Low-level Method Research Articles

Reaction Rate Rules of Intramolecular H-Migration Reaction Class for RIORIIOO·Radicals in Ether Combustion.

The intramolecular H-migration reaction of RIORIIOO· radicals constitute a key class of reactions in the low-temperature combustion mechanism of ethers. Despite this, there is a dearth of direct computations regarding the potential energy surface and rate constants specific to ethers, especially when considering large molecular systems and intricate branched-chain structures. Furthermore, combustion kinetic models for large molecular ethers generally utilize rate constants derived from those of structurally similar alcohols or alkane fuels. Consequently, chemical kinetic studies involve the calculation of energy barriers and rate rules for the intramolecular H-migration reaction class of RIORIIOO· radicals, which are systematically conducted using the isodesmic reaction method (IRM). The geometries of the species participating in these reactions are optimized, and frequency calculations are executed using the M06-X method in tandem with the 6-31+G(d,p) basis set by the Gaussian 16 program. Moreover, the M06-2X/6-31+G(d,p) method acts as the low-level ab initio method, while the CBS-QB3 method is utilized as the high-level ab initio method for calculating single-point energies. Rate constants at the high-pressure-limit are computed based on the reaction class transition state theory (RC-TST) by ChemRate program, incorporating asymmetric Eckart tunneling corrections for intramolecular H-migration reactions across a temperature range of 500 to 2000 K. It was found that the isodesmic reaction method gives accurate energy barriers and rate constants, and the rate constants of the H-migration reaction for RIORIIOO· radicals diverge from those of comparable reactions in alkanes and alcohol fuels. There are significant disparities in energy barriers and rate constants across the entire reaction classes of the H-migration reaction for RIORIIOO· radicals, necessitating the subdivision of the H-migration reaction into subclasses. Rate rules are established by averaging the rate constants of representative reactions for each subclass, which is pivotal for the advancement of accurate low-temperature combustion reaction mechanisms for ethers.

Large-Scale Solar-Powered UAV Attitude Control Using Deep Reinforcement Learning in Hardware-in-Loop Verification

Large-scale solar-powered unmanned aerial vehicles possess the capacity to perform long-term missions at different altitudes from near-ground to near-space, and the huge spatial span brings strict disciplines for its attitude control such as aerodynamic nonlinearity and environmental disturbances. The design efficiency and control performance are limited by the gain scheduling of linear methods in a way, which are widely used on such aircraft at present. So far, deep reinforcement learning has been demonstrated to be a promising approach for training attitude controllers for small unmanned aircraft. In this work, a low-level attitude control method based on deep reinforcement learning is proposed for solar-powered unmanned aerial vehicles, which is able to interact with high-fidelity nonlinear systems to discover optimal control laws and can receive and track the target attitude input with an arbitrary high-level control module. Considering the risks of field flight experiments, a hardware-in-loop simulation platform is established that connects the on-board avionics stack with the neural network controller trained in a digital environment. Through flight missions under different altitudes and parameter perturbation, the results show that the controller without re-training has comparable performance with the traditional PID controller, even despite physical delays and mechanical backlash.

Exact derivative propagation method to compute the generalized compliance matrix for continuum robots: Application to concentric tubes continuum robots

This paper introduces the concept of Generalized Compliance for continuum robots, specifically for those modeled with the Cosserat Rod theory. Unlike existing models based on tip compliance, the proposed approach considers interactions along the entire body of the flexible robot. The paper also presents a novel method referred to as the Low-Level Derivative Propagation Method, which is designed for the computationally efficient derivation of the Generalized Compliance matrix. The proposed method streamlines calculations and reduces integration time. The presented method, which is general and applies to various types of continuum robot models, is demonstrated on the case of a Concentric Tubes Continuum Robot. We provide detailed derivations of the equations and computation techniques leading to the derivation of the Generalized Compliance matrix, as well as a large-scale numerical validation of the method. The code used in this paper is available on the following GitHub website: https://github.com/benoitrosa/Generalized-Compliance-Computation-for-Continuum-Robots.

SLE diagnosis research based on SERS combined with a multi-modal fusion method

As artificial intelligence technology gains widespread adoption in biomedicine, the exploration of integrating biofluidic Raman spectroscopy for enhanced disease diagnosis opens up new prospects for the practical application of Raman spectroscopy in clinical settings. However, for systemic lupus erythematosus (SLE), origin Raman spectral data (ORS) have relatively weak signals, making it challenging to obtain ideal classification results. Although the surface enhancement technique can enhance the scattering signal of Raman spectroscopic data, the sensitivity of the SERS substrate to airborne impurities and the inhomogeneous distribution of hotspots degrade part of the signal. To fully utilize both kinds of data, this paper proposes a two-branch residual-attention network (DBRAN) fusion technique, which allows the ORS to complement the degraded portion and thus improve the model's classification accuracy. The features are extracted using the residual module, which retains the original features while extracting the deep features. At the same time, the study incorporates the attention module in both the upper and lower branches to handle the weight allocation of the two modal features more efficiently. The experimental results demonstrate that both the low-level fusion method and the intermediate-level fusion method can significantly improve the diagnostic accuracy of SLE disease classification compared with a single modality, in which the intermediate-level fusion of DBRAN achieves 100% classification accuracy, sensitivity, and specificity. The accuracy is improved by 10% and 7% compared with the ORS unimodal and the SERS unimodal modalities, respectively. The experiment, by fusing the multimodal spectral, realized rapid diagnosis of SLE disease by fusing multimodal spectral data, which provides a reference idea in the field of Raman spectroscopy and can be further promoted to clinical practical applications in the future.

Hybrid RPA:DFT Approach for Adsorption on Transition Metal Surfaces: Methane and Ethane on Platinum (111).

The hybrid QM:QM approach is extended to adsorption on transition metal surfaces. The random phase approximation (RPA) as the high-level method is applied to cluster models and, using the subtractive scheme, embedded in periodic models which are treated with density functional theory (DFT) that is the low-level method. The PBE functional, both without dispersion and augmented with the many-body dispersion (MBD), is employed. Adsorption of methane and ethane on the Pt(111) surface is studied. For methane in a 2 × 2 surface cell, the hybrid RPA:PBE and RPA:PBE+MBD results, -14.3 and -16.0 kJ mol-1, respectively, are in close agreement with the periodic RPA value of -13.8 kJ mol-1 at significantly reduced computational cost (factor of ∼50). For methane and ethane, the RPA:PBE results (-14.3 and -17.8 kJ mol-1, respectively) indicate underbinding relative to energies derived from experimental desorption barriers for relevant loadings (-15.6 ± 1.6 and -27.2 ± 2.9 kJ mol-1, respectively), whereas the hybrid RPA:PBE+MBD results (-16.0 and -24.9 kJ mol-1, respectively) agree with the experiment well within experimental uncertainty limits (deviation of -0.4 ± 1.5 and +2.3 ± 2.9 kJ mol-1, respectively). Finding a cluster that adequately and robustly represents the adsorbate at the bulk surface is important for the success of the RPA-based QM:QM scheme for metals.

Matched or moved? Asymmetry in high- and low-level visual processing of motion events

AbstractConsensus on the extent to which cross-linguistic differences affect event cognition is currently absent. This is partly because cognitive influences of language have rarely been examined within speakers of different languages in tasks that manipulate the level of visual processing. This study presents a novel combination of a high-level approach upregulating the involvement of language, namely self-paced sentence-video verification, and a low-level visual detection method without language use, namely breaking continuous flash suppression (b-CFS) (Yang et al., 2014). The results point to cross-linguistic effects on event cognition by revealing variations in visual processing patterns of manner and path by English versus Mandarin Chinese speakers. Language specificity was found on both levels of processing. An asymmetry in response speed across tasks highlights an important difference between facilitation of detecting contrasts when recruitment of verbal labels is automatic, versus facilitation of verifying correspondences when labels are overt.

Accelerating Anharmonic Spectroscopy Simulations via Local-Mode, Multilevel Methods.

Ab initio computer simulations of anharmonic vibrational spectra provide nuanced insight into the vibrational behavior of molecules and complexes. The computational bottleneck in such simulations, particularly for ab initio potentials, is often the generation of mode-coupling potentials. Focusing specifically on two-mode couplings in this analysis, the combination of a local-mode representation and multilevel methods is demonstrated to be particularly symbiotic. In this approach, a low-level quantum chemistry method is employed to predict the pairwise couplings that should be included at the target level of theory in vibrational self-consistent field (and similar) calculations. Pairs that are excluded by this approach are "recycled" at the low level of theory. Furthermore, because this low-level pre-screening will eventually become the computational bottleneck for sufficiently large chemical systems, distance-based truncation is applied to these low-level predictions without substantive loss of accuracy. This combination is demonstrated to yield sub-wavenumber fidelity with reference vibrational transitions when including only a small fraction of target-level couplings; the overhead of predicting these couplings, particularly when employing distance-based, local-mode cutoffs, is a trivial added cost. This combined approach is assessed on a series of test cases, including ethylene, hexatriene, and the alanine dipeptide. Vibrational self-consistent field (VSCF) spectra were obtained with an RI-MP2/cc-pVTZ potential for the dipeptide, at approximately a 5-fold reduction in computational cost. Considerable optimism for increased accelerations for larger systems and higher-order couplings is also justified, based on this investigation.

An integrated path-tracking and control allocation method for autonomous racing electric vehicles

In recent years, path-tracking controllers for autonomous passenger vehicles and Control Allocation (CA) methods for handling and stability control have both received extensive discussion in the literature. However, the integration of the path-tracking control with CA methods for autonomous racing vehicles has not attracted much attention. In this study, we design an integrated path-tracking and CA method for a prototype autonomous racing electric vehicle with a particular focus on the maximising the turning speed in tight cornering. The proposed control strategy has a hierarchical structure to improve the computational efficiency: the high-level path-tracking Model Predictive Control (MPC) based on a rigid body model is designed to determine the virtual control forces according to the desired path and desired maximum velocity profile, while the low-level CA method uses a Quadratically Constrained Quadratic Programming (QCQP) formulation to distribute the individual control actuator according to the desired virtual control values. The proposed controller is validated in a high-fidelity simulation vehicle model with the computational time of the optimisation controller presented to demonstrate the real-time control performance.

Low Cost and Sustainable Test Methods to Study Vulnerabilities of Large-Scale Systems against EMP

Intense electromagnetic pulses are electromagnetic waves with sharp rise time, high field strength and short duration. They have attracted more and more attention in recent years because they can cause destructions or malfunctions of some key national core infrastructures, such as power grids, communication and financial networks, etc. Hence, it is important to harden these facilities to ensure that they can survive in the face of electromagnetic pulse attacks. A direct way to investigate the vulnerabilities of these facilities is placing them in the electromagnetic environments generated by EMP simulators. However, the scale of facilities under test are limited by the working volumes of simulators. With the increase in working volumes and field strength, the price and technical difficulty of simulators are increased. Therefore, low cost and sustainable test methods to investigate vulnerabilities of large-scale systems against EMP are proposed in this study. The study takes advantage of continuous wave immersion (CWI) test and pulse current injection (PCI) test methods, which are low cost and sustainable to predict the pulse responses and assess the nonlinear effect of large-scale facilities under EMP attacks. In a CWI test, the magnitude of the transfer function of large-scale systems or facilities can be measured, and the corresponding phase information of the transfer function can be reconstructed by minimum phase algorithm (MPA) if the systems meet the minimum phase condition. After acquiring the entire information of the transfer function, we can predict the responses of a system under threat-level EMP attacks. However, these responses are obtained under the assumption that the system does not have nonlinear effects. Because the CWI is a low-level test, it cannot simulate the threat-level EMP attacks. In the PCI test proposed here, a bulky pulse current is coupled into the system to stimulate enough current intensity, just as the system was attacked by threat-level EMPs. In this situation, the system would be destroyed, or any other nonlinear effect would occur in the system. After that, the problem is to determine the quantities of the injected current, and a few kinds of norms are introduced in this paper to define the quantities. The method proposed here innovatively takes the experimental results of CWI as reference inputs of PCI tests. In this paper, the accuracy of the response prediction is validated by means of simulations and experiments. Results show that as a low-level test method in the frequency domain, the CWI test method can not only analyze couplings of external electromagnetic energy from frequency domain but also predict responses of the facilities under high amplitude electromagnetic pulses. The nonlinear effect of large-scale facilities can be assessed by applying the PCI test method with the results from CWI prediction. Therefore, if infrastructures or facilities are too large to be tested under EMP simulators, an alternative approach is to carry out the CWI and PCI experiments.

Dynamic Voltage Regulation and Unbalance Compensation in a Low-Voltage Distribution Network Using Energy Storage System

Modern distribution grids may suffer problems of voltage distortion, especially along radial low-voltage feeders with a high penetration of intermittent, unbalanced and distorted loads and generation sources. It is a challenge to develop an effective voltage-regulation method using a straightforward implementation. This paper proposes a novel method for local voltage control and balancing using a shunt-connected energy storage system. The compensation principles are explained, and a complete controller design is proposed. The algorithm is designed to be implemented in power electronic converters that provide the interface between the storage and the grid. The original contribution is the development of a low-level control method, which includes voltage balancing and a method to minimize the compensator current and is to be implemented in power electronic converters that provide the interface between the storage and the grid. The calculation of active and reactive compensator currents is explained with relation to the estimated grid impedance. The efficacy of the designed controller is verified by laboratory experiments. It is shown that voltage regulation using the proposed method is achieved with less apparent power compared to a system where only reactive power is used. The controller presents a very good dynamic response to rapid voltage variations, such as unbalanced voltage dips. The applicability and constraints of the method are discussed with respect to the present state of the art in low-voltage-grid voltage regulation.

IiDAQ: An Affordable Data Acquisition System as a Tool for Teaching-Learning Process in Instrumentation and Control

Teaching-learning process in undergraduate courses requires alternative methods to succeed, even more in a blended mode. Particularly, for instrumentation and control courses, practical activities are mandatory to strengthen the theoretical content. However, accessing to laboratory workbench and devices is not straightforward due to several constraints. In this work, we introduce the so-called iiDAQ, a low-cost and affordable solution as a data acquisition (DAQ) device devoted to didactic practices. The system is based on open hardware devices to acquire and generate signals from sensors and actuators. Moreover, we develop a full Matlab compliant toolbox with a graphical user interface (GUI) interaction and a low-level method scripting capabilities. We show the iiDAQ design and details on its functionality. The performance is shown in three didactic case studies. Results show that the proposal is an attractive tool to leverage teaching-learning processes for undergraduate instrumentation and control courses.

An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds

Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force fields with high accuracy to achieve correct folding. However, the current force fields are inaccurate, inapplicable and inefficient. We propose a machine learning protocol, the inductive transfer learning force field (ITLFF), to construct protein force fields in seconds with any level of accuracy from a small dataset. This process is achieved by incorporating an inductive transfer learning algorithm into deep neural networks, which learn knowledge of any high-level calculations from a large dataset of low-level method. Here, we use a double-hybrid density functional theory (DFT) as a case functional, but ITLFF is suitable for any high-precision functional. The performance of the selected 18 proteins indicates that compared with the fragment-based double-hybrid DFT algorithm, the force field constructed by ITLFF achieves considerable accuracy with a mean absolute error of 0.0039kcal/mol/atom for energy and a root mean square error of 2.57$\mathrm{kcal}/\mathrm{mol}/{\AA}$ for force, and it is more than 30000 times faster and obtains more significant efficiency benefits as the system increases. The outstanding performance of ITLFF provides promising prospects for accurate and efficient protein dynamic simulations and makes an important step toward protein folding simulation. Due to the ability of ITLFF to utilize the knowledge acquired in one task to solve related problems, it is also applicable for various problems in biology, chemistry and material science.

Taking Advantage of a Systematic Energy Non-linearity Error in Density Functional Theory for the Calculation of Electronic Energy Levels.

We present an approximate approach for the calculation of ionization potential (IP) and electron affinity (EA) by exploiting the complementary energy non-linearity errors for a species M and its one-electron-ionized counterpart (M+). Reasonable IPs and EAs are thus obtained by averaging the orbital energies of M and M+, even with a low-level method such as BLYP/6-31G(d). By combining the corrected IPs and EAs, we can further obtain reasonable excitation energies. The errors in uncorrected valence IPs and uncorrected virtual-orbital energies show systematic trends. These characteristics provide a convenient and computationally efficient avenue for qualitative estimation of these properties with single corrections for multiple IPs and excitation energies.

Dynamical Self-energy Mapping (DSEM) for Creation of Sparse Hamiltonians Suitable for Quantum Computing.

We present a two-step procedure called the dynamical self-energy mapping (DSEM) that allows us to find a sparse Hamiltonian representation for molecular problems. In the first part of this procedure, the approximate self-energy of a molecular system is evaluated using a low-level method and subsequently a sparse Hamiltonian is found that best recovers this low-level dynamic self-energy. In the second step, such a sparse Hamiltonian is used by a high-level method that delivers a highly accurate dynamical part of the self-energy that is employed in later calculations. The tests conducted on small molecular problems show that the sparse Hamiltonian parameterizations lead to very good total energies. DSEM has the potential to be used as a classical-quantum hybrid algorithm for quantum computing where the sparse Hamiltonian containing only O(n2) terms on a Gaussian orbital basis, where n is the number of orbitals in the system, could reduce the depth of the quantum circuit by at least an order of magnitude when compared with simulations involving a full Hamiltonian.

Integration of vibration and optical techniques for watermelon firmness assessment

Fruit firmness is one of the key quality indices of fruit at different stages of the food supply chain. In this study, a new detection system based on the acoustic vibration method and visible/near-infrared (Vis/NIR) spectroscopy was developed to evaluate watermelon firmness. To diminish the influences of the shape index and white peel thickness on the second resonance frequency (f2), a multiple linear regression model was established by the finite element analysis (FEA) method to optimize the measured f2 to the modified second resonance frequency (f0) of a single-tissue watermelon. Besides, the machine vision system was designed to obtain the real white peel thickness and shape index. And the peel thickness was non-destructively predicted by Vis/NIR spectroscopy. In addition, a new puncture probe with ten cylinders was developed to determine the destructive firmness of the whole watermelon. The flesh firmness (FF) of watermelon was selected as an output variable in the prediction models due to its high correlation coefficients with vibration parameters. Regarding the acoustic vibration method, better prediction performance of FF was obtained in a back propagation neural network (BPNN) model by using the modified elasticity index (EI0), the peak width at half height (w), the peak value at f2 (A) and the peak area (S) as input variables. The correlation coefficient of prediction (rp) and root mean square error of prediction (RMSEP) were 0.655 and 0.971 N. Considering the merits and limitations of acoustic vibration method, a low-level data fusion method was applied to combine the vibration and Vis/NIR spectrum data to acquire more comprehensive firmness information. The successive projections algorithm (SPA)-BPNN model using 13 spectrum features achieved better prediction results than other prediction models (rp = 0.841, RMSEP = 0.932 N). It was found that the integration of vibration and optical techniques could be an effective approach to provide more detailed information about watermelon firmness.

Segmentation Based Biomedical Image Retrieval with Low-Level Feature Extraction

Content based image retrieval (CBIR) is an imperative and testing errand in numerous fields, for example, military, common, restorative and even in web applications. Here, we implemented medical image retrieval focused on novel material with decimated bi-orthogonal spline wavelet filter banks. Proposed algorithm is an extension for the existing low-level feature extraction method done by using spline wavelet filters, also utilized iterative partitioning (IP) for extracting the similar patterns of Pictures of query & database. In terms of consistency & memory, simulation results indicate that the approach suggested was better than the current scheme.

Comparison of the effects between low-level assisted ventilation and T-piece method on respiratory mechanics during weaning of mechanically ventilated patients

To compare the difference of low-level assisted ventilation and T-piece method on respiratory mechanics of patients with invasive mechanical ventilation during spontaneous breathing trial (SBT) within 3 days before extubation. A retrospective observational study was conducted. Twenty-five patients with difficulty in weaning or delayed weaning from invasive mechanical ventilation who were admitted to department of critical care medicine of the First Affiliated Hospital of Guangzhou Medical University from December 2018 to June 2020, and were in stable condition and entered the weaning stage after more than 72 hours of invasive mechanical ventilation were studied. A total of 119 cases of respiratory mechanical indexes were collected, which were divided into the low-level assisted ventilation group and the T-piece group according to the ventilator method and parameters used during the data collection. The different ventilation modes related respiratory mechanics indexes such as the esophageal pressure (Pes), the gastric pressure (Pga), the transdiaphragmatic pressure (Pdi), the maximum Pdi (Pdimax), Pdi/Pdimax ratio, the esophageal pressure-time product (PTPes), the gastric pressure-time product (PTPga), the transdiaphragmatic pressure-time product (PTPdi), the diaphragmatic electromyography (EMGdi), the maximum diaphragmatic electromyography (EMGdimax), PTPdi/PTPes ratio, Pes/Pdi ratio, the inspiratory time (Ti), the expiratory time (Te) and the total time respiratory cycle (Ttot) at the end of monitoring were recorded and compared between the two groups. Compared with the T-piece group, Pes, PTPes, PTPdi/PTPes ratio, Pes/Pdi ratio and Te were higher in low-level assisted ventilation group [Pes (cmH2O, 1 cmH2O = 0.098 kPa): 2.84 (-1.80, 5.83) vs. -0.94 (-8.50, 2.06), PTPes (cmH2O×s×min-1): 1.87 (-2.50, 5.93) vs. -0.95 (-9.71, 2.56), PTPdi/PTPes ratio: 0.07 (-1.74, 1.65) vs. -1.82 (-4.15, -1.25), Pes/Pdi ratio: 0.17 (-0.43, 0.64) vs. -0.47 (-0.65, -0.11), Te (s): 1.65 (1.36, 2.18) vs. 1.33 (1.05, 1.75), all P < 0.05], there were no significant differences in Pga, Pdi, Pdimax, Pdi/Pdimax ratio, PTPga, PTPdi, EMGdi, EMGdimax, Ti and Ttot between the T-piece group and the low-level assisted pressure ventilation group [Pga (cmH2O): 6.96 (3.54,7.60) vs. 7.74 (4.37, 11.30), Pdi (cmH2O): 9.24 (4.58, 17.31) vs. 6.18 (2.98, 11.96), Pdimax (cmH2O): 47.20 (20.60, 52.30) vs. 29.95 (21.50, 47.20), Pdi/Pdimax ratio: 0.25 (0.01, 0.34) vs. 0.25 (0.12, 0.41), PTPga (cmH2O×s×min-1): 7.20 (2.54, 9.97) vs. 7.97 (5.74, 13.07), PTPdi (cmH2O×s×min-1): 12.15 (2.95, 19.86) vs. 6.87 (2.50, 12.63), EMGdi (μV): 0.05 (0.03, 0.07) vs. 0.04 (0.02, 0.06), EMGdimax (μV): 0.07 (0.05, 0.09) vs. 0.07 (0.04, 0.09), Ti (s): 1.20 (0.95, 1.33) vs. 1.07 (0.95, 1.33), Ttot (s): 2.59 (2.22, 3.09) vs. 2.77 (2.35, 3.24), all P > 0.05]. When mechanically ventilated patients undergo SBT, the use of T-piece method increases the work of breathing compared with low-level assisted ventilation method. Therefore, long-term use of T-piece should be avoided during SBT.

Effects of Combined Mental and Physical Practices on Walking and Daily Living Activities in Individuals With Multiple Sclerosis

Objectives: Mental practice, as a neuropsychological factor effective in motor recovery, is a cognitive rehearsal of a physical skill without muscular activity. Considering the high level of fatigue in patients with Multiple Sclerosis (MS), we hypothesized that using mental practice as a low-level energy-consuming method added to physical practice could be a useful therapeutic strategy. Therefore, the current study aimed to investigate the effects of combined mental and physical practices on walking and daily living activities in patients with MS. Methods: A randomized double-blind controlled trial was applied in the present research. In total, 22 subjects with MS were randomly allocated into the occupational therapy and mental practice groups; all study subjects received equal occupational therapy interventions 3 days a week for 6 weeks. However, in addition to occupational therapy services, the study group received mental exercises. Such practices included the visual and kinesthetic imagery of walking activity in the presence of external cues. Walking ability and daily living activities were assessed at pre-treatment, post-treatment, and 2 weeks after the treatment (follow-up). Gait parameters (distance and speed) were measured by the functional scales of the 6-Minute Walk Test and the Timed 25-Foot Walk Test. The Barthel Index was used to test individuals’ performance in daily living activities. Results: The presented combined mental and physical practice significantly improved walking distance and walking speed in post-treatment (P=0.047, P&lt;0.001) and follow-up (P=0.044, P=0.001) assessments, respectively. The Barthel Index scores significantly changed per group; however, no significant differences were found between the control and test groups in this regard (P=0.386). Discussion: The present study data revealed that performing mental practice along with occupational therapy interventions are more effective than regular interventions alone in the gait rehabilitation of patients with MS. These significant differences in walking performance in the intervention group remained obvious till the follow-up stage.

Measurements of the Specific Activities of 137Cs in Antarctica Environmental Samples by Using the Low-Level Radiation Analysis Method

Since the King Sejong Korean Antarctic Research Station began operation in 1988, studies of various fields have been then carried out in polar regions and significant achievements have been yielded. However, environmental and biological radiations have not been dealt with compared to other research areas. In this study, the 137Cs distribution is investigated for environmental elements in the vicinity of the two research stations, the Jang Bogo Station and the King Sejong Station, operated by the Korea Polar Research Institute (KOPRI) in Antarctica by using a low-level radiation analysis method. Three different types of environmental samples, soils, mosses, and lichens are investigated for identification of 137Cs. In order to discriminate low levels of radiations from background radiations and estimate their specific activities with high reliability and precision, we used a heavily shielded HPGe detector in an underground laboratory to perform the activity measurements. GEANT4 simulations were carried out for efficiency calibrations corresponding to the shape of the pre-processed sample. 137Cs has been identified in all the samples and the energy spectrum has been found to reflect their physical and ecological characteristics.

Low Frequency Injection as a Method of Low-Level DC Microgrid Communication

This paper provides a simple low-level unidirectional global communication method for DC microgrids, and requires no hardware modifications to the microgrid and interfacing power electronic converters. The underlying premise to this communication method is injecting low-frequency low-voltage sinusoidal components into the DC microgrid power lines. This method deviates from the common bit-level communication scheme by relating parameters and commands with certain frequency components. Communication structures are included as a basis for communication protocols, and a detection method is proposed for detecting the injected frequencies. The injection method, communication structure, and detection method are implemented on a live-scale DC microgrid.