Artificial neural network based on strong track and square root UKF for INS/GNSS intelligence integrated system during GPS outage.

When INS/GNSS (inertial navigation system/global navigation satellite system) integrated system is applied, it will be affected by the insufficient number of visible satellites, and even the satellite signal will be lost completely. At this time, the positioning error of INS accumulates with time, and the navigation accuracy decreases rapidly. Therefore, in order to improve the performance of INS/GNSS integration during the satellite signals interruption, a novel learning algorithm for neural network has been presented and used for intelligence integrated system in this article. First of all, determine the input and output of neural network for intelligent integrated system and a nonlinear model for weighs updating during neural network learning has been established. Then, the neural network learning based on strong tracking and square root UKF (unscented Kalman filter) is proposed for iterations of the nonlinear model. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical UKF to avoid the filter divergence caused by the negative definite state covariance matrix. Meanwhile, the strong tracking coefficient is introduced to adjust the filter gain in real-time and improve the tracking capability to mutation state. Finally, an improved calculation method of strong tracking coefficient is presented to reduce the computational complexity in this algorithm. The results of the simulation test and the field-positioning data show that the proposed learning algorithm could improve the calculation stability and robustness of neural network. Therefore, the error accumulation of INS/GNSS integration is effectively compensated, and then the positioning accuracy of INS/GNSS intelligence integrated system has been improved.

Spurring SMEs' performance through business intelligence, organizational and network learning, customer value anticipation, and innovation - Empirical evidence of the creative economy sector in East Java, Indonesia.

Several studies have explored firm performance in the post-Covid-19 pandemic era. However, there is not much research to find reports divulging the complex relationship dynamics between business intelligence, organizational and network learning, customer value anticipation, and creative economy-based small-medium enterprises (SMEs) performance in developing countries. This study aims to uncover the complexity of those relationships. The quantitative data were collected from 313 creative economy-based SMEs in East Java, Indonesia. Using PLS-SEM, this study disclosed that business intelligence practices could not directly impact SMEs' performance. Business intelligence will be crucial to SMEs' performance with the support of organizational learning as a mediator. The finding also confirmed the presence of serial mediation of organizational learning and innovation in the relationship between business intelligence and SMEs' performance. However, the role of network learning and innovation is also important, considering their relatively large direct impact on SMEs' performance. The theoretical implications of this research broke the boundaries of strategic management theory in resource-based view and knowledge-based view in the latest era, where creative economy-based SMEs have been able to mobilize resources to carry out business intelligence to realize innovation and high performance. Further research is suggested to explore the role of business intelligence in promoting specific performance areas, such as marketing performance, financial performance, and human resource management. In addition, it is advisable to choose more specific research subjects, including those in the culinary subsector, and pay attention to other areas, e.g., the demographics of respondents in the model as a control variable.

Causal inference approaches reveal both positive and negative unintended effects of agricultural and urban management practices on instream biological condition.

Agricultural and urban management practices (MPs) are primarily designed and implemented to reduce nutrient and sediment concentrations in streams. However, there is growing interest in determining if MPs produce any unintended positive effects, or co-benefits, to instream biological and habitat conditions. Identifying co-benefits is challenging though because of confounding variables (i.e., those that affect both where MPs are applied and stream biota), which can be accounted for in novel causal inference approaches. Here, we used two causal inference approaches, propensity score matching (PSM) and Bayesian network learning (BNL), to identify potential MP co-benefits in the Chesapeake Bay watershed portion of Maryland, USA. Specifically, we examined how MPs may modify instream conditions that impact fish and macroinvertebrate indices of biotic integrity (IBI) and functional and taxonomic endpoints. We found evidence of positive unintended effects of MPs for both benthic macroinvertebrates and fish indicated by higher IBI scores and specific endpoints like the number of scraper macroinvertebrate taxa and lithophilic spawning fish taxa in a subset of regions. However, our results also suggest MPs have negative unintended effects, especially on sensitive benthic macroinvertebrate taxa and key instream habitat and water quality metrics like specific conductivity. Overall, our results suggest MPs offer co-benefits in some regions and catchments with largely degraded conditions but can have negative unintended effects in some regions, especially in catchments with good biological conditions. We suggest the number and types of MPs drove these mixed results and highlight carefully designed MP implementation that incorporates instream biological data at the catchment scale could facilitate co-benefits to instream biological conditions. Our study underscores the need for more research on identifying effects of individual MP types on instream biological and habitat conditions.

Learning the structure of the mTOR protein signaling pathway from protein phosphorylation data.

Statistical learning of the structures of cellular networks, such as protein signaling pathways, is a topical research field in computational systems biology. To get the most information out of experimental data, it is often required to develop a tailored statistical approach rather than applying one of the off-the-shelf network reconstruction methods. The focus of this paper is on learning the structure of the mTOR protein signaling pathway from immunoblotting protein phosphorylation data. Under two experimental conditions eleven phosphorylation sites of eight key proteins of the mTOR pathway were measured at ten non-equidistant time points. For the statistical analysis we propose a new advanced hierarchically coupled non-homogeneous dynamic Bayesian network (NH-DBN) model, and we consider various data imputation methods for dealing with non-equidistant temporal observations. Because of the absence of a true gold standard network, we propose to use predictive probabilities in combination with a leave-one-out cross validation strategy to objectively cross-compare the accuracies of different NH-DBN models and data imputation methods. Finally, we employ the best combination of model and data imputation method for predicting the structure of the mTOR protein signaling pathway.

SAA-SDM: Neural Networks Faster Learned to Segment Organ Images.

In the field of medicine, rapidly and accurately segmenting organs in medical images is a crucial application of computer technology. This paper introduces a feature map module, Strength Attention Area Signed Distance Map (SAA-SDM), based on the principal component analysis (PCA) principle. The module is designed to accelerate neural networks' convergence speed in rapidly achieving high precision. SAA-SDM provides the neural network with confidence information regarding the target and background, similar to the signed distance map (SDM), thereby enhancing the network's understanding of semantic information related to the target. Furthermore, this paper presents a training scheme tailored for the module, aiming to achieve finer segmentation and improved generalization performance. Validation of our approach is carried out using TRUS and chest X-ray datasets. Experimental results demonstrate that our method significantly enhances neural networks' convergence speed and precision. For instance, the convergence speed of UNet and UNET + + is improved by more than 30%. Moreover, Segformer achieves an increase of over 6% and 3% in mIoU (mean Intersection over Union) on two test datasets without requiring pre-trained parameters. Our approach reduces the time and resource costs associated with training neural networks for organ segmentation tasks while effectively guiding the network to achieve meaningful learning even without pre-trained parameters.

Long-Horizon Associative Learning Explains Human Sensitivity to Statistical and Network Structures in Auditory Sequences.

Networks are a useful mathematical tool for capturing the complexity of the world. In a previous behavioral study, we showed that human adults were sensitive to the high-level network structure underlying auditory sequences, even when presented with incomplete information. Their performance was best explained by a mathematical model compatible with associative learning principles, based on the integration of the transition probabilities between adjacent and nonadjacent elements with a memory decay. In the present study, we explored the neural correlates of this hypothesis via magnetoencephalography (MEG). Participants (N = 23, 16 females) passively listened to sequences of tones organized in a sparse community network structure comprising two communities. An early difference (∼150 ms) was observed in the brain responses to tone transitions with similar transition probability but occurring either within or between communities. This result implies a rapid and automatic encoding of the sequence structure. Using time-resolved decoding, we estimated the duration and overlap of the representation of each tone. The decoding performance exhibited exponential decay, resulting in a significant overlap between the representations of successive tones. Based on this extended decay profile, we estimated a long-horizon associative learning novelty index for each transition and found a correlation of this measure with the MEG signal. Overall, our study sheds light on the neural mechanisms underlying human sensitivity to network structures and highlights the potential role of Hebbian-like mechanisms in supporting learning at various temporal scales.

A universal lesion detection method based on partially supervised learning.

Partially supervised learning (PSL) is urgently necessary to explore to construct an efficient universal lesion detection (ULD) segmentation model. An annotated dataset is crucial but hard to acquire because of too many Computed tomography (CT) images and the lack of professionals in computer-aided detection/diagnosis (CADe/CADx). To address this problem, we propose a novel loss function to reduce the proportion of negative anchors which is extremely likely to classify the lesion area (positive samples) as a negative bounding box, further leading to an unexpected performance. Before calculating loss, we generate a mask to intentionally choose fewer negative anchors which will backward wrongful loss to the network. During the process of loss calculation, we set a parameter to reduce the proportion of negative samples, and it significantly reduces the adverse effect of misclassification on the model. Our experiments are implemented in a 3D framework by feeding a partially annotated dataset named DeepLesion, a large-scale public dataset for universal lesion detection from CT. We implement a lot of experiments to choose the most suitable parameter, and the result shows that the proposed method has greatly improved the performance of a ULD detector. Our code can be obtained at https://github.com/PLuld0/PLuldl.

Deterministic learning-based neural network control with adaptive phase compensation.

Under the persistent excitation (PE) condition, the real dynamics of the nonlinear system can be obtained through the deterministic learning-based radial basis function neural network (RBFNN) control. However, in this scheme, the learning speed and accuracy are limited by the tradeoff between the PE levels and the approximation capabilities of the neural network (NN). Inspired by the frequency domain phase compensation of linear time-invariant (LTI) systems, this paper presents an adaptive phase compensator employing the pure time delay to improve the performance of the deterministic learning-based adaptive feedforward control with the reference input known a priori. When the adaptive phase compensation is applied to the hidden layer of the RBFNN, the nonlinear approximation capability of the RBFNN is effectively improved such that both the learning performance (learning speed and accuracy) and the control performance of the deterministic learning-based control scheme are improved. Theoretical analysis is conducted to prove the stability of the proposed learning control scheme for a class of systems which are affine in the control. Simulation studies demonstrate the effectiveness of the proposed phase compensation method.

Application of a Bayesian Network Learning Model to Predict Longitudinal Trajectories of Executive Function Difficulties in Elementary School Students.

Executive function is the mental ability to modulate behavior or thinking to accomplish a task. This is developmentally important for children's academic achievements and ability to adjust to school. We classified executive function difficulties (EFDs) in longitudinal trajectories in Korean children from 7 to 10 years old. We found predictors of EFDs using latent class growth analysis and Bayesian network learning methods with Panel Study data. Three types of latent class models of executive function difficulties were identified: low, intermediate, and high EFDs. The modeling performance of the high EFD group was excellent (AUC = .91), and the predictors were the child's gender, temperamental emotionality, happiness, DSM (Diagnostic and Statistical Manual of Mental Disorders) anxiety problems, and the mother's depression as well as coparenting conflict recognized by the mother. The results show that using latent class growth analysis and Bayesian network learning are helpful in classifying the longitudinal EFD patterns in elementary school students. Furthermore, school-age EFD is affected by emotional problems in parents and children that continue from early life. These findings can support children's development and prevent risk by preclassifying children who may experience persistent EFD and tracing causes.

La virtualización de la superación profesional para el mejoramiento del desempeño pedagógico durante la COVID-19 / Virtualization of professional upgrading for improving teaching performance during COVID-19

Introducción: La pandemia generada por la COVID-19 promovió que la superación profesional adquiriera nuevos matices. Las universidades fomentaron el uso de las tecnologías de la información y las comunicaciones, y el aprendizaje en los entornos virtuales. Objetivo: Exponer criterios sobre la virtualización de la superación profesional para el mejoramiento del desempeño pedagógico durante la COVID-19. Posicionamiento del autor: La situación generada por la COVID-19 exigió realizar el proceso de enseñanza aprendizaje en los entornos virtuales, por esta razón los profesores del Departamento de Ciencias Sociales asumieron este reto sin preparación previa. La superación profesional realizada en el Aula Virtual constituyó una vía eficaz para la aplicación de los recursos educativos virtuales. Conclusiones: La superación profesional en circunstancias sanitarias de emergencia se realizó en el Aula Virtual de la Escuela Latinoamericana de Medicina, donde se logró el mejoramiento del desempeño pedagógico de los docentes del Departamento de Ciencias Sociales(AU)

Introduction: The COVID-19 pandemic caused professional upgrading to acquire new shades. Universities encouraged the use of information and communication technologies, as well as learning in virtual settings. Objective: To present criteria on the virtualization of professional upgrading for the improvement of pedagogical performance during COVID-19. Author's stance: The situation caused by COVID-19 demanded to carry out the teaching-learning process in virtual settings; therefore, the professors from the social sciences area took up this challenge without previous preparation. The professional upgrading process carried out in the virtual classroom proved to be an effective way for the application of virtual educational resources. Conclusions: Professional upgrading during public health circumstances of emergency was carried out in the virtual classroom of the Latin American School of Medicine, where pedagogical performance could be improved among the professors from the social sciences area(AU)

Entrepreneurial Learning, Self-Efficacy, and Firm Performance: Exploring Moderating Effect of Entrepreneurial Orientation.

Although the impact entrepreneurial learning on firm performance has attracted significant attention, a comprehensive understanding by integrating entrepreneurial orientation and individual self-efficacy remain poorly understood. We fill this void by integrating the above variables into a model and examine these relations. Findings from a sample of 411 nascent entrepreneurs support that entrepreneurial learning is positively related to firm performance, and this relationship is fully mediated by entrepreneurial self-efficacy (ESE). We also found entrepreneurial orientation strengthens the positive impact of entrepreneurial learning on ESE. The findings indicate that ESE must be in place to maximize the effect of entrepreneurial learning on performance, and entrepreneurial orientation is an important contingency in shaping entrepreneurial learning's impact on nascent entrepreneur's self-efficacy.

Feasibility of Deep Learning Algorithms for Reporting in Routine Spine Magnetic Resonance Imaging.

BACKGROUND: Artificial intelligence is gaining traction in automated medical imaging analysis. Development of more accurate magnetic resonance imaging (MRI) predictors of successful clinical outcomes is necessary to better define indications for surgery, improve clinical outcomes with targeted minimally invasive and endoscopic procedures, and realize cost savings by avoiding more invasive spine care. OBJECTIVE: To demonstrate the ability for deep learning neural network models to identify features in MRI DICOM datasets that represent varying intensities or severities of common spinal pathologies and injuries and to demonstrate the feasibility of generating automated verbal MRI reports comparable to those produced by reading radiologists. METHODS: A 3-dimensional (3D) anatomical model of the lumbar spine was fitted to each of the patient's MRIs by a team of technicians. MRI T1, T2, sagittal, axial, and transverse reconstruction image series were used to train segmentation models by the intersection of the 3D model through these image sequences. Class definitions were extracted from the radiologist report for the central canal: (0) no disc bulge/protrusion/canal stenosis, (1) disc bulge without canal stenosis, (2) disc bulge resulting in canal stenosis, and (3) disc herniation/protrusion/extrusion resulting in canal stenosis. Both the left and right neural foramina were assessed with either (0) neural foraminal stenosis absent, or (1) neural foramina stenosis present. Reporting criteria for the pathologies at each disc level and, when available, the grading of severity were extracted, and a natural language processing model was used to generate a verbal and written report. These data were then used to train a set of very deep convolutional neural network models, optimizing for minimal binary cross-entropy for each classification. RESULTS: The initial prediction validation of the implemented deep learning algorithm was done on 20% of the dataset, which was not used for artificial intelligence training. Of the 17,800 total disc locations for which MRI images and radiology reports were available, 14,720 were used to train the model, and 3560 were used to validate against. The convergence of validation accuracy achieved with the deep learning algorithm for the foraminal stenosis detector was 81% (sensitivity = 72.4.4%, specificity = 83.1%) after 25 complete iterations through the entire training dataset (epoch). The accuracy was 86.2% (sensitivity = 91.1%, specificity = 82.5%) for the central stenosis detector and 85.2% (sensitivity = 81.8%, specificity = 87.4%) for the disc herniation detector. CONCLUSIONS: Deep learning algorithms may be used for routine reporting in spine MRI. There was a minimal disparity among accuracy, sensitivity, and specificity, indicating that the data were not overfitted to the training set. We concluded that variability in the training data tends to reduce overfitting and overtraining as the deep neural network models learn to focus on the common pathologies. Future studies should demonstrate the accuracy of deep neural network models and the predictive value of favorable clinical outcomes with intervention and surgery. LEVEL OF EVIDENCE: 3. CLINICAL RELEVANCE: Feasibility, clinical teaching, and evaluation study.

Artificial Intelligence Comparison of the Radiologist Report With Endoscopic Predictors of Successful Transforaminal Decompression for Painful Conditions of the Lumber Spine: Application of Deep Learning Algorithm Interpretation of Routine Lumbar Magnetic Resonance Imaging Scan.

BACKGROUND: Identifying pain generators in multilevel lumbar degenerative disc disease is not trivial but is crucial for lasting symptom relief with the targeted endoscopic spinal decompression surgery. Artificial intelligence (AI) applications of deep learning neural networks to the analysis of routine lumbar MRI scans could help the primary care and endoscopic specialist physician to compare the radiologist's report with a review of endoscopic clinical outcomes. OBJECTIVE: To analyze and compare the probability of predicting successful outcome with lumbar spinal endoscopy by using the radiologist's MRI grading and interpretation of the radiologic image with a novel AI deep learning neural network (Multus Radbot™) as independent prognosticators. METHODS: The location and severity of foraminal stenosis were analyzed using comparative ordinal grading by the radiologist, and a contiguous grading by the AI network in patients suffering from lateral recess and foraminal stenosis due to lumbar herniated disc. The compressive pathology definitions were extracted from the radiologist lumbar MRI reports from 65 patients with a total of 383 levels for the central canal - (0) no disc bulge/protrusion/canal stenosis, (1) disc bulge without canal stenosis, (2) disc bulge resulting in canal stenosis, and (3) disc herniation/protrusion/extrusion resulting in canal stenosis. Both neural foramina were assessed with either - (0) neural foraminal stenosis absent, or (1) neural foramina are stenosis present. Reporting criteria for the pathologies at each disc level and, when available, the grading of severity were extracted and assigned into two categories: "Normal," and "Stenosis." Clinical outcomes were graded using dichotomized modified Macnab criteria considering Excellent and Good results as "Improved," and Fair and Poor outcomes as "Not Improved." Binary logistic regression analysis was used to predict the probability of the AI- and radiologist grading of stenosis at the 88 foraminal decompression sites to result in "Improved" outcomes. RESULTS: The average age of the 65 patients was 62.7 +/- 12.7 years. They consisted of 51 (54.3%) males and 43 (45.7%) females. At an average final follow-up of 57.4 +/- 12.57, Macnab outcome analysis showed that 86.4% of the 88 foraminal decompressions resulted in Excellent and Good (Improved) clinical outcomes. The stenosis grading by the radiologist showed an average severity score of 4.71 +/- 2.626, and the average AI severity grading was 5.65 +/- 3.73. Logit regression probability analysis of the two independent prognosticators showed that both the grading by the radiologist (86.2%; odds ratio 1.264) and the AI grading (86.4%; odds ratio 1.267) were nearly equally predictive of a successful outcome with the endoscopic decompression. CONCLUSIONS: Deep learning algorithms are capable of identifying lumbar foraminal compression due to herniated disc. The treatment outcome was correlated to the decompression of the directly visualized corresponding pathology during the lumbar endoscopy. This research should be extended to other validated pain generators in the lumbar spine. LEVEL OF EVIDENCE: 3. CLINICAL RELEVANCE: Validity, clinical teaching, evaluation study.

Reliability Analysis of Deep Learning Algorithms for Reporting of Routine Lumbar MRI Scans.

BACKGROUND: Artificial intelligence could provide more accurate magnetic resonance imaging (MRI) predictors of successful clinical outcomes in targeted spine care. OBJECTIVE: To analyze the level of agreement between lumbar MRI reports created by a deep learning neural network (RadBot) and the radiologists' MRI reading. METHODS: The compressive pathology definitions were extracted from the radiologist lumbar MRI reports from 65 patients with a total of 383 levels for the central canal: (0) no disc bulge/protrusion/canal stenosis, (1) disc bulge without canal stenosis, (2) disc bulge resulting in canal stenosis, and (3) disc herniation/protrusion/extrusion resulting in canal stenosis. For both, neural foramina were assessed with either (0) neural foraminal stenosis absent or (1) neural foramina stenosis present. Reporting criteria for the pathologies at each disc level and, when available, the grading of severity were extracted, and the Natural Language Processing model was used to generate a verbal and written report. The RadBot report was analyzed similarly as the MRI report by the radiologist. MRI reports were investigated by dichotomizing the data into 2 categories: normal and stenosis. The quality of the RadBot test was assessed by determining its sensitivity, specificity, and positive and negative predictive value as well as its reliability with the calculation of the Cronbach alpha and Cohen kappa using the radiologist MRI report as a gold standard. RESULTS: The authors found a RadBot sensitivity of 73.3%, a specificity of 88.4%, a positive predictive value of 80.3%, and a negative predictive value of 83.7%. The reliability analysis revealed the Cronbach alpha as 0.772. The highest individual values of the Cronbach alpha were 0.629 and 0.681 when compared to the MRI report by the radiologist, rending values of 0.566 and 0.688, respectively. Analysis of interobserver reliability rendered an overall kappa for the RadBot of 0.627. Analysis of receiver operating characteristics (ROC) showed a value of 0.808 for the area under the ROC curve. CONCLUSIONS: Deep learning algorithms, when used for routine reporting in lumbar spine MRI, showed excellent quality as a diagnostic test that can distinguish the presence of neural element compression (stenosis) at a statistically significant level (P < .0001) from a random event distribution. This research should be extended to validated and directly visualized pain generators to improve the accuracy and prognostic value of the routine lumbar MRI scan for favorable clinical outcomes with intervention and surgery. LEVEL OF EVIDENCE: 3. CLINICAL RELEVANCE: Validity, clinical teaching, and evaluation study.

Aportes del Centro Nacional de Información de Ciencias Médicas frente a la COVID-19 / Contributions of the National Medical Sciences Information Center to the fight against COVID-19

La rápida propagación del SARS-CoV-2 ha ocasionado grandes desafíos, algunos de ellos relacionados con la gestión y la visibilidad de la información. Los profesionales de las instituciones de información enfrentaron el reto de tener que reorganizar sus servicios en un breve tiempo, con un componente tecnológico esencial para lograr la virtualidad ante el imperativo indiscutible del distanciamiento físico. Como parte del Plan de enfrentamiento desplegado por Cuba durante esta crisis sanitaria, el Centro Nacional de Información de Ciencias Médicas (CNICM/Infomed) desempeña un rol fundamental, al actuar como proveedor y facilitador de las fuentes, herramientas y servicios de información científica especializada, además de garantizar la plataforma tecnológica para todo el sistema nacional de salud y la población. El presente documento se propone describir el flujo de trabajo concebido por esta institución, los espacios y modos empleados para la producción y difusión de la información científica durante la epidemia, así como los aportes realizados (elaboración de productos y desarrollo e implementación de herramientas y servicios( para apoyar las investigaciones científicas y la toma de decisiones en salud durante la pandemia de la COVID-19(AU)

The fast spread of SARS-CoV-2 has posed big challenges, some of which are related to information management and visibility. Professionals from information institutions have been faced with having to reorganize their services in a short time, with an essential technological component to achieve virtuality in face of the unavoidable imperative of physical distancing. The National Medical Sciences Information Center (CNICM/Infomed) plays a fundamental role in the response plan implemented by Cuba during this health crisis, serving as facilitator and provider of specialized scientific information services, sources and tools, and supplying the required technological platform for the entire national health system and the population. The present document is aimed at describing the workflow devised by this institution, the spaces and modes used for the production and dissemination of scientific information during the epidemic, and the contributions made to the creation of products and the development and implementation of tools and services to support health scientific research and decision making during the COVID-19 pandemic(AU)

Functional brain network architecture supporting the learning of social networks in humans.

Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. We found that participants learned the community structure of the networks, as evidenced by a slower reaction time when a trial moved between communities than when a trial moved within a community. Learning the community structure of social networks was also characterized by significantly greater functional connectivity of the hippocampus and temporoparietal junction when transitioning between communities than when transitioning within a community. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions for social networks than for non-social networks. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies.

Adaptive fault-tolerant consensus for a class of leader-following systems using neural network learning strategy.

In this paper, the leader-following consensus problem of a class of nonlinearly multi-dimensional multi-agent systems with actuator faults is addressed by developing a novel neural network learning strategy. In order to achieve the desirable consensus results, a neural network learning algorithm composed of adaptive technique is proposed to on-line approximate the unknown nonlinear functions and estimate the unknown bounds of actuator faults. Then, on the basis of the approximations and estimations, a robust adaptive distributed fault-tolerant consensus control scheme is investigated so that the bounded results of all signals of the resulting closed-loop leader-following system can be achieved by using Lyapunov stability theorem. Finally, efficiency of the proposed adaptive neural network learning strategy-based consensus control strategies is demonstrated by a coupled nonlinear forced pendulums system.

PEnBayes: A Multi-Layered Ensemble Approach for Learning Bayesian Network Structure from Big Data.

Discovering the Bayesian network (BN) structure from big datasets containing rich causal relationships is becoming increasingly valuable for modeling and reasoning under uncertainties in many areas with big data gathered from sensors due to high volume and fast veracity. Most of the current BN structure learning algorithms have shortcomings facing big data. First, learning a BN structure from the entire big dataset is an expensive task which often ends in failure due to memory constraints. Second, it is quite difficult to select a learner from numerous BN structure learning algorithms to consistently achieve good learning accuracy. Lastly, there is a lack of an intelligent method that merges separately learned BN structures into a well structured BN network. To address these shortcomings, we introduce a novel parallel learning approach called PEnBayes (Parallel Ensemble-based Bayesian network learning). PEnBayes starts with an adaptive data preprocessing phase that calculates the Appropriate Learning Size and intelligently divides a big dataset for fast distributed local structure learning. Then, PEnBayes learns a collection of local BN Structures in parallel using a two-layered weighted adjacent matrix-based structure ensemble method. Lastly, PEnBayes merges the local BN Structures into a global network structure using the structure ensemble method at the global layer. For the experiment, we generate big data sets by simulating sensor data from patient monitoring, transportation, and disease diagnosis domains. The Experimental results show that PEnBayes achieves a significantly improved execution performance with more consistent and stable results compared with three baseline learning algorithms.

Body region localization in whole-body low-dose CT images of PET/CT scans using virtual landmarks.

PURPOSE: Radiological imaging and image interpretation for clinical decision making are mostly specific to each body region such as head and neck, thorax, abdomen, pelvis, and extremities. In this study, we present a new solution to trim automatically the given axial image stack into image volumes satisfying the given body region definition. METHODS: The proposed approach consists of the following steps. First, a set of reference objects is selected and roughly segmented. Virtual landmarks (VLs) for the objects are then identified by using principal component analysis and recursive subdivision of the object via the principal axes system. The VLs can be defined based on just the binary objects or objects with gray values also considered. The VLs may lie anywhere with respect to the object, inside or outside, and rarely on the object surface, and are tethered to the object. Second, a classic neural network regressor is configured to learn the geometric mapping relationship between the VLs and the boundary locations of each body region. The trained network is then used to predict the locations of the body region boundaries. In this study, we focus on three body regions - thorax, abdomen, and pelvis, and predict their superior and inferior axial locations denoted by TS(I), TI(I), AS(I), AI(I), PS(I), and PI(I), respectively, for any given volume image I. Two kinds of reference objects - the skeleton and the lungs and airways, are employed to test the localization performance of the proposed approach. RESULTS: Our method is tested by using low-dose unenhanced computed tomography (CT) images of 180 near whole-body 18 F-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) scans (including 34 whole-body scans) which are randomly divided into training and testing sets with a ratio of 85%:15%. The procedure is repeated six times and three times for the case of lungs and skeleton, respectively, with different divisions of the entire data set at this proportion. For the case of using skeleton as a reference object, the overall mean localization error for the six locations expressed as number of slices (nS) and distance (dS) in mm, is found to be nS: 3.4, 4.7, 4.1, 5.2, 5.2, and 3.9; dS: 13.4, 18.9, 16.5, 20.8, 20.8, and 15.5 mm for binary objects; nS: 4.1, 5.7, 4.3, 5.9, 5.9, and 4.0; dS: 16.2, 22.7, 17.2, 23.7, 23.7, and 16.1 mm for gray objects, respectively. For the case of using lungs and airways as a reference object, the corresponding results are, nS: 4.0, 5.3, 4.1, 6.9, 6.9, and 7.4; dS: 15.0, 19.7, 15.3, 26.2, 26.2, and 27.9 mm for binary objects; nS: 3.9, 5.4, 3.6, 7.2, 7.2, and 7.6; dS: 14.6, 20.1, 13.7, 27.3, 27.3, and 28.6 mm for gray objects, respectively. CONCLUSIONS: Precise body region identification automatically in whole-body or body region tomographic images is vital for numerous medical image analysis and analytics applications. Despite its importance, this issue has received very little attention in the literature. We present a solution to this problem in this study using the concept of virtual landmarks. The method achieves localization accuracy within 2-3 slices, which is roughly comparable to the variation found in localization by experts. As long as the reference objects can be roughly segmented, the method with its learned VLs-to-boundary location relationship and predictive ability is transferable from one image modality to another.

Competencias docentes en entornos virtuales: un reto para la anestesiología cubana / Teaching competences in virtual environments: A challenge for cuban anesthesiology

Introducción: La educación virtual es el proceso de formación desarrollado mediante la incorporación de las tecnologías de la información y las comunicaciones, a través de internet. Para un adecuado desarrollo de este nuevo método pedagógico se necesitan profesores preparados y motivados en ese sentido. Objetivos: Describir los principales aspectos relacionados con el desarrollo de las competencias docentes en los entornos virtuales de aprendizaje. Método: Se realizó un resumen de la información extraída de diferentes fuentes bibliográficas. Desarrollo: El nivel de competencias docentes en los entornos virtuales de aprendizaje se ve afectado por el escaso conocimiento y dominio de las herramientas tecnológicas y la poca motivación. Conclusiones: La presente revisión aporta elementos para trazar una estrategia destinada a mantener una superación profesoral continua en la que se incluya el uso de las de las tecnologías de la información y las comunicaciones(AU)

Introduction: Virtual education is the training process developed through the usage of information and communication technologies, through the Internet. For an adequate development of this new pedagogical method, teachers are needed who are qualified and motivated in that respect. Objectives: To describe the main aspects related to the development of teaching competences in virtual learning environments. Method: A summary was made of the information extracted from different bibliographic sources. Development: The level of teaching competences in virtual learning environments is affected by poor knowledge and mastery of technological tools, as well as by low motivation. Conclusions: This review provides elements to design a strategy aimed at maintaining the continuous improvement of professors including the use of information communication technologies(AU)