Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Kalman Filter Integration of a Global Positioning System Receiver and an LN-94 Inertial Navigation System

Abstract : This research develops and attempts to implement a Kalman filter integration of a Phase III Global Positioning System (GPS) five-channel receiver and an LN-94 Inertial Navigation System (INS). The GPS provides highly accurate position and velocity information in low dynamic environments. An INS provides position and velocity information with lower accuracy over long periods of time, but it is highly responsive in dynamic maneuvers or at high frequencies. The INS has the added advantage of requiring no signals external to the vehicle to function. The integration of these two systems provide more precise information under a wider variety of situations. A truth model for the INS is verified. A GPS error model is developed and combined with the INS model to provide GPS- aided INS navigation. This model is used to predict baseline performance of all full-ordered filter. Attempts are made to utilize the filter with empirical data. The data is analyzed, and suggestions are made about ways to account for the errors in evidence. Results to date are presented and analyzed. Keywords: Global positioning system, Inertial navigation system, GPS/INS Integration, GPS- aided-INS, Theses.
Course-Keeping Control for Unmanned Sailboat Robot with Dynamic Event-Triggered Rule

This note focuses on the course-keeping control problem for unmanned sailboat robot (USR) in presence of the communication burden and time-varying disturbances. The robust adaptive course-keeping control algorithm has been proposed based on dynamic event-triggered mechanism to increase the transmission efficiency. Compared with existing event-triggered control with fixed threshold parameters, the triggering thresholds in proposed algorithm are updated online according to dynamic rule to decrease the resource waste and channel occupancy. Finally, the closed-loop control system stability is proofed by the Lyapunov theorem. The superiorities of the proposed algorithm have been illustrated through numerical simulation.
Distributed Anode Current Classification Method Based on Spectral Filtering and Convolutional Neural Networks

Distributed anode current (DAC) is a spatio-temporal sequence containing important spatial distribution information that can be obtained online by the aluminum electrolysis cell control system. DAC classification is of great significance in the knowledge discovery of aluminum electrolysis industrial production process data. Due that DAC has the dynamic cell noise and complex temporal-spatial dependency characteristics, DAC classification is a challenging problem and existing classification methods cannot effectively capture these spatial features under the interference of industrial noise. To solve the above problems, DAC classification method based on spectral filtering and convolutional neural networks (CNN) is proposed in this paper. Firstly, the dynamic graph structure of aluminum electrolysis cell is obtained based on the similarity between each dimensional signal of DAC to capture the dynamic spatial dependence of DAC. Secondly, taking into account the real anode spatial structure information (ASSI) and the feed mode of the aluminum electrolysis cell, a static graph structure is designed to capture the static spatial dependence of the DAC. Then, two different spectral filtering modes are proposed, and the spectral filtering results of the DAC are converted into gray-scale images, which are used as the input of the CNN to achieve the DAC classification task. Finally, the classification results on the actual DAC datasets compared with existing methods demonstrate the effectiveness of our method.
Fast Path Planning Based on Bi-Directional RRT* for Mobile Robot in Complex Maze Environments

The Rapidly-exploring Random Tree (RRT) algorithm and various improved algorithms are being used for path planning in many different environments. Because of the probabilistic completeness of the RRT algorithm, if there is a feasible path, the RRT algorithm should be able to find it. However, in maze environments, the cost of computation and computation time will increase. There, in this paper, a new algorithm Exploration and Exploitation RRT (EE-RRT) based on Bi-directional RRT*(B-RRT*) is proposed for path planning in maze environments. During the initial stage of path planning, two trees with starting point and ending point as root nodes grow at the same time, exploring the global environment. Once all feasible areas are discovered, the algorithm stops searching and begins to exploit the feasible areas to find better paths. The simulation results verify the effectiveness and superiority and of our proposed algorithm which has the fastest speed of path planning and shorter path length than RRT*-connect and Bi-directional RRT* algorithms.
Towards Randomized Algorithms and Models that We Can Trust: a Theoretical Perspective

In the last decade it became increasingly apparent the inability of technical metrics to well characterize the behavior of intelligent systems. In fact, they are nowadays requested to meet also ethical requirements such as explainability, fairness, robustness, and privacy increasing our trust in their use in the wild. The final goal is to be able to develop a new generation of more responsible and trustworthy machine learning. In this paper, we focus our attention on randomized machine learning algorithms and models questioning, from a theoretical perspective, if it is possible to simultaneously optimize multiple metrics that are in tension between each other towards randomized machine learning algorithms that we can trust. For this purpose we will leverage the most recent advances coming from the statistical learning theory: distribution stability and differential privacy.
Innovative multidimensional gait evaluation using IMU in multiple sclerosis: introducing the semiogram

Background Quantifying gait using inertial measurement units has gained increasing interest in recent years. Highly degraded gaits, especially in neurological impaired patients, challenge gait detection algorithms and require specific segmentation and analysis tools. Thus, the outcomes of these devices must be rigorously tested for both robustness and relevancy in order to recommend their routine use. In this study, we propose a multidimensional score to quantify and visualize gait, which can be used in neurological routine follow-up. We assessed the reliability and clinical coherence of this method in a group of severely disabled patients with progressive multiple sclerosis (pMS), who display highly degraded gait patterns, as well as in an age-matched healthy subjects (HS) group. Methods Twenty-two participants with pMS and nineteen HS were included in this 18-month longitudinal follow-up study. During the follow-up period, all participants completed a 10-meter walk test with a U-turn and back, twice at M0, M6, M12, and M18. Average speed and seven clinical criteria (sturdiness, springiness, steadiness, stability, smoothness, synchronization, and symmetry) were evaluated using 17 gait parameters selected from the literature. The variation of these parameters from HS values was combined to generate a multidimensional visual tool, referred to as a semiogram. Results For both cohorts, all criteria showed moderate to very high test–retest reliability for intra-session measurements. Inter-session quantification was also moderate to highly reliable for all criteria except smoothness, which was not reliable for HS participants. All partial scores, except for the stability score, differed between the two populations. All partial scores were correlated with an objective but not subjective quantification of gait severity in the pMS population. A deficit in the pyramidal tract was associated with altered scores in all criteria, whereas deficits in cerebellar, sensitive, bulbar, and cognitive deficits were associated with decreased scores in only a subset of gait criteria. Conclusions The proposed multidimensional gait quantification represents an innovative approach to monitoring gait disorders. It provides a reliable and informative biomarker for assessing the severity of gait impairments in individuals with pMS. Additionally, it holds the potential for discriminating between various underlying causes of gait alterations in pMS.
Descriptive Epidemiology of Pediatric Inflammatory Bowel Disease in Oceania: A Systematic Review and Meta-Analysis

Objectives: Pediatric inflammatory bowel diseases (IBDs) are chronic, idiopathic illnesses of the digestive tract, which can impact adversely on children’s quality of life and burden health systems. International studies have shown these diseases are increasing. The aim was to describe pediatric IBD epidemiology across Oceania by conducting a systematic review and meta-analysis of incidence and prevalence. Methods: Medline, EMBASE and Web of Science databases were searched in October 2022 for studies reporting rates of IBD, Crohn disease (CD), or ulcerative colitis (UC) in children (≤19 years). Several data collection methodologies were included and pooled estimates of incidence and prevalence were calculated using a random effects model with I2 measures of heterogeneity. Results: Nineteen articles provided 15 incidence and 7 prevalence studies. Fourteen studies were from Australia, 8 studies from New Zealand, and no studies were found from the Pacific Islands. Study dates ranged from 1950 to 2020 with 11 studies using population-based designs. Pooled estimates for annual incidence were IBD 4.1 (3.4–4.8, I2 = 98.7), CD 2.3 (1.9–2.7, I2 = 98.6), and UC 0.9 (0.6–1.1, I2 = 96.8) per 100,000 person-years. Prevalence rates were IBD 36.0 (23.5–48.5, I2 = 98.4), CD 23.2 (6.6–39.8, I2 = 97.8), and UC 7.6 (2.7–12.5, I2 = 99.6) per 100,000 persons. Conclusions: Pediatric IBD is prevalent in Oceania with high incidence rates, particularly for CD. Low rates of IBD were observed in indigenous Australian, Māori, and New Zealand Pacific children and there were no studies from the Pacific Islands highlighting this as an area in need of further research.
A Bibliometric Analysis of Blockchain Use for the RPL Technology: A Systematic Literature Review

: Internet of Things (IoT) has been the most emerging technology in the last two decade because the number of smart intelligent sensors and its associated technologies has rapidly grown in both industrial and research prospectives. Recent trends have suggested convergence to WSNs becoming IPv6based. To this effect, the ROLL working group of the IETF is currently specifying an IPv6based unicast routing protocol for WSNs, denoted IPv6 Routing Protocol for Low power and Lossy Networks (RPL) of low power consumption, and other constraints of nodes in the network. In this study, we performed a Systematic Literature Review, evaluate, and identify the security issues that exist in the RPL network. Second, identity the use of blockchain technology in protecting the nodes in RPL network. Thirdly, as per blockchain technology, we provide some security solutions and provide robot blockchain taxonomy. The detailed analysis, including enabling technology and integration of IoT technologies, is explained. We review relevant works that have proposed Blockchain based solution to strength the security of RPL network. We further contribute to present, analyze, and compare best authentication solutions. Also, we present the solutions that integrate Blockchain. Finally, several research directions and open challenges are identified.
Association between offensive behaviors and burnout and depression risks in health workers

Objective: to evaluate the occurrence of offensive behaviors at work, their characteristics and association with sex, stress, burnout and depression in health workers. Method: a cross-sectional, descriptive and quantitative study carried out with 125 workers from the Brazilian Unified Health System. The data were collected from June 2021 to April 2022 through three self-applied questionnaires that assess personal and occupational characteristics; offensive behaviors, stress and burnout; and depressive symptoms. Descriptive statistics, the chi-square association test and logistic regression analysis were applied. Results: 44% of the sample reported 83 behaviors, with threats of violence as the most frequent ones (26%). Nursing technicians/assistants, nurses and physicians were the most exposed professionals. The main aggressors were the patients, except for bullying, which was perpetrated by co-workers (48%). There was an association between offensive behaviors and burnout (OR: 4.73; 95% CI: 1.29-17.3; p=0.02) and between offensive behaviors and depression symptoms (OR: 1.05; 95% CI: 1.01-1.10; p=0.02). Conclusion: the occurrence of offensive behaviors in health work is frequent and characteristic and burnout and depressive symptoms respectively increased 4.73 and 1.05 times the chances of workers suffering these offensive behaviors in the work environment.
Physical Activity as A Major Low Back Pain Complaints Among Office Worker in Their Working Life: Cross Sectional Study

Low back pain is a very common problem that can affect a variety of ages. The symptoms of low back pain can be caused by lumbar spine problems such as root pain, radiculopathy, and spinal tract stenosis. The intervertebral disk and the joint facet are two lumbar structures that can cause pain. One element that contributes to low back discomfort is physical activity. The study design used correlation analysis with a cross-sectional approach. The sampling technique used in this study is consecutive sampling. The sample is 37 teachers with an age range of 35 – 60 years. To indentify physical activity is used Short-Form International Physical Activity Questionnaire and for identifying low back pain complaints is used Pain and Distress Scale (William J.K. Zunga) in the Primala study was analyzed using the Chi-Square test. The teachers and perform moderate activities, don’t have complaints of low back pain of 15 (40,5%), heavy activity, did have a complaint of low back pain of 22 (59,5%). The results of the Chi-square test showed that there was a significant relationship between physical activity and low back pain complaints (p value=0,000;α<0,05). Appropriate and consistent activity that is adapted to the body's capacities and age will have a positive influence on the building of stronger muscles and bones. Adequate and regular physical activity can help prevent low back pain complaints
Fractional-Order Global Fast Terminal Sliding Mode Control Strategy for Fractional-Order Hydraulic Turbine Regulating System

Hydraulic turbine regulating system (HTRS) directly affects the safety and stability of hydro-generator units. In this paper, the control and modeling problems of the HTRS are mainly investigated based on the fractional-order theory, aiming to design a novel control strategy to raise the dynamic response of the HTRS and ensure the frequency stability of the hydro-generator units. Firstly, a nonlinear model of the fractional-order HTRS is established mathematically. Secondly, a fractional-order global fast terminal sliding mode controller is designed to improve the dynamic performance as well as the robustness of the system. Finally, relevant simulations are carried out to illustrate the effectiveness and rationality of the proposed control strategy in comparison with two other fractional-order control strategies.
Label Propagation Algorithm Based on Random Wandering for Community Detection

Community structure is an important property on complex networks, and finding communities in the field of social network analysis is of greater information value. The label propagation algorithm is widely used to detect communities on networks due to its advantage of near linear time complexity. However, the randomness of the LPA algorithm in the label selection process may lead to the detection of poor quality communities. Therefore, we propose a label propagation algorithm based on random wandering and updates the label propagation rules while taking the network information obtained from random wandering into account in our algorithm. The final experimental results show that the proposed algorithm detects communities satisfactorily on real networks.
Method of Classified Counting of Mixed Breeding Chickens Based on YOLOV5

Accurately identifying and calculating the number of chickens in a large-capacity mixed-raising environment is a key step in applying machine vision technology to automatically monitor the behavior, status, health, and other precise animal husbandry. This paper proposes a chicken counting method based on YOLOV5, taking the Silky chicken and Xianghuang chicken mixed in a chicken house as recognition targets. Use the camera to record the situation of the mixed chickens before and after entering and leaving the chicken house from 17:00 every day to 7:00 the next day for a week, screen out 321 pictures, mark the Xianghuang chicken (17543 samples) and the Silky chicken (8905 samples), and use the YOLO series of target detection algorithms to identify. The experimental results showed that the yolov5L algorithm had the best effect, and the precision rate of Xianghuang chickens was 89.9% and the recall rate was 87.2%, while the precision rate of black chickens was 85.1% and the recall rate was 84.2%. This method can replace the manual monitoring of the number of chickens returned to the nest, providing the possibility of precise breeding.
Multifunctional Lithium-Doped Mesoporous Nanoparticles for Effective Dentin Regeneration in vivo

Introduction Effective infection control without irritating the pulp tissue is the key to successful vital pulp therapy. Developing a novel antibacterial biomaterial that promotes dentin regeneration for pulp capping is thus a promising strategy for enhancing vital pulp therapy. Methods Lithium-doped mesoporous nanoparticles (Li-MNPs) were synthesized using an alkali-catalyzed sol-gel method. The particle size, elemental distribution, surface morphology, pore structure, and ion release from Li-MNPs were measured. Human dental pulp stem cells (hDPSCs) and Streptococcus mutans (S. mutans) were used to evaluate the biological effects of Li-MNPs. In addition, a dental pulp exposure mouse model was used to evaluate the regenerative effects of Li-MNPs. Results Li-MNPs had a larger surface area (221.18 m2/g), a larger pore volume (0.25 cm3/g), and a smaller particle size (520.92 ± 35.21 nm) than MNPs. The in vitro investigation demonstrated that Li-MNPs greatly enhanced the biomineralization and odontogenic differentiation of hDPSCs through the Wnt/β-catenin signaling pathway. Li-MNPs showed a strong antibacterial effect on S. mutans. As expected, Li-MNPs significantly promoted dentin regeneration in situ and in vivo. Conclusion Li-MNPs promoted dentin regeneration and inhibited S. mutans growth, implying a possible application as a pulp capping agent in vital pulp therapy.
Impact of Human Activity On Streamfiow in The Huaihe River Basin, China : Analysis and Simulation

A distributed hydrological model coupled with a coarse grid land surface model is set up to simulate hydrological processes in the Huaihe River Basin, China. Parameters of the land surface model are interpolated from global soil and vegetation data sets. The characteristics of the basin, including topography, river networks and aquifer geology, are derived from a digital elevation model (DEM) and a national geological survey atlas. The NCEP/NCAR re-analysis data set and observed precipitation data are used as meteorological inputs. The coupled model is firstly calibrated and validated by using observed streamfiow over the period 1980-1987. A long-term continuous simulation is then carried out for 1980-2003 forced with observed rainfall data. Results indicate that streamfiow is over-estimated for dry years since the 1990s when water withdrawal increased substantially due to the growing industrial activities and the development of water projects. Two methods are proposed to study the human dimension in the hydrological cycle. One is to reconstruct the natural streamfiow series using local volumes of withdrawals. The simulated results are consistent with the reconstructed hydrographs. The other method is to integrate a designed modular into the coupled model to represent the impact of human activities. This method can significantly improve the models performance in streamflow simulation. This study shows that the coupling of hydrological and atmospheric models is a powerful tool for studying the human impact on the hydrological cycle.
Applying Probabilistic Forecasting of Multidimensional Time Series to Electricity Load Analysis

Electricity load is a crucial time series for electricity power systems, and its analysis and forecasting are essential for making informed decisions regarding future power infrastructure construction and power generation. Current deterministic point forecasting methods for electricity load forecasting have limitations. Probabilistic forecasting, on the other hand, provides more comprehensive analysis information by offering the interval and probability distribution of future changes. However, existing probabilistic schemes for time series forecasting, such as Variational Auto-Encoder (VAE), suffer from time-consuming training, large volume of model parameters, and inability to capture long-term dependencies in series. To address this challenge, this paper proposes a new probabilistic forecasting model V-trans that combines the self-attention mechanism and VAE. The model uses the self-attention scheme to capture long dependencies and VAE to extrapolate probability distribution in prediction results while minimizing computation costs and model storage. Evaluation experiments demonstrate that the proposed model has higher forecasting accuracy.
Development and validation of an experimental verbal Episodic Memory task in Spanish

ABSTRACT Purpose To develop and validate an experimental verbal episodic memory task in Spanish. Methods Six encoding blocks were elaborated, three deep and three superficial, each one with different demands of cognitive effort. The blocks were reviewed by four expert judges and tested in a pilot application. The agreement was assessed on whether the task allowed combined processing level and cognitive effort to be manipulated during incidental encoding of words, as well as clarity of instructions, examples, and workflow. Results Variables such as lexical availability, metrics, and strength of association were useful to differentiate the cognitive effort between each block. The judges agreed that the processing blocks allowed a combined manipulation of the level of processing and cognitive effort and that the instructions are precise. After the pilot, the participants agreed that the instructions, examples, and way of working were easy to understand and perform. Conclusion The results provide evidence of validity related to the content for the proposed experimental task, thus becoming a viable alternative to consider in research aimed at identifying environmental factors that contribute to compensating the defects shown by episodic memory with age.
Serum neurofilament light chain: a promising early diagnostic biomarker for hereditary transthyretin amyloidosis?

Hereditary transthyretin amyloidosis (ATTRv) is a life‐threatening disease caused by mutations in the gene encoding transthyretin (TTR). The recent therapeutic advances have underlined the importance of easily accessible, objective biomarkers of both disease onset and progression. Preliminary evidence suggests a potential role in this respect for neurofilament light chain (NfL). In this study, the aim was to determine serum NfL (sNfL) levels in a late‐onset ATTRv population and evaluate whether it might represent a reliable biomarker of disease onset (i.e., ‘conversion’ from the asymptomatic status to symptomatic disease in TTR mutation carriers).