Structuring peer assessment: Comparing the impact of the degree of structure on peer feedback content

The present study examines the added value of structuring the peer assessment process, by providing students with a peer feedback template with a varying structuring degree, for the peer feedback content quality in a wiki environment in higher education. The present study took place in the 1st year of a university course in Instructional Sciences (N = 176) and more specifically compared three conditions: no structure peer feedback (control), basic structure peer feedback, and elaborate structure peer feedback condition. Quantitative content analysis of students’ (n = 41) peer feedback messages was performed, and an analysis of (co)variance revealed some discrepancies between the conditions regarding the proportion of peer feedback content categories: (1) peer feedback style, (2) verification type, (3) verification focus, (4) elaboration type, and (5) elaboration focus. This study demonstrated that a higher structuring degree in a peer feedback template during the peer assessment process might have an impact on peer feedback content with respect to the above-mentioned categories; the peer feedback content. Results revealed significant differences between the three conditions regarding the peer feedback content categories. This study illustrated how a practical instructional intervention in the feedback process can increase the potential impact of peer assessment and boost students’ learning in higher education.     

Human lower extremity joint moment prediction: A wavelet neural network approach

Joint moment is one of the most important factors in human gait analysis. It can be calculated using multi body dynamics but might not be straight forward. This study had two main purposes; firstly, to develop a generic multi-dimensional wavelet neural network (WNN) as a real-time surrogate model to calculate lower extremity joint moments and compare with those determined by multi body dynamics approach, secondly, to compare the calculation accuracy of WNN with feed forward artificial neural network (FFANN) as a traditional intelligent predictive structure in biomechanics.To aim these purposes, data of four patients walked with three different conditions were obtained from the literature. A total of 10 inputs including eight electromyography (EMG) signals and two ground reaction force (GRF) components were determined as the most informative inputs for the WNN based on the mutual information technique. Prediction ability of the network was tested at two different levels of inter-subject generalization. The WNN predictions were validated against outputs from multi body dynamics method in terms of normalized root mean square error (NRMSE (%)) and cross correlation coefficient (ρ).Results showed that WNN can predict joint moments to a high level of accuracy (NRMSE < 10%, ρ > 0.94) compared to FFANN (NRMSE < 16%, ρ > 0.89). A generic WNN could also calculate joint moments much faster and easier than multi body dynamics approach based on GRFs and EMG signals which released the necessity of motion capture. It is therefore indicated that the WNN can be a surrogate model for real-time gait biomechanics evaluation.     

An expert protein loop refinement protocol by molecular dynamics simulations with restraints

Protein structure prediction (PSP) is a long standing problem in structural biology and bioinformatics. Within the PSP problem loop refinement is a major bottleneck. In this article we report the latest version of the CReF expert predictor system for the PSP problem with emphasis on loop refinement of the approximate 3-D structure 1ZDD_P of the Z34C mini protein predicted by CReF. We designed a loop refinement protocol based on seven molecular dynamics (MD) simulations runs at different temperatures. We found that, by letting the loop residues move freely during dynamics at 325 K and restraining the internal coordinates of the correctly predicted helical structures, while allowing them to move relative to each other, the refinement protocol was very effective in predicting an accurate loop conformation in the first 100 ps of a 1000 ps MD simulation. The quality of the predictions was confirmed by the RMSD between refined and experimental structures which varied from 0.6 to 1.3 Å. In addition, stereochemical analyses showed that 100% of all residues of the refined 1ZDD_P, including those in the loop, populates the most favorable core regions of the Ramachandran plot. Our study suggests that the proposed protocol may be suitable to refine more complex mini proteins with different classes and architectures.     

A micro shear stress sensor based on laterally aligned carbon nanotubes

This paper reports the development of a micro thermal shear stress sensor that utilizes multiwalled carbon nanotubes as the sensing element. The sensor was fabricated by laterally aligning randomly distributed nanotubes into a 360 μm long and 90 μm wide conductive trace between two triangular shaped micro electrodes through the use of a high frequency AC electric field. During operation, the aligned nanotubes are electrically heated to an elevated temperature and surface shear stress is measured indirectly by the amount of convective heat transfer from the heated nanotubes to the surrounding fluid flow.The nanotube alignment process was primarily controlled by three different phenomena: dielectrophoresis, joule heating, and Brownian motion. Numerical simulations, together with experimental verifications, indicated that a successful alignment could only be realized if: (1) the dielectrophoretic force was positive, (2) the electro-thermal force was also positive, and (3) the dielectrophoretic force was high enough to overcome Brownian motion. The aligned nanotube trace has a room-temperature resistance of 580 Ω, which corresponds to a conductivity of 2.7 × 10⁴ S/m. The absolute temperature coefficient of resistivity ranges from 0.01 to 0.04% °C⁻¹. This is about one order of magnitude smaller than the highly doped polysilicon sensing material used in the MEMS micro shear stress sensor. The shear stress sensitivity of the nanotube trace operated at a 3% overheat ratio is found to follow the theoretical sensor power ∝ (shear stress)^1/3 relationship, provided the shear stress level is higher than 0.34 mPa. This result confirms the feasibility of using aligned multi-walled carbon nanotubes as a thermal shear stress sensing material.     

Piezoelectric fuel injection: Cycle-to-cycle control of tightly spaced injections

Piezoelectric fuel injectors will require closed-loop control to realize tightly spaced injections. This paper describes an estimation algorithm for cycle-to-cycle determination of an injection flow profile for use as feedback for closed-loop control. A control design-amenable, 2-pulse approximate model is outlined to represent the dynamics of simultaneously controlling the quantity of, and realized dwell time between, injection pulses. A control law is developed to provide an overdamped response of realized dwell time to prevent pulse bleeding. Closed-loop performance is validated with the experimental data. Desired fuel quantity, and very short dwell times of 0.0002 s (two crank angle degrees at 1800 RPM), are realized with tracking error convergence time constants of approximately four engine cycles (0.27 s at 1800 RPM).     

A Cognitive-Affective Model of Perceived User Satisfaction (CAMPUS): The complementary effects and interdependence of usability and aesthetics in IS design

Affective dimensions of human–computer interaction design have the potential to elicit emotions and behaviours. However, there is little research into which affective treatments are systematically tested, let alone assessed in light of additional cognitive dimensions. In this study, we formulate and empirically test a Cognitive-Affective Model of Perceived User Satisfaction (CAMPUS) that displays high explanatory power (R² = .69). CAMPUS offers a comprehensive framework for assessing both direct effects of perceptions of cognitive and affective dimensions on satisfaction and the complex interplay between these two in terms of system design and use. Implications for theory and practice are discussed.     

Stabilizing global mean surface temperature: A feedback control perspective

In this paper, we develop a discrete time, state variable feedback control regime to analyze the closed-loop properties associated with stabilizing the global mean surface temperature anomaly at 2 °C within a sequential decision making framework made up of 20 year review periods beginning in 2020. The design of the feedback control uses an optimal control approach that minimizes the peak deceleration of anthropogenic CO₂ emissions whilst avoiding overshooting the 2 °C target. The peak value for emissions deceleration that satisfies the closed-loop optimization was found to be linearly related to climate sensitivity and a climate sensitivity of 3.5 °C gave a deceleration of ?1.9 GtC/a/20 years². In addition to accounting for the predicted climate dynamics, the control system design includes a facility to emulate a robust corrective action in the face of uncertainty. The behavior of the overall control action is evaluated using an uncertainty scenario for climate model equilibrium sensitivity.     

Structural study of carboxylesterase from hyperthermophilic bacteria Geobacillus stearothermophilus by molecular dynamics simulation

Carboxylesterases are ubiquitous enzymes with important physiological, industrial and medical applications such as synthesis and hydrolysis of stereo specific compounds, including the metabolic processing of drugs, and antimicrobial agents. Here, we have performed molecular dynamics simulations of carboxylesterase from hyperthermophilic bacterium Geobacillus stearothermophilus (GsEst) for 10 ns each at five different temperatures namely at 300 K, 343 K, 373 K, 473 K and 500 K. Profiles of root mean square fluctuation (RMSF) identify thermostable and thermosensitive regions of GsEst. Unfolding of GsEst initiates at the thermosensitive α-helices and proceeds to the thermostable β-sheets. Five ion-pairs have been identified as critical ion-pairs for thermostability and are maintained stably throughout the higher temperature simulations. A detailed investigation of the active site residues of this enzyme suggests that the geometry of this site is well preserved up to 373 K. Furthermore, the hydrogen bonds between Asp188 and His218 of the active site are stably maintained at higher temperatures imparting stability of this site. Radial distribution functions (RDFs) show similar pattern of solvent ordering and water penetration around active site residues up to 373 K. Principal component analysis suggests that the motion of the entire protein as well as the active site is similar at 300 K, 343 K and 373 K. Our study may help to identify the factors responsible for thermostability of GsEst that may endeavor to design enzymes with enhanced thermostability.     

Analysis of the structure and morphology of fenoxycarb crystals

In this paper, we have explored the relationship between surface structure and crystal growth and morphology of fenoxycarb (FC). Experimental vs. predicted morphologies/face indices of fenoxycarb crystals are presented. Atomic-scale surface structures of the crystalline particles, derived from experimentally indexed single crystals, are also modelled. Single crystals of fenoxycarb exhibit a platelet-like morphology which closely matches predicted morphologies. The solvent choice does not significantly influence either morphology or crystal habit. The crystal morphology is dominated by the {0 0 1} faces, featuring weakly interacting aliphatic or aromatic groups at their surfaces. Two distinct modes of interaction of a FC molecule in the crystal can be observed, which appear to be principal factors governing the microscopic shape of the crystal: the relatively strong collateral and the much weaker perpendicular bonding. Both forcefield-based and quantum-chemical calculations predict that the aromatic and aliphatic terminated {0 0 1} faces have comparably high stability as a consequence of weak intermolecular bonding. Thus we predict that the most developed {0 0 1} surfaces of fenoxycarb crystals should be terminated randomly, favouring neither aliphatic nor aromatic termination.     

Action recognition via bio-inspired features: The richness of center–surround interaction

Motion is a key feature for a wide class of computer vision approaches to recognize actions. In this article, we show how to define bio-inspired features for action recognition. To do so, we start from a well-established bio-inspired motion model of cortical areas V1 and MT. The primary visual cortex, designated as V1, is the first cortical area encountered in the visual stream processing and early responses of V1 cells consist in tiled sets of selective spatiotemporal filters. The second cortical area of interest in this article is area MT where MT cells pool incoming information from V1 according to the shape and characteristic of their receptive field. To go beyond the classical models and following the observations from Xiao et al. [61], we propose here to model different surround geometries for MT cells receptive fields. Then, we define the so-called bio-inspired features associated to an input video, based on the average activity of MT cells. Finally, we show how these features can be used in a standard classification method to perform action recognition. Results are given for the Weizmann and KTH databases. Interestingly, we show that the diversity of motion representation at the MT level (different surround geometries), is a major advantage for action recognition. On the Weizmann database, the inclusion of different MT surround geometries improved the recognition rate from 63.01 ± 2.07% up to 99.26 ± 1.66% in the best case. Similarly, on the KTH database, the recognition rate was significantly improved with the inclusion of MT different surround geometries (from 47.82 ± 2.71% up to 92.44 ± 0.01% in the best case). We also discussed the limitations of the current approach which are closely related to the input video duration. These promising results encourage us to further develop bio-inspired models incorporating other brain mechanisms and cortical areas in order to deal with more complex videos.     

Credit rating by hybrid machine learning techniques

It is very important for financial institutions to develop credit rating systems to help them to decide whether to grant credit to consumers before issuing loans. In literature, statistical and machine learning techniques for credit rating have been extensively studied. Recent studies focusing on hybrid models by combining different machine learning techniques have shown promising results. However, there are various types of combination methods to develop hybrid models. It is unknown that which hybrid machine learning model can perform the best in credit rating. In this paper, four different types of hybrid models are compared by ‘Classification + Classification’, ‘Classification + Clustering’, ‘Clustering + Classification’, and ‘Clustering + Clustering’ techniques, respectively. A real world dataset from a bank in Taiwan is considered for the experiment. The experimental results show that the ‘Classification + Classification’ hybrid model based on the combination of logistic regression and neural networks can provide the highest prediction accuracy and maximize the profit.     

Securing high resolution grayscale facial captures using a blockwise coevolutionary GA

In biometric systems, reference facial images captured during enrollment are commonly secured using watermarking, where invisible watermark bits are embedded into these images. Evolutionary Computation (EC) is widely used to optimize embedding parameters in intelligent watermarking (IW) systems. Traditional IW methods represent all blocks of a cover image as candidate embedding solutions of EC algorithms, and suffer from premature convergence when dealing with high resolution grayscale facial images. For instance, the dimensionality of the optimization problem to process a 2048 × 1536 pixel grayscale facial image that embeds 1 bit per 8 × 8 pixel block involves 49k variables represented with 293k binary bits. Such Large-Scale Global Optimization problems cannot be decomposed into smaller independent ones because watermarking metrics are calculated for the entire image. In this paper, a Blockwise Coevolutionary Genetic Algorithm (BCGA) is proposed for high dimensional IW optimization of embedding parameters of high resolution images. BCGA is based on the cooperative coevolution between different candidate solutions at the block level, using a local Block Watermarking Metric (BWM). It is characterized by a novel elitism mechanism that is driven by local blockwise metrics, where the blocks with higher BWM values are selected to form higher global fitness candidate solutions. The crossover and mutation operators of BCGA are performed on block level. Experimental results on PUT face image database indicate a 17% improvement of fitness produced by BCGA compared to classical GA. Due to improved exploration capabilities, BCGA convergence is reached in fewer generations indicating an optimization speedup.     

Tuning and hybrid parallelization of a genetic-based multi-point statistics simulation code

One of the main difficulties using multi-point statistical (MPS) simulation based on annealing techniques or genetic algorithms concerns the excessive amount of time and memory that must be spent in order to achieve convergence. In this work we propose code optimizations and parallelization schemes over a genetic-based MPS code with the aim of speeding up the execution time. The code optimizations involve the reduction of cache misses in the array accesses, avoid branching instructions and increase the locality of the accessed data. The hybrid parallelization scheme involves a fine-grain parallelization of loops using a shared-memory programming model (OpenMP) and a coarse-grain distribution of load among several computational nodes using a distributed-memory programming model (MPI). Convergence, execution time and speed-up results are presented using 2D training images of sizes 100 × 100 × 1 and 1000 × 1000 × 1 on a distributed-shared memory supercomputing facility.     

Sensitivity analysis for biometric systems: A methodology based on orthogonal experiment designs

The purpose of this paper is to introduce an effective and structured methodology for carrying out a biometric system sensitivity analysis. The goal of sensitivity analysis is to provide the researcher/developer with insight and understanding of the key factors—algorithmic, subject-based, procedural, image quality, environmental, among others—that affect the matching performance of the biometric system under study. This proposed methodology consists of two steps: (1) the design and execution of orthogonal fractional factorial experiment designs which allow the scientist to efficiently investigate the effect of a large number of factors—and interactions—simultaneously, and (2) the use of a select set of statistical data analysis graphical procedures which are fine-tuned to unambiguously highlight important factors, important interactions, and locally-optimal settings. We illustrate this methodology by application to a study of VASIR (Video-based Automated System for Iris Recognition)—NIST iris-based biometric system. In particular, we investigated k = 8 algorithmic factors from the VASIR system by constructing a (2^6?1 × 3¹  × 4¹) orthogonal fractional factorial design, generating the corresponding performance data, and applying an appropriate set of analysis graphics to determine the relative importance of the eight factors, the relative importance of the 28 two-term interactions, and the local best settings of the eight algorithms. The results showed that VASIR’s performance was primarily driven by six factors out of the eight, along with four two-term interactions. A virtue of our two-step methodology is that it is systematic and general, and hence may be applied with equal rigor and effectiveness to other biometric systems, such as fingerprints, face, voice, and DNA.     

Computational analysis of Amsacrine resistance in human topoisomerase II alpha mutants (R487K and E571K) using homology modeling,docking and all-atom molecular dynamics simulation in explicit solvent

Amsacrine is an effective topoisomerase II enzyme inhibitor in acute lymphatic leukemia. Previous experimental studies have successfully identified two important mutations (R487K and E571K) conferring 100 and 25 fold resistance to Amsacrine respectively. Although the reduction of the cleavage ligand-DNA-protein ternary complex has been well thought as the major cause of drug resistance, the detailed energetic, structural and dynamic mechanisms remain to be elusive. In this study, we constructed human topoisomerase II alpha (hTop2α) homology model docked with Amsacrine based on crystal structure of human Top2β in complex with etoposide. This wild type complex was used to build the ternary complex with R487K and E571K mutants. Three 500 ns molecular dynamics simulations were performed on complex systems of wild type and two mutants. The detailed energetic, structural and dynamic analysis were performed on the simulation data. Our binding data indicated a significant impairment of Amsacrine binding energy in the two mutants compared with the wild type. The order of weakening (R487K > E571K) was in agreement with the order of experimental drug resistance fold (R489K > E571K). Our binding energy decomposition further indicated that weakening of the ligand-protein interaction rather than the ligand-DNA interaction was the major contributor of the binding energy difference between R487K and E571K. In addition, key residues contributing to the binding energy (ΔG) or the decrease of the binding energy (ΔΔG) were identified through the energy decomposition analysis. The change in ligand binding pose, dynamics of protein, DNA and ligand upon the mutations were thoroughly analyzed and discussed. Deciphering the molecular basis of drug resistance is crucial to overcome drug resistance using rational drug design.     

Realization strategies of dedicated path protection: A bandwidth cost perspective

Communication networks have to provide a high level of availability and instantaneous recovery after failures in order to ensure sufficient survivability for mission-critical services. Currently, dedicated path protection (or 1 + 1) is implemented in backbone networks to provide the necessary resilience and instantaneous recovery against single link failures with remarkable simplicity. However, in order to satisfy strict availability requirements, connections also have to be resilient against Shared Risk Link Group (SRLG) failures. In addition, switching matrix reconfigurations have to be avoided after a failure in order to guarantee instantaneous recovery. For this purpose, there are several possible realization strategies improving the characteristics of traditional 1 + 1 path protection by lowering reserved bandwidth while conserving all its favorable properties. These methods either utilize diversity coding, network coding, or generalize the disjoint-path constraint of 1 + 1.In this paper, we consider the cost aspect of the traditional and the alternative 1 + 1 realization strategies. We evaluate the bandwidth cost of different schemes both analytically and empirically in realistic network topologies. As the more complex realizations lead to NP-complete problems even in the single link failure case, we propose both Integer Linear Programming (ILP) based optimal methods, as well as heuristic and meta-heuristic approaches to solve them. Our findings provide a tool and guidelines for service providers for selecting the path protection method with the lowest bandwidth cost for their network corresponding to a given level of reliability.     

On the utility of pictorial feedback in computer-based learning environments

Extensive research has added to what is known about the nature of feedback and how to best incorporate it into instruction. Yet, many questions related to learner feedback remain unanswered. One problem of practical importance is the utility of incorporating semantically related pictures into the feedback. Decades of research on feedback have largely focused on the use of verbal feedback in written instruction. This research included two experiments. The first experiment (n = 63) addressed the incorporation of pictorial feedback into instruction; the second experiment (n = 69) extended this study through the use of a more ecologically valid intervention. Results suggest that the use of pictures in feedback did not influence learning any more than text-only treatments. A discussion and recommendations for future research are provided.     

Effects of visual feedback on medical students’ procrastination within web-based planning and reflection protocols

Procrastination is a very common problem among students that results from ineffective selfregulation. In two field-experimental studies (N = 18 and N = 49), we investigated whether visual feedback on students’ previous procrastination was effective in provoking a decrease in students’ future procrastination as well as improvements in self-regulated learning. The visual feedback was implemented as a dynamic line chart in a web-based planning and reflection protocol used once a week by medical students to record their class preparation and homework once a week. In the protocols, the students planned and reflected on their personal learning processes and they estimated retrospectively their inclination to procrastinate. The results of both studies consistently showed that presenting students a line chart that adaptively visualizes the course and extent of their self-reported previous procrastination led to a statistically significant and practically relevant decrease in their future procrastination. Furthermore, the visualization had positive effects on other variables central to self-regulated learning. The studies provide converging evidence that the inclination to procrastinate can successfully be counteracted both by a parsimonious and easy-to-implement method. They are suggestive of ways how Internet technology can be used support students’ self-regulated learning.     

Crystal morphology prediction of 1,3,3-trinitroazetidine in ethanol solvent by molecular dynamics simulation

In order to understand the mechanism of the effect of solvent on the crystal morphology of explosives, and be convenient for the choice of crystallization solvent, the attachment energy (AE) model was performed to predict the growth morphology and the main crystal faces of 1,3,3-trinitroazetidine (TNAZ) in vacuum. The molecular dynamics simulation was applied to investigate the interactions of TNAZ crystal faces and ethanol solvent, and the growth habit of TNAZ in ethanol solvent was predicted using the modified AE model. The results indicate that the morphology of TNAZ crystal in vacuum is dominated by the six faces of [0 2 1], [1 1 2], [0 0 2], [1 0 2], [1 1 1] and [0 2 0], and the crystal shape is similar to polyhedron. In ethanol solvent, The binding strength of ethanol with TNAZ faces changes in the order of [0 2 1] > [1 1 2] > [0 0 2] > [1 0 2] > [1 1 1] > [0 2 0], which causes that [1 1 1] and [0 2 0] faces disappear and the crystal morphology becomes more regular. The radial distribution function analysis shows that the interactions between solvent and crystal faces mainly consist of coulomb interaction, van der Waals force and hydrogen bonds.