Ultrasonic investigation on aqueous polysaccharide (starch) at 298.15 K

AbstractThe ultrasonic velocity, density and viscosity at 298.15K have been measured in the binary system of starch in aqueous medium. The acoustical parameters such as adiabatic compressibility (β), free length (Lf), free volume (Vf), internal pressure (πi), acoustical impedance (Z), relative association (RA), Rao’s constant (R), Wada’s constant (W), classical absorption coefficients (α/f2), relaxation time (τ) and relaxation strength (r) are calculated. The results are interpreted in terms of molecular interaction between the components of the mixtures

Affinity study of -lactalbumin nanoparticles in a mixed solvent environment using Laplace transform technique

ABSTRACT. Effect of pH and cosolvent on the stabilization of protein structure is a well established study in protein or food science. Of the various interesting applications of protein nanoparticles, making it as a drug or bioactive compound carrier is of vital importance. This application of protein nanoparticle demands the affinity priority of protein with the available components of the medium. The basis of such studies lies in the synthesis of such protein nanoparticles and their characterizations. Secondly the knowledge of priority in affinity of protein to a particular solvent is essential. On this basis, the present work deals with the ultrasonic analysis of hydophobic interactions exhibited by the α-lactalbumin nanoparticle synthesised by heat treatment using acetone as desolvating agent. In order to enrich the variations in hydrophobicity, pH and cosolvent (fructose) are included in the study. The results are compared with one of our earlier work and are interpreted in terms of the interactions existing among the components and the evolved discussions reveal that the bulk nature of the medium is controlled by the existing hydrophobicity interactions. Further, as a novel attempt, the preference of protein particle to interact with a particular solvent in mixed solvent environment is elucidated using Laplace transform technique. This approach is expected to torch light in protein science in fixing the most desirable solvent in mixed solvent environment.                     KEY WORDS: a-Lactalbumin, Fructose, Laplace Transform, Diffusion, Hydrophobic interactions   Bull. Chem. Soc. Ethiop. 2021, 35(3), 659-668. DOI: https://dx.doi.org/10.4314/bcse.v35i3.1

Analogue mouse pointer control via an online steady state visual evoked potential (SSVEP) brain-computer interface

The steady state visual evoked protocol has recently become a popular paradigm in brain–computer interface (BCI) applications. Typically (regardless of function) these applications offer the user a binary selection of targets that perform correspondingly discrete actions. Such discrete control systems are appropriate for applications that are inherently isolated in nature, such as selecting numbers from a keypad to be dialled or letters from an alphabet to be spelled. However motivation exists for users to employ proportional control methods in intrinsically analogue tasks such as the movement of a mouse pointer. This paper introduces an online BCI in which control of a mouse pointer is directly proportional to a user's intent. Performance is measured over a series of pointer movement tasks and compared to the traditional discrete output approach. Analogue control allowed subjects to move the pointer faster to the cued target location compared to discrete output but suffers more undesired movements overall. Best performance is achieved when combining the threshold to movement of traditional discrete techniques with the range of movement offered by proportional control

Multi-agent system with iterative auction mechanism for master bay plan problem in marine logistics

The support of containerization to trade development demands an efficient solution method for the container loading problem in order to reduce shipment and handling time. Hence, the stowage planning of containers is critical to provide speedy delivery of resources from the area of supply to the area of demand. Moreover, information on container terminal activities, structure of ship, and characteristics of containers is distributed among stowage planners. This information imposes constraints, and so the master bay plan problem (MBPP) becomes NP-hard. Therefore, a multi-agent systems (MAS) methodology is designed to effectively communicate the information and solve the MBPP sustainably. In the designed MAS methodology, an information exchange system (IES) is created for stowage planners to bid for ship slots in each experimental iterative combinatorial auction (ICA) market. The winner in the ICA experiments is provided with the ship slots, and the entire bay plan is prepared. Further, the ship-turnaround time is validated using the data obtained from the benchmark problem

Vortex Images and q-Elementary Functions

In the present paper problem of vortex images in annular domain between two coaxial cylinders is solved by the q-elementary functions. We show that all images are determined completely as poles of the q-logarithmic function, where dimensionless parameter $q = r^2_2/r^2_1$ is given by square ratio of the cylinder radii. Resulting solution for the complex potential is represented in terms of the Jackson q-exponential function. By composing pairs of q-exponents to the first Jacobi theta function and conformal mapping to a rectangular domain we link our solution with result of Johnson and McDonald. We found that one vortex cannot remain at rest except at the geometric mean distance, but must orbit the cylinders with constant angular velocity related to q-harmonic series. Vortex images in two particular geometries in the $q \to \infty$ limit are studied.Comment: 17 page

Robust Long-Term Hand Grasp Recognition With Raw Electromyographic Signals Using Multidimensional Uncertainty-Aware Models

Hand grasp recognition with surface electromyography (sEMG) has been used as a possible natural strategy to control hand prosthetics. However, effectively performing activities of daily living for users relies significantly on the long-term robustness of such recognition, which is still a challenging task due to confused classes and several other variabilities. We hypothesise that this challenge can be addressed by introducing uncertainty-aware models because the rejection of uncertain movements has previously been demonstrated to improve the reliability of sEMG-based hand gesture recognition. With a particular focus on a very challenging benchmark dataset (NinaPro Database 6), we propose a novel end-to-end uncertainty-aware model, an evidential convolutional neural network (ECNN), which can generate multidimensional uncertainties, including vacuity and dissonance, for robust long-term hand grasp recognition. To avoid heuristically determining the optimal rejection threshold, we examine the performance of misclassification detection in the validation set. Extensive comparisons of accuracy under the non-rejection and rejection scheme are conducted when classifying 8 hand grasps (including rest) over 8 subjects across proposed models. The proposed ECNN is shown to improve recognition performance, achieving an accuracy of 51.44% without the rejection option and 83.51% under the rejection scheme with multidimensional uncertainties, significantly improving the current state-of-the-art (SoA) by 3.71% and 13.88%, respectively. Furthermore, its overall rejection-capable recognition accuracy remains stable with only a small accuracy degradation after the last data acquisition over 3 days. These results show the potential design of a reliable classifier that yields accurate and robust recognition performance

Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks

Hand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, addressing the reliability of such classifiers has been absent, to our best knowledge. This may be due to the lack of consensus on the definition of model reliability in this field. An uncertainty-aware model has the potential to self-evaluate the quality of its inference, thereby making it more reliable. Moreover, uncertainty-based rejection has been shown to improve the performance of sEMG-based hand gesture recognition. Therefore, we first define model reliability here as the quality of its uncertainty estimation and propose an offline framework to quantify it. To promote reliability analysis, we propose a novel end-to-end uncertainty-aware finger movement classifier, i.e., evidential convolutional neural network (ECNN), and illustrate the advantages of its multidimensional uncertainties such as vacuity and dissonance. Extensive comparisons of accuracy and reliability are conducted on NinaPro Database 5, exercise A, across CNN and three variants of ECNN based on different training strategies. The results of classifying 12 finger movements over 10 subjects show that the best mean accuracy achieved by ECNN is 76.34%, which is slightly higher than the state-of-the-art performance. Furthermore, ECNN variants are more reliable than CNN in general, where the highest improvement of reliability of 19.33% is observed. This work demonstrates the potential of ECNN and recommends using the proposed reliability analysis as a supplementary measure for studying sEMG-based hand gesture recognition