Research has shown that emotional states may influence the subjective experience of time. The present study involved 111 healthy adult participants and examined the impact of emotional stimuli on time perception, using a time estimation task with four conditions: in one of the conditions, participants estimated the exposure time of a black screen, wherein the duration could vary be-tween 2 to 7 s (condition without emotional interference); in the other conditions, participants es-timated the exposure time (also ranging from 2 to 7 s) of images that would induce pleasant, un-pleasant, or neutral emotions, to which participants were instructed to focus their attention (pleasant, unpleasant, or neutral emotions). The results showed that participants estimated “shorter” time intervals (underestimation) for pleasant and unpleasant images compared to the condition without interference, but no significant differences were found between the neutral condition and the condition without interference. It is suggested that increased attention to an ex-ternal stimuli, regardless of its emotional valence, produces an underestimated perception of time.

China's economic development has shifted from high speed to high quality. This study constructs an evaluation index for China’s high-quality development utilizing a new development concept. The entropy method and cluster analysis were used to assess the level of high-quality development in China between 2015 and 2021. Kernel density estimation method was employed to explore spatiotemporal dynamics. The results reveal significant differences in the level of economic development among provinces in China; the eastern region is significantly better than the central, northeastern, and western regions. From the perspective of the sub-item evaluation, challenges with uneven, uncoordinated, and insufficient economic development exist in various Chinese provinces.

This manuscript presents a novel approach to estimating the potential within Schrödinger equations, with a particular focus on applications to the Navier-Stokes problem in fluid dynamics. By establishing new theoretical estimates, we delve into the intricate dynamics of fluid flow, aiming to unveil previously obscured aspects of the Navier-Stokes equations. Through a rigorous mathematical framework, we explore how these new potential estimates can provide a fresh perspective on fluid mechanics, contributing to the ongoing quest to solve some of its most persistent challenges.

Positioning based on wireless signals such as mobile communication networks has become an important means to provide high-precision location services in environments where satellite signals are blocked. In complex environments such as indoor and underground, wireless signal propagation is obstructed and non-line-of-sight (NLOS) phenomena appears due to serious occlusion and reflection. The time delay caused by NLOS effects has little impact on communication system but can significantly increase positioning errors in positioning systems. Therefore, effective suppression of NLOS errors is crucial to improving 5G positioning accuracy. To address the insufficient feature extraction of existing NLOS error suppression methods, the neglect of residual NLOS measurement errors, and poor stability of position estimation results, this paper innovatively proposes a NLOS mitigation and location estimation method for 5G positioning terminals. Simulation and experimental test results demonstrate that the proposed method outperforms the comparative methods both theoretically and practically, achieving an average positioning accuracy of 1.85 meters in complex indoor NLOS environments. The method proposed in this paper provides a new strategy for NLOS error suppression in indoor 5G positioning, which can significantly contribute to high-precision location services based on commercial 5G networks.

The high reliable correlations between CBR and DCP in general proposed are presented in this paper. We analyzed the test data and correlations from many researchers to find the best equation for prediction the CBR from DCP test. The findings show that all correlations from existing paper are based on local material and cannot apply to soil in a different location in general purpose. New correlations between CBR and DCP were proposed and validated in this paper for general uses. The validation confirmed that these new correlations can predict well with higher R2.

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.

Using UAV-based multispectral images for quickly and accurately monitoring chlorophyll content is critical for field management and yield estimation. However, the lower model accuracy and the poor robustness of the estimation models are still preventing the widespread application of UAV-based multispectral images. We carried out two field trials at various experimental sites to further enhance the precision and applicability of the model used to estimate the chlorophyll content of potato plants. Firstly, the texture features and vegetation indices derived from multispectral images were screened using the Pearson correlation coefficient method, and Normalized difference red edge (NDRE) performed the best over the two growth periods. Secondly, principal component analysis (PCA) was applied to recombine five bands of multispectral images, and third PCA results (PCA3) was selected to combined with NDRE according to the construction principle of NDRE, and the newly constructed parameter was named improved NDRE (INDRE). Finally, INDRE was used to establish a chlorophyll content estimation model of potato plants, and compared with some traditional parameters. The results demonstrated that the INDRE had the maximum accuracy (R2 = 0.7865, RMSE = 2.1378), and corresponding R2 increased by 0.1481 and RMSE decreased by 1.2994 than NDRE. Additionally, the model was validated using independent data from Experiment 2, and INDRE considerably increased estimation accuracy compared to other factors. In conclusions, the INDRE suggested in this study significantly enhances the accuracy and applicability of the chlorophyll content inversion model and can serve as an additional reference for fertilization management.

Estimating parameters for multiple datasets can be time consuming, especially when the number of datasets is large. One solution is to sample from multiple datasets simultaneously using Bayesian methods such as adaptive multiple importance sampling (AMIS). Here we use the AMIS approach to fit a von Misses distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from a 3D meteorological data. The primary objective is to characterise the uncertainties in wind trajectories in a form that can be used as inputs for predictive models of wind-dispersed insect pests and pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.

The paper presents an error model of a measurement chain containing link performing discrete wavelet transform algorithm which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose.

Lower limb exoskeletons have the potential to reduce the prevalence of work-related musculoskeletal disorders; however, they often lack user-oriented control strategies. Human-in-the-loop (HITL) controls can adapt an exoskeleton’s assistance in real-time, to optimize the user-exoskeleton interaction. This study presents a HITL control for a knee exoskeleton to minimize the users’ physical effort, a parameter innovatively evaluated by their interaction torque with the exoskeleton (a muscular effort indicator) and metabolic cost. This work innovates by estimating the user’s metabolic cost within the HITL control through a machine-learning model. The regression model was able to estimate the metabolic cost, in real-time, with a root-mean-squared error of 0.66 W/kg and a mean absolute percentage error of 26%, making faster (10s) and less noisy estimations than a respirometer (K5, Cosmed) device. The HITL reduced the user’s metabolic cost and interaction torque by 7.3% and 32.3%, respectively, when compared to a zero-torque control. The developed HITL control surpassed both a non-exoskeleton and a zero-torque condition regarding the user’s physical effort, even for a simple task such as slow walking. Furthermore, the user-specific control enabled a lower metabolic cost than the non-user-specific assistance. This proof-of-concept demonstrated the potential for HITL controls in assisted working.

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.

Conclusions: Our findings suggest that the NMS-E represents a valuable preoperative diagnostic tool for endometriosis, effectively correlating with the disease's severity and surgical outcomes. Incorporating the NMS-E into clinical practice could significantly enhance the management of endometriosis by addressing current diagnostic limitations and guiding surgical planning.

In this paper, four probability functions are compared with the purpose of establishing a methodology to improve the accuracy of the estimated wind energy in a desert city in northwestern Mexico. For the statistical modeling, 3 time series of wind speed data were used, corresponding to the years 2017, 2018, and 2019, recorded with a sonic anemometer at a sampling frequency of 10 Hz. From these, the analysis is performed with different stationarity periods (5, 30, 60, and 600 seconds). The estimation of the parameters characterizing the probability density functions was performed using different methods; the statistical models were evaluated by the coefficient of determination and the Nash-Sutcliffe efficiency coefficient, while their accuracy by the measured quadratic error, the mean square error, the mean absolute error, and the mean absolute percentage error. It was found that Weibull with the energy pattern factor method and Gamma using the method of moments were the probability density functions that best describe the statistical behavior of the wind speed and, therefore, better estimate the energy generated, so it is concluded that the proposed methodology will allow having greater confidence, both in the estimation of the wind speed and in the small wind energy available for its use.

Classically the walk in biped robotics was obtained controlling balance during the whole step, i.e. guaranteeing that the pressure point under the soles always stayed in the polygon of the supporting feet. However, this assumed that the feet were able to transfer torque to the ground during the whole gait cycle. In spite of the fact that the amount of transferrable torque in the feet-ground contact is limited, it is possible only during some phases of the step, and the overall process is energetically inefficient. On the other side, starting from the passive motion of the rimless wheel falling on an inclined surface, and ending to the inverted pendulum with a compass, balance in the whole was proven in spite of dynamical instability inside each step. Along this line results of Foot Placement Estimation (FPE) in 2-D and 3-D showed how energy efficient walk was possible, emulating the human walk with a free fall on the swing foot and energy restitution at the foot collision with the ground for the next step. This model assumes pointy feet, so without torque transfer to the ground. In the realm of FPE, in previous papers the present author adopted the 3-D inverted pendulum in polar coordinates (Spherical Inverted Pendulum - SIP) to introduce omnidirectional walks with arbitrarily changing characteristics. No torque control was used during the step, i.e the pendulum was always in free fall at each step, the only control actions were at the beginning of the next step. These actions are: the change of angular velocities at the start of a new step, with respect to those given after the collision (emulating the torque action in the brief double stance period), to recover for the losses, and the preparation of the position in the frontal and sagittal planes of the swing foot for the next collision. The present paper improves this paradigm, proposing a general model to account for all characteristics of the biped and of the gait, with adding a minimum of dynamical complexity with respect to the SIP. This model allows, not only to walk omnidirectionally on a flat surface, but also to go up and down staircases.

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.

The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them appropriately in biomedical research, such as protein-protein interaction studies, protein design, and drug discovery. With the advent of more accurate protein complex (multimer) prediction tools, such as AlphaFold2-Multimer and ESMFold, the estimation of the accuracy of protein complex structures has attracted increasing attention. Many deep learning methods have been developed to tackle this problem; however, there is a noticeable absence of a comprehensive overview of these methods to facilitate future development. Addressing this gap, we present a review of deep learning EMA methods for protein complex structures developed in the past several years, analyzing their methodologies and impacts. We also provide a prospective summary of some potential new developments for further improving the accuracy of the EMA methods.

This paper proposes a novel formulation of the fuzzy newsvendor problem for inventory management applications. This new formulation allows the use of any profit function. A new credibility estimation is proposed to explore the neighborhood around the most impactful demand scenarios. Further, a simulation procedure was designed for the different demand scenarios, which allows the comparison of the proposed approach with probabilistic demand curves. The fuzzy newsvendor problem is solved using a modified genetic algorithm (GA), where an initialization mechanism with null values is proposed. The new formulation of the fuzzy newsvendor problem together with the modified GA have shown to improve the average profit up to 55% in problems with low-budget scenarios.

The design and operation of quantum networks are both decisive in the current push towards a global quantum internet. Although space-enabled quantum connectivity has already been identified as a beneficial candidate for long-range quantum channels for over two decades, the architecture of a hybrid space-ground network is still a work in progress. Here, we propose an analysis of such a network based on a best-path approach, where either fiber- or satellite-based elementary links can be concatenated to form a repeater chain. The network consisting of quantum information processing nodes, equipped with both ground and space connections, is mapped into a graph structure, where edge weights represent the achievable secret key rates, chosen as the figure of merit for the network analysis. A weight minimization algorithm allows identifying the best path dynamically, i.e., as the weather conditions, stray light radiance, and satellite orbital position changes. From the results we conclude that satellite links will play a significant role on the future large-scale quantum internet, and both a constellation of satellites and significant advances in filtering technology are required to fulfill continuous coverage.

This longitudinal study examined the development of executive function and calibration accuracy in pre-adolescents. Two hundred sixty two students(127 females) from grades 4 (n = 91), 5 (n = 89) and 6 (n = 82) took measures of executive function and performance calibration in a sport task, three times within a period of 20 months. A latent growth curve modeling analysis showed a significant relation between the rates of change of executive function and calibration. Further, the results showed a dynamic interplay in the development of executive function and calibration. There were significant inter-individual differences in the estimated population means both in executive function and calibration and in the rate of change of executive function, but not in the rate of change for calibration. The age of the participants had a positive effect only on the estimated population mean of executive function.

The manuscript presents the applicability of the Gumbel distribution in the frequency analysis of extreme events in hydrology. The advantages and disadvantages of using the distribution are highlighted, as well as recommendations regarding its proper use. A literature review was also carried out regarding the methods of estimating the parameters of the Gumbel distribution. Thus, for the verification of the methods, case studies are presented regarding the determination of the maximum annual flows and precipitations using nine methods for parameter estimation. The influence of the observed data lengths on the estimation of the statistical indicators, the parameters and the quantiles corresponding to the field of low annual exceedance probabilities (p <1%) is also highlighted. The results are compared to those obtained with the Generalized Extreme Value distribution, respectively the Burr and the Wakeby distributions with parameters estimated using the L-moments. The results highlight and reaffirm the statistical, mathematical and hydrological recommendation regarding the avoidance of using the Gumbel distribution in Flood Frequency Analysis and its use with reservations in the case of maximum precipitation, especially when the statistical indicators of the analyzed data are not close to the characteristic ones and unique to the distribution.

Sustainable continuous cover forestry is defined and analyzed in several ways. The differential equation representing growth of the basal areas of individual trees, motivated by fundamental biological production theory by Lohmander (2017a), is analyzed and extended in different directions. From the solution of the differential equation, the basal area and the tree diameter are obtained as explicit functions of time. The diameter is a strictly increasing function of time. In the absence of competitors, the diameter increment is shown to be a strictly decreasing function of time. Hence, the diameter increment can also be interpreted as a strictly decreasing function of the diameter. Alternative forms of adjustment of the differential equation, with consideration of competition, are defined. If the competition is strong, with large trees in the vicinity of a particular tree, then the basal area increment, and the diameter increment, are reduced. The growth of a large tree is less sensitive than the growth of a small tree, to competition from other trees. Under strong competition, the basal area increment, and the diameter increment, are strictly concave functions of the size of the tree. The unique maximum of the diameter increment occurs at a higher diameter, if the competition increases. In dynamic equilibrium, the tree size frequency distribution is stationary. If natural tree mortality can be avoided via the harvest strategy, the tree size frequency distribution is a function of the size and competition dependent growth function, and the harvest strategy. Empirical tree size frequency data are used to simultaneously estimate parameters of a size and competition dependent growth function and the applied harvest strategy, via nonlinear optimization. The properties of the estimated growth function are consistent with the corresponding properties of the production theoretically motivated hypothetical function, and the properties of the estimated harvest strategy confirm the corresponding hypotheses. The R2 of the nonlinear regression exceeds 0.97. With access to an empirically estimated equilibrium tree size distribution, it is possible to: 1. Estimate size frequency relevant parameters of tree size and competition dependent growth functions for individual trees. 2. Estimate the applied harvest strategy. 3. Explain and reproduce the empirically estimated tree size equilibrium distribution.

Descriptive statistics for parametric models are currently highly sensative to departures, gross errors, and/or random errors. Here, leveraging the structures of parametric distributions and their central moment kernel distributions, a class of estimators, consistent simultanously for both a semiparametric distribution and a distinct parametric distribution, is proposed. These efficient estimators are robust to both gross errors and departures from parametric assumptions, making them ideal for estimating the mean and central moments of common unimodal distributions. This article also illuminates the understanding of the common nature of probability distributions and the measures of them.

Centimeter-level localization and rotation angle estimation of flatbed trucks are major problems in unmanned forklift automated loading scenes. To solve it, this study proposes a method for high-precision positioning and rotation angle estimation of flatbed trucks based on BeiDou and vision. First, an unmanned forklift equipped with a ToF camera and double antenna mobile receiver for BeiDou positioning collects depth images and localization data near the front and rear endpoints of the flatbed. Then DDRNet-23-slim was used to segment the flatbed from the depth image and extract the straight lines at the edges of the flatbed using the Hough transform and compute the set of intersection points of the lines. A neighborhood feature vector was designed to locate the endpoint of a flatbed from a set of intersection points by feature screening. Finally, the relative coordinates of the endpoints were converted to a customized forklift navigation coordinate system by BeiDou positioning, and rotation angle estimation was performed by the front and rear endpoints. Experiments showed that the endpoint positioning error was less than 3 cm and the rotation angle estimation error was less than 0.1°. The precision of the method meets the demand for automated flatbed truck loading by unmanned forklifts.

In this paper, an adaptive proportional and integral (PI) controller for speed control of electric drives is developed. The adaptive PI controller is designed based on model reference adaptive identification and the Lyapunov function so that the system stability can be proved. To identify the inertia, the viscous friction torque coefficient, and the load torque simultaneously and smoothly, sinusoidal speed reference is usually employed. A modified motion equation considering the low-pass filter for feedback speed and the Coulomb friction torque is introduced and the relationship between the identified parameters and their actual values are clarified. A high-precision digital simulation model is built based on MATLAB/Simulink. The effectiveness of the proposed method is verified by simulations under various conditions.

Caring for dolphins is a delicate process that requires experienced caretakers to pay close attention to their behavioral characteristics. However, caretakers may sometimes lack experience or not be able to give their full attention, which can lead to misjudgment or oversight. To address these issues, a dolphin behavior analysis system has been designed to assist caretakers in making accurate assessments. The study utilized image preprocessing techniques to reduce sunlight reflection in the pool and enhance the outline of dolphins, making it easier to analyze their movements. The dolphins were divided into 11 keypoints using an open-source tool called DeepLabCut, which helped accurately mark various body parts for skeletal detection. The AquaAI Dolphin Decoder (ADD) was then used to analyze six dolphin behaviors. To improve behavior recognition accuracy, the Long Short-Term Memory (LSTM) neural network was introduced. The ADD and LSTM models were integrated to form the ADD-LSTM system. Several classification models, including unidirectional and bidirectional LSTM, GRU, and SVM, were compared. The results showed that the ADD module combined with a double-layer bidirectional LSTM method achieved high accuracy in dolphin behavior analysis. The accuracy rates for each behavior were over 90%.

(1) Background/Objectives: Accurate determination of chronological age is crucial in legal dental identification. This study aimed to compare the effectiveness of different formulas in estimating the age of a Brazilian subpopulation by analyzing the pulp/tooth ratio of the maxillary canine and mandibular second premolar in panoramic and periapical radiographs. (2) Methods: The sample consisted of panoramic and periapical radiographs of 247 individuals. The file of each radiograph was opened in the Adobe Photoshop CS4® program to outline and obtain values in pixels for calculating the pulp/tooth ratio. Statistical analysis was conducted using the SPSS program, with a significance level set at 5%. (3) Results: The interclass correlation coefficient demonstrated excellent intra-observer agreement (0.990 – 0.999). The determination coefficients (R2) suggested that only 30-35% of the actual age results could be explained by the pulp/tooth ratio. Smallest differences were observed with Cameriere's formula for the mandibular second premolar on panoramic radiographs (+4.1 years). The greatest differences were found with the formulas for the mandibular second premolar in panoramic radiographs of the Korean (+12.5 years) and Portuguese (-12.1 years) populations. (4) Conclusions: The equations employed allowed for estimating the age of a Brazilian subpopulation and showed little agreement between the actual age and the estimated age.

Human Pose Estimation (HPE) is a technique in computer vision and AI for detecting and tracking human body parts and poses from images or videos. Widely used in augmented reality, animation, fitness applications, and surveillance, HPE methods using monocular cameras are highly versatile due to their applicability in standard video and CCTV footage. These methods have evolved from 2D to 3D pose estimation. However, current 3D HPE methods trained on laboratory-based motion capture data encounter challenges such as limited training data, depth perception ambiguity, left/right switching, and issues with occlusions when applied in real-world environments. This study compares two representative 3D HPE methods by assessing their strengths and weaknesses with real-world videos. Then, we propose data processing techniques to eliminate and correct anomalies like left/right inversion and false detections of joint positions in daily life motions. Finally, we obtain joint angle trajectories using an optimization method based on a 3D humanoid simulator, taking as input the joint coordinate data corrected by applying the proposed human joint data processing technique. The efficacy of the proposed 3D HPE method is verified by applying it to three-dimensional freehand gymnastics exercises and comparing the joint angle trajectories during the motion.

This study explores the capital structure determinants of Korean firms over an extensive period (1995-2021), encompassing both stable and crisis conditions. Employing a dynamic panel data model and the Generalized Method of Moments (GMM) estimation, we address the endogeneity issue introduced by the inclusion of lagged dependent variables. Our research integrates firm-specific internal factors with macroeconomic external variables to provide a comprehensive understanding of the influence of varying economic environments on capital structure. Our study suggests that in times of economic stability, the capital structure decisions of a firm are more influenced by internal factors such as profitability. However, in periods of economic downturns, it's the external macroeconomic market conditions that tend to have a greater impact on these decisions. It's also noteworthy that both book leverage (BL) and market leverage (ML) exhibit quicker adjustments during stable periods as opposed to periods of crisis. This indicates a higher agility of firms in adapting their capital structures in stable normal conditions. Our findings contribute to existing literature by offering a holistic view of capital structure determinants in Korean firms. They underscore the necessity of adaptable financial strategies that account for both internal dynamics and external economic conditions. This study fills a gap in current research, presenting new insights into the dynamics of capital structure in Korean firms and suggesting a multifaceted approach to understanding capital structure in diverse economic contexts.

The hyperspectral vegetation index was defined based on the distinctive features of the spectral curve. In alignment with the growth and development characteristics of cotton, the spectral reflectivity of the cotton canopy was computed at different growth stages. The aim of this study was to clarify the association between cotton yield and canopy spectral indices and to develop yield estimation models utilizing hyperspectral imaging. The ASD Field Spec Pro VNIR 2500 spectrometer radiometer was employed to collect spectral reflectance data from cotton canopies at various growth stages. Using spectral analysis techniques, quantitative models were developed based on hyper-spectral vegetation indices, including Normalized Difference Vegetation Index (NDVI) (NDVI) and Radar Vegetation Index (RVI) to extract characteristic information from the cotton canopies. Following thorough testing and precise monitoring of the estimation models, the most optimal models representing cotton canopy structure parameters were identified. Results demonstrate that the power function model, relying on NDVI, provides the most accurate forecasting for Leaf Area Index (LAI)). Additionally, the exponential function model based on RVI proves to be the most effective for predicting cotton unit area above-ground fresh biomass, while another exponential function model based on RVI is identified as the best for predicting cotton unit area above-ground fresh biomass. Clearly, the application of hyper-spectral remote sensing technology enables the analysis, simulation, assessment, and providing scientific basis for precision cotton planting and cotton field management strategies.

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.

The implementation of a progressive rehabilitation training model to promote patients’ motivation efforts can greatly restore the damaged central nervous system function in patients. The patients’ active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot assisted torque which focuses on the evaluation only of the affected limb's movement ability. Moreover, the expected effect of assistance depends on the designer, which deviates from the patient's expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient’s healthy limbs. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner loop position control module for the pneumatic swing cylinder in the joint space. An upper limb endpoint stiffness estimation model is constructed and a parameter identification algorithm is designed. The upper limb endpoint stiffness which characterizes the patient's ability to complete training movements is obtained by collecting surface electromyographic (sEMG) signals and human-robot interaction forces during patient movement. Then the motor needs of the affected limb when completing the same movement are quantified based on the performance of the healthy limb. A stiffness mapping algorithm is designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method can effectively estimate the patient's movement needs and achieve progressive rehabilitation training. The rehabilitation training robot that simulates the movement characteristics of the patient's healthy limbs drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient's pre-morbid limb use habits, and also beneficial for the consistency of bilateral limb movements.

In Chuanming Zong’s program to attack Hadwiger’s covering conjecture, which is a longstanding open problem from Convex and Discrete Geometry, it is essential to estimate covering functionals of convex bodies effectively. Recently, He et al. [J. Optim. Theory Appl. 2023, 196, 1036–1055.] and Yu et al. [Mathematics, 2023, 11, 2000] provided two deterministic global optimization algorithms having high computational complexity for this purpose. Since satisfactory estimations of covering functionals will be sufficient in Zong’s program, we propose a stochastic global optimization algorithm based on CUDA and provide an error estimation for the algorithm. The accuracy of our algorithm is tested by comparing numerical and exact values of covering functionals of convex bodies including the Euclidean unit disc, the three-dimensional Euclidean unit ball, the regular tetrahedron, and the regular octahedron. We also present estimations of covering functionals for the regular dodecahedron and the regular icosahedron.

We propose Context-aware Feature Transformer Network (CaFTNet), a novel network for human pose estimation. To address the issue of limited modeling of global dependencies in convolutional neural networks, we design Transformerneck to strengthen the expressive power of features. Transformerneck directly substitutes the 3×3 convolution in bottleneck of HRNet with Contextual Transformer (CoT) block, while reducing the complexity of the network. Specifically, CoT first produces keys with static contextual information through 3×3 convolution. Then, relying on the query and contextualization keys, the dynamic contexts are generated through two concatenated 1×1 convolutions. Static and dynamic contexts are eventually fused as an output. Additionally, for the multi-scale networks, in order to further refine the features of the fusion output, we propose an Attention Feature Aggregation Module(AFAM). Technically, given an intermediate input, AFAM successively deduces attention maps along channel and spatial dimensions. Then, Adaptive refinement module(ARM) is exploited to activate the obtained attention maps. Finally, the input undergoes adaptive feature refinement through multiplication with the activated attention maps. Through the above studies, our lightweight network provides a powerful clue for detection of keypoints. Experiments are implemented on the COCO and MPII datasets. The model achieves 76.2 AP on the COCO val2017. Compared to other methods with the CNN as the backbone, CaFTNet reduces the number of parameters by 72.9 %. On the MPII, our method uses only 60.7% of the number of parameters, acquiring semblable results to other methods with the CNN as the backbone.

Lump sum contracts are specific contracts used in the construction industry to give owners financial certainty and decrease risk while incentivizing contractors to finish projects on schedule and within budget. This article discusses the usage of lump sum contracts in construction projects, covering the types of projects appropriate for lump sum contracts, critical considerations for entering a lump sum contract, and the tender and preliminary expenses involved. The article also covers lump sum contracts' benefits and drawbacks and how owners and contractors allocate risk. Finally, the difficulties of implementing lump sum contracts, such as potential disagreements over scope changes and quality issues, are investigated. In general, lump sum contracts can be a valuable tool for managing construction projects, but their execution necessitates careful planning and collaboration to ensure successful completion.

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).

Advanced methods of treatment are needed to fight the threats of virus-transmitted deseases and pandemics. Often they are based on an improved biophysical understanding of virus replication strategies and processes in their host cells. For instance, an essential component of the replication of the Hepatitis C virus (HCV) proceeds under the influence of non-structural HCV proteins (NSPs) that are anchored to the endoplasmatic reticulum (ER), such as the NS5a protein. The diffusion of NSPs has been studied by in-vitro fluorescence recovery after photobleaching (FRAP) experiments. The diffusive evolution of the concentration field of NSPs on the ER can be described by means of surface partial differential equations (sufPDE). Knodel et al, Viruses 2018, 10, 28 estimated the diffusion coefficient of the NS5a protein by minimizing the discrepancy of an extended set of sufPDE simulations and experimental FRAP time series data. Here, we provide a scaling analysis of the sufPDEs that describe the diffusive evolution of the concentration field of NSPs on the ER. This analysis provides an estimate of the diffusion coefficient that is based only on the ratio of the membrane surface area in the FRAP region and its contour length. The quality of this estimate is explored by comparison to numerical solutions of the sufPDE for a flat geometry and for ten different 3D embedded 2D ER grids that are derived from fluorescence z-stack data of the ER. Finally, we apply the new data analysis to the experimental FRAP time-series data analyzed in our previous paper, and we discuss the opportunities of the new approach.

Gesture recognition technology has been quickly developed in the field of human-computer interaction. The multiple-input multiple-output (MIMO) radar has been widely adopted in gesture recognition because of its notable spatial resolution. In this work, a highly accurate MIMO radar-based hand gesture recognition algorithm with very low complexity is proposed. To make the proposed system applicable in the industry and work well even when the size of the training data is limited, we applied several low-complexity adaptive signal processing methods to extract features and reduce the noise effect. First, spectrum analysis is applied to range-Doppler maps (RDMs) and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness to noise. Afterward, the features related to the distance, speed, direction, and the elevation angle of the moving object are determined by the proposed adaptive signal analysis techniques and the random forest is applied for classification. The proposed system has the capability to precisely distinguish and identify eight motions, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anticlockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.

In the literature, estimation of weighted extropy are least considered. In this paper some non parametric estimators of weighted extropy are given. The validation and comparison of the estimators are implemented with the help of simulation study and data illustration. The usefulness of the estimators are demonstrated using real data sets.

This study advances the field of solar irradiance nowcasting by introducing a discrete-level classification approach, diverging from traditional continuous measurement methods. Grounded in a need for cost-effective and modular solutions, the research employs high-resolution computer vision coupled with a deep learning framework to predict Direct Normal Irradiance (DNI). By harnessing the capabilities of a Logitech C900 1080p HD camera and an NVIDIA Jetson module, the study achieves real-time data processing, pivotal for CSP systems' operational efficiency. The core of the methodology is the ResNet-50 convolutional neural network, refined via transfer learning on a bespoke dataset, culminating in a predictive accuracy of 85.78%. This discrete classification model contrasts with conventional, costly instruments like the MS-57, offering a novel and accessible alternative for DNI estimation. Such innovation not only demonstrates high predictive accuracy but also signifies a shift towards less resource-intensive and more adaptable solar energy forecasting tools, contributing a significant leap towards optimizing renewable energy systems.

Earthquakes often entail alterations in the groundwater flow regime, in the phreatic level, in the surge and loss of springs, and discharge in brooks and springs. As for the latter, there are diverse feasible mechanisms, so there is a variety of theoretical approaches to elucidate the post-earthquake effects on spring discharge. This study adopts a conceptual approach, primarily presenting various methods for estimating water released by earthquakes by calculating discharge surpluses in springs. This study delves into refined techniques rooted in groundwater hydraulics, displaying applications of analytical and simulation methodologies to quantify seismic-induced groundwater discharge in springs. Investigating springs as natural indicators, this research applies mathematical precipitation-runoff models, specifically the CREC model, for simulating hydrographs in post-earthquake scenarios. We apply analytical procedures or mathematical simulation techniques employed in groundwater hydraulics for natural aquifer recharge calculations. Firstly, we briefly describe the methods based on the analysis of depletion curves of hydrographs in spring discharge. Additionally, certain mathematical rainfall-runoff models utilized in simulating hydrographs of karstic springs, along with derived analytical approximations, are adapted for this scenario. These hydraulic calculations involve the depletion coefficient and hydrodynamic volumes of aquifers, parameters that reveal certain aspects of the relation between groundwater and earthquakes, such as: a) Acknowledging faults as the primary geological structures in transmitting pore pressures due to earthquakes. b) Clarifying mechanisms of permeability enhancement during earthquakes. c) Highlighting the substantial volumes in motion within the Earth's interior, which, for instance, could constitute a significant source for the origin of mineral deposits. The findings highlight diverse features of the complex interplay between hydrogeological and tectonic processes at challenging spatial and temporal scales.

The Stepped Frequency Waveform (SFW) is commonly employed in radar technology to synthesize wideband signals through the aggregation of narrow-band pulses, thereby achieving high-resolution range profiles without the need to increase the radar's instantaneous bandwidth. However, the inherent large time-bandwidth product of SFW introduces substantial ranging errors and energy dispersion, which significantly hinders its efficacy in detecting high-speed objects. This paper introduces a pioneering velocity estimation technique utilizing the fractional Fourier transform (FrFT) to address these limitations. Leveraging the characteristic of the Doppler signal from a moving target, which manifests as a chirp signal with a rate proportional to the target's velocity, the FrFT is utilized for precise velocity estimation. Subsequent to this, velocity compensation is applied using the deduced metrics, followed by the application of the inverse fast Fourier transform (iFFT) to pinpoint the target's exact location. To optimize the computational efficiency of determining the FrFT's optimal order, we propose an iterative algorithm founded on the golden section search method. The effectiveness of the proposed approach is verified by simulation data, and the results demonstrate that the proposed approach can accurately estimate the velocity and the range of the high-speed targets with a relatively low computational complexity.

Reliable motor control design is a critical part of the electric vehicle application. Control techniques and motor control devices play an important role in achieving system reliability. In this study, motor control devices have been proposed to implement control techniques in sensorless PMSM. Motor control devices including PWM signal generation by FPGA, gate driver circuit (GDC), and power inverter were developed to be integrated into single-stage motor control devices for sensorless motor control applications. Improvements in GDC design have been the focus to increase the performance of the motor control devices used. Switching losses due to the parasitic elements caused ringing switching behavior of the power inverter circuit, which reduces the performance when the system is integrated. Moreover, in the implementation of sensorless motor control using observer techniques, these conditions affect the accuracy of rotor position estimation, which uses the current or voltage information of the power converter as a variable in the observer design model. Therefore, a GDC design using IR2110 with additional ringing suppression circuit configuration was proposed for the application of motor device control topology to improve the performance of sensorless motor control system. Furthermore, the reduction of switching losses, reduction of current distortion, and accuracy of rotor position information were confirmed by experimental results.

This paper considers the direction of arrival (DOA) estimation problem of phase ambiguity for unfolded coprime linear array (UCLA). The existing most common stacking subarray-based method can tackle the phase ambiguity problem. However, the method is not always true. For a given DOA, it has its corresponding steering vector, but if there are two DOAs which have the same steering vectors with the given DOA for different subarrays, the phase ambiguity problem still arises. A modified method, which defines a decision variable and uses the classic beamforming technique to estimate the DOAs is proposed. However, this method needs additional algorithm to distinguish the true DOAs besides multiple signal classification (MUSIC) spectrum. And sometimes this method is not reliable if the decision variable is not chosen appropriately. Therefore, based on the UCLA, we reconstruct the array to design an improved UCLA called IUCLA. With moving the reference element, the linear relationship among the directional vectors can be broken. In this way, we can directly utilize the MUSIC algorithm to estimate with no additional algorithm. The proposed method can solve the phase ambiguity problem and it does not demand other technique to decrease the complexity. Moreover, the Cramer-Rao Bound (CRB), as the lower bound of the unbiased estimation, is provided and numerical simulations are given to demonstrate the effectiveness and superiority of the proposed method.

This study introduces a multilayer perceptron (MLP) error compensation method for real-time camera orientation estimation, leveraging a single vanishing point and road lane lines within a steady-state framework. The research emphasizes cameras with a roll angle of 0°, predominant in autonomous vehicle contexts. The methodology estimates pitch and yaw angles using a single image and integrates two Kalman filter models with inputs from image points (u, v) and derived angles (pitch, yaw). Performance metrics, including AE, MINE, MAXE, SSE, and STDEV, were utilized, testing the system in both simulator and real-vehicle environments. The outcomes indicate that our method notably enhances the accuracy of camera orientation estimations, consistently outpacing competing techniques across varied scenarios. This method’s potency is evident in its adaptability and precision, holding promise for advanced vehicle systems and real-world applications.

In this paper, a special recurrent neural network (RNN) called \emph{Long Short-Term Memory (LSTM)} is used to design a virtual load sensor that estimates the mass of heavy vehicles. The estimation algorithm consists of a two-layer LSTM network, with the two layers based on sequence-to-sequence and sequence-to-one logic, respectively. The network estimates the vehicle mass based on the vehicle speed, longitudinal acceleration, engine speed, engine torque, and the accelerator pedal position. The network is trained and tested with a data set collected in a high-fidelity simulation environment called Truckmaker. Training data is generated in acceleration maneuvers in a range of speeds whereas test data is obtained by simulating the vehicle in a Worldwide harmonized light vehicles test cycle (WLTC). Preliminary results show that, with the proposed approach, the heavy-vehicle mass can be estimated as accurately as commercial load sensors in a range of load mass as wide as four tons.

Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a dominant approach due to its capacity to capture intricate data patterns and relationships. However, a prevalent challenge lies in the black-box nature of many deep learning algorithms, which often operate without adhering to the underlying physical characteristics intrinsic to the studied phenomena. To address this limitation and enhance the fusion of data-driven methodologies with the fundamental physics of the system under study, this paper leverages physics-informed neural networks (PINNs). Specifically, a simple but realistic numerical case study of an extended Jeffcott rotor model, encompassing damping effects and anisotropic supports for a more comprehensive modelling, is considered. PINNs are used for the estimation of five parameters that characterize the health state of the system. These parameters encompass the radial and angular position of the static unbalance due to the disk installed on the shaft, the stiffness along the principal axes of elasticity, and the non-rotating damping coefficient. The estimation is conducted solely by exploiting the displacement signals from the centre of the disk and, to showcase the efficacy and precision provided by this novel methodology, various scenarios involving different constant rotational speeds are examined. Additionally, the impact of noisy input data is also taken into account within the analysis.

Lithium-ion batteries with improved energy densities have made understanding the Solid Electrolyte Interphase (SEI) generation mechanisms that cause mechanical, thermal, and chemical failures more complicated. SEI processes reduce battery capacity and power. Thus, a review of this area's understanding is important. It is essential to know how batteries degrade in EVs to estimate battery lifespan as it goes, predict, and minimize losses, and determine the ideal time for a replacement. Lithium-ion batteries used in EVs mainly suffer two types of degradation: calendar degradation and cycling degradation. Despite the existence of several existing works in the literature, several aspects of battery degradation remain unclear or have not been analyzed in detail. This work presents a systematic review of existing works in the literature. The results of the present investigation provide insight into the complex relationships among various factors affecting battery degradation mechanisms. Specifically, this systematic review examined the effects of time, side reactions, temperature fluctuations, high charge/discharge rates, depth of discharge, mechanical stress, thermal stress, and the voltage relationship on battery performance and longevity. The results revealed that these factors interact in complex ways to influence the degradation mechanisms of batteries. For example, high charge currents and deep discharges were found to accelerate degradation, while low temperatures and moderate discharge depths were shown to be beneficial for battery longevity. Additionally, the results showed that the relationship between cell voltage and State-of-Charge (SOC) plays a critical role in determining the rate of degradation. Overall, these findings have important implications for the design and operation of battery systems, as they highlight the need to carefully manage a range of factors to maximize battery performance and longevity. The result is an analysis of the main articles published in this field in recent years. This work aims to present new knowledge about fault detection, diagnosis, and management of lithium-ion batteries based on battery degradation concepts. The new knowledge is presented and discussed in a structured and comprehensive way.

Accurate weight measurement is pivotal for monitoring the growth and well-being of cattle. However, the conventional weighing process, which involves physically placing cattle on scales, is labor-intensive and distressing for the animals. Hence, the development of automated cattle weight prediction techniques assumes critical significance. This study proposes a weight prediction approach for Korean cattle using 3D segmentation-based feature extraction and regression machine learning techniques from incomplete 3D shapes acquired from real farm environments. In the initial phase, we generated mesh data of 3D Korean cattle shapes using a multiple-camera system. Subsequently, deep learning-based 3D segmentation with the PointNet network model was employed to segment two dominant parts of the cattle. From these segmented parts, three crucial dimensions of Korean cattle were extracted. Finally, we implemented five regression machine learning models (CatBoost regression, LightGBM, Polynomial regression, Random Forest regression, and XGBoost regression) for weight prediction. To validate our approach, we captured 270 Korean cattle in various poses, totaling 1190 poses of 270 cattle. The best result was achieved with mean absolute error (MAE) of 25.2 kg and mean absolute percent error (MAPE) of 5.81% using the random forest regression model.

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.

3D human pose estimation has made significant advancements through the integration of deep learning techniques. This survey provides a comprehensive review of recent 3D human pose estimation methods, with a focus on monocular images, videos, and multi-view cameras. Our approach stands out through a systematic literature review methodology, ensuring an up-to-date and meticulous overview. Unlike many existing surveys that categorize approaches based on learning paradigms, our survey offers a fresh perspective, delving deeper into the subject. For image-based approaches, we not only follow existing categorizations but also introduce and compare significant 2D models. Additionally, we provide a comparative analysis of these methods, enhancing the understanding of image-based pose estimation techniques. In the realm of video-based approaches, we categorize them based on the types of models used to capture inter-frame information. Furthermore, in the context of multi-person pose estimation, our survey uniquely differentiates between approaches focusing on relative poses and those addressing absolute poses. Our survey aims to serve as a pivotal resource for researchers, highlighting state-of-the-art deep learning strategies and identifying promising directions for future exploration in 3D human pose estimation.

Quantum amplitude estimation is a numerical integration technique and a candidate for the first operationally used quantum algorithm providing a quantum advantage in the NISQ era. Its main metric is the generated Fisher information per second, as it describes the accuracy of the numerical integration. The development of this metric can be coupled to the quantum volume of the used quantum processor with a phenomenological noise model. Since the increase of the quantum volume seems to follow an exponential pattern, insight in the integration power of quantum amplitude estimation for the upcoming years can be obtained. This shows that quantum amplitude estimation as a numerical integration technique will not provide an advantage for functions without significant improvements to the connectivity, reliability and speed of quantum processors. Beyond that, this method may be used the estimate the development of many more NISQ era algorithms.

To obtain a better estimation frequency band for source bearing estimation, source bearing estimation experiments using an ice-mounted geophone were performed at three sites on the Arctic ice cap. By transforming to the frequency domain, errors of the bearing estimation in a number of frequency bands within 1000 Hz and the horizontal peak displacements received by the geophone in the corresponding frequency bands were obtained. After comparing the variation of errors to the peak displacements in the same frequency band, it is found that the errors are related to the peak displacements, and higher peak displacement values mean smaller errors. When the incident angle of the acoustic source and the ice thickness meet certain conditions, some modes of the Lamb waves of the ice plate are excited, the peak displacements at the same frequency band are increased, the errors of bearing estimation are reduced, and the effective frequency band for bearing estimation is broadened. These results provide some new ideas for the bearing estimation of acoustic sources beneath Arctic ice.

The present work fronts the human pose estimation problem, developing a method that enables automated ergonomic risk assessment. A research methodology is developed to calculate joint angles from digital snapshots or videos by using computer vision and machine learning techniques to get a more accurate ergonomic risk assessment. Starting from an ergonomic analysis, the study explores the use of a semi-supervised training method to detect the skeletons of workers. The research methodology developed aims to infer the positions and angles of the joints, to calculate the criticality index based on the RULA scores and fuzzy rules. Then, to prevent work-related musculoskeletal disorders (WMSD), improve production capacity and decrease the ergonomic risk, the system uses joint with a collaborative robot to support the worker in carrying out the most critical operations. The method has been tested on a real industrial case in which manual assembly activities of electrical components are conducted. The approach developed can overcome the limitations of recent developments based on computer vision or wearable measurement, sensors by performing an assessment with an objective and flexible ap-proach to postural analysis development.

Age estimation is an important tool when dealing with human remains or undocumented minors. Although the skull, the skeleton or the hand-wrist are used on age estimation as maturity indicators, they often present lack of good conditions for a correct identification or estimation. Few systematic reviews (SRs) have been recently published; therefore, this umbrella review critically assesses their level of evidence and provides a general comprehensive view. An electronic database search was conducted in four databases (PubMed, Cochrane, WoS, LILACS) up to December 2022, focusing on SRs of age estimation through forensic dentistry procedures. The methodological quality was analyzed using the measurement tool to assess SRs criteria (AMSTAR2). Eighteen SRs were included, five of critically low quality, six of low quality, three of moderate quality and four of high quality. The SRs posited that Willems’ method is more accurate and less prone to overestimation; most methods seem to be geographically sensitive; and 3D-Imaging and artificial intelligence tools demonstrate high potential. The quality of evidence on age estimation using dental approaches was rated as low to moderate. Well-designed clinical trials and high standard systematic reviews are essential to corroborate the accuracy of the different procedures of age estimation in forensic dentistry.

One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. The state-of-the-art envisages various methods for estimating the claims reserve but without being applied to systems that do not present huge variability and heterogeneity, such as the healthcare sector. The methodology is based on generalized linear models using the Overdispersed Poisson distribution as shown in the state-of-the-art. In this context, we developed a method to estimate the parameters of the quasi-likelihood function using a Gauss-Newton algorithm optimized through a genetic algorithm. The genetic algorithm plays a crucial role in glimpsing the position of the global minimum to ensure a correct convergence of the Gauss-Newton method, where the choice of the initial guess is fundamental. The proposed methodology has been entirely developed using MATLAB and has been applied to estimate the claims reserve in the healthcare system of the Tuscany Region in Italy, as case study. The results obtained were validated by comparing them with the state-of-the-art by measuring the confidence intervals of the Overdispersed Poisson distribution parameters. Therefore local healthcare authorities could use the proposed and improved methodology to allocate the resources dedicated to healthcare and global management.

In this article we introduce an extension of the Akash distribution. We use the slash 1 methodology to make the kurtosis of the Akash distribution more flexible. We study the general 2 density of this new distribution, some properties, moments, coefficients of asymmetry and kurtosis. 3 Statistical inference is performed using the methods of moments and maximum likelihood via the 4 EM algorithm. A simulation study is carried out to observe the behavior of the maximum likelihood 5 estimator. An application to a real data set with high kurtosis is considered, where it is shown that 6 the new distribution fits better than other extensions of the Akash distribution.

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.

This Article demonstrates how an eigendecomposition problem is inputted into a quantum circuit, how gates are applied in the quantum circuit, and how the output measurements are the correct eigenvalues. This process is known as quantum phase estimation (QPE). A quantum harmonic oscillator example, a foundational quantum physical chemistry problem, is demonstrated within the context of QPE. A particle in a box example, another quantum physical chemistry problem, may be solved by QPE with a caveat. These examples are of the limiting cases of diagonal matrices. Future advances in taking matrix inverses for solving linear sets of equations or finding ground state energies in the Schrödinger equation will use the principles implemented in this Article.

As the application of cyber-physical systems (CPSs) becomes more and more widespread, its security is becoming a focus of attention. Currently, there has been much research on the security defense of the physical layer of the CPS. However, most of the research only focuses on one of the aspects, for example, attack detection, security state estimation or recovery control. Obviously, the effectiveness of security defense targeting only one aspect is limited. Therefore, in this paper, a set of security defense processes is proposed for the case that a CPS containing multiple sensors is subject to three kinds of stealthy attacks (i.e., zero-dynamics attack, covert attack, and replay attack). Firstly, the existing attack detection method based on improved residuals is used to detect stealthy attacks. Secondly, based on the detection results, an optimal state estimation method based on improved Kalman filtering is proposed to estimate the actual state of the system. Then, based on the optimal state, internal model control (IMC) is introduced to complete the recovery control of the system. Finally, the proposed methods are integrated to give a complete security defense process, and the simulation is verified for three kinds of stealthy attacks. The simulation results show that the proposed methods are effective.

Techniques and tools meant to aid fire management activities in the Cerrado, such as accurately determining fuel load and composition spatially and temporally, are pretty scarce. The need to have fuel information for more efficient management in a considerably heterogeneous, biodiverse, and fire-dependent environment makes a constant search for the improvement of the use of remote sensing techniques in determining fuel characteristics. This study presents the following objectives: (1) to assess the use of data from Landsat 8 OLI images to estimate the fine surface fuel load of the Cerrado during the dry season by adjusting multiple linear regression equations; (2) to estimate fuel load through Random Forest and k-Nearest Neighbor (k-NN) algorithms in comparison with re-gression analyses; and (3) to evaluate the importance of predictor variables from satellite images. Therefore, 64 sampling units were collected, and the pixel values associated with the field plots were extracted in a 3 x 3 pixel window surrounding the reference pixel. For the multiple linear regression analyses, the R² values ranged from 0.63 to 0.78, and the models fitted by the Random Forest al-gorithm ranged from 0.52 to 0.83, while those fitted by k-NN ranged from 0.30 to 0.68. Adopting the Random Forest algorithm resulted in improvements in the statistical metrics of evaluation of the fuel load estimates for Cerrado grassland relative to the multiple linear regression analyses.

OpenToolFlux is an open-source software to estimate soil gas fluxes from gas concentration time-series data generated by automatic chamber systems. This paper describes the physical equipment used as well as software design and workflow. The software is a command-line application that imports tabular time-series data from the analyzer following the instructions specified in a configuration file by the user, performs configurable data-cleaning operations, and outputs a data file with volumetric flux estimates as well as diagnostic plots. The software can be configured according to the specifics of physical equipment and experimental setup and is therefore applicable in a wide range of studies.

Backgrounds Linear association has widely been assumed for prediction of aging-related fractional anisotropy (FA) decline in white matter of the brain. While useful for testing significance of the aging effect, it fails to identify a threshold age before and after which the age-FA association changes. Identification of such a threshold is often of clinical interest for timely intervention. Methods We employed a sigmoidal growth function to test a threshold effect in age triggering onset of cerebral decline in 21 white matter tracts, and compared its fitting performance to those of linear, and power regression. The study sample was a normal healthy cohort of 106 participants with ages in mid-life ranging from 18 to 60 years. Results Of the 21 white matter tracts analyzed, the posterior thalamic radiation showed better fit with sigmoidal curve model, compared to a linear or power regression. The estimated threshold age in years (95% confidence interval) were 47.2 (44.1-48.4). Conclusion While available evidence regarding the presence of a specific age threshold for cerebral decline in mid-life based on FA was limited, the posterior thalamic radiation exhibited a threshold age of 47.2. Beyond this age point, we observed a significant change in the FA risk pattern.

The flour milling industry, a vital component of global food production, is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality, and sustainability in flour production. Accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient, and cost-effective method for measuring the aforementioned essential parameters in wheat and flour, by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the under-study parameters.

In recent years, the Chinese government pays more and more attention to agricultural development and ecological protection. While improving the cultivated land green use efficiency(CLGUE) is the key to promote the sustainable development of agriculture. This study aims to study the current situation and influencing factors of agricultural production from the perspective of green utilization efficiency of cultivated land. It takes 39 cities in the upper, middle and lower reaches of the Yangtze River basin in China as an example. The CLGUE values in those 39 cities from 2011 to 2020 were specifically measured, using the Super-SBM model, kernel density estimation and geographic detector method. Their temporal and spatial heterogeneity was described, and the influencing factors were detected at both single and interactive levels. The results showed that: （1）From 2011 to 2020, the green utilization efficiency value of cultivated land in the Yangtze River Basin showed an upward trend on the whole; （2）There is a clear spatial heterogeneity the CLGUE values in the Yangtze River Basin cities, as shown by: downstream region > midstream region > upstream region; (3) Cultivated land resource endowment, socioeconomic development, and agricultural production technology are important factors affecting the variability of CLGUE values. However, there are some differences in the degree and direction of influence of different influencing factors on different sample subgroups.

This study presents a cost-effective IoT-based remote monitoring system for solar PV energy systems, along with a machine learning-based PV power estimator. Remote access is crucial for tracking PV systems installed in remote areas. In this system, an open-source and IoT-compatible data logger is employed. The data logger collects important performance data of the PV system and transfers it to a server. Real-time visualization of this data is displayed in the designed web and mobile monitoring interfaces. The measured data includes the current, voltage, and temperature information of the PV generator and battery, as well as environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, this data is used for PV power estimation using machine learning methods. This enables the identification of maintenance requirements and the prediction of potential issues in the PV system. When a problem occurs in the PV system, the user is alerted through the mobile application. Early detection and intervention prevent power loss and damage to system components. When comparing the results of linear regression, SVM, decision trees, random forests, and KNN machine learning methods for power estimation based on performance evaluation criteria, it was observed that the random forests algorithm provided the best results. In conclusion, the developed monitoring and estimation system, along with web and mobile interfaces, is suitable for large-scale PV energy systems.

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.

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.

Dynamic adaptive video streaming over HTTP (DASH) plays a crucial role in video transmission across networks. Traditional adaptive bitrate (ABR) algorithms adjust the quality of video segments based on network conditions and buffer occupancy. However, these algorithms rely on fixed rules within a complex environment, making it challenging to achieve optimal decisions considering the overall context. In this paper, we propose a novel Deep Reinforcement Learning-based approach for streaming DASH, focusing on maintaining consistent perceived video quality throughout the streaming session to enhance user experience. Our approach optimizes the Quality of Experience (QoE) by dynamically controlling the quality distance factor between consecutive video segments. We evaluate this approach through a simulation model that encompasses diverse wireless network environments and various video sequences. Additionally, we compare our proposed approach with state-of-the-art methods. The experimental results demonstrate significant improvements in QoE, ensuring users enjoy stable, high-quality video streaming sessions.

Continuous-variable quantum key distribution (CV-QKD) shows potential for the rapid development of an information-theoretic secure global communications network; however, the complexities of CV-QKD implementation remain a restrictive factor. Machine learning (ML) has recently shown promise in alleviating these complexities. ML has been applied to almost every stage of CV-QKD protocols, including ML-assisted phase error estimation, excess noise estimation, state discrimination, parameter estimation and optimization, key sifting, information reconciliation, and key rate estimation. This survey provides a comprehensive analysis of the current literature on ML-assisted CV-QKD. In addition, the survey compares the ML algorithms assisting CV-QKD with the traditional algorithms they aim to augment, as well as providing recommendations for future directions for ML-assisted CV-QKD research.

Previous studies have indicated that facial expressions can serve as an objective evaluation method for hedonics (overall pleasure) of food and beverages. In this study, we aimed to validate the findings of our previous research, which demonstrated that facial expressions induced by tastants can predict the perceived hedonic ratings of these tastants. Facial expressions of 29 female participants (aged 18-55 years) were recorded using a digital camera while they consumed 12 different concentrations of solutions representing five basic tastes. The facial expressions were then analyzed using the widely-used facial expression analysis application, FaceReader, to identify seven emotions (surprise, happiness, scare, neutral, disgust, sadness, and anger) with scores ranging from 0 to 1. Participants also rated the hedonics of each solution on a scale from -5 (extremely unpleasant) to +5 (extremely pleasant). A multiple linear regression analysis was conducted to develop a formula to predict perceived hedonic ratings. The formula's applicability was tested by analyzing emotion scores for 11 additional taste solutions consumed by 20 other participants. The predicted hedonic ratings demonstrated good correlation and concordance with the perceived ratings, supporting the validity of our previous findings using different software and taste stimuli among diverse participants.

River monitoring has the potential to grow substantially with present-day available affordable and locally sourced hardware. Yet river monitoring networks are still under pressure due to lack of resources, difficulties with maintenance, and rapidly changing conditions which in part may be due to climate change. We advocate that to turn around this trend, monitoring stations must rely on local people, work with locally available devices, work without contact with water and operate through openly available knowledge. River observations with camera videos have this potential. IP cameras, drones or smartphones are widely available as observation platforms. The scientific methods are well established in literature. Yet current attempts to establish scalable open-source software solutions that can be operated anywhere are lacking. In fact, currently available software is either research-oriented, more aimed at incidental observations, is restricted to single-use licenses, entirely proprietary, or restricted to operations through a third-party Software as a Service. To overcome this obstacle, we present OpenRiverCam, a free and open-source software ecosystem for river observations that can fulfill a wide variety of use cases and business cases through a well-documented and simple to use Application Programming Interface, service workflows, cloud scalability and interoperability, and options to extend to several applications within the hydrological, hydrodynamic, environmental and geospatial domains. We demonstrate its current technical abilities through three different case studies that all originate from a user perspective. We discuss the future developments to meet further requirements, which include documentation, widely available training materials and embedding in curricula, and further hardware and software developments.

Distributions with bounded support show considerable sparsity over those with unbounded support, despite the fact that there are a number of real-world contexts where observations take values from a bounded range (proportions, percentages, and fractions are typical examples). For proportion modelling, a flexible family of two-parameter distribution functions associated with the exponential distribution is proposed here. Mathematical and statistical properties of the novel distribution are examined, including quantiles, mode, moments, hazard rate function, and its characterization. The parameter estimation procedure using the maximum likelihood method was carried out, and applications to environmental and engineering data were also considered. To this end, various statistical tests are used, along with some other information criterion indicators to determine how well the model fits the data. The proposed model is found to be the most efficient plan in most cases for the datasets considered.

Bipolar disorder is a severe mood disorder and is one of the top 20 reasons of disability in the world. It causes a huge burden on society. In this study, the prediction models of bipolar disorder were constructed based on the concept of knowledge distillation. The input data consisted of patients of bipolar disorder and matched controls, all of which were selected from the open database MIMIC. The method of kernel density estimation (KDE) was exploited to generate probability density functions (PDF) which identify distributions of input data. The PDF values referred to as the soft labels were combined with the input data to construct the prediction models of bipolar disorder using decision tree and artificial neural network respectively. According to the evaluation results, indicators for identifying positive samples of bipolar disorder were improved. Meanwhile, the indicators for identifying negative samples have also been advanced. In addition, the branching attributes selected by the decision trees can be mapped back to specific disease diagnoses, which are all associated with bipolar disorder. In conclusion, using KDE to generate the soft label information of the input data can make knowledge distillation work and has improved the performances of prediction models for bipolar disorder.

Object-level Simultaneous Localization and Mapping (SLAM) has gained popularity in recent years since it can provide a means for intelligent robot-to-environment interactions. However, most of these methods assume that the distribution of the errors is gaussian. This assumption is not valid under many circumstances. Further, these methods use a delayed initialization of the objects in the map. During this delayed period, the solution relies on the motion model provided by an Inertial Measurement Unit (IMU). Unfortunately, the errors tend to accumulate quickly due to the dead-reckoning nature of these motion models. Finally, the current solutions depend on a set of salient features on the object’s surface and not the object’s shape. This research proposes an accurate object-level solution to the SLAM problem with a 4.1 to 13.1 cm error in the position (0.005 to 0.021 of the total path). The developed solution is based on Rao-blackwellized Particle Filtering (RBPF) that does not assume any predefined error distribution for the parameters. Further, the solution relies on the shape and thus can be used for objects that lack texture on their surface. Finally, the developed tightly coupled IMU/camera solution is based on an undelayed initialization of the objects in the map.

In the Cerrado-Amazonian ecotone in the State of Mato Grosso, intensely altered by anthropic action, the knowledge and processes of energy conversion and energy balance are still incipient, making the monitoring and modeling of diffuse radiation essential for several applications. The objective of this study was to evaluate the seasonality of the diurnal evolution and estimate the hourly diffuse radiation (Hdh) incident radiation in the horizontal plane, between June 2011 and October de 2016. The instantaneous measurements (5 minutes) of diffuse radiation underwent geometric, astronomical, and anisotropic corrections, with subsequent hourly integrations. The seasonality of diffuse radiation and its radiometric fractions was evaluated. The estimates were made considering total and seasonal data groupings (water stations in the region) and in different cloudiness classes (atmospheric transmissivity index - KTh). The diurnal behavior of diffuse radiation (Hdh) was similar to that of global radiation and at the top of the atmosphere, with maximum values at solar noon. The correlations between Kdh and KTh showed third-order polynomial behavior, with maximum observed values of Kdh ranging from 0.8 to 0.9, for KTh less than 0.2. Estimation equations based on radiometric fractions underestimated the values of diffuse radiation, with a better performance presented by models adjusted in annual data groupings. Among the parameterized models for estimating diffuse radiation obtained in the literature, those calibrated regionally in this study, together with those developed for tropical regions, presented better statistical performances.

In the Versatile Video Coding (VVC) standard, affine motion models have been applied to enhance the resolution of complex motion patterns. However, due to the high computational complexity involved in affine motion estimation, real-time video processing applications face significant challenges. This paper focuses on optimizing affine motion estimation algorithms in the VVC environment and proposes a fast gradient iterative algorithm based on edge detection for efficient computation. Firstly, we establish judging conditions during the construction of affine motion candidate lists to streamline the redundant judging process. Secondly, we employ the Canny edge detection method for gradient assessment in the affine motion estimation process, thereby enhancing the iteration speed of affine motion vectors. Experimental results demonstrate that our affine motion estimation algorithm reduces encoding time by approximately 15%-35% while maintaining video bitrate and quality.

Artificial intelligence is increasingly used in various branches of engineering. In this article, artificial neural networks are used to predict the crush resistance of corrugated packaging. Among the analysed packages were boxes with ventilation openings, packages with perforations, and typical flap boxes, which makes the proposed estimation method very universal. Typical shallow feedforward networks were used, which are perfect for regression problems, mainly when the set of input and output parameters is small, so no complicated architecture or advanced learning techniques are required. The input parameters of the neural networks are selected so as to take into account not only the material used for the production of the packaging, but also the dimensions of the box and the impact of ventilation holes and perforations on the load capacity of individual walls of the packaging. In order to maximize the effectiveness of neural network training process, the group of input parameters was changed so as to eliminate those to which the sensitivity of the model was the lowest. This allowed the selection of the optimal configuration of training pairs for which the estimation error was on the acceptable level. Finally, models of neural networks were selected, for which the training and testing error did not exceed 10%. The demonstrated effectiveness allows to conclude that the proposed set of universal input parameters is suitable for efficient training of a single neural network model capable of predicting the compressive strength of various types of corrugated packaging.

This study presents the numerical investigation of bioconvective nanofluid (NF) flow compris-ing gyrotactic microorganisms through convective boundary conditions (CBC) enclosing heat and mass transmission analysis above an inclined extending cylinder. Via appropriate transfor-mation, the set of Partial Differential Equations is transformed into a scheme of nonlinear ODEs. A new set of variables are presented in the directive to get the first-order convectional equations and, then solved numerically by using bvp4c. The influence of several factors on velocity, ener-gy, concentration, and the density of motile microorganisms (DMM) is investigated and exam-ined. The analysis describes and addresses all physical measures of concentration such as Skin Friction (SF), Sherwood number (SN), DMM, and Nusselt number (NN). The present conse-quences display a validation with published. For confirmation of bvp4c, the ND-solve approach is also applied. Error analysis is also examined for the confirmation of the mathematical model.

This paper presents preliminary research that investigates the relationship between the flow of a group of jazz musicians, quantified through multi-person pose synchronization, and their collective emotions. Building upon previous studies that measured the physical synchronicity of team members by tracking their body movements and measuring the difference in arm, leg, and head movements, we introduce a novel metric termed "team entanglement". We employ facial expression recognition to evaluate the musicians’ collective emotions. Through correlation and regression analysis, we establish that higher levels of synchronized body and head movements correspond to lower levels of disgust, anger, sadness and higher levels of joy among the musicians. Furthermore, we utilize a Convolutional Neural Network (CNN) based deep learning model to predict the collective emotions of the musicians. This model leverages 17 body synchrony keypoint vectors as features, resulting in a training accuracy of 61.47% and a test accuracy of 66.17%.

Financial markets require a great deal of decision making from the investors and market makers. One metric that can help ease the process of decision making is investment risk which can be measured in two parts; systematic risk and idiosyncratic risk. Clear understanding of the volatilities in each risk component can be a powerful signal in recognizing the right assets to maximize the investment returns. In this paper, we focus on the idiosyncratic volatility values and pre-calculate the idiosyncratic volatility values for 31,198 members of NYSE, Amex and Nasdaq markets for the trades occurring between January 1963 and December 2019. Utilizing a subset of dataset, limited to Nasdaq100 index, we consider the application of machine learning techniques in predicting the idiosyncratic volatility values using the raw trade data to explore a data extension option for the future market trade records that have not yet occurred. We offer a deep learning based regression model and compare it with traditional tree-based methods on a small subset of our per-calculated idiosyncratic volatility dataset. Our analytical results show that the performance of the deep learning techniques is much more robust in comparison to that of the traditional tree-based baselines.

This paper addresses the problem of low accuracy of conventional channel estimation and signal detection in complex fields like mines; and the problem of replacing the deep receiver at the receiver side with a deep neural network requires a large amount of data for training and slow training time. Combining the receiver side with traditional communication knowledge, a channel estimation and signal detection method based on the joint ReEsNet and SDRNet under the mine is proposed. The detailed step is to use ReEsNet network for interpolation of conventional channel estimation. Then, on the basis of ReEsNet, the output data is channel equalized and pilots removed, and the previously processed data is input to SDRNet to perform signal recovery. In the complex mine environment, the MSE values are 2e-2 for LS, 6e-3 for MMSE, and 8.44e-5 for ReEsNet at the 1500th sample data for interpolation compared to the conventional channel estimation. the MSE of ReEsNet is the smallest and is closer to the real channel estimation. Compared with the proposed joint channel estimation and signal detection methods, fewer trainable parameters reduce 97% of the parameters compared to CCRNet and 84% of the parameters compared to CNNNet; their detection results, however, are not very different.

The common dolphin is the second most bycaught species in Korean waters. To provide key information about their habitat boundaries and hotspots for spatial conservation and management, the spatial use of this species was examined using data obtained from sighting and bycatch surveys of cetacean during the past 20 years. The 95% minimum convex polygon and 95% density contour of fixed kernel suggested that the boundary of home range of common dolphins is limited to the coastal region (Busan-Sokcho) of the East Sea/Sea of Japan. From 50% density contours dawn by kernel density estimation, it was suggested that their hotspots are around the cost of Ulsan-Pohang, Doghae, and Sokcho within the home range. Common dolphins are not at all distributed in the Yellow Sea. Hence, shallow waters, such as the coastal region of the Yellow Sea, are not suitable habitats for common dolphins.

The robust fusion estimation problem of a multi-sensor system with communication constraints, random packet drops, d-step state delays, and deterministic control inputs is investigated. The state augmentation method is used to transform the time delay system into a non-time delay system, and then a robust fusion estimation algorithm based on sensitivity penalty is proposed and the pseudo-cross-covariance matrix is updated. An event-triggered robust fusion estimator with state delay and deterministic control inputs is considered to make the model closer to reality while effectively reducing network congestion and lowering communication costs. Conditions are given to ensure that the estimation errors of the robust fusion estimator are uniform boundedness. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator.

Deformation monitoring is beneficial to ensure the service safety of long tunnel. An three-direction (vertical, transverse and axial) deformation estimation method using long-gauge fiber Bragg grating (FBG) sensing technology is proposed and applied to a long tunnel by integrating mechanical analysis and field monitoring during the service state. The key issues of the proposed method for identifying the three-direction deformation are discussed, such as establishing the separation model of coupling strain on cross section, deriving the theory of deformation identification in three directions, and determining the sensor layout of the long tunnel. A major advantage of the proposed method is that the three-direction deformation of the tunnel can be monitored in real time using distributed long-gauge strain sensors. The numerical analysis and field test results verify the feasibility of the proposed method.

Nonparametric density estimation for nonnegative data is considered in a situation where a random sample is not directly available but the data are instead observed from the length-biased sampling. Due to the boundary bias problem of the location-scale kernel, the approach in this paper is an application of asymmetric kernel. Two nonparametric density estimators are proposed. The mean integrated squared error, strong consistency, and asymptotic normality of the estimators are investigated. Some simulations illustrate the finite sample performance of the estimators.

Peroxiredoxins play central roles in the detoxification of reactive oxygen species and have been modelled across multiple organisms using a variety of kinetic methods. However, the peroxiredoxin dimer-to-decamer transition has been underappreciated in these studies despite the 100-fold difference in activity between these forms. This is due to the lack of available kinetics and theoretical framework for modelling this process. Using published isothermal titration calorimetry data, we obtained association and dissociation rate constants of 93.0 µM-4·s-1 and 102 s-1, respectively, for the dimer-decamer transition of human PRDX1. We developed an approach that greatly reduces the number of reactions and species needed to model the peroxiredoxin decamer oxidation cycle. Using these data, we simulated horse radish peroxidase competition and NADPH-oxidation linked assays and found that the dimer-decamer transition had an inhibition-like effect on peroxidase activity. Further, we incorporated this dimer-decamer topology and kinetics into a published and validated in vivo model of PRDX2 in the erythrocyte and found that it almost perfectly reconciled experimental and simulated responses of PRDX2 oxidation to hydrogen peroxide insult. By accounting for the dimer-decamer transition of peroxiredoxins, we were able to resolve several discrepancies between experimental data and available kinetic models.

Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images. In this paper, we propose MSTPose (Multi-Scale Transformer for human Pose estimation). Specifically, MSTPose leverages a high-resolution Convolution neural network (CNN) to extract texture information from images. For the feature maps from three different scales produced by the backbone network, each branch performs the coordinate attention operations. The feature maps are then spatially and channel-wise flattened, combined with keypoint tokens generated through random initialization, and fed into a parallel Transformer structure to learn spatial dependencies between features. As the Transformer outputs one-dimensional sequential features, the mainstream two-dimensional heatmap method is abandoned in favor of one-dimensional coordinate vector regression. The experiments show that MSTPose outperforms other CNN-based pose estimation models and demonstrates clear advantages over CNN + Transformer networks of similar types.

In econometrics and finance, volatility modelling has long been a specialised field for addressing a variety of issues pertaining to the risk and uncertainties of an asset. This study presents a robust framework, through a step-by-step design, that is relevant for effective Monte Carlo simulation (MCS) with empirical verifications to estimate volatility using the Generalized Autoregressive Score (GAS) model. The framework describes an organised approach to the MCS experiment that includes "background (optional), defining the aim, research questions, method of implementation, and summarised conclusion". The method of implementation is a workflow that consists of writing the code, setting the seed, setting the true parameter a priori, data generation process, and performance assessment through meta-statistics. Among the findings, it is experimentally demonstrated in the study that the GAS model with a lower unconditional shape parameter value can generate a dataset that adequately reflects the behaviour of financial time series data, relevant for volatility estimation. This dynamic framework is intended to help interested users on MCS experiments utilising the GAS model for reliable volatility calculations in finance and other areas.

Noise model selection criteria has a significant impact on identifying the stochastic noise proper-ties of any GNSS daily coordinate time series. The low-frequency random walk noise existing in these time series could lead to overestimation of the tectonic rate, so it is of great significance to accurately detect the random walk component. This study focuses on noise model estimation cri-terion (BIC_tp) derived from the AIC and the BIC by introducing 2π factors. It is more sensitive to abnormal steps (random jumps). Using observation data from 72 GNSS stations from 1992 to 2022 and simulated data, four combined noise models are used to explore the impacts of time se-ries lengths (ranging from2 to 24 years) and data loss (between 2% and 30%) on noise models and velocity estimation. The results show that as the time length increased, the selected optimal noise model, and the estimated uncertainty of the tectonic trend with different data gap, gradually con-verge. When the time length is short (less than 8 years), it could lead to the FNRWWN, FNWN, and PLWN models being mistakenly estimated as GGMWN models, thereby affecting the accura-cy of determining the station velocity parameters. When the time length is 12 years, the RW noise component is more probably detected, As the time length increases, the impact of RW on velocity uncertainty is weakened. Finally, we conclude that for a time series with a minimum time length of 12 years, both the selection of the optimal stochastic noise model and the estimation of the ve-locity parameters are reliable.

Purpose- Betel nut (Areca catechu) is one of the major plantation crops grown in Assam. However, due to inefficiency in production, the productivity of betel nuts in the state is very low. Therefore, this study attempted to measure the level of technical efficiency and the factors affecting the technical inefficiency of betel nut growers in the Nagaon district of Assam.Design/methodology/approach – Primary data were collected from 240 households in the Nagaon district of Assam. Based on the Cobb-Douglas Production function for betel nut growers, the present study uses stochastic frontier analysis to estimate the production frontier and examine the effects of exogenous variables on farm-level technical inefficiency. Findings - The findings of the study reveal that, on average, 85 per cent of betel nut growers are technically efficient in the study area. Moreover, experience in betel nut plantations and gender have a significantly negative effect on technical inefficiency. In contrast, the age of the grower had a positive and significant effect on the technical inefficiency of betel nut cultivation. The findings suggest the availability of space for an increase in the productivity of betel nut plantations in the study area and scope for increasing the level of output through technical efficiency without raising inputs.Originality/Value: This study contribute to the estimation of the technical efficiency of betel nut growers and examines the factors responsible for technical inefficiency.The dataset is original and was especially collected for the present study.

Background. Pose estimation based on deep learning has been expected to be a breakthrough method to increase the accuracy of clinical photographic evaluation and to decrease interobserver errors. The purpose is to quantify pose estimation from photography of patients with adolescent idiopathic scoliosis (AIS) using open-source software packages and determine correlations between parameters obtained by radiography and photography. Methods. We included 12 consecutive patients with AIS treated with spinal correction surgery. Photographs were taken preoperatively using a tripod-mounted camera (iPhone 13Pro) on an X-ray tube head. To assess photographic parameters obtained by photography, we defined 17 points to analyze posture and define parameters. Results. In the sagittal plane, there was a significant correlation between the radiographic trunk tilt angle and the photographic sagittal trunk tilt angle of the shoulder–hip and ear–hip. In the coronal plane, there was a significant correlation between the radiographic clavicle angle and the photographic shoulder height angle, and the radiographic C7–CSVL and the photographic coronal trunk tilt angle. Conclusions. Posture analysis by photography using popular mobile devices has clinical utility for improving and promoting the screening and early detection of AIS because it is simple, without patient exposure to X-ray radiation.

This paper aims at trying to answer the question of whether a low cost drone equipped with a regular camera can serve as a simple device to estimate the size of objects. Ad-ditionally, it assesses if the accuracy of measurement is good enough for using a drone for measurements, relying solely on GPS position and camera. A review of the literature pertaining to ground object measurements was conducted. The availability of such in-struments was evaluated in terms of purchase and rental costs, the number of instru-ments on the market, and the time to obtain them. The most common measurement methods were considered and their unit costs were reviewed. The cost of professional surveying and measuring equipment was compared to the possibility of using low-cost, non-commercial drones to measure objects and surfaces. The quality of the images ob-tained was compared with high-resolution satellite systems. Is an ordinary drone with a standard camera suitable for length measurement without professional measuring equipment? Should the results be considered only in the category of size estimation?

Channel estimation is an important module to enhance the performance of orthogonal frequency division multiplexing (OFDM) systems. However, the presence of a large amount of noise in multipath fading time-varying channels significantly affects the channel estimation accuracy and thus the recovery quality of the received signals. Therefore, this paper proposes a double-threshold (DT) channel estimation method based on adaptive frame statistics (AFS). The method first adaptively determines the number of statistical frames based on the temporal correlation of the received signals, and preliminarily detects the channel structure by analyzing the distribution characteristics of multipath sampling points and noise sampling points during adjacent frames. Subsequently, a multi-frame averaging technique is used to expand the distinction between multipath and noise sampling points. Finally, the DT is designed to better recover the channel based on the preliminary detection results. Simulation results show that the proposed adaptive frame statistics-double-threshold (AFS-DT) channel estimation method is effective and has better performance compared with many existing channel estimation methods.

With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of the fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surfaces (RIS), one of the potential technologies for the sixth generation (6G), has received a lot of attention. Since it is easier to deploy, consumes less power, and is inexpensive. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This paper conducts a comprehensive investigation of the existing channel estimation methods of RIS, analysis and comparison of channel model building and CSI acquisition schemes in different frequency bands, in addition to a comprehensive description of generic channel estimation methods, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook in the future development of RIS channel estimation.

High-dimensional measurement error data are becoming more prevalent across various fields. Research on measurement error regression models has gained increasing attention due to the risk of drawing inaccurate conclusions if measurement errors are ignored. When the dimension p is larger than the sample size n, it is challenging to develop statistical inference methods for high-dimensional measurement error regression models due to the existence of bias, nonconvexity of objective function, high computational cost and many other difficulties. Over the past few years, some works have overcome the aforementioned difficulties and proposed several statistical inference methods. This paper mainly reviews the current development on estimation, hypothesis testing and variable screening methods for high-dimensional measurement error regression models and shows the theoretical results of these methods with some directions worthy of exploring for future research.

Most petrochemical plants still maintain a Proportional-Integral-Differential controller(PID) system, which is a feedback control system. However, gradually, the PID system is being extended and introduced to the Advanced Process Controller (APC) system, which is an integrated control system of feedforward and feedback that predicts external influences in advance. In the process of conducting on-site plant tests and calculating APC model parameter for the application of APC systems, a problem arises that Model Parameter are implemented differently depending on the proficiency of APC engineers. To minimize this problem, a technique for estimating APC model parameter without a plant test is required. In order to estimate the APC model parameter, it is necessary to train on dynamic interval data. In this paper, we use statistical techniques such as PELT, Linear Kernel, and Radial Basis Function Kernel of Change Point Detection (CPD) to extract dynamic data with minimum Mean Absolute Error (MAE) from time series data of a real petrochemical plant. Then, the hyper parameter is fixed and the APC model parameter is estimated by learning the dynamic section data. By applying the estimated APC model parameter to the APC Model Tool and measuring the fitting rate, it was confirmed that it is possible to estimate the APC model parameter with excellent control performance without plant test.

Centrifugal pumps are widely employed in the oil refinery industry due to their efficiency and effectiveness in fluid transfer applications. The reliability of pumps plays a pivotal role in ensuring uninterrupted plant productivity and safe operations. Analysis of failure history data shows that bearings have been identified as critical components in oil refinery pump groups. However, traditional reliability estimation theories may not apply when data is limited or subject to right censoring. This paper addresses the complexity of estimating the Weibull distribution parameters using the maximum-likelihood method under the abovementioned conditions. The likelihood equation lacks an explicit analytical solution, necessitating the use of numerical methods for resolution. The proposed approach presented in this article leverages the Expectation-Maximization (EM) algorithm for estimating the Weibull distribution parameters. This method provides more accurate estimates of failure rates and probabilities by accounting for limited and censored data. The findings are demonstrated through a case study, showcasing the practical application of the proposed approach.

This study rolls out a robust framework relevant for simulation studies through the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model using the rugarch package. The package is thoroughly investigated, and novel findings are identified for improved and effective simulations. The focus of the study is to provide necessary simulation steps for volatility estimation that involve "background (optional), defining the aim, research questions, method of implementation, and summarised conclusion". The method of implementation is a workflow that includes writing the code, setting the seed, setting the true parameters a priori, data generation process and performance assessment through meta-statistics. This novel, easy-to-understand steps are demonstrated on financial returns using illustrative Monte Carlo simulation with empirical verification. Among the findings, the study shows that regardless of the arrangement of the seed values, the efficiency and consistency of an estimator generally remain the same as the sample size increases. The study also derived a new and flexible true-parameter-recovery measure which can be used by researchers to determine the level of recovery of the true parameter by the MCS estimator. It is anticipated that the outcomes of this study will be broadly applicable in finance, with intuitive appeal in other areas, for volatility estimation.

Discrete distribution estimation is a fundamental statistical tool, which is widely used to perform data analysis tasks in various applications involving sensitive personal information. Due to privacy concerns, individuals may not always provide their raw information, which leads to unpredictable biases in the final results of estimated distribution. Local Differential Privacy (LDP) is an advanced technique for privacy protection of discrete distribution estimation. Currently, typical LDP mechanisms provide same protection for all items in the domain, which imposes unnecessary perturbation on less sensitive items and thus degrades the utility of final results. Although, several recent works try to alleviate this problem, the utility can be further improved. In this paper, we propose a novel notion called Item-Oriented Personalized LDP (IPLDP), which independently perturbs different items with different privacy budgets to achieve personalized privacy protection. Furthermore, to satisfy IPLDP, we propose the Item-Oriented Personalized Randomized Response (IPRR) based on the observation that the sensitivity of data shows an inverse relationship with the population size of respective individuals. Theoretical analysis and experimental results demonstrate that our method can provide fine-grained privacy protection and improve data utility simultaneously.

Pose estimation has various applications in analyzing human movement and behavior, including providing feedback to users about their movements so they could adjust and improve their movement skills. To investigate the current research status and possible gaps, we searched Scopus and Web of Science for articles that (1) human `body' pose estimation is used and (2) user movement is assessed and communicated. We used either a bottom-up or top-down approach to analyze 20 articles for methods used to estimate human pose, assess movement, provide feedback to users, as well as methods to evaluate them. Our review found that pose estimation systems typically used CNNs while movement assessment methods varied from mathematical formulas or models, rule-based approaches, to machine learning. Feedback was primarily presented visually in verbal forms and nonverbal forms. The experiments to evaluate each part ranged from the use of public datasets to human participants. We found that while there was an improvement, the majority of pose estimation challenges remain. The effectiveness and factors for choosing movement assessment methods for a new context are still unclear. In the end, we suggest that studies about feedback prioritization and erroneous feedback are needed.