Notes

Group the Heart failure Remodeling With circRNAs.
'You uses up hope': a good search for low-income parents' encounters with foods uncertainty making use of Photovoice.
Effect of the interior medication nocturnist assistance about care of individuals together with most cancers at a significant Canada teaching clinic: any quality-improvement examine.
nity rehabilitation. Community healthcare professionals and health services may need to consider a broader approach to management including lifestyle assessment and intervention of patients with various conditions. Implications for rehabilitationAlmost two-thirds of adults in community rehabilitation had underlying, undetected metabolic syndrome.Adults with metabolic syndrome completed less objectively measured physical activity and had lower health literacy levels than those without metabolic syndrome.A broader approach to management may need to be considered in community rehabilitation where patients presenting for rehabilitation of various conditions would likely benefit from lifestyle assessment and intervention.
In adults, low-weight restrictive eating disorders, including anorexia nervosa (AN), are marked by chronicity and diagnostic crossover from restricting to binge-eating/purging. Less is known about the naturalistic course of these eating disorders in adolescents, particularly atypical AN (atyp-AN) and avoidant/restrictive food intake disorder (ARFID). To inform nosology of low-weight restrictive eating disorders in adolescents, we examined outcomes including persistence, crossover, and recovery in an 18-month observational study.

We assessed 82 women (ages 10-23years) with low-weight eating disorders including AN (
=40; 29 restricting, 11 binge-eating/purging), atyp-AN (
=26; 19 restricting, seven binge-eating/purging), and ARFID (
=16) at baseline, nine months (9M; 75% retention), and 18 months (18M; 73% retention) via semi-structured interviews. First-order Markov modeling was used to determine diagnostic persistence, crossover, and recovery occurring at 9M or 18M.

Among all diagnoses, the likelirgent need for innovative treatment approaches to these illnesses. Frequent crossover between AN and atyp-AN supports continuity between typical and atypical presentations, whereas no crossover to ARFID supports its distinction.
Patient values, preferences, and circumstances are critical to decision-making in both patient-centred and evidence-based practice models of healthcare. Despite the established importance of integrating these patient attributes, the ways they are elicited in rehabilitation remain unclear. This study aimed to explore how health professionals elicit and share patients' 'values', 'preferences', and 'circumstances', and what they understand by the terms.

This exploratory qualitative descriptive study used interviews with 13 clinicians from interprofessional teams in inpatient neurological rehabilitation. Data were analysed using a general inductive approach.

Participants understood 'values' to mean what is important and meaningful; 'preferences' as likes/dislikes and choices; and 'circumstances' as the social, physical, and environmental context surrounding the person. Formal and informal strategies were used to gather information directly from patients or indirectly from other sources. The processes of eliion is foundational to these processes.
The strategies used and the approach used to implement these strategies were both essential to eliciting patient values, preferences, and circumstances in neurological rehabilitation. These findings offer insights into the practices of interprofessional rehabilitation clinicians. Implications for rehabilitationEliciting patient values, preferences, and circumstances involves a combination of strategies and approaches that are applied gradually throughout the continuum of rehabilitation.These processes are flexible, and strategies should be tailored to individual patients/families and phases of rehabilitation.Clinicians should be attentive to informal opportunities to gather valuable information throughout rehabilitation.Establishing positive relationships and using effective communication is foundational to these processes.Ectomycorrhizal fungi (EMFs) form symbioses with plant roots to promote nutrient uptake by plants but it is controversial as to whether they induce disease resistance in plants. selleck chemical Here, we inoculated pine seedlings with Sphaeropsis sapinea, which was presymbiotic with the EMF Hymenochaete sp. Rl, and the mycorrhizal helper bacterium (MHB) Bacillus pumilus HR10, which promotes the formation of Pinus thunbergia-Hymenochaete sp. Rl mycorrhizae. The results showed that inoculation with Hymenochaete sp. Rl, B. selleck chemical pumilus HR10, and the consortium significantly reduced pine shoot blight disease caused by S. sapinea. After inoculation with pathogenic fungi, callose deposition was significantly increased in needles of pine seedlings inoculated with Hymenochaete sp. Rl, B. pumilus HR10, and the consortium, together with an increase in enzymatic and nonenzymatic systemic antioxidant activity as well as early priming for upregulated expression of PR3 and PR5 genes. Our findings suggest that ectomycorrhizal colonization enhances the resistance of pine seedlings to Sphaeropsis shoot blight by triggering a systemic defense response and that interactions between EMFs and MHBs are essential for mycorrhizal-induced disease resistance.Primary acquired perineal hernias are rare defects through the pelvic floor diaphragm. The optimal surgical technique for repair remains unknown, and recurrence rates approach 50%. We present a 65-year-old female without previous obstetric or pelvic surgical history who was found to have herniated sigmoid colon through a 2×2 cm levator ani defect. The patient underwent robotic transabdominal hernia repair with a synthetic self-fixating underlay mesh. The peritoneum was primarily closed and the patient was discharged the same day. There is no sign of recurrence to date. Our minimally invasive approach with extraperitoneal mesh placement provided us with several advantages ambulatory surgery; excellent visualization of the defect; easier suturing in the deep pelvis compared to traditional laparoscopy; and mesh reinforcement while minimizing the risks of erosion, migration, adhesion, and fistula formation.Isogenic populations of mammalian cells exhibit significant gene expression variability. This variability can be separated into two components. Variance arises from events specific to the transcribed gene (i.e., cis or allele-specific sources) and variance from events that impact many genes at once (i.e., trans and global processes). Furthermore, the activity of the different regulatory factors that influence gene expression fluctuates at different timescales. Fast timescales will result in rapid fluctuation of gene expression, whereas slow timescales will result in longer persistence of gene expression levels over time. Here, we investigated sources of gene expression that are intrinsic, i.e., coming from cis-regulatory factors and follow slow timescales. To do so, we developed a reporter system that isolates allele-specific variability and measures its persistence in imaging and long-term fluctuation analysis experiments. Our results identify a new source of gene expression variability that is allele-specific but that fluctuates on timescales of days. We hypothesized that allele-specific fluctuations of epigenetic regulatory factors are responsible for the newly discovered allele-specific and slow source of gene expression variability. Using mathematical modeling, we showed that adding this effect to the two-state model is sufficient to account for all empirical observations. Furthermore, using direct assays of chromatin markers, we find fluctuation in H3K4me3 levels that match the observed changes in gene expression levels providing direct experimental support of our model. Collectively, our work shows that slow fluctuations of regulatory chromatin modifications contribute to the variability in gene expression.An adequate understanding of molecular structure-property relationships is important for developing new molecules with desired properties. Although deep learning optical spectroscopy (DLOS) has been successfully applied to predict the optical and photophysical properties of organic chromophores, how specific functional groups and solvents affect the optical properties is not clearly understood. Here, we employed an explainable DLOS method by applying the integrated gradients method to DLOS. The integrated gradients method allows us to obtain attributions, indicating how much the functional group contributes to the optical properties including the absorption wavelength and bandwidth, extinction coefficients, emission wavelength and bandwidth, photoluminescence quantum yield, and lifetime. The attributions of 54 functional groups and 9 solvent molecules to seven optical properties are quantified and can be used to estimate the optical properties of chromophores as in the Woodward-Fieser rule. Unlike the Woodward-Fieser rule for only the absorption wavelength, the attributions obtained in this work can be applied to estimate all seven optical properties, which makes a significant extension of the Woodward-Fieser rules. In addition, we demonstrated a strategy for utilizing the attributions in the design of molecules and in tuning the optical properties of the molecules. The design of molecular structures using attributions can revolutionize the development of optimal molecules.There are several studies stating that many types of microplastics cannot be retained completely by conventional wastewater treatment systems. Therefore, it is necessary to prevent the discharge of these microplastics to the ecological system. The objective of this study was to investigate the biodegradation ability of two different size of PE (50 and 150 µm) by using two Gram-positive, spore-forming, rod-shaped, and motile thermophilic bacteria, called strain Gecek4 and strain ST5, which can hydrolyse starch, were isolated from the soil's samples of Gecek and Ömer hot-springs in Afyonkarahisar, Turkey, respectively. Phenotypic features and 16S rRNA analyzing of strains also studied. According to these results, Gecek4s and ST5 were identified as Anoxybacillus flavithermus Gecek4s and Bacillus firmus ST5, respectively. Results showed that A. flavithermus Gecek4s could colonise the polymer surface and cause surface damage whereas B. firmus ST5 could not degrade bigger-sized particles efficiently. In addition, morphological changes on microplastic surface were investigated by scanning electron microscopy (SEM) where dimensional changes, irregularities, crack, and/or holes were detected. This finding suggests that there is a high potential to develop an effective integrated method for plastic bags degradation by extracellular enzymes from bacteria.High-performance warm-white light-emitting diode (LED) devices are in great demand toward green and comfortable solid-state lighting. Herein, we report a creative green-emission CaY2HfGa(AlO4)3Ce3+ phosphor. CaY2HfGa(AlO4)3Ce3+ compounds with different cerium ion doping contents have been successfully prepared through a conventional high-temperature solid-state method, and their phase and crystal structure have been revealed via the powder X-ray diffraction and Rietveld refinement. Impressively, the CaY2HfGa(AlO4)3Ce3+ phosphors exhibit a broad-band excitation, which well covers the wavelength region from the 300 to 500 nm, corresponding to the commercial blue-emitting LED chip. Upon 450 nm excitation, the optimal CaY2HfGa(AlO4)32%Ce3+ sample shows an intense broad-band green emission (the corresponding testing spectral range 460-750 nm) with a strongest peak about 534 nm. In addition, the CaY2HfGa(AlO4)32%Ce3+ sample possesses a broad full width at half-maximum equal to 120 nm; moreover, its CIE chromaticity coordinate and the internal quantum efficiency are determined to be (0.3541, 0.5427) and 72.8%, respectively. A high-quality warm-white LED has been fabricated through incorporating our CaY2HfGa(AlO4)32%Ce3+ green phosphors and commercial red phosphors with the 450 nm blue LED chip. When upon the 20 mA bias driving current, the LED device demonstrates a bright warm-white light emission, which possesses a satisfactory color rendering index of 91, a low correlated color temperature of 4080 K, as well as a good luminous efficacy of 85.14 lm W-1. The creative green-emitting CaY2HfGa(AlO4)3Ce3+ garnet phosphor has a bright application prospect toward high-quality warm-white LED lighting.Multiview subspace clustering has turned into a promising technique due to its encouraging ability to discover the underlying subspace structure. In recent studies, a lot of subspace clustering methods have been developed to strengthen the clustering performance of multiview data, but these methods rarely consider simultaneously the nonlinear structure and multilevel representation (MLR) information in multiview data as well as the data distribution of latent representation. To address these problems, we develop a new Multiview Subspace Clustering with MLRs and Adversarial Regularization (MvSC-MRAR), where multiple deep auto-encoders are utilized to model nonlinear structure information of multiview data, multiple self-expressive layers are introduced into each deep auto-encoder to extract multilevel latent representations of each view data, and diversity regularizations are designed to preserve complementary information contained in different layers and different views. Furthermore, a universal discriminator based on adversarial training is developed to enforce the output of each encoder to obey a given prior distribution, so that the affinity matrix for spectral clustering (SPC) is more realistic. Comprehensive empirical evaluation with nine real-world multiview datasets indicates that our proposed MvSC-MRAR achieves significant improvements than several state-of-the-art methods in terms of clustering accuracy (ACC) and normalized mutual information (NMI).
The morphological and hemodynamic characterization of the microvascular network around the gastrointestinal (GI) tract can be of significant clinical value for the early diagnosis and treatment of GI tract cancer. Ultrasound localization microscopy (ULM) imaging has been demonstrated to be capable of resolving the microvascular network. However, the endoscopic application of ULM imaging techniques is still unknown. link= selleck chemical In this study, an endoscopic ultrasound localization microscopy (e-ULM) imaging technique was developed to evaluate the changes of microvasculature during the GI tract tumor growth.

A customized circular array transducer (center frequency 6.8 MHz) and the coherent diverging wave compounding method were used to generate B-mode images. Spatiotemporal singular value decomposition processing was used to eliminate the background signals before signal localizations. The centroids of spatially isolated signals were localized and summed to generate the final super-resolution image.

The final microvasculature map of rabbit GI tract tumor reveals that e-ULM can be used to surpass the diffraction limit in traditional endoscopic ultrasound (EUS) imaging. Furthermore, it is observed that data from different stages of tumor growth exhibit significant differences in microvascular pattern and density.

Our study demonstrated the implementations and applications of in vivo e-ULM imaging techniques for the evaluation of the microvasculature of GI tumors.

Efficient e-ULM imaging technique shows promise for clinical translational studies, particularly for the early diagnosis of GI tract cancers.
Efficient e-ULM imaging technique shows promise for clinical translational studies, particularly for the early diagnosis of GI tract cancers.
Diffuse intrinsic pontine glioma (DIPG) is the most common and deadliest brainstem tumor in children. Focused ultrasound combined with microbubble-mediated BBB opening (FUS-BBBO) is a promising technique for overcoming the frequently intact blood-brain barrier (BBB) in DIPG to enhance therapeutic drug delivery to the brainstem. Since DIPG is highly diffusive, large-volume FUS-BBBO is needed to cover the entire tumor region. The objective of this study was to determine the optimal treatment strategy to achieve efficient and homogeneous large-volume BBBO at the brainstem for the delivery of an immune checkpoint inhibitor, anti-PD-L1 antibody (aPD-L1).

Two critical parameters for large-volume FUS-BBBO, multi-point sonication pattern (interleaved vs. serial) and microbubble injection method (bolus vs. infusion), were evaluated by treating mice with four combinations of these two parameters. 2D Passive cavitation imaging (PCI) was performed for monitoring the large-volume sonication.

Interleaved sonication cs from this study suggest that efficient and homogeneous large-volume FUS-BBBO can be achieved by interleaved sonication combined with bolus injection of microbubbles, and the efficiency and homogeneity can be monitored by PCI.
This study introduces a deep learning approach to accurately predict challenging mechanical environments that possibly cause decreasing postural stability.

Dual-axis robotic platforms were utilized to simulate various environments and collect center-of-pressure data during narrow and wide stance. A convolutional neural network (CNN) was developed to predict environmental conditions given segmented time-series balance data. Different window sizes were examined to investigate its minimal length for reliable prediction. Effectiveness of the presented CNN was additionally compared with that of conventional machine learning models. Its applicability with low sampled data or more natural stance data was then evaluated.

The CNN achieved above 94.5% in the overall prediction accuracy even with 2.5-second length postural sway data, which cannot be achieved by traditional machine learning (ps < 0.05). Increasing data length beyond 2.5 seconds slightly improved the accuracy of CNN but substantially increased training time (60% longer). Importantly, results from averaged normalized confusion matrices revealed that CNN is much more capable of differentiating the mid-level environmental condition. Deep learning could also produce comparable performance in predicting environments even with much lower sampled data or with standing posture changed.

CNN removed the burden of feature preparation and accurately predicted environments when dealing with short-length data. It also indicated potentials to real life applications.

This study contributes to the advancement of wearable devices and human interactive robots (e.g., exoskeletons and prostheses) by predicting environmental contexts and preventing potential falls.
This study contributes to the advancement of wearable devices and human interactive robots (e.g., exoskeletons and prostheses) by predicting environmental contexts and preventing potential falls.Objective Fontan surgical planning involves designing grafts to perform optimized hemodynamic performance for the patient's long-term health benefit. The uncertainty of post-operative boundary conditions (BC) and graft anastomosis displacements can significantly affect optimized graft designs and lead to undesirable outcomes, especially for hepatic flow distribution (HFD). We aim to develop a computation framework to automatically optimize patient-specific Fontan grafts with the maximized possibility of keeping post-operative results within clinical acceptable thresholds. Methods The uncertainties of BC and anastomosis displacements were modeled using Gaussian distributions according to prior research studies. By parameterizing the Fontan grafts, we built surrogate models of hemodynamic parameters taking the design parameters and BC as input. A two-phase reliability-based robust optimization (RBRO) strategy was developed by combining deterministic optimization (DO) and optimization under uncertainty (OUU) to s and can also be used in other pediatric and adult cardiac surgeries.Point cloud-based place recognition is a fundamental part of the localization task, and it can be achieved through a retrieval process. Reranking is a critical step in improving the retrieval accuracy, yet little effort has been devoted to reranking in point cloud retrieval. In this paper, we investigate the versatility of rigid registration in reranking the point cloud retrieval results. Specifically, after obtaining the initial retrieval list based on the global point cloud feature distance, we perform registration between the query and point clouds in the retrieval list. We propose an efficient strategy based on visual consistency to evaluate each registration with a registration score in an unsupervised manner. The final reranked list is computed by considering both the original global feature distance and the registration score. In addition, we find that the registration score between two point clouds can also be used as a pseudo label to judge whether they represent the same place. Thus, we can create a self-supervised training dataset when there is no ground truth of positional information. Moreover, we develop a new probability-based loss to obtain more discriminative descriptors. The proposed reranking approach and the probability-based loss can be easily applied to current point cloud retrieval baselines to improve the retrieval accuracy. Experiments on various benchmark datasets show that both the reranking registration method and probability-based loss can significantly improve the current state-of-the-art baselines.Deep models trained in supervised mode have achieved remarkable success on a variety of tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a new paradigm for making use of large amounts of unlabeled samples. SSL has achieved promising performance on natural language and image learning tasks. Recently, there is a trend to extend such success to graph data using graph neural networks (GNNs). In this survey, we provide a unified review of different ways of training GNNs using SSL. Specifically, we categorize SSL methods into contrastive and predictive models. In either category, we provide a unified framework for methods as well as how these methods differ in each component under the framework. Our unified treatment of SSL methods for GNNs sheds light on the similarities and differences of various methods, setting the stage for developing new methods and algorithms. We also summarize different SSL settings and the corresponding datasets used in each setting. To facilitate methodological development and empirical comparison, we develop a standardized testbed for SSL in GNNs, including implementations of common baseline methods, datasets, and evaluation metrics.The single cell RNA sequencing (scRNA-seq) technique begins a new era by revealing gene expression patterns at single-cell resolution, enabling studies of heterogeneity and transcriptome dynamics of complex tissues at single-cell resolution. However, existing large proportion of dropout events may hinder downstream analyses. Thus imputation of dropout events is an important step in analyzing scRNA-seq data. link2 We develop scTSSR2, a new imputation method which combines matrix decomposition with the previously developed two-side sparse self-representation, leading to fast two-side sparse self-representation to impute dropout events in scRNA-seq data. The comparisons of computational speed and memory usage among different imputation methods show that scTSSR2 has distinct advantages in terms of computational speed and memory usage. Comprehensive downstream experiments show that scTSSR2 outperforms the state-of-the-art imputation methods. A user-friendly R package scTSSR2 is developed to denoise the scRNA-seq data to improve the data quality.The understanding of protein functions is critical to many biological problems such as the development of new drugs and new crops. To reduce the huge gap between the increase of protein sequences and annotations of protein functions, many methods have been proposed to deal with this problem. These methods use Gene Ontology (GO) to classify the functions of proteins and consider one GO term as a class label. However, they ignore the co-occurrence of GO terms that is helpful for protein function prediction. We propose a new deep learning model, named DeepPFP-CO, which uses Graph Convolutional Network (GCN) to explore and capture the co-occurrence of GO terms to improve the protein function prediction performance. In this way, we can further deduce the protein functions by fusing the predicted propensity of the center function and its co-occurrence functions. We use Fmax and AUPR to evaluate the performance of DeepPFP-CO and compare DeepPFP-CO with state-of-the-art methods such as DeepGOPlus and DeepGOA. The computational results show that DeepPFP-CO outperforms DeepGOPlus and other methods. Moreover, we further analyze our model at the protein level. The results have demonstrated that DeepPFP-CO improves the performance of protein function prediction. DeepPFP-CO is available at https//csuligroup.com/DeepPFP/.Snake bite is a serious medical emergency often leading to untimely fatalities. Serotherapy is the only treatment method adapted for this, whose efficacy depends on identification of the Snake species and venom type. As a specific antivenom has to be implicated for saving the victim, in most of the cases, such identification is challenging, thus, leading to mortality due to delay in treatment or side effects of injecting polymeric non-specific antivenom. Therefore, a point-of-care, venom specific detection device could be an impactful diagnostic tool. Herein, a prototype of miniaturized electrochemical sensing platform is presented for detection of Crotaline, venom from various common pit viper snakes. A three electrode based micro-platform with carbon fibre microelectrode, modified with mesoporous carbon, embedded and laminated in commercial OHP sheet, has been developed. The dimensions of the miniaturized platform was 25 mm × 35 mm, size of electrode was 0.5 mm × 25 mm with an electrochemical testing zone of diameter 10 mm, electrode spacing as 3 mm. The microscopic characterization revealed immobilization of porous carbon on fine fibrous structure. The device gave highly stable and sensitive electro-catalytic oxidation of Crotaline at E'= at 0.81 V, and provided a linear range of 50-300 μM, limit of detection as 18.98 μM and limit of quantification as 63 μM. The device exhibited negligible interference from physiological blood serum biochemicals, high stability and reproducibility. Further, real blood serum samples, analysis via standard addition approach, was performed which showcased appreciable recovery values confirming the practical applicability of the device.With the increased demands of human-machine interaction, haptic feedback is becoming increasingly critical. However, the high cost, large size and low efficiency of current haptic systems severely hinder further development. As a portable and efficient technology, cutaneous electrotactile stimulation has shown promising potential for these issues. This paper presents a review on and insight into cutaneous electrotactile perception and its applications. Research results on perceptual properties and evaluation methods have been summarized and discussed to understand the effects of electrotactile stimulation on humans. Electrotactile applications are presented in categories to understand the methods and progress in various fields such as prostheses control, sensory substitution, sensory restoration and sensorimotor restoration. State of the art has demonstrated the superiority of electrotactile feedback, its efficiency and its flexibility. However, the complex factors and the limitations of evaluation methods made it challenging for precise electrotactile control. Groundbreaking innovation in electrotactile theory is expected to overcome challenges such as precise perception control, information capacity increasing, comprehension burden reducing and implementation costs.Internet of Things assisted healthcare services grants reliable clinical diagnosis and analysis by exploiting heterogeneous communication and infrastructure elements. Communication is enabled through point-to-point or cluster-to-point between the users and the diagnosis center. In this process, the complication is the resource sharing and diagnosis swiftness invalidating multiple resources. IoT's open and ubiquitous nature results in proactive resource sharing, resulting in delayed transmissions. This manuscript introduces the Redemptive Resource Sharing and Allocation (R2SA) scheme to address this issue. The available health data is accumulated on a first-come-first-serve basis, and the transmitting infrastructure is selected. In this process, the data-to-capacity of the available infrastructure is identified for non-redemptive resource allocation. The extremity of the capacity and unavailability of the resource is then analyzed for parallel processing and allocation. Therefore, the data accumulation and exchange rely on concurrent sharing and resource allocation processes, deferring a better accumulation ratio. The concurrent redemptive selection and sharing reduces transmission delay, improves resource allocation, and reduces transmission complexity. The entire process is managed for transfer learning, data-to-capacity validation, and concurrent recommendation. The first validation knowledge base remains the same/shared for different data accumulation and sharing intervals.Wireless charging devices for implantable cardiac pacemakers have not been clinically applied. link2 For actual applications, safety assessments of a wireless charging system must be conducted. For systems with a certain power, frequency is one of the important factors that directly affect safety. This paper aims to study the safety evaluation method and optimal operation frequency of a cardiac pacemaker wireless charging system. The wireless power transfer (WPT) model considering the coils' AC resistance is established, which is more in line with the actual situation. The analytical solution to the current in coupling coils is derived, which reveals the effect of the frequency. The currents used in electromagnetic and thermal simulations are calculated or measured for different charging prototypes. A safety evaluation method that comprehensively considers specific absorption rate (SAR), electric field, efficiency, temperature rise and electromagnetic interference (EMI) is proposed. In particular, the temperature rise is an innovative perspective as it has rarely been studied in previous literatures. The optimal frequency of a 3 W wireless charging system for cardiac pacemaker is determined based on the results of safety evaluation. The theoretical temperature rise reaches the minimum at 203 kHz, and the theoretical energy loss reaches the minimum at 260 kHz. The comfort and safe frequency band is approximately from 150 kHz to 370 kHz based on theoretical and experimental results, and the optimal frequency band from 200 kHz to 300 kHz is recommended.Multiview clustering has received great attention and numerous subspace clustering algorithms for multiview data have been presented. However, most of these algorithms do not effectively handle high-dimensional data and fail to exploit consistency for the number of the connected components in similarity matrices for different views. In this article, we propose a novel consistency-induced multiview subspace clustering (CiMSC) to tackle these issues, which is mainly composed of structural consistency (SC) and sample assignment consistency (SAC). To be specific, SC aims to learn a similarity matrix for each single view wherein the number of connected components equals to the cluster number of the dataset. SAC aims to minimize the discrepancy for the number of connected components in similarity matrices from different views based on the SAC assumption, that is, different views should produce the same number of connected components in similarity matrices. CiMSC also formulates cluster indicator matrices for different views, and shared similarity matrices simultaneously in an optimization framework. Since each column of similarity matrix can be used as a new representation of the data point, CiMSC can learn an effective subspace representation for the high-dimensional data, which is encoded into the latent representation by reconstruction in a nonlinear manner. We employ an alternating optimization scheme to solve the optimization problem. Experiments validate the advantage of CiMSC over 12 state-of-the-art multiview clustering approaches, for example, the accuracy of CiMSC is 98.06% on the BBCSport dataset.Decomposition methods have been widely employed in evolutionary algorithms for tackling multiobjective optimization problems (MOPs) due to their good mathematical explanation and promising performance. However, most decomposition methods only use a single ideal or nadir point to guide the evolution, which are not so effective for solving MOPs with extremely convex/concave Pareto fronts (PFs). To solve this problem, this article proposes an effective method to adapt decomposed directions (ADDs) for solving MOPs. Instead of using one single ideal or nadir point, each weight vector has one exclusive ideal point in our method for decomposition, in which the decomposed directions are adapted during the search process. In this way, the adapted decomposed directions can evenly and entirely cover the PF of the target MOP. The effectiveness of our method is analyzed theoretically and verified experimentally when embedding it into three representative multiobjective evolutionary algorithms (MOEAs), which can significantly improve their performance. When compared to seven competitive MOEAs, the experiments also validate the advantages of our method for solving 39 artificial MOPs with various PFs and one real-world MOP.Unmanned aerial vehicle (UAV) swarms are becoming increasingly attractive as highly integrated miniature sensors and processors deliver extraordinary performance. The employment of UAV swarms on complex real-life tasks has motivated exploration on allocation problems involving multiple UAVs, complex constraints, and multiple tasks with coupling relationships. Such problems have been summarized domain independently as multirobot task allocation problems with temporal and ordering constraints (MRTA/TOC). The majority of MRTA/TOC works have hitherto focused on deterministic settings, while their stochastic counterparts are sparsely explored. In this article, allocation problems incorporating classification uncertainty of targets and soft ordering constraints of tasks are considered. To address such problems, a novel market-based allocation algorithm, the probability-tuned market-based allocation (PTMA), is proposed. PTMA consists of iterations between two phases 1) the first phase updates local perception of gloability of the proposed PTMA.This article investigates the distributed adaptive fuzzy finite-time fault-tolerant consensus tracking control for a class of unknown nonlinear high-order multiagent systems (MASs) with actuator faults and high powers (ratio of positive odd rational numbers). The fault models include both loss of effectiveness and bias fault. Compared with existing similar results, the MASs considered here are more general and complex, which include the special case when the powers are equal to 1. Besides, the functions in this article are completely unknown and do not need to satisfy any growth conditions. In the backstepping framework, an adaptive fuzzy fault-tolerant consensus tracking controller is designed via adding one power integrator technique and directed graph theory so that the controlled systems are semiglobal practical finite-time stability (SGPFTS). Finally, numerical simulation results further verify the effectiveness of the developed control scheme.An efficient energy scheduling strategy of a charging station is crucial for stabilizing the electricity market and accommodating the charging demand of electric vehicles (EVs). Most of the existing studies on energy scheduling strategies fail to coordinate the process of energy purchasing and distribution and, thus, cannot balance the energy supply and demand. Besides, the existence of multiple charging stations in a complex scenario makes it difficult to develop a unified schedule strategy for different charging stations. In order to solve these problems, we propose a multiagent reinforcement learning (MARL) method to learn the optimal energy purchasing strategy and an online heuristic dispatching scheme to develop a energy distribution strategy in this article. Unlike the traditional scheduling methods, the two proposed strategies are coordinated with each other in both temporal and spatial dimensions to develop the unified energy scheduling strategy for charging stations. Specifically, the proposed MARL method combines the multiagent deep deterministic policy gradient (MADDPG) principles for learning purchasing strategy and a long short-term memory (LSTM) neural network for predicting the charging demand of EVs. Moreover, a multistep reward function is developed to accelerate the learning process. The proposed method is verified by comprehensive simulation experiments based on real data of the electricity market in Chicago. The experiment results show that the proposed method can achieve better performance than other state-of-the-art energy scheduling methods in the charging market in terms of the economic profits and users' satisfaction ratio.In this article, we shall investigate the event-triggered communication control problem for strict-feedback nonlinear systems with measurement outputs. First, two event-triggered communication schemes are designed. link3 Based on both event-triggered schemes, the measurement output and control input signals are only transmitted at triggering time instants, which saves communication costs from the sensor to the controller and from the controller to the actuator. Meanwhile, Zeno behavior can be excluded under the proposed triggering schemes. Second, since the full-state information is not available to the controller, by developing an observer, the system state is estimated and a controller based on estimated state information is designed. Due to the irregular sampling of information communication and state estimation error affects each other, the parameters of the state observer, the controller, and the event-triggering mechanism should be jointly designed. It is proved that the closed-loop system state converges to the origin. Finally, a simulation example verifies the validity of the obtained theoretical result.Machine-learning solutions for pattern classification problems are nowadays widely deployed in society and industry. However, the lack of transparency and accountability of most accurate models often hinders their safe use. Thus, there is a clear need for developing explainable artificial intelligence mechanisms. There exist model-agnostic methods that summarize feature contributions, but their interpretability is limited to predictions made by black-box models. An open challenge is to develop models that have intrinsic interpretability and produce their own explanations, even for classes of models that are traditionally considered black boxes like (recurrent) neural networks. In this article, we propose a long-term cognitive network (LTCN) for interpretable pattern classification of structured data. Our method brings its own mechanism for providing explanations by quantifying the relevance of each feature in the decision process. For supporting the interpretability without affecting the performance, the model incorporates more flexibility through a quasi-nonlinear reasoning rule that allows controlling nonlinearity. Besides, we propose a recurrence-aware decision model that evades the issues posed by the unique fixed point while introducing a deterministic learning algorithm to compute the tunable parameters. The simulations show that our interpretable model obtains competitive results when compared to state-of-the-art white and black-box models.Evolving Android malware poses a severe security threat to mobile users, and machine-learning (ML)-based defense techniques attract active research. Due to the lack of knowledge, many zero-day families' malware may remain undetected until the classifier gains specialized knowledge. The most existing ML-based methods will take a long time to learn new malware families in the latest malware family landscape. Existing ML-based Android malware detection and classification methods struggle with the fast evolution of the malware landscape, particularly in terms of the emergence of zero-day malware families and limited representation of single-view features. In this article, a new multiview feature intelligence (MFI) framework is developed to learn the representation of a targeted capability from known malware families for recognizing unknown and evolving malware with the same capability. The new framework performs reverse engineering to extract multiview heterogeneous features, including semantic string features, API call graph features, and smali opcode sequential features. It can learn the representation of a targeted capability from known malware families through a series of processes of feature analysis, selection, aggregation, and encoding, to detect unknown Android malware with shared target capability. We create a new dataset with ground-truth information regarding capability. Many experiments are conducted on the new dataset to evaluate the performance and effectiveness of the new method. The results demonstrate that the new method outperforms three state-of-the-art methods, including 1) Drebin; 2) MaMaDroid; and 3) N-opcode, when detecting unknown Android malware with targeted capabilities.The problem of fault prognosis in the context of discrete event systems (DESs) is a crucial subject to study the security and maintenance of cyber-physical systems. In this article, the decentralized fault prognosis of partially observed DESs is analyzed with a universal state-estimate-based protocol. It follows (M,K) as the performance bound of any expected decentralized prognosers, where any fault can be predicted K steps before its occurrence and the fault is guaranteed to occur within M steps once a corresponding fault alarm is issued. To determine whether expected decentralized prognosers exist, the notion of state-estimate-coprognosability (SE-coprognosability) under the case of one fault type is proposed. Compared with existing other kinds of coprognosability, SE-coprognosability is a more generalized concept. Meanwhile, combining the formal method and algebraic state space approach, a novel state estimation algorithm is presented and based on which, the verification of SE-coprognosability is also solved.Modern classifier systems can effectively classify targets that consist of simple patterns. However, they can fail to detect hierarchical patterns of features that exist in many real-world problems, such as understanding speech or recognizing object ontologies. Biological nervous systems have the ability to abstract knowledge from simple and small-scale problems in order to then apply it to resolve more complex problems in similar and related domains. It is thought that lateral asymmetry of biological brains allows modular learning to occur at different levels of abstraction, which can then be transferred between tasks. This work develops a novel evolutionary machine-learning (EML) system that incorporates lateralization and modular learning at different levels of abstraction. The results of analyzable Boolean tasks show that the lateralized system has the ability to encapsulate underlying knowledge patterns in the form of building blocks of knowledge (BBK). Lateralized abstraction transforms complex problems into simple ones by reusing general patterns (e.g., any parity problem becomes a sequence of the 2-bit parity problem). By enabling abstraction in evolutionary computation, the lateralized system is able to identify complex patterns (e.g., in hierarchical multiplexer (HMux) problems) better than existing systems.While AUC maximizing support vector machine (AUCSVM) has been developed to solve imbalanced classification tasks, its huge computational burden will make AUCSVM become impracticable and even computationally forbidden for medium or large-scale imbalanced data. In addition, minority class sometimes means extremely important information for users or is corrupted by noises and/or outliers in practical application scenarios such as medical diagnosis, which actually inspires us to generalize the AUC concept to reflect such importance or upper bound of noises or outliers. In order to address these issues, by means of both the generalized AUC metric and the core vector machine (CVM) technique, a fast AUC maximizing learning machine, called ρ-AUCCVM, with simultaneous outlier detection is proposed in this study. ρ-AUCCVM has its notorious merits 1) it indeed shares the CVM's advantage, that is, asymptotically linear time complexity with respect to the total number of sample pairs, together with space complexity independent on the total number of sample pairs and 2) it can automatically determine the importance of the minority class (assuming no noise) or the upper bound of noises or outliers. Extensive experimental results about benchmarking imbalanced datasets verify the above advantages of ρ-AUCCVM.The dendritic neural model (DNM) is computationally faster than other machine-learning techniques, because its architecture can be implemented by using logic circuits and its calculations can be performed entirely in binary form. To further improve the computational speed, a straightforward approach is to generate a more concise architecture for the DNM. Actually, the architecture search is a large-scale multiobjective optimization problem (LSMOP), where a large number of parameters need to be set with the aim of optimizing accuracy and structural complexity simultaneously. However, the issues of irregular Pareto front, objective discontinuity, and population degeneration strongly limit the performances of conventional multiobjective evolutionary algorithms (MOEAs) on the specific problem. Therefore, a novel competitive decomposition-based MOEA is proposed in this study, which decomposes the original problem into several constrained subproblems, with neighboring subproblems sharing overlapping regions in the objective space. The solutions in the overlapping regions participate in environmental selection for the neighboring subproblems and then propagate the selection pressure throughout the entire population. Experimental results demonstrate that the proposed algorithm can possess a more powerful optimization ability than the state-of-the-art MOEAs. Furthermore, both the DNM itself and its hardware implementation can achieve very competitive classification performances when trained by the proposed algorithm, compared with numerous widely used machine-learning approaches.Navigation of underactuated wheeled inverted pendulum (WIP) vehicles in unknown environments is still facing great difficulties, especially when the optimal motion is required. This article proposes an optimal trajectory planning method for the navigation of WIP vehicles in unknown environments, where various performance demands, such as security, smoothness, efficiency, etc., are all considered. First, a map-building algorithm based on the improved Rao-Blackwellized particle filter is applied for the WIP vehicle to construct the environmental map. Then, a multiobjective optimization using the genetic algorithm is performed to find an optimized path between the given start and target point with path length, path curvature, and safe distance being taken into consideration simultaneously. Moreover, on the basis of kinematical and dynamical analysis, velocity, and acceleration constraints are parameterized with a path parameter, and the minimum-time trajectory along the optimized path is further planned with a sequence of maximum acceleration and deceleration trajectories. Finally, a WIP vehicle platform based on the robot operating system is designed, and related experiments in a real obstacle environment are conducted to validate the feasibility of the proposed method.Graph classification aims to predict the label associated with a graph and is an important graph analytic task with widespread applications. Recently, graph neural networks (GNNs) have achieved state-of-the-art results on purely supervised graph classification by virtue of the powerful representation ability of neural networks. However, almost all of them ignore the fact that graph classification usually lacks reasonably sufficient labeled data in practical scenarios due to the inherent labeling difficulty caused by the high complexity of graph data. The existing semisupervised GNNs typically focus on the task of node classification and are incapable to deal with graph classification. To tackle the challenging but practically useful scenario, we propose a novel and general semisupervised GNN framework for graph classification, which takes full advantage of a slight amount of labeled graphs and abundant unlabeled graph data. In our framework, we train two GNNs as complementary views for collaboratively learning high-quality classifiers using both labeled and unlabeled graphs. To further exploit the view itself, we constantly select pseudo-labeled graph examples with high confidence from its own view for enlarging the labeled graph dataset and enhancing predictions on graphs. Furthermore, the proposed framework is investigated on two specific implementation regimes with a few labeled graphs and the extremely few labeled graphs, respectively. Extensive experimental results demonstrate the effectiveness of our proposed semisupervised GNN framework for graph classification on several benchmark datasets.A moored floating platform has great potential in ocean engineering applications because the mooring system is necessary to keep a floating platform in the station. link3 It relates directly to operational efficiency and safety of a floating platform. This study presents a comprehensive assessment of the dynamics of a moored semi-submersible in waves by performing model test and numerical simulation. First, a three-dimensional panel method was used to estimate the motion of a moored semi-submersible in waves. A semi-submersible is modelled as a rigid body with six degrees-of-freedom (6DOF) motion. Dynamic response analysis of a semi-submersible is performed in regular wave and irregular wave. Second, the model test is performed in various wave directions. An Optical-based system is used to measure 6DOF motion of a semi-submersible. Numerical results are compared with the experimental results in various wave directions. Wavelength and wave direction showed significant effects on the motion response of a semi-submersible in regular wave. Third, to obtain a better understanding of response frequencies, the time histories of motion responses in irregular wave are converted from the time domain to the frequency domain. Effects of the wave frequency component on motion responses and mooring dynamics are analyzed. Motion spectrum in irregular wave has a strong response to the natural frequency of a moored semi-submersible and the peak of wave frequency. Finally, exceedance probability is estimated to predict probable extreme values of motion responses of a moored semi-submersible as well as mooring dynamics.
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