Walking Strategy Research Articles

Lower-extremity inter-joint coordination variability in active individuals with transtibial amputation and healthy males during gait.

This study was aimed to compare the variability of inter-joint coordination in the lower-extremities during gait between active individuals with transtibial amputation (TTAs) and healthy individuals (HIs). Fifteen active male TTAs (age: 40.6±16.24years, height: 1.74±0.09m, and mass: 71.2 ± 8.87kg) and HIs (age: 37.25±13.11years, height: 1.75±0.06m, and mass: 74 ± 8.75kg) without gait disabilities voluntarily participated in the study. Participants walked along a level walkway covered with Vicon motion capture system, and their lower-extremity kinematics data were recorded during gait. The spatiotemporal gait parameters, lower-extremity joint range of motion (ROM), and their coordination and variability were calculated and averaged to report a single value for each parameter based on biomechanical symmetry assumption in the lower limbs of HIs. Additionally, these parameters were separately calculated and reported for the intact limb (IL) and the prosthesis limb (PL) in TTAs individuals. Finally, a comparison was made between the averaged values in HIs and those in the IL and PL of TTAs subjects. The results showed that the IL had a significantly lower stride length than that of the PL and averaged value in HIs, and the IL had a significantly lower knee ROM and greater stance-phase duration than that of HIs. Moreover, TTAs showed different coordination patterns in pelvis-to-hip, hip-to-knee, and hip-to-ankle couplings in some parts of the gait cycle. It concludes that the active TTAs with PLs walked with more flexion of the knee and hip, which may indicate a progressive walking strategy and the differences in coordination patterns suggest active TTA individuals used different neuromuscular control strategies to adapt to their amputation. Researchers can extend this work by investigating variations in these parameters across diverse patient populations, including different amputation etiologies and prosthetic designs. Moreover, Clinicians can use the findings to tailor rehabilitation programs for TTAs, emphasizing joint flexibility and coordination.

An enhanced sparrow search swarm optimizer via multi-strategies for high-dimensional optimization problems

With the development of science and technology, high-dimensional global optimization problems have become increasingly prevalent for scientific research and engineering, such as gene recognition, vehicle routing, job scheduling, and network topology. These problems are typically characterized by enormous and complex search spaces and numerous local minima, making it challenging to find the global optimal solution with limited computing resources. This paper introduces an enhanced sparrow search swarm optimizer (ESSSO) based on a bio-mimetic method. The ESSSO employs an adaptive sinusoidal walk strategy based on the von Mises distribution, a learning strategy utilizing roulette wheel selection, a two-stage evolution strategy, and a selection mutation strategy to address these issues. The proposed sinusoidal walk strategy, grounded in the von Mises distribution, supports a balanced evolutionary search. This mechanism disperses the individuals in a swarm in various directions based on a circular normal distribution. It then leads the search and adaptively adjusts their step sizes according to the size of the search domain during each generation of evolution. The learning strategy, based on roulette wheel selection, enhances the diversity of the population and improves the global search capability of the algorithm during the initial iterations. The two-stage evolution strategy involves a sine-learning mechanism based on the von Mises distribution and an adaptive mutation mechanism. The former is designed to boost the convergence speed of ESSSO, while the latter prevents ESSSO from getting trapped in a local optimum. Additionally, the selection mutation strategy further enhances convergence speed while maintaining population diversity. These strategies promote exploration in the early stages of evolution and exploitation in the later stages, enabling a well-balanced search for optimal solutions. We conducted comprehensive experiments two standard benchmark sets (i.e., CEC2010 and CEC2013), antenna array optimization, feature selection, and four engineering design problems. The results indicate that ESSSO outperforms ten comparison algorithms, especially in scenarios with smaller population sizes. This confirms its effectiveness in high-dimensional global optimization tasks and demonstrates that it can achieve better results with less computational resource consumption.

Engineering the substrate acceptance of l-amine dehydrogenase enables the collective biocatalytic synthesis of N-heterocyclic primary amines

Chiral N-heterocyclic primary amines are highly attractive synthons in the pharmaceutical industry. Amine dehydrogenases (AmDHs)-catalyzed direct asymmetric reductive amination of the readily available N-heterocyclic ketones represents a promising approach for synthesizing N-heterocyclic primary amines. However, the limited substrate acceptance of AmDHs restricts their application in biocatalytic synthesis. Here, we engineered the substrate acceptance of L-EsAmDH from Exiguobacterium sibiricum to access a panel of structurally diverse N-heterocyclic ketones. Through reverse substrate design and combining with substrate walking strategy, two active mutants with extended substrate specificity toward N-Boc-4-acetylpiperidine were identified, and two additional rounds of iterative site mutagenesis further increased the activity by 116.3-fold. The optimal mutant M4-2 (L49G/V303A/L307A/T143A) exhibited significantly expanded N-Boc, N-Bn, and N-Cbz-substituted heterocyclic ketones scope, and its practical biocatalytic synthesis performance was verified in the gram-scale synthesis of (R)-4-aminoethyl-1-Boc-piperidine, achieving 95 % conversion, >99 % ee, and an overall isolated yield of 86 % (9.8 g). Our study lays the foundation for the collective biocatalytic synthesis of structurally diverse N-heterocyclic primary amines and gives referable guidance for engineering AmDH family members into versatile biocatalysts.

A Temperature Control Method of Lysozyme Fermentation Based on LRWOA-LSTM-PID

In order to overcome the difficulty of parameter tuning caused by the large lag and time-varying nonlinearity of the tank for lysozyme fermentation, a temperature control method based on LRWOA-LSTM-PID is proposed in this paper. Firstly, according to the intrinsic mechanism of the fermenter, a temperature mechanism model based on a dynamic equation is designed, which can better reflect the temperature changes in the fermenter. Secondly, a Proportional Integral Derivative (PID) parameter tuning method based on a Long-Short Term Memory Network (LSTM) is proposed, which takes advantage of the ability of LSTM to learn time sequence information and obtains the variation trend between error sequences under continuous time sampling, thereby adjusting network weights more reasonably and accelerating PID parameter tuning. Finally, a Whale Optimization Algorithm (WOA) based on the Lévy flight and random walk strategy (LRWOA) is proposed for the initialization of LSTM parameters; this algorithm has excellent optimization capabilities and overcomes the problem of LSTM falling into local optimal solutions prematurely during parameter randomization. The results show that the method proposed in this paper can achieve rapid tuning of PID parameters, thereby improving the convergence speed of the system and reducing system overshoot.

Modelling and optimisation of PV-PEMEC hydrogen generation system considering partial shading conditions

This study addresses the adverse effects of PSC on both PV output and PEMEC performance. It establishes a PV-PEMEC hydrogen production simulation model considering complex PSC scenarios. The analysis delves into the impacts of PSC on electrolyzer performance, including internal overpotential, internal pressure variation, and electrolysis efficiency. A hybrid GMPPT algorithm based on IGWO and AP&Q is proposed to mitigate PSC effects on the electrolyzer. The algorithm enhances population initialization with chaotic mapping and antagonistic learning mechanisms within the GWO algorithm. Additionally, it incorporates Levy flight and random walk strategies to improve local search capability, coupled with AP&Q to enhance convergence speed. Results indicate significant impacts of PV array PSC on PEM electrolyzer internal electrochemical mechanisms, particularly affecting Ohmic and diffusion overvoltages. Different local shading scenarios cause continuous fluctuations in electrolysis efficiency, more pronounced at lower current densities. PSC induces substantial fluctuations in hydrogen partial pressure and fewer fluctuations in oxygen partial pressure. The IGWO-AP&Q hybrid algorithm exhibits high responsiveness and tracking efficiency, yielding the highest PV-PEMEC electrolysis efficiency. This study effectively integrates complex multi-scenario PSC with PEMEC for solar-powered hydrogen production, laying a solid theoretical foundation for regions with high solar indices.

WalkGAN: Network Representation Learning With Sequence-Based Generative Adversarial Networks.

Network representation learning, also known as network embedding, aims to learn the low-dimensional representations of vertices while capturing and preserving the network structure. For real-world networks, the edges that represent some important relationships between the vertices of a network may be missed and may result in degenerated performance. The existing methods usually treat missing edges as negative samples, thereby ignoring the true connections between two vertices in a network. To capture the true network structure effectively, we propose a novel network representation learning method called WalkGAN, where random walk scheme and generative adversarial networks (GAN) are incorporated into a network embedding framework. Specifically, WalkGAN leverages GAN to generate the synthetic sequences of the vertices that sufficiently simulate random walk on a network and further learn vertex representations from these vertex sequences. Thus, the unobserved links between the vertices are inferred with high probability instead of treating them as nonexistence. Experimental results on the benchmark network datasets demonstrate that WalkGAN achieves significant performance improvements for vertex classification, link prediction, and visualization tasks.

The Effectiveness of Gallery Walk Strategy to Students’ Writing Skill

This research is aimed to get empirical evidence on the effect of gallery walk technique on students’ writing skillt eleventh Grade of SMA Muhammadiyah Kalosi Kabupaten Enrekang in Academic Year 2022/2023. This research used quantitative method and the research design used is a quasi-experimental design. The population of this research is all eleventh Grade students of SMA Muhammadiyah Kalosi Kabupaten Enrekang. The sample technique used purposive sampling that consist of 60 students. The samples are assigned into two groups: class VII-2 as an experimental class and VII-3 as a control class and each class consist of 30 students. The instrument of this research was written test. The result of this research was obtained from calculating the data using t-test with the significance level (α) = 0.05 and also calculated the effect size. The result showed that the mean score of post-test in experimental class is higher than control class (79>65.73). Therefore, statistic hypothesis test showed that ρ-value of post-test and gained score is 0.000 < sig. α = 0.05. Also, the effect size of this result is 1.53. It proved that the alternatives hypothesis (H a ) is accepted and the null hypothesis (H ) is rejected. To sum up, gallery walk technique is effective with strong level on students’ writing skill.

SHuffled Ant Lion Optimization approach with an exponentially weighted random walk strategy

Ant Lion Optimization (ALO) method is one of the population-based nature-inspired optimization algorithms which mimics the hunting strategy of antlions. ALO is successfully employed for solving many complicated optimization problems. However, it is reported in the literature that the original ALO has some limitations such as the requirement of high number of iterations and possibility of trapping to local optimum solutions, especially for complex or large-scale problems. For this purpose, the SHuffled Ant Lion Optimization (SHALO) approach is proposed by conducting two improvements in the original ALO. Performance of the proposed SHALO approach is evaluated by solving some unconstrained and constrained problems for different conditions. Furthermore, the identified results are statistically compared with the ones obtained by using the original ALO, two improved ALOs which are the self-adaptive ALO (saALO) and the exponentially weighted ALO (EALO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) approaches. Identified results indicated that the proposed SHALO approach significantly improves the solution accuracy with a mean success rate of 76% in terms of finding the global or near-global optimum solutions and provides better results than ALO (22%), saALO (25%), EALO (14%), GA (28%), and PSO (49%) approaches for the same conditions.

Neural Oscillations and Functional Significances for Prioritizing Dual-Task Walking in Parkinson's Disease.

Task prioritization involves allocating brain resources in a dual-task scenario, but the mechanistic details of how prioritization strategies affect dual-task walking performance for Parkinson's disease (PD) are little understood. We investigated the performance benefits and corresponding neural signatures for people with PD during dual-task walking, using gait-prioritization (GP) and manual-prioritization (MP) strategies. Participants (N = 34) were asked to hold two inter-locking rings while walking and to prioritize either taking big steps (GP strategy) or separating the two rings (MP strategy). Gait parameters and ring-touch time were measured, and scalp electroencephalograph was performed. Compared with the MP strategy, the GP strategy yielded faster walking speed and longer step length, whereas ring-touch time did not significantly differ between the two strategies. The MP strategy led to higher alpha (8-12 Hz) power in the posterior cortex and beta (13-35 Hz) power in the left frontal-temporal area, but the GP strategy was associated with stronger network connectivity in the beta band. Changes in walking speed and step length because of prioritization negatively correlated with changes in alpha power. Prioritization-related changes in ring-touch time correlated negatively with changes in beta power but positively with changes in beta network connectivity. A GP strategy in dual-task walking for PD can enhance walking speed and step length without compromising performance in a secondary manual task. This strategy augments attentional focus and facilitates compensatory reinforcement of inter-regional information exchange.

Using Gallery Walk Strategy to Improve Students' Participation in Speaking Activity

This study aimed to improve the year-10 students’ participation in speaking activities through the gallery walk strategy. Classroom action research was applied to conduct this study. The year-10 students of SMK Subur Insani Sambas in Academic Year 2020/2021 became the research participant. The researcher used an observation checklist and field note to find out and get data from the research participant. This study was conducted in two cycles. This research result showed good progress in improving the students’ participation through the gallery walk strategy, even though only a few students participated enthusiastically in the speaking activity and had enough bravery to speak English in the first cycle. The researcher modified the gallery walk strategy in the second cycle to actively talk in English during the presentation, meaning the gallery walk strategy could improve the year-10 students’ participation. The result showed the percentage of the students categorized as very active and active was approximately 71%. The researcher recommended that the gallery walk strategy can also be applied in the teaching and learning process.Keywords: Gallery Walk, students’ participation, speaking

Does hearing impairment affect gait under realistic conditions?

Hearing loss is associated with increased risk of falling, but little is known about how hearing impairment might affect mobility. Here, we report preliminary results from an ongoing experimental study investigating dynamic properties of gait while walking with and without impaired hearing. Young- and middle-aged adult participants with self-reported normal hearing completed four walking tasks, 2 indoors, 2 outdoors, each conducted with and without a simulated hearing loss. In the impaired condition, participants wore an insert earplug in one ear combined with binaural, circumaural noise-damping headphones. We quantified gait parameters using data from inertial measurement units (IMUs) affixed to participants’ ankles and waist, allowing participants to walk farther than in typical gait assessments. Parameters included standard gait metrics, such as limb acceleration as well as a novel spatiotemporal index quantifying variability in step patterns. Data have been collected from 13 of 30 projected participants. Analysis will focus on how temporal-spatial gait parameters and variability differ between indoor and outdoor walking with and without hearing impairment. We expect gait parameters to reflect a more variable and potentially less mobile walking strategy in the outdoor condition and with impaired hearing due to increased cognitive load and/or reduced spatial awareness in those conditions.

Kinematic difference and asymmetries during level walking in adolescent patients with different types of mild scoliosis

BackgroundAdolescent idiopathic scoliosis (AIS), three-dimensional spine deformation, affects body motion. Previous research had indicated pathological gait patterns of AIS. However, the impact of the curve number on the walking mechanism has not been established. Therefore, this study aimed to compare the gait symmetry and kinematics in AIS patients with different curve numbers to healthy control.ResultsIn the spinal region, double curves AIS patients demonstrated a smaller sagittal symmetry angle (SA) and larger sagittal convex ROM of the trunk and lower spine than the control group. In the lower extremities, the single curve patients showed a significantly reduced SA of the knee joint in the frontal plane, while the double curves patients showed a significantly reduced SA of the hip in the transverse plane.ConclusionThe curve number indeed affects gait symmetry and kinematics in AIS patients. The double curves patients seemed to adopt a more "careful walking" strategy to compensate for the effect of spinal deformation on sensory integration deficits. This compensation mainly occurred in the sagittal plane. Compared to double curves patients, single curve patients unitized a similar walking strategy with healthy subjects.

Fine mapping an AUXIN RESPONSE FACTOR, SmARF18, as a candidate gene of the PRICKLE LOCUS that controls prickle absence/presence on various organs in eggplant (Solanum melongena L.)

Prickle is a sharp protrusion that covering plant and fruit calyxes of eggplant are considered undesirable agronomic traits, for they bring troubles and create additional costs for farmers. However, little is known about its regulatory genes and molecular mechanism of morphogenesis. In this study, two eggplant inbred lines, prickly ‘140’ and prickleless ‘145’ were applied to construct F1, BC1, and F2 offspring populations. Genetic analysis results showed that prickle's absence/presence on various organs in eggplant was controlled by only one dominant nuclear gene. The PRICKLE LOCUS (Pl) was fine mapped into a candidate interval with 28.3 Kb on chromosome 6 ultimately by adopting bulked segregant analysis combined with genome walking strategy. An auxin response factor SmARF18 (Smechr0602826.1) was deduced to be a candidate gene encoded by Pl, which possesses a non-synonymous single nucleotide polymorphism co-segregating with prickle phenotype in F2 population. The finding here could provide a basis to reveal the molecular regulatory mechanism of prickle morphogenesis in plants and breed prickle-free eggplant cultivars.

How to walk to reduce footstep noise in multi-story residential buildings

Loud footsteps from upstairs cause disturbance to downstairs neighbours in multi-story residential buildings. In this experiment, we examined how participants walk when asked to walk quietly and evaluated the efficiency of their quiet walking patterns. Changes in vertical impact loading rates during the early stance phase, walking speed, and lower limb muscle activity when asked to walk quietly were evaluated from twenty-six young participants. Study data show that participants who struck the ground with the rearfoot reduced the impact loading rate by 44.6% with 29.3% slower walking speed than normal walking. Those who struck with the fore- or mid-foot reduced the impact loading by 69.2% with a 23.4% decrease in speed. Quiet walking with the non-rearfoot strike pattern reduced the impact loading by 48.7%, even when asked to walk as fast as normal walking. The results support the non-rearfoot strike pattern as an efficient walking strategy for lowering footstep impact. Practitioner summary: Data of this study show that voluntary gait alteration, such as adopting a non-rearfoot strike pattern, can reduce footstep impact. The study results propose that implementing such changes could be beneficial in addressing floor noise issues of multi-story residential buildings. Abbreviations: RFS: Rearfoot strike; NRFS: non-rearfoot strike; COP: Center of pressure; NW: Normal walking; QWs: Quiet walking at a preferred slower speed; QWn: Quiet walking at the speed of normal walking; EMG: Electromyography; BW: Body weight; iNEMG: integrated normalized EMG.

Compliant gait control method based on CVSLIP-FF model for biped robot walking over uneven terrain

Bipedal walking over uneven terrain remains a challenging task due to the environmental complexity and unavoidable landing impact. To realize the stable and robust walking of biped robots, this paper proposes a compliant gait control method, which focuses on walking compliance and conducts research on two levels. In the gait generation level, a Continuous-Variable Spring-Loaded Inverted Pendulum with Finite-sized Foot (CVSLIP-FF) model is provided with the consideration of the ankle joint and compliant spring-loaded leg. Then, a CVSLIP-FF based gait generation pattern with relevant walking strategies is provided to enhance the mobility of biped robots. In the joint control level, an ankle joint admittance control strategy is applied to achieve compliant robot-environment interaction. Experimental results indicate that compared with the traditional SLIP model, the proposed method performs better adaptability to uneven terrain with a 217.77% improvement, and enables biped robots to cope with slight unknown disturbance.

Enhanced dung beetle optimizer for Kriging-assisted time-varying reliability analysis

<p>During the engineering structure's operation, the mechanical structure's performance and loading will change with time, so the parameter uncertainty and structural reliability will also have dynamic characteristics. The time-varying reliability analysis method can more accurately evaluate structural reliability by fully using this dynamic uncertainty. However, the time-varying reliability analysis was mainly based on the spanning rate method, which was complex and difficult to obtain the final result. Therefore, this study proposed an enhanced dung beetle optimization (EDBO) assisted time-varying reliability analysis method based on the adaptive Kriging model. With the help of the adaptive Kriging model and the EDBO optimization algorithm, the efficiency of the time-varying reliability analysis method was improved. At the same time, to prevent prematurely falling into the local search trap, the method improved the uniformity of the sample by initializing the sample through improved tent chaotic mapping (ITCM). Next, the Gaussian random walk strategy was used to search the updated position, which further improved the accuracy of the reliability analysis results. Finally, the accuracy and effectiveness of the proposed time-varying reliability analysis method were verified by four mechanical structure model examples. From the calculation results, it can be seen that with the help of the new DBO optimization algorithm, the relative error of the proposed reliability analysis results was about 20%~30% lower than that of the traditional reliability analysis method. What's more, the calculation efficiency was higher than that of other reliability analysis methods.</p>

DNA Polymerase-Steered Self-Propelled and Self-Enhanced DNA Walker for Rapid and Distinctly Amplified Electrochemical Sensing.

The development of a simple, rapid, easy-to-operate, and ultrasensitive DNA walker-based sensing system is challenging but would be very intriguing for the enormous applications in biological analysis and disease monitoring. Herein, a new self-propelled and self-enhanced DNA walking strategy was developed on the basis of a simple DNA polymerase-steered conversion from a typical alternate DNA assembly process. The sensing platform was fabricated easily by immobilizing only one hairpin probe (H1) and the sensing process was based on a simple one-step mixing with another hairpin-like DNA probe (H2) and DNA polymerase. The DNA polymerization could achieve target recycling and successive DNA walking steps. Interestingly, along with each DNA walking step, the new DNA walker sequence could be autonomously accumulated for a self-enhanced DNA walking effect. This provided a multilevel signal amplification ability for the ultrasensitive detection of the target with a low detection limit of 0.18 fM. Moreover, it could greatly reduce the reaction time with the sensing process finished within 1 h. The detection selectivity and the applicative potential in a complicated biological matrix were also demonstrated. Furthermore, the flexible control of sensing modes (self-enhanced DNA walking or the alternate DNA assembly) by using DNA polymerase or not offered a powerful means for sensing performance modulation. It thus opens a new avenue toward the development of a DNA walker-based sensing platform with both rapid and ultrasensitive features and might hold a huge potential for point-of-care diagnostic applications.

Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods

Cryptocurrency price forecasting is attracting considerable interest due to its crucial decision support role in investment strategies. Large fluctuations in non-stationary cryptocurrency prices motivate the urgent need for accurate forecasting models. The lack of seasonal effects and the need to meet a number of unrealistic requirements make it difficult to make accurate forecasts using traditional statistical methods, leaving machine learning, particularly ensemble and deep learning, as the best technology in the area of cryptocurrency price forecasting. This is the first work to provide a comprehensive comparative analysis of ensemble learning and deep learning forecasting models, examining their relative performance on various cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin) and exploring their potential trading applications. The results of this study reveal that gated recurrent unit, simple recurrent neural network, and LightGBM methods outperform other machine learning methods, as well as the naive buy-and-hold and random walk strategies. This can effectively guide investors in the cryptocurrency markets.

Multi-strategy enhanced kernel search optimization and its application in economic emission dispatch problems

Abstract The kernel search optimizer (KSO) is a recent metaheuristic optimization algorithm that is based on kernel theory, eliminating the need for hyper-parameter adjustments, and demonstrating excellent global search capabilities. However, the original KSO exhibits insufficient accuracy in local search, and there is a high probability that it may fail to achieve local optimization in complex tasks. Therefore, this paper proposes a multi-strategy enhanced KSO (MSKSO) to enhance the local search ability of the KSO. The MSKSO combines several control strategies, including chaotic initialization, chaotic local search mechanisms, the high-altitude walk strategy (HWS), and the Levy flight (LF), to effectively balance exploration and exploitation. The MSKSO is compared with ten well-known algorithms on 50 benchmark test functions to validate its performance, including single-peak, multi-peak, separable variable, and non-separable variable functions. Additionally, the MSKSO is applied to two real engineering economic emission dispatch (EED) problems in power systems. Experimental results demonstrate that the performance of the MSKSO nearly optimizes that of other well-known algorithms and achieves favorable results on the EED problem. These case studies verify that the MSKSO outperforms other algorithms and can serve as an effective optimization tool.

Vertical mixing strategies in the Opendrift platform: analytical solution and Random Walk scheme

Predicted scenarios for microplastic displacement in the ocean is still a challenging task, where particle trajectory evolution is based on stochastic strategies at each timestep. The Opendrift platform presents a microplastic trajectory evolution solution using two strategies for turbulent vertical mixing. This work simulated microplastic particles emission in the Rio de Janeiro region (Guanabara Bay) and observed its depth over one year. Attentive to subtle differences, a statistical analysis was proposed to observe how particles are affected by the strategies individually. The preliminary observations showed no significant modification on the surface, but the depth had important motion modification. A dispersion was observed in XY coordinates of each particle using statistical analysis. The results demonstrated significant individual movement of microplastic particles. This study encourages the need for a protocol to decide which strategy to be used in computational simulations so that monitoring tasks can have optimum efficiency.