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The Edible Campus project was begun in spring 2007 in Montréal. An action-research project launched by volunteers and researchers from two leading local NGOs and university-based researchers (Alternatives, [online]; Santropol Roulant, [online]; McGill University's Minimum Cost Housing Group, [online]), it sought creative solutions to turn underutilised urban spaces into productive places. It involved citizens in the creation of green community spaces by incorporating productive growing in containers on a prominent but concrete-covered part of McGill University's downtown campus. Not only is it an investigation into making cities more food-secure by increasing urban food production, it is also a live demonstration of how ‘edible landscapes’ can be woven into urban spaces without diminishing their utility or functionality.

This study reviews the existing literature on the U.S. peer review system and the Public Company Accounting Oversight Board (PCAOB) inspection system to assess our knowledge of audit regulation. The traditional self-regulatory system of the accounting profession came to an end, in 2002, when the PCAOB was established to oversee the audit firms of publicly traded companies. This paper contributes to the controversial debate about self-regulation versus independent regulation by analyzing, categorizing, and comparing the research findings on the peer review system and the PCAOB system along three dimensions: the validity of peer reviews and PCAOB inspections, the recognition of reviews and inspections by decision-makers (e.g., investors, bankers, committees), and the effect of reviews and inspections on audit quality. Synthesizing the research on the regulatory regimes suggests that the notion of external quality control, both through peer reviews and government inspections, is positively linked with an improvement of audit quality. At the same time, the analysis indicates that external users do not seem to recognise peer review and PCAOB reports as very useful instruments for decision-making, which is in line with an identified rather skeptical perception of the audit profession on reviews and inspections. Overall, this study reveals that although the academic literature on peer review and PCAOB inspection is extensive it has not produced definitive conclusions concerning various aspects of audit regulation. This paper shows how this blurred picture is due to conflicting research findings, the dominance of the quantitative research paradigm, and unchallenged assumptions within the literature, and concludes by proposing research opportunities for the future.

The set of 2,509 documents related to the human-centric aspect of manufacturing were retrieved from Scopus database and systmatically analyzed. Using an unsupervised machine learning approach based on Latent Dirichlet Allocation we were able to identify latent topics related to human-centric aspect of Industry 5.0.

This study aims to create a scientific map of the human-centric aspect of manufacturing and thus provide a systematic framework for further research development of Industry 5.0.

In this study a 140 unique research topics were identified, 19 of which had sufficient research impact and research interest so that we could mark them as the most significant. In addition to the most significant topics, this study contains a detailed analysis of their development and points out their connections.

Industry 5.0 has three pillars – human-centric, sustainable, and resilient. The sustainable and resilient aspect of manufacturing has been the subject of many studies in the past. The human-centric aspect of such a systematic description and deep analysis of latent topics is currently just passing through.

This paper is concerned with estimation of the parameters of a high-frequency VAR model using mixed-frequency data, both for the stock and for the flow case. Extended Yule–Walker estimators and (Gaussian) maximum likelihood type estimators based on the EM algorithm are considered. Properties of these estimators are derived, partly analytically and by simulations. Finally, the loss of information due to mixed-frequency data when compared to the high-frequency situation as well as the gain of information when using mixed-frequency data relative to low-frequency data is discussed.

This study aims to evaluate a culture-based blended learning multilingual course created for adult learners in ten languages and the development of learners’ 21st-century skills during its implementation in six European countries – Croatia, Latvia, Slovenia, Romania, Poland and Czechia in the COVID-19 period.

A cross-sectional survey of 638 participants was conducted using a paper-based questionnaire. Data analysis was carried out applying Bereday’s four-step model comprising description, interpretation, juxtaposition and comparison to find out similarities and differences among various groups of learners.

Overall, learners have developed their 21st-century skills, but the improvement has not been the same for all target audiences. Learners with economic, social and cultural barriers were more positive in their evaluation than those with geographic and learning obstacles.

The research was conducted during the COVID-19 pandemic, and the face-to-face stage was replaced with online learning on virtual platforms, which impacted the research results. The results cannot be generalized to all adult learners as significant differences were discovered among various target groups of learners.

The course may be implemented for formal and non-formal adult education when face-to-face teaching/learning is restricted.

The findings indicate that the course is especially suitable for learners with economic, social and cultural obstacles to learning.

The article focuses on the use of blended learning in non-formal/informal adult education, which is a less widely researched area. The target course implemented during the COVID-19 pandemic shows a novel way of engaging adult learners in lifelong learning, including those with certain barriers to learning.

To investigate the general necessary condition for synthesis of square, real rational matrices of complex frequency as admittance matrices of active multiports with resistors, inductors, capacitors and possibly multiport transformers and to prove that this condition is also sufficient for synthesis of stable, square, real rational matrices of complex frequency as admittance matrices of balanced active multiports having only resistors, capacitors and voltage‐amplifiers with sufficiently large amplifications. The main aim of the paper is to provide a new and general method for stable admittance matrices synthesis and to develop strict realization algorithm by active balanced transformerless multiport networks.

The objectives of the paper are achieved by using factorization of regular polynomial matrices in complex frequency with certain degree as products of other regular polynomial matrices with specified degrees. A set of sufficient conditions for such a factorization is presented and derived a pertinent algorithm as the starting point for investigation and solving network synthesis problem and generation of class of equivalent realizations.

Theorem 1 states that sufficient condition for factorization of Pth order, generally regular polynomial matrix P(s) in complex frequency s with degree L, whose determinant has K distinct zeros, in form P(s)=P₁(s)·P₂(s), where 1≤p₂=P₂⁰≤L−1 is degree of polynomial matrix P₂(s), reads: K>(P−1)·L+p₂−1. The coefficient‐matrices of s, s²,… in P₁(s) and P₂(s) are real or complex depending on whether distinct zeros of det P(s) are real or complex, respectively. Theorem 2 states that: (a) for realization of Pth order matrix of real rational functions in complex frequency s (i.e. RRF matrix) as admittance matrix of active balanced RLC P‐port network with multiport transformers, or without them, P generalized controlled‐sources and P controlling‐ports are necessary, in general; and (b) P balanced voltage‐controlled voltage‐sources (VCVSs) with real and by module greater than unity controlling coefficients (“voltage amplifications”) are sufficient for realization of stable admittance RRF matrix by active, balanced, transformerless, RC P‐port network.

This is a research paper with the following two main contributions (original results). First, a theorem on sufficient conditions for factorization of regular polynomial matrices in complex frequency; and second, a theorem relating to sufficient conditions for synthesis of matrices of real rational functions in complex frequency by active, balanced, transformerless networks. The results may be interesting for network theorists and researchers in the field of electric circuits and systems.

Nobody concerned with political economy can neglect the history of economic doctrines. Structural changes in the economy and society influence economic thinking and, conversely, innovative thought structures and attitudes have almost always forced economic institutions and modes of behaviour to adjust. We learn from the history of economic doctrines how a particular theory emerged and whether, and in which environment, it could take root. We can see how a school evolves out of a common methodological perception and similar techniques of analysis, and how it has to establish itself. The interaction between unresolved problems on the one hand, and the search for better solutions or explanations on the other, leads to a change in paradigma and to the formation of new lines of reasoning. As long as the real world is subject to progress and change scientific search for explanation must out of necessity continue.

Examines the thirteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

The purpose of this paper is to study the effect law of roundness error on the properties of gas foil conical bearing (GFCB), and the performance of bearings with different non-circular sleeve shapes are calculated.

For the bump-type GFCB, the nonlinear bump foil stiffness model and 1-D beam top foil stiffness model are built. On this basis, the finite element method and finite difference method are used to solve the Reynolds equation and the film thickness equation coupled, and the static and dynamic properties of GFCB are calculated. The effect law of sleeve roundness error on the static performance under different conditions is obtained. Moreover, the dynamic stiffness and damping characteristics under different errors are also studied.

The roundness error will decrease the load capacity and friction torque of GFCB, and increase the attitude angle. The error effect is more dramatic when there is larger eccentric, small nominal clearance, larger error value and more error lobes, and the static performance exhibits a periodic change in the circumferential direction. The roundness error can also decrease the direct stiffness and cross-coupled damping of GFCB, while the cross-coupled stiffness increases largely, which will reduce the bearing stability.

The roundness error adversely affects the static and dynamic characteristics of GFCB, which should be concerned by bearing designers, researchers and academicians.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2020-0019/

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.

Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.

The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.

The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.

This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.