The factors influencing municipal solid waste generation in bauchi town, Nigeria

This study investigated the factors influencing municipal solid waste generation (MSWG) in Bauchi town, the administrative headquarters of Bauchi state, Nigeria. The study used quantitative method, while experiments and questionnaire surveys, were used as the major instruments for data collection. Data on solid waste generation and the socioeconomic attributes of residents from 400 households were collected from residential zones within Bauchi town, the study area. Descriptive statistics, Correlations and Standard Multiple Regressions (SMR) were computed for data analysis using SPSS 2.2 software. Twelve (12) socioeconomic factors were computed in SMR to determine the significant factors of MSWG in the study. The results showed that five factors, namely: household size, income, education, house head age and occupation have exerted significant influence on MSWG in the study area. The results also showed that F (5:362) = 84.058 at p ≤ 0.01 and adjusted R² = 0.531; which indicated that the factors in the SMR model have sufficiently explained the variance in MSWG in Bauchi town. The study concluded that the five significant predictor factors have adequately explained the variance of MSWG in the study area. Therefore, the factors have implications for planning of effective waste management system in Bauchi town, Nigeria

The effect of chemical treatment, fibre length, fibre content and injection moulding parameters to uv irradiation resistance of oil palm fibre reinforced composites

There are many types of polymer used in engineering materials expose to UV irradiation, such as automotive parts (car body, bumper, dashboard etc.) which can cause material degradation. Some polymers are used in pure polymer and some of them in composite material. This study has investigated the composite material degradation. In this study, polypropylene was used as a matrix of the composite material samples, while oil palm fibre as reinforcement. The effect of the fibre length, fibre content, fibre treatment, coupling agent and injection moulding parameter to ultraviolet (UV) light resistance of this composite and also the optimum setting of them were investigated. The UV resistance was examined via the change of mechanical properties after UV exposured in the UV accelerated weathering tester. The Linear regression models were generated for tensile strength, strain at maximum stress, break stress, break strain and Charpy Impact strength based on six different UV exposure time i.e.: 0, 96, 336, 504, 1008, and 1512 hours. The significance of the regression models were tested by Analysis Of Variance (ANOVA) and verified by two expose time i.e: 168 and 672 hours. This study found that all of the mechanical properties decrease after UV irradiated. The largest property decrease was break strain of the samples, which was decrease in the range 44.54% to 79.21% after 1512 hours UV irradiated. The lowest decrease was break stress in the range 27.38 % to 63.82%. It was also found in this study that fibre content, and UV irradiation time, significantly affect all properties. Coupling agent and alkali treatment significantly affect all properties except strain at maximum stress. Whereas fibre length and injection moulding parameter only significantly affect the Impact strength of the specimens. All of the regression models are significant which are signed by the p value of each of regression models were lower than 0.05. The equation for predicting the lifetime of UV exposed of oil palm fibre reinforced composite have been generated. The lifetime UV irradiated specimen can be predicted using this equation

One machine, one minute, three billion tetrahedra

This paper presents a new scalable parallelization scheme to generate the 3D Delaunay triangulation of a given set of points. Our first contribution is an efficient serial implementation of the incremental Delaunay insertion algorithm. A simple dedicated data structure, an efficient sorting of the points and the optimization of the insertion algorithm have permitted to accelerate reference implementations by a factor three. Our second contribution is a multi-threaded version of the Delaunay kernel that is able to concurrently insert vertices. Moore curve coordinates are used to partition the point set, avoiding heavy synchronization overheads. Conflicts are managed by modifying the partitions with a simple rescaling of the space-filling curve. The performances of our implementation have been measured on three different processors, an Intel core-i7, an Intel Xeon Phi and an AMD EPYC, on which we have been able to compute 3 billion tetrahedra in 53 seconds. This corresponds to a generation rate of over 55 million tetrahedra per second. We finally show how this very efficient parallel Delaunay triangulation can be integrated in a Delaunay refinement mesh generator which takes as input the triangulated surface boundary of the volume to mesh

Distributed simulation of city inundation by coupled surface and subsurface porous flow for urban flood decision support system

We present a decision support system for flood early warning and disaster management. It includes the models for data-driven meteorological predictions, for simulation of atmospheric pressure, wind, long sea waves and seiches; a module for optimization of flood barrier gates operation; models for stability assessment of levees and embankments, for simulation of city inundation dynamics and citizens evacuation scenarios. The novelty of this paper is a coupled distributed simulation of surface and subsurface flows that can predict inundation of low-lying inland zones far from the submerged waterfront areas, as observed in St. Petersburg city during the floods. All the models are wrapped as software services in the CLAVIRE platform for urgent computing, which provides workflow management and resource orchestration.Comment: Pre-print submitted to the 2013 International Conference on Computational Scienc

Resource Optimized Quantum Architectures for Surface Code Implementations of Magic-State Distillation

Quantum computers capable of solving classically intractable problems are under construction, and intermediate-scale devices are approaching completion. Current efforts to design large-scale devices require allocating immense resources to error correction, with the majority dedicated to the production of high-fidelity ancillary states known as magic-states. Leading techniques focus on dedicating a large, contiguous region of the processor as a single "magic-state distillation factory" responsible for meeting the magic-state demands of applications. In this work we design and analyze a set of optimized factory architectural layouts that divide a single factory into spatially distributed factories located throughout the processor. We find that distributed factory architectures minimize the space-time volume overhead imposed by distillation. Additionally, we find that the number of distributed components in each optimal configuration is sensitive to application characteristics and underlying physical device error rates. More specifically, we find that the rate at which T-gates are demanded by an application has a significant impact on the optimal distillation architecture. We develop an optimization procedure that discovers the optimal number of factory distillation rounds and number of output magic states per factory, as well as an overall system architecture that interacts with the factories. This yields between a 10x and 20x resource reduction compared to commonly accepted single factory designs. Performance is analyzed across representative application classes such as quantum simulation and quantum chemistry.Comment: 16 pages, 14 figure

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201