Comparative exploration on bifurcation behavior for integer-order and fractional-order delayed BAM neural networks

In the present study, we deal with the stability and the onset of Hopf bifurcation of two type delayed BAM neural networks (integer-order case and fractional-order case). By virtue of the characteristic equation of the integer-order delayed BAM neural networks and regarding time delay as critical parameter, a novel delay-independent condition ensuring the stability and the onset of Hopf bifurcation for the involved integer-order delayed BAM neural networks is built. Taking advantage of Laplace transform, stability theory and Hopf bifurcation knowledge of fractional-order differential equations, a novel delay-independent criterion to maintain the stability and the appearance of Hopf bifurcation for the addressed fractional-order BAM neural networks is established. The investigation indicates the important role of time delay in controlling the stability and Hopf bifurcation of the both type delayed BAM neural networks. By adjusting the value of time delay, we can effectively amplify the stability region and postpone the time of onset of Hopf bifurcation for the fractional-order BAM neural networks. Matlab simulation results are clearly presented to sustain the correctness of analytical results. The derived fruits of this study provide an important theoretical basis in regulating networks

Dysregulation of bile acids increases the risk for preterm birth in pregnant women

Preterm birth (PTB) is the leading cause of perinatal mortality and newborn complications. Bile acids are recognized as signaling molecules regulating a myriad of cellular and metabolic activities but have not been etiologically linked to PTB. In this study, a hospital-based cohort study with 36,755 pregnant women is conducted. We find that serum total bile acid levels directly correlate with the PTB rates regardless of the characteristics of the subjects and etiologies of liver disorders. Consistent with the findings from pregnant women, PTB is successfully reproduced in mice with liver injuries and dysregulated bile acids. More importantly, bile acids dose-dependently induce PTB with minimal hepatotoxicity. Furthermore, restoring bile acid homeostasis by farnesoid X receptor activation markedly reduces PTB and dramatically improves newborn survival rates. The findings thus establish an etiologic link between bile acids and PTB, and open an avenue for developing etiology-based therapies to prevent or delay PTB

Percutaneous angioplasty and/or stenting versus aggressive medical therapy in patients with symptomatic intracranial atherosclerotic stenosis: a 1-year follow-up study

BackgroundSymptomatic intracranial atherosclerotic stenosis (sICAS) is one of the common causes of ischemic stroke. However, the treatment of sICAS remains a challenge in the past with unfavorable findings. The purpose of this study was to explore the effect of stenting versus aggressive medical management on preventing recurrent stroke in patients with sICAS.MethodsWe prospectively collected the clinical information of patients with sICAS who underwent percutaneous angioplasty and/or stenting (PTAS) or aggressive medical therapy from March 2020 to February 2022. Propensity score matching (PSM) was employed to ensure well-balanced characteristics of two groups. The primary outcome endpoint was defined as recurrent stroke or transient ischemic attack (TIA) within 1 year.ResultsWe enrolled 207 patients (51 in the PTAS and 156 in the aggressive medical groups) with sICAS. No significant difference was found between PTAS group and aggressive medical group for the risk of stroke or TIA in the same territory beyond 30 days through 6 months (P = 0.570) and beyond 30 days through 1 year (P = 0.739) except for within 30 days (P = 0.003). Furthermore, none showed a significant difference for disabling stroke, death and intracranial hemorrhage within 1 year. These results remain stable after adjustment. After PSM, all the outcomes have no significant difference between these two groups.ConclusionThe PTAS has similar treatment outcomes compared with aggressive medical therapy in patients with sICAS across 1-year follow-up

An experimental study and axial tensile constitutive model of the toughness of PP-SACC for rapid repairs

To improve the economic benefits of engineered cementitious composites and control the repair cycle, repair materials were designed, with the key components of the mixture being low-cost polypropylene (PP) fibers and fast-setting sulfoaluminate cement. The effects of water/binder ratio, fiber content, and aggregate particle size on the flowability, mechanical properties, and toughness of the polypropylene fiber-reinforced sulfoaluminate cementitious composite (PP-SACC) were explored. Based on experimentally measured axial tensile stress–strain curves, a constitutive model of PP-SACC was derived in terms of fiber content and water/binder ratio. Additionally, the correlation coefficients representing the relationships of the mixture indices with the tensile properties were explored based on revised gray relational analysis. Test results indicated that fiber content and water/binder ratio were the most important factors affecting the mechanical properties, toughness, and fluidity of the material; in contrast, the influence of aggregate size was slight. The PP-SACC mixture with an aggregate size of 75 µm, a water/binder ratio of 0.30, and a fiber content of 3.0% demonstrated an excellent degree of toughness and exhibited a flexural hardening phenomenon under bending load

Prediction of protein binding sites in protein structures using hidden Markov support vector machine

<p>Abstract</p> <p>Background</p> <p>Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. Recent research on protein binding site prediction has been mainly based on widely known machine learning techniques, such as artificial neural networks, support vector machines, conditional random field, etc. However, the prediction performance is still too low to be used in practice. It is necessary to explore new algorithms, theories and features to further improve the performance.</p> <p>Results</p> <p>In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.</p> <p>Conclusion</p> <p>The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.</p

Linking Superior Developmental Feedback with Employee Job Satisfaction? A Conservation of Resources Perspective

Previous studies have shown that superior developmental feedback (SDF) has a mixed impact on employees’ long-term development, but its effect on job satisfaction (JS) has been generally ignored. Therefore, this study proposes and tests a model based on the conservation of resources theory to shed light on how feedback from a leader or superior may increase employees’ JS. In this study, researchers analyzed responses from a two-stage questionnaire distributed to 296 employees to test the proposed hypotheses using MPlus 7.4 software. The results show that employee resilience (ER) partially mediates the link between SDF and JS. The results also indicate that the relationship between SDF and ER is strengthened by job complexity (JC). The results provide novel avenues for further study and practice in the areas of SDF and JS

Improved Multiview Decomposition for Single-Image High-Resolution 3D Object Reconstruction

As a representative technology of artificial intelligence, 3D reconstruction based on deep learning can be integrated into the edge computing framework to form an intelligent edge and then realize the intelligent processing of the edge. Recently, high-resolution representation of 3D objects using multiview decomposition (MVD) architecture is a fast reconstruction method for generating objects with realistic details from a single RGB image. The results of high-resolution 3D object reconstruction are related to two aspects. On the one hand, a low-resolution reconstruction network represents a good 3D object from a single RGB image. On the other hand, a high-resolution reconstruction network maximizes fine low-resolution 3D objects. To improve these two aspects and further enhance the high-resolution reconstruction capabilities of the 3D object generation network, we study and improve the low-resolution 3D generation network and the depth map superresolution network. Eventually, we get an improved multiview decomposition (IMVD) network. First, we use a 2D image encoder with multifeature fusion (MFF) to enhance the feature extraction capability of the model. Second, a 3D decoder using an effective subpixel convolutional neural network (3D ESPCN) improves the decoding speed in the decoding stage. Moreover, we design a multiresidual dense block (MRDB) to optimize the depth map superresolution network, which allows the model to capture more object details and reduce the model parameters by approximately 25% when the number of network layers is doubled. The experimental results show that the proposed IMVD is better than the original MVD in the 3D object superresolution experiment and the high-resolution 3D reconstruction experiment of a single image

Soil Nitrogen Transformations Respond Diversely to Multiple Levels of Nitrogen Addition in a Tibetan Alpine Steppe

Elevated reactive nitrogen (N) input could modify soil N transformations, regulating ecosystem functions such as soil N retention and loss. Although multiple hypotheses advocate nonlinear variations in soil N transformations with continuous N input, there still lacks empirical evidences for the responses of soil N transformations to multiple N additions. Here, based on a manipulative N addition experiment and a N-15 pool dilution approach, we explored changes in soil gross N transformations with eight N addition levels and associated mechanisms in a Tibetan alpine steppe. Our results showed that soil gross N mineralization rate (GNM) increased first and then stabilized with increasing N additions. Meanwhile, soil microbial immobilization rate (MIM) exhibited an initially increased and subsequently declined pattern under various N addition levels. In contrast, soil gross nitrification rate (GN) increased linearly across multiple N addition levels. Our results also revealed that variations in GNM were mainly regulated by aboveground vegetation N pool-induced changes in dissolved organic N content along the N addition gradient. Meanwhile, changes in GN were dominantly modified by soil pH-induced variations in ammonia-oxidizing archaea abundance across multiple N addition levels. Additionally, alterations in MIM under various N input levels were primarily controlled by microbial biomass which was regulated by dissolved organic carbon content under low N input and NH4+-N content at high N level, respectively. Overall, patterns and drivers of soil N transformations observed in this study provide valuable benchmark for Earth system models to better predict ecosystem N dynamics under global N-enrichment scenarios

Chaos Control for a Fractional-Order Jerk System via Time Delay Feedback Controller and Mixed Controller

In this study, we propose a novel fractional-order Jerk system. Experiments show that, under some suitable parameters, the fractional-order Jerk system displays a chaotic phenomenon. In order to suppress the chaotic behavior of the fractional-order Jerk system, we design two control strategies. Firstly, we design an appropriate time delay feedback controller to suppress the chaos of the fractional-order Jerk system. The delay-independent stability and bifurcation conditions are established. Secondly, we design a suitable mixed controller, which includes a time delay feedback controller and a fractional-order PDσ controller, to eliminate the chaos of the fractional-order Jerk system. The sufficient condition ensuring the stability and the creation of Hopf bifurcation for the fractional-order controlled Jerk system is derived. Finally, computer simulations are executed to verify the feasibility of the designed controllers. The derived results of this study are absolutely new and possess potential application value in controlling chaos in physics. Moreover, the research approach also enriches the chaos control theory of fractional-order dynamical system