Construction is one of the strongholds for development in every country. The objective of the research is to identify the critical technical risk factors encountered by engineering firms on the global milieu. Based on questionnaire surveys, statistical analyses and previous studies on risk management, 23 risks were selected and ranked as technical factors that hinder construction projects. The relative importance index (RII) was used to determine the rank of each factor surveyed. The study revealed that industrial disputes, shortage in power supply, shortage in supply of water and problems of partner difference practice of construction globally were among the highly ranked technical risks. The study is beneficial as it adopts case studies in Africa to elicit the technical effects on the international frontlines. With the ranking of the technical risk factors, international construction firms can adequately enhance critical planning, avoid casualties and engage in economically viable projects. The study will be useful for analysing and investigating the hypothesised relationships technical risk variables that influence performance of international joint ventures. The risk factors displayed in the study can be used in supporting data for many other research development studies in the area of construction, risk analysis and global construction development studies.

This paper develops simple system-level energy models for different kinds of transmitter implementations. Different communication protocols such as ASK, OOK, FSK, PSK are considered to evaluate the energy cost of both the architecture of the system as well as the electronic circuitry. These models are developed for the systems operating in the Medical Implant Communications Service frequency band. These models are useful for the optimisation of system and circuit parameters and provided some guidelines to choose the right modulation and circuit implementation for energy limited wireless transmitters.

In this study, a cloud service optimisation model is presented for resource adaptation control, in which the cost to the user, various levels of virtual machine efficacy, and rental price are considered. A cloud service middleware that supports flexible web services by using heterogeneous platforms is proposed. Developers can conveniently register web services and deploy them to the middleware. The middleware provides an energy-minimisation load balance (EM-LB) mechanism, which considers the temperature of the machine, its power consumption, and the computing resources of virtual machines. The EM-LB mechanism maintains a safe temperature threshold and minimises power usage, thereby reducing hotspots and conserving energy. Furthermore, a two-stage cloud service optimisation model is proposed to analyse users’ requirements and existed computing resources for finding the Pareto optimality, and the aspirational level is found in accordance with the Pareto optimality for the lower cost and lower execution time. The simulation results show the efficiency of the proposed cloud service optimisation model and EM-LB mechanism. Finally, the proposed EM-LB mechanism is implemented to prove the feasibility of the model.

A reconfigurable X-band microstrip bandpass filter based on the stepped impedance resonator topology with two folded stubs is proposed. Reconfiguration is enabled in the microstrip filter using positive–intrinsic–negative (PIN) diode and the proposed filter performs frequency switching between uplink and downlink X-band services. The prototype filter is realised ideally using open-circuit and short-circuit conditions that are designed in a way similar to the design using PIN diodes that provide the same results. From the simulation and measured results, it is inferred that by altering the ON/OFF states, the filter can work from downlink frequency range of 7.25–7.9 GHz to the uplink frequency range of 8.00–8.4 GHz. Comprehensive studies are conducted on a fabricated prototype to assess its performance from which encouraging results are obtained.

The electroencephalogram (EEG) signal is an electrical representation of brain's working that reflects various physiological and pathological activities such as alcoholism. Alcohol can affect whole parts of the body but, it particularly affects the brain, heart, liver, and the immune system; its effects on the brain are called brain disorders. Nowadays, automatic identification of alcoholic subjects based on EEG signals has become one of the challenging problems in biomedical research. In this study, an automatic classification method for classifying alcoholic and normal EEG signals, based on empirical mode decomposition (EMD), is proposed. The uniqueness of EMD method is to decompose non-stationary and non-linear signals into a set of stationary intrinsic mode functions (IMFs) that are band limited signals. These IMFs are transformed into analytic representations by applying the Hilbert transform. From these analytic IMFs, various features namely mean, kurtosis, skewness, entropy, and negentropy are extracted; these features are used as input to least squares support vector machines (LS-SVMs) classifier with radial basis function (RBF) kernel and polynomial kernel. The accuracy results achieved for LS-SVM classifier with polynomial and RBF kernels are found to be 96.67 and 97.92%, respectively, which are found to be better as compared with other state-of-the-art methods.