The politics of amnesty in Nigeria: a comparative analysis of the Boko Haram and Niger Delta insurgencies

Special Edition issue - Terrorism and Counter-Terrorism in Sub-Saharan AfricaThis paper presents a comparative analysis of the Niger Delta amnesty programme and the proposed amnesty for Boko Haram insurgents in Nigeria. The motivation for comparing the two groups derives from the growing demand from some notable groups and individuals, mainly from the northern part of Nigeria, that the Boko Haram insurgents be granted amnesty just as the Niger Delta armed militants. One of such strong voices in favour of amnesty for Boko Haram insurgents is the Sultan of Sokoto, AlhajiSa’adAbubakar, who, on the 7th of March 2013, called for “total and unconditional” amnesty for Boko Haram. Sultan Abubakar’s demand has attracted mixed reaction as it is largely supported by the northern group-Arewa Consultative Forum (ACF) and rejected by others such as the Christian Association of Nigeria (CAN). The most serious reaction comes from the presidency in its commission, on April 24th 2013, of a presidential Committee on Dialogue and Peaceful Resolution of Security Challenges in the North. After an extension by 2 months of its initial 90 days task, the Turaki-led Committee on Dialogue and Peaceful Resolution of Security Challenges in the North has finally submitted its recommendations to the president. Two key recommendations are: the need to set up an advisory committee for continuous dialogue with Boko Haram (as the leadership of Boko Haram refused to dialogue) and a victims’ support fund to help victims of Boko Haram.Publisher PD

Process Complexity impact on IS Audit Service Quality: An Enterprise System Perspective

Information System Auditors rely on clients business processes in order to make a determination whether the information system can safeguard assets and maintain data integrity. However, as the level of business process complexities in these organizations increases, IS auditors may find it difficult to grasp these business processes. The central theme of this paper is to examine the impact of business process complexity on IS audit quality using the construct of SERVQUAL. From the IS auditors view of an organizations ability to deploy appropriate compliance measures in an enterprise system, this paper contends that as business process complexity increases in an enterprise system environment the IS audit service quality will witness a corresponding decrease as IS auditors will grapple with understanding the business process hence lowering their expectations on audit reliability, assurance, empathy, and responsiveness

The manifestation of stuttering in Spanish-English bilingual speakers : a systematic review

textThe purpose of this systematic review was to analyze the descriptions of bilingual Spanish-English (SE) participants provided in stuttering literature to determine whether critical factors were used to define bilingualism and appropriate criteria were used to classify stuttering. The method included a systematic search of published studies that included bilingual Spanish-English participants who stutter and reviewed the level of detail provided regarding language history, function, proficiency, stability, mode, accent, covert speech, and affective factors. The identification and diagnosis of stuttering was also analyzed using disfluency factors that were reported across the studies. Those factors included formal diagnosis, monolingual guidelines, self-report, parent/teacher concern, informal observation, family history, and disfluency types. Ten studies qualified to be included the bilingualism analysis, consisting of over 15 different speakers who were identified as bilinguals who stutter. Nine out of the 10 studies qualified to be included in the stuttering analysis of the systematic review. Of the 10 studies analyzing bilingualism, the most frequently reported language profile information involved language proficiency, language history, and language function. Affect, accent, mode, and language stability information were the least mentioned language factors in the studies. Of the nine studies included in the disfluency analysis, the most commonly reported disfluency profile information involved disfluency types, comparison to monolingual normative data, and informal observation. The least reported disfluency factors in the studies were formal diagnosis, parent/teacher concern, self-reported stutterer, and family history of stuttering. Results demonstrate that the definition of bilingualism and the classification of stuttering among bilinguals is lacking and inconsistent.Communication Sciences and Disorder

Anne Harley and Eurig Scandrett (eds) (5th June 2019 Environmental Justice, Popular Struggle and Community Development

The book is a very extensive collection of richly informative experiences of career activists, academics and academic activists. Besides the wide variety and extensive range of the nature of issues covered in the book, it captures experiences of actors (activists, academics and academic activists) from across the globe. Consequently, in my opinion, there is something for anyone interested in the subject of environmentalism, environmental justice and popular struggle, irrespective of ethnic background. &nbsp

PowerGrid - A Computation Engine for Large-Scale Electric Networks

This Final Report discusses work on an approach for analog emulation of large scale power systems using Analog Behavioral Models (ABMs) and analog devices in PSpice design environment. ABMs are models based on sets of mathematical equations or transfer functions describing the behavior of a circuit element or an analog building block. The ABM concept provides an efficient strategy for feasibility analysis, quick insight of developing top-down design methodology of large systems and model verification prior to full structural design and implementation. Analog emulation in this report uses an electric circuit equivalent of mathematical equations and scaled relationships that describe the states and behavior of a real power system to create its solution trajectory. The speed of analog solutions is as quick as the responses of the circuit itself. Emulation therefore is the representation of desired physical characteristics of a real life object using an electric circuit equivalent. The circuit equivalent has within it, the model of a real system as well as the method of solution. This report presents a methodology of the core computation through development of ABMs for generators, transmission lines and loads. Results of ABMs used for the case of 3, 6, and 14 bus power systems are presented and compared with industrial grade numerical simulators for validation

Enhancing remanufacturing automation using deep learning approach

In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces.In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces

Advances in optimisation algorithms and techniques for deep learning

In the last decade, deep learning(DL) has witnessed excellent performances on a variety of problems, including speech recognition, object recognition, detection, and natural language processing (NLP) among many others. Of these applications, one common challenge is to obtain ideal parameters during the training of the deep neural networks (DNN). These typical parameters are obtained by some optimisation techniques which have been studied extensively. These research have produced state-of-art(SOTA) results on speed and memory improvements for deep neural networks(NN) architectures. However, the SOTA optimisers have continued to be an active research area with no compilations of the existing optimisers reported in the literature. This paper provides an overview of the recent advances in optimisation algorithms and techniques used in DNN, highlighting the current SOTA optimisers, improvements made on these optimisation algorithms and techniques, alongside the trends in the development of optimisers used in training DL based models. The results of the search of the Scopus database for the optimisers in DL provides the articles reported as the summary of the DL optimisers. From what we can tell, there is no comprehensive compilation of the optimisation algorithms and techniques so far developed and used in DL research and applications, and this paper summarises these facts

History and contexts of municipal solid waste management in Aba – Recounting the stories of residents

Stanley Nwankpa - ORCID 0000-0002-5393-0426 https://orcid.org/0000-0002-5393-0426Replaced AM with VoR 2020-10-12.To review the history of MSW management in a place, authors often review the development drivers of MSW management and or how such drivers have affected MSW management policy or practice in the place. This study focuses on the lived experiences of the residents. The researchers believe that a phenomenological review of the lived experiences of long term residents of the city of Aba, provide unique and more useful account of the development of MSW management in the city especially in the absence of any significant development in the methods and or processes of managing MSW. It is also recommended that similar methods be utilised in reviewing the development of MSW management in similar cities in Nigeria and other cities in Sub Saharan Africa. The study finds that over the period in review, the process of MSW management in Aba has remained rudimentary, primarily consisting of evacuation of refuse from one point to another without any form of treatment or processing. Responses from participants of this study – drawn from an extended peer community of long-term residents of the city, suggest that except for a period between 2013 and 2014, the overall MSW management situation in the city have worsened. From post-independence in 1960 to 2017, four distinct eras characterised mainly by the leadership and clarity of purpose was identified by analysing the responses from participants. There are widespread accusations of nepotism, corruption, ineptitude and high handedness levelled against the current leadership of Abia State Environmental Protection Agency (ASEPA) - the agency responsible for managing MSW in Aba. However, most of the current problems and challenges can be traced back to several years of negligence and subsequent dilapidation of infrastructure. For a sustainable progress to be made and maintained, MSW managers in the city must find a way to involve the wider community of stakeholders in the design, implementation and evaluation of the city’s waste management policies and processes.https://doi.org/10.7176/JEES/10-9-0610pubpub