Step Size Research Articles

Musculoskeletal System and Gait Characteristics in Patients with Osteogenesis imperfecta.

The aim of the study was to assess the muscoloskeletal system and spatiotemporal gait parameters of patients in three types of osteogenesis imperfecta. Retrospective observational study. The Department of Rehabilitation, Children's Memorial Health Institute in Warsaw, Poland. This study investigated individuals with various types of osteogenesis imperfecta: 33 with osteogenesis imperfecta I (aged 13.9), 16 with osteogenesis imperfecta III (aged 10.4), and 14 with osteogenesis imperfecta IV (aged, 15.8), as well as a reference group of 400 healthy individuals. The musculoskeletal assessment included: medical record review, clinical evaluation, functional tests, long bone deformity assessment via clinical and X-ray examination, and objective gait analysis with the Vicon Motion Systems (Ltd, Oxford, UK). The study revealed notable differences in clinical presentation, deformities within the musculoskeletal system, gait parameters across the various types of osteogenesis imperfecta (p < 0.001). The most affected gait parameters were: cadence, gait speed and step length. The greatest deformities of lower limbs and spine were presented in patients with osteogenesis imperfecta type III. These findings are significant for understanding gait abnormalities in osteogenesis imperfecta patients and designing customized physiotherapy programs to help them participate fully in daily life. Improvement of muscle strength is one of the key for easier engagement in activities like walking or stair-climbing.

Consensus Paper: Cerebellum and Reward.

Cerebellum is a key-structure for the modulation of motor, cognitive, social and affective functions, contributing to automatic behaviours through interactions with the cerebral cortex, basal ganglia and spinal cord. The predictive mechanisms used by the cerebellum cover not only sensorimotor functions but also reward-related tasks. Cerebellar circuits appear to encode temporal difference error and reward prediction error. From a chemical standpoint, cerebellar catecholamines modulate the rate of cerebellar-based cognitive learning, and mediate cerebellar contributions during complex behaviours. Reward processing and its associated emotions are tuned by the cerebellum which operates as a controller of adaptive homeostatic processes based on interoceptive and exteroceptive inputs. Lobules VI-VII/areas of the vermis are candidate regions for the cortico-subcortical signaling pathways associated with loss aversion and reward sensitivity, together with other nodes of the limbic circuitry. There is growing evidence that the cerebellum works as a hub of regional dysconnectivity across all mood states and that mental disorders involve the cerebellar circuitry, including mood and addiction disorders, and impaired eating behaviors where the cerebellum might be involved in longer time scales of prediction as compared to motor operations. Cerebellar patients exhibit aberrant social behaviour, showing aberrant impulsivity/compulsivity. The cerebellum is a master-piece of reward mechanisms, together with the striatum, ventral tegmental area (VTA) and prefrontal cortex (PFC). Critically, studies on reward processing reinforce our view that a fundamental role of the cerebellum is to construct internal models, perform predictions on the impact of future behaviour and compare what is predicted and what actually occurs.

Amelioration of gait and balance disorders by rosuvastatin is associated with changes in cerebrovascular reactivity in older patients with hypertensive treatment.

To investigate the effect of rosuvastatin on gait and balance disorder progression and elucidate the role of cerebrovascular reactivity (CVR) on this effect. From April 2008 to November 2010, 943 hypertensive patients aged ≥60 years were enrolled from the Shandong area of China. Patients were randomized into rosuvastatin and placebo groups. Gait, balance, CVR, fall and stroke were assessed. During an average 72 months of follow-up, the decreasing trends for step length, step speed, and Berg balance scale scores and the increasing trends for step width and chair rising test were slower in the rosuvastatin group when compared to the placebo group. The hazard ratio of incident balance impairment and falls was 0.542 [95% confidence interval (CI) 0.442-0.663] and 0.532 (95% CI 0.408-0.694), respectively, in the rosuvastatin group compared with placebo group. For CVR progression, the cerebrovascular reserve capacity and breath-holding index were increased and the pulsatility index decreased in the rosuvastatin group, while the cerebrovascular reserve capacity and breath-holding index were decreased, and pulsatility index increased in the placebo group. The changes in gait stability and balance function were independently associated with the changes in the CVR. The odds risks of balance impairment and falls were 2.178 (95% CI: 1.491-3.181) and 3.227 (95% CI: 1.634-6.373), respectively, in the patients with CVR impairment and patients without CVR impairment. Rosuvastatin ameliorated gait and balance disorder progression in older patients with hypertension. This effect might result from the improvement in the CVR. This double-blind clinical trial recruited 943 hypertensive patients aged ≥60 years who were randomly administered rosuvastatin and placebo interventions. The data indicates that rosuvastatin significantly ameliorated the progressions of gait and balance disorders in older hypertensive patients. The cerebrovascular reactivity might play an important mediating role in this amelioration.

Integrating gated recurrent unit in graph neural network to improve infectious disease prediction: an attempt

ObjectiveThis study focuses on enhancing the precision of epidemic time series data prediction by integrating Gated Recurrent Unit (GRU) into a Graph Neural Network (GNN), forming the GRGNN. The accuracy of the GNN (Graph Neural Network) network with introduced GRU (Gated Recurrent Units) is validated by comparing it with seven commonly used prediction methods.MethodThe GRGNN methodology involves multivariate time series prediction using a GNN (Graph Neural Network) network improved by the integration of GRU (Gated Recurrent Units). Additionally, Graphical Fourier Transform (GFT) and Discrete Fourier Transform (DFT) are introduced. GFT captures inter-sequence correlations in the spectral domain, while DFT transforms data from the time domain to the frequency domain, revealing temporal node correlations. Following GFT and DFT, outbreak data are predicted through one-dimensional convolution and gated linear regression in the frequency domain, graph convolution in the spectral domain, and GRU (Gated Recurrent Units) in the time domain. The inverse transformation of GFT and DFT is employed, and final predictions are obtained after passing through a fully connected layer. Evaluation is conducted on three datasets: the COVID-19 datasets of 38 African countries and 42 European countries from worldometers, and the chickenpox dataset of 20 Hungarian regions from Kaggle. Metrics include Average Root Mean Square Error (ARMSE) and Average Mean Absolute Error (AMAE).ResultFor African COVID-19 dataset and Hungarian Chickenpox dataset, GRGNN consistently outperforms other methods in ARMSE and AMAE across various prediction step lengths. Optimal results are achieved even at extended prediction steps, highlighting the model’s robustness.ConclusionGRGNN proves effective in predicting epidemic time series data with high accuracy, demonstrating its potential in epidemic surveillance and early warning applications. However, further discussions and studies are warranted to refine its application and judgment methods, emphasizing the ongoing need for exploration and research in this domain.

Comparison of Learning Golf Putting Skill in Different Choices: A Test of the OPTIMAL Theory

Background: The OPTIMAL theory of motor learning posits that autonomy support (AS), enhanced expectancies (EE), and an external focus of attention (EF) facilitate improved motor learning and performance. However, the applicability of this theory by simultaneously presenting all three factors in task-related and Irrelevant choices in learning golf skills has not been investigated. Objectives: The purpose of this study was to investigate the sequential implementation of AS, EE, and EF factors in golf putting tasks. Methods: During a pre-test under neutral conditions and acquisition phase (six blocks), thirty-six participants attempted to putt golf balls into a hole cup placed in the center of four circles with different diameters on the green. The participants were divided into two groups for optimized practices: The optimal group with task-relevant choices and the optimal group with task-irrelevant choices, both groups performed the optimized factors (EE, AS, and EF) successively and identically to each other. After 24 hours, learning was measured by a retention test. Results: The optimized group with relevant choices performed better than the optimized group with irrelevant choices in the autonomy variable. In the retention test, the aiming accuracy score of both groups increased compared to the pre-test, but no significant difference was observed in the learning rate between the two optimized groups. Conclusions: The present findings demonstrate that the key factors of OPTIMAL theory can be further benefited by task-related choices during practice for beginners' learning.

An analysis of high order FEM and IGA for explicit dynamics: Mass lumping and immersed boundaries

SummaryWe investigate the behavior of different shape functions for the discretization of hyperbolic problems. In particular, we consider classical Lagrange polynomials and B‐splines. The studies focus on the performance of the these functions as a spatial discretization approach combined with an explicit time marching scheme. In this regard, a major concern is the maximum eigenvalue that imposes restrictions on the critical time step size and suitable lumping techniques that yield a diagonal mass matrix. The accuracy of the discretization methods is assessed in an asymptotic manner in terms of the convergence of eigenvalues and eigenvectors. Further, the global accuracy is investigated in terms of the full spectrum. The results show that B‐spline discretization with a consistent mass matrix are more accurate than those based on Lagrange shape functions, which holds true in the boundary‐fitted as well as in the immersed setting. On the other hand, Lagrange shape functions are more robust with respect to standard lumping techniques, which cannot be directly applied for B‐splines without loss of accuracy. In general, we observe that none of the standard lumping schemes yields optimal results for B‐splines, even in the boundary‐fitted setting. For the immersed setting, also Lagrange shape functions show a drop in accuracy which depends on the position of the boundary that cuts the element. Several remedies are considered in order to overcome these issues, including interpolatory B‐spline bases as well as eigenvalue stabilization methods. While accuracy and stability can be improved using these remedies, we conclude from our study that it is still an open question, how to design a discretization method that achieves large critical time step sizes in combination with a diagonal mass matrix and high accuracy in the immersed setting. We note that these considerations primarily relate to linear structural dynamics applications, such as for example, structural acoustics. In nonlinear problems, such as automotive crash dynamics, other considerations predominate. An example of a one‐dimensional elastic‐plastic bar impacting a rigid wall is illustrative.

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.

A novel framework using 3D-CNN and BiLSTM model with dynamic learning rate scheduler for visual speech recognition

A novel framework using 3D-CNN and BiLSTM model with dynamic learning rate scheduler for visual speech recognition

Dynamical stability and chaos in artificial neural network trajectories along training

The process of training an artificial neural network involves iteratively adapting its parameters so as to minimize the error of the network’s prediction, when confronted with a learning task. This iterative change can be naturally interpreted as a trajectory in network space–a time series of networks–and thus the training algorithm (e.g., gradient descent optimization of a suitable loss function) can be interpreted as a dynamical system in graph space. In order to illustrate this interpretation, here we study the dynamical properties of this process by analyzing through this lens the network trajectories of a shallow neural network, and its evolution through learning a simple classification task. We systematically consider different ranges of the learning rate and explore both the dynamical and orbital stability of the resulting network trajectories, finding hints of regular and chaotic behavior depending on the learning rate regime. Our findings are put in contrast to common wisdom on convergence properties of neural networks and dynamical systems theory. This work also contributes to the cross-fertilization of ideas between dynamical systems theory, network theory and machine learning.

The Relationship between Take-Off Parameters and Relative Vertical Momentum of Free Limbs at the Take-Off in Hurdle Clearance

This study aimed to clarify the kinetics of the relative vertical momentum to the proximal joint of each free limb and their contribution to the increase in the centre of mass height at the take-off of hurdle clearance, as well as their relationship with take-off variables. Thirteen male hurdlers cleared one hurdle at the height of their centre of mass, and their attempts were filmed using six high-speed cameras. The hurdle height was 96.54 ± 2.63 cm (55.35 ± 0.29% of body height). The approach distance was set at 15 m and adjusted by each hurdler in the range of 10–50 cm so as not to involve any noticeable step length adjustment before the take-off. The combined free limb relative vertical momentum tended to increase until mid-support and was maintained until the toe off. The smaller the whole-body vertical momentum at the toe off and the increase in relative vertical momentum of the lead leg during the take-off, the higher the take-off velocity, the shorter the support time, and the smaller the deceleration. The higher the relative vertical momentum of the forward swing arm during touchdown and the smaller the relative vertical momentum increase of the combined free limb and the forward swing arm during the take-off, the smaller the deceleration. In conclusion, hurdlers should reduce the increase in whole-body vertical momentum at the take-off by suppressing the increase in relative vertical momentum of the lead leg and the forward swing arm.

Application of machine-learning model to optimize colonic adenoma detection in India.

There is limited data on the prevalence and risk factors of colonic adenoma from the Indian sub-continent. We aimed at developing a machine-learning model to optimize colonic adenoma detection in a prospective cohort. All consecutive adult patients undergoing diagnostic colonoscopy were enrolled between October 2020 and November 2022. Patients with a high risk of colonic adenoma were excluded. The predictive model was developed using the gradient-boosting machine (GBM)-learning method. The GBM model was optimized further by adjusting the learning rate and the number of trees and 10-fold cross-validation. Total 10,320 patients (mean age 45.18 ± 14.82years; 69% men) were included in the study. In the overall population, 1152 (11.2%) patients had at least one adenoma. In patients with age > 50years, hospital-based adenoma prevalence was 19.5% (808/4144). The area under the receiver operating curve (AUC) (SD) of the logistic regression model was 72.55% (4.91), while the AUCs for deep learning, decision tree, random forest and gradient-boosted tree model were 76.25% (4.22%), 65.95% (4.01%), 79.38% (4.91%) and 84.76% (2.86%), respectively. After model optimization and cross-validation, the AUC of the gradient-boosted tree model has increased to 92.2% (1.1%). Machine-learning models may predict colorectal adenoma more accurately than logistic regression. A machine-learning model may help optimize the use of colonoscopy to prevent colorectal cancers. ClinicalTrials.gov (ID: NCT04512729).

A distributed algorithm with network‐independent step‐size and event‐triggered mechanism for economic dispatch problem

AbstractThe economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk of communication failures. To address this challenge, we propose a distributed algorithm with network‐independent step sizes and an event‐triggered mechanism, which reduces communication requirements and enhances adaptability. Unlike traditional methods, our algorithm uses network‐independent step sizes derived from each agent's local cost functions, thus eliminating the need for detailed network topology knowledge. The theoretical derivation identifies a range of step size values that depend solely on the objective function's strong convexity and the gradient's Lipschitz continuity. Furthermore, the proposed algorithm is shown to achieve a linear convergence rate, assuming the event triggering threshold criteria are met for linear convergence. Numerical experiments further validate the effectiveness and advantages of our proposed distributed algorithm by demonstrating its ability to maintain good convergence characteristics while reducing communication frequency.

Effects of running-induced fatigue on joint kinematics and kinetics during overground running: a systematic review and meta-analysis.

This study evaluated the acute fatigue-inducing effect of distance running on kinematics and kinetics during overground running. Standardised mean differences (SMD) with 95% confidence intervals (95% CI) were used to pool data across 16 studies. Effects during consistent (pre- and post-fatigue running speed within ± 5%) versus varied speed running (difference of >5% between running speeds) were analysed separately. There was strong evidence that running-induced fatigue significantly increases ground contact times at consistent running speeds (SMD 0.52 [95% CI 0.22, 0.82]) and moderate evidence that step length shortens at varied running speeds (SMD -1.27 [95% CI -1.79, -0.75]). There was strong evidence that fatigue does not change peak: hip and knee flexion angles, hip adduction angle, hip and knee internal rotation angles, hip and knee extension moments, hip and knee abduction moments, knee abduction angle, knee flexion and extension moments, knee adduction moment, rearfoot eversion angle, and plantarflexion moments, or knee flexion and plantarflexion range of motion during stance. Running-induced fatigue increases contact times and reduces step length, whereas lower-body joint angles and moments are unchanged. Minimising changes in stride parameters could provide a mechanism for reducing the effects of fatigue on running performance.

Learning Rates: A Correction of Gain Scores to Assess Math Learning Interventions

Learning Rates: A Correction of Gain Scores to Assess Math Learning Interventions

Nonlinear Dynamic Properties of Rigid–Elastic–Liquid Coupled Ball Bearings Considering External Load Excitation

In this study, a novel dynamic model of a ball bearing based on rigid–elastic–liquid (REL) coupling structure considering external load excitation is established, and the dynamic response of its outer ring under external load excitation is examined. First, the differential equation of the journal motion (i.e. the outer ring) is derived based on Newton’s law, and the oil film force induced by the REL coupling structure is determined by using the central finite difference method, planar bending theory of thin ring, and Simpson’s rule. Further, the fourth and fifth-order variable step length Runge–Kutta method is used to solve the kinematic differential equation. Second, the dynamic characteristics of the outer ring of the bearing under different speeds and elastic support structural parameters are examined. Finally, the feasibility of the theoretical model is verified through experiments. The results show that compared with the traditional squeeze film damper (SFD) system, the ball bearing with REL coupling structure is more stable, and the vibration damping effect is better under higher bearing speed, lower squirrel cage stiffness, and smaller elastic ring flexibility coefficient. The above findings provide useful insights into the dynamic characteristics of REL coupled ball bearings under unbalanced excitation, which can serve as a reference for the optimization design of such ball bearings.

R-linear and strong convergence of Tseng methods for solving certain split and non-split problems

In the recent past, authors have fondly assumed the co-coercive condition for solving certain class of split inverse problems. Recently, some attempts have been made to relax this very strict condition, however, only few results exists in literature in such case. In this paper, we present some new Mann-type Tseng methods for solving split monotone variational inclusion and common fixed point problems for a finite collection of quasi-pseudocontractive operators. We prove strong convergence and R − linear convergence rate results of our methods, while the co-coerciveness property is dispensed with. Our methods incorporate the relaxation, inertial extrapolation and self-adaptive step size techniques to achieve better performance and faster convergence. With the aid of relevant numerical experiments, we showcase the performance profile of our methods in comparison with recent methods in literature.

Deep Learning based Semantic Segmentation for Buildings Detection from Remote Sensing Images

Building extraction from remote sensing images is the process of automatically identifying and extracting the boundaries of buildings from high-resolution aerial or satellite images. The extracted building footprints can be used for a variety of applications, such as urban planning, disaster management, city development, land management, environmental monitoring, and 3D modeling. The results of building extraction from remote sensing images depend on several factors, such as the quality and resolution of the image and the choice of algorithm.The process of building extraction from remote sensing images typically involves a series of steps, including image pre-processing, feature extraction, and classification. Building extraction from remote sensing images can be challenging due to factors such as varying building sizes and shapes, shadows, and occlusions. However, recent advances in deep learning and computer vision techniques have led to significant improvements in the accuracy and efficiency of building extraction methods. This research presents a deep learning semantic segmentation architecture-based model for developing building detection from high resolution remote sensing images. The open-source Massachusetts dataset is used to train the suggested UNet architecture. The model is optimized using the RMSProp algorithm with a learning rate of 0.0001 for 100 epochs. After 1.52 hours of training on Google Colab the model achieved an 83.55% F1 score, which indicates strong precision and recall.

The Influence of YOLOv5 Hyperparameters for Construction Details Detection

Computer vision has become a fundamental area of interest in recent decades. Each area has unique data which object detection methods can analyse. However, it is important to find the most suitable parameters for the model that detects different object groups. In this research has been investigated the influence of pre-trained YOLOv5 (nano (n), small (s), medium (m), large (l), extralarge (x)) models, hyperparameters (learning rate, momentum, and weight decay) and different image augmentation (hsv_h, degrees, translate, flipud, mosaic, mixup, shear, perspective) efficiency for similar construction details detection. A newly collected dataset with twenty-two labelled categories of construction details was prepared. A total of 270 models were trained and evaluated. Every model was evaluated with 3,300 test images which backgrounds were mixed, neutral, and white backgrounds. The most accurate model was YOLOv5l with learning rate – 0.001, momentum – 0.950 and weight decay – 0.0001. This model achieved – 0.5015 (50.15%) accuracy.

Nonsmooth Reduced Interface Models and Their Use in Co-Simulation of Mechanical Systems

Abstract In a co-simulation setup, the entire system is decomposed into a collection of individual subsystems that are interfaced together, with each subsystem being modeled and integrated separately according to its own requirements. To maintain the interconnectivity and consolidation of the primary system, these subsystems must communicate with each other through the interface and transfer certain information at the end points of a defined time interval termed macro time step. Inside the macro time step, the evolution of the interface variables has to be approximated as information about them will only be available again at the end of the step. In real-time simulations, the size of the macro time step and the accuracy of the approximated interface variables are critical factors; if the interface variables are approximated accurately, the size of the macro time step can be kept large enough to provide interactive rates without loss of accuracy and stability. This work focuses on systems where unilateral contact interactions are important and proposes reduced interface model concepts for such nonsmooth systems. The use of the proposed reduced interface model (RIM) is demonstrated in co-simulation to provide model-based approximation of the interface variables. The advantages of the proposed method are demonstrated through two representative case studies.

Dynamical behavior of a hepatitis B epidemic model and its NSFD scheme

AbstractHepatitis is inflammation of the liver, and one of its types, hepatitis B, is a contagious infection. Using mathematical models, the nature of the spread of the Hepatitis B virus can be predicted. In the present paper, a hepatitis B epidemic model with a Beddington–DeAngelis type incidence rate and a constant vaccination rate is considered. Some dynamical properties of this model, such as non-negativity, boundedness character, the basic reproduction number $$\mathcal {R}_0$$ R 0 , stability nature, and the bifurcation phenomenon, are investigated. By the Bendixson theorem, it is demonstrated that the disease-free equilibrium is globally asymptotically stable. It is shown that a transcritical bifurcation phenomenon occurs when $$\mathcal {R}_0=1$$ R 0 = 1 . It is concluded that the endemic equilibrium is globally asymptotically stable when $$\mathcal {R}_0&gt;1$$ R 0 &gt; 1 , by utilizing Dulac’s criteria. Also, a discrete system of difference equations is obtained by constructing a non-standard finite difference (NSFD) scheme for the continuous model. It is shown that the solutions of this discrete system are dynamically consistent for all finite step sizes. The theoretical results obtained are also supported and visualized by numerical simulations. These simulations also demonstrate that the NSFD scheme produces much more efficient results than the Euler or RK4 schemes, as shown in the theoretical results obtained.