints. The method was successfully applied to characterize the ribbon core material of a fluxset magnetic field sector. In this case the hysteresis loop and the lumped magnetic circuit were extracted. Comparison with experimental results validates the proposed method.

Housing prices have been increasing tremendously in Pakistan, there should be many reasons but the haphazard urbanization and rapidly growing population. To find out the causes of this price rise, this study aims to assess the impact of the foreign capital inflow and some domestic factors on housing prices.

To get the benefits of high-frequency data, it has been converted into a monthly, quarterly and yearly basis. The unit root is performed to see the stationarity, Johansen test is used for cointegration and coefficients are obtained through the ordinary least squares technique. The robustness of the results is checked with dynamic ordinary least squares and the Chow breakpoint test is used to detect structural breaks.

The housing prices have increased over time; this has been reflected in all the data sets under observation. The country has observed a rapid growth in population and urbanization that has badly affected almost every activity of city life. The impact of foreign capital inflow is positive on the house price appreciation. There is a dire need to divert such foreign funds in the housing sector so that it cannot create an artificial price hike. The government should regularly publish a housing policy for the guidance of investors and the public at large. Also, public authorities should provide housing finance facility.

This is a novel work to the best of the authors’ knowledge because no one has studied the impact of foreign capital inflow on the housing market for the economy of Pakistan. Furthermore, this study is different in the sense that it has disaggregated annual data into a monthly and quarterly basis to get the benefits of high-frequency data.

In contrast to rents which are prescribed by contract to remain fixed or constant during the term of a lease, the expression variable rents simply implies that the rent to be paid during the tenure of occupancy will be subject to change with the passage of time.

This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading?

Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted.

Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information.

To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information.