Distance Parameters Research Articles

Active fault diagnosis for stochastic systems subject to non-convex input constraints

In this paper, the problem of active fault diagnosis (AFD) is investigated for a class of continuous-time stochastic systems subject to non-convex input constraints. The input is designed to minimize the probability of misdiagnosis. Since the misdiagnosis probability cannot be expressed in the closed form, its Chernoff upper bound likely tighter than its widely used Bhattacharyya upper bound is exploited as an alternative. The closed-form expression of the Chernoff distance under Gaussian distributions is correctly deduced. For calculation simplification and diagnosis performance enhancement purposes, a novel input design criterion is constructed for AFD based on the Chernoff distance. The optimization problem for AFD is formulated as a bilevel optimization, where the outer and inner layers optimize the Chernoff distance parameter and the input respectively. A convex relaxation is proposed to relax non-convex input constraints into convex ones, and the optimal solution to the relaxed inner input design problem is proven to be also optimal for the original inner problem whose objective function and input constraints are both non-convex under certain conditions, which is referred to as lossless relaxation. The relaxed inner input design problem under the constructed design criterion can be solved to global optimums, and the relaxed outer optimization is solved by a grid search method. Given the optimal input, faults are diagnosed based on the minimum misdiagnosis probability decision rule. The effectiveness of the proposed AFD approach is demonstrated by utilizing a planetary landing example.

Cardiac effort and 6-min walk distance correlate with stroke volume measured by cardiac magnetic resonance imaging.

Right ventricular (RV) dysfunction in pulmonary arterial hypertension (PAH) is associated with poor outcomes. Cardiac magnetic resonance imaging (cMRI) is the gold standard for volumetric assessment, and few reports have correlated 6-min walk distance (6MWD) and cMRI parameters in PAH. Cardiac Effort, (the number of heart beats used during 6-min walk test)/(6MWD), incorporates physiologic changes into walk distance and has been associated with stroke volume (SV) measured by nuclear imaging and indirect Fick. Here, we aimed to interrogate the relationship of Cardiac Effort and 6MWD with SV measured by the gold standard, cMRI. This was a single-center, observational, prospective study in Group 1 PAH patients. Subjects completed 6-min walk with heart rate monitoring (Cardiac Effort) and cMRI within 24 h. cMRI was correlated to Cardiac Effort and 6MWD using Spearman Correlation Coefficient. Twenty-five participants with a wide range of RV function completed both cMRI and Cardiac Effort. There was a strong correlation between left ventricle SV index and both Cardiac Effort (r = -0.70, p = 0.0001) and 6MWD (r = 0.67, p = 0.0002). Cardiac Effort and 6MWD were statistically separated in patients at prognostically significant thresholds of left ventricle SV index (>31 ml/m2), RV Ejection Fraction (>35%), and SV/End Systolic Volume ( > 0.53). Cardiac Effort and 6MWD are noninvasive ways to gain insight into those with impaired SV. 6MWD may correlate better with SV than previously thought and heart rate monitoring provides physiologic context to the walk distance obtained.

A Ship Path Tracking Control Method Using a Fuzzy Control Integrated Line-of-Sight Guidance Law

A fuzzy control improvement method is proposed with an integral line-of-sight (ILOS) guidance principle to meet the needs of autonomous navigation and high-precision control of ship trajectories. Firstly, a three-degree-of-freedom ship motion model was established with the battery-powered container ship ZYHY LVSHUI 01 built by the COSCO Shipping Group. Secondly, a ship path-following controller based on the ILOS algorithm was designed. To satisfy the time-varying demand of the look-ahead distance parameters during the following process, especially under different navigation conditions, fuzzy logic controllers were designed for different navigation conditions to automatically adjust the look-ahead distance parameters. Thirdly, a controller was applied that uses a five-state extended Kalman filter (EKF) to estimate the heading, speed, and heading rate based on the ship’s motion model with the assistance of Global Navigation Satellite System (GNSS) position measurements. This provides the necessary navigational information, reduces the algorithm’s dependence on sensors, and improves its generalizability. Finally, path-following experiments were carried out in the MATLAB experimental platform, and the results were compared with different following algorithms. The simulation results showed that the new algorithm has a better following performance, and it can maintain a smooth rudder angle output. The research results provide a reference for the path-following control of ships.

Predictive modelling of flow dynamics in micropolar hybrid nanofluids subjected to magnetic dipole influence using nonlinear autoregressive artificial neural networks with exogenous input

In the present research article, we explore the intelligence-based numerical computation of a nonlinear autoregressive artificial neural network with exogenous input using the Levenberg-Marquardt algorithm (ANN-LMA) trained to analyze the unsteady two-dimensional micro-polar flow of an incompressible hybrid nanofluid (Al 2 O 3−Fe 3 O 4/Ethylene Glycol) under the impact of the magnetic field introduced by the magnetic dipole on a nonlinear stretched sheet. In the first step of the research, partial differential equations are converted into nonlinear ordinary differential equations using appropriate transmission and then solved numerically via the Successive Over Relaxation method after applying the finite difference method. The impact of numerous emerging parameters on the solutions is displayed graphically, and the physical significance is discussed. The results show that the concentration profile displays a dwindling trend for the Brownian motion parameter, and the opposite trend is witnessed for the thermophoretic parameter. Moreover, for increasing values of the dimensionless distance parameter and ferromagnetic interaction parameter, the temperature and velocity profiles exhibit the opposite tendency. In the second step of the study, we validated the Successive Over Relaxation outcomes with an ANN-LMA. For this purpose, the suggested ANN-LMA is trained for six different scenarios of the reference dataset points of the given flow model. The analysis shows that Successive Over Relaxation and LM-SNNs yield similar results, with a decreasing trend for the Brownian motion parameter and an opposite trend for the thermophoretic parameter.

Exploring the Spatiotemporal Effects of the Built Environment on the Nonlinear Impacts of Metro Ridership: Evidence from Xi’an, China

Exploring the correlation of the built environment with metro ridership is vital for fostering sustainable urban growth. Although the research conducted in the past has explored how ridership is nonlinearly influenced by the built environment, less research has focused on the spatiotemporal ramifications of these nonlinear effects. In this study, density, diversity, distance, destination, and design parameters are utilized to depict the “5D” traits of the built environment, while Shapley Additive Explanations with eXtreme Gradient Boosting (XGBoost-SHAP) are adopted to uncover the spatial and temporal features concerning the nonlinear relationship of the built environment with ridership for metro stations located in Xi’an. We conducted a K-means clustering analysis to detect different site clusters by utilizing local SHAP coefficients. The results show that (1) built environment variables significantly influence metro ridership in a nonlinear manner at different periods and thresholds, with the POI facility density being the most critical variable and the other variables demonstrating time-driven effects; (2) the variables of population density and parking lot density exhibit spatial impact heterogeneity, while the number of parks and squares do not present a clear pattern; and (3) based on the clustering results, the metro stations are divided into four categories, and differentiated guidance strategies and planning objectives are proposed. Moreover, the current work offers a more developed insight into the spatiotemporal influence of built environments on metro travel in Xi’an, China, using nonlinear modeling, which has vital implications for coordinated urban–metro development.

Analysis based on surface and kerf performance in multi-simultaneous cutting of S235JR structural steel with abrasive water jet

The use of S235JR structural steel materials is common in the manufacturing industry. These materials are typically supplied as plates and cut to the desired dimensions. However, conventional machining methods used to obtain smaller parts from these plates have quality, cost and time difficulties. Advanced manufacturing methods for mass production can cut materials in bulk, resulting in more parts in a shorter time. This study investigates the effects of an abrasive water jet on stacked plates through multi-simultaneous cutting. The experimental parameter of the gap distance between the stacks was varied to examine its effect on surface roughness and kerf widths. The results were used to explain the cutting mechanisms. Based on an analysis of surface and kerf performance, it was found that an inter-stack distance of 2 mm behaves like a single plate cutting for the stacks. The implemented concept defines inter-stack distance values that contain the optimum cutting performance outputs for multi-simultaneous cutting.

The utilization of decision trees on orthopantomographic and lateral panoramic graphs for the diagnosis of unilateral anterior disc displacement of the temporomandibular joint

BackgroundInvestigation is to utilize decision trees in conjunction with orthopantomography (OPT) and lateral panoramic graphy (LPG) to diagnose unilateral anterior disc displacement (ADD) of the temporomandibular joint.MethodsIn this study, 161 patients with images obtained through all three imaging methods, MRI, OPT, and LPG, were selected from the archives. The participants were categorized into two groups: the study group, comprising 89 patients with unilateral anterior disc displacement, and the control group, consisting of 72 healthy individuals. Measurements, including 2 angles (antero-posterior angle and superior-inferior angle) and 3 distance parameters (anterior joint space distance, superior joint space distance, and posterior joint space distance), were conducted on each imaging modality dataset. To assess the obtained measurement data within each patient, the differences from each measurement were calculated. Statistical analysis of the measurement differences between the control and study groups was carried out with independent t test, and decision trees were generated using the SPSS 25 decision tree module 5.0.ResultsIn ADD patients, it was statistically significantly found that the APA increased while the SIA decreased for angle measurements. But for linear measurements, AS increased while the SS and PS decreased in MRI, OPT, and LPG.ConclusionADD can be diagnosed in OPT and LPG. The identification of the specific type of ADD that occurs in the temporomandibular joint is not feasible.

ANDClust: An Adaptive Neighborhood Distance‐Based Clustering Algorithm to Cluster Varying Density and/or Neck‐Typed Datasets

AbstractAlthough density‐based clustering algorithms can successfully define clusters in arbitrary shapes, they encounter issues if the dataset has varying densities or neck‐typed clusters due to the requirement for precise distance parameters, such as eps parameter of DBSCAN. These approches assume that data density is homogenous, but this is rarely the case in practice. In this study, a new clustering algorithm named ANDClust (Adaptive Neighborhood Distance‐based Clustering Algorithm) is proposed to handle datasets with varying density and/or neck‐typed clusters. The algorithm consists of three parts. The first part uses Multivariate Kernel Density Estimation (MulKDE) to find the dataset's peak points, which are the start points for the Minimum Spanning Tree (MST) to construct clusters in the second part. Lastly, an Adaptive Neighborhood Distance (AND) ratio is used to weigh the distance between the data pairs. This method enables this approach to support inter‐cluster and intra‐cluster density varieties by acting as if the distance parameter differs for each data of the dataset. ANDClust is tested on synthetic and real datasets to reveal its efficiency. The algorithm shows superior clustering quality in a good run‐time compared to its competitors. Moreover, ANDClust could effectively define clusters of arbitrary shapes and process high‐dimensional, imbalanced datasets may have outliers.

Estimating flashover occurrence in distribution lines: A novel approach focused on the current-peak-to-distance ratio

Lightning location systems provide an estimation of both lightning current peak amplitudes and stroke locations, which are useful information to correlate lightning strokes and faults in power distribution networks. The paper presents a novel method for assessing the correlation between a lightning-induced flashover and the lightning current-peak-to-distance ratio of indirect lightning events. This is motivated by the consideration that a minimum threshold/value of the ratio between current peak and stroke distance from the line must be reached for an insulation flashover to occur. The proposed procedure uses existing equations and models that relate induced voltage peak amplitudes, distance, current peak and other parameters. Specifically, the paper compares the results predicted by Rusck's and Darveniza's formulas with those provided by the more detailed model implemented in the LIOV code. It is found that a minimum lightning current-peak-to-distance ratio must be exceeded to trigger an insulation flashover and that Rusck's and Darveniza's formulas may be too conservative in estimating such a threshold value. The paper provides guidelines for properly estimating the minimum collecting area around the line including all hazardous events, as this information is essential for calculating the lightning performance of a power distribution line using a Monte Carlo-based approach.

Raychaudhuri equation and the dynamics of cosmic evolution

The paper deals with the Raychaudhuri equation (RE) which is a non-linear ordinary differential equation in Θ, the expansion scalar corresponding to a geodesic flow. Focusing theorem which follows as a consequence of the RE has been restated in terms of the cosmic parameter q (deceleration parameter) both for Einstein gravity and for modified gravity theories. Measurable quantities namely the luminosity distance and density parameter are shown to have an upper bound using the Raychaudhuri scalar. An analogy between geometric and cosmological RE has been made. Subsequently, to find the solution of the non-linear RE a transformation of variable related to the metric scalar of the hyper-surface has been identified which converts the former to a second order differential equation. Finally, the first integral of this second order differential equation gives the entire picture of the dynamics of cosmic evolution

Computational models of compound nerve action potentials: Efficient filter-based methods to quantify effects of tissue conductivities, conduction distance, and nerve fiber parameters.

Peripheral nerve recordings can enhance the efficacy of neurostimulation therapies by providing a feedback signal to adjust stimulation settings for greater efficacy or reduced side effects. Computational models can accelerate the development of interfaces with high signal-to-noise ratio and selective recording. However, validation and tuning of model outputs against in vivo recordings remains computationally prohibitive due to the large number of fibers in a nerve. We designed and implemented highly efficient modeling methods for simulating electrically evoked compound nerve action potential (CNAP) signals. The method simulated a subset of fiber diameters present in the nerve using NEURON, interpolated action potential templates across fiber diameters, and filtered the templates with a weighting function derived from fiber-specific conduction velocity and electromagnetic reciprocity outputs of a volume conductor model. We applied the methods to simulate CNAPs from rat cervical vagus nerve. Brute force simulation of a rat vagal CNAP with all 1,759 myelinated and 13,283 unmyelinated fibers in NEURON required 286 and 15,860 CPU hours, respectively, while filtering interpolated templates required 30 and 38 seconds on a desktop computer while maintaining accuracy. Modeled CNAP amplitude could vary by over two orders of magnitude depending on tissue conductivities and cuff opening within experimentally relevant ranges. Conduction distance and fiber diameter distribution also strongly influenced the modeled CNAP amplitude, shape, and latency. Modeled and in vivo signals had comparable shape, amplitude, and latency for myelinated fibers but not for unmyelinated fibers. Highly efficient methods of modeling neural recordings quantified the large impact that tissue properties, conduction distance, and nerve fiber parameters have on CNAPs. These methods expand the computational accessibility of neural recording models, enable efficient model tuning for validation, and facilitate the design of novel recording interfaces for neurostimulation feedback and understanding physiological systems.

Effect of microstructural and texture evolution of ECAP-processed Mg-Zn-Zr alloy on the corrosion and wear behaviours for bone repair applications

Mg-Zn-Zr alloys show great potential for replacing the currently used materials in biodegradable applications. They exhibit similar mechanical properties to that of human bones, while possessing favorable biodegradable properties that can be engineered to allow the complete, safe degradation of the alloy in the human body around the time the bones fully heal, without the need for a removal surgery. Literature confirms that grain refinement through processing via severe plastic deformation (SPD) techniques enhances both corrosion and wear behavior of Mg alloys; thus, in this study Mg-3Zn-0.6 Zr (ZK30) billets were ECAP-processed for up to 4-passes of route Bc to investigate their wear and corrosion behaviors improvement in relation to their microstructural and texture evolutions. Electron back-scatter diffraction (EBSD) was used to study microstructural and texture evolution of the billets, their electrochemical behavior was investigated using a body simulated fluid as a corrosive agent, and wear tests with load, velocity and distance parameters were carried out to investigate the improvements resulting from the produced ultrafine-grained structure and crystallographic texture. EBSD revealed that 4-pass ECAP processing yielded a grain size reduction of 92.7%. Accordingly, this ultrafine-grained structure reduced the corrosion rate by 94%, improved the corrosion resistance by 726%, and notably improved the billets’ wear behavior, compared to the as-annealed condition. Therefore, this in-depth analysis confirms the promising potential of ZK30 alloy for industrial use in bone applications.

Hydrophobic effects on a solid sphere translating in a Brinkman couple stress fluid covered by a concentric spherical cavity

In this study, a fluid flow with an incompressible axisymmetric steady couple stress translated through a porous media is analyzed between a hollow sphere and a concentric rigid sphere. In the permeable region, the flow field is regulated by Brinkman's equation. The slip and spin slip conditions are applied on both the rigid sphere and spherical cavity surfaces. Modified Bessel functions provide a systematic approach to the problem by utilizing the principle of a stream function. On the inner sphere, the wall correction factor that an incompressible couple stress fluid encounters is calculated. The effects of the slip, spin slip, coupling stress parameter, separation distance, and permeability parameter on the field functions and the normalized drag force are also graphically shown. The corresponding results are contrasted with the outcomes reported for particular cases of couple stress fluid and viscous fluid flow in two permeability-free concentric circles. Furthermore, graphs of the streamlines for various values of the relevant parameters have been included.

Friction and wear analysis of basalt micro-filler loaded various epoxies and esters based thermoset polymer composites

In this study, thermoset polymers (synthetic epoxy, bio-epoxy, polyester, and vinyl ester) and basalt-reinforced (30 wt%) polymer composites were examined for their hardness and tribological characteristics. Under two levels of basalt weight percentage (0% and 30%), and three levels of load (3, 6, and 9 N), speed (100, 150, and 200 rpm), and distance (100, 150, and 200 m) parameters, the coefficient of friction (COF) and specific wear rate (SWR) are examined. According to the L18 orthogonal array design, which was created using the statistical software MINITAB-19, the tests are carried out using Pin-on-Disk Tribometer as per ASTM G99 standard. The optimum parameters are determined for COF and SWR using Taguchi statistical method. The significance of each parameter and its contribution is analyzed using the ANOVA technique. For synthetic epoxy samples, the optimum set of parameters for the COF response is 30 wt% basalt (2nd rank), 9 N load (4th rank), 100 rpm speed (1st rank), and 150 m distance (3rd rank); for the SWR response, the parameters are 30 wt% basalt (1st rank), 3 N load (2nd rank), 100 rpm speed (4th rank), and 200 m distance (3rd rank). For bio-epoxy samples, the optimum set of parameters for the COF response is 30 wt% basalt (4th rank), 9 N load (1st rank), 150 rpm speed (3rd rank), and 100 m distance (2nd rank); for the SWR response, the parameters are 30 wt% basalt (4th rank), 6 N load (2nd rank), 200 rpm speed (3rd rank), and 200 m distance (1st rank). For polyester samples, the optimum set of parameters for the COF response are 30 wt% basalt (2nd rank), 6 N load (4th rank), 200 rpm speed (1st rank), and 200 m distance (3rd rank); for the SWR response, the parameters are 30 wt% basalt (1st rank), 3 N load (4th rank), 100 rpm speed (3rd rank), and 150 m distance (2nd rank). For vinyl ester samples, the optimum set of parameters for the COF response is 30 wt% basalt (1st rank), 9 N load (3rd rank), 100 rpm speed (4th rank), and 100 m distance (2nd rank); for the SWR response, the parameters are 30 wt% basalt (2nd rank), 6 N load (3rd rank), 100 rpm speed (1st rank), and 100 m distance (4th rank). The ANOVA method demonstrated that the addition of 30 wt% basalt considerably reduced both COF and SWR. The optimum parameters that are not included in the proposed array underwent confirmation tests, and the results showed that the optimum parameters had an error rate that was less than 10% and within the bounds of statistical acceptability. According to the SEM morphology, the basalt particles reduced the material's plastic flow under different processing conditions.

Evaluation of Lower Extremity and Core Strength in Young Female and Male Volleyball Players

Volleyball is an anaerobic team sport that involves various complex movements and requires adequate body position sense. Therefore, volleyball players' most important physical characteristics are balance, agility, endurance, reaction speed, explosive strength, and quick strength. This study aimed to evaluate the relationship between core strength and functional performance of lower extremities in male and female volleyball players. A total of 19 (11 female, eight male) volleyball players aged 16-20 years (11 female, eight male) who were students of Ondokuz Mayıs University Yaşar Doğu Faculty of Sports Sciences participated in the study voluntarily. The inclusion criteria were determined as athletes with at least three years of active sports history and regular training. Height, weight, and Body Mass Index measurements were taken at the first visit. Then, shuttle test, plank test, push-up, single hop for distance (SH), triple hop for distance (TH), crossover triple hop for distance (CH), medial side triple hop for distance (MSTH), and medial rotation ( ) with hop for distance (MRH) and Y Balance Test were performed with randomized practice cards. When the push-up, sit-up, and plank tests were compared between men and women, a significant difference was found in the shuttle parameter (p 0.05). When the athletes' Single Leg Hop test parameters were evaluated, there was no significant difference, only in SH between the sides in men. At the same time, there was no significant difference only in medial rotation ( ) with hop in women (p 0.05). While there was a significant difference in triple hop for distance, crossover triple hop for distance, medial side triple hop for distance (MSTH) and medial rotation ( ) with hop between the parties in men, a statistically significant difference was found in single hop for distance, triple hop for distance, crossover triple hop for distance, medial side triple hop for distance parameters in women (p 0.05). When single-leg hop Tests LSIs were compared between male and female groups, no statistically significant difference was found in all parameters (p 0.05). When the findings of our study were examined, it was determined that although there was a significant difference in Single Leg Hop test parameters between dominant and non-dominant sides in the groups, the LSI values seen in male and female groups were between normal values, and there was no limb asymmetry. However, there was no difference between genders in LSIs, and similar results were found for men and women. In the core strength tests, while similar results were seen between genders in push-up and plank parameters, the significant difference in the shuttle test revealed that men had better core strength than women.

Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

The Kalman filter algorithm is very important as a recursive algorithm method to optimize sensor output from physical parameter measurement systems, especially physics practicum demonstration systems. One of the distance parameter measurement demonstration systems used in Hooke’s law demonstration system is applied in physics practicum, the system has problems related to fluctuating or unstable sensor output. This research implements the Kalman filter algorithm on the Arduino IDE sketch to reduce noise that appears at the ultrasonic sensor output. The methodology used in this study includes the application of the Kalman filter algorithm to the Arduino IDE sketch with the variable value of the Kalman filter algorithm equation modified with a value of R=10, H=1, and Q=1, and returns the filtered Kalman out value. The Arduino output results are exported to Ms. Excel for further analysis and generate a filtered ultrasonic sensor output signal graph compared without using the Kalman filter. The ultrasonic sensor output noise filtration effectively reduces noise by showing a decrease in the mean squared error (MSE) value and obtaining the best performance of up to 89.23 %. The accuracy of Kalman filter filtration results can be seen from the calculation that the spring constant of filtered metal materials is smaller than the conventional measurement spring constant. Accurate and effective results with the implementation of the Kalman filter algorithm can be developed for the variation values of distance parameters and Kalman filter algorithm variables (R, Q, and H) with other value variations, especially variables that produce filtering curves close to straight lines. It was concluded that the Kalman filter algorithm was able to improve the performance of Hooke’s law prop system

Parameters influencing electrospun nanofiber diameter of polylactide incorporated with cellulose nanofibrils and nanocrystals

The main purpose of this study was to investigate effects of various electrospinning parameters on the morphology and diameter of cellulose nanofibril (CNF) filled polylactide (PLA) nanofibers. For this purpose, first of all effects of three important electrospinning parameters; polymer solution concentration, solution feeding rate and collector distance to feeding tip were studied. Then, effects of using higher amount of CNF, effects of using cellulose nanocrystal (CNC) particles, and effects of adding potassium chloride salt were also investigated. It was observed that when optimum electrospinning parameters were determined, then it was possible to obtain almost “bead-free” morphology and “finest” average diameter of 232 nm for PLA/CNF electrospun fibers. Increasing values of feeding rate and collector distance parameters resulted in bead formation and thicker diameters. On the other hand, increasing CNF amount, using CNC particles and adding KCl salt, all resulted in further decreases in the diameter down to 152 nm; mainly due to increased charge density of the polymer solution. Moreover, in vitro degradation analysis of all types of electrospun nanofiber mats in a simulated body fluid revealed that increasing the immersion period increased their degradation rate in terms of “% weight loss”. It was also observed that, mats with fine diameter fibers had higher degradation rate.

Postural Control Characteristics in Alzheimer's Disease, Dementia With Lewy Bodies, and Vascular Dementia.

Dementia often results in postural control impairment, which could signify central nervous system dysfunction. However, no studies have compared postural control characteristics among various types of dementia. This study aimed to compare static postural control in patients with Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and vascular dementia (VaD). Cross-sectional relationship between the clinical diagnoses (AD, DLB, VaD, or normal cognition [NC]) of outpatients at a memory clinic and their upright postural control characteristics were examined. In the postural control test, participants were instructed to maintain a static upright standing on a stabilometer for 60 seconds under the eyes-open and eyes-closed conditions. Forty postural control parameters, including distance, position, and velocity in the anterior-posterior and medio-lateral directions, derived from the trajectory of the center of mass sway, were calculated. The characteristics of each type of dementia were compared to those of NC, and the differences among the 3 types of dementia were evaluated using linear regression models. The study included 1789 participants (1206 with AD, 111 with DLB, 49 with VaD, and 423 with NC). Patients with AD exhibited distinct postural control characteristics, particularly in some distance and velocity parameters, only in the eyes-closed condition. Those with DLB exhibited features in the mean position in the anterior-posterior direction. In patients with VaD, significant differences were observed in most parameters, except the power spectrum. Patients with AD, DLB, and VaD display disease-specific postural control characteristics when compared to cognitively normal individuals.

A spatio-temporal temperature prediction model for coal spontaneous combustion based on back propagation neural network

Coal spontaneous combustion (CSC) occurs with the change of temperature and gas concentration which is useful for early warning. To explore the intrinsic correlation between temperature evolution and gas distribution, a Back Propagation Neural Network (BPNN) model of coal temperature - gas composition - spatial distance was constructed, which is based on the gas characteristics and spatial distribution of coal temperature in large-scale coal spontaneous combustion experiments. The results showed that the O2 concentration reached the lowest value (8.058%) at 45 cm on Day 52 with the time. CO and CO2 also formed concentration peaks at 45 cm, and the gas distribution continuously moved towards the inlet position. The hydrocarbon gas components showed periodic increases, with values below 100 ppm. When the supply air time increased, the temperature rise rate in the initial stage of oxidation was slow but relatively uniform. Affected by ventilation conditions and coal density, two high-temperature zones were observed at 45 cm and 85 cm in the experimental furnace. With the inclusion of spatial distance parameters in coal temperature prediction, the BPNN model exhibits accurate predictive capability, with a total error of 0.94997. This can provide a method for predicting temperature changes in coal mine.

Optimization Algorithm for AoI-Based UAV-Assisted Data Collection

Regarding the issue of information freshness in systems that aid in data collection using unmanned aerial vehicles (UAVs), a data collection algorithm that is based on freshness and UAV assistance is proposed. Under the limitations of wireless sensor node communication distance and UAV parameters, the optimization problem of minimizing the average spatial correlation age of information (SCAoI) of all nodes in the area is set up. This problem is solved by optimizing the number of clusters, UAV flight trajectories, and the order of data collection from cluster member nodes. The maximum communication distance of the nodes is used as the cluster formation radius, and the maximum-minimum distance clustering algorithm is used to cluster the nodes in the region to obtain the minimum number of clusters. After it has been proven that the trajectory optimization problem in this study is NP-hard, the ant colony algorithm is applied to obtain the minimum flight time and the corresponding trajectory. By using the greedy algorithm to determine the member nodes in the sequence of data collection for a cluster, the instantaneous SCAoI of the UAV arriving at the cluster head is solved. Simulation results show that the proposed algorithm in this paper can effectively improve the freshness of data and reduce the average SCAoI of the system compared with the algorithm in the comparative literature, reducing the average SCAoI by about 61%.