Environmental uncertainties and hyperconnectivity force technosocio-economic systems to introspect and adapt to succeed and survive. Current practices in decision-making are predominantly intuition-driven with attendant challenges for precision and rigor. We propose to use the concept of digital twins by combining results from Modelling & Simulation, Artificial Intelligence, and Control Theory to create a risk free 'in silico' experimentation aid to help: (i) understand why a system is the way it is, (ii) be prepared for possible outlier conditions, and (iii) identify plausible solutions for mitigating the outlier conditions in an evidence-backed manner. We use reinforcement learning to systematically explore the digital twin solution space. Our proposal is significant because it advances the effective use of digital twins to new problem domains that have new potential for impact. Our approach contributes an original meta model for simulatable digital twin of industry scale techno-socioeconomic systems, agent-based implementation of the digital twin, and an architecture that serves as a risk-free experimentation aid to support simulation-based evidence-backed decision-making. We also discuss the rigor of our validation of the proposed approach and associated technology infrastructure through a representative sample of industry-scale real-world use cases.

This article proposes a novel Industrial Internet of Things framework to monitor the machine health conditions (MHCs) in a smart factory. The framework utilizes the reconfigurable intelligent surface (RIS) to address propagation blockages while employing a novel power mapping scheme and an autoencoder to facilitate the transmission and classification of the MHCs. Analytical and numerical analyses are then performed to study the ergodic capacity (primary information) and the MHC accuracy (secondary information) in terms of the RIS size ($K$) and the transmit power ($P$). We observe that the accuracy of detecting MHCs does not change significantly with$K$and$P$, implying that the MHC alerts can be efficiently conveyed in parallel with the primary information. In contrast, a careful choice of different power mapping levels is necessary in order to achieve the two main goals: 1) reasonably high data rate for primary transmission and 2) high accuracy for secondary MHC information.

Effective decision-making of modern organisation requires deep understanding of various aspects of organisation such as its goals, structure, business-as-usual operational processes etc. The large size and complex structure of organisations, socio-technical characteristics, and fast business dynamics make this decision-making a challenging endeavour. The state-of-practice of decision-making that relies heavily on human experts is often reported as ineffective, imprecise and lacking in agility. This paper evaluates a set of candidate technologies and makes a case for using actor based simulation techniques as an aid for complex dynamic decision-making. The approach is justified by enumeration of basic requirements of complex dynamic decision-making and the conducting a suitability of analysis of state-of-the-art enterprise modelling techniques. The research contributes a conceptual meta-model that represents necessary aspects of organisation for complex dynamic decision-making together with a realisation in terms of a meta model that extends Actor model of computation. The proposed approach is illustrated using a real life case study from business process outsourcing industry

The downlink problem of a cloud‐based central station (CCS) to multiple base stations (BSs) in a heterogeneous cloud radio access network (HCRAN) sharing the same time and frequency resources is studied. The authors adopt nonorthogonal multiple access (NOMA) and propose power allocation for the wireless downlink in the HCRAN. Taking into account practical channel modelling with power consumptions at BSs of different cell types (e.g. macrocell, micro‐cell etc.) and backhauling power, they analyse the energy efficiency (EE) of the practical HCRAN utilising NOMA. Simulation results indicate that the proposed NOMA for the HCRAN outperforms the conventional orthogonal frequency division multiple access scheme in terms of providing higher EE of up to four times. Interestingly, the results reveal a fact that the EE of the NOMA varies as a quasi‐concave function. An optimisation problem is accordingly introduced to find the optimal number of BSs that maximises the EE of the HCRAN. It is shown that, with a low power supply at the CCS, a double number of micro BSs can be served providing an improved EE of up to 1.6 times compared with the macro BSs and remote radio heads, while they achieve the same EE performance with high‐power CCS.

Enterprise agility, generally defined as the ability of an enterprise to detect and respond to changes timely and effectively, is a core imperative for effective change management and optimal performance in contemporary enterprises. It can improve operational efficiency, and enhance competitive ability. At the same time, it is elusive, challenging, and difficult to achieve. A data centric approach can support change management process by providing an avenue to capture, store, and manage information, activities, and knowledge relating to changes. In addition, it can provide an avenue for re-using previous (successful) change management strategies to adapt to similar changes in the future. In this paper, we examine change management concepts and requirements, integrate them into a conceptual data model. To demonstrate utility of this data model, we apply it to real world industry case study. Results show that this approach can be useful to enterprise change management by providing information and intelligence to support change management decisions.

The role of software systems on societal sustainability has generally not been the subject of substantive research activity. In this paper we examine the role of software engineering practice as an agent of change/impact for societal sustainability through the manifestation of value sensitive concerns. These concerns remain relatively neglected by software design processes except at early stages of user interface design. Here, we propose a conceptual model that can contribute to a translation of value sensitive design from its current focus in participatory design to one located in mainstream software engineering processes. Addressing this need will have an impact of societal sustainability and we outline some of the key research challenges for that journey.

This paper takes a practitioner's perspective on the problem of organisational decision-making. Industry practice follows a refinement based iterative method for organizational decision-making. However, existing enterprise modelling tools are not complete with respect to the needs of organizational decision-making. As a result, today, a decision maker is forced to use a chain of non-interoperable tools supporting paradigmatically diverse modelling languages with the onus of their co-ordinated use lying entirely on the decision maker. This paper argues the case for a model-based approach to overcome this accidental complexity. A bridge meta-model, specifying relationships across models created by individual tools, ensures integration and a method, describing what should be done when and how, and ensures better tool integration. Validation of the proposed solution using a case study is presented with current limitations and possible means of overcoming them outlined.

This paper investigates double-threshold based energy detection for cooperative spectrum sensing in cognitive wireless radio networks. We first propose a hybrid double-threshold based energy detector (HDTED) to improve sensing performance at the secondary users (SUs) by exploiting both their local decisions and the global binary decisions at the fusion centre (FC). We then derive closed- form expressions for the probability of missed detection and the probability of false alarm considering a practical scenario where all channel links suffer from Rayleigh fading. The derived expressions not only show the improved performance but also enable us to develop an optimal SU selection algorithm for forwarding the local decisions to the FC. Finally, numerical results are provided to demonstrate the validity of the analytical findings.

In this paper, we consider cooperative transmission in cognitive wireless relay networks (CWRNs) over frequency-selective fading channels. We propose a new distributed space-time-frequency block code (DSTFBC) for a two-hop nonregenerative CWRN, where a primary source node and multiple secondary source nodes convey information data to their desired primary destination node and multiple secondary destination nodes via multiple cognitive relay nodes with dynamic spectrum access. The proposed DSTFBC is designed to achieve spatial diversity gain as well as allow for low-complexity decoupling detection at the receiver. Pairwise error probability is then analysed to study the achievable diversity gain of the proposed DSTFBC for different channel models including Rician fading and mixed Rayleigh-Rician fading.

Industry has been practicing model-driven development in various flavours. In general it can be said that modelling and use of models have delivered on the promises of platform independence, enhanced productivity, and delivery certainty as regards development of software-intensive systems. Globalization market forces, increased regulatory compliance, ever-increasing penetration of internet, and rapid advance of technology are some of the key drivers leading to increased business dynamics. Increased number of factors impacting the decision and interdependency amongst the key drivers is leading to increased complexity in making business decisions. Also, enterprise software systems need to commensurately change to quickly support the business decisions. The paper presents synthesis of our experience over a decade and half in developing model-driven development technology and using it to deliver several business-critical software systems worldwide.

Business and IT alignment remains an ongoing concern for organizations. Enterprise Architecture has emerged as a possible tool for understanding and planning for BIA, but the current range of methods and frameworks for EA do not support precise measurement. In this paper, we propose a set of technologies and concepts - notably goals and computable functions which can be used to provide a measure of equivalence between as-is and to-be enterprise architectures. The goals enable description of business level requirements whilst the functions enable the goals to be evaluated at a technical architecture level. The technology is evaluated with a detailed and authentic case study from higher education.

Can e-learning systems effectively provide useful information to help teachers prevent students dropping out and failing as well as other access patterns useful to improve the pace of teaching? We present the data resulting from the analysis of a series of university courses in two different universities, one in Madrid, Spain, and the other one in London, United Kingdom. In order to do so, we have collected data from October 2006 to July 2007 from 400 students from 6 different courses interacting one way or the other with their e-learning platform. Thus, in these two countries a different e-learning tracking system was used to obtain the information about access; in the first one, an Ad-hoc Tracking System called Merlin placed in an in house e-learning system, and in the second one, the standard Blackboard tracking system was used.

Abstract Graphical interfaces and interactive graphical programmes are awkward to write because of a lack of top‐down structure. A methodology for constructing graphical programs will be described, together with a system that generates the basic interaction requirements for such applications.