Anaerobic fermentation of xylose by pichia stipitis: The effect of forced cycling of ph

Xylose fermentation by Pichia stipitis was examined using a two-stage batch process. The cells were first grown aerobically on D-xylose (5 g/L), whereafter additional xylose (10 g/L) was added and fermented during anaerobic conditions (T=30°C). The optimum pH value for a fermentation with constant pH was found to be 4.5 (maximum specific ethanol production rate 0.21 g/(g h). Forced square wave cycling of the pH between 4 and 5, and 3.5 and 5.5 (cycle time 30 min) during the fermentation stage resulted in a fermentation rate lower than the maximum rate, but with unchanged ethanol yields.     

An Empirical Study on Views of Importance of Change Impact Analysis Issues

Change impact analysis is a change management activity that previously has been studied much from a technical perspective. For example, much work focuses on methods for determining the impact of a change. In this paper, we present results from a study on the role of impact analysis in the change management process. In the study, impact analysis issues were prioritised with respect to criticality by software professionals from an organisational perspective and a self-perspective. The software professionals belonged to three organisational levels: operative, tactical and strategic. Qualitative and statistical analyses with respect to differences between perspectives as well as levels are presented. The results show that important issues for a particular level are tightly related to how the level is defined. Similarly, issues important from an organisational perspective are more holistic than those important from a self-perspective. However, our data indicate that the self-perspective colours the organisational perspective, meaning that personal opinions and attitudes cannot easily be disregarded. In comparing the perspectives and the levels, we visualise the differences in a way that allow us to discuss two classes of issues: high-priority and medium-priority. The most important issues from this point of view concern fundamental aspects of impact analysis and its execution.     

Industry evaluation of the Requirements Abstraction Model

Software requirements are often formulated on different levels and hence they are difficult to compare to each other. To address this issue, a model that allows for placing requirements on different levels has been developed. The model supports both abstraction and refinement of requirements, and hence requirements can both be compared with each other and to product strategies. Comparison between requirements will allow for prioritization of requirements, which in many cases is impossible if the requirements are described on different abstraction levels. Comparison to product strategies will enable early and systematic acceptance or dismissal of requirements, minimizing the risk for overloading. This paper presents an industrial evaluation of the model. It has been evaluated in two different companies, and the experiences and findings are presented. It is concluded that the requirements abstraction model provides helpful improvements to the industrial requirements engineering process.

An evaluation of methods for prioritizing software requirements

This article describes an evaluation of six different methods for prioritizing software requirements. Based on the quality requirements for a telephony system, the authors individually used all six methods on separate occasions to prioritize the requirements. The methods were then characterized according to a number of criteria from a user's perspective. We found the analytic hierarchy process to be the most promising method, although it may be problematic to scale-up. In an industrial follow-up study we used the analytic hierarchy process to further investigate its applicability. We found that the process is demanding but worth the effort because of its ability to provide reliable results, promote knowledge transfer and create consensus among project members.     

RAFALE: Rethinking the provisioning of virtuAl network services using a Fast and scAlable machine LEarning approach

Network Function Virtualization (NFV) has been identified to revamp the provisioning of next-generation network services. This new paradigm allows cloud and network/service providers to compose their network services, also known as service function chains (SFCs), in an agile way since the software of the network function is decoupled from the legacy hardware. To reap the benefits of this new technology, there is a need for novel mechanisms that help cloud and network/service providers deploy the increasingly complex virtual network services seamlessly, efficiently, and in a time-efficient way. Existing state-of-the-art techniques often rely on the Integer Linear Programming framework, heuristics/metaheuristics, and greedy methods to deploy the services function chains. However, these techniques although reasonable and acceptable, still suffer from several key limitations: convergence time and scalability. To this end, we propose RAFALE, a suite of solution techniques, to tame this complexity by leveraging the concept of similarity from machine learning and skip-gram modeling framework. To the best of our knowledge, we are the first to tackle these key limitations and propose a suite of solutions to them. RAFALE, a novel approach proposed to find the similarity between the new incoming virtual network service request and all the already-deployed services to learn from the previous experience of deploying techniques and use the same or close similar provisioning techniques. RAFALE is the first and the only method that develops the idea of detecting the similarity between virtual network services. Experimental results show that RAFALE reduces greatly the convergence time needed for provisioning virtual network services and can scale to 100 virtual network functions per virtual network service compared to the state-of-the-art. The Experimental results prove that RAFALE accomplished the NFV promises; decreasing the time and complexity of managing and deploying the virtual services, and providing a solution that is agile, faster, and scalable to deploy the new service requests by skipping one or more service provisioning steps (i.e., detecting and resolving the conflicts among policies, placement, and chaining) while satisfying the validated NFV policies.

     

Bark as feedstock for dual fluidized bed gasifiers—Operability,efficiency, and economics

The demand for biofuels and biochemicals is expected to increase in the future, which will in turn increase the demand for biomass feedstock. Large gasification plants fueled with biomass feedstock are likely to be a key enabling technology in a resource‐efficient, bio‐based economy. Furthermore, the costs for producing biofuels and biochemicals in such plants could potentially be decreased by utilizing inexpensive low‐grade residual biomass as feedstock. This study investigates the usage of shredded tree bark as a feedstock for the production of biomethane in the GoBiGas demonstration plant in Gothenburg, Sweden, based on a 32 MW_th industrial dual fluidized bed gasification unit. The plant was operated with bark feedstock for 12 000 hours during the period 2014 to 2018. Data from the measurement campaign were processed using a stochastic approach to establish the plant's mass and energy balances, which were then compared with operation of the plant with wood pellets. For this comparison, an extrapolation algorithm was developed to predict plant performance using bark dried to the same moisture content as wood pellets, ie, 8%_w.b. Plant operation with bark feedstock was evaluated for operability, efficiency, and feedstock‐related cost. The gas quality achieved during the test period was similar to that obtained for operation with wood pellets. Furthermore, no significant ash sintering or agglomeration problems were observed more than 750 hours of operation. The calculated biomass‐to‐biomethane efficiency is 43% to 47% (lower heating value basis) for operation with wet bark. However, the predicted biomass‐to‐biomethane efficiency can be increased to 55%–65% for operation with bark feedstock dried to 8% moisture content, with corresponding feedstock costs in the range of 24.2 to 32.7 EUR/MWh; ie, a cost reduction of about 40% compared with wood pellets.     

Histomorphological, histomorphometrical and biomechanical analysis of ceramic bone substitutes in a weight-bearing animal model

It was the purpose of this investigation to prove the biomechanical properties, the osteoconductive capacity and the degradation rate of tricalcium phosphate ( TCP), a neutralized glass ceramics (GB9N) and a composite material (GB9N+copolymers). In a weight-bearing animal model six substitutes each were implanted in the medial tibial head of the right lower leg of adult Merino-sheep in a standardized surgical technique. After nine months the implants were harvested and prepared for histomorphological and histomorphometrical investigations (undecalcified Masson Goldner staining). For additional biomechanical testing of the specimens, non-operated bone blocks from the contralateral tibia as well as native implants served as controls. No significant differences for the maximum fracture load as well as for the yield strength were detected between harvested specimens and bone blocks from the contralateral tibia. However there were marked differences to ceramics that were not implanted. All substitutes showed osteoconduction, leading to a continuous ingrowth of new formed bone. However in the composite material soft tissue could be identified within the scaffold and there were signs of ongoing bone remodeling, nine months after implantation. The bone per tissue volume of -TCP in conjunction to new bone (=percentage of trabecular bone volume plus percentage of residual substitute) was higher than for GB9N and the composite material. Nine months after implantation the percentage of residual -TCP was 48%, it was 32% for GB9N and 28% for the composite.The intention of further studies should be to accelerate the degradation rates of substitutes and to improve biomechanical properties of implants by either modifying the chemical composition or combining materials with agents as, e.g. growth factors.     

Establishment of a knee-joint coordinate system from helical axes analysis--a kinematic approach without anatomical referencing

This study establishes a functional knee-joint coordinate system (FCS) derived from active motion. The scale invariant properties of helical axes were used in order to avoid inter-observer errors associated with the traditional anatomical referencing techniques. The algorithm was tested with six cadaveric specimens in a knee-joint motion and loading apparatus. To determine the FCS sensitivity to variable loading, rotational moments were applied to the tibia while extending and flexing the knee. Each derived FCS was compared with the clinically derived anatomical coordinate system (ACS). The FCS was reproducible when the loading condition was the same. Changing the rotational moments from internal to external affected the orientations and the positions of the FCS. The largest displacement of 20.8 mm in average occurred in the medio/lateral direction. The FCS corresponded with the ACS for all specimens and loading conditions. The origin was always located within the femur along the transepicondylar line. The orientations differed less than 16.6 degrees in average, thus allowing the use of clinical terminology. These findings suggest that the FCS might improve the ability to clinically assess kinematic alterations provided that the reference motion is reproducible.     

Assessing Project Success Using Subjective Evaluation Factors

Project evaluation is essential to understand and assess the key aspects of a project that make it either a success or failure. The latter is influenced by a large number of factors, and many times it is hard to measure them objectively. This paper addresses this by introducing a new method for identifying and assessing key project characteristics, which are crucial for a project's success. The method consists of a number of well-defined steps, which are described in detail. The method is applied to two case studies from different application domains and continents. It is concluded that patterns are possible to detect from the data sets. Further, the analysis of the two data sets shows that the proposed method using subjective factors is useful, since it provides an increased understanding, insight and assessment of which project factors might affect project success.