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In recent years, it is imperative to establish the structure of manufacturing industry in the context of smart factory. Due to rising demand for exchange of information with various devices, and huge number of sensor nodes, the industrial wireless networks (IWNs) face network congestion and inefficient task scheduling. For this purpose, software-defined network (SDN) is the emerging technology for IWNs, which is integrated into cognitive industrial Internet of things for dynamic task scheduling in the context of industry 4.0.

In this paper, the authors present SDN based dynamic resource management and scheduling (DRMS) for effective devising of the resource utilization, scheduling, and hence successful transmission in a congested medium. Moreover, the earliest deadline first (EDF) algorithm is introduced in authors’ proposed work for the following criteria’s to reduce the congestion in the network and to optimize the packet loss.

The result shows that the proposed work improves the success ratio versus resource usage probability and number of nodes versus successful joint ratio. At last, the proposed method outperforms the existing myopic algorithms in terms of query response time, energy consumption and success ratio (packet delivery) versus number of increasing nodes, respectively.

The authors proposed a priority based scheduling between the devices and it is done by the EDF approach. Therefore, the proposed work reduces the network delay time and minimizes the overall energy efficiency.

In the pharmaceutical industry, marketing and sales managers often deal with massive amounts of marketing and sales data. One of their biggest concerns is to recognize the impact of actions taken on sold-out products. Data mining discovers and extracts useful patterns from such large data sets to find hidden and worthy patterns for the decision-making. This paper, too, aims to demonstrate the ability of data-mining process in improving the decision-making quality in the pharmaceutical industry.

This research is descriptive in terms of the method applied, as well as the investigation of the existing situation and the use of real data and their description. In fact, the study is quantitative and descriptive, from the point of view of its data type and method. This research is also applicable in terms of purpose. The target population of this research is the data of a pharmaceutical company in Iran. Here, the cross-industry standard process for data mining methodology was used for data mining and data modeling.

With the help of different data-mining techniques, the authors could examine the effect of the visit of doctors overlooking the pharmacies and the target was set for medical representatives on the pharmaceutical sales. For that matter, the authors used two types of classification rules: decision tree and neural network. After the modeling of algorithms, it was determined that the two aforementioned rules can perform the classification with high precision. The results of the tree ID3 were analyzed to identify the variables and path of this relationship.

To the best of the authors’ knowledge, this is one of the first studies to provide the real-world direct empirical evidence of “Analytics of Physicians Prescription and Pharmacies Sales Correlation Using Data Mining.” The results showed that the most influential variables of “the relationship between doctors and their visits to pharmacies,” “the length of customer relationship” and “the relationship between the sale of pharmacies and the target set for medical representatives” were “deviation from the implementation plan.” Therefore, marketing and sales managers must pay special attention to these factors while planning and targeting for representatives. The authors could focus only on a small part of this study.

One of the main problems of selective laser sintering (SLS) manufacturing process is the dimensional accuracy of products. Main causes of dimensional deviations are material shrinkage and size of laser heat affected zone (LHAZ). This paper aims to present a new method of adapting SLS manufacturing shrinkage and LHAZ compensation parameters to the geometrical characteristics of processed parts to improve their accuracy.

The first part of this work presents a hypothesis asserting that the shrinkage and the LHAZ size depend on geometrical properties of products. A method that defines geometrical properties by numerical influence factors is described in the continuation. A multi-factorial experiment with adaptable test part is set up. Then, test builds are manufactured on an SLS machine and measured with a three-dimensional optical scanner. Afterwards, the results are analysed in relation to the presumed hypothesis.

The analysis of variance of multi-factorial experiment proves the hypothesis and the influence of the geometrical properties on the accuracy of the SLS manufacturing process. Afterwards, a part is manufactured with adapted values of compensation parameters and the archived accuracy is discussed.

Presented research is limited on a single SLS material. Also, some numerical factors are directly linked to the build volume dimensions of the SLS machine that was used in the experiment. However, results can be generalised and some guidelines for shrinkage and LHAZ compensation method are presented. Also, some guidelines for future research are proposed.

Based on the presented results, it can be determined that using constant shrinkage and LHAZ values on an SLS machine will not yield the same results in terms of accuracy if the geometrical properties of parts change significantly.

By correctly adapting compensation values, the overall achievable accuracy of the SLS process can be achieved, enabling a more reliable production of mass-customised end-user parts such as customised medical accessories and devices for example.

A similar method of numerically describing geometrical properties of part in regard to SLS and directly adapting shrinkage and LHAZ compensation values to them for every individual build has not yet been proposed.

Drawing on Shakespeare, and in particular Hamlet's psychological crisis, this paper examines the relationship between emotions and meaning, a key theme in artistic work since Aristotle but, it is argued, largely ignored in psychology and the social sciences. Now, however, against a background of international terrorism, lessons are being learned from literature's insights.

The purpose of this paper is to provide a platform for young British Muslims in Tower Hamlets to share their perspectives on British values and identity, in light of the increased pressure schools are facing to actively promote “British values”.

Three focus groups were convened of 16-18 year olds, two all-male (one with five and one with six participants) and one all-female (five participants). Discussions were audio recorded with the data subjected to a form of thematic analysis that divided the raw data into three different categories: individual, group and group interaction data.

All but one of the participants defined themselves as British, largely due to a strong connection with British values. A minority felt this understanding was reflected back to them by society. However, the majority felt that, as ethnically Bengali and as Muslims, the opposite was the case. By judging the strength of an individual’s Britishness against the strength of their adherence to British values the government’s British values agenda is only serving to reinforce the isolation of those that feel excluded.

While the identities of young people, British people, and Muslims have been widely explored, there is little research that looks at the intersection of all three.

The purpose of this paper is to report experimental investigations performed to analyze the effect of process parameters on the shape accuracy of selective laser sintered (SLS) parts.

The effect of process parameters, namely build orientation, laser power, scan speed, cylinder diameter and build chamber temperature has been studied on shape accuracy by using geometric tolerances such as cylindricity and flatness. Central composite design (CCD) is used to plan the experiments and a second order regression model has been developed to predict flatness and cylindricity. The significance of process variables on flatness and cylindricity has been evaluated using analysis of variance technique.

It is observed that interaction effects are more dominant than individual effects. In case of cylindricity, it is found that the interaction between the scan speed and orientation is the dominant factor next to the orientation and quadratic effect of the geometry. In case of flatness, the interaction between build chamber temperature and scan speed is the dominant factor.

The empirical models presented in this paper work within the range of values used for the experiments and most of these models need to be redeveloped for use with other materials.

The empirical models developed in this work would be useful in deciding the process parameters for parts with improved geometrical tolerances. The optimum parameters identified from the empirical model are found to yield accurate parts with minimum shape error.

The paper establishes the interactions between this build orientation, geometry and process parameters on the shape accuracy of SLS process.

This paper aims to propose a view similarity-based shape complexity metric to guide part selection for additive manufacturing (AM) and advance the goals of design for AM. The metric helps to improve the selection process by objectively screening a large number of parts and identifying the parts most suited for AM and enabling experts to prioritize parts from a smaller set based on relevant subjective/contextual factors.

The methodology involves calculating a part’s shape complexity based on the concept of view similarity, that is, the similarity of different views of the outer shape and internal cross-sectional geometry. The combined shape complexity metric (weighted sum of the external shape and internal structure complexity) has been used to rank various three dimensional (3D) models. The metric has been tested for its sensitivity to various input parameters and thresholds are suggested for effective results. The proposed metric’s applicability for part selection has also been investigated and compared with the existing metric-based part selection.

The proposed shape complexity metric can distinguish the parts of different shapes, sizes and parts with minor design variations. The method is also efficient regarding the amount of data and computation required to facilitate the part selection. The proposed method can detect differences in the mass properties of a 3D model without evaluating the modified parameters. The proposed metric is effective in initial screening of a large number of parts in new product development and for redesign using AM.

The proposed metric is sensitive to input parameters, such as the number of viewpoints, design orientation, image resolution and different lattice structures. To address this issue, this study suggests thresholds for each input parameter for optimum results.

This paper evaluates shape complexity using view similarity to rank parts for prototyping or redesigning with AM.

This paper aims to present a survey concerning the accuracy of thermoplastic polymeric parts fabricated by additive manufacturing (AM). Based on the scientific literature, the aim is to provide an updated map of trends and gaps in this relevant research field. Several technologies and investigation methods are examined, thus giving an overview and analysis of the growing body of research.

Permutations of keywords, which concern materials, technologies and the accuracy of thermoplastic polymeric parts fabricated by AM, are used for a systematic search in peer-review databases. The selected articles are screened and ranked to identify those that are more relevant. A bibliometric analysis is performed based on investigated materials and applied technologies of published papers. Finally, each paper is categorised and discussed by considering the implemented research methods.

The interest in the accuracy of additively manufactured thermoplastics is increasing. The principal sources of inaccuracies are those shrinkages occurring during part solidification. The analysis of the research methods shows a predominance of empirical approaches. Due to the experimental context, those achievements have consequently limited applicability. Analytical and numerical models, which generally require huge computational costs when applied to complex products, are also numerous and are investigated in detail. Several articles deal with artificial intelligence tools and are gaining more and more attention.

The cross-technology survey on the accuracy issue highlights the common critical aspects of thermoplastics transformed by AM. An updated map of the recent research literature is achieved. The analysis shows the advantages and limitations of different research methods in this field, providing an overview of research trends and gaps.

Ubiquitous penetration of the internet fuelled by the interactive nature of digital media has created a new genre of entrepreneurs who leverage social media to propel their businesses. This study aims to explore the transformation of one such digital entrepreneurial cohort: Food YouTubers (YTs), who have metamorphosed from being mere content creators to entrepreneurs in the context of an emerging economy.

The authors invoke a qualitative interpretive phenomenological approach to unravel the entrepreneurial transition process. A combination of personal interviews and open-ended surveys is used to collect relevant data from 14 digital entrepreneurs. The transcripts are analysed to extract meaningful codes and themes.

The study shows that these entrepreneurs’ innovativeness and readiness to meet the real-time demands of their customers made their ventures a runaway success. This is the first study to target food YTs in India, an emerging economy. While the study is exploratory in nature, it provides basic insights into the transition of a novel group of influencers into entrepreneurs.

To the best of the authors’ knowledge, this is the first study of its kind to analyse the transition of YouTube influencers into entrepreneurs in emerging economies, as it relies more on entrepreneurial activities for their economic and social well-being.

In this study, we examined the attributes that determine shoppers’ satisfaction with food courts in shopping malls and the effects of this satisfaction on behavioural intentions towards shopping malls.

The data for this study were collected from 380 shoppers who visited food courts in shopping malls located in Hyderabad, India. The data were analysed using structural equation modelling.

The results indicate that fair prices, authenticity and the quality of the food, service and environment predict shoppers’ dining satisfaction and behavioural intentions. The findings also indicate that satisfaction with food court experiences has a strong positive influence on shoppers’ intention to revisit shopping malls.

Food service providers in Indian shopping malls should recognise that offering suitably priced and high-quality experiences is vital for sustaining their businesses in the long term as well as offer prompt service and warm hospitality to visitors to enhance customer satisfaction.

This study has implications for mall operators and food court managers seeking to enhance customer satisfaction with food courts and sustain the success of shopping malls. To the best of our knowledge, this is the first study to examine the factors that influence shoppers’ dining satisfaction and behavioural intentions in this context.