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.

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.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

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.

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.

Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on eight subjects while performing 5-minute sitting and walking tasks. Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity.

The variation of aerosols, especially dust aerosol, in time and space plays an important role in climate forcing studies. Aerosols can effectively reduce land surface longwave emission and re-emit energy at a colder temperature, making estimation of downwelling surface longwave radiation (DSLR) with satellite data difficult. Using the latest atmospheric radiative transfer code (MODTRAN 5.0), we simulate the outgoing longwave radiation (OLR) and DSLR under different land surface and atmospheric profile conditions. The results show that dust aerosol has an obvious “warming” effect to longwave radiation compared with other aerosols, that aerosol longwave radiative forcing (ALRF) increased with increasing aerosol optical depth (AOD), and that the atmospheric water vapor content (WVC) is critical to the understanding of ALRF. A method is proposed to improve the accuracy of DSLR estimation from satellite data for the skies under heavy dust aerosols. The AOD and atmospheric WVC under cloud-free conditions with a relatively simple satellite-based radiation model that yields the high accurate DSLR under heavy dust aerosol are used explicitly as model input to reduce the effects of dust aerosol on the estimation of DSLR. Validations of the proposed model with satellites data and field measurements show that it estimates the DSLR accurately under heavy dust aerosol skies. The root mean square errors (RMSEs) are 20.4 W/M² and 24.2 W/M² for Terra and Aqua satellites, respectively, at the Yingke site, and the biases are 2.7 W/M² and 9.6 W/M², respectively. For the Arvaikheer site, the RMSEs are 23.2 W/M² and 19.8 W/M² for Terra and Aqua, respectively, and the biases are 7.8 W/M² and 10.5 W/M², respectively. The proposed method is especially applicable to acquire relatively high accurate DSLR under heavy dust aerosol using MODIS data with available WVC and AOD data.

Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity and low cost. However, the thorniest issue for wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRU) in composite material samples via pulsed thermography (PT). Carbon Fiber Reinforced Polymer(CFRP) embedded with flat bottom holes were designed via Finite Element Method (FEM) modeling in order to precisely control the depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

Accurate power curve modelling is essential to continuously evaluate the performance of a wind turbine (WT). In this work, we characterize the wind power curves using SCADA data acquired at a frequency of 5 minutes in a wind farm (WF) consisting of 5 WTs. Regarding the non-parametric methods, we select artificial neural networks (ANNs) to make curve estimations. Given that, we have the curves provided by the manufacturer of the WTs given by some very precisely measured pair of wind speed and power points. We can evaluate the difference between the manufacturer characterization and the ones estimated with the data provided by the SCADA system. Before the estimation, we propose a method of filtering the anomalies based on the characteristics provided by the manufacturer. We use three-quarters of the available data for curve estimation and one-quarter for the test. One WT suffered a break in the test part, so we can check how the test estimates reflect this problem in its wind-power curve compared to the estimations obtained in the WTs that worked adequately.

Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location's recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area's local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.

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.

The main objective of this work is the assessment of the annual number of hospital admissions for respiratory disease (HARD) due to the exposure to in-healable particulate matter (PM10), within the greater Athens area (GAA), Greece. Towards this aim, the time series of the particulate matter with aerodynamic diameter less than 10μm (PM10) recorded in six monitoring stations located in the GAA, for a 13-year period 2001-2013, is used. Initially, a descriptive statistical treatment of PM10 concentrations took place. Furthermore, the AirQ2.2.3 software developed by the WHO was used to evaluate adverse health effects by PM10 in the GAA during the examined period. The results show that, during the examined period PM10 concentrations present a significant decreasing trend. Also, the mean annual HARD cases per 100,000 inhabitants ranged between 20 (suburban location) and 40 (city centre location). Approximately 70% of the annual HARD cases are due to city centre residents. In all examined locations, a declining trend in the annual number of HARD cases is appeared. Moreover, a strong relation between the annual number of HARD cases and the annual number of days exceeding the European Union daily PM10 threshold value was found.

Employing remote sensing techniques to evaluate crop residue cover (CRC) enables rapid mapping of tillage practices over large cropland areas, which has significant practical implications for monitoring the implementation of conservation tillage practices. This research utilized Sentinel-2 MSI and Landsat-8 OLI remotely sensed data, various spectral indices were calculated and the datasets were correlated with the field-measured CRC data. Selected spectral indices, highly significantly correlated, were chosen to build a correlation model between the indices and the CRC. Considering Sentinel-2 MSI and Landsat 8 OLI remotely sensed data variations in spectral and spatial scales, the accuracy of CRC models was evaluated using R2 and RMSE values. From the results, it's evident that all six spectral indices, including STI, NDTI, SRNDR, NDI5, NDI7 and NDSVI, registered correlation coefficients with the CRC exceeded 0.3 in the study area. STI and NDTI showed a stronger correlation to CRC, with coefficients of 0.810 and 0.803, respectively. For the Landsat 8 data, the lowest correlation obtained was for NDSVI with the Landsat 8 data with a correlation coefficient of 0.358. Applying the linear regression technique, STI and NDTI obtained higher accuracy compared to other indices in the Sentinel-2 MSI data. For NDTI and STI, The R2 values were 0.650 and 0.665, respectively, while their corresponding RMSE values were 3.96% and 4.03%. The highest model accuracy was obtained for the STI and NDTI constructed from the Landsat 8 OLI data (R2 values 0.41 and 0.40; RMSE values 5.25% and 5.27%, respectively). The CRC model built using partial least squares regression (PLSR) exhibited greater precision when applied to the Sentinel-2 MSI data (R2 value 0.894; RMSE value 2.18%) in comparison to the Landsat 8 OLI data (R2 value 0.695; RMSE value 3.70%). PLSR has the potential to enhance the precision of the model. In addition, the study area shows that the estimation of the CRC is better in the one-dimensional linear regression model and the PLSR model created with the Sentinel-2 MSI optical data compared to the Landsat 8 OLI optical remotely sensed data. The validation suggests that the CRC estimation in the study area is more precise when using the model relying on Sentinel-2 satellite imagery rather than the Landsat 8 OLI imagery.

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map on a pixel-by-pixel basis, the actual evapotranspiration (ET_a) of the Wonji Shoa Sugarcane Estate using the Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0 - 6.85, 0.0 – 9.36, 0.0 – 3.61, 0.0 – 6.83 mm/day; using SSEB 0.0 - 6.78, 0.0 – 7.81, 0.0 – 3.65, 0.0 – 6.46 mm/day, and SSEBop were 0.05 - 8.25, 0.0 – 8.82, 0.2 – 4.0, 0.0 – 7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ET_a over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop had higher ET_a values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season, ET_a is limited to water surplus areas only and in wet season, ET_a was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ET_a over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ET_a estimated for the whole estate were 107 Mm³, 140 Mm³, and 178 Mm³ using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.

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.

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.

Introduction: Hypertension is a serious public health problem in Morocco. The objective of this pilot study was to estimate the salt intake of Moroccan adults by measuring 24-hour urinary sodium excretion. Methods: 132 participants were recruited from the central region of Morocco. Sodium, potassium and creatinine levels were measured using the 24-hour urine samples. Additional data included anthropometric measurements and socio-demographic questionnaire. Results: The daily sodium, potassium and creatinine excretions were 2779.1±1334.9 mg, 1350.0±642.8 mg and 820.3 ± 381.2 mg, respectively. Data analysis revealed that 71.2% of the participants had a daily sodium intake more than 2000 mg (5g of salt) recommended by the WHO. The mean of potassium excretion was 1350.0±642.8 mg /day, this average was lower than 3.51g per day recommended by the WHO. Conclusion: Public health measures to reduce sodium and increase potassium consumption in order to decrease the population’s risk of high blood pressure and heart disease are recommended.

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.

With the progression of AI, embedding advanced AI technologies into small robotics and mobile devices has become essential, driving research towards lightweighting AI models. Our study enhances the EPINET depth estimation model for light field images, aiming for compactness and faster inference while preserving accuracy. We conducted two-step experiments aimed at enhancing inference efficiency: Initially, by adjusting input streams and convolution layers, we simplified the CNN model, achieving faster inference times at the cost of reduced accuracy. To address this reduction in accuracy, we then applied knowledge distillation, allowing the simplified model to learn from the original model’s more complex patterns. In our quantitative experiments using two error metrics, MSE (Mean Squared Error) and BadPix, we identified optimal knowledge positions and evaluated the required complexity for the student model. As a result, our method improved MSE by 21% and BadPix by 14% compared to training without it. Furthermore, the student model achieved an inference speed 13% faster than the teacher model and surpassed its accuracy by 10% in MSE. Additionally, we demonstrated that repeatedly applying our approach could further enhance both model compactness and accuracy.

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.

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.

Understanding the dynamics of Organic Carbon mineralization is fundamental in forecasting biosphere to atmosphere Net Carbon Ecosystem Exchange (NEE). With this perspective, we developed 3D-CMCC-PSM, a new version of the hybrid Process Based Model 3D‐CMCC FEM where also heterotrophic respiration (R_h) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting Ecosystem Respiration (R_eco) to Gross Primary Productivity (GPP). To do so, we developed a simplification of the Soil Carbon dynamics routine proposed in DNDC [1]. The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameters distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggesting that it may need further improvements in addressing evapotranspiration and water dynamics.

Forest ecosystems make a greater contribution to carbon (C) stocks than any other terrestrial ecosystem. To understand the role of regional forest ecosystems in global climate change and carbon exchange, forest C stock and its spatial distribution within the small (2,300 km2) Liuxihe River basin were analyzed to determine the different contributors to the C stock. Forest C stocks were quantified by measuring the biomass of trees, understory vegetation, litter and roots, as well as soil organic C, using data from field samples and laboratory experiments. The results showed that forests stored 38.04 Tg C in the entire basin, with secondary and planted forests accounting for 89.82% and 10.18%, respectively, of the stored C. Five types of forests, a subtropical evergreen broad-leaved forest, a subtropical coniferous and broad-leaved mixed forest, a subtropical coniferous forest, a timber forest, and a non-wood forest, stored 257.55 ± 15.01, 218.92 ± 9.59, 195.24 ± 18.29, 177.42 ± 17.55, and 117.86 ± 6.04 Mg C ha−1, respectively. In the forest ecosystem C stocks of the basin, soils averagely contribute about 73.78%, not including root underground biomass. It provides a comprehensive method for forest ecosystem carbon investigation and forest management in small basin scale.

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.

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.

In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper better communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as a helpful tool to enhance UAV communication networks. However, due to the high mobility of UAV, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. Simulation results showed that the proposed GRU model effectively and accurately estimates the link quality of the ground users in the RIS-assisted UAV-enabled wireless communication network.

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.

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.

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.

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. Fruit Harvest Helper seeks to simplify their process. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. The old and new applications were designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app utilizes a color ratio-based image segmentation algorithm implemented in OpenCV C++ to detect apples in apple tree images selected for processing. The results of testing the algorithm with a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture, nearing readiness for farmers to use in yield monitoring and farm management.

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.

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

Eutrophication, a global concern, impacts water quality, ecosystems, and human health. It’s crucial to monitor algal blooms in freshwater reservoirs, as they indicate the trophic condition of a waterbody through Chlorophyll-a (Chla) concentration. Traditional monitoring methods, however, are expen-sive and time-consuming. Addressing this hindrance, we developed models using remotely sensed data from the Sentinel-2 satellite for large-scale coverage, including its bands and spectral indexes, to estimate the Chla concentration on 149 freshwater reservoirs in Ceará, Brazil. Several machine learning models were trained and tested, including k-nearest neighbours, random forests, extreme gradient boosting, the least absolute shrinkage, group method of data handling (GMDH), and sup-port vector machine models. A stepwise approach determined the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH, achieving an R2 of 0.91, MAPE of 102.34%, and RMSE of 20.38 g/L, which are values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near infra-red bands.

Time estimation was investigated in 24 healthy adults, 12 women and 12 men, before and after an exhaustive exercise. We compared the capability of estimating time intervals in the range from 1 to 5 s using tasks requiring mental count and tasks that did not allow the mental count. Time estimation and blood lactate levels were evaluated before and at the end of the exercise. We found that the perception of time intervals between 1 and 5 s was affected at the end of the exercise. The observed effects, associated with a significant increase in blood lactate levels, were different in the two types of time estimation used in the present study. In experimental conditions in which participants had to evaluate duration of the time interval by mental counting, at the end of exercise a significant reduction in the overestimation of time made at rest was observed. On the other hand, when participants had to assess the difference in duration between two events without the possibility to mentally count, at the end of the exercise a significant deterioration in performance was observed. In both cases, no gender differences were seen.

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.

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.

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.

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.

1) Landsat operational land imager (OLI) data and consequent laboratory measurements were used to predict Chlorophyll-a (Chl-a) concentration and the trophic states for an inland lake within the East Kolkata Wetland, India; 2) The most suitable band ratio was identified by performing Pearson correlation analysis between Chl-a concentrations and possible OLI band and band ratios from the study points; 3) The results showed highest correlation coefficient from the band ratio OLI5/OLI4 with an R value of 0.85. The prediction model was then developed by applying regression analysis between the band ratio OLI5/OLI4 and Chl-a concentration of the study points. The reflectance ratios of the validation points were given as input on the prediction model and the model output was considered as predicted Chl-a values of the validation points to check the efficiency of the prediction model. The regression model between laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points revealed a high correlation with an R² value of 0.78. Trophic State Index (TSI) of the lake was also calculated from laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points. The study presented a high correlation of TSI determined from predicted data with TSI from laboratory reference data (R = 0.88). The TSI values of the lake ranged from 65 to 75 which indicate that the lake is appeared to be eutrophic to hypereutrophic conditions. 4) This empirical study showed that Landsat 8 OLI imagery can be effectively applied to estimate Chl-a levels and trophic states for inland lakes.

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.

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.

Wireless communication has proliferated due to the colossal augmentation of smart phones, mobile phones, tablets and laptops etc. Multimedia applications such as live videos, audios, still images, animated graphics and live communications all requires accompaniments of quality of service (QoS) as well as reliable end-to-end transfer of data. Information about the availability of resources in channel is must in order to improve the QoS. QoS can be determined in terms of capacity of channel, available bandwidth (ABW), and bulk transfer capacity (BTC). Bandwidth availability is a key check for improving the QoS in a network. The performance of a multimedia application is directly affected by the bandwidth availability. One of the most important QoS characteristic is ABW at a wireless route and it can be demarcated as least unused capacity of links instituting a network route. Since now there have been many bandwidth estimation techniques are available in the literature to increase the network performance. Bandwidth estimation techniques have been arranged into three leading classifications: i) Active probing bandwidth estimation techniques, ii) Passive bandwidth estimation technique and iii) Model based bandwidth estimation techniques. Each of these techniques are briefly discussed in this paper.

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.

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 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.

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.

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.

The stock beta coefficient literature extensively discusses the proper methods for the estimation of beta as well as its use in asset valuation. However, there are relatively few references with respect to the appropriate time horizon that investors should utilize when evaluating the risk-return relationship of a stock. We examine the appropriate time horizon for beta estimation differentiating our results by sector according to the Industry Classification Benchmark. We employ data from the NYSE and we estimate varying lengths of beta employing data from 30 to 250 trading days. The constructed beta series is then examined for the presence of breaks using the endogenous structural break literature. Results show evidence against the use of betas that employ more than 90 trading days of data provisional to the sector under study.

This work addresses the problem of automatic head pose estimation and its application in 3D gaze estimation using low quality RGB--D sensors without any subject cooperation or manual intervention. The previous works on 3D head pose estimation using RGB--D sensors require either an offline step for supervised learning or 3D head model construction which may require manual intervention or subject cooperation for complete head model reconstruction. In this paper, we propose a 3D pose estimator based on low quality depth data, which is not limited by any of the aforementioned steps. Instead, the proposed technique relies on modeling the subject's face in 3--D rather than the complete head, which in turn, relaxes all of the constraints with the previous works. The proposed method is robust, highly accurate and fully automatic. Moreover, it does not need any offline step. Unlike some of the previous works, the method only uses depth data for pose estimation. The experimental results on the Biwi head pose database confirm the efficiency of our algorithm in handling large pose variations and partial occlusion. We also evaluate the performance of our algorithm on IDIAP database for 3D head pose and eye gaze estimation.

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.

This article explores the intricate journey of neural progenitor cells from the ventricular zones of the embryonic brain, focusing on their pivotal role in the formation of rich club networks, which are integral to the brain's functional architecture. Initially, the ventricular zones, particularly the cerebral ventricular zone, serve as the primary sites for the proliferation and differentiation of neural progenitors, giving rise to various types of neurons and glial cells. We delve into the sequential migration patterns of these cells, emphasizing how early-migrating neurons, destined to form deeper cortical layers, establish extensive and robust connections, potentially evolving into rich club nodes. These nodes are characterized by their high-degree connectivity and serve as crucial hubs for integrating and disseminating information across disparate brain regions.The article further discusses the implications of this developmental pathway in the context of brain network efficiency, resilience, and cognitive function. We highlight how disruptions in early cell migration can lead to anomalies in rich club formation, drawing connections to various neurodevelopmental disorders such as autism, ADHD and schizophrenia. Additionally, the significance of rich club nodes in maintaining efficient information transfer and network resilience is elucidated, underscoring their role in supporting complex cognitive processes and overall brain dynamics.In summary, this article offers a comprehensive overview of the developmental trajectory from the ventricular zones to the establishment of rich club networks, providing insights into the fundamental processes that shape the brain's intricate connectivity and functional capabilities. Ric

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).

The goal of this study was to characterize the initial growth trajectory of Epicrates assisi, 1 Epicrates cenchria, and Epicrates crassus snakes raised in captivity. An exponential model with 2 parameters for birth weight (A) and growth rate (k) was fitted for each animal. There was a significant 3 difference A and k among the three species, the means (standard error) for A and k parameters were 4 31 (0.3) g and 0.0027 (0.00006) for E. assisi, 27.6 (0.6) g and 0.0046 (0.00013) for E. cenchria and 25.9 5 (1.4) g and 0.0033 (0.00027) for E. crassus. We showed a significative variability between species for 6 initial growth trajectory. In addition, we also demonstrated the existence of variability within species, 7 which can be used to change the growth trajectory of snakes, being a tool for selection and genetic 8 improvement.9

As the problem of air pollution becomes more urgent, the precise determination of the characteristics of air pollution and the associated mechanisms of transport of pollutants becomes an increasingly urgent task. The simulation results of the Copernicus Atmospheric Monitoring Service (CAMS) [18] were used as initial data sets. The area of the Crimean Peninsula (within coordinates 44.50 latitude, 33.50 longitude) in 23 - 26 January, 2019 was chosen for the study. That time concentrations of pollutants (aerosol particles with a diameter of 2.5 (PM2.5) and 10 micrometers (PM10)) steadily increased and reached the maximum value. Four kinds of meteorological data sets were used in the work. The first and second data sets were the global NCEP/NCAR and (ERA-5) re-analyses. The third and fourth meteorological data sets were obtained using the Weather tracking and Forecasting model (WRF) as domains on high-resolution grids: 9 km and 2 km. The analysis of meteorological data sets was carried out to analyze the nature of the air masses movement in the studied region. Using the HYSPLIT model simulation was carried out to determine area of the sources of negative impact. The emissions transfer from the supposed sources of negative impact on the Crimea region is analyzed. The sensitivity of simulation the inverse and direct trajectories of air masses transfer was studied based a set of simulation results. On the basis of HYSPLIT model the simulation was carried out separately using meteorological data sets having different spatial and temporal resolutions. In general, the simulation results allowed us to conclude that the HYSPLIT model in combination with high-resolution meteorological input data of the WRF model are good to identify the sources of possible negative effects on atmospheric air located at long distances from the study site.

Traffic congestion and frequent traffic accidents have become main problems affecting urban traffic. Effective location prediction of vehicle trajectory can help alleviate traffic congestion, reduce the occurrence of traffic accidents, and optimize the urban traffic system. Vehicle trajectory is closely related to the surrounding Points of Interest (POI). POI can be considered as the spatial feature and can be fused with trajectory points to improve prediction accuracy. A Local Dynamic Graph Spatiotemporal- Long Short-Term Memory (LDGST-LSTM) is proposed in this paper to extract and fuse the POI knowledge and realize next location prediction. POI semantic information is learned by constructing the traffic knowledge graph, and spatial and temporal features are extracted by combining Graph Attention Network (GAT) and temporal attention mechanism. Moreover, the weights of POI that influence location prediction are visualized to improve the interpretability of the proposed model. The effectiveness of LDGST-LSTM is verified on two datasets, including Chengdu taxi trajectory data in October 2018 and August 2014. The accuracy and robustness of the proposed model are significantly improved compared with the benchmark models.

Reducing the construction time for large logistics buildings can result in reduced construction management costs and economic gains from early operation. A large logistic building with a heavy load and long spans was constructed as a precast concrete (PC) structure, which requires the use of a sizable crane to lift heavy PC members. A basic analytical approach is employed to resolve potential errors in the planning of PC member erection and to build a systematic erection plan. Calculation techniques for the trajectory distance, which use the crane location, are applied to select an erection plan that minimizes crane work. A crane trajectory distance calculation algorithm for sustainable PC member erection in large logistic buildings with heavy loads and long spans has been developed. The developed model aids in creating simulation and optimization models to ensure minimal usage of cranes in the future and determining cost, construction time, CO2 emissions, and energy use for each erection plan.

The mRNA vaccines for SARS-CoV-2 have demonstrated efficacy and immunogenicity in the re-al-world setting. However, most of the research on vaccine immunogenicity has been centered on characterizing the antibody response, with limited exploration into the persistence of spike-specific memory B cell response. Here we monitored the durability of the memory B cell response and characterize the trajectory of spike-specific B cell phenotypes in healthy individuals who have received two doses of the BNT162b2 vaccine up to 9 months post-vaccination. To profile the spike-specific B cell response we applied the tSNE and Cytotree automated approaches. Our data showed the induction of spike-specific IgA+ and IgG+ plasmablasts and IgA+ activated cells 7 days after the second dose which disappeared 3 months later, while subsets of spike-specific IgG+ resting memory B cells became predominant 9 months after vaccination, and they were capable to differentiate into spike-specific IgG secreting cells when in vitro restimulated. Other subsets of spike-specific B cells, such as IgM+ or unswitched IgM+IgD+ or IgG+ double negative/atypical cells, were also elicited by the BNT162b2 vaccine and persisted up to month 9. The analysis of circulating spike-specific IgG, IgA and IgM was in line with the plasmablasts observed. The longitudinal analysis of the antigen-specific B cell response elicited by mRNA-based vaccines provides valuable insights into our understanding of the immunogenicity of this novel vaccine platform destined to a future widespread use, and it is crucial in guiding future decisions and vaccination schedules.

Reasonable trajectory planning is the precondition for the parafoil airdrop system to achieve autonomous accurate homing and safe landing. In order to successfully realize the self-homing of the parafoil airdrop system, a new trajectory optimization design scheme is proposed in this paper. The scheme is based on the parafoil's unique flight and control characteristics and adopts a segmented homing design. Compared with the current common trajectory design method, its core feature is to avoid the problem that the straight-line flight distance before landing is limited by the radius of the height-reducing area and ensure landing accuracy and safety. Firstly, the different starting states of the parafoil airdrop system and the landing requirements were comprehensively considered, and the homing trajectory is reasonably segmented. Based on the requirements of energy control, stable flight, and landing accuracy, the optimal objective function of the trajectory was established, and the trajectory parameters, calculation methods, and constraints were given. Secondly, the cuckoo search algorithm was applied to optimize the objective function to obtain the final home trajectory. Finally, the trajectory planning under different airdrop conditions was simulated and verified. The results showed that the planned trajectories could reach the target point accurately and meet the flight direction requirements, proving the proposed scheme's correctness and feasibility.

With the great development of intelligent transportation systems (ITS), travel time prediction has attracted the attentions of many researchers and a large number of prediction methods have been developed. However, as an unavoidable topic, the predictability of travel time series is the basic premise for travel time prediction has received less attention than the methodology. Based on the analysis of the complexity of travel time series, this paper defines travel time predictability to express the probability of correct travel time prediction and proposes an entropy-based method to measure the upper bound of travel time predictability. Multiscale entropy is employed to quantify the complexity of travel time series, and the relationships between entropy and the upper bound of travel time predictability are presented. Empirical studies are made with vehicle trajectory data in an express road section. The effectiveness of time scales, tolerance, and series length to entropy and travel time predictability are analysis, and some valuable suggestions about the accuracy of travel time predictability are discussed. Finally, the comparisons between travel time predictability and actual prediction results from two prediction models, ARIMA and BPNN, are conducted. Experimental results demonstrate the validity and reliability of the proposed travel time predictability.

Existing methods for simulating missile dynamics and training often rely on three degrees of freedom (3DOF) motions, limiting their ability to represent the complex maneuvers and targeting strategies of modern missiles. This paper proposes integrating virtual reality (VR) technology with six degrees of freedom (6DOF) dynamics to enhance the accuracy and efficiency of missile simulation and training. By incorporating 6DOF dynamics, simulations can capture the full range of missile motion, providing a comprehensive understanding of their capabilities and limitations. Through VR, users can immerse themselves in realistic missile scenarios, interact with dynamic behaviors in real-time, and refine their targeting and interception skills. The integration of VR with 6DOF dynamics also facilitates real-time feedback and evaluation, enhancing training efficiency and proficiency in missile operations. This approach aims to revolutionize missile training by bridging the gap between traditional simulation methods and real-world dynamics.

The rate constants of elementary processes in the atom-diatom system O(³P)+H₂(v), including the processes of vibrational relaxation and dissociation, were studied using the quasi-classical trajectory method. All calculations were carried out along the ground potential energy surface (PES) ³A′′ that was approximated by a neural network. Approximation data were obtained using ab initio quantum chemistry methods at the extended multi-configuration quasi-degenerate second-order perturbation theory XMCQDPT2 in a basis set limit. The calculated cross sections of the reaction channels are in good agreement with the literature data. A complete set of state-to-state rate constants was obtained for the metathesis reaction, the dissociation and VT relaxation of the H₂ molecule upon collision with an O atom. According to this data Arrhenius approximations over a wide temperature range were obtained for the thermal rate constants of considered processes. Data obtained on the dissociation constants and VT relaxation of vibrationally excited H₂ molecules can be used in constructing kinetic models describing the oxidation of hydrogen at high temperatures or highly nonequilibrium conditions.

The novel Ionized Gas Thermoelectric Generator (IG-TEG) system that has been studied theoretically showing capabilities to continually extracting energy from the thermal energy of the ambient air, at low temperatures within the standard room temperature and below. This system does not need a temperature gradient in order to work, unlike the other TEGs that use Seebeck effect, and therefore this new system can be utilized for cooling purposes, by extracting energy instead of wasting energy in compressing the gas for cooling. This novel system was designed based on Static Ratchet Potential (SRP), which is known as a spatially asymmetric electric potential produced by an array of positive and negative electrodes. The ratchet potential produces electrical current from random Brownian Motion of charged particles that are driven by thermal energy. Ratchet potential was studied and investigated by several researches in the fields of sensing and energy harvesting. The main ratchet potential system parameter is the particles transportation, this parameter was studied under the condition of flashing ratchet potentials, and was analyzed based on several methods. In this study, a different approach is pursued to estimate particles transportation, by evaluating the charged particles distribution, and applying the other conditions of the SRP.

A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as the Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we both study the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard, we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators; the multiple point Maximum A-Posteriori (MAP) estimators; an efficient class of multiple-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators; and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.

During the conceptual and preliminary design stage of a ship, designers need to ensure that the selected principal dimensions and parameters are good enough to deliver a stable ship (statically and dynamically) besides deadweight and speed. To support this, the initial intact stability of the proposed ship is required to be calculated, and in doing so, the lightship weight and its detailed breakdown are necessary to be known. After hull steel weight, machinery weight, mainly, marine propulsion and power generation machinery, play a vital role in the lightship weight estimate of a ship due to its robustness. The correct estimation of respective weights improves the accuracy of calculating a ship's initial stability typically to be designed and built. Hence, it would be advantageous for the designer to convince the ship owner. A total of 3006 marine propulsion (main marine diesel) engines and 348 power generation (auxiliary marine diesel) engines/generators of various power output (generators output for auxiliary engines), engine RPM and cylinder number of different engine makers are collected. These are analyzed and presented in both tabular and graphical forms to demonstrate the possible relationship between marine propulsion engine weight and power generation engine weight, and their respective power output, RPM, cylinder number, power-RPM ratio and power-RPM ratio per cylinder.In this article, the authors have attempted to investigate the behavior of marine propulsion engine weight and power generation engine/generator weight regarding engine power output, generator power output, engine RPM and cylinder number (independent variables). Further attempts have been made to identify those independent variables that influence the weight of the marine propulsion engine and power generation engine/generator (dependent variables), and their interrelationships. A mathematical model has thus been developed and proposed, as a guiding tool, for the designer to estimate the weight of main and auxiliary engines more accurately during the conceptual and preliminary design stage.

Estimation and planning play a vital role in the construction of an autonomous navigation framework. However, these problems are often considered separately, while planning gives robot a free-collision path towards the desired goal, estimation algorithm presents the executed trajectory in the sense that it has to be closed to the ground truth path as much as possible. Recently, a unified probabilistic framework, which supports solving these problems simultaneously, dubbed STEAP has been proposed. Nevertheless, its current version is only designated for an omni wheels robot, which allows robot to move and turn in vertical direction. Differential drive robot, on the other hand, though limited to move along only one direction, has been used in various situations due to its flexibility and lower cost in hardware designing. Thus, in this extension, our aim is to control a differential drive robot via STEAP. Moreover, in a more complicated environment such as labyrinth or maze, the original STEAP sometimes fails to find a path. Indeed, this problem is mainly caused by the poor initialization and the non-linearity in optimizer constraints. In our implementation, instead of dealing with these constraints, we employ a global planner algorithm such as Dijkstra or RRT to treat STEAP as an effective local planner module that focus on following the global path. Consequently, the experimental results show that the extended STEAP not only able to navigate a differential drive robot but also in a more complicated and unstructured environment.

Modelling diameter distribution is an integral part of forest management. It requires the use of a suitable probability density function or cumulative distribution function with the appropriate fitting method. In this study, we compared the suitability of eight probability density functions fitted by derivative and optimization methods used for diameter distribution estimation of forest stands. The derivation and optimization were used on A Charlier, beta, generalized beta, gamma, Gumbel, Johnson’s SB, and Weibull (2 and 3-parameter). We used data from 167 permanent sample plots from Atlantic forest (Quercus robur) and 59 temporary sample plots from tropical forests (Tectona grandis). The quality of the fits was evaluated with different indices such as Kolmogorov–Smirnov, Cramer-von Mises, mean absolute error, bias and mean squared error. The results showed Johnson’s SB function was more suitable for describing the diameter distribution of the stands. Johnson’s SB, 3-parameter Weibull and generalized beta had consistent performance regardless of the fitting methods. The quality of fits produced by gamma, Gumbel and 2-parameter Weibull was poor.

This interdisciplinary investigation of the human remains of Balduin Helm, one of the most important abbots of Fürstenfeld monastery, provides novel information on that historic individual. This is particularly interesting since Balduin, during early 18th century, was involved in the renovation of this large Bavarian monastery. Metastatic prostate cancer was found as evidenced by multiple mixed osteolytic-osteoblastic bone lesions in all available vertebral bodies, fragments of both hemipelvises and isolated metastases in skull bones. Distribution, radio- and histomorphology, and especially the immunohistochemical detection of prostate-specific antigen in those metastases, definitively confirm this diagnosis. Further investigation, especially by stable isotope analysis, showed a balanced high-level diet with considerable contribution from animal protein and significant freshwater fish. These additional findings suggest a significant radiocarbon reservoir effect as an explanation for a “too old” radiocarbon dating. Finally, the obviously high-level protein diet may have contributed to the tumorigenesis which caused the death of the abbot at an advanced age.

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.

The population’s mobility in urban areas is a necessary variable in the modeling of risk scenarios caused by atmospheric contamination. The inclusion of this concept makes static models more dynamic while considering people within a city to be an entity with complex mobility processes. We propose a conceptual and methodological tool to make the representation of the social, economic and territorial components, as well as the patterns in the population´s mobility to delimitate risk areas for human health by exposure of contaminants. In the volatile organic compounds (VOC), benzene, ethylbenzene, toluene and xylene (BTEX) are amongst the most dominant substances in fugitive vapor emissions in gas stations (GS). In urban areas, the exposure to BTEX by residential proximity and proximity to other facilities, which cause intra-urban agglomeration, can impact and affect human health. This model seeks to facilitate the focalization, identification and prioritization of risk areas by BTEX environmental contamination. This article goes beyond de conceptual framework. It suggests methodological and instrumental aspects to be applied in other cities. The government agencies must consider these results when establishing rules, permissions and procedures to reduce environmental pollution for managing the risk in a complex urban environment.

Software project estimating (SPE) is a powerful but necessary step in the production of projects for software. Software project estimating includes several components, estimating software time, software resources, software costs, and software effort estimation (SEE). SEE seeks to estimate the number of "hours-person" and "months-person" of work required for developing or managing a software program. During the initial phase of the development of software, future efforts are impossible to predict. To estimate effort, many deep learning (DL) and machine learning (ML) models were previously created. For estimate, single-model techniques and ensemble methods were investigated in this research. Ensemble methods involve a combination of multiple separate models. Bagging, boosting, and voting were the ensemble methods looked into for estimation. Datasets considered for estimation were COCOMO81, COCOMONasa I, COCOMONasa II, Albrecht, Maxwell, China, Desharnais, Desharnais_1_1, Kitchenham, Boehm, and Belady. The evaluation criteria used MAE, RAE, RMSE, RRSE, and CC. The result from previous studies is that the total size of the set of data utilized is small in predicting the effort, as the largest size of the set of data used is the China dataset containing only 499 projects. So, in this study, the dataset has been merged to increase the total size of the set of data and increase the accuracy of prediction. The results indicated that the suggested merged dataset increased effort estimation accuracy.

Crowd density estimation is an important task for crowd monitoring. Many efforts have been done to automate the process of estimating crowd density from images and videos. Despite series of efforts, it remains a challenging task. In this paper, we proposes a new texture feature-based approach for the estimation of crowd density based on Completed Local Binary Pattern (CLBP). We first divide the image into blocks and then re-divide the blocks into cells. For each cell, we compute CLBP and then concatenate them to describe the texture of the corresponding block. We then train a multi-class Support Vector Machine (SVM) classifier, which classifies each block of image into one of four categories, i.e. Very Low, Low, Medium, and High. We evaluate our technique on the PETS 2009 dataset, and from the experiments, we show to achieve 95% accuracy for the proposed descriptor. We also compare other state-of-the-art texture descriptors and from the experimental results, we show that our proposed method outperforms other state-of-the-art methods.

Cycle times at workstations in offsite construction factories fluctuate widely due to various influencing factors. Consequently, relying on average rates, such as length per unit of time, for estimating cycle times proves to be inaccurate, often leading to significant deviations between production schedules and actual operations. To address this issue, this study proposes an estimation system that leverages machine-learning-based prediction, statistical methods, 3D simulation, and computer vision to predict cycle times at the workstation level. Testing of the system on a semi-automated wood-wall framing workstation in a panelized construction factory shows that it reduces the mean absolute error and sum of errors by approximately 36% and 68%, respectively, compared to the fixed rate method. The results also highlight the efficacy of using computer vision data for training machine-learning models for cycle time estimation, the importance of identifying and understanding the factors influencing cycle times, and the impact of random delays on the accuracy of cycle time estimation systems.

In many research fields, statistical probability models are often used to analyze real-world data. However, data from many fields, such as the environment, economics, and health care, do not necessarily fit traditional models. New empirical models need to be developed to improve their fit. In this paper, we explore a further extension of the quasi-Lindley model. Maximum likelihood, least square error, Anderson-Darling algorithm, and expectation-maximization algorithm are four techniques for estimating the parameters under study. All techniques provide accurate and reliable estimates of the parameters. However, the mean square error of the expectation maximization approach was lower. The usefulness of the proposed model was demonstrated by analyzing a dynamical systems data set, and the analysis shows that it outperforms the other models in all statistical models considered.

The power function distribution is a flexible waiting time model that may provide better fit for some failure data. This paper presents the comparison of the maximum likelihood estimates and the Bayes estimates of two-parameter power function distribution. The Bayes estimates are obtained, using conjugate priors, under five loss functions consist of square error, precautionary, weighted, LINEX and DeGroot loss function. The Gibbs sampling algorithm is proposed to generate samples from posterior distributions and in result the Bayes estimates are computed. The comparison of the maximum likelihood estimates and the Bayes estimates are done through the root mean squared errors. One real-life data set is analyzed to illustrate the evaluation of proposed methods of estimation. Finally, results from the simulation are discussed to assess the performance behavior of the maximum likelihood estimates and the Bayes estimates.

Structural displacement is an important metric for assessing structural conditions because it has a direct relationship with the structural stiffness. Many bridge displacement measurement techniques have been developed, but most methods require fixed reference points in the vicinity of the target structure which limits field implementations. A promising alternative is to use reference-free measurement techniques that indirectly estimate the displacement by using measurements such as acceleration, and strain. This paper proposes novel reference-free bridge displacement estimation by the fusion of single acceleration with pseudo-static displacement derived from co-located strain measurements. First, we propose a conversion of the strain at the center of a beam into displacement based on the geometric relationship between strain and deflection curves with reference-free calibration. Second, an adaptive Kalman filter is proposed to fuse the displacement generated by strain with acceleration by recursively estimate the noise covariance of displacement from strain measurements which is vulnerable to measurement condition. Both numerical and experimental validations are presented to demonstrate the efficiency and robustness of the proposed approach.

Visual inertial odometry (VIO) has recently received much attention for efficient and accurate ego-motion estimation of unmanned aerial vehicle systems (UAVs). Recent studies have shown that optimization-based algorithms achieve typically high accuracy when given enough amount of information, but occasionally suffer from divergence when solving highly non-linear problems. Further, their performance significantly depends on the accuracy of the initialization of inertial measurement unit (IMU) parameters. In this paper, we propose a novel VIO algorithm of estimating the motional state of UAVs with high accuracy. The main technical contributions are the fusion of visual information and pre-integrated inertial measurements in a joint optimization framework, and the stable initialization of scale and gravity using relative pose constraints. To count for ambiguity and uncertainty of VIO initialization, a local scale parameter is adopted in the online optimization. Quantitative comparisons with the state-of-the-art algorithms on the EuRoC dataset verify the efficacy and accuracy of the proposed method.

Inflammation is a cause of number of disorders. Many anti-inflammatory drugs are available to cure and prevent inflammation and its related disorders. Some of the drugs are from plant and animal origin. Among animals snake venom is also utilized as treatment option of certain disorders. In the current study the venom of N. oxiana from Northern areas of Pakistan is assayed to determine its anti-inflammatory activity. The Naja oxiana is also called central Asian cobra is endemic to Central Asia. Naja oxiana belong to family Elapidae and it is highly venomous snake. The venom has many enzymatic proteins as well as nonenzymatic proteins that is neurotoxins. The venom is lethal having very high mortality rate and the death may occur due to respiratory failure in 45 minutes to 24 hrs depending upon the nature and amount of venom. As the venom is highly toxic so initially after venom milking its protein estimation was done and toxicity profile and LD50 was determined to find out its therapeutic dose at which anti-inflammatory activity was observed. The toxicity and anti-inflammatory activity was determined on mice model. Inflammation was induced by carrageenan induced peritonitis method. The inflammatory markers ROS, TNF-α and IL-β were determined by ELIZA in peritoneal fluid. The LD50 of crude venom, fraction 1 and fraction 2 was found as 34 µg/kg, 7.4 µg/kg and 416 µg/kg respectively. Anti-inflammatory activity crude venom, fraction 1 and fraction 2 was found at dose 19 µg/kg, 1.8 µg/kg and 267 µg/kg respectively. The levels of inflammatory markers reduced after therapeutic dose administration of venom and its fractions that indicates that it can be utilized as anti-inflammatory agent and so can be effective in various disorders.

This paper analyzes low-Earth orbit (LEO) satellite downlinks when an airborne interference source moves parallel to the satellite trajectory by considering the relative angle differences be-tween the satellites and the interference sources. To make the experimental interference situations more like actual environments, the LEO trajectories are obtained from two-line element set (TLE) data. Airborne interference sources with various altitudes move parallel to the LEO trajectories, and the jamming to signal (J/S) ratio is calculated based on the relative angle differences between the ground station, the LEO satellite, and the interference source. In order to calculate the relative angle difference psi, the coordinates of the satellite and the interference source are converted from the World Geodetic System 1984 (WGS84) to the ground station-centered east-north-up (ENU) system. By applying the relative angle difference psi, we can obtain the sidelobe gain of the ground station antenna in the direction from which the interference comes. The results of the study confirm that, from a J/S ratio perspective, the distances between the ground station, LEO satellite, and airborne interference source are important, and in particular, the relative angle difference psi between the interference source and the satellite is more critical factor.

Against the backdrop of the current carbon peaking and carbon neutrality policies, higher requirements have been put forward for the upgrading and construction of smart grids. Non-intrusive load monitoring (NILM) technology is a key technology for advanced measurement systems at the end of the power grid. It obtains detailed power information of the load, without the need for traditional hardware deployment. The key step to solve this problem is load decomposition and identification. This paper first utilized Long Short Term Memory-Denoising Autoencoder(LSTM-DAE) to decompose the mixed current signal on the household busbar and obtain the current signals of multiple independent loads that constituted the mixed current. Then, the obtained independent current signals were combined with voltage signals to generate multi-cycle colored Voltage-Current(VI) trajectories, which were color-coded according to the background. These colored VI trajectories with background colors formed a feature library. When the CNN network was used for load recognition, considering the influence of hyperparameters on recognition results, the BOA algorithm was used for optimization, and the optimized CNN network was employed for VI trajectory recognition. Finally, the proposed method was validated using the PLAID dataset. Experimental results showed that the proposed method exhibited better performance in load decomposition and identification.

Nitrogen is one of the most important macronutrients for crops, and in conjunction with artificial intelligence algorithms, it is possible to estimate it with the aid of vegetation indices through remote sensing. Various indices were calculated and those with a correlation ≥ 0.7 were selected for subsequent use in Random Forest, Gradient Boosting, and Artificial Neural Networks to determine their relationship with nitrogen levels measured in the laboratory. Random Forest showed no relationship, yielding an R2 of zero, whereas Artificial Neural Networks yielded the best results with an R2 of 0.93. Thus, it is reliable to estimate nitrogen levels using this algorithm by feeding it with data from TCARI, MCARI, TCARI/OSAVI, and MCARI/OSAVI, assisted by technological tools.

A number of accidents at sea are primarily caused by low visibility due to sea fog therefore, it is important to estimate sea fog intensity and visibility in real-time in the ocean environment. However, the existing estimation systems have significant costs so that it is not viable to install them at various locations, such as small vessels, minor ports, and a number of buoys. Therefore, this paper proposes an algorithm called RDCP (Reduced Dark Channel Prior) which provides the estimation at a low complexity and a low cost. For the estimation, the RDCP algorithm firstly acquires dark channels from an ocean image, then applies an optimized threshold value to the dark channels, and crops out the sky region in the ocean image. For the estimation performance evaluation, 320 raw images captured from cameras at four different ports in South Korea are used rather than artificially composed images. Considering facilities in oceans which are usually operated in the power limited environment, the processing time performance of PDCP are also examined. The experiments demonstrate that the RDCP algorithm provides reliable estimation performance in real-time.

Rice canopy height and density are directly usable crop phenotypic traits for the direct estimation of crop biomass. Therefore, it is crucial to rapidly and accurately estimate rice canopy phenotypic parameters. To achieve non-destructive detection and estimation of essential phenotypic parameters in rice, a platform based on LiDAR point cloud data for rice phenotypic parameter detection was established. Data collection of rice canopy layers was performed across multiple plots. The LiDAR-detected canopy top point clouds were selected using a method based on the highest percentile, and the rice canopy surface model was calculated. Canopy height estimation was the difference between ground elevation and percentile value. To determine the optimal percentile defining the rice canopy top, testing was conducted incrementally from 0.8 to 1 with an increment of 0.005. The optimal percentile value was found to be 0.975. The root mean square error (RMSE) between LiDAR-detected canopy height and manually measured canopy height was calculated. The prediction model based on canopy height (R2=0.941, RMSE=0.019) exhibited a strong correlation with actual canopy height. Linear regression analysis was conducted between gap fraction of different plots and manually detected average Leaf Area Index (LAI) of rice canopy. Prediction models for canopy LAI based on ground return counts (R2=0.24, RMSE=0.1) and ground return intensity (R2=0.28, RMSE=0.09) showed strong correlations but had lower correlation with rice canopy LAI. Regression analysis was performed between LiDAR-detected canopy height and manually measured rice canopy LAI. The results indicated that the prediction model based on canopy height (R2=0.77, RMSE=0.03) was more accurate.

Foundational and state-of-the-art anomaly detection methods through power system state estimation are reviewed. The traditional components for bad data detection such as chi-square testing, residual-based methods, and hypothesis testing are discussed to explain the motivations for recent anomaly detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest change detection and artificial intelligence are discussed and directions for research are suggested with particular emphasis on considerations of the future smart grid.