Tree species selection for land rehabilitation in Ethiopia: from fragmented knowledge to an integrated multi-criteria decision approach

Dryland regions worldwide are increasingly suffering from losses of soil and biodiversity as a consequence of land degradation. Integrated conservation, rehabilitation and community-based management of natural resources are therefore of vital importance. Local planting efforts should focus on species performing a wide range of functions. Too often however, unsuitable tree species are planted when both ecological suitability for the targeted area or preferences of local stakeholders are not properly taken into account during selection. To develop a decision support tool for multi-purpose species selection, first information needs to be pooled on species-specific ranges, characteristics and functions for a set of potentially valuable species. In this study such database has been developed for the highly degraded northern Ethiopian highlands, using a unique combination of information sources, and with particular attention for local ecological knowledge and preferences. A set of candidate tree species and potentially relevant criteria, a flexible input database with species performance scores upon these criteria, and a ready-to-use multi-criteria decision support tool are presented. Two examples of species selection under different scenarios have been worked out in detail, with highest scores obtained for Cordia africana and Dodonaea angustifolia, as well as Eucalyptus spp., Acacia abyssinica, Acacia saligna, Olea europaea and Faidherbia albida. Sensitivity to criteria weights, and reliability and lack of knowledge on particular species attributes remain constraints towards applicability, particularly when many species are jointly evaluated. Nonetheless, the amount and diversity of the knowledge pooled in the presented database is high, covering 91 species and 45 attributes

Multi User Context-Aware Service Selection for Mobile Environments - A Heuristic Technique

Modern service systems build on top of service dominant designs which encompass contextualization (value-in-context) and collaboration (value-in-use) between users and service providers. Processes in this domain often require the consideration of both context information (e.g., location or time of day) and multiple participating users where each user probably has its own preferences and constraints (e.g., restricted overall budget). However, selecting a suitable service provider for each action of a process, especially when some of these actions are conducted together by several users, can be a complex decision problem in multi user context-aware service systems. Consequently, exact approaches are not fit to solve such a service selection problem in appropriate time. Thus, the paper proposes a heuristic technique applying a decomposition of the users’ global constraints and a local service selection. In this way, the aim is to determine a feasible service composition for each participating user while taking the users’ individual preferences and constraints as well as context information into account. The evaluation of the heuristic technique shows, based on a real-world scenario in the tourism domain, that the proposed approach is able to achieve close-to-optimal solutions while efficiently scaling with problem size and therefore can support decision makers in multi user context-aware service Systems

An Improved Energy-Aware Distributed Unequal Clustering Protocol using BBO Algorithm for Heterogeneous Load Balancing

With the rapid extension of IoT-based applications various distinct challenges are emerging in this area Among these concerns the node s energy efficiency has a special importance since it can directly affect the functionality of IoT-Based applications By considering data transmission as the most energy-consuming task in IoT networks clustering has been proposed to reduce the communication distance and ultimately overcome node energy wastage However cluster head selection as a non-deterministic polynomial-time hard problem will be challenging notably by considering node s heterogeneity and real-world IoT network constraints which usually have conflicts with each other Due to the existence of conflict among the main system parameters various solutions have been proposed in recent years that each of which only considered a few real-world limitations and parameter

An Adaptive Tabu Search Heuristic for the Location Routing Pickup and Delivery Problem with Time Windows with a Theater Distribution Application

The time constrained pickup and delivery problem (PDPTW) is a problem of finding a set of routes for a fleet of vehicles in order to satisfy a set of transportation requests. Each request represents a user-specified pickup and delivery location. The PDPTW may be used to model many problems in logistics and public transportation. The location routing problem (LRP) is an extension of the vehicle routing problem where the solution identifies the optimal location of the depots and provides the vehicle schedules and distribution routes. This dissertation seeks to blend the PDPTW and LRP areas of research and formulate a location scheduling pickup and delivery problem with time windows (LPDPTW) in order to model the theater distribution problem and find excellent solutions. This research utilizes advanced tabu search techniques, including reactive tabu search and group theory applications, to develop a heuristic procedure for solving the LPDPTW. Tabu search is a metaheuristic that performs an intelligent search of the solution space. Group theory provides the structural foundation that supports the efficient search of the neighborhoods and movement through the solution space

Engineering Crowdsourced Stream Processing Systems

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort

Two-Stage Stochastic Program Optimizing the Total Cost of Ownership of Electric Vehicles in Commercial Fleets

The possibility of electric vehicles to technically replace internal combustion engine vehicles and to deliver economic benefits mainly depends on the battery and the charging infrastructure as well as on annual mileage (utilizing the lower variable costs of electric vehicles). Current studies on electric vehicles’ total cost of ownership often neglect two important factors that influence the investment decision and operational costs: firstly, the trade-off between battery and charging capacity; secondly the uncertainty in energy consumption. This paper proposes a two-stage stochastic program that minimizes the total cost of ownership of a commercial electric vehicle under uncertain energy consumption and available charging times induced by mobility patterns and outside temperature. The optimization program is solved by sample average approximation based on mobility and temperature scenarios. A hidden Markov model is introduced to predict mobility demand scenarios. Three scenario reduction heuristics are applied to reduce computational effort while keeping a high-quality approximation. The proposed framework is tested in a case study of the home nursing service. The results show the large influence of the uncertain mobility patterns on the optimal solution. In the case study, the total cost of ownership can be reduced by up to 3.9% by including the trade-off between battery and charging capacity. The introduction of variable energy prices can lower energy costs by 31.6% but does not influence the investment decision in this case study. Overall, this study provides valuable insights for real applications to determine the techno-economic optimal electric vehicle and charging infrastructure configuration

Carbon Free Boston: Energy Technical Report

Part of a series of reports that includes: Carbon Free Boston: Summary Report; Carbon Free Boston: Social Equity Report; Carbon Free Boston: Technical Summary; Carbon Free Boston: Buildings Technical Report; Carbon Free Boston: Transportation Technical Report; Carbon Free Boston: Waste Technical Report; Carbon Free Boston: Offsets Technical Report; Available at http://sites.bu.edu/cfb/INTRODUCTION: The adoption of clean energy in Boston’s buildings and transportation systems will produce sweeping changes in the quantity and composition of the city’s demand for fuel and electricity. The demand for electricity is expected to increase by 2050, while the demand for petroleum-based liquid fuels and natural gas within the city is projected to decline significantly. The city must meet future energy demand with clean energy sources in order to meet its carbon mitigation targets. That clean energy must be procured in a way that supports the City’s goals for economic development, social equity, environmental sustainability, and overall quality of life. This chapter examines the strategies to accomplish these goals. Improved energy efficiency, district energy, and in-boundary generation of clean energy (rooftop PV) will reduce net electric power and natural gas demand substantially, but these measures will not eliminate the need for electricity and gas (or its replacement fuel) delivered into Boston. Broadly speaking, to achieve carbon neutrality by 2050, the city must therefore (1) reduce its use of fossil fuels to heat and cool buildings through cost-effective energy efficiency measures and electrification of building thermal services where feasible; and (2) over time, increase the amount of carbon-free electricity delivered to the city. Reducing energy demand though cost effective energy conservation measures will be necessary to reduce the challenges associated with expanding the electricity delivery system and sustainably sourcing renewable fuels.Published versio