Data Transformation in Cross-project Defect Prediction

Software metrics rarely follow a normal distribution. Therefore, software metrics are usually transformed prior to building a defect prediction model. To the best of our knowledge, the impact that the transformation has on cross-project defect prediction models has not been thoroughly explored. A cross-project model is built from one project and applied on another project. In this study, we investigate if cross-project defect prediction is affected by applying different transformations (i.e., log and rank transformations, as well as the Box-Cox transformation). The Box-Cox transformation subsumes log and other power transformations (e.g., square root), but has not been studied in the defect prediction literature. We propose an approach, namely Multiple Transformations (MT), to utilize multiple transformations for cross-project defect prediction. We further propose an enhanced approach MT+ to use the parameter of the Box-Cox transformation to determine the most appropriate training project for each target project. Our experiments are conducted upon three publicly available data sets (i.e., AEEEM, ReLink, and PROMISE). Comparing to the random forest model built solely using the log transformation, our MT+ approach improves the F-measure by 7, 59 and 43% for the three data sets, respectively. As a summary, our major contributions are three-fold: 1) conduct an empirical study on the impact that data transformation has on cross-project defect prediction models; 2) propose an approach to utilize the various information retained by applying different transformation methods; and 3) propose an unsupervised approach to select the most appropriate training project for each target project.     

Colour and ergonomics: On the selection of a “colour of the year”

The key role of colour in ergonomics has been emphasized by a number of researchers and design professionals. Although several research studies have been published regarding the use of colour in ergonomics, there are still some areas that need to be considered. The issue of deciding a “colour of the year” is an example, which represents a new challenge for researchers in the field of ergonomics. This is of particular interest considering the fact that the nature of research on the selection of a “colour of the year” is generally based on user experience. This paper argues that ergonomics should play a more prominent role in this field to ensure better user experience and performance. This paper highlights specific areas that need further study and development.     

Robust Diversity-based Sine-Cosine Algorithm for Optimizing Hydropower Multi-reservoir Systems

Hydropower energy generation depends on the available water resources. Therefore, planning and operation of the water resource systems are paramount tasks for energy management. Since reservoirs are one of the important components of water resources systems, extracting optimal operating policies for proper management of energy generated from these systems is an imperative step. Optimizing reservoir system operation (ORSO) is a non-linear, large-scale, and non-convex problem with a large number of constraints and decision variables. To solve ORSO problem effectively, a robust diversity-based, sine-cosine algorithm (RDB-SCA) is developed in the present study by introducing several strategies to balance the global exploration and local exploitation ability and to achieve accurate and reliable solutions. An efficient linear operation rule is coupled with the RDB-SCA to maximize the energy generation. The proposed method is then applied to a real-world, multi-reservoir system to extract optimal operational policies and, consequently, maximize the energy production. It is shown that the RDB-SCA is able to generate 24, 14, and 6% more energy than the original SCA, respectively for 2-, 3-, and 4-reservoir systems. The present findings are useful to suggest guidelines for efficient operation of hydropower multi-reservoir systems. This paper is supported by https://imanahmadianfar.com/codes.

     

Efficient repairing effect of PEG based tri-block copolymer on mechanically damaged PC12 cells and isolated spinal cord

Membrane sealing effects of polymersomes made of tri-block copolymer, PEG-co-FA/SC-co-PEG, (PFSP) were studied on isolated spinal cord strips, PC12 cell lines and artificial bilayer following mechanical impact implemented by aneurism clip, sonication and electric shock, respectively. The homogeneity and size of PFSP, membrane permeability and cell viability were assessed by dynamic light scattering, LDH release and MTT assays. According to the results, the biocompatible, physico-chemical, size, surface charge and amphipathic nature of PFSP polymersome makes it an ideal macromolecule to rapidly reseal damaged membranes of cells in injured spinal cord as well as in culture medium. Compound action potentials recorded from intentionally damaged spinal cord strips incubated with PFSP showed restoration of neural excitability by 82.24 % and conduction velocity by 96.72 % after 5 min that monitored in real time. Thus, they triggered efficient instant and sustained sealing of membrane and reactivation of temporarily inactivated axons. Treatment of ultrasonically damaged PC12 cells by PFSP caused efficient cell membrane repair and led to their increased viability. The optimum effects of PFSP on stabilization and impermeabilizing of the lipid bilayer occurred at the same concentrations applied to the damaged cells and spinal cord fibers and was approved by restoration of membrane conductance and calcein release manifested by NanoDrop technique. The unique physico-chemical characteristics of novel polymersomes introduced here, make them capable to reorganize membrane lipid molecules, reseal the breaches and restore the hydrophobic insulation in spinal cord damaged cells. Thus, they might be considered in the clinical treatment of SCI at early stages.     

From Side Chains Rattling on Picoseconds to Ensemble Simulations of Protein Folding

Simulations of biological macromolecules have evolved tremendously since the discoveries of the 1970s. The field has moved from simple simulations in vacuo on picosecond scales to milliseconds of accurate sampling of large proteins, and it has become a standard tool in biochemistry and biophysics, rather than a dedicated theoretical one. This is partly due to increasing computational power, but it would not have been possible without huge research efforts invested in new algorithms and software. Here, we illustrate some of this development, both past and future challenges, and in particular, discuss how the recent introduction of modern ensemble methods is breaking the trend of ever-longer simulations to instead focus on throughput and sampling. This has not only helped simulations become much more accurate, but it provides statistical error estimates, which are critical, as simulations are increasingly used to predict properties that have not yet been measured experimentally.     

Pattern matching of alarm flood sequences by a modified Smith–Waterman algorithm

Alarm flooding is one of the main problems in alarm management. Alarm flood pattern analysis is helpful for root cause analysis of historical floods and for incoming flood prediction. This paper deals with a data driven method for alarm flood pattern matching. An alarm flood is represented by a time-stamped alarm sequence. A modified Smith–Waterman algorithm considering the time stamp information is proposed to calculate a similarity index of alarm floods. The effectiveness of the algorithm is validated by a case study on actual chemical process alarm data.     

Development and Validation of an Artificial Mechanical Skin Model for the Study of Interactions between Skin and Microneedles

Microneedles are small needle‐like structures that are almost invisible to the naked eye. They have an immense potential to serve as a valuable tool in many medical applications, such as painless vaccination. Microneedles work by breaking through the stratum corneum, the outermost barrier layer of the skin, and providing a direct path for drug delivery into the skin. A lot of research has been presented over the past two decades on the applications of microneedles, yet the fundamental mechanism of how they interact, pressure, and penetrate the skin in its native state is worth examining further. As such, a major difficulty with understanding the mechanism of microneedle–skin interaction is the lack of an artificial mechanical human skin model to use as a standardized substrate. In this research news, the development of an artificial mechanical skin model based on a thorough mechanical study of fresh human and porcine skin samples is presented. The artificial mechanical skin model can be used to study the mechanical interactions between microneedles and skin, but not diffusion of molecules across skin. This model can assist in improving the performance of microneedles by enhancing the reproducibility of microneedle depth insertions for optimal drug delivery and biosensing.