Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness

Skin lesion segmentation is one of the pivotal stages in the diagnosis of melanoma. Many methods have been proposed but, to date, this is still a challenging task. Variations in size and color, the fuzzy boundary and the low contrast between lesion and normal skin are the adverse factors for deficient or excessive delineation of lesions, or even inaccurate lesion location detection. In this paper, to counter these problems, we introduce a deep learning method based on U-Net architecture, which performs three tasks, namely lesion segmentation, boundary distance map regression and contour detection. The two auxiliary tasks provide an awareness of boundary and shape to the main encoder, which improves the object localization and pixel-wise classification in the transition region from lesion tissues to healthy tissues. Moreover, concerning the large variation in size, the Selective Kernel modules, which are placed in the skip connections, transfer the multi-receptive field features from the encoder to the decoder. Our methods are evaluated on three publicly available datasets: ISBI2016, ISBI 2017 and PH2. The extensive experimental results show the effectiveness of the proposed method in the task of skin lesion segmentation.

Correlation of inhibitions to structural aspects of chloroplast proteins: A murburn perspective

In this manuscript, we we first present a brief review of the structural awareness on chloroplasts, the two photosystems (PS I & PS II, along with the respective light harvesting complexes and chlorophyll binding proteins). Thereafter, with an in silico approach, we attempt to correlate the photoactive proteins’ inhibition by various class of molecules, particularly weedicides. The prevailing understanding relies on topographical-affinity driven binding based explanations for electron transfers and inhibitions. The murburn model of photosynthesis deems DROS (diffusible reactive oxygen species) mediated constructive outcomes as the physiological mechanism for overall outcomes. We unravel key anomalous observations on the inhibitory aspects of the photosynthetic machinery, which were also noted in DROS involving systems like microsomal xenobiotic metabolism and mitochondrial oxidative phosphorylation. Via a comparative logic and applying Ockham’s razor, we infer that inhibitory outcomes in several photolysis-photophosphorylation processes are better explained by the murburn model.

Training Structural Awareness with StatHand: A 1 Year Follow-Up

Background: Allen, Fielding, East, et al. demonstrated experimentally that structural awareness, or the ability to disregard a research problem’s topic and instead focus on its structural features, can be trained using StatHand ( https://stathand.net ). Most training benefits persisted for 1 week. Objective: The objective was to assess the longer-term effects of training. Method: One year after training (or control activities), 54 participants were re-administered 5 measures of structural awareness and 1 statistic selection measure. Results: Trained participants continued to reliably out-perform control participants on 4 measures of structural awareness, though no longer on the 5th. Over the year, decrements in trained participants’ performance on the 5 structural awareness measures were mostly small. However, 1 year after training, the trained participants’ statistic selection advantage had largely disappeared. Conclusion: Brief structural awareness training can have long-term benefits, though selecting an appropriate statistical test for common research scenarios without assistance remains a difficult task. Teaching Implications: Structural awareness can be trained. However, even structurally aware students cannot reliably select appropriate statistics without assistance. Training plus easy access to a decision-making aid should maximize statistic selection accuracy. Our evidence-based training methods and materials, including StatHand, can be freely used and adapted for these purposes.

STUDENTS’ REFLECTIVE ABSTRACTION ABILITY ON LINEAR ALGEBRA PROBLEM SOLVING AND RELATIONSHIP WITH PREREQUISITE KNOWLEDGE

This study aims to describe the achievement of the ability of students' reflective abstraction in solving Linear Algebra problems and the relationship with prerequisite knowledge. The important of this research because the characteristic of Linear Algebra requiring reflectif abstraction skill that must be support by the prerequisite knowledge. The reflective abstraction abilities studied in this study are level, i.e.1) recognition,2) representation, 3) structural abstraction, and 4) structural awareness. These stages are adjusted to Polya's problem solving stages, namely: understanding the problem, devising a plan, carrying out the plan, and looking back. This type of research is descriptive-quantitative. The subjects of this study were students of the Mathematics Education Study Program, Faculty of Tarbiyah and Teacher Training of UIN Sunan Gunung Djati Bandung Indonesia. Collecting data through tests and interviews, data were analyzed with percentage and the pearson product-moment correlation.The results showed that the achievement level consisiting of ) recognition,2) representation, 3) structural abstraction, and 4) structural awareness of the students’ reflective abstraction abilities on linear algebra problem solving are very good, this can be seen from the percentage achieved at stages of the recognition,the representation,the structural abstraction, and the structural awareness which is associated with Polya problem solving measures above an average of 73,31% (moderat category); there are relationship between students' reflective abstraction abilities and their prerequisite knowledge; and prerequisite knowledge influences the students’reflective abstraction abilities

The Process of Student's Mathematic Abstract from Spatial Intelligence

<p>Every student must have mathematical abstraction skills. Research with a qualitative approach aims to identify the mathematical abstraction process of students when working on geometric material problems in terms of spatial intelligence. By using a descriptive design, apart from the researcher as the main instrument, the mathematical abstraction test, the spatial intelligence test, and the interview reference were used as auxiliary instruments. A total of 6 students from class VIII were selected through purposive sampling technique which was taken from each category of spatial ability which had been classified into high, medium and low criteria. Based on data analysis, students' mathematical abstraction can be grouped into 4 levels, namely: recognition, representation, structural abstraction, and structural awareness. The conclusions of this study are: 1) students with a high level of spatial intelligence can achieve all four levels of mathematical abstraction characteristics and activities, namely recognition, representation, structural abstraction, and structural awareness. 2) students with moderate spatial intelligence can only achieve two levels of mathematical characteristics and abstraction activities, namely recognition and representation. 3) students with low-level spatial intelligence are only able to achieve one level of mathematical abstraction characteristics and activity, namely recognition where students are able to remember previous activities and experiences related to the problems at hand. This shows that students with moderate and low-level spatial intelligence do not have adequate abstraction skills in the concept of geometry.</p>

Dynamic Characterization and Control of a Metamaterials-Inspired Smart Composite

The potential to utilize metamaterials concepts to realize smart composites with adaptive mechanical wave manipulation, energy harvesting, and structural health monitoring functionalities was investigated. A proof-of-concept metamaterials-inspired smart composite having CFRP face sheets bonded to additively manufactured polymer cores equipped with harvesting coils and sandwiching a chemically-etched multifunctional plate was fabricated. This plate consists of a periodic array of re-entrant cantilever beam resonators with center-loaded neodymium magnets, which acts as the multifunctional kernel. Experiments demonstrate isolation of a payload from mechanical disturbances within tunable frequency bands. Moreover, energy sequestered by resonators is harvested as useable electrical power. Using a coupled electromechanical harvesting model, predictions for multifunctional responses were obtained and correlated with experiments. The harvesting circuitry doubles as an active control system for the resonators as well as a sensing and monitoring system to detect structural defects. Both offline and online active control algorithms were investigated to reduce phase shift between harvesting coils, thereby improving the efficacy of the harvesting process. Potential applications include use as structural material for equipment or vehicles used in adverse or remote environments, where maximizing energy recovery and structural awareness in addition to payload isolation is desirable.