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A myriad of researchers have presented methodologies and frameworks for implementing knowledge‐management. However, frameworks do not provide sufficient detail for executing knowledge‐management initiatives, and existing methods do not adequately address all of the requirements for effective knowledge management. The field of knowledge management has been slow in formulating a universally accepted methodology. This paper reviews the status quo of knowledge‐management methodologies and presents a detailed, comprehensive methodology that addresses existing shortcomings. The presented method, SMARTVision, extends previous research on frameworks by taking a more micro‐view of knowledge management – a specific methodology.

In order to help explain experimental findings related to the stabbing- and ballistic-penetration resistance of flexible body-armor, single-yarn pull-out tests, involving specially prepared fabric-type test coupons, are often carried out. The purpose of this paper is to develop a finite-element-based computational framework for the simulation of the single-yarn pull-out test, and applied to the case of Kevlar® KM2 fabric.

Three conditions of the fabric are considered: neat, i.e, as-woven; polyethylene glycol (PEG)-infiltrated; and shear-thickening fluid (STF)-infiltrated. Due to differences in the three conditions of the fabric, the computational framework had to utilize three different finite-element formulations: standard Lagrangian formulation for the neat fabric; combined Eulerian-Lagrangian formulation for the PEG-infiltrated fabric (an Eulerian subdomain had to be used to treat the PEG solvent/dispersant); and combined continuum Lagrangian/discrete-particle formulation for the STF-infiltrated fabric (to account for the interactions of the particles suspended in PEG, which give rise to the STF character of the suspension, with the yarns, the particles had to be treated explicitly).

The results obtained for the single-yarn pull-out virtual tests are compared with the authors’ experimental counterparts, and a reasonably good agreement is obtained, for all three conditions of the fabric.

To the authors’ knowledge, the present work represents the first attempt to simulate single-yarn pull-out tests of Kevlar® KM2 fabric.

With an increasing urbanization trend over the last decades, urban agglomerations are facing different challenges that affect its inhabitants: pollution, traffic congestion, thriving population growth rates, and economic uncertainty. In the context of Latin America, where less than 20% of its inhabitants live in rural areas and with a projection to decrease to close to 10% by the year 2030, providing solutions to reduce the impact of this increase of population, on at least one of the issues, seems logical.

This study focuses on the urban logistics component to propose a classification method for homogeneous areas, using Factor Analysis (FA) and analysis of variance (ANOVA) as the main supporting tools. The proposed methodology builds up on the square kilometer (KM2) methodology developed by MIT Center for Transportation and Logistics, applying it in a neuralgic section of the downtown area of a mid-sized city in Latin America: Córdoba, Argentina. The selection was made considering the logistic restrictions, commercial density, and the relevance of the area for the city. Our proposed methodology uses relevant variables for urban logistics to perform the statistical analysis. The main goal is to develop a data-driven methodology to identify clusters to guide Córdoba's urban logistics policy and decision-making processes.

The results suggest a clear relationship between the different commercial activities and the location inside the area, splitting the area under study clearly into two main sections with similar overall characteristics and two subsections inside each one of them, which should be considered as a basis for further urban logistic analysis and implementation of specific best practices that fit the particular needs.

Demand for goods/services has increased in Latin America due to urbanization, leading to a complex delivery system and increased logistical activities. In Quito, the Historic Center and La Mariscal are two zones that face logistical challenges. The objective of this chapter is to analyze the commercial logistic activities related to loading and unloading goods in these zones. To address this urban freight problem, this chapter proposes a solution through the calculation of the optimal number and location of loading and unloading bays in each zone based on actual commercial activity data. First, a delivery survey was completed in each zone regarding frequency and amount of deliveries. Then, based on the data obtained, an optimization model is proposed to determine the optimal number and location of loading and unloading bays. Finally, a simulation model of the delivery process is performed to readjust the bay’s optimal number. A total number of 75 and 98 bays were calculated to serve the total shopping district of a representative square kilometer (km²) of the Historic Center and La Mariscal. This solution aims to minimize the delivery time and the distance for deliveries, improve urban freight transportation, and reduce traffic. This study could be used as a baseline and guide for further research in urban logistics, especially in Latin America, where urban logistics is still under study. This chapter is part of a Research Project of Urban Logistics in Quito, led by Universidad San Francisco de Quito (USFQ), in association with the Megacity Logistics Lab of MIT.

Biomass waste can be used as fuel in biomass power plants to generate electricity. It is a type of renewable energy widely available in Malaysia because 12 million tons of the biomass waste is produced every year. At present, only 5 per cent of the total biomass waste in Sabah, one of the states in Malaysia, is used to generate electricity for on-site consumption. The remaining 95 per cent of the biomass waste has not been utilized because the transportation cost for shifting the waste from the plantations to the power plants is substantial, hence making the cost of the biomass generated electricity to be high. Therefore, a methodology is developed and presented in this paper to determine the optimum geographic distribution and capacities of the biomass power plants around a region so that the cost of biomass generated electricity can be minimized. The paper aims to discuss these issues.

The methodology is able to identify the potential locations of biomass power plants on any locations on a region taking into account the operation and capital costs of the power plants as well as the cost of connecting the power plants to the national grid. The methodology is programmed using Fortran.

This methodology is applied to Sabah using the real data. The results generated from the methodology show the best locations and capacities of biomass power plants in Sabah. There are 20 locations suitable for biomass power plants. The total capacity of these biomass power plants is 4,996 MW with an annual generation of 35,013 GWh. This is sufficient to meet all the electricity demand in Sabah up to 2030.

The methodology is an effective tool to determine the best geographic locations and sizes of the biomass power plants around a region.

The purpose of this paper is to show the extent of desertification and land degradation as threat to sustainable environmental, agricultural and land development in the Sahel of Nigeria with its consequences; with also some efforts to control desertification.

Several desertification attenuation projects in Nigeria are employing different methods for maximum benefits obtainable from the objectives) of the particular project. Specific methods will be cited for particular projects mentioned as appropriate. It is noted however that environmental impact assessment, community reconnaissance or needs assessments were initial part of pre-project activities.

Desertification has reached an alarming state in Nigeria. The frontline desert threatened States of Nigeria constitute 40 per cent of the land mass of the country. With increased pressure of desertification, exacerbated by a period of prolonged drought of about 20 years, climate change and human activities, it is becoming increasingly difficult to obtain sustainability in the management of the fragile lands and the region’s ecosystem. Strategic interventions in combating the problem of desertification in Nigeria have attenuated some of the detrimental social, economic and environmental impacts on the affected communities of the Sahel of the country. Programmes and projects are designed to strengthen the resilience of the people in the affected region with sound ecosystems’ management; support the efforts of the communities resulting in increased agricultural yields. Programmes and projects have strengthened the resilience of the people, participating in sand dune stabilization, the Great Green Wall Sahara Sahel Initiative and other shelterbelt development. Government has sustained inputs in environmentally friendly agriculture and also encouraged synergetic collaborative activities with national agencies, international agencies and local institutions.

These results/activities give evidence of the increased public awareness of environmental degradation due to desertification in Nigeria; the realization in environmental stabilization needs with ready participation of the communities for improved livelihoods in arid agriculture; resulting in internalization of these problems for Nigeria.

Adopting a personal knowledge management (PKM) scope, this paper aims to report the resulting experience of a four‐year qualitative research project on the dynamics of social skills development strategies in knowledge‐intensive, e‐learning workplace environments.

By adopting a grounded encased study approach, the research explored how practitioners develop strategies for adapting to emerging e‐learning spaces while developing networking skills. The encased study stemmed from research completed at the University of Manchester. The study aimed to gain perspectives and make sense of social skills development (communication, networking and collaboration skills) in the particular setting of knowledge facilitation within virtual environments in three universities of the Manchester city region.

The personal lifelong learning journey that educational and academic staff undertake in order to become skilled knowledge facilitators online is perceived and represented as a full learning cycle of multiple dimensions. Also, by identifying specific roles of knowledge facilitators according to context, the existing institutional social systems and city networks of learning were made evident in the practitioners' learning scope within their own institutions and beyond.

The paper uses a multiple‐layer, third‐generation knowledge management framework to explore the different emerging roles of knowledge workers in knowledge‐intensive communities online, and how they facilitate multiple tacit knowledge conversion into explicit scholarly knowledge.

The purpose of this paper is to assess the vulnerability of the Kesennuma area in Japan to a tsunami disaster and to map the area of inundation.

Digital elevation model (DEM) data and ALOS image were used to create maps of the parameters of this study area: elevation, slope, coastal proximity, river, and land use. An analytical hierarchy process was used to assign weights to each parameter and a spatial multi-criteria analysis was applied through cell-based modelling for vulnerability mapping.

The vulnerability map shows that 17.679 km2 of the area could be inundated by a tsunami. High vulnerability areas were mostly found in coastal areas with a sloping coast and a cape area. A low elevation and the presence of rivers or water channels are factors that increase the impact of tsunamis. Inundation areas were predicted to spread in areas identified as having either high vulnerability or slightly high vulnerability.

Because of the limited geospatial data, the authors encourage further studies using DEM data with a high spatial resolution.

The results of this research can be used as basic information for disaster mitigation and urban planning in coastal areas.

This research creates a new approach for assessing which areas could be inundated by tsunamis, based on the vulnerability map generated through remote sensing and spatial multi-criteria analysis. Moreover, the parameters used are very close to those of actual inundation maps.