Mammarenavirus Wenzhouense (WENV) is broadly distributed in Asia, while the he dynamics process of WENV infection remains unclear. A field derived strain of WENV was used to inoculate Sprague-Dawley (SD) rats by intramuscular injection, the process of virus infection was de-tected by controlling time for 28 days. Viral RNA became detectable in the blood at 3 d pi, and remained detectable for about 12 d. In most organ tissues, viral RNA peaked at 7 d pi, then began to decline by 14 d, however remained detectable in intestine and brain tissues at 21 and 28 d pi. Virus shedding had been detected from fecal samples for 5 days from 6 to 11 d pi using qRT-PCR, and was recovered from feces collected at 8 d pi. Horizontal contact infection occurred among cage-mates at 14 and 21 d pi. Antibodies against the nucleocapsid were detected at 5 dpi, next increased and persisted until the end of experiment. The results enabled to discriminate the kinetic of viremic response, virus shedding in feces and horizontal transmission, as well as the potential sites for WENV replication or maintenance of the virus in nature.

Abstract. This paper proposes a Second Order Terminal Sliding Mode (2TSM) approach to the trajectory tracking of Differential Drive Mobile Robot (DDMR). Within this cascaded control scheme, the 2TSM dynamic controller, at the inner most loop, tracks the robot’s velocity quantities; while a kinematic controller, at the outer most loop, regulates the robot’s positions. In this manner, chattering is greatly attenuated and finite time convergence is guaranteed by the second order TSM manifold which involves higher order derivatives of the state variables, resulting in inherently robust as well as fast and better tracking precision. The simulation results demonstrate the merit of the proposed control methods.

The paper addresses the challenging issue of trajectory tracking for uncertain quadrotor UAVs, particularly under the constraints of input saturation and external disturbances. It introduces adaptive laws for managing uncertainties in mass and inertia moments without requiring prior knowledge, ensuring effective control even with varying system parameters. To counteract the effects of input saturation, the study incorporates an auxiliary system designed to compensate for these limitations. Additionally, a disturbance observer(DO) is utilized to manage and mitigate the impact of time-varying external disturbances. The proposed control strategy integrates a sliding mode adaptive control approach with an inner-outer loop structure, enhancing robustness and adaptability. Numerical simulations demonstrate the effectiveness of the designed control strategy.

This paper proposes a novel hybrid control strategy for quadrotor unmanned aerial vehicles (UAVs) based on bio-inspired neural dynamics. This strategy addresses the velocity jump issue prevalent in traditional backstepping control and the control signal chattering problem common in traditional sliding mode control. The control system comprises an outer-loop bio-inspired backstepping controller and an inner-loop bio-inspired sliding mode controller, ensuring smooth trajectory tracking even in the presence of external disturbances. The stability of the quadrotor UAV control system is rigorously analyzed using Lyapunov stability theory. Extensive comparative simulation experiments are conducted to assess the robustness and effectiveness of the control system. Results demonstrate superior trajectory tracking performance of the proposed control system compared to other control systems for quadrotor UAVs.

This study presents a novel kinematic tracking model, designed for a networked exoskeleton system that is asynchronously taught by a remote therapist. On the server side, the therapist’s rehabilitation exercises are quantitatively assessed using a monocular passive vision system. The resultant analytical metrics are then transmitted asynchronously over the network to patients equipped with exoskeletons. On the client side, the exoskeleton utilizes these analytical metrics as reference paths for exercises, complemented by electromyography (EMG) feedback. This work introduces a calibration approach aimed at estimating angular positions by utilizing EMG observations. The calibration model establishes real-time correlations between polynomial reference positions. This calibration mechanism is integrated into simulations of both upper and lower limb exoskeletons. We further explore redundant kinematics, incorporating an EMG observer for linear, time-variant rehabilitation tracking control. Our methodology is validated using vision-based metric data and experimental EMG measurements for various exercises, including shoulder flexion, elbow flexion, and rowing-like movements. This work also includes computer simulations for tracking the control of rehabilitation exercises, demonstrating the adaptability of the system in reliably, robustly, and effectively following desired trajectories.

This article addresses the trajectory tracking between two non-identical systems with chaotic properties. We employ the Rossler chaotic and RCL-shunted Josephson junctions model in similar phase space to study trajectory tracking. In order to achieve the goal tracking, we afford two stages to approximate the target tracking. The first stage utilizes the active control technique to transfer the output signal from the RCLs-J system into the quasi-Rossler system. Then next, the RCLs-J system employs the proposed the iterative learning control scheme and the control signal from the drive system to trace the trajectory of Rossler system. The numerical results demonstrate the proposed method and the tracking system is asymptotically stable.

This paper presents a new adaptive and optimal algorithm for the trajectory tracking control of a quadrotor using iterative learning algorithm (ILA) and enumerative learning algorithm. Ordinarily the ILA, as an adaptive method, can perform well with PID control to improve the controller’s performance for a nonlinear system. Quadrotors are considered as non-linear and unstable systems which the use of an adaptive and optimal controller can increase its stability and decrease error level. In this method, a PID controller is proposed for the outer and inner control loops of a quadrotor and the ILA is used to adapt PID control gains. Subsequently, an enumerative learning algorithm is used to optimize the learning rates of the ILA. For this purpose, at first, the dynamic model of the quadrotor is acquired. After that, the structure of the inner and outer control loops is defined. In the end, the simulation results for the trajectory tracking control of a quadrotor are demonstrated. Through simulation, it is concluded that as time increases, the performance of the suggested control method in trajectory tracking control becomes better and better and error signals convergence to zero.

The motion of Unmanned Surface Vehicles (USVs) is frequently disturbed by the ocean wind, wave and current. A poorly designed controller will lead to failures and even safety problems during actual navigation. In order to obtain satisfied control performance of the motion for the USVs, a Model Predictive Control (MPC) based on improved Nonlinear Disturbance Observer (NDO) is proposed. Firstly, the USV model is approximately linearized and MPC is designed for the multivariable system with constraints. In order to compensate the influence of disturbances, an improved NDO is designed, with which the calculation time for the MPC saved a lot. Finally, comparison experiments are conducted between the MPC with NDO and the MPC with improved NDO, and the results show that they have similar performance to the USVs. However, the proposed method has less parameter to be tuned and it is much more time-saving compared to the MPC with traditional NDO.

Recently, a large number of Transformer-based solutions have emerged for the trajectory prediction task, but there are shortcomings in the effectiveness of Transformers in trajectory prediction. Specifically, while position encoding preserves some of the ordering information, the self-attention mechanism at the core of the Transformer has its alignment invariance that leads to the loss of temporal information, which is crucial for trajectory prediction. For this reason, we design a simple and efficient strategy for temporal information extraction and prediction of trajectory sequences using the self-attention mechanism and linear layers. Experimental results show that the strategy we designed can effectively combine the advantages of the linear layer and self-attention mechanisms, compensating for the shortcomings of the Transformer. Additionally, we conducted an empirical study to explore the effectiveness of the linear layer and sparse self-attention mechanisms in trajectory prediction.

Single particle tracking is a powerful technique to investigate the motion of molecules or particles. Here we review methods for analyzing the reconstructed trajectories, step needed for deciphering the underlying mechanisms driving the motion. First, we review the traditional analysis based on the mean square displacement (MSD), highlighting sometimes-neglected factors potentially affecting results accuracy. We then report methods that exploit distributions of parameters other than displacements, e.g. angles, velocities, and times and probabilities of reaching a target, discussing how they are more sensitive in characterizing heterogeneities and transient behaviors masked in MSD analysis. Hidden Markov Models are also used for this purpose, and these allow identifying different states, their populations and the switching kinetics. Finally, we discuss a rapidly expanding field: trajectory analysis based on machine learning. Various approaches, from random forest to deep learning, are used to classify trajectory motions, which can be identified by motion models or by model-free sets of trajectory features, either previously defined or automatically identified by the algorithms. We also review free software available for some of the analysis methods. We emphasize that approaches based on combined different methods, including classical statistics and machine learning, may be the way to obtain the most informative and accurate results.

To address the trajectory tracking issue for quadrotors working under the influence of external disturbances, a control scheme is designed for position and attitude trajectory tracking. Firstly, the quadrotor dynamic model is derived for control design. Secondly, adaptive integral backstepping control (AIBS) is employed in the position loop in which adaptive estimation is used to estimate the upper bounds of disturbances. Subsequently, a new adaptive backstepping fast nonsingular integral terminal sliding mode control (ABFNITSM) is proposed to follow desired Euler angles. The intro-duction of fast nonsingular terminal sliding mode and integral element is to realize fast convergence and accurate tracking. Dead zone technique is applied to minimize estimation errors. Besides, a saturation function is utilized to eliminate chattering phenomenon. Finally, simulation experiments are conducted under the Simulink environment. The proposed control is verified by comparing it with traditional integral terminal sliding mode control (ITSM) and integral backstepping control (IBS).

In this paper, a voice coil motor (VCM) actuated fast tool servo (FTS) system is developed for diamond turning. To guide motions of the VCM actuator, a crossed double parallelogram flexure mechanism is selected featuring totally symmetric structure with high lateral stiffness. To facilitate the determination of the multi-physical parameters, analytical models of both electromagnetic and mechanical systems are developed. The designed FTS with balanced stroke and natural frequency is then verified through the finite element analysis. Finally, the prototype of the VCM actuated FTS is fabricated and experimentally demonstrated to have a stroke of ±59.02 μm and a first natural frequency of 253 Hz. By constructing a closed-loop control using PID controller with the internal-model based resonant controller, the error for tracking a harmonic trajectory with ±10 μm amplitude and 120 Hz frequency is obtained to be ±0.2 μm, demonstrating the capability of the FTS for high accuracy trajectory tracking.

Although Cable driven rehabilitation devices (CDRD) have advantages over traditional link-driven devices, including lightweight, ease of reconfiguration, remote actuation, etc, the majority of existing lower-limb CDRD is limited to rehabilitation in the sagittal plane. In this work, we extend our previous sagittal plane model (2DOF) to accommodate hip motion in the frontal plane (abduction/adduction) toward studying the feasibility of tracking bi-planar motion (combined frontal and sagittal plane motion) via intra-planar cable routing. Two optimization problems have been formulated to estimate an optimal location of the hip cuffs, first to estimate the optimal cuff location at each time step to identify a single ‘averaged’ optimal cuff location and second to calculate a single optimal cuff location for the entire gait cycle. The optimization solutions identified for the 3DOF model revealed that optimization of the location of cuffs on the anterior and posterior side of the hip joint for 4 cable configuration is not sufficient to generate the desired bi-planar motion. For simultaneous tracking of the bi-planar motion, 2 additional cables have been added at hip joints and are routed in an intra-planar manner. The simulation result with the 3DOF model confirmed successful bi-planar trajectory tracking. The various number of cables and cable routings for tracking bi-planar motion will be studied in future work.

Due to constraints in launch platform and cost, the maneuverability of gliding-guided projectiles is limited, necessitating a rational design of their trajectory schemes. To reduce the sensitivity of trajectory schemes to uncertainties while ensuring compatibility between flight schemes and guidance control systems, and fully exploiting the control capability of the projectile, a closed-loop robust trajectory planning method is proposed. Models of major uncertain factors and states deviation at the control-start point are established. Based on the NIPCE method, the stochastic dynamic model is transformed into a high-dimensional deterministic model with PCE coefficients as state variables, and the uncertainty propagation law is obtained. A PID algorithm is employed to design a tracking guidance law based on position error feedback, and open-loop and closed-loop robust trajectory planning models are established accordingly. The optimal control problem is solved by transforming it into a nonlinear programming problem using the direct shooting method. Simulation results indicate that the NIPCE method can significantly improve the computational efficiency of uncertainty propagation while ensuring accuracy. Open-loop robust planning can effectively mitigate the sensitivity of gliding trajectories to uncertainties but cannot completely eliminate terminal dispersion. Closed-loop robust planning effectively improves control effort consumption on the basis of open-loop planning.

Violence against healthcare workers (HCWs) is a widespread, underreported and inadequately prevented problem. Only a few companies have efficient systems for assessing the extent of the phenomenon. In 2005, the health surveillance service of a public health company introduced a system that monitored violence experienced by HCWs by means of three items from the Violent Incident Form (VIF), integrated with departmental in-depth analyses using the participatory ergonomics group technique. In 2005, the annual rate of physical assaults was 8.2%, that of threats 12.0%, while the harassment rate was 19.6%. Over the past twenty years of observation (2005-2024), the percentage of workers who reported experiencing a physical attack in the previous year at their periodic medical examination, has fluctuated between 5.8% and 11.1%, except for the years 2020 and 2021 when, during the COVID-19 pandemic, the rate was 3.9% and 3.2%, respectively. During the same pandemic period, the annual threat rate that ranged from 9.4% to 20.1%, dropped to 7.7%, while the prevalence of harassment, that was between 13.5 and 19.6, fell to 7.2%. HCWs believe that (i) limiting visitor access (ii) a better balance of the demand for services and (iii) a better attitude towards HCWs were the causes of the reduced rate of violence during the pandemic. Recording the violence experienced during health surveillance is an economical, reliable and sustainable risk assessment method.

In modern warfare, the diversified tasks can sometimes be challenging to fulfill with a single projectile, necessitating the incorporation of cooperation constraints in trajectory planning. Limited by the stringent requirements of the launch platform and lower costs, the maneuverability and ability to adjust the flight time range of projectiles are weak. Therefore, the rationality of performance indicators and flight time coordination strategies is especially critical. To fully exploit the control capabilities of projectiles, a battlefield area segmentation method is proposed, differentiated performance indicators and a cooperative trajectory planning model that considers the entire range of projectiles are established. Considering the limitations of existing flight time coordination strategies in applications, targeted improvements have been made in different cooperation scenarios, and a bi-level adaptive cooperation strategy(BACS) with both optimality and flexibility is proposed. To rapidly determine feasible conditions, a simple and universal method for obtaining the capability ranges of cooperation is introduced. The multi-phase optimal control problems are transformed into nonlinear programming problems using the Radau pseudo-spectral method, and simulation results show that the proposed methods are capable of handling trajectory planning tasks in different cooperation scenarios. The improved strategies effectively enhance the planning success rate under relatively harsh conditions and BACS demonstrates good compatibility with the problems studied.

Pedestrian trajectories are crucial for self-driving cars to plan their paths effectively. The sensors implanted in these self-driving vehicles, despite being state-of-the-art ones, often face inaccuracies in the perception of surrounding environments due to technical challenges for adverse weather conditions, interference from other vehicles sensors and electronic devices, and signal reception failure, leading to incompleteness in trajectory data. But for real-time decision making for autonomous driving, the trajectory imputation is no less crucial. Previous attempts to address this issue such as statistical inference and machine learning approaches have shown promise. Yet, the landscape of deep learning is rapidly evolving with new and more robust models emerging. In this research, we have proposed an encoder-decoder architecture, Human Trajectory Imputation Model, coined as HTIM, to tackle these challenges. This architecture aims to fill in the missing parts of pedestrian trajectories. The model has been evaluated using the Intersection drone inD dataset, containing trajectory data at suitable altitudes preserving naturalistic pedestrian behavior with varied dataset sizes. To assess the effectiveness of our model, we have utilized L1, MSE, Quantile and ADE loss. Our experiments have demonstrated that HTIM outperforms the majority of the state-of-the-art methods in this field, thus indicating its superior performance in imputing pedestrian trajectories.

Predicting human trajectories poses a significant challenge due to the complex interplay of pedestrian behavior, which is influenced by environmental layout and interpersonal dynamics. This complexity is further compounded by variations in scene density. To address this, we introduce a novel dataset from the Festival of Lights in Lyon 2022, characterized by a wide range of densities (0.2-2.2 ped/m2). Our analysis demonstrates that density-based classification of data can significantly enhance the accuracy of predictive algorithms. We propose an innovative two-stage processing approach, surpassing current state-of-the-art methods in performance. Additionally, we utilize a collision-based error metric to better account for collisions in trajectory predictions. Our findings indicate that the effectiveness of this error metric is density-dependent, offering prediction insights. This study not only advances our understanding of human trajectory prediction in dense environments but also presents a methodological framework for integrating density considerations into predictive modeling, thereby improving algorithmic performance and collision avoidance.

Vibration problems have become one of the most important factors affecting robot performance. To this end, a terminal residual vibration suppression method based on joint trajectory optimization is proposed to improve the accuracy and stability of robot motion. Firstly, based on the characteristics of the friction nonlinearity due to joint coupling and physical feasibility of dynamic parameters, a semidefinite programming method is used to identify dynamic parameters with actual physical meaning, thereby obtaining an accurate dynamic model. Then, based on the result of the residual vibration time domain analysis, a joint trajectory optimization model with the goal of minimizing joint tracking error is established. The Chebyshev collocation method is used to discretize the optimization model. The dynamic model is used as the optimization constraint, and barycentric interpolation is used to obtain the optimized joint motion trajectory. Finally, industrial robot experiments prove that the vibration suppression method proposed in this article can reduce the maximum acceleration amplitude of residual vibration by 62% and the vibration duration by 71%. Compared with the input shaping method, the method proposed in this paper can reduce the terminal residual vibration more effectively and ensure the consistency of running time and trajectory.

Clubhouse is an auditory app that allows users to host various rooms surrounding a diverse range of topics from Artificial Intelligence to Philosophy. Along with its educational and serene approach, it is known for its popularity amongst celebrities, including Elon Musk, Mark Zuckerburg, CEO of Shopify, and its elusive invite and ios-only pass into gaining access into Clubhouse. Waiting lists are available in the case of not achieving an invite, but to further speed the process, various sellers on eBay, Reddit, Twitter, etc., charging invites from $10-$200. This research paper covers the phenomenon of Clubhouse and the emergence of audio-only rooms, along with a hypothesis of why Clubhouse and other apps of a similar kind are experiencing a harsh downfall despite its seemingly successful business model.

With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment–based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms.

Over the past decade and a half, dynamic functional imaging has revolutionized the neuroimaging field. Since 2009, it has revealed low dimensional brain connectivity measures, has identified potential common human spatial connectivity states, has tracked the transition patterns of these states, and has demonstrated meaningful alterations in these transition and spatial patterns in neurological disorders, psychiatric disorders, and over the course of development. Recently, researchers have begun to analyze this data from the perspective of dynamic system and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Consciousness, in particular, appears to be strongly linked to entropy production in the brain. Little attention has been paid to the effects of psychiatric disease on entropy production in the human brain, however. Even disorders characterized by substantial changes in conscious experience have not been widely analyzed from this perspective. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls, and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients’ brain function.

The increasing frequency and severity of phytoplankton blooms worldwide highlight the need for advancements in monitoring technologies. Optical remote sensors, such as Landsat, have proven to be a cost-effective method for large-scale, near-real-time assessments of phytoplankton biomass in lakes. However, its effectiveness is often compromised by atmospheric interferences like clouds, dust, and wildfire smoke, which can obscure the clear-sky conditions essential for accurate remote sensing. While partial atmospheric correction (removing only the Rayleigh effect) is commonly applied to address these interferences, it remains inadequate for mitigating the impact of wildfire smoke. This study investigates the potential of Landsat's coastal/aerosol band (B1) for assessing wildfire smoke interference effects on Chlorophyll-a (Chl-a) retrieval models, which serve as proxies for phytoplankton biomass. We employed cluster analysis of B1 values to create a screening system based on aerosol reflectance, categorizing smoke interference into low, moderate, and high levels. Subsequently, we applied both partial (Rayleigh-corrected reflectance) and full (Landsat 8 Level 2 surface reflectance) atmospheric corrections before developing the Chl-a retrieval models. Excluding high wildfire smoke interference (B1 > 0.07) from the Chl-a calibration dataset significantly enhanced model performance, increasing the r-squared value from approximately 0.55 to 0.80. Moderate smoke interference (0.05 < B1 < 0.07) yielded results comparable to low-interference conditions. Notably, Rayleigh-corrected reflectance, even without additional aerosol band filtering, achieved higher r-squared values for the Chl-a retrieval model than full atmospheric correction. B1 thus proves to be a valuable tool for identifying low smoke-impacted observations, offering an effective method to monitor phytoplankton biomass amid increasing wildfire activity and improving the capacity to monitor aquatic environments in a changing global landscape.

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested to solve this problem using sequential Monte Carlo (SMC, a.k.a. particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper extends the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

The traditional receiver employs scalar tracking loops, resulting in degraded navigation performance in weak signal and high dynamic scenarios. An innovative design of a vector tracking receiver based on nonlinear Kalman filter (KF) tracking loops is proposed in this paper, which combines the strengths of both vector tracking and KF tracking loops. Firstly, a comprehensive description of the vector tracking receiver model is presented, and unscented Kalman filter (UKF) is applied to nonlinear tracking loop. Secondly, to enhance the stability and robustness of the KF tracking loop, we introduce square root filtering and an adaptive mechanism. The tracking loop based on square root UKF (SRUKF) can dynamically adjust its filtering parameters based on signal noise and feedback Doppler error. Finally, the proposed method is implemented on a software-defined receiver (SDR), and the field vehicle experiment demonstrates the superiority of this method over other receivers in complex dynamic environments.

This study investigates the possibility of using machine learning models created in DeepLabCut and Create ML to automate aspects of behavioral coding and aid in behavioral analysis. Two models with different capabilities and complexities were constructed and compared to a manually observed control period. The accuracy of the models was assessed before being applied to 7 nights of footage of the nocturnal behavior of two African elephants (Loxodonta africana). The resulting data was used to draw conclusions regarding behavioral differences between the two elephants and between individually observed nights, thus proving that such models can aid researchers in be-havioral analysis. The models were capable of tracking simple behaviors with high accuracy, but had certain limitations regarding detection of complex behaviors, such as the stereotyped behavior sway, and displayed confusion when deciding between visually similar behaviors. Further expansion of such models may be desired to create a more capable aid with the possibility of automating behavioral coding.

To elucidate mesenchymal stromal cells (MSC)-tumor intricate and ambiguous interactions, the reliable cell labeling and tracking techniques are imperative. Traditional methods of cell labelling have limitations mostly relevant to need for genetic modification what demands the exploration for alternative strategies. Here, we propose using fluorescent photoconvertible microcapsules containing Rhodamine B (RhB) to study mMSC migration in brain tumors. These 3 μm sized microcapsules are readily internalised by cells and undergo photoconversion under 561 nm laser exposure with fluorescence blue shift on demand. Optimal photoconversion was achieved with a laser irradiation duration of 0.4 ms, which provide a maximal brightness of photoconverted label without an excessive laser exposure on cells. Capsules modified with polyallylamine hydrochloride demonstrated the best potential for intracellular uptake without significantly affecting cell viability, motility, or proliferation. The optimal ratio of 20 capsules per mMSC was determined for labeling. Moreover, migration of individual mMSCs within 2D and 3D glioblastoma cell (EPNT-5) colonies over 2 days and in vivo tumor settings over 7 days was tracked. Our study unveils a robust platform for investigating MSC-tumor dynamics, offering insights into therapeutic strategies. Photoconvertible microcapsules could also become an indispensable tool for studying complex fundamental processes of cell-cell interactions for a wide range of problems in biology and medicine.

This paper describes the development of a controller that enables trajectory control and vibration control. The controller performance was verified the using a 3D 2-link, flexible manipulator. On trajectory control using inverse kinematics, it was confirmed that the deflection due to its own weight deteriorated the track following performance. The vibration component of the resonance frequency of the flexible manipulator was generated, and the tip position accuracy is deteriorated. Using the results of control experiments based on the inverse kinematics, the system is identified and then created an inverse system for simultaneous control of trajectory control and vibration control. The target trajectories were the three joint angles. Finally, it was demonstrated through experiments on actual manipulator, that the system could sufficiently follow the ideal trajectory and suppress link vibrations.

Intraoperative load sensors have been developed with the aim of balancing the soft tissue in the knee during Total Knee Replacements (TKRs). Literature has shown success in soft tissue bal-ancing however, concerns regarding the accuracy of these sensors remain. For this reason, pre-vious research has attempted to create robust sensors for intraoperative use. The design of the sensor in this research has aimed to address the concerns outlined in the literature to create a new and novel smart-sensor for soft-tissue balancing. This new intraoperative load sensor included design features to increase accuracy while artificial intelligence allowed for comprehensive sensing across the entirety of the sensor, providing unparalleled insight during the operation. Notably, the sensor was designed to withstand loads of at least 450 N, ensuring robust perfor-mance of the sensor. To optimize the design process while minimizing cost, Finite Element Analysis was employed. This approach ensured the design features fulfilled their function while maintaining structural integrity to withstand loads. Moreover, the novelty of using training data from simulated data will reduce the iterative process of the labor-intensive collection of training data for the artificial intelligence while reducing human error associated with it. Results from the evaluation demonstrated the ability of this design to successfully bridge the current gap in the market by fulfilling all essential design criteria established within the literature. This innovative smart-sensing tool will in turn enhance patient outcomes and alleviate financial burdens to pa-tients and the healthcare systems by reducing the need for early revision surgeries associated with improper joint tension.

With the development of Global Navigation Satellite Systems (GNSS), the quality and anti-jamming capability of satellite navigation signals are crucial. Binary Offset Carrier (BOC) modulation faces issues of false locking due to multi-peak ambiguity in its autocorrelation function, limiting its application. This paper examines FH-BOC modulation, which integrates BOC with frequency hopping technology, significantly improving anti-jamming, anti-multipath performance, and spectral efficiency. To meet the high dynamic tracking demands of FH-BOC signals, this study explores three types of carrier tracking loops: the traditional second-order frequency-locked loop assisted a third-order phase-locked loop, a linear Kalman filter-based tracking algorithm, and an adaptive Kalman filter-based tracking algorithm. A composite code tracking loop is also proposed. Simulations show that the adaptive Kalman filter algorithm achieves high-precision carrier tracking in dynamic environments and quickly converges during transitions. The composite code tracking loop, aided by the carrier loop, ensures precise synchronization of pseudocode and frequency-hopping subcarrier phases, demonstrating the superior performance of FH-BOC modulation in dynamic environments.

The next generation network requires not only ultra high data rate, global coverage and connectivity, but also reducing network deployment costs and energy consumption. The emergence of reconfigurable intelligent surface (RIS) technology provides an effective way to improve efficiency and reduce cost, while the passive elements bring new challenges of channel estimation (CE) and beam tracking. For a RIS-aided multiple-input and single-output (MISO) system, in this paper, to achieve the channel state information (CSI), we propose a principle component analysis (PCA) based staged channel estimation method. Based on the estimated channel, we propose a deep learning (DL) based beam tracking scheme to realize low-complexity RIS reflection coefficient design, which effectively improves the signal-to-noise ratio (SNR) at the user side. Simulation results verify our proposed channel estimation scheme based on PCA and beam tracking scheme based on DNN for the semi-active RIS-aided MISO systems can obtain approximate performances with much lower computational complexity.

To make wind power more competitive, it is necessary to reduce turbine downtime and reduce costs associated with wind turbine Operation and Maintenance (O&M). Incorporating machine learning in developing condition-based predictive maintenance methodologies for wind turbines can enhance their efficiency and reliability. This paper presents a monitoring method that utilizes Base Density for the Support Vector Machine (BDSVM) and the evolutionary Fourier spectra of vibrations. This method allows smart monitoring of the function evolution of the turbine. A complex optimal function (FO) for 5-degree order has been developed that will be the boundary function of the BDSVM to timely determine the magnitude, frequency, and place of the failure occurring in wind turbine drive.

Identifying human actions in complex scenes is widely considered as a challenging research problem due to the unpredictable behaviors and variation of appearances and postures. For extracting variations in motion and postures, trajectories provide meaningful way. However, simple trajectories are normally represented by vector of spatial coordinates. In order to identify human actions, we must exploit structural relationship between different trajectories. In this paper, we propose a method that divides the video into N number of segments and then for each segment we extract trajectories. We then compute trajectory descriptor for each segment which capture the structural relationship among different trajectories in the video segment. For trajectory descriptor, we project all extracted trajectories on the canvas. This will result in texture image which can store the relative motion and structural relationship among the trajectories. We then train Convolution Neural Network (CNN) to capture and learn the representation from dense trajectories. . Experimental results shows that our proposed method out performs state of the art methods by 90.01% on benchmark data set.

Underwater natural gas pipelines constitute critical infrastructure for energy transportation. Any damage or leakage in these pipelines poses serious security risks, directly threatening marine and lake ecosystems and potentially causing operational issues and economic losses in the energy supply chain. Due to the difficulty in detecting deterioration over time and regularly inspecting these submerged pipelines by divers, the use of unmanned underwater vehicles (UUVs) becomes crucial in this field. In this study, an underwater pipeline tracking experiment was carried out by providing autonomous features to a remote-controlled unmanned underwater vehicle. During the tracking of the underwater pipeline, damages were identified, and the locations of these damages were determined. The navigation information of the underwater vehicle, including orientation in the x, y, z axes (roll, pitch, yaw) from a gyroscope integrated with magnetic compass, speed and position information in the three axes from an accelerometer, and the distance to the water surface from a pressure sensor, was integrated into the vehicle. Pre-tests determined the necessary pulse width modulation values for the thrusters of the vehicle, enabling autonomous operation by providing these values as input to the thruster motors. In this study, the vehicle moves in 3D was achived by the vertical thruster of the vehicle was activated to maintain a specific depth, and applying equal force to the vehicle’s right and left thrusters forward movement, while differential force induced deviation angles. In pool experiments, the unmanned underwater vehicle autonomously tracked the pipeline as intended, identifying damages on the pipeline using images captured by the vehicle's camera. The images for damage assessment were processed using a convolution neural network (CNN) algorithm, which is a deep learning method. The position of the damage relative to the vehicle was estimated from the pixel dimensions of the identified damage. The location of the damage relative to its starting point was obtained by combining these two positional pieces of information from the vehicle's navigation system. The all study was performed within the Python environment.

Viral variant analysis is a bedrock of the disease surveillance. When combined with temporospatial analysis variant analysis can further the knowledge of disease spread in a study area. This paper suggests a method to perform the analysis in an operational setting which will allow for real-time surveillance of viral variants and allow local public health professionals to rapidly respond to changes in the evolution of the disease. This method includes three main subprocesses: preprocessing, analysis, and rendering. This method can be performed across multiple software platforms. A use case is given in which it was found that this method helped a hospital system understand the spread of SARS-CoV-2 in Northeast, Ohio.

Automated lying-posture tracking is important in preventing bed-related disorders such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research about how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article, we propose a comprehensive approach to design a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are most effective in accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F-Score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoders, showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.

Based on our interest in properties of human movement, we investigated Japanese GPS data, and arrived at the following three observations: 1) there is a strong correlation between the area of polygons created by human movement trajectories and their perimeters; 2) short-distance movement trajectories less than 5 km tend to enclose a large area like a circle; 3) long-distance movement trajectories over 5 km tend to be straight. We also clarified the following four observations on individual attributes and external factors related to long-distance movements: 1) women tend to travel more linearly than men; 2) linearity is stronger on weekends and national holidays in areas with a large theme park; 3) linearity is weaker on weekends and holidays in areas with many historical tourist attractions; 4) these variations are created by people visiting such areas. These properties should be incorporated when modeling the movement trajectories of humans.

A large amount of data is produced every day by stakeholders of the Air Traffic Management (ATM) system, in particular airline operators, airports, and air navigation service provider (ANSP). Most data is kept private for many reasons, including commercial and security concerns. More than data, shared information is precious, as it leverages intelligent decision-making support tools designed to smooth daily operations. We present a framework to detect, identify and characterise anomalies in past aircraft trajectory data. It is based on an open source of ADS-B based aircraft trajectories, and extracted information can benefit a wide range of stakeholders: Air Traffic Control (ATC) training centres could play more realistic simulations; ANSP may improve capacity indicators; academics improve safety models and risk estimations; and commercial stakeholders, like airlines and airports, may use such information to improve short-term predictions and optimise their operations. The technique is based on autoencoding artificial neural networks applied on flows of trajectories, which provide a useful reading grid associating cluster analysis with quantified level of abnormality. In particular, we find that the highest anomaly scores correspond to poor weather conditions, whereas anomalies with a lower score relate to ATC tactical actions.

Since 2019, the SARS-CoV-2 virus has caused a global pandemic, resulting in widespread infections and ongoing mutations. Analysing these mutations is essential for predicting future impacts. Unlike influenza mutations, SARS-CoV-2 mutations displayed distinct selective patterns that were concentrated in the spike protein and small ORFs. In contrast to the gradual accumulation seen in influenza mutations, SARS-CoV-2 mutations lead to the abrupt emergence of new variants and subsequent outbreaks. This phenomenon may be attributed to their targeted cellular substances; unlike the influenza virus, which has mutated to evade acquired immunity, SARS-CoV-2 appeared to mutate to target individuals who have not been previously infected. The Omicron variant, which emerged in late 2021, demonstrates significant mutations that set it apart from previous variants. The rapid mutation rate of SARS-CoV-2 has now reached a level comparable to 30 years of influenza variation. The most recent variant, JN.1, exhibits a discernible trajectory of change distinct from previous Omicron variants.

Robots in space are necessarily extremely light and lack structural stiffness resulting in natural frequencies of resonance so low as to reside inside the attitude controller’s bandwidth. A variety of input trajectories can be used to drive a controller’s attempt to ameliorate the control-structural interactions where feedback is provided by low-quality, noisy sensors. Traditionally, step functions are used as the ideal input trajectory. However, step functions are not ideal in many applications, as they are discontinuous. Alternative input trajectories are explored in this manuscript and applied to an example system that includes a flexible appendage attached to a rigid main body. The main body is controlled by a reaction wheel. The equations of motion of the flexible appendage, rigid body, and reaction wheel are derived. A feedback controller is developed to account for the rigid body modes. Additional filters are added to compensate for the system’s flexible modes. Sinusoidal trajectories are autonomously generated to feed the controller. Whiplash compensation is additionally implemented for comparison. The control method without random errors with the smallest error is the sinusoidal trajectory method, which showed a 97.39% improvement when compared to the baseline response when step trajectories were commanded, while the sinusoidal method was inferior to traditional step trajectories when sensor noise and random errors were present.

This paper discusses the use of networks of Inertial Measurement Units (IMU) for the reconstruction of trajectories from sensor data. Logistics is a natural application domain, to verify the quality of the handling of goods. This is a mass application and the economics of logistics impose that IMUs to be used must be low cost and use basic computational devices. The approach in the paper converts a strategy from the literature, used in the multi-target following problem, to reaching a consensus in a network of IMUs. The paper presents results on how to achieve the consensus in trajectory reconstruction, along with covariance intersection data fusion of the information obtained by all nodes in the network.

In this paper, we analyzed the live fish trajectory recorded from an experiment in an experimental vertical slot fishway. Combined with a numerical simulation, we demonstrated that randomness shown in fish trajectory might not merely be attributed to fish's random choices in its swimming, also could be an adaption consequence to the bulk unsteady turbulent flow structure. Simple superposing the fish trajectory on the time-averaged flow field obtained either by interpolating on discrete point measurements or numerical simulation is not an ideal method for fish movement description in fishway engineering. How to model the fish paths in transient flow and the necessity of simultaneous recording of the flow field and the fish locomotion are challenging topics. The suggested spectrum analysis of the flow field may provide a new general method to reproduce the fish trajectory in a complex turbulent flow.

The tsscds method, recently developed in our group, discovers chemical reaction mechanisms with minimal human intervention. It employs accelerated molecular dynamics, spectral graph theory, statistical rate theory and stochastic simulations to uncover chemical reaction paths and to solve the kinetics at the experimental conditions. In the present review, its application to solve mechanistic/kinetics problems in different research areas will be presented. Examples will be given of reactions involved in photodissociation dynamics, mass spectrometry, combustion chemistry and organometallic catalysis. Some planned improvements will also be described.

Aiming at the APF algorithm in the collision-free path planning and LQR algorithm tracking process, there are problems such as unreachable target points, local optimization, low safety coefficient of autonomous lane change collision avoidance tracking control and poor stability, this paper proposes an optimized APF method, which solves the defects of the traditional artificial potential field method by adding the distance adjustment factor, the safety distance protection model and the sub-target virtual potential field, and at the same time generates the autonomous driving car collision-free path. The path tracking control system is designed based on feed-forward control and DLQR controller, in which the vehicle dynamics state space model is used as the basis, the model is linearized and discretized, and the corresponding constraint variables are set to minimize the control command objective cost function under the constraints, and the objective of the DLQR controller.The objective is to accurately determine the steering angle in order to achieve path tracking and eliminate the lateral error between the reference path and the actual path caused by external interference.Finally, the simulink model and CarSim parameter configuration joint simulation platform are built, and different obstacle scenarios and different speeds are selected for collision-free path planning and tracking motion simulation research and analysis, which verifies the accuracy and practicability of the algorithm, and shows more obvious advantages in obstacle avoidance ability, path smoothing degree, and collision avoidance safety stability. The ideas proposed in this paper are expected to be further extended and popularized to actual path planning problems, which has important practical significance and engineering value.

In Thailand, parking time violation is a major problem, especially for mini-marts. At present the task of detecting parking time violation is mainly conducted manually using Closed-Circuit Television (CCTV). This method requires additional human labour to track incoming and outgoing vehicles. Therefore, low cost time violation tracking is needed. To the best of our knowledge, there has not been any research for parking violation detection and tracking conducted for parking time limits. This paper introduces a novel parking time violation detection algorithm using the Yolov8 and DeepSORT tracking algorithms to track vehicles in consecutive frames. The presented parking violation tracking algorithm can provide a guideline for research in parking time violation detection.

Sports video analysis is an interesting and emerging field in the domain of computer vision. It has become a very popular subject among scholars all over the world in the last decade due to the need of automated classification of events such as offsides, in/out situations, goals and so on. The most important problem in sports video analysis is detection and tracking of ball and players. Extraction of court lines is also another important problem since it is necessary in the classification of aforementioned events. In this study, considering some of the previous work on the subject, we develop a method for tracking the ball and players in tennis videos where the camera position and orientation is supposed fixed.

Porcine metabolomics research has been extensively conducted, primarily focusing on conventional cohorts or animal models. However, the composition and functions of fecal metabolites in pigs at various ages, particularly in the elderly, remain poorly understood. In the present study, an untargeted metabolomics approach was employed to analyze the fecal metabolomes of pigs at three distinct age stages, with the aim of elucidating age-associated changes in metabolite composition and functionality under standardized rearing conditions. Through the untargeted metabolomic analysis of fecal samples from young (one year), middle-aged (four years), and elderly (eight years) pig cohorts, a variety of age-related metabolites were identified. For example, L-methionine sulfoxide was observed to increase with age, while cytidine-5-monophosphate exhibited a concomitant gradual decrease during the aging process. These metabolites were found to undergo alterations across multiple biological pathways, including energy metabolism, pyrimidine metabolism, lipid metabolism, and amino acid metabolism. Furthermore, the age-dependent metabolic changes were associated with known senescence biomarkers, offering new insights into the mechanistic understanding of aging in pigs.

Space exploration presents vast prospects for scientific, industrial, and economic progress. This paper introduces the MeSat mission as a pioneering approach to Mars exploration. MeSat aims to deepen our understanding of Martian conditions and resources by employing an optimized Earth-to-Mars trajectory, enabling a comprehensive study of the Martian atmosphere and surface. The mission comprises a Cargo Microsatellite hosting three 6U CubeSats and two 3U CubeSats, deployed into four separate Mars orbits forming a constellation. Each CubeSat carries distinct payloads: a THz radiometer for water detection, a high-resolution surface camera, a top-tier spectrometer, and a Fourier Transform Spectrometer (FTS) for wind speed readings. This paper focuses on two pivotal innovations: the development of a 183GHz radiometer payload for detecting water on Mars and a optimal mission design algorithm that analyzes a fuel-efficient low-thrust trajectory from Earth to Mars. Regarding the THz payload for water detection, a subharmonic Schottky diode-based mixer is meticulously designed to efficiently down-convert radio signals. The down-converted signals are carefully analyzed using a customized Do-It-Yourself (DIY) spectrum meter, which facilitated the capture and processing of acquired data. To provide the necessary Local Oscillator signals for the mixer, a Tripler employing three-anode Schottky diodes is developed. Additionally, the Tripler requires a signal generator for its entry frequency, which is addressed by developing a DIY signal generator. Together, these components, comprising the Schottky diode mixer, Tripler, and DIY circuits, collectively form the complete THz payload, demonstrating exceptional power efficiency with a total consumption of only 3.7W. The study employs a dual-step hybrid optimization algorithm (PSO-Homotopy) to analyze fuel-efficient low-thrust trajectories from Earth to Mars, incorporating the Ephemeris dynamics model to account for gravitational perturbations in the entire solar system. In practical mission design, crucial factors like hyperbolic excess velocity, diverse opportunities for Earth launch and Mars rendezvous, varied propulsion systems, and Time of Flight (TOF) play vital roles in trajectory optimization.

Delta parallel robot has been widely used in precision parts processing, handling, sorting, and precision assembly, with high efficiency and motion stability being its important performance indexes. Any small line segment corner in trajectory planning causes tangential discontinuous trajectory abrupt change, and the process vibration and shock caused by it seriously affect its high-speed and high-precision performance. In this paper, a trajectory planning method combining Cartesian space and joint space is proposed. Firstly, the vector method and micro-element integration method are used to establish the complete kinematics and dynamics equations of the Delta parallel robot, and the inverse kinematics/dynamics model-solving program is written based on Matlab software. Secondly, the end-effector trajectory of the Delta parallel robot is planned in Cartesian space, and the data points and inverse control points of the end-effector trajectory are obtained by the normalization method. Finally, the data points and control points are mapped to the joint space through the inverse kinematics equation, and the fifth-order B-spline curve is adopted for quadratic trajectory planning, which realizes the high-order continuous smoothing of trajectory planning. The simulation and experiment results show that the trajectory smoothing performance of continuous high-order curvature change can be improved using the proposed method, the tracking error of the end-effector is reduced by 21.12%, and the peak torques change of three joints is reduced by 3.5%, 11.6% and 1.6%, respectively.

In recent years many different deep neural networks were developed, but due to a large number of layers in deep networks, their training requires a long time and a large number of datasets. Today is popular to use trained deep neural networks for various tasks, even for simple ones in which such deep networks are not required. The well-known deep networks such as YoloV3, SSD, etc. are intended for tracking and monitoring various objects, therefore their weights are heavy and the overall accuracy for a specific task is low. Eye-tracking tasks need to detect only one object - an iris in a given area. Therefore, it is logical to use a neural network only for this task. But the problem is the lack of suitable datasets for training the model. In the manuscript, we presented a dataset that is suitable for training custom models of convolutional neural networks for eye-tracking tasks. Using data set data, each user can independently pre-train the convolutional neural network models for eye-tracking tasks. This dataset contains annotated 10,000 eye images in an extension of 416 by 416 pixels. The table with annotation information shows the coordinates and radius of the eye for each image. This manuscript can be considered as a guide for the preparation of datasets for eye-tracking devices.

Trajectory Optimization (TO) is the sequence of processes that are considered in order to produce the best path that mends the overall performance or reduces the consumption of the resources where the restriction system remains maintained. In this survey, an inclusive review of the latest advancements in modeling and optimization of trajectory generation in robotic applications will be discussed broadly. In recent times, numerous studies have employed optimal control techniques involving direct and indirect methods in order to convert the authentic Trajectory Optimization problem into a constrained parameter optimization problem. Moreover, a huge variety of optimization algorithms such as Genetic Algorithms (GA), Simulated Annealing (SA), Sequential Quadratic Programming (SQP), and Particle Swarm Optimization (PSO) are used aiming to find the optimal solutions for trajectory planning. It is observable that, minimizing the jerk, energy consumption, and execution time among the most widely used design objectives, on the other hand, the robot’s joints configurations and the motor torques are the most used design variables. This paper aims to review the fundamental techniques and their coincident robotic applications in the field of trajectory optimization aiming to afford some steering for related researchers.

In this paper, first the Self-Written Waveguide (SWW) process in wet photopolymer media (liquid solutions), are examined for three examples: single-, counter-, and co-fibers exposure. Then the SWWs formed inside solid material are examined including the effects of manipulating the alignment of the fibers. In all cases high precision measurements are used to position the fiber optic cables (FOCs) before exposure using a microscope. The self-writing process is indirectly monitored by observing (imaging) the light emerging from the side of the material sample during SWW formation. In this way the optical waveguide trajectories formed in an Acrylamide/Polyvinyl Alcohol (AA/PVA) a photopolymer material (sensitized at 532 nm) are examined. First the transmission of light by this material is characterized. Then the bending and merging of the waveguides which occur are investigated. The predictions of our model are shown to qualitatively agree with the observed trajectories. The largest index changes taking place at any time during the exposure, i.e. during SWW formation, are shown to take place at the positions where the largest exposure light intensity is present. Typically, such maxima exist close to the input face and the first maximum is referred to as the location of the Primary Eye. Other local maxima also appear further along the SWW and are referred to as Secondary Eyes, i.e. deeper within the material.

In Brazil, during the four last administrations of Worker’s Party (PT) 2003-2015, the support to the agrarian reform seems to have stagnated, even with the influence of landless workers' movements. Thus in 2016, the impeachment President Dilma Roussef have marked a brutal stop in the agrarian reform process. How to explain that which seems at first to be a contradiction and has become a decadence of an important federal public policy?. Furthermore, how can we evaluate the debates within Brazilian society and the federal government on this theme? The article analyzes the tensions, debates, advances and impasses of the past fifteen years of agrarian reform policy in Brazil looking at the interaction between social movements and public policies. The method associates bibliography, official statistic synthesis and research results in Northeast, Amazônia and Cerrado regions among several projects. The first part results put on evidence the crescent reduction of agrarian reform settlements and beneficiary families since 2006. The second part presents the main reasons offering an analysis of government and society debates in Brazil about land reform. The analysis conclude to the less of power and representation in the society of the pro agrarian reform large and popular coalition.

Despite their prevalence in almost all publicly orientated conservation settings, sign design receives limited attention in the research literature. We used a remote eye-tracking device to test how visitors read educational signs, maps, and species signage. Three designs, plus a plain-text control sign, were tested for each sign type. Sign content was based on Brackenhurst Botanic Garden, Kenya and included four different information texts (156-256 words long) and a species information text (64 words long). Four presentations of extinction threat-status were also tested as were depictions of trail routes on maps. Data were collected from 51 participants at six cultural venues across Nottingham, UK. Signs positioned centre-left of an exhibit were read first. Information on the left-hand side of signs was read first and dwelled upon the longest. Signs with a single large image and a single block of text were generally preferred, and images were most frequently recalled. Extinction threat-status was most frequently viewed and best recalled from green-red thermometer diagrams and least from the IUCN red-list diagram. Map routes were clearest when presented as coloured solid lines. This study indicates the potential benefit of eye-tracking research for measuring sign use and assisting in sign design.

Goal-conditioned reinforcement learning (RL) holds promise for addressing intricate control challenges by enabling agents to learn and execute desired skills through separate decision modules. However, the irregular occurrence of required skills poses a significant challenge to effective learning. In this paper, we demonstrate the detrimental effects of this imbalanced skill(sub-goal) distribution and propose a novel training approach, Classified Experience Replay (CER), designed to mitigate this challenge. We demonstrate that adapting our method to conventional RL methods significantly enhances the performance of the RL agent. Considering the challenges inherent in tasks such as driving, characterized by biased occurrences of required sub-goals, our study demonstrates the improvement in trained outcomes facilitated by the proposed method. In addition, we introduce a specialized framework tailored for self-driving tasks on highways, integrating Model Predictive Control (MPC) into our RL trajectory optimization training paradigm. Our approach, utilizing CER with the suggested framework, yields remarkable advancements in trajectory optimization for RL agents operating in highway environments.

The objective of this study was to analyze the mental workload using EEG and eye tracking data and classify it using machine learning algorithms. The machine learning model was developed based on the simultaneous recording of eye tracking and EEG measurements during the experimental process. The experiments involved 15 university students, consisting of 7 women and 8 men. Throughout the experiments, the researchers utilized the n-back memory task and the NASA-Task Load Index (TLX) subjective rating scale to assess various levels of mental workload. The findings revealed that as the task difficulty increased, there was an increase in the diameter of both the right and left pupils, the number of fixations, the number and duration of saccades, and the number and duration of blinks. Conversely, variables related to fixation duration decreased. The EEG results indicated that theta power in the prefrontal, frontal, and front central regions increased with task difficulty. Additionally, alpha power increased in the frontal regions but decreased in the temporal, parietal, and occipital regions as the task became more challenging. Furthermore, low beta power significantly decreased in almost all brain regions as the task difficulty increased. In terms of the four-class classification problem, the mental workload level can be predicted with an accuracy rate of 76.59% using 34 selected features. This study has made a significant contribution to the literature by presenting a four-class mental workload estimation model that utilizes different machine learning algorithms.

This paper aims to provide a comprehensive review of the historical trajectory of deforestation in Ethiopia, spanning an extensive timeframe and exploring its major driving factors. Over the span of three millennia, Ethiopia has experienced persistent deforestation, resulting in a significant reduction of its once-large forest and woodland resources. Currently, small, degraded forests and woodlands exist in isolated patches, located in remote areas such as the southwest and southeast. Major deforestation in Ethiopia unfolded progressively, first along a north-south axis and more recently shifting from the highlands to the lowlands. The northern and central highlands lost most of their natural forests before the turn of the 20th century, while significant loss in the southern highlands occurred over the past 150 years, and in the lowlands over the past 50 years. This deforestation is primarily attributed to the expansion of ox-plow agriculture and population pressure resulting from natural birth and internal migration. Despite efforts to counter deforestation and promote restoration, mostly since the 1970s, successes remain limited. A noteworthy exception in this regard is the introduction of Eucalyptus during the turn of the 20th century and the subsequent expansion of it, mainly in the form of small-scale farmer-led farm forestry. Today, Eucalyptus become the most preferred, well-adapted, widely planted, and the major supplier of wood materials in Ethiopia. The limited success in restoration is attributed to the reliance on campaign-based strategy, lack of private sector involvement, the intertwining of restoration with political agenda, and poorly defined property rights on land, forests, and trees. The consequences of deforestation and resultant environmental degradation have profoundly influenced the socio-economic dynamics of Ethiopia, giving rise to issues such as food insecurity, poverty, migration, and political instability.

Directed Energy Deposition (DED) processes necessitate a consistent material flow to the melt pool, typically achieved through pneumatic conveying of metal powder via thin pipes. This study aims to record and analyze the multiphase fluid-solid flow. An experimental setup utilizing a high-speed camera and specialized optics was constructed, the flow through thin transparent pipes was recorded. The resulting information was analyzed and compared with coupled Computational Fluid Dynamics - Discrete Element Modeling (CFD-DEM) simulations, with special attention to the solids flow fluctuations. The paper details the experimental design, video analysis using Particle Tracking, and a novel method for deriving volumetric concentrations and flowrate from flat images. The findings confirm the accuracy of the CFD-DEM simulations and provide insights into the dynamics of pneumatic conveying, which could be used to enhance the efficiency of DED processes.

Studies on particulate air pollution indicate that a new type of pollutant should be considered from mainly fossil fuel combustion and automobile exhaust emissions, i.e. environmentally persistent free radicals. These radicals, ubiquitous in the environment, have a long life span and are capable of producing harmful reactive oxygen species. Samples of dandelion were collected in 2020 and 2021 in spring and late summer. Roots, leaves, flower stalks, and inflorescences of Taraxaum sp., were collected from 6 sites with 3 plants each, along with monitoring of PM air pollution. The free radical content in each part of the plant was measured by electron paramagnetic resonance. The leaf was selected as the most appropriate part of the plant for the measurement of carbon-derived free radicals. The geff value and the total number of spins were calculated. Relationships were found between location, season, and measurements. The electron paramagnetic resonance spectrum consists of at least two components, which can be attributed to C-type radicals, and mixed C+O radicals. Their increase in numbers in the fall seasons, compared to the spring seasons, is also noticeable. It has also been observed that leaves collected in autumn have a higher geff value, which is probably related to the higher amount of oxygen- and carbon-derived free radicals.

Trajectory planning determines whether a UAV can successfully complete its mission, and the reasonable planning of UAV trajectory in 3D environment is a complex global optimization problem, which needs to take into account many constraints such as urban environment, mountain terrain, obstacles, no-fly zones, flight boundaries, flight distances, and trajectory change rates. In view of the shortcomings of the whale optimization algorithm in 3D trajectory planning, such as slow convergence speed, low accuracy and easy to fall into the local optimum, this paper increases the diversity of the initial population through the introduction of the reverse learning mechanism, and optimizes the coordination of the global and local search ability by integrating the nonlinear convergence factor and the random number generating mechanism, so as to realize the improved whale optimization algorithm that can cope with the higher degree of freedom and the more complex constraints. The simulation results show that the optimization algorithm has improved the convergence accuracy by 22.1% and reduced the standard deviation by 74.1%, which can effectively deal with the 3D UAV path planning problem.

This paper proposes training improvement methods of artificial neural networks (ANN) trajectory predictors. First, a dynamic power system time-series trajectory is split into several different segments to simplify the original ANN training problem. Moreover, the time-derivative of the trajectory is included to obtain an augmented loss function. Compared to previous studies which mainly focused on increasing the prediction accuracy, the aim of these novel techniques is to reduce the computational burden where the ANN output performance is still acceptable. The effectiveness of the developed methods is validated based on the WSCC three-machine nine-bus and IEEE 39-bus system models. The mean absolute error (MAE) and trajectory prediction results are analyzed, in which the numbers of neurons, hidden layers, and training epochs are constrained during the ANN training process. Rotor-angle difference between generators and the system frequency are investigated as the dynamic trajectories of the power system models. It is revealed that when the ANN architecture and epochs are constrained, the MAE results can be reduced, and the ANN training results can better reflect the original trajectory using the improvement approaches.

This research investigates the utilization of human driving models in autonomous vehicles, particularly in scenarios with minimal or no interactions with other vehicles. Human driving models provide valuable insights into driver behavior and play a crucial role in shaping the behavior of autonomous vehicles, enhancing their performance and user experience. The primary focus of the study is the creation of a planning model for autonomous vehicles when navigating roundabouts in the absence of traffic. This model seeks to emulate human driving behavior, ensuring predictability, safety, optimization of traffic flow, and adaptation to various roundabout geometries. To achieve this, the research introduces a trajectory model that takes into account geometric attributes and speed variations within roundabouts. The model is calibrated using empirical data and generalizes parameters through statistical regression methodologies. This model is referred to as "MRoundabout" and is evaluated for its consistency in generating plans that closely mimic human driving behavior within roundabouts. While the study presents a promising approach, it acknowledges limitations related to the model's reliance on geometric attributes and its inability to account for external factors like weather conditions. This research underscores the importance of bridging the gap between theoretical research and practical application, with the aim of enhancing safety and the overall user experience in real-world driving scenarios.

In tracking-by-detection paradigm for multi-target tracking, target association is modeled as an optimization problem that is usually solved through network flow formulation. In this paper, we proposed combinatorial optimization formulation and used a bipartite graph matching for associating the targets in the consecutive frames. Usually, the target of interest is represented in a bounding box and track the whole box as a single entity. However, in the case of humans, the body goes through complex articulation and occlusion that severely deteriorate the tracking performance. To partially tackle the problem of occlusion, we argue that tracking the rigid body organ could lead to better tracking performance compared to the whole body tracking. Based on this assumption, we generated the target hypothesis of only the spatial locations of person’s heads in every frame. After the localization of head location, a constant velocity motion model is used for the temporal evolution of the targets in the visual scene. Qualitative results are evaluated on four challenging video surveillance dataset and promising results has been achieved.

Natural vegetation restoration can take decades to achieve and requires information on species composition and trend to inform management strategies. Here we demonstrate the potential and portability of drone, dove and Sentinel-2 imagery for species level classification and trajectory monitoring using three study sites with different revegetation histories. Drone imagery (4 cm) was classified using random forests. Dove (3 m) and Sentinel-2 (10 m) images were acquired close to the anniversary of the drone flights and converted into fractional cover maps showing the proportion of exotics, natives and other land covers using ordinary least squares (OLS) and geographically weighted regression (GWR). The time series of fractional cover maps were converted into positive and negative slope and second-order curvature and summarised as one RGB composite to illustrate trend and observe exotic expansion. Drone classification accuracies ranged from 73%-76% when applied to the full plant species list and between 93% and 97% when grouped into natives, exotics, and other land covers. GWR outperformed OLS for all fractional cover mapping and GWR with dove imagery was more accurate than with Sentinel imagery. The combination of very high-resolution drone acquired imagery with the versatility of dove acquired imagery arms land managers with highly accurate species composition and their spatio-temporal dynamics to promote timely intervention.

Event-based vision is an emerging field of computer vision that offers unique properties such as asynchronous visual output, high temporal resolutions, and dependence on brightness changes to generate data. These properties can enable robust high-temporal-resolution object detection and tracking when combined with frame-based vision. In this paper, we present a hybrid, high-temporal-resolution, object detection and tracking approach, that combines learned and classical methods using synchronized images and event data. Off-the-shelf frame-based object detectors are used for initial object detection and classification. Then, event masks, generated per each detection, are used to enable inter-frame tracking at varying temporal resolutions using the event data. Detections are associated across time using a simple low-cost association metric. Moreover, we collect and label a traffic dataset using the hybrid sensor DAVIS 240c. This dataset is utilized for quantitative evaluation using state-of-the-art detection and tracking metrics. We provide ground truth bounding boxes and object IDs for each vehicle annotation. Further, we generate high-temporal-resolution ground truth data to analyze the tracking performance at different temporal rates. Our approach shows promising results with minimal performance deterioration at higher temporal resolutions (48 – 384 Hz) when compared with the baseline frame-based performance at 24 Hz.

The event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye tracking tasks. However, the asynchronous incremental nature of event camera output has not been fully utilized, and there are also issues related to missing event datasets. Combining the temporal information encoding and state-preserving properties of spiking neural network (SNN) with event camera, a near-range eye tracking algorithm is proposed as well as a novel event-based dataset for validation and evaluation. According to experimental results, the proposed solution outperforms artificial neural network (ANN) algorithms, while computational time remains only 12.5\% of that of traditional SNN algorithms. Furthermore, the proposed algorithm allows for self-adjustment of time resolution, with a maximum achievable resolution of 0.081ms, enhancing tracking stability while maintaining accuracy.

Intelligent ship technology is currently an international research hotspot, and model predictive control is widely used in the path-tracking control of intelligent vehicles. To construct an intelligent ship anti-disturbance path-tracking control method, firstly, an environmental disturbance model was constructed with the actual meteorological data of the target sea area. Secondly, the Fossen ship equation of motion is linearized and discretized as the ship motion model. Thirdly, the expression of the prediction equation is derived from the ship motion model. Fourthly, the cost function is constructed by using the polar diameter and polar angle values of the ship. Fifth, the power function in the cost function is replaced with an exponential function to obtain an improved cost function. Sixthly, according to the Lyapunov theory and the MPC terminal constraint theory, the stability of the improved cost function is verified. Seventh, different test paths are set up, the environmental disturbance model is taken as the external disturbance, the ship motion model, the prediction equation, and the improved cost function are used to design the anti-disturbance path-tracking control algorithm according to the model prediction control idea for simulation experiments. Finally, different MATLAB simulation results show that the improved cost function can resist disturbance of the external wave, current, and wind, and effectively track the target path. Therefore, this study provides a reference for improving the navigation safety of ship path-tracking.

Concentrator photovoltaic (CPV) technology offers an alternative to conventional photovoltaic systems, focusing on the concentration of solar radiation through the optics of the system onto smaller and more efficient solar cells. However, the surface on which the solar beam is incident must remain oriented towards the position of the sun, which requires the use of large and heavy external elements known as solar trackers. In addition to their size, these elements must be as accurate as possible to avoid misalignment. To avoid the need of these solar trackers, the concept of tracking-integrated concentrating systems has been developed. This technology has been researched to make CPV systems accessible in places where conventional systems are not viable, such as building roofs or agrivoltaic fields. This review presents a detailed classification of the existing designs in the literature and providing an overview of this type of system. By eliminating the need for external solar trackers, tracking-integrated CPV systems offer a promising solution to increase the adoption of this technology. Therefore, it is expected that these systems will be a viable and effective option for solar power generation.

In this paper, we conduct a comprehensive analysis of the RounD dataset to enhance the understanding of motion forecasting for autonomous vehicles in complex roundabout environments. We implement a trajectory prediction framework that combines Long Short-Term Memory (LSTM) networks with graph-based modules to model vehicle interactions. Our primary objective is to assess the generalizability of a standard trajectory prediction model across diverse training and testing datasets. Through extensive experiments, we analyze how varying data distributions, including different road configurations and recording times, influence the model’s prediction accuracy and robustness. This study provides key insights into the challenges of domain generalization in autonomous vehicle trajectory prediction.

The Global Carbon Budget is the cumulative carbon emissions that human activities can generate while limiting the global temperature increase to less than 2°C. On this basis, most countries ratified the Paris Agreement 2015, pledging to reduce national emissions and the impacts of climate change. The European Union has planned to reduce emissions by 80% of their 1990 value by 2050 but such a target needs to be coupled with a further constraint on the cumulative greenhouse gases released along the path to 2050. The aim and the novelty of this study are to propose, for the first time, a carbon budget for the European Union, which represents the most significant physical characteristic to assess the feasibility of current EU-28 greenhouse gas reduction objectives under the goals of the 2015 Paris treaty

Advances in accelerator technology have enabled the use of exotic and intense radioactive ion beams. Enhancements to tracking detectors are necessary to accommodate increased particle rates. Recent advancements in digital electronics have led to the construction or planning of next-generation detectors. To conduct kinematically complete measurements, it is essential to track and detect all particles produced as a result of the reaction. Furthermore, the need for high-precision physics experiments has led to significant developments in the detector field. In recent years, highly efficient and highly granular tracking detectors have been developed. These detectors significantly enhance the physics programme at dedicated facilities. An overview of the charged-particle tracking detectors in low-energy nuclear physics will be given.

With the vast amount of goods being shipped around the world, there is a need to track and manage various types of assets, particularly shipping containers. Logistics industries dealing with small facilities, shipments, equipment, and vehicles must be tracked. There are many applied asset tracking systems such as Radio-Frequency Identification (RFID), Bluetooth Low Energy (BLE) Beacon, and Long-Range Radio (LoRa). This paper presents these systems and focuses on locating assets using LoRaWAN technology, which has a positive impact on the responsive and sustainable cities in Egypt. IoT-based Long Range (LoRa) is a low-power, wide-area communication technology that uses radio frequencies to transmit data over long distances. The extended range, low power consumption, low maintenance, and the ability to store location data when the end node that needs to connect to the asset is out of the gateway's coverage make LoRa a good choice for developing asset-tracking applications. This paper introduces a real-time tracking experiment as a result of a project implementation, whose goals are aligned with industry, innovation, and infrastructure and Sustainable Cities and Communities, the Sustainable Development Goals (SDGs) No. 9 and 11.

The commercialization of historic streets constitutes a pivotal aspect of urban cultural heritage, and the comprehension of their visual perception serves as a valuable asset to urban planning and cultural conservation efforts. However, current research concerning the disparities in visual perception among diverse demographics within historic streets, as well as their spatiotemporal dynamics, remains insufficient. This study aims to utilize mobile eye-tracking technology to delve deeply into the visual perception characteristics of diverse demographics within commercialized historic streets, while also analyzing their spatiotemporal dynamics. We explore the disparities in visual perception of historic streets among groups with varying degrees of architectural knowledge and professional literacy. Through meticulous observation using mobile eye-tracking technology, we elucidate the visual perception patterns of different demographic characteristics through statistical analysis and spatiotemporal modeling. The findings reveal significant discrepancies in visual attention and perception among various groups. Participants with a higher understanding of architecture tend to focus more on architectural details and structures within the streetscape compared to those with a lower understanding, who exhibit greater interest in elements such as shops and signage. Moreover, during the process of perceiving the streetscape, participants’ eye movements transition from surrounding landscapes and historical edifices upon initial entry to the district, gradually shifting towards the commercial activities and distinctive architecture within the district. This study delves deeply into the visual perception characteristics and spatiotemporal dynamics of commercialized historic streets, providing invaluable insights for urban planning and cultural preservation endeavors. By revealing the impact of different demographic characteristics on the visual perception of historic streets, it offers new insights for urban planning and cultural heritage conservation.

Current effort with light field displays is mainly concentrated on the widest possible viewing angle, while a single viewer only needs to view the display in a specific viewing direction. To make light field display a practical practice, a super multi-view light field display is proposed to compress the information in the viewing zone of a single user by reducing the redundant viewpoints. A quasi-directional backlight is proposed and a lenticular lens array is applied to achieve the restricted viewing zone. Eye-tracking technique is applied to extend the viewing area. Experimental results show that the proposed scheme can present vivid 3D scene with smooth motion parallax. Only 16.7% conventional light field display data are required to achieve 3D display. Furthermore, an illumination power of 3.5 watt is sufficient to lighten a 31.5-inch light field display，which takes up 1.5% of the illumination power required for planar display of similar configuration.

The dynamic factors in the environment violate the static environment assumption of the SLAM algorithm, leading to a decrease in the accuracy of camera positioning. In recent years, many studies have attempted to address dynamic objects by combining semantic constraints and geometric constraints, but issues such as poor real-time performance, the potential for mistakenly treating humans as rigid objects, and subpar performance in high-dynamic scenes still persist. This paper proposes a dynamic scene visual SLAM algorithm called Dynamic SLAM based on target tracking and multi-view geometry (TKG-SLAM), based on object detection, Kalman filters, and multi-view geometry. This algorithm takes into consideration both real-time performance and algorithm accuracy. It combines semantic constraints and multi-view geometry constraints, selectively running the algorithm in different scenarios, filtering and preserving static points for camera pose estimation. Experimental results demonstrate that, compared to current state-of-the-art dynamic SLAM methods, our approach achieves optimal performance in some scenarios and exhibits stronger real-time capabilities.

This paper is regarding design and program an Micro-controller Arduino Uno board by using Arduino software to work as a photo-sensor(Active) single axial solar tracker system(SASTS). A solar panel, two photo-resistors (LDR) in two sides (north/south) of the photo-voltaic(PV) and a servo motor are connected to the Uno board, which is running a code that prepared by Arduino software IDE in advanced then it works as a tracking system. Here, the LDRs send the signal of presence or absence of the light to the board and based on that sent signal the Uno reflects a new signal to the servo motor to rotate and finds the light source. Lastly, the photo-sensor single axis tracker is made while Continuously, the system tries to face the panel to the sun and whilst changing the irradiance intensity it starts searching to find the angle of highest irradiance. Based on results that are extracted from the data, the tracker system significantly boosts the output efficiency of the solar panel. By using the Micro-controller Uno board, LDRs, servo motor and special designed mechanical base, the tracking system is constructed, based on acquired data the influence of the STS on the increasing the solar panel efficiency is more obvious. Significantly, the tracker system rises the efficiency of the PV .

Recent advancements have revolutionized interplanetary spacecraft missions by combining low-thrust propulsion systems with planetary Gravity-Assist (GA) maneuvers. This study investigates/improved a novel dual-step efficient-robust homotopy algorithm, grounded in indirect optimal control principles, to analyze fuel-optimal bang-bang control problems. The methodology integrates efficient techniques, including the homotopic method, compatible normalization, and improved switching function detection, aimed at enhancing optimal convergence in solving low-thrust gravity-assist spacecraft missions. To illustrate practical application, an Earth-to-Jupiter interplanetary scenario utilizing Mars-GA is examined. Results demonstrate the algorithm's superior efficacy in achieving fuel efficiency, robust convergence, and remarkable accuracy compared to existing solutions.

In this paper, a swarm trajectory planning method is proposed for multiple autonomous surface vehicles (ASVs) in an unknown and obstacle-rich environment. Specifically, based on the point cloud information of the surrounding environment obtained from local sensors, a kinodynamic path searching method is used to generate a series of waypoints in the discretized control space at first. Next, after fitting B-spline curves to the obtained waypoints, a nonlinear optimization problem is formulated to optimize the B-spline curves based on gradient-based local planning. Finally, a numerical optimization method is used to solve the optimization problems in real-time to obtain collision-free, smooth and dynamically feasible trajectories relying on a shared network. Simulation results demonstrate the effectiveness and efficiency of the proposed swarm trajectory planning method for a network of ASVs.

Human milk is recommended for feeding preterm infant. Yet the potential impact of specific breast-milk lipid components on the initial growth rate of very-preterm infants has received scant attention. The current pilot study aims to determine whether breast-milk lipidome had any impact on the early growth pattern of preterm infants fed their own mother’s milk. A prospective monocentric observational birth cohort was established, enrolling 147 preterm infants, who received their own mother’s breast-milk throughout hospital stay. Among that cohort, infants who experienced slow (n=15) or fast (n=11) growth were selected, based on the change in their weight Z-score between birth and hospital discharge (-1.54± 0.42 and -0.48± 0.19 Z-score, respectively). Liquid chromatography-high resolution-mass spectrometry was used to obtain lipidomic signatures in breast-milk. Multivariate analyses made it possible to identify breast-milk lipid species that allowed clear-cut discrimination between the 2 infants’ groups. Validation of the selected biomarkers was performed by means of various multidimensional statistical techniques, false-discovery rate and ROC curve computation. Breast-milk associated with fast growth contained more medium chain-saturated fatty acid and -sphingomyelin, dihomo-γ-linolenic acid (DGLA)-containing phosphethanolamine, and less oleic acid-containing triglyceride and DGLA-oxylipin. Their predictive ability of preterm early-growth rate was validated in presence of confounding clinical factors.

: The radar multi target tracking (MTT) technique requires prior knowledge of a number of parameters about the sensor, the target and backgrounds. The Integrated Track Splitting (ITS) is a fully automatic track-while-scan (TWS) target tracking algorithm capable of extracting and tracking a target in a dense clutter environment using quality false track discrimination (FTD) methodology. The computational complexity in ITS algorithm is limited, compared to other algorithms they use statistical methods to discriminate between false and true tracks, such as multiple hypothesis tracking (MHT), mainly due to the FTD performed. The paper provides an analysis of tracking parameters that allows determining the limit of the possibility of successful target tracking. Extensive experiments have confirmed that the recursive determination of the probability of the existence of a track during tracking can confirm a true track and reject a false track. The clutter density, number of random occurred targets, targets load during the maneuver and the target detection probability were varied. The results of experiments, carried out via Monte Carlo simulations, shown over representative confirmed true tracks (CTT) diagrams, root mean square error position and normalized tracking efficiency parametric diagrams allow the user to select optimal multi-target tracking parameters for different scenarios and clutter densities.

Clonal evolution and cellular plasticity are the genetic and non-genetic driving forces of tumor heterogeneity that in turn determines the tumor cell response towards therapeutic drugs. Several lines of evidence suggest that therapeutic interventions foster the selection of drug resistant neural crest stem-like cells (NCSCs) that establish minimal residual disease (MRD) in melanoma. Here we established a dual reporter system enabling the tracking of NGFR expression and mRNA stability, providing insights into the maintenance of NCSC-states. We observed that the transcriptional reporter that contained a 1kb fragment of the human NGFR promoter was activated only in a minor subset (0.72±0.49%, range 0.3-1.5) and ~2-4% of A375 melanoma cells revealed stable NGFR mRNA. The combination of both reporters provided insights into phenotype switching and revealed that both cellular subsets gave rise to cellular heterogeneity. Moreover, whole transcriptome profiling and gene set enrichment analysis (GSEA) of the minor cellular subset revealed hypoxia-associated programs that might serve as potential drivers of an in vitro switching of NGFR-associated phenotypes and relapse of post-BRAF inhibitor treated tumors. Concordantly, we observed that the minor cellular subset increased in response to dabrafenib over time. In summary, our reporter-based approach provided insights into plasticity and identified a cellular subset that might be responsible for the establishment of MRD in melanoma.

This paper presents research on the application of trajectory design, optimization, and control to an orbital transfer from Mars-Phobos Distant Retrograde Orbits to the surface of Phobos. Given a Distant Retrograde Orbits and a landing location on the surface of Phobos, landing trajectories for which total Δv for a direct 2-burn maneuver is minimized are computed. This is accomplished through the use of Particle Swarm Optimization in which the required Δv and time of flight are optimization parameters. The non-uniform gravitational environment of Phobos is considered in the computation. Results show how direct transfers can be achieved with Δv in the order of ∼30 m/s.

1. Background: The global COVID-19 lockdowns are unique psychological factors, requiring adjustment and coping. This illustrative, South African case study investigated the influence the COVID-19 lockdown context would have on meditation and physical activity. As these were lifestyle patterns the null hypothesis was of no change. 2. Method: The methodology consisted of quantitative and qualitative phases. The quantitative phase was an empirical, case study review of data from the author’s HeartMath and Fitbit electronic devices, as from 9 November 2019 to 31 July 2021. This consisted of coherence and achievement data from the HeartMath Inner Balance application (app), as well as distance and activity data from the Fitbit app. The qualitative phase consisted of the selection and illustration of meditation sessions documented over lockdown. 3. Results: The highly significant increases in physical activity and meditation coherence and achievement collectively indicated significant health promotion over the COVID-19 Lockdown period. Subtle energetic, experiential descriptions of meditation apprehensions resonated and supported these quantitative findings. 4. Conclusions: COVID-19 lockdown has meant transformation, humility, and reordering of priorities. The meditation descriptions amplified information from the HeartMath and Fitbit electronic devices, together co-creating conscious, embodied, relationship science for discussion, instruction, and further research.

: In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous landing. This technology improves the UAV's environment perception and autonomous flight capabilities in GPS-free scenarios. In particular, we are researching tracking and landing as a cohesive unit, devising a switching plan for various UAV tracking and landing modes, and creating a flight controller that has an inner and outer loop structure based on relative position estimation. The inner and outer nested markers aid in the independent monitoring and touchdown of UAV. Optimal parameters are determined via optimized experiments on the measurements of the inner and outer markers. An indoor experimental platform for tracking and landing UAV was established. Tracking performance was verified by tracking three trajectories of unmanned ground vehicle (UGV) at varying speeds, and landing accuracy was confirmed through static and dynamic landing experiments. The experimental results show that the proposed scheme has good dynamic tracking and landing performance.

Due to the advantages of low latency, low power consumption and high flexibility of FPGA-based acceleration technology, it has been more and more widely studied and applied in the field of computer vision in recent years. An FPGA-based feature extraction and tracking accelerator for real-time visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) is proposed, which can realize the complete acceleration processing capability of the image front-end and directly output the feature point ID and coordinates to the backend. The accelerator consists of image preprocessing, pyramid processing, optical flow processing, and feature extraction and tracking modules. For the first time, it implements a hardware solution that combines features from accelerated segment test (FAST) corners with Gunnar Farneback (GF) dense optical flow, to achieve better feature tracking performance and provide more flexible technical route selection. In order to solve the scale invariance and rotation invariance lacking problem of FAST features, an efficient pyramid module with a five-layer thumbnail structure is designed and implemented. The accelerator is implemented on a modern Xilinx Zynq FPGA. The evaluation result shows that the accelerator can achieve stable tracking of features of violently shaking images, and is consistent with the results of MATLAB code running on PC. When operating at 100MHz, the accelerator can process 108 frames per second for 720P images and 48 frames per second for 1080P images. Compared to PC CPUs that consume seconds of time, the processing latency is greatly reduced to the order of milliseconds, making GF dense optical flow an efficient and practical technical solution on the edge side.

The rapid development of technology has led to the implementation of data-driven systems whose performance heavily relies on the amount and type of the data itself. In the latest decades, in the fields of bioengineering data management, among others, eye-tracking data has become one of the most interesting and essential components for many medical, psychological, and engineering research applications. However, despite the large usage of eye-tracking data in many studies and applications, a strong gap is still present in the literature regarding real-time data collection and management, which led to strong constraints for the reliability and accuracy of on-time results. To address this gap, this study aims to introduce a system that enables the collection, processing, real-time streaming, and storage of eye-tracking data. The system is developed by using Java programming language, WebSocket protocol, and Representational State Transfer (REST), improving the efficiency in transferring and managing eye-tracking data. Results were computed in two test conditions, i.e., local and online scenarios, within a time window of 100 seconds. The experiments conducted for this study were carried out by comparing the time delay between two different scenarios. Even if preliminary, results showed a significantly improved performance of data management systems in managing real-time data transfer. Overall, this system can significantly benefit the research community by providing real-time data transfer and storing the data, enabling more extensive studies using eye-tracking data.

In this contribution a new configuration of a DC-DC Boost and Buck-Boost converters is proposed to track sinusoidal signals exactly, with zero tracking error. The stage is conformed by two DC-DC converters connected to the same load, this is, they are not connected in cascade. To track exactly the sinusoidal reference the control scheme is base on the indirect control algorithm and the harmonic balance method. The proposed algorithm considers a second control input with the end of cancel out the second harmonic, this second harmonic is produced by the non linearity of the model and must be cancelled since there must be a single frequency in the output. The result is such that the converters can track sinusoidal signals with exactly zero error, some numerical results are presented to corroborate the proposal.

This study proposes a new algorithm termed rain cell identification and tracking (RCIT) to identify and track rain cells from high resolution weather radar data. Previous algorithms have limitations when tracking non-consequent rain cells owing to their use of maximum correlation coefficient methods and their lack of an alternative way to handle the variation stages of rain cells during their life cycles. To address these deficiencies, various methods are implemented in the new algorithm. These include the particle image velocimetry (PIV) method for motion estimation and the rain cell matching rule to obtain the stage changes of rain cells. High resolution (5-min and 1-km) radar reflectivity data from three rainy days over the German federal state North Rhine Westphalia (NRW) are used to evaluate the proposed algorithm. The performance of the new algorithm is compared with a radar reflectivity map and verified by two object-oriented methods: structure–amplitude–location (SAL) and geometric index. The verification results suggest that the performance of the new algorithm is good. Application of the RCIT algorithm to the selected cases shows that the inner structure of rainfall events in the experimental region present extreme value distributions, with most rainfall events having a short duration with less intensity. The new algorithm can effectively capture the stage changes of rain cells during their life cycles. The proposed algorithm can serve as the basis for further hydro-meteorological applications such as spatial and temporal analysis of rainfall events and short-term flood forecasting.

This paper uses panel data to find the determinants of tracking errors in exchange traded funds (ETFs) in the Hong Kong stock market. A comparison of tracking errors between physical and synthetic ETFs also indicates that the synthetic ETFs have higher tracking errors. The magnitude of tracking errors is found to be negatively related to size but positively related to dividend yield, trading volumes of funds, and market risk. However, this study also finds that expense ratio has a negative impact on tracking error, which is not consistent with previous studies, and which this paper addresses.

Robot manipulators are robotic systems that are frequently used in automation systems and able to provide increased speed, precision and efficiency in the industrial applications. Due to their nonlinear and complex nature, it is crucial to optimize the robot manipulator systems in terms of trajectory control. In this study, positioning analysis based on artificial neural networks (ANN) were performed for robot manipulator systems used in the textile industry and the optimal ANN model for the high-accuracy positioning was improved. The inverse kinematic analysis of a 6 degree of freedom (DOF) industrial denim robot manipulator were carried out via 4 different learning algorithms of Delta-Bar-Delta (DBD), Online Back Propagation (OBP), Quick Back Propagation (QBP) and Random Back Propagation (RBP) for proposed neural network predictor. From the results obtained, it was observed that QBP based 3-10-6 type ANN structure produced the optimal results in terms of estimation and modelling of trajectory control. In addition, 3-5-6 type ANN structure was also improved and its Root Mean Square Error (RMSE) and statistical R2 performances were compared to that of 3-10-6 ANN structure. Consequently, it can be concluded that the proposed neural predictors can successfully be employed in real-time industrial applications for robot manipulator trajectory analysis.

The development and use of highly automated vehicles (HAV) in agriculture is a highly important task because of the lack of machine drivers and unsatisfactory level of their qualifications, reducing the impact of harmful factors on employees of agricultural enterprises. HAVs allow to avoid these problems: increase productivity and the efficiency of seasonal use of equipment, and reduce the impact of harmful factors on people. Modern HAVs in agriculture are based on the following methods: parallel driving according to geo coordinates without automatic turning and without obstacles tracking, and on technical vision without the use of a geographic information system (for example Cognitive AgroPilot) and a digital field map, which makes it impossible to generate the optimal path around the field.

Deploying UAVs as aerial base stations is an exceptional approach to reinforce terrestrial infrastructure owing to their remarkable flexibility and superior agility. However, it is essential to design their flight trajectory effectively to make the most of UAV-assisted wireless communications. This paper presents a novel method for improving wireless connectivity between UAVs and terrestrial users through effective path planning. This is achieved by developing a goal-directed trajectory planning method using active inference. First, we create a global dictionary using TSPWP instances executed on various training examples. This dictionary contains letters representing available hotspots, tokens representing local paths, and words depicting complete trajectories and hotspot order. By using this world model, the UAV can understand the TSPWP’s decision-making grammar and how to use the available letters to form tokens and words at various levels of abstraction and time scales. With this knowledge, the UAV can assess encountered situations and deduce optimal routes based on the belief encoded in the world model. Our proposed method outperforms traditional Q-learning by providing fast, stable, and reliable solutions with good generalization ability.

In this paper, we investigated the trajectory of the massive particle in the vicinity of a general spherical symmetric black hole. Also, in the framework of general sphericalily symmetric black hole, Pseudo-Newtonian potential (PNP) and effective potentials has been investigated. As an example, static spherically symmetric black hole in f(R) gravity is considered and presented the brief discussion on the structure of spacetime and horizons. We calculated energy and angular momentum in the framework of general relativity as well as in Pseudo-Newtonian theory. A graphical comparison of angular momentum in this both framework has been studied.

This article addresses the question how the religious narrative identity and subjective religiosity change over the course of 15 years. The cases portrayed are deconverts who have changed their religious affiliations multiple times. It will be carved out what led to their deconversion and what remains as a core of their faith after they have turned away from organized religion for good. Interviews have been conducted at three time points and are analyzed using content analysis. It will become clear that the needs and expectations of the two individuals differ highly, as well as the reasons for turning away from a religious community; yet what is a common core in this joint faithful journey is their need to live their religiosity, now in a private setting.

In a previous paper\cite{simplified}, a variation of the Collision-time Statistics method was applied to identify the relevant perturbers for line broadening under the action of a constant magnetic field. As discussed, that version was simplified and inadequate for low magnetic field and/or large perturber mass (ions). The purpose of the present work is to augment the previous work, so that such cases, corresponding to the Larmor radius exceeding the maximum impact parameter, can be handed efficiently. The results may also be used to construct analytic, i.e. impact/unified models under the usual assumptions in these models.

Keyhole neurosurgery implies reaching a target area inside the brain through an entry point specified by the neurosurgeon. In order to avoid complications, a risk assessment procedure must be done to establish the minimum risk trajectory from the entry point to the target area. The neurosurgeon establishes the risk values for the brain structure according to the type of intervention. The preset brain structure risk value is used to assess the risk value for each voxel of the brain. This paper proposes an improved risk assessment methodology based on the sum of N maximum risk values for each voxel. Then, risk assessment for a trajectory is done by adding the risk of all voxels that are part of the path. The safest trajectory is defined as the trajectory with the lower risk. Our proposed search trajectory methodology includes a Genetic Algorithm (GA) for finding the safest trajectories. The use of a GA drastically reduces the number of trajectories to analyze, speeding up the planning procedure. The achieved results were qualified by expert neurosurgeons as satisfactory. Our proposed method allows neurosurgeons to calibrate the surgical planning system by allowing them to establish the risk brain structure and the risk value for each structure.

Aiming at the problem of model error and tracking dependence in the process of intelligent vehicle motion planning, an intelligent vehicle model transfer trajectory planning method based on deep reinforcement learning is proposed, which obtain an effective control action sequence directly. Firstly, an abstract model of the real environment is extracted. On this basis, Deep Deterministic Policy Gradient (DDPG) and vehicle dynamic model are adopted to jointly train a reinforcement learning model, and to decide the optimal intelligent driving maneuver. Secondly, the actual scene is transferred to equivalent virtual abstract scene by transfer model, furthermore, the control action and trajectory sequences are calculated according to trained deep reinforcement learning model. Thirdly, the optimal trajectory sequence is selected according to evaluation function in the real environment. Finally, the results demonstrate that the proposed method can deal with the problem of intelligent vehicle trajectory planning for continuous input and continuous output. The model transfer method improves the model generalization performance. Compared with the traditional trajectory planning, the proposed method output continuous rotation angle control sequence, meanwhile, the lateral control error is also reduced.

Introduction: The disease caused by Coronavirus-2019 (COVID-19) was initially described in December 2019. Severe acute respiratory syndrome type 2 (Sars-Cov-2) is responsible for an important multisystem inflammatory spectrum. Cardiovascular conditions have been reported with an estimated frequency of 8-28%. In this context, transthoracic echocardiography associated with the analysis of the two-dimensional Global Longitudinal Strain (GLS) of the left ventricle derived from Speckle Tracking emerges as a promising modality. Objective: This study aims to evaluate the applicability of GLS and segmental deformity assessment in detecting subclinical myocardial dysfunction in patients that recovered from COVID-19 infection. Methods: The observational study involved 18 patients (mean age 52 years) with recent evidence of COVID-19 infection that underwent detailed echocardiographic evaluation with GLS, and cardiac magnetic resonance (CMR) according to segmental assessment results. The degree of correlation between the methods was analyzed and a systematic literature review was also conducted. Results: The mean left ventricular ejection fraction (LVEF) was 57.50 ± 9.98%. Only 16.6% of patients had reduced LVEF. The average GLS was – 18.70 ± 3.54% and in 44% of cases only one myocardial segment was affected, particularly the inferolateral basal. CMR revealed inflammatory cardiomyopathy. Discussion: Subclinical myocardial involvement is associated with major adverse cardiac events. Literature review supports the use of GLS for detecting early cardiac involvement. Conclusion: Speckle-tracking echocardiography has been shown to have clinical utility in a variety of settings and to offer superior prognostic value with a potential to enhance subclinical detection of myocardial changes in patients after COVID-19 infection.