Smart and Sustainable Engineering for Next Generation Applications

Robotic wheelchairs help in improving the mobility and safety of people with severe motor handicaps. In this paper, we propose a microelectromechanical sensor integrated in a data glove to record the hand tilt angle. An Arduino microcontroller processes the formulated angle and transmits a corresponding hand command wirelessly to a prototype robotic wheelchair via a Zigbee module employing IEEE 802.15.4 protocol. A Proportional, Integrative, and Derivative controller is added by interfacing the MATLAB® SIMULINK and Arduino® programming environments to optimise the DC motor speed control response of the robotic wheelchair. The resulting automatic tuning system causes the wheelchair speed to remain constant in both ramping up and down conditions. Experimental outcomes show that the proposed mechanism responds accurately to hand gestures of users.

The aim of this paper is to apply Invasive Weed Optimization (IWO), Jaya Algorithm (JA) and a proposed Jaya-based Invasive Weed Optimization (JIWO) technique to the tuning of proportional-integral-derivative (PID) controllers for load frequency control (LFC) and to determine which method is more appropriate. The Matlab/Simulink environment is used for the implementations and simulations. Taking into account performance measures, such as peak values, settling times and steady-state errors, as well as the optimality of the solutions and the convergence rates, the most suitable approach is selected. The results obtained show that all of the three optimization techniques are able to deliver improved responses over the uncompensated system. However, JIWO provides the best results, both in terms of convergence and optimality.

This paper proposes of a hybrid optimisation algorithm consisting of the ant-colony optimisation and whale optimisation algorithm for the tuning of PID controllers in AVR systems. Two excitation models are chosen, namely the Type DC1A and Type AC5A excitation systems which are coupled with either a first order or a fourth order generator model. A Proportional Integral Derivative (PID) controller is incorporated into the system to correct the step response in terms of percentage overshoot, settling time and steady state error. The hybrid algorithm is then tested against the individual optimisation schemes and the results obtained were found to be very promising.

The aim of this paper is to apply three optimization techniques, namely Genetic Algorithm (GA), Cultural Algorithm (CA) and Jaya-based Invasive Weed Optimization (JIWO), to the tuning of proportional-integral-derivative (PID) controllers for the load frequency control (LFC) of a non-linear two-area system, in which the frequency is to be kept uniform and tie-line power deviations are to be mitigated. The Matlab/Simulink environment is used for the implementations and simulations. The results obtained show that, while all three optimization techniques improve the responses relative to the uncompensated system, JIWO gives the best results in terms of convergence and optimality.

In this paper, we investigate the behaviour of scalar controlled AC Variable Frequency Drives for induction motors when coupled with different types of loads. It is well known that slip is more pronounced at high speeds rather than low speeds in loads such as in pumps and fans operation because the torque load varies as the square or cube of the speed. Slip compensation in Voltage/Frequency controlled Variable Frequency Drives may work only up to a certain limit since the problem can be exacerbated by overcompensation causing low magnetisation flux of the stator and the Variable Frequency Drive becomes uncontrollable. In this research work, we investigate the behaviour of the motor and Variable Frequency Drive assembly with regards to slowly changing loads from low to high speeds. Results show that at reduced speeds for the Variable Frequency Drive and motor assembly, the efficiency is lowest for square and cubic torques loads compared to linear and constant torque loads. Highest efficiency and torque are achieved only in a restricted interval of speed. This problem therefore compels us to avoid using the Variable Frequency Drives in the Voltage/Frequency mode at low speeds for pumps and fans.

Motor drives are required to be tested under various loading conditions so as to ascertain the effectiveness of their speed control schemes and the ability of the machine to withstand sudden load changes. This paper presents the modeling and design of a reconfigurable load emulator which can apply typical load torque profiles on the shaft of the motor under test. The proposed system uses a current-controlled dc chopper driven dc machine, and does not require torque measurement. Simulation tests confirm the ability of the load emulator to produce commonly encountered torque-speed characteristics and bidirectional step load profiles for multiple quadrant operation.

Due to the increased concern about the environment, distributed generation (DG) units have been widely introduced to the power system. However, DG units may have positive and negative impacts on the voltage profile and active power loss of a power system, depending on their size and location. In this paper, three algorithms namely multi-objective particle swarm optimisation (MOPSO), non-dominated sorting genetic algorithm (NSGA-II) and strength pareto evolutionary algorithm (SPEA2) were used to identify the optimum size, location and type of a DG unit in an unbalanced distribution system. The simulations were performed on the IEEE 34 Node Test Feeder System using OpenDSS and MATLAB such that the total active power loss and the voltage deviation are reduced. The effectiveness of the algorithms were evaluated based on the computation time and performance metrics such as generational distance, pure diversity and spread. It was found that all the three algorithms were suitable for the optimisation. However, NSGA-II had the lowest average computation time.

This paper presents the preliminary results of the experiments to quantify the effects of four roof conditions on the power production of three photovoltaic (PV) technologies. Amorphous silicon, polycrystalline silicon and copper indium selenide PV technologies were set up on small scale bare, white coated, black water-proofed and grass-covered concrete roofs. Two MOSFET-based current-voltage curve tracers were implemented to acquire the maximum powers of the PV modules through LabVIEW software and myDAQ hardware from National Instruments. Under the prevailing irradiance and temperature conditions during the measurements, it was found that relative to bare concrete roof, white-coated roof reduced the modules’ output powers by 2.6–58.8%, the black waterproofed-roof reduced the output powers by 20.8–50.7%, while the grass-covered roof increased the output powers by 15.1–81.4%. The results conclude that roofs conditions should not be neglected when PV systems are being considered.

Solar tracking conventionally uses mathematical calculations to know the exact position of the sun. In an attempt to further increase yield efficiency, this paper looks at control techniques to achieve maximum power. Converting irradiation into electricity is the main function of a photovoltaic panel. Then, why not orientate the solar panel in a position where it can yield maximum power? In other words, this research tests the feasibility of building a ‘power tracker’ instead of a solar tracker. The two proposed algorithms are namely, Extremum Seeking Control and Perturb and Observe. Simulations were done on MATLAB/Simulink and a prototype was built for a comparative study of different tracking control techniques.

Precision agriculture is a modern farming practice that makes production more efficient. It can help determine everything from what factors may be stressing a crop at a specific point in time to estimating the amount of moisture in the soil. One important aspect in precision agriculture is precision irrigation. This paper provides the design and implementation of a Smart Precision Irrigation and Monitoring System which uses Microsoft Azure along with Internet of Things technologies to provide for automatic precision irrigation. Sensors are used to collect water level, temperature, humidity and soil moisture data and Azure Cloud services are utilized to perform real-time analytics on the data obtained. A Web App and a Mobile App have been implemented for the farmer to manage the system, control the automatic and manual irrigation processes and receive important notifications. Azure Machine Learning has also been used to generate the chance of rain, hence facilitating the decision-making process of the farmer.

Over the course of the last few years, people have begun to acknowledge the full potential imposed by the smart devices, also known as “Internet of Things”. The use of these devices is increasing day-by-day, from authentication devices to home automation and even weather forecast. A lot of emphasis is being made about how to use it and what it is for. Amidst all these discussions, an important problem has been brought up. The latter is the networking protocol used in the intercommunication between these devices. The IPv4 protocol is being completely depleted by the enormous number of devices. While a lot has tried to overcome this problem by using 6LoWPAN an adapter layer, the drawback remains visible due to the lack of range and small data rate. This paper aims at developing an IPv6 IoT network based on the 802.11 standard while overcoming weaknesses associated with using them on tiny Internet of Things devices.

The technological innovations in wireless communications of modern time have accustomed human population on presence of electromagnetic fields (EMF) in environment. Those fields are produced artificially and have become an inevitable element of the environment, which potentially can have influence on living organisms. Thus, the considerable effort is devoted to analysis of their interaction with human body, as well as prevention of the unsafe EMF levels. The modern approaches of EMF investigation recommend continuous and long-term EMF monitoring, introducing several advanced EMF monitoring systems, such as newest one, the Serbian EMF RATEL system. In this paper, the measurement results acquisition from Narda AMS 8061 area monitoring stations, is presented, introducing the dedicate parser function, as a central part of information logistic for such monitoring stations in the EMF RATEL system.

A huge technological development, mostly prominent in the area of wireless communications, in recent years, has led to a considerable growth of artificial sources of electromagnetic fields (EMF). Consequently, an issue of EMF exposure has become very important regarding environmental protection. These facts increased the necessity for development of smart systems, designed for long-term and continuous EMF monitoring in the environment, as well as appropriate exposure assessment. One of such systems has recently started in the Republic of Serbia, managed by Regulatory Agency for Electronic Communications and Postal Services – RATEL. This paper brings details on small parts of the system and its adaptive boundary exposure assessment approach, recently realized and implemented into system. It is described the software realization of exposure assessment, containing the specific function for exposure boundaries calculation. In addition, the logical interpretation of the function is presented. Finally, it is given the example of graphical presentation of exposure boundaries at publicly available Internet portal of the EMF RATEL system.

Modelling processes is one of the core objectives of the scientific world. Since mobile communication has become one of the most booming technologies of this era, mobile operators around the world call for proper modelling and forecasting of the traffic for smarter investment of their resources. In this paper, a powerful stochastic modelling technique, the Hidden Markov Modelling (HMM) has been used to develop a forecasting model for voice traffic over 2G and 3G, and internet data traffic over 3G and 4G during peak time via supervised learning. The designed model has been developed to forecast mobile communication traffic in Mauritius, a country with limited prediction models in this specific field.

The increasing use of hierarchical modulation techniques having fixed parameters with non-binary Turbo codes has risen the interest to obtain optimized parameters with a view to enhance the error performance. In this paper, a derivation of optimum parameters for performing Hierarchical modulation with16-QAM integrated with triple-binary turbo codes have been performed. The validation of the enhanced performance achieved using Hierarchical modulation with optimized parameters has been presented using EXIT charts. Additionally, a classification using K-NN method has been presented. The technique is used to forecast the optimized parameters at every Eb/N0. Results demonstrate that Triple-binary turbo codes with triple length of 152, code-rates = 1/3 and 1/2, and 16-QAM provide an average gain of 0.4 dB.

Rough Set Theory has been widely explored in the past decades and many hybrids have been developed as well. In this paper, rough set theory and temporal flow graphs have been used to detect distortions in sinusoidal signals. An episode information system is created in which data are stored in the form of integers, more specifically, 1, −1 and 0. The main objective of this work is to detect different kinds of disturbances that can occur in a specific range of sinusoidal signals. The design of the algorithm for the software was programmed in Java language. Several types of distortions have been tested and the results obtained from the temporal flow graphs show that the different distortions could be identified successfully.

In this work, the effects of different types of noise on Power Line Communications (PLC) channel have been measured and analysed. Adaptive Noise Canceller (ANC) using Least Mean Square (LMS) filter algorithm has been used to mitigate the noise. The LMS filter has proved to be effective in cancellation of both impulsive and Gaussian noise. Simulation results show that the use of the LMS filter decreases the bit error rate (BER) from 0.22 to 4.1 × 10−5 for the same signal to noise ratio (SNR) value.

Telemedicine, Electronic Health Records (EHR) and social media seem to have a promising direction for an intensive approach to deal with diabetes. With the rapid advances in ICT, various diabetes information systems have evolved. The availability of new technologies for monitoring and treating diabetes is helping to achieve recommended metabolic control. The usage of social media in healthcare is also gaining much popularity as people wish to share their ideas and ask for advice with others having the same disease. The paper therefore presents a model for classifying people who could potentially be at risk of Diabetes Mellitus using social media analytics. A prototype has been implemented based on the model.

This paper proposes a Java application for detecting network anomalies due to DDoS attacks and congestion on a host in the idle state. It is also very challenging to detect and identify such problems especially when there is congestion in a network. The application uses parameters such as upload speed, download speed, number of packets transmitted and received, to analyse network traffic. The Multi-variate Gaussian technique has been used to detect anomalies in network traffic caused by DDoS attacks and congestion. However, in order to ensure that the anomalies detected over a specific interval of time are significant, t-tests have been used to test for their statistical significance.

Cloud Storage has been designed for storing large file that could occupy several HDDs (Hard Disk Drive). Most Cloud Storage Systems use Block Storage, which is efficient for large file, however, wasteful for small and medium files that are generally less than the size of a typical block (64/128 Mb). Research shows that most of the files created for personal usage is most of the time less than block size. As such for a Network Attached Storage (NAS), it is imperative to adopt another approach than Block Storage. Object Storage is the technique for storing of a group of files as one object and associated metadata stored independently. In this work, an Object Based NAS system of 12 TB is being implemented, and it has been developed using Hadoop 3.0. Given that Redundancy is important to guard against failure, the novelty of OSSREC is that is uses the default 3x Replication for individual files and coupled with Erasure Coding for Objects. A web interface is implemented to interact with Hadoop through the use of WebHDFS API and added to that, the functionality of Erasure Coding is also developed. The system is then evaluated in terms of Storage Capacity, Availability and Write/Read Performances. And finally, the Object Storage solution of OSSREC is benchmarked against the default Block Storage of Hadoop.

During recent years, there has been an increase in cyber-crime and cybercriminal activities around the world and as countermeasures, effective attack prevention and detection mechanisms are needed. A popular tool to augment existing attack detection mechanisms is the Honeypot. It serves as a decoy for luring attackers, with the purpose to accumulate essential details about the intruder and techniques used to compromise systems. In this endeavor, such tools need to effectively listen and keep track of ports on hosts such as servers and computers within networks. This paper investigates, analyzes and predicts destination port numbers targeted by attackers in order to improve the effectiveness of honeypots. To achieve the purpose of this paper, the J48 decision tree classifier was applied on a database containing information on cyber-attacks. Results revealed insightful information on key destination port numbers targeted by attackers, in addition to how these targeted ports vary within different regions around the world.

Drug Repositioning is the use of existing drugs to treat new diseases. Drug molecules exert their actions by binding to specific 3D biological molecules. Working and reasoning with 3D structures is complex, thus researchers prefer working with 1D or text data. Furthermore, drug-repositioning studies often use data sets from various independent sources, which make data processing and analysis time consuming due to different file formats, missing data, and complex cross-referencing. Here, we integrate 12 publicly available data sets on various biological/chemical entities like disease, gene, protein, pathway, drug, and side effect and 5 ontologies to provide an abstraction paradigm. The resulting integrated repository, which we called Sirius (for shedding light on drug-disease relationships) contains 7,321 disease related phenotypes, 47,063 protein functions, 2,226 drugs functions and 72,787 drug side effects having 12, 11, 9 and 4 abstraction levels, respectively. We illustrate the usefulness of our repository by studying the relationships between drugs and diseases, using side effect and pathway data. Our study predicted 117 associations, of which 93 are confirmed by the CTD database. The database is available on request from the authors as an SQL dump file.

Studies have shown that interaction with the subject content in class is important to allow students to enrich their learning. Augmented Reality (AR) provides this unique capability of blending real and virtual worlds to allow students to be engaged in practical experiences. According to studies, the application of AR in the education sector is minimal mainly because government is not giving support financially. In Mauritius, the government vision is to equipped schools with tablets thereby increasing ICT literacy among students. Hence an AR application based on android platform has been developed to help Mauritian primary students to better comprehend the history and Geography subject. The AR application has been tested on different mobile phones in order to know the minimum requirements of device that can support the application. Furthermore, the application has been deployed on mobile phones of 10 teachers and feedback received from teachers was encouraging.

Marine litter has been considered as a growing concern within different coastal areas around the world and to address this issue, there have been apprehensions from various stakeholders including international regulatory bodies and governmental institutions, among others. Amongst the different technologies being promoted by key stakeholders, mobile-based marine debris tracking is being promoted due to the widespread utilization of mobile devices. However, although a few mobile based marine debris reporting, and tracking tools have emerged, limited research has been undertaken about the acceptance of such solution by end users. Assessment of acceptance of this technology is important in order to understand aspects that impact future adoption. To address this gap, this paper investigates and analyses the acceptance of mobile-based marine debris tracking. In order to achieve the purpose of this paper, an application called “Mau Marine-Litter Watch” was developed and assessed through application of the Technology Acceptance Model.

Learning computer programming today is still difficult at any age. However, several studies have been done to encourage learning computer programming in a ubiquitous environment. In this paper, we identify the requirements needed to support ubiquitous learning in existing software tools for teaching and learning computer programming to children of age between 4–10 years old. Twenty two tools were analysed and contrasted with five known characteristics of ubiquitous learning. Permanency, accessibility, immediacy, interactivity and context-awareness are the five mentioned characteristics. They are each evaluated and elaborated in the study carried out. On an end note, we highly recommend to add ubiquitous learning characteristics in children’s programming tools.

Given the boom in the number of cars in Mauritius and worldwide, car parks are becoming increasingly in demand. Timely allocation and monitoring of car park is important for the proper management. The main aim of our implementation is to amalgamate two existing technologies readily available, namely CCTV cameras and Smart Phones, to build a system, that using Computer Vision detection will be able to identify free and occupied slots, and then store this information in a cloud database, to be retrieved by a mobile app for real-time communication. Secondly, we have devised a procedure through our system, that can be trained and be adapted for any car park. We evaluated our system by comparing it to existing ones based on features and performance.

The number of un-institutionalized older adults is on the rise and it is vital that appropriate support is given to them so they can continue living independently in their own homes. This unequivocally poses several challenges since elderlies are prone to ageing problems such as falls, cognitive impairment, loneliness and even depression. This paper looks at the different aspects of ageing and presents an un-intrusive monitoring system, the Elderly Care Assistant (ECA) to provide these senior citizens with solutions for their ageing problems and grant their caretakers the confidence of their well-being. Medication adherence, fall, boredom and loneliness are addressed by the different subsystems of the ECA and the data generated by the system is logged on a cloud infrastructure while important notifications about the health and well-being of the elderlies are sent to the caretakers via their smartphone.

Being one of the latest trends in technology, big data is proving to be fundamental in various fields and domains. Analyzing the large volume of data leads to fruitful information and depicts new methods of achieving growth and innovation in this competitive world. Similarly, analyzing large data sets from social media can enhance recommendations provided by recommender systems in a proximity based social network. This research work presents a hybrid approach for performing recommendations in a proximity based social network by using three recommendation techniques namely Content-based filtering, Collaborative filtering and Link Analysis. Additionally, big data from social media is analyzed to enhance the recommendations. The Hadoop ecosystem is used to help for processing large datasets. A prototype has been implemented and evaluated.

In this era of education and technology, it is undeniable that there is a growing interaction between machine and humans. Student performance is of prime importance as education is the key to success. At the university of Mauritius, the number of students enrolled in a course does not match the number of students graduating as not every student complete their academic cycle of 3 or 4 years. Some extend their course duration as they have to repeat the whole year or several modules, while others exit with a certificate or diploma since they lack the required number of credits to obtain a degree. Unfortunately, the registration of some students with very low average marks are terminated. This research work investigates a machine learning model to predict the performance of university students on a yearly basis. The model will forecast student performance and help take necessary actions before it is too late. The classification technique is used to train the proposed model using an existing student dataset. The training phase generates a training model that can then be used to predict student performance based on parameters such as attendance, marks, study hours, health or average performance. Different algorithms are evaluated and the classification and prediction algorithms which are more accurate are recommended.

A chatbot is a software that is able to autonomously communicate with a human being through text and due to its usefulness, an increasing number of businesses are implementing such tools in order to provide timely communication to their clients. In the past, whilst literature has focused on implementing innovative chatbots and the evaluation of such tools, limited studies have been done to critically comparing such conversational systems. In order to address this gap, this study critically compares the Artificial Intelligence Mark-up Language (AIML), and Sequence-to-Sequence models for building chatbots. In this endeavor, two chatbots were developed to implement each model and were evaluated using a mixture of glass box and black box evaluation, based on 3 metrics, namely, user’s satisfaction, the information retrieval rate, and the task completion rate of each chatbot. Results showed that the AIML chatbot ensured better user satisfaction, and task completion rate, while the Sequence-to-Sequence model had better information retrieval rate.

Currently, a hundreds of satellites are orbiting the sky of our home earth. The probability of the collusion in space of the satellite is very high to happen. This research paper is mainly about developing a program namely Accurate Satellite Positioning Space Footprint Dataset: Geographic using a novelty improved and advanced software named Accurate Satellite Positioning Space System (ASPSS). The satellite movement indication in orbit is examined to the ASPSS package. The aim of this research study are to have an online tracking database (datasets) of coordinate in space location signals of defined orbiting satellites over the earth and also, the predicate and or the calculate the current position of any defined satellite in space. The details of the ASPSS first prototype is given such as fundamental of the computing the space (orbit) coordinate’s equations and system flowchart. This ASPSS type of program is new and it has the measurements of computing and predicating the coordination of any satellite orbiting earth’s sky at selecting or given time. The ASPSS could be improve by adding an advanced sensors to it and develop an innovative function/procedure and Graphic User Interface (GUI) in new ASPSS versions.

Mobile video is a primary service category in 5G systems. Enhanced mobile broadband (eMBB) is oriented to the service in human user access to multimedia content. Video formats UltraHD and 3D are the most important traffic contributors to the cellular networks, thus quality of experience (QoE) requirements create new research and development opportunities. The new radio access network with multimedia broadcast eMBMS enables high-quality live video streaming as well as new audio-visual services. Standardization frameworks of ITU-R and 3GPP, minimum technical requirements and key performance indicators (KPI) are outlined in this paper. The results of recent development in MPEG network-based multimedia processing framework are also included. Technological solutions of network content caching and mobile edge computing in selected proofs of concept (PoC) are pointed out.

This work seeks to provide the 5G connectivity technologies for the Internet of Things (IoT) research and development together with future directions in the field. The requirements of Massive IoT and Critical IoT use cases and communication technologies are reviewed from the point of view for 5G devices and the corresponding low-power networks. 3GPP cellular networks are addressed as a major connectivity solution for IoT applications. Also, one of the main focus is to support the traffic growth for enabling the IoT. Market drivers and requirements for IoT use cases are included, too. Thus, 5G connectivity has a huge influence in the move of IoT from infrastructure to business models. This article presents a comparative study of cellular IoT support and evolution for the 5G system. Research activities including the area of software-defined sensor networks, network function virtualization, cognitive radio technology, network management, interoperability, and new radio access are also presented.

The fifth generation (5G) mobile communication system has reached the standardization phase, after more than five years of research and development activities. Design of an ultra-reliable and low-latency communication (uRLLC) is much more challenging then 5G mobile broadband and massive IoT primary service categories. It is shown that uRLLC with stringent requirements is valid use case for latency sensitive and computing intensive real-time applications. The fundamental tradeoffs in terms of reliability-latency-throughput are outlined in this work. The requirements for achieving wireless augmented and virtual reality communications are described in the second part of the paper. The key enabling technologies for future immersive applications such as mmWave and mobile edge computing are pointed out. Directional communications above 30 GHz provide sufficient connection capacity. The concept of edge computing enables high computational resources close to the users at the same time balancing between communication and computer latency.

Business resiliency in the financial services industry has become critically important with more data and information required to support decision making in real time using predictive modelling and analytics underpinned with artificial intelligence and machine learning. The application of Systems Thinking to Business Continuity Management systems using Big Data as an asset can significantly enhance the responsiveness of banks to realise a full stack business model that is resilient enough to survive in future industrial revolutions. Through the successful implementation and operation of a Business Continuity System underpinned by managed data and information, banks can not only meet regulatory obligations, but can also gain significant advantage through a coordinated response to disruption in the industry.Systems thinking and the adoption of the ISO15288 provides a framework by which banks can achieve a competitive business model with sound processes and operating practices. This paper will provide a Big Data Systems Framework for a Business Continuity System in the context of financial services that can be used in conjunction with the ISO22301 Good Practice Guide.

The application of Human Factors is to ensure a fit between the person and their environment, where the task, environment or equipment must be adapted to fit the capabilities and limitations of people rather than the other way around. Failure to do this can result in the risk of forcing people to operate in unsuitable conditions and use poorly designed equipment. A better designed workplace, task and environment has benefits for individuals (improved well-being and safety) and employers (improved work performance and efficiency).This paper will explore the discipline and profession of Human Factors, and illustrates how a more systematic approach, the socio-technical system viewpoint, is favoured in understanding the interactions within a system that influence its performance. The outcome is to ensure that the system as a whole operates within a safe boundary. By way of example, a port and its operations in the maritime domain are discussed to illustrate this new way of thinking.

Economic development shares interdependence with energy investment, a complex interaction of systems. Thus, advanced modelling tools are required to support the development of strategic integrated energy plans, inclusive of the technological complexities in the electricity value chain. This paper looks at a system dynamics modelling approach with elements of control systems engineering to determine the impact of the electric vehicle (EV) technology market penetration on the electricity demand profile and the related environmental impact in the energy and transport sectors in South Africa. Results indicate that the approach provided a robust framework in which to design the model and conduct sensitivity analyses of additional EVs entering the system due to the feedback loops inherent in the system structure.

As the level of project complexity also increases the pace of change in business and technology continues to accelerate. The adoption of Industrie 4.0 technologies by most sectors and specifically by the transport logistics sector, means that products and processes will be evolving at a more rapid pace and as a result the lifecycle of things we use and rely on is getting shorter. There is clearly a need to improve the rate of successful project execution. This paper investigates the existence of project management and systems engineering elements in selected projects with a view that specific focus on one element over the other is more likely to lead to project failure.The lack of an integrated project management and requirements management approach behind selected projects were explored to indicate the applicabiliy of a systems methodology – towards successful project management and execution.We investigate four main elements of a project – the project challenge, project planning, requirements development and management, and the project team. It was concluded that it is not about choosing systems engineering over project management to enable successful project management, or vice-versa. It is rather about the whole being bigger than the sum of the individual parts of project management and systems engineering. To achieve this, there is a need to move from the apparent multidisciplinary approach to a transdisciplinary one.