Istraživanje simultane lokalizacije, kalibracije i kartiranja umreženim robotskim sustavima

In a network robot system, a robot and a sensor network are integrated smoothly to develop their advantages and benefit from each other. Robot localization, sensor network calibration and environment mapping are three coupled issues to be solved once network robot system is introduced into a service environment. In this article, the problem of simultaneous localization, calibration and mapping is raised in order to improve their precision. The coupled relations among localization, calibration and mapping are denoted as a joint conditional distribution and then decomposed into three separate analytic terms according to Bayesian and Markov properties. The framework of Rao-Blackwellized particle filtering is used to solve the three analytic terms, in which extended particle filter is used for localization and unscented Kalman filter is used for both calibration and mapping. Simulations have been performed to demonstrate the validity and efficiency of the proposed solutions.U umreženom robotskom sustavu, robot i senzorska mreža su međusobno integrirani i povezani na način da i jedan i drugi iskoriste svoje prednosti, te da imaju koristi jedan od drugoga. Kako bi umreženi robotski sustav mogao djelovati u radnom okruženju potrebno je riješiti tri međusobno povezana problema: lokalizaciju, kalibraciju senzorske mreže i kartiranje prostora. U ovom radu razmatraju se problemi istodobne lokalizacije, kalibracije i kartiranja te se razmatraju mogućnosti poboljšanja njihove preciznosti. Povezanost lokalizacije, kartiranja i kalibracije predstavljena je pomoću zajedničke uvjetne razdiobe i zatim rastavljena u tri razdvojena analitička izraza korištenjem Bayesovih i Markovljevih svojstava. Za rješavanje svih triju analitičkih izraza koristi se Rao-Blackwell čestično filtriranje, pri čemu se prošireni čestični filtar koristi kod lokalizacije a nederivirajući Kalmanov filtar za kalibraciju i kartiranje. Ispravnost i efikasnost predloženog pristupa pokazana je kroz provedene simulacije

An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling

Due to the NP-hard nature, the permutation flowshop scheduling problem (PFSSP) is a fundamental issue for Industry 4.0, especially under higher productivity, efficiency, and self-managing systems. This paper proposes an improved genetic-shuffled frog-leaping algorithm (IGSFLA) to solve the permutation flowshop scheduling problem. In the proposed IGSFLA, the optimal initial frog (individual) in the initialized group is generated according to the heuristic optimal-insert method with fitness constrain. The crossover mechanism is applied to both the subgroup and the global group to avoid the local optimal solutions and accelerate the evolution. To evolve the frogs with the same optimal fitness more outstanding, the disturbance mechanism is applied to obtain the optimal frog of the whole group at the initialization step and the optimal frog of the subgroup at the searching step. The mathematical model of PFSSP is established with the minimum production cycle (makespan) as the objective function, the fitness of frog is given, and the IGSFLA-based PFSSP is proposed. Experimental results have been given and analyzed, showing that IGSFLA not only provides the optimal scheduling performance but also converges effectively

A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China

IntroductionPulmonary embolism (PE) is a common thrombotic disease and potentially deadly cardiovascular disorder. The ratio of clinical misdiagnosis and missed diagnosis of PE is very large because patients with PE are asymptomatic or non-specific.MethodsUsing the clinical data from the First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China), we proposed a swarm intelligence algorithm-based kernel extreme learning machine model (SSACS-KELM) to recognize and discriminate the severity of the PE by patient’s basic information and serum biomarkers. First, an enhanced method (SSACS) is presented by combining the salp swarm algorithm (SSA) with the cuckoo search (CS). Then, the SSACS algorithm is introduced into the KELM classifier to propose the SSACS-KELM model to improve the accuracy and stability of the traditional classifier.ResultsIn the experiments, the benchmark optimization performance of SSACS is confirmed by comparing SSACS with five original classical methods and five high-performance improved algorithms through benchmark function experiments. Then, the overall adaptability and accuracy of the SSACS-KELM model are tested using eight public data sets. Further, to highlight the superiority of SSACS-KELM on PE datasets, this paper conducts comparison experiments with other classical classifiers, swarm intelligence algorithms, and feature selection approaches.DiscussionThe experimental results show that high D-dimer concentration, hypoalbuminemia, and other indicators are important for the diagnosis of PE. The classification results showed that the accuracy of the prediction model was 99.33%. It is expected to be a new and accurate method to distinguish the severity of PE

The value of luteinizing hormone basal values and sex hormone-binding globulin for early diagnosis of rapidly progressive central precocious puberty

ObjectiveThis study aimed to investigate the diagnostic value of luteinizing hormone (LH) basal values and sex hormone-binding globulin (SHBG) for rapidly progressive central precocious puberty (RP-CPP).MethodsA total of 121 girls presenting with secondary sexual characteristics were selected from the Department of Pediatric Endocrinology, Lianyungang Clinical Medical College of Nanjing Medical University, from May 2021 to June 2023. The children were followed up for 6 months and were divided into three groups: RP-CPP group (n=40), slowly progressive central precocious puberty (SP-CPP) group (n=40), and premature thelarche (PT) group (n=41). The differences in LH basal values and SHBG among girls in the three groups were compared. ROC curves were drawn to analyze the value of LH basal values and SHBG in identifying RP-CPP.ResultsSignificant differences were observed in age, height, predicted adult height (PAH), weight, body mass index (BMI), bone age (BA), BA-chronological age (CA), LH basal, LH peak, FSH basal, LH peak/FSH peak, estradiol (E2), testosterone, and SHBG levels between the RP-CPP group and the SP-CPP and PT groups (P < 0.05). The LH basal value in the RP-CPP group was higher than that in the SP-CPP group and the PT group, while SHBG levels were lower than in the latter two groups, and these differences were statistically significant (P < 0.05). When the LH basal value was ≥0.58 IU/L and SHBG was ≤58.79 nmol/L, the sensitivity for diagnosing RP-CPP was 77.5% and 67.5%, and the specificity was 66.7% and 74.1%.ConclusionDetection of basal LH and SHBG levels allows for early diagnosis of the progression of central precocious puberty

Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques

IntroductionPulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients.MethodsCombining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed.ResultsTo confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital.DiscussionThe experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model’s accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE

Allicin Attenuates Inflammation and Suppresses HLA-B27 Protein Expression in Ankylosing Spondylitis Mice

Here we aimed to determine the therapeutic effect of allicin on ankylosing spondylitis (AS) and explore the mechanism(s) of action. AS mouse model was constructed by transferring the HLA-B2704 gene into Kunming mice and verified by RT-PCR and CT imaging. Verified AS mice were randomly divided into model group () and allicin-treated groups (50, 100, and 200 mg/kg, resp., , p.o., for 2 months). Wild type mice were used as control (). The levels of AS-related inflammatory factors were measured by ELISA. mRNA and protein expressions of HLA-B27 were checked by RT-PCR and western blotting. As the results, the mouse model of AS was successfully established, and high-dose allicin could markedly alleviate spine inflammatory injury possibly via reducing the secretion of the inflammatory factors (IL-6, IL-8, and TNF-α) sharply in AS mice. Moreover, allicin significantly inhibited HLA-B27 protein translation but failed to suppress HLA-B27 gene transcription in AS mice, indicating a posttranscriptional mechanism of this modulation. In conclusion, allicin has potential to be used for AS treatment as an anti-inflammatory nutraceutical

A novel bipartite consensus tracking scheme for unknown nonlinear multi-agent systems: Theoretical analysis and applications

This article proposes a novel distributed data-driven bipartite consensus tracking scheme for bipartite consensus tracking problems of multi-agent systems with bounded disturbances and coopetition networks. The proposed scheme only uses the input/output data of each agent without requiring the agents’ dynamics. We obtain the equivalent dynamic linearization data model for a controlled plant using the dynamic linearization technique based on the pseudo partial derivative. Considering the cooperative and competitive interactions among agents, the proposed method ensures that agents with adversarial relationships implement bipartite consensus tracking tasks even if only a subset of agents can access the information from the virtual leader. Moreover, the strict proof process of convergence properties reveals that the tracking error coverages to a small range around the origin. We also establish a set of software and hardware platform to demonstrate the effectiveness of the proposed distributed data-driven bipartite consensus tracking method