In this study, we propose a modified particle swarm optimization (PSO) algorithm, which is an improved version of the conventional PSO algorithm. To improve the performance of the conventional PSO, a ...novel method is applied to intelligently control the number of particles. The novel method compares the cost value of the global best (gbest) in the current iteration to that of the gbest in the previous iteration. If there is a difference between the two cost values, the proposed algorithm operates in the exploration stage, maintaining the number of particles. However, when the difference in the cost values is smaller than the tolerance values assigned by the user, the proposed algorithm operates in the exploitation stage, reducing the number of particles. In addition, the algorithm eliminates the particle that is nearest to the best particle to ensure its randomness in terms of the Euclidean distance. The proposed algorithm is validated using five numerical test functions, whose number of function calls is reduced to some extent in comparison to conventional PSO. After the algorithm is validated, it is applied to the optimal design of an interior permanent magnet synchronous motor (IPMSM), aiming at minimizing the total harmonic distortion (THD) of the back electromotive force (back EMF). Considering the performance constraint, an optimal design is attained, which reduces back EMF THD and satisfies the back EMF amplitude. Finally, we build and test an experimental model. To validate the performance of the optimal design and optimization algorithm, a no-load test is conducted. Based on the experimental result, the effectiveness of the proposed algorithm on optimal design of an electric machine is validated.
Big web data from sources including online news and Twitter are good resources for investigating deep learning. However, collected news articles and tweets almost certainly contain data unnecessary ...for learning, and this disturbs accurate learning. This paper explores the performance of word2vec Convolutional Neural Networks (CNNs) to classify news articles and tweets into related and unrelated ones. Using two word embedding algorithms of word2vec, Continuous Bag-of-Word (CBOW) and Skip-gram, we constructed CNN with the CBOW model and CNN with the Skip-gram model. We measured the classification accuracy of CNN with CBOW, CNN with Skip-gram, and CNN without word2vec models for real news articles and tweets. The experimental results indicated that word2vec significantly improved the accuracy of the classification model. The accuracy of the CBOW model was higher and more stable when compared to that of the Skip-gram model. The CBOW model exhibited better performance on news articles, and the Skip-gram model exhibited better performance on tweets. Specifically, CNN with word2vec models was more effective on news articles when compared to that on tweets because news articles are typically more uniform when compared to tweets.
Given that surgical stress response and surgical excision may increase the likelihood of post-surgery cancer dissemination and metastasis, the appropriate choice of surgical anesthetics may be ...important for oncologic outcomes. We evaluated the association of anesthetics used for general anesthesia with overall survival and recurrence-free survival in patients who underwent esophageal cancer surgery. Adult patients (922) underwent elective esophageal cancer surgery were included. The patients were divided into two groups according to the anesthetics administered during surgery: volatile anesthesia (VA) or intravenous anesthesia with propofol (TIVA). Propensity score and Cox regression analyses were performed. There were 191 patients in the VA group and 731 in the TIVA group. In the entire cohort, VA was independently associated with worse overall survival (HR 1.58; 95% CI 1.24-2.01; P < 0.001) and recurrence-free survival (HR 1.42; 95% CI 1.12-1.79; P = 0.003) after multivariable analysis adjustment. Similarly, in the propensity score matched cohorts, VA was associated with worse overall survival (HR 1.45; 95% CI 1.11-1.89; P = 0.006) and recurrence-free survival (HR 1.44; 95% CI 1.11-1.87; P = 0.006). TIVA during esophageal cancer surgery was associated with better postoperative survival rates compared with volatile anesthesia.
A synthesis strategy for the preparation of ultrathin free‐standing ternary‐alloy nanosheets is reported. Ultrathin Pd‐Pt‐Ag nanosheets with a thickness of approximately 3 nm were successfully ...prepared by co‐reduction of the metal precursors in an appropriate molar ratio in the presence of CO. Both the presence of CO and the interplay between the constituent metals provide fine control over the anisotropic two‐dimensional growth of the ternary‐alloy nanostructure. The prepared Pd‐Pt‐Ag nanosheets were superior catalysts of ethanol electrooxidation owing to their specific structural and compositional characteristics. This approach will pave the way for the design of multicomponent 2D nanomaterials with unprecedented functions.
Ultrathin Pd‐Pt‐Ag nanosheets with a thickness of approximately 3 nm were successfully prepared by the co‐reduction of suitable metal precursors in an appropriate molar ratio in the presence of CO. These nanosheets are superior catalysts of ethanol electrooxidation owing to their specific structural and compositional characteristics.
Q-learning is arguably one of the most applied representative reinforcement learning approaches and one of the off-policy strategies. Since the emergence of Q-learning, many studies have described ...its uses in reinforcement learning and artificial intelligence problems. However, there is an information gap as to how these powerful algorithms can be leveraged and incorporated into general artificial intelligence workflow. Early Q-learning algorithms were unsatisfactory in several aspects and covered a narrow range of applications. It has also been observed that sometimes, this rather powerful algorithm learns unrealistically and overestimates the action values hence abating the overall performance. Recently with the general advances of machine learning, more variants of Q-learning like Deep Q-learning which combines basic Q learning with deep neural networks have been discovered and applied extensively. In this paper, we thoroughly explain how Q-learning evolved by unraveling the mathematical complexities behind it as well its flow from reinforcement learning family of algorithms. Improved variants are fully described, and we categorize Q-learning algorithms into single-agent and multi-agent approaches. Finally, we thoroughly investigate up-to-date research trends and key applications that leverage Q-learning algorithms.
Recent advances in positioning techniques, along with the widespread use of mobile devices, make it easier to monitor and collect user trajectory information during their daily activities. An ...ever-growing abundance of data about trajectories of individual users paves the way for various applications that utilize user mobility information. One of the most common analysis tasks in these new applications is to extract the sequential transition patterns between two consecutive timestamps from a collection of trajectories. Such patterns have been widely exploited in diverse applications to predict and recommend next user locations based on the current position. Thus, in this paper, we explore the computation of the transition patterns, especially with a trajectory dataset collected using differential privacy, which is a de facto standard for privacy-preserving data collection and processing. Specifically, the proposed scheme relies on geo-indistinguishability, which is a variant of the well-known differential privacy, to collect trajectory data from users in a privacy-preserving manner, and exploits the functionality of the expectation-maximization algorithm to precisely estimate hidden transition patterns based on perturbed trajectory datasets collected under geo-indistinguishability. Experimental results using real trajectory datasets confirm that a good estimation of transition pattern can be achieved with the proposed method.
The widespread use of thermoelectric technology is constrained by a relatively low conversion efficiency of the bulk alloys, which is evaluated in terms of a dimensionless figure of merit (zT). The ...zT of bulk alloys can be improved by reducing lattice thermal conductivity through grain boundary and point-defect scattering, which target low- and high-frequency phonons. Dense dislocation arrays formed at low-energy grain boundaries by liquid-phase compaction in Bi0.5Sb1.5Te3 (bismuth antimony telluride) effectively scatter midfrequency phonons, leading to a substantially lower lattice thermal conductivity. Full-spectrum phonon scattering with minimal charge-carrier scattering dramatically improved the zT to 1.86 ± 0.15 at 320 kelvin (K). Further, a thermoelectric cooler confirmed the performance with a maximum temperature difference of 81 K, which is much higher than current commercial Peltier cooling devices.
Outer membrane vesicles (OMVs) containing various bacterial compounds are released from mainly gram-negative bacteria. Secreted OMVs play important roles in the ability of a bacterium to defend ...itself, and thus contribute to the survival of bacteria in a community. In this study, we collected OMVs from β-lactam antibiotic-resistant Escherichia coli established by conjugation assay and the parental β-lactam antibiotic-susceptible strain, and performed comparative proteomic analysis to examine whether these OMVs carried β-lactam-resistant compounds. We also investigated whether both types of OMVs could protect susceptible cells from β-lactam-induced death and/or directly degrade β-lactam antibiotics. Several proteins that can be involved in degrading β-lactam antibiotics were more abundant in OMVs from β-lactam-resistant E. coli, and thus OMVs from β-lactam resistant E. coli could directly and dose-dependently degrade β-lactam antibiotics and fully rescue β-lactam-susceptible E. coli and other bacterial species from β-lactam antibiotic-induced growth inhibition. Taken together, present study demonstrate that OMVs from β-lactam-resistant E. coli play important roles in survival of antibiotic susceptible bacteria against β-lactam antibiotics. This finding may pave the way for new efforts to combat the current global spread of antibiotic resistances, which is considered to be a significant public health threat.
During the process of plant growth, such as during the flowering stages and fruit development, the plants need to be provided with the various minerals and nutrients to grow. Nutrient deficiency is ...the cause of serious diseases in plant growth, affecting crop yield. In this article, we employed artificial neural network models to recognize, classify, and predict the nutritional deficiencies occurring in tomato plants (Solanum lycopersicum L.). To classify and predict the different macronutrient deficiencies in the cropping process, this paper handles the captured images of the macronutrient deficiency. This deficiency during the fruiting and leafing phases of tomato plant are based on a deep convolutional neural network (CNN). A total of 571 images were captured with tomato leaves and fruits containing the crop species at the growth stage. Among all images, 80% (461 captured images) were used for the training dataset and 20% (110 captured images) were applied for the validation dataset. In this study, we provide an analysis of two different model architectures based on convolutional neural network for classifying and predicting the nutrient deficiency symptoms. For instance, Inception-ResNet v2 and Autoencoder with the captured images of tomato plant growth under the greenhouse conditions. Moreover, a major type of statistical structure, namely Ensemble Averaging, was applied with two aforementioned predictive models to increase the accuracy of predictive validation. Three mineral nutrients, i.e., Calcium/Ca2+, Potassium/K+, and Nitrogen/N, are considered for use in evaluating the nutrient status in the development of tomato plant with these models. The aim of this study is to predict the nutrient deficiency accurately in order to increase crop production and prevent the emergence of tomato pathology caused by lack of nutrients. The predictive performance of the three models in this paper are validated, with the accuracy rates of 87.273% and 79.091% for Inception-ResNet v2 and Autoencoder, respectively, and with 91% validity using Ensemble Averaging.
Gram-negative bacteria have an outer membrane inhibiting the entry of antibiotics. Porins, found within the outer membrane, are involved in regulating the permeability of β-lactam antibiotics. ...β-lactamases are enzymes that are able to inactivate the antibacterial properties of β-lactam antibiotics. Interestingly, porins and β-lactamase are found in outer membrane vesicles (OMVs) of β-lactam-resistant
and may be involved in the survival of susceptible strains of
in the presence of antibiotics, through the hydrolysis of the β-lactam antibiotic. In this study, OMVs isolated from β-lactam-resistant
and from mutants, lacking porin or β-lactamase, were evaluated to establish if the porins or β-lactamase in OMVs were involved in the degradation of β-lactam antibiotics. OMVs isolated from
deficient in β-lactamase did not show any degradation ability against β-lactam antibiotics, while OMVs lacking OmpC or OmpF showed significantly lower levels of hydrolyzing activity than OMVs from parent
. These data reveal an important role of OMVs in bacterial defense mechanisms demonstrating that the OmpC and OmpF proteins allow permeation of β-lactam antibiotics into the lumen of OMVs, and antibiotics that enter the OMVs can be degraded by β-lactamase.
PDF
