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At the one‐stop carpal tunnel syndrome clinic, patients undergo neurophysiological studies followed by clinical assessment by the orthopaedic consultant on the same day. Patients with paraesthesia or numbness in the median nerve distribution for greater than three months duration without a history of a previous soft tissue neck injury were selected for assessment in the one‐stop carpal tunnel syndrome clinic based on a proforma completed by their general practitioners. Data of patients attending the one‐stop carpal tunnel syndrome clinic over a ten‐month period were compared with that over the same period from a conventional hand clinic. A total of 77 patients attended the one‐stop carpal tunnel syndrome clinic over a ten‐month period. The mean time from referral to surgery was 23 weeks in this group compared to 44 weeks for a conventional clinic. On average, the one‐stop carpal tunnel syndrome clinic reduced the time from referral to surgery by 21 weeks. The one‐stop carpal tunnel syndrome clinic is convenient and cost effective for patients and hospitals.

The purpose of this paper is to investigate the potential for using chitin and chitosan sustainable materials to absorb copper from PCB manufacturing effluent and to report the results of an initial feasibility study aimed at demonstrating proof of concept.

Crab shells and prawn shells, both waste products of the seafood industry, as well as chitosan, were evaluated as potential absorbents for recovering copper present at low levels in the manufacturing effluent produced in a UK‐based PCB manufacturing facility. Various conditions were investigated and efforts were also made to recover absorbed copper via a regeneration process that enabled the metal to be electroplated from solution.

Although only a short feasibility study, conditions were found that enabled copper to be absorbed by the ground crab shells and chitosan and then subsequently recovered by electrowinning to produce the metal.

Although successful as a feasibility study, the experimental work highlighted the large number of variables that need to be investigated and optimised in order to obtain the most efficient copper capture and recovery. Further work needs to be carried out to determine these optimum conditions and to investigate the potential for recovery of other metals from a wider range of solutions.

The paper details how individual treatment technologies can be combined to enable a much more sustainable approach to PCB manufacturing which offers the benefits of reduced effluent metal levels, metal recovery and a novel use for another sector's waste products.

This paper aims to predict Dogecoin price by using artificial intelligence (AI) methods and comparing the results with the econometrics models.

An artificial neural network (ANN) was applied as a prediction method without any optimization techniques. Additionally, the genetic algorithm (GA) is used to select the most appropriate input variables. Additionally, based on the literature review and the relationships between crypto-price and global indices, 20 economic indicators, such as Coinbase Bitcoin, Coinbase Litecoin and US dollars, along with main global stock indices such as FTSE100 and NIFTY50, are identified as input variables for the model. Lichtenberg algorithm (LA) and aquila optimization (AO) algorithm are used to make the ANN more robust. To validate our algorithms, they have been implemented on daily data for the last three years. To demonstrate the superiority of the models over traditional methods such as econometrics, regression analysis and curve fitting techniques are used. The effectiveness of these models is then evaluated and compared using criteria such as recall, accuracy and precision.

The results indicate that AI-based algorithms not only enhance the accuracy, recall and precision of calculations but also expedite the process without requiring the numerous and restrictive assumptions associated with time series and econometric models.

The main contribution of this paper is the application of novel approaches such as AO and LA to improve the predictive capabilities of the ANN method for various cryptocurrencies’ prices. It demonstrates the superiority of the proposed algorithms over traditional econometric models using real-life data.

The purpose of this paper is two-fold: first, it highlights the importance of the presence of active teaching experiences in architecture courses. Such experiences can lead to an improvement in the teaching of technical disciplines, such as structural engineering. Second, it purports to demonstrate the relation and interaction between the active teaching strategy here presented and the learning outcomes required by the study programme.

The paper reports an active didactic experience (addressed to students of architecture and performed at Politecnico di Milano, Italy, and Université catholique de Louvain, Belgium), from its conception to its development in the classroom with the students. The experience is reported by discussing the three main steps of which an active didactic experience should be composed: the stimulus, the practice and the discussion.

The experience seeks to find innovative methods to stimulate the study of structural engineering by students of architecture. Through this experience, based on the study of a square silicon pot mat, students are able to learn concepts related to the mechanics of structures. In addition, students find in their experience direct connections with structures of considerable architectural importance, such as the structure of the New National Gallery by Mies van der Rohe.

This experience is original in two aspects. First of all, the introduction of an active didactic experience to improve courses that are generally structured in a passive way. Second, in an era where the importance of numerical technology is growing, this experience goes in a different direction by choosing a low-tech but no less interesting approach.

This study aims to explore the impact of store atmospherics on urban bottom-of-the-pyramid consumers’ behavioural intentions to purchase apparel in an emerging African market context. This study also considers purchase antecedents to attitude, perceived behavioural control and social norms as determinants of urban bottom-of-the-pyramid consumers’ apparel behavioural intentions.

Using non-probability sampling, specifically purposive and interlocking sampling, data collection was secured from 881 economically active Namibian urban bottom-of-the-pyramid respondents through interviewer-administered questionnaires. Covariance-based structural equation modelling assessed the significant relationships among all constructs in the conceptual model.

This study found that for favourable apparel behavioural intentions of urban bottom-of-the-pyramid consumers to occur, apparel retailers should emphasise trust, perceived awareness and self-identity through apparel assortment and groupings, easy-to-read visible signage, together with competent, friendly and respectful sales personnel in their store atmospherics.

The findings of this study may guide apparel retailers in other emerging African markets to develop regional integration, market-based solutions and inclusive economic growth focusing on “non-essential” products, such as apparel, among urban bottom-of-the-pyramid consumers.

This study expands the intellectual boundaries of urban bottom-of-the-pyramid consumers’ behavioural intentions towards “non-essential” products. The theoretical framework supports the integration of both the stimulus-organism-response model and the theory of planned behaviour into one single model for empirical investigation. Additionally, adopting a novel theoretical framework helped identify the impact of store atmospherics from a bottom-of-the-pyramid perspective in an emerging African market context, such as Namibia.

The purpose of this paper is to analyze the innovation strategies of the sugar–energy industry in Central-Southern Brazil with regard to its resource structure and results.

The data were collected through a questionnaire covering the three-year period from 2015 through 2017. The results were extracted based on a combination of factor analysis and multiple regression analysis.

The innovation strategies of Brazilian sugar–energy companies are characterized as defensive and imitative based on the resources dedicated to innovation and the implementation of innovation with respect to products, processes, eco-innovation and cost reductions.

The sample is representative, but it does not cover all companies in the Brazilian sugar–energy industry. The data were acquired using a survey, and the results could not be compared to the financial results of the companies.

Sugar–energy companies need to define their innovation strategies, as these strategies lead to different results that can be achieved only through the management of resources dedicated to the generation and implementation of innovations. Managers and analysts need to know the profile of these innovation strategies to evaluate the effectiveness of the results based on the resources that were invested.

This study presents an exploratory model that identified two factors related to innovation efforts and three results. It is possible to define the innovation strategies of companies in the Brazilian sugar–energy industry, which is one of the most competitive in Brazil.