Effects of Carrier Materials on Anaerobic Hydrogen Production by Continuous Mixed Immobilized Sludge Reactors

To enhance hydrogen production rate and increase substrate utilization efficiency of anaerobic fermentation, three carrier materials, Granular Activated Carbon (GAC), Zeolite Molecular Sieve (ZMS) and Biological Ceramic Ring (BCR), were used as carrier materials in Continuous Mixed Immobilized Sludge Reactors (CMISRs). The effects of carrier materials and substrate organic loading rate (OLR, OLR = 12, 24, 36, 48 kg/m3/d) on biohydrogen production were investigate, respectively. The highest HPRs of ZMS, GAC and BCR were achieved under the OLR of 36 kg COD/m3/d, and were 2.01, 1.81, and 2.86 L/L/d, respectively. The highest COD removal efficiencies of ZMS, GAC and BCR were 38.95 % (OLR = 24 kg COD/m3/d), 36.47 % (OLR = 36 kg COD/m3/d), and 41.03 % (OLR = 36 kg COD/m3/d), respectively. The best substrate degradation rate of ZMS, GAC and BCR were 40.33 % (OLR = 24 kg COD/m3/d), 38.30 % (OLR = 24 kg COD/m3/d) and 45.60 % (OLR = 12 kg COD/m3/d). The results indicated that biological ceramic ring get better hydrogen production and wastewater treatment performance as sludge carrier material for hydrogen production in immobilized bioprocesses

Architectural design decisions that incur technical debt — An industrial case study

Context: During software development, some architectural design decisions incur technical debt, either deliberately or inadvertently. These have serious impact on the quality of a software system, and can cost significant time and effort to be changed. While current research efforts have explored general concepts of architectural design decisions and technical debt separately, debt-incurring architectural design decisions have not been specifically explored in practice. Objective: In this case study, we explore debt-incurring architectural design decisions (DADDs) in practice. Specifically, we explore the main types of DADDs, why and how they are incurred in a software system, and how practitioners deal with these types of design decisions. Method: We performed interviews and a focus group with practitioners working in embedded and enterprise software companies, discussing their concrete experience with such architectural design decisions. Results: We provide the following contributions: 1) A categorization for the types of DADDs, which extend a current ontology on architectural design decisions. 2) A process on how deliberate DADDs are made in practice. 3) A conceptual model which shows the relationships between the causes and triggers of inadvertent DADDs. 4) The main factors that influence the way of dealing with DADDs. Conclusion: The results can support the development of new approaches and tools for Architecture Technical Debt management from the perspective of Design Decisions. Moreover, they support future research to capture architecture knowledge related to DADDs

Identification and Remediation of Self-Admitted Technical Debt in Issue Trackers

Technical debt refers to taking shortcuts to achieve short-term goals, which might negatively influence software maintenance in the long-term. There is increasing attention on technical debt that is admitted by developers in source code comments (termed as self-admitted technical debt or SATD). But SATD in issue trackers is relatively unexplored. We performed a case study, where we manually examined 500 issues from two open source projects (i.e. Hadoop and Camel), which contained 152 SATD items. We found that: 1) eight types of technical debt are identified in issues, namely architecture, build, code, defect, design, documentation, requirement, and test debt; 2) developers identify technical debt in issues in three different points in time, and a small part is identified by its creators; 3) the majority of technical debt is paid off, 4) mostly by those who identified it or created it; 5) the median time and average time to repay technical debt are 872.3 and 25.0 hours respectively.Comment: The 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA

Identifying self-admitted technical debt in issue tracking systems using machine learning

Technical debt is a metaphor indicating sub-optimal solutions implemented for short-term benefits by sacrificing the long-term maintainability and evolvability of software. A special type of technical debt is explicitly admitted by software engineers (e.g. using a TODO comment); this is called Self-Admitted Technical Debt or SATD. Most work on automatically identifying SATD focuses on source code comments. In addition to source code comments, issue tracking systems have shown to be another rich source of SATD, but there are no approaches specifically for automatically identifying SATD in issues. In this paper, we first create a training dataset by collecting and manually analyzing 4,200 issues (that break down to 23,180 sections of issues) from seven open-source projects (i.e., Camel, Chromium, Gerrit, Hadoop, HBase, Impala, and Thrift) using two popular issue tracking systems (i.e., Jira and Google Monorail). We then propose and optimize an approach for automatically identifying SATD in issue tracking systems using machine learning. Our findings indicate that: 1) our approach outperforms baseline approaches by a wide margin with regard to the F1-score; 2) transferring knowledge from suitable datasets can improve the predictive performance of our approach; 3) extracted SATD keywords are intuitive and potentially indicating types and indicators of SATD; 4) projects using different issue tracking systems have less common SATD keywords compared to projects using the same issue tracking system; 5) a small amount of training data is needed to achieve good accuracy.Comment: Accepted for publication in the EMSE journa

Learning to Grasp 3D Objects using Deep Residual U-Nets

Grasp synthesis is one of the challenging tasks for any robot object manipulation task. In this paper, we present a new deep learning-based grasp synthesis approach for 3D objects. In particular, we propose an end-to-end 3D Convolutional Neural Network to predict the objects’ graspable areas. We named our approach Res-U-Net since the architecture of the network is designed based on U-Net structure and residual network-styled blocks. It devised to plan 6-DOF grasps for any desired object, be efficient to compute and use, and be robust against varying point cloud density and Gaussian noise. We have performed extensive experiments to assess the performance of the proposed approach concerning graspable part detection, grasp success rate, and robustness to varying point cloud density and Gaussian noise. Experiments validate the promising performance of the proposed architecture in all aspects. A video showing the performance of our approach in the simulation environment can be found at http://youtu.be/5_yAJCc8owo<br/