Characterizing and Diagnosing Architectural Degeneration of Software Systems from Defect Perspective

The architecture of a software system is known to degrade as the system evolves over time due to change upon change, a phenomenon that is termed architectural degeneration. Previous research has focused largely on structural deviations of an architecture from its baseline. However, another angle to observe architectural degeneration is software defects, especially those that are architecturally related. Such an angle has not been scientifically explored until now. Here, we ask two relevant questions: (1) What do defects indicate about architectural degeneration? and (2) How can architectural degeneration be diagnosed from the defect perspective? To answer question (1), we conducted an exploratory case study analyzing defect data over six releases of a large legacy system (of size approximately 20 million source lines of code and age over 20 years). The relevant defects here are those that span multiple components in the system (called multiple-component defects - MCDs). This case study found that MCDs require more changes to fix and are more persistent across development phases and releases than other types of defects. To answer question (2), we developed an approach (called Diagnosing Architectural Degeneration - DAD) from the defect perspective, and validated it in another, confirmatory, case study involving three releases of a commercial system (of size over 1.5 million source lines of code and age over 13 years). This case study found that components of the system tend to persistently have an impact on architectural degeneration over releases. Especially, such impact of a few components is substantially greater than that of other components. These results are new and they add to the current knowledge on architectural degeneration. The key conclusions from these results are: (i) analysis of MCDs is a viable approach to characterizing architectural degeneration; and (ii) a method such as DAD can be developed for diagnosing architectural degeneration

Application of Absorption and Scattering Properties Obtained through Image Pre-Classification Method Using a Laser Backscattering Imaging System to Detect Kiwifruit Chilling Injury

Kiwifruit chilling injury (CI) damage occurs after long-term exposure to low temperature. A non-destructive approach to detect CI injury was tested in the present study, using a laser backscattering image (LBI) technique calibrated with 56 liquid phantoms for providing absorption coefficient (µa) and reduced scattering coefficient (µs’). Calibration of LBI resulted in a true-positive (TP) classification of 91.5% and 65.6% of predicted µs’ and µa, respectively. The optical properties of ‘SunGold™’and ‘Hayward’ kiwifruit were analysed at 520 nm with a two-step protocol capturing pre-classification according to the LBI parameters used in the calibration and estimation with the Farrell equation. Severely injured kiwifruit showed white corky tissue and water soaking, reduced soluble solids content and firmness measured destructively. Non-destructive classification results for ‘SunGold™’ showed a high percentage of TP for severe CI of 92% and 75% using LBI parameters directly and predicted µa and µs’ after pre-classification, respectively. The classification accuracy for severe CI ‘Hayward’ kiwifruit with LBI parameter was low (58%) and with µa and µs’ decreased further (35%), which was assumed to be due to interference caused by the long trichomes on the fruit surface

Telerehabilitation Combined Speech-Language and Cognitive Training Effectively Promoted Recovery in Aphasia Patients

The present study investigated the efficacy of a computerized intervention for aphasia that combined speech-language and cognitive training delivered on an inpatient unit or via telerehabilitation to discharged patients. Forty inpatient and discharged aphasia patients were recruited and randomly assigned to the training group or control group. Computerized speech-language and cognitive training was provided for 14 days to the inpatients and 30 days to the discharged patients. Compared with the control group, training group had significantly more improved language function as assessed by the Western Aphasia Battery (WAB) and practical communication skills as assessed by the Communicative Abilities in Daily Living Test (CADL). It was also found that the positive effects of the computerized training when delivered via telerehabilitation to the discharged group were smaller than the effects when delivered on the inpatient unit. The results suggest that combining speech-language and cognitive training program is efficacious in promoting the recovery of patients with aphasia, both inpatients and discharged patients, and that the program works even when administered from a remote location

Characterization of textural failure mechanics of strawberry fruit

This is an accepted manuscript of an article published by Elsevier in Journal of Food Engineering on 05/03/2020, available online: https://doi.org/10.1016/j.jfoodeng.2020.110016 The accepted version of the publication may differ from the final published version.Fresh strawberry fruit is highly susceptible to damage during mechanical handlings. To prevent fruit macro-damage from external forces and predict damage evolution in internal tissues, the textural failure mechanics of strawberry fruit and its tissues were characterized by loading-unloading tests at different compression speeds. Strawberry fruit showed expected three stages of deformation during the loading phase, namely elastic, local plastic and structural failure deformation. Their cut-off points depended on the compression speed and loading direction, which was validated further by the corresponding visible browning processes in tissues from fruit longitudinal equatorial section. The peak force and absorbed energy depended on the loading direction and compression speed while the percentage of damaged mass only depended on the loading direction. The fruit was most susceptible to mechanical damage when it was compressed along its stem-blossom axis at low percentage deformation and along its radial axis at high percentage deformation. The absorbed energy and percentage of damaged mass of the strawberry fruit was correlated, which suggested that the absorbed energy could be an appropriate and easily measurable mechanical parameter for quantitatively assessing the degree of fruit damage. The failure stress, failure energy and elastic modulus of fruit tissues increased with the compression speed, while this factor did not affect the failure strain. The average failure stress, failure strain, failure energy and elastic modulus of fruit inner tissue were 0.093 MPa, 17.7%, 8.09 mJ, 0.53 MPa, which was 1.27, 1.14, 1.47, 1.15 times enhanced compared to values of outer tissue (p < 0.05), respectively.This work was supported by a European Marie Curie International Incoming and Return Fellowship (326847 and 912847), a Special Foundation for Talents of Northwest A&F University, China (Z111021801), two Key Research and Development Plans of Shaanxi Province, China (2019NY-172 and 2019TSLNY01-01) and a Project for Sino-German Cooperation on Agricultural Science and Technology in 2018–2019 (15).Published versio

Fuzzy sliding mode control of a multi-DOF parallel robot in rehabilitation environment

Multi-degrees of freedom (DOF) parallel robot, due to its compact structure and high operation accuracy, is a promising candidate for medical rehabilitation devices. However, its controllability relating to the nonlinear characteristics challenges its interaction with human subjects during the rehabilitation process. In this paper, we investigated the control of a parallel robot system using fuzzy sliding mode control (FSMC) for constructing a simple controller in practical rehabilitation, where a fuzzy logic system was used as the additional compensator to the sliding mode controller (SMC) for performance enhancement and chattering elimination. The system stability is guaranteed by the Lyapunov stability theorem. Experiments were conducted on a lower limb rehabilitation robot, which was built based on kinematics and dynamics analysis of the 6-DOF Stewart platform. The experimental results showed that the position tracking precision of the proposed FSMC is sufficient in practical applications, while the velocity chattering had been effectively reduced in comparison with the conventional FSMC with parameters tuned by fuzzy systems

Attention-based multi-semantic dynamical graph convolutional network for eeg-based fatigue detection

IntroductionEstablishing a driving fatigue monitoring system is of utmost importance as severe fatigue may lead to unimaginable consequences. Fatigue detection methods based on physiological information have the advantages of reliable and accurate. Among various physiological signals, EEG signals are considered to be the most direct and promising ones. However, most traditional methods overlook the functional connectivity of the brain and fail to meet real-time requirements.MethodsTo this end, we propose a novel detection model called Attention-Based Multi-Semantic Dynamical Graph Convolutional Network (AMD-GCN). AMD-GCN consists of a channel attention mechanism based on average pooling and max pooling (AM-CAM), a multi-semantic dynamical graph convolution (MD-GC), and a spatial attention mechanism based on average pooling and max pooling (AM-SAM). AM-CAM allocates weights to the input features, helping the model focus on the important information relevant to fatigue detection. MD-GC can construct intrinsic topological graphs under multi-semantic patterns, allowing GCN to better capture the dependency between physically connected or non-physically connected nodes. AM-SAM can remove redundant spatial node information from the output of MD-GC, thereby reducing interference in fatigue detection. Moreover, we concatenate the DE features extracted from 5 frequency bands and 25 frequency bands as the input of AMD-GCN.ResultsFinally, we conduct experiments on the public dataset SEED-VIG, and the accuracy of AMD-GCN model reached 89.94%, surpassing existing algorithms.DiscussionThe findings indicate that our proposed strategy performs more effectively for EEG-based driving fatigue detection

Effects of heat treatment on the microstructure of amorphous boron carbide coating deposited on graphite substrates by chemical vapor deposition

A two-layer boron carbide coating is deposited on a graphite substrate by chemical vapor deposition from a CH4/BCl3/H-2 precursor mixture at a low temperature of 950 degrees C and a reduced pressure of 10 KPa. Coated substrates are annealed at 1600 degrees C, 1700 degrees C, 1800 degrees C, 1900 degrees C and 2000 degrees C in high purity argon for 2 h, respectively. Structural evolution of the coatings is explored by electron microscopy and spectroscopy. Results demonstrate that the as-deposited coating is composed of pyrolytic carbon and amorphous boron carbide. A composition gradient of B and C is induced in each deposition. After annealing, B4C crystallites precipitate out of the amorphous boron carbide and grow to several hundreds nanometers by receiving B and C from boron-doped pyrolytic carbon. Energy-dispersive spectroscopy proves that the crystallization is controlled by element diffusion activated by high temperature annealing, after that a larger concentration gradient of B and C is induced in the coating. Quantified Raman spectrum identifies a graphitization enhancement of pyrolytic carbon. Transmission electron microscopy exhibits an epitaxial growth of B4C at layer/layer interface of the annealed coatings. Mechanism concerning the structural evolution on the basis of the experimental results is proposed. (C) 2010 Elsevier B.V. All rights reserved.National Natural Science Foundation of China [50532010, 90405015