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The aim of this paper is to propose a basis upon which accounting reporting can be developed to reflect real values and the real economy. It aims to address the environmental considerations discussed in the UN debate (Bebbington and Unerman, 2020) and the concern for a “better life-world”, which is the theme of this special issue.

Addressing the task involves the application of the philosophy of pragmatic constructivism (which explains how people can relate to their reality in ways that lead to successful action) and the philosophical concept of the “good life” (which establishes the values to be pursued through action and so defines action success). Also, it outlines the necessary characteristics of measurement frameworks if they are to be effective in the development and control of human practices to achieve desired values.

This paper proposes a conceptual framework for guiding the measurement of how a sustainable good life has improved and/or deteriorated as a result of organisational activities. It outlines a system of concepts on basic and instrumental values for analysing the condition of maintaining a sustainable good life in real terms. This is related to the financial results and societal regulations to analyse and adjust controls according to the real economic goals. Also, it provides a system of value measurands to produce valid information about the development of a sustainable good life. The measurand makes accounting reporting reflect the conditions of the good life that constitute the real economy instead of merely the financial economy driven by shareholder capitalism. Providing tools to analyse whether the existing practices of business and social regulations promote or counteract the real economic goals of producing a sustainable good life means the measurement system proposed makes the invisible hand of the market visible.

The mechanism proposed to enable accounting reporting to reflect real values and the real economy is a new conceptual framework that will allow accounting to more fully realise its potential to contribute to a “better world”. In aiming to serve a sustainable good life, accounting reporting will inherently foster ethical social practices.

The assumption that a set of observed variables is a function of an underlying common factor plus some error has dominated measurement in marketing and the social sciences in general for decades. This view of measurement comes with assumptions, which, however, are rarely discussed in research. In this article, we question the legitimacy of several of these assumptions, arguing that (1) the common factor model is rarely correct in the population, (2) the common factor does not correspond to the quantity the researcher intends to measure, and (3) the measurement error does not fully capture the uncertainty associated with measurement. Our discussions call for a fundamental rethinking of measurement in the social sciences. Adapting an uncertainty-centric approach to measurement, which has become the norm in in the physical sciences, offers a means to address the limitations of current measurement practice in marketing.

In the determination of the measurement uncertainty, the GUM procedure requires the building of a measurement model that establishes a functional relationship between the measurand and all influencing quantities. Since the effort of modelling as well as quantifying the measurement uncertainties depend on the number of influencing quantities considered, the aim of this study is to determine relevant influencing quantities and to remove irrelevant ones from the dataset.

In this work, it was investigated whether the effort of modelling for the determination of measurement uncertainty can be reduced by the use of feature selection (FS) methods. For this purpose, 9 different FS methods were tested on 16 artificial test datasets, whose properties (number of data points, number of features, complexity, features with low influence and redundant features) were varied via a design of experiments.

Based on a success metric, the stability, universality and complexity of the method, two FS methods could be identified that reliably identify relevant and irrelevant influencing quantities for a measurement model.

For the first time, FS methods were applied to datasets with properties of classical measurement processes. The simulation-based results serve as a basis for further research in the field of FS for measurement models. The identified algorithms will be applied to real measurement processes in the future.

The purpose of this paper is to develop and implement a general sensor model under normal and abnormal operational conditions including nine functional categories (FCs) to provide additional tools for the design, testing and evaluation of unmanned aerial systems within the West Virginia University unmanned air systems (UAS) simulation environment.

The characteristics under normal and abnormal operation of various types of sensors typically used for UAS control are classified within nine FCs. A general and comprehensive framework for sensor modeling is defined as a sequential alteration of the exact value of the measurand corresponding to each FC. Simple mathematical and logical algorithms are used in this process. Each FC is characterized by several parameters, which may be maintained constant or may vary during simulation. The user has maximum flexibility in selecting values for the parameters within and outside sensor design ranges. These values can be set to change at pre-defined moments, such that permanent and intermittent scenarios can be simulated. Sensor outputs are integrated with the autonomous flight simulation allowing for evaluation and analysis of control laws.

The developed sensor model can provide the desirable levels of realism necessary for assessing UAS behavior and dynamic response under sensor failure conditions, as well as evaluating the performance of autonomous flight control laws.

Due to its generality and flexibility, the proposed sensor model allows detailed insight into the dynamic implications of sensor functionality on the performance of control algorithms. It may open new directions for investigating the synergistic interactions between sensors and control systems and lead to improvements in both areas.

The implementation of the proposed sensor model provides a valuable and flexible simulation tool that can support system design for safety purposes. Specifically, it can address directly the analysis and design of fault tolerant flight control laws for autonomous UASs. The proposed model can be easily customized to be used for different complex dynamic systems.

In this paper, information on sensor functionality is fused and organized to develop a general and comprehensive framework for sensor modeling at normal and abnormal operational conditions. The implementation of the proposed approach enhances significantly the capability of the UAS simulation environment to address important issues related to the design of control laws with high performance and desirable robustness for safety purposes.

The paper aims to develop a method for improving the accuracy of smart sensors (deemed as digital measuring instruments) by organizing combined measurements and processing their results by the parametric adjustment method at heterogeneous dispersion of the random error of the applied regression model.

When carrying out combined measurements, the problem of joint processing of measurement results of functionally related quantities must be solved. The function type can be known in advance or obtained experimentally. The number of combined measurements exceeds the number of unknown measured quantities. The redundant measurements can improve the accuracy of estimates of measured values but lead to inconsistency of the measurement results. The problem of inconsistency is solved by the parametric adjustment method, which is rather widely used mainly in the field of geodetic measurements, wherein the parametric equations are linear and the measured quantities are additive.

The proposed method allows to reduce the uncertainty of type B of a measurement result, caused by the maximum permissible error of a digital measuring instrument, by 1.2–4 times in comparison with the direct estimation method.

A compact description of the parametric adjustment method in matrix form is given. Recommendations are given on shaping a sensitivity matrix of functions for the proposed method. The geometric interpretation of the proposed method is considered. The results of the proposed method experimental testing are given when evaluating resistance values.

Measurement uncertainty is present in all measurement processes in the field of production engineering. However, this uncertainty should be minimized to avoid erroneous decisions. Present methods to determine the measurement uncertainty are either only applicable to certain processes and do not lead to valid results in general or require a high effort in their application. To optimize the costs and benefits of the measurement uncertainty determination, a method has to be developed which is valid in general and easy to apply. The paper aims to discuss these issues.

This paper presents a new technique for determining the measurement uncertainty of complex measurement processes. The approximation capability of artificial neural networks with one hidden layer is proven for continuous functions and represents the basis for a method for determining a measurement model for continuous measurement values.

As this method does not require any previous knowledge or expertise, it is easy to apply to any measurement process with a continuous output. Using the model equation for the measurement values obtained by the neural network, the measurement uncertainty can be derived using common methods, like the Guide to the expression of uncertainty in measurement. Moreover, a method for evaluating the model performance is presented. By comparing measured values with the output of the neural network, a range in which the model is valid can be established. Combining the evaluation process with the modelling itself, the model can be improved with no further effort.

The developed method simplifies the design of neural networks in general and the modelling for the determination of measurement uncertainty in particular.

Fibre optic sensors offer many potential advantages for the measurement of physical parameters in process plants

It is currently difficult to measure temperature and pressure in harsh environments. Such measurements are limited by either the ability of the sensing element or the associated electrical wiring to withstand the operating environment. This is unfortunate as temperature and pressure are important measurands in various engineering structures as they provide critical information on the operating condition of the structure. Hence, there is a need to address this shortcoming. Such a sensor in place would enhance the operating efficiency thereby reducing the pollution burden and its impact on the environment. The purpose of this paper is to present theoretical and preliminary experimental results for a co‐integrated pressure and temperature sensor for harsh environments.

This work describes a co‐integrated pressure‐temperature wireless sensing scheme. The approach presented herein provides the possibility of measuring dynamic pressure and temperature within an enclosed volume using acoustic signals. Resonance tube physics is exploited for the temperature sensing. A microphone is used to obtain the acoustic signal whose frequency is a function of the temperature and the tube geometry.

The dynamic pressure is measured from the calibrated amplitude of the pressure wave signal measured by the microphone. The temperature can be measured through the shift of the standing wave frequency with a resolution of <1°C. The resonance tube can be fabricated using any material that resists harsh environments. The geometry of the tube can be tailored for any specific frequency range, as the application warrants. Also, this provides a means for accurate temperature compensation of pressure sensor data from high temperature environments. A Matlab/Simulink model is developed and presented for the acquisition of acoustic signals through the wall of an enclosed volume. For these applications the standing wave signal transmitted through the enclosure wall becomes a function of the wall material and wall thickness. Preliminary experimental results are presented in which a DC fan is used for generating the dynamic pressure in a varying temperature environment.

The major issue is the separation of the noise from the signal. As various applications yield specific signal noise, the problem needs detailed data to be addressed.

Temperature and dynamic pressure could be recorded/monitored in very harsh environment conditions such as chemical reactors.

This work demonstrates the possibility of employing a co‐integrated acoustic sensing scheme in which both pressure and temperature are measured simultaneously with a sole sensor. The major advantage with acoustic sensing is the wireless transmission of data. This allows for non‐invasive measurement from within enclosed systems. Direct real‐time temperature compensation is possible that does not require any compensation circuitry. Hence, pressure and temperature data may be obtained from caustic operating environments whose access is otherwise not feasible.

Introduction Optical sensors, and especially fibre optic sensors, offer some significant technical advantages over conventional electronic sensors. These technical advantages have been perceived to be sufficiently significant to have stimulated a large amount of research activity in the UK and elsewhere. Much of the original research has been carried out in universities and polytechnics, but there has also been considerable corporate R&D activity aimed at developing commercial products and systems. The results of much of this corporate work have not been published, except in the form of patents. Patents therefore provide a useful literature which, though of considerable interest to companies, is difficult to analyse and assimilate. For this reason, the DTI Advanced Sensors Technology Transfer Programme commissioned a study of recent optical sensor patents. The aim was to classify and analyse the patents, and to present the findings in a manner which a small or medium size instrumentation company could readily digest.

In Aerospace applications, the inlet tubes are used to mount strain gauge type pressure sensors on the engine under static test to measure engine chamber pressure. This paper aims to focus on the limitations of the inlet tube and its design aspects to serve better in the static test environment. The different sizes of the inlet tubes are designed to meet the static test and safety requirements. This paper presents the performance evaluation of the designed inlet tubes with calibration results and the selection criteria of the inlet tube to measure combustion chamber pressure with the specified accuracy during static testing of engines.

Two sensors, specifically, one cavity type pressure sensor with the inlet tube of range 0-6.89 MPa having natural frequency of the diaphragm 17 KHz and another flush diaphragm type pressure sensor of the same range having −3 dB frequency response, 5 KHz are mounted on the same pressure port of the engine under static test to study the shortcomings of the inlet tube. The limitations of the inlet tube have been analyzed to aid the tube design. The different sizes of inlet tubes are designed, fabricated and tested to study the effect of the inlet tube on the performance of the pressure sensor. The dynamic calibration is used for this purpose. The dynamic parameters of the sensor with the designed tubes are calculated and analyzed to meet the static test requirements. The diaphragm temperature test is conducted on the representative hardware of pressure sensor with and without inlet tube to analyze the effect of the inlet tube against the temperature error. The inlet tube design is validated through the static test to gain confidence on measurement.

The cavity type pressure sensor failed to capture the pressure peak, whereas the flush diaphragm type pressure sensor captured the pressure peak of the engine under a static test. From the static test data and dynamic calibration results, the bandwidth of cavity type sensor with tube is much lower than the required bandwidth (five times the bandwidth of the measurand), and hence, the cavity type sensor did not capture the pressure peak data. The dynamic calibration results of the pressure sensor with and without an inlet tube show that the reduction of the bandwidth of the pressure sensor is mainly due to the inlet tube. From the analysis of dynamic calibration results of the sensor with the designed inlet tubes of different sizes, it is shown that the bandwidth of the pressure sensor decreases as the tube length increases. The bandwidth of the pressure sensor with tube increases as the tube inner diameter increases. The tube with a larger diameter leads to a mounting problem. The inlet tube of dimensions 6 × 4 × 50 mm is selected as it helps to overcome the mounting problem with the required bandwidth. From the static test data acquired using the pressure sensor with the selected inlet tube, it is shown that the selected tube aids the sensor to measure the pressure peak accurately. The designed inlet tube limits the diaphragm temperature within the compensated temperature of the sensor for 5.2 s from the firing of the engine.

Most studies of pressure sensor focus on the design of a sensor to measure static and slow varying pressure, but not on the transient pressure measurement and the design of the inlet tube. This paper presents the limitations of the inlet tube against the bandwidth requirement and recommends dynamic calibration of the sensor to evaluate the bandwidth of the sensor with the inlet tube. In this paper, the design aspects of the inlet tube and its effect on the bandwidth of the pressure sensor and the temperature error of the measured pressure values are presented with experimental results. The calibration results of the inlet tubes with different configurations are analyzed to select the best geometry of the tube and the selected tube is validated in the static test environment.