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The purpose of this paper is to obtain a better understanding on robust performance of a hardening and tempering process producing component worm shaft used in the steam power plant. This research is capable to explaining the variation of process capability in terms of robustness.

This paper proposed a methodology (a combination of simulation, regression modelling and robust design technique) to study robustness of a hardening and tempering process producing component worm shaft used in the steam power plant and process capability acts as a surrogate measure of robustness. In each experimental run, the values of responses and the corresponding multivariate process capability indices across the outer array are determined. The variation of process performance (process capability values) due to random noise variation is studied using a general purpose process control chart (R-chart).

The results provide useful information in term of insensitiveness of the process against the noise (raw material and process noise) variation where the process capability acts as a surrogate measure of process robustness and explains the variation of process capability in term of robustness.

This paper adds to the body of knowledge on robustness of a manufacturing process. This paper may be of particular interest to practicing engineers as it suggests what factors should be more emphasis to achieve robust (consistent) performance from the process.

The originality of this paper lies within the context in which this study is to address key relationships between process robustness and process capability in a manufacturing industry.

Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when deciding on a residential neighbourhood. Unlike the market for most tangible goods, the market for environmental quality does not yield an observable per unit price effect. As no explicit price exists for a unit of environmental quality, this paper aims to use the housing market to derive its implicit price and test whether these constituent elements of health and well-being are indeed capitalised into property prices and thus implicitly priced in the market place.

A considerable number of studies have used hedonic pricing models by incorporating spatial effects to assess the impact of air quality, noise and proximity to noise pollutants on property market pricing. This study presents a spatial analysis of air quality and noise pollution and their association with house prices, using 2,501 sale transactions for the period 2013. To assess the impact of the pollutants, three different spatial modelling approaches are used, namely, ordinary least squares using spatial dummies, a geographically weighted regression (GWR) and a spatial lag model (SLM).

The findings suggest that air quality pollutants have an adverse impact on house prices, which fluctuate across the urban area. The analysis suggests that the noise level does matter, although this varies significantly over the urban setting and varies by source.

Air quality and environmental noise pollution are important concerns for health and well-being. Noise impact seems to depend not only on the noise intensity to which dwellings are exposed but also on the nature of the noise source. This may suggest the presence of other externalities that arouse social aversion. This research presents an original study utilising advanced spatial modelling approaches. The research has value in further understanding the market impact of environmental factors and in providing findings to support local air zone management strategies, noise abatement and management strategies and is of value to the wider urban planning and public health disciplines.

The purpose of this paper is to determine whether noise is affected by psychological factors rather than simply by physical metrics. For example, personality type, age, perceived control and screening ability were explored, as well as the choice of primary workplace.

An online survey was conducted which resulted in 517 valid responses. The survey included the personality profiling along with questions related to noise and personal circumstances. The key noise metrics were perceived performance, ability to work, well-being and stress plus three noise indices: concentration, distraction and speech interference.

The survey revealed that personality type does affect noise perception, in particular extroversion and neuroticism. Perceived control, screening ability, age, workplace, design and focused work are also factors. Personal variables accounted for 25 per cent of the variance in the ability to carry out work, and for 40 per cent of the variance in concentration and speech interference.

Whilst statistically significant differences were found for most of the psychological and personal variables, the size of effect was smaller than anticipated. This is likely because the survey was carried out across a range or workplaces, rather than in a laboratory, with a number of uncontrolled extraneous factors.

The research has resulted in the development of a design guidance document for controlling noise distractions based on more psychoacoustic, people-centred, principles than purely physical ones.

Most acoustics research is conducted in the laboratory and focuses on the physical sound properties. This research took a psychoacoustic approach focusing more on psychological and personal factors, and was carried out in the real world.

The purpose of this paper is to present computer‐generated combined arrays as efficient alternatives to Taguchi's crossed arrays to solve robust parameter problems.

The alternative combined array designs were developed for the cases including six to twelve variables where CMR designs are not smaller than Taguchi's designs. The efficiency to estimate the effects of interest was calculated and compared to the efficiency of the corresponding CMR designs.

For all the cases investigated at least one computer generated combined array design was found with the same size as the CMR design and with higher efficiency.

Robust parameter design identifies appropriate levels of controllable variables in a process for the manufacturing of a product. The designed experiments involve the controllable variables along with the uncontrollable or noise variables to design a product or process that will be robust to changes in these noise variables. Response surface methodology estimates the actual relationship between the response and the input variables with an empirical model based on the designed experiment. Once the empirical model is fitted, the surface described by the model can be used to describe the behavior of the response over the experimental region. The higher efficiency of the computer generated combined array designs proposed in this research produces lower variances for the parameter estimates and lower variance of prediction for the model. As a result, the response will be described in a more realistic form.

The paper shows that using a computer‐generated design to solve a robust parameter problem will result in a better approximation to the true response of the process. Consequently, optimizing the fitted model will produce settings for the parameters closer to the real optimal settings.

The authors examine the relation between noise trading in equity markets and stochastic volatility by estimating a two-factor jump diffusion model. Their analysis shows that contemporaneous price deviations in the derivatives market are statistically significant in explaining movements in index futures prices and option-market volatility measures.

To understand the impact noise may have in the S&P 500 derivatives market, the authors first measure and evaluate the influence noise exerts on futures prices and then investigate its influence on option volatility.

In the period from 1996 to 2003, this study finds significant changes in the volatility and mean reversion in the noise level and a significant increase in its relation to implied volatility in option prices. The results are consistent with a bubble in technology stocks that occurred with significant increases in noise trading.

This study provides estimates for this model during the periods preceding and during the technology bubble. The study analysis shows that the volatility and mean reversion in the noise level are much stronger during the bubble period. Furthermore, the relation between noise trading and implied volatility in the futures market was of a significantly larger magnitude during this period. The study results support the importance of noise trading in market bubbles.

Bloomfield, O'Hara and Saar (2009) find that noise traders lower bid–ask spreads and improve liquidity through increases in trading volume and market depth. Such improved market conditions could have positive effects on market quality, and this impact could be evidenced by lower implied volatility when noise traders are more active. Indeed, the results in this study indicate that the level and characteristics of noise trading are fundamentally different during the technology bubble, and this noise trading activity has a larger impact during this period on implied volatility in the options market.

This paper uniquely analyzes derivatives on the S&P 500 Index in order to detect the presence and influence of noise traders. The authors derive and implement a two-factor jump diffusion noise model. In their model, noise rectifies the difference of analysts' opinions, market information and beliefs among traders. By incorporating a reduced-form temporal expression of heterogeneities among traders, the model is rich enough to capture salient time-series characteristics of equity prices (i.e. stochastic volatility and jumps). A singular feature of the authors’ model is that stochastic volatility represents the random movements in asset prices that are attributed to nonmarket fundamentals.

Signalling theory suggests how “strong” or “weak” the signal quality detected by a receiver (defined as signalling strength) is distorted by noisy factors (defined as noise). Although corporate cooperation signals are known to lead to receiver reaction, the effects of distortion factors on signal credibility are generally unexplored in signalling process. The paper aims to discuss this issue.

After analysing 264 contract announcements in 2013–2015 that befall publicly listed firms in China, the authors explore the signalling impact of contract value. the authors also incorporate the signalling noises, namely, signalling environment, external referents and other signallers, into the contracting context and investigate their effects on distorting the relationship between signal strength and receiver reaction.

Results indicate that firms’ contract-signing announcement conveys an effective signal to investors: the larger the contract scale is, the more investor reaction the firms experience. The signalling effects of contract scale on investor reaction are moderated by the three distorting factors.

The findings contribute to the signalling theory literature on the effects of signalling noise on receivers’ perception of signal observability.

Develops a robust maintenance float policy, that considers system design parameter settings that not only satisfy the system performance criteria but are also insensitive to various noise conditions. The experimental design strategy employed in the study involves the use of discrete event simulation. In the study, the strategy proposed involves solving a maintenance float policy using both the inner and outer arrays as advocated by Genichi Taguchi. Initial system variables and their parameter settings were chosen based on a prior study. These system variables were then classified into design factors and noise factors. An experimental design was developed using Taguchi’s orthogonal array, after which a simulation experiment was performed and additional data collected. Based on the results, regression was performed with the significant factors and interactions. From the regression analysis, a robust metamodel was developed. A cost model was also proposed.

In two experiments on reactions to persistent annoyance from another person, participants employed a very orderly verbal escalation sequence that fit a cascading Guttman scale. This began with requests and moved on to demands, and then to complaints, angry statements, threats, harassment, and abuse, in that order. The more escalated the tactic, the fewer people used it. People seldom skipped a step on the way to their most escalated tactic. Two possible explanations for this pattern seemed plausible in light of the data, that it is due to either a widely snared try‐try‐again script or a declining hierarchy of thresholds. Verbal escalation was associated with a negative view of the annoyer's character, while physical escalation was associated with blame and feelings of frustration and anger. Escalation was discouraged by membership in the same group as the annoyer. Loud noise did not encourage escalation in general but promoted the use of angry statements.

Following the optimal contracting hypothesis, this study investigates the issue of whether the board of director's ex ante choice to incorporate individual performance evaluation (IPE) measures into the CEO bonus plan rewards managerial decisions not reflected in measures of the firm's current financial performance. Empirical results provide evidence that the use of IPE in the CEO bonus plan is an increasing function of the proportion of outsider directors on the board and a decreasing function of the informativeness of financial performance measures. This study also demonstrates how the use of IPE in incentive contracting can explain CEO cash compensation that is not explained by the firm's current performance and governance variables. Finally, the CEO incentive cash compensation not explained by observable performance measures or governance structure is positively associated with firm future performance one year after its award. Overall, results support the optimal contracting hypothesis. IPE appears to be used to increase the informativeness of CEO actions and determine the level of current CEO cash incentive compensation.

China has proposed two-stage goals of carbon peaking by 2030 and carbon neutralization by 2060. The carbon emission reduction effect of the power industry, especially the thermal power industry, will directly affect the progress of the goal. This paper aims to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency of the thermal power industry and proposes policy suggestions for realizing China’s carbon peak and carbon neutralization goals.

This paper evaluates and compares the carbon emission efficiency of the thermal power industry in 29 provinces and regions in China from 2014 to 2019 based on the three-stage slacks-based measure (SBM) of efficiency in data envelopment analysis (DEA) model of undesired output, excluding the influence of environmental factors and random errors.

Empirical results show that during the sample period, the carbon emission efficiency of China’s thermal power industry shows a fluctuating upward trend, and the carbon emission efficiency varies greatly among the provincial regions. The carbon emission efficiency of the interregional thermal power industry presents a pattern of “eastern > central > western,” which is consistent with the level of regional economic development. Environmental factors such as economic level and environmental regulation level are conducive to the improvement of carbon emission efficiency of the thermal power industry, but the proportion of thermal power generation and industrial structure is the opposite.

This paper adopts the three-stage SBM–DEA model of undesired output and takes CO₂ as the undesired output to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency in China’s thermal power industry. The results provide a more comprehensive perspective for regional comparative evaluation and influencing factors of carbon emission efficiency in China’s thermal power industry.