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Aims: COVID-19 Vaccination Hesitancy is a public health concern in the world. The present study aimed to determine the prevalence of vaccination hesitancy and its relevant factors using the health belief model among people in northeast Iran.
Instrument & Methods: The present cross-sectional study examined 626 people using convenience sampling. Data were collected through an online survey using a questionnaire based on the health belief model at intervals of 10 September 2021 and 15 October 2021. Data were analyzed by using SPSS 16.
Findings: Overall, 70.6% of the samples reported that they would use the vaccine for free after it becomes available. Health belief model constructs predicted 40% of the variance of vaccination behavioral intention. The results indicated that the perceived barriers were significantly related to using the vaccine, thereby reducing the probability of using the vaccine by 10% (Relative Risk Ratio=0.90, 95% CI: 0.82-0.99). Perceived benefits (Relative Risk Ratio =1.21, 95% CI: 1.11-1.32), perceived susceptibility (Relative Risk Ratio =1.54, 95% Cl: 1.23-1.92), and behavioral intention (Relative Risk Ratio =3.06, 95% CI: 2.23-4.20) had a significant relationship with the probability of using the vaccine.
Conclusion: About one-third of the participants had COVID-19 vaccination hesitancy. Interventions are necessary to increase compliance with vaccination, especially among people with low education levels. The health belief model constructs have a high power of predicting hesitancy and acceptance of COVID-19 vaccination and can be used in intervention programs.
 

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Identification and classification of signals which are heard by underwater microphones (hydrophones) can be used extensively in harbor traffic management, especially in economical harbors. However, automatic identification and classification of acoustic signals which are received by passive sonar system is a challenging problem, because of variation in temporal and frequency characteristics of signals (even they are received from a same source). In this paper, a novel method for classification of acoustic signals is presented, based on DWT as preprocessing, a diverse range of feature extraction methods (principal component analysis and its variations (6 methods) and discriminant analysis and its variations (3 methods)), and 4 ensemble learning methods with 3 classifiers (multilayer perceptron (MLP), probabilistic neural network (PNN) and support vector machine (SVM)). Performing a diverse range of performance tests, the performances of different methods are assessed and the best ones are chosen for the proposed method. The proposed method is used to extract features and classify acoustic signals of 8 ships. Using the proposed method, some real signals and their noisy version are classified. The accuracy of the proposed method in classification of test signals with Gaussian white noise with -5, -10 and -15 signal-to-noise ratio is obtained as 99.83%, 97.06% and 83.56%, respectively.

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Rye (Secale cereale) chromosome 1RS harbors multiple genes including Lr26, Sr31, Yr9 and Pm8 conferring disease resistance and tolerance to abiotic stresses. The introgression of the rye 1R chromosome short arm has enormously contributed to increase of genetic diversity in wheat. Utilization of such translocations in breeding programs demands identification of wheat germplasm possessing the wheat-alien chromosome translocation. This study was designed to screen a set of 102 Pakistani wheat cultivars and candidate lines to identify the rye T1BL.1RS translocation, using cytological, biochemical, and molecular techniques. Results revealed that 12 out of the 40 wheat cultivars were found to have this alien introgression. In the National Uniform Wheat Yield Trials (NUWYT) group, 10 of 23 entries of the rainfed category were identified as carrying 1BL.1RS translocation, while 4 out of 39 genotypes were present in the irrigated category of both NUWYT crop seasons. The valuable information generated can be useful in the crop improvement programs for the production of germplasm possessing T1BL.1RS translocation, in order to enhance the genetic variability in local wheat cultivars and, also, avoid the preponderance of T1BL.1RS candidates.

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Passive bipeds have become an interesting field of research for investigators. Probably all of passive bipeds which have been modeled previously are considered as a rigid model with point-mass. In this paper, 2D planner compass-gait biped with elastic link is modeled and simulated and its period-one gait is investigated. The stance leg of the passive biped is modeled as an Euler-Bernoulli beam and its vibrations are modeled by using Assumed Modes Method and the equations of motion for the swing phase are developed. Then behavior of the elastic biped is simulated by suing numerical methods in MATLAB software and the changes in leg angles and angular velocities of the biped are discussed. Computer simulations showed that when the vibrations of the stance leg are large, angular velocities become oscillating. Vibrations of the stance leg and the effects of Young's Modulus and damping coefficient on the motion of the elastic biped are discussed. Then model is simulated for the small vibrations of the stance leg and the results show that when the vibrations are small the elastic biped behaves like a rigid biped which verifies our simulations. When the vibrations are small, period-one gait can be found for the elastic biped for the ramp slopes of 0<γ≤0.0328 rad. The split in the eigenvalues of the period-one gait happens at the ramp slope of γ=0.029 rad.

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The Artificial Potential Fields approach is amongst the widely used path planning methods in continuous environments. However, the implementation of it in multi-robot path planning encounters challenges such as the local-minima and an increase in traffic probability with the rise in the number of robots. The purpose of the proposed method is to improve multi-robot path planning in complex environments. A new adaptive potential function is introduced that reduces the probability of the robots entering an area at the same time and thus reducing the probability of traffic. Also, new potential functions have been proposed that lead to smoother paths with less traverse time when the robot encounters obstacles. In these functions, in addition to the position of robots and obstacles, heading of the robot and the position of the target are also considered. In order to evaluate this method, a distributed software architecture has been designed and implemented in the framework of the robot operating system. In this architecture, as robots move, new robots can join the operation or new tasks can be assigned to robots. Two series of real-time simulations are carried out in the Gazebo environment. The results show that the use of the proposed potential functions leads to a decrease in the convergence of the robots. In the simulation done for 2 robots, proposed method has resulted in a 35% reduction in the traversal time. While in case of 15 robots in the same map, a 50% reduction in the traversal time has been achieved.

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This article investigates the challenging problem of simultaneous control of discrete and continuous inputs in switching systems. Switching systems, which are a special type of hybrid systems, require advanced control methods that can simultaneously manage continuous and discrete inputs due to their hybrid nature. An innovative approach is introduced in this paper, combining Adaptive Fuzzy Sliding Mode Control (AFSMC) with Reinforcement Learning (RL). This method not only manages both types of inputs simultaneously but also operates adaptively and robustly with minimal model information, performing learn and optimize online. To evaluate the performance and verify the proposed algorithm, the two-tank system is selected as a benchmark example in this field. The simulation results showed that the tank level tracking error is reduced to less than 1 cm despite the noise in the measurement with a standard deviation of 0.005 and also the sudden change of the system parameter. Additionally, the number of valve position changes decreased to 6 after 1000 episodes, indicating a significant reduction in switching frequency and an improvement in system stability. This algorithm achieves desired objectives with lower control costs compared to non-hybrid methods (management of discrete and continuous inputs). Furthermore, this approach can serve as a scalable framework for controlling other complex systems with combined inputs across various engineering domains.