The Prevention of Oil and Chemical Pollution: Proactive Approaches in the UK and Canada

In the UK, various initiatives have been launched in an attempt to provide guidance on the occurrence and avoidance of oil and chemical pollution. Despite these efforts, oil and chemicals remain a major cause of pollution. In Canada, similar initiatives have been more successful due to a participative approach to campaign development and implementation. This paper compares UK and Canadian initiatives and identifies factors which led to the success of the Canadian campaigns.     

An Effective Model for Indirect Trust Computation in Pervasive Computing Environment

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. An effective model for indirect trust computation is proposed which is capable of identifying dishonest recommendations. Dishonest recommendations are identified by using deviation based detecting technique. The concept of measuring the credibility of recommendation (rather than credibility of recommender) using fuzzy inference engine is also proposed to determine the influence of each honest recommendation. The proposed model has been compared with other existing evolutionary recommendation models in this field, and it is shown that the model is more accurate in measuring the trustworthiness of unknown entity.     

Kinetic parameter estimation and simulation of trickle-bed reactor for hydrodesulfurization of crude oil

Hydrodesulfurization (HDS) of crude oil has not been reported widely in the literature and it is one of the most challenging tasks in the petroleum refining industry. In order to obtain useful models for HDS process that can be confidently applied to reactor design, operation and control, the accurate estimation of kinetic parameters of the relevant reaction scheme are required. In this work, an optimization technique is used in order to obtain the best values of kinetic parameters in trickle-bed reactor (TBR) process used for hydrodesulfurization (HDS) of crude oil based on pilot plant experiment. The optimization technique is based on minimization of the sum of the square errors (SSE) between the experimental and predicted concentrations of sulfur compound in the products using two approaches (linear (LN) and non-linear (NLN) regressions).A set of experiments were carried out in a continuous flow isothermal trickle-bed reactor using crude oil as a feedstock and the commercial cobalt–molybdenum on alumina (Co–Mo/γ-Al₂O₃) as a catalyst. The reactor temperature was varied from 335 to 400 °C, the hydrogen pressure from 4 to 10 MPa and the liquid hourly space velocity (LHSV) from 0.5 to 1.5 h⁻¹, keeping constant hydrogen to oil ratio (H₂/oil) at 250 L/L.A steady-state heterogeneous model is developed based on two-film theory, which includes mass transfer phenomena in addition to many correlations for estimating physiochemical properties of the compounds. The hydrodesulfurization reaction is described by Langmuir–Hinshelwood kinetics. gPROMS software is employed for modelling, parameter estimation and simulation of hydrodesulfurization of crude oil in this work. The model simulations results were found to agree well with the experiments carried out in a wide range of the studied operating conditions. Following the parameter estimation, the model is used to predict the concentration profiles of hydrogen, hydrogen sulfide and sulfur along the catalyst bed length in gas, liquid and solid phase, which provides further insight of the process.     

6D direct-drive technology for planar motion stages

Achieving 6D direct drive motion control over long planar strokes on a single mover can greatly improve machine performance. Existing solutions are either limited to small-stroke applications (a fraction of the stage size) or require a large number of coils with enormous control complexity. This paper presents a novel 6D direct drive technology for planar stages with many advantages: (1) stroke can be on the order of several meters; (2) the number of coils increases linearly with motion range; (3) no end effects or force coupling between axes; and (4) ease of control and superior force linearity.     

A review on conversion of triglycerides to on‐specification diesel fuels without additional inputs

Dependence on fossil fuels for global energy supply has continued to generate concerns about climate change and sustainable development. It has motivated the search for carbon‐neutral alternative resources for the production of transportation fuels to replace crude oil. Although biodiesels have recently emerged as a close substitute to petrol diesel, their use in compression ignition engines designed to run on petro‐diesel fuels are linked to adverse effects on the engines' performance and life span. This informed efforts at upgrading biodiesel or direct conversion of triglycerides to hydrocarbon mixtures that are identical or similar to that of petro‐diesel through hydrodeoxygenation. Moreover, it seems that commercial methods for the conversion of triglycerides to diesel fuels depends on inputs (methanol and hydrogen) derived from fossil fuels. However, it will be desirable to do so without inputs from fossil fuels. Hence, reaction paths for direct conversion of triglycerides to on‐specification hydrocarbons fuels without hydrogen gas input are discussed and suggested strategies are in cognisance of green chemistry principles. Copyright © 2012 John Wiley & Sons, Ltd.     

Way forward in meeting energy challenges in Pakistan

In this review the energy potential of Pakistan from hydro, coal, wind, solar and nuclear sources has been discussed. The new projects that are being commissioned on coal, wind, solar, hydel and nuclear-based technologies have also been mentioned. The review has also explored the future prospects of the country’s energy requirements. Pakistan has potential to produce 100,000?MW of electricity from Thar coal for 20 years, 56,000?MW of hydroelectricity, 150,000?MW of wind energy and ～50,000?MW from solar sources. The coal-fired power generation is expected to 11,500?MW in period 2017–2019. The country is moving in the right direction to cater its energy needs. However, there is a need to find local and foreign investment in the country to meet high energy demands in the country in future.     

Barriers to effective configuration management application in a project context: An empirical investigation

Configuration management (CM) is not a new set of ideas, what it does represent is an effective way for project managers to use a formalized methodology in order that they can manage status and changes to it throughout the lifecycle. This research sets out to identify and prioritize the obstacles in the effective implementation of the CM practices, categorize these obstacles into more manageable groups of factors, and analyse the effects of multiple factors on the identification and rating of these barriers. Nineteen barriers are finalized and prioritized on the basis of their criticality and as a result three groups (managerial and organizational barriers, implementation barriers, and planning and process barriers) are extracted with the help of factor analysis. This study will help both configuration management and project management professionals to plan better and avoid the impacts of these key obstacles from much earlier in the definition phase.     

Self and static interference mitigation scheme for coexisting wireless networks

High density of coexisting networks in the Industrial, Scientific and Medical (ISM) band leads to static and self interferences among different communication entities. The inevitability of these interferences demands for interference avoidance schemes to ensure reliability of network operations. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets. DAFR employs intelligent hopping procedures in order to mitigate self interferences, weeds out the static interferer efficiently and ensures sufficient frequency diversity. We compare the performance of our proposed technique with the widely used existing frequency hopping techniques, namely, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR). Simulation studies validate the significant improvement in goodput and hopping diversity of our scheme compared to other schemes and demonstrate its potential benefit in real world deployment.     

An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation

This research presents an autonomous robotic framework for academic, vocational and training purpose. The platform is centred on a 6 Degree Of Freedom (DOF) serial robotic arm. The kinematic and dynamic models of the robot have been derived to facilitate controller design. An on-board camera to scan the arm workspace permits autonomous applications development. The sensory system consists of position feedback from each joint of the robot and a force sensor mounted at the arm gripper. External devices can be interfaced with the platform through digital and analog I/O ports of the robot controller. To enhance the learning outcome for beginners, higher level commands have been provided. Advanced users can tailor the platform by exploiting the open-source custom-developed hardware and software architectures. The efficacy of the proposed platform has been demonstrated by implementing two experiments; autonomous sorting of objects and controller design. The proposed platform finds its potential to teach technical courses (like Robotics, Control, Electronics, Image-processing and Computer vision) and to implement and validate advanced algorithms for object manipulation and grasping, trajectory generation, path planning, etc. It can also be employed in an industrial environment to test various strategies prior to their execution on actual manipulators.     

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.