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Distributed linear algebraic equation over networks, where nodes hold a part of problem data and cooperatively solve the equation via node-to-node communications, is a basic distributed computation task receiving an increasing research attention. Communications over a network have a stochastic nature, with both temporal and spatial dependence due to link failures, packet dropouts or node recreation, etc. In this paper, we study the convergence and convergence rate of distributed linear equation protocols over a $\ast$-mixing random network, where the temporal and spatial dependencies between the node-to-node communications are allowed. When the network linear equation admits exact solutions, we prove the mean-squared exponential convergence rate of the distributed projection consensus algorithm, while the lower and upper bound estimations of the convergence rate are also given for independent and identically distributed (i.i.d.) random graphs. Motivated by the randomized Kaczmarz algorithm, we also propose a distributed randomized projection consensus algorithm, where each node randomly selects one row of local linear equations for projection per iteration, and establish an exponential convergence rate. When the network linear equation admits no exact solution, we prove that a distributed gradient-descent-like algorithm with diminishing step-sizes can drive all nodes' states to a least-squares solution at a sublinear rate. These results collectively illustrate that distributed computations may overcome communication correlations if the prototype algorithms enjoy certain contractive properties or are designed with suitable parameters., 17 pages

The decision making and management of many engineering networks involves multiple parties with conflicting interests, while each party is constituted with multiple agents. Such problems can be casted as a multi-cluster game. Each cluster is treated as a self-interested player in a non-cooperative game where agents in the same cluster cooperate together to optimize the payoff function of the cluster. In a large-scale network, the information of agents in a cluster can not be available immediately for agents beyond this cluster, which raise challenges to the existing Nash equilibrium seeking algorithms. Hence, we consider a partial-decision information scenario in generalized Nash equilibrium seeking for multi-cluster games in a distributed manner. We reformulate the problem as finding zeros of the sum of preconditioned monotone operators by the primal-dual analysis and graph Laplacian matrix. Then a distributed generalized Nash equilibrium seeking algorithm is proposed without requiring fully awareness of its opponent clusters' decisions based on a forward-backward-forward method. With the algorithm, each agent estimates the strategies of all the other clusters by communicating with neighbors via an undirected network. We show that the derived operators can be monotone when the communication strength parameter is sufficiently large. We prove the algorithm convergence resorting to the fixed point theory by providing a sufficient condition. We discuss its potential application in Energy Internet with numerical studies.

In this work, we design a distributed algorithm for time-varying convex optimization over networks with quantized communications. Each agent has its local time-varying objective function, while the agents need to cooperatively track the optimal solution trajectories of global time-varying functions. The distributed algorithm is motivated by the alternating direction method of multipliers, but the agents can only share quantization information through an undirected graph. To reduce the tracking error due to information loss in quantization, we apply the dynamic quantization scheme with a decaying scaling function. The tracking error is explicitly characterized with respect to the limit of the decaying scaling function in quantization. Furthermore, we are able to show that the algorithm could asymptotically track the optimal solution when time-varying functions converge, even with quantization information loss. Finally, the theoretical results are validated via numerical simulation.

Heat shock is a feasible means to break rock mass in engineering duo to the large additional thermal stress caused by thermal expansion. This paper established a coupled thermal transfer and rock deformation model based on the energy conservation and the elastic deformation theory of rock. In the model, the failure and damage of rock are judged according to the Drucker-Prager criterion. A finite element method of COMSOL Multiphysics to study the characteristics of temperature, stress distribution and damage zone on a rock surface is proposed. Results show that the failure of rock mass occurs at the cusps of the heater first duo to stress concentration and then grows at both sides of the heater greatly.

There are significant advantages to investigate underwater attachments, which would be valuable in providing inspirations and design strategies for multi-functional surfaces and underwater robots. Here, an abalone-inspired sucker integrating an elastic body and a membrane structure is proposed and fabricated filled with rigid quartz particles to adjust the backing stiffness of the contact like abalone. The membrane is used to conform and contact surfaces well, the center area of which can be pulled in exposed to a negative pressure differential, to create a suction cavity. The pulling experiments indicate that the sucker can adhere to three-dimensional surfaces with both suction and adhesion mechanisms in both dry and liquid environments. The switching between soft/hard contact states leads to the change of adhesive strength over 30 times. Furthermore, we provide theoretical analysis on how the sucker work well in both dry and liquid environments. Finally, the developed sucker can easily lift up smooth planar objects and 3D objects, and can grip objects both smaller and larger than the size of the sucker, which have a difficulty for conventional suckers or friction-based grippers. The potential application of the sucker in flexible transfer robot is demonstrated on various surfaces and environments, paving the way for further bio-inspired adhesive designs for both dry and wet scenarios.

In this paper, we investigate the interval consensus for a network of agents with flocking dynamics, where each agent imposes an interval constraint on its preferred consensus values. Specifically, we work on two different frameworks, viz., the first one that the node states are constrained in their own constraint intervals and the second one that the node states are constrained in their neighbors' constraint intervals. For both frameworks, we provide a complete solution to the equilibrium seeking problem by resolving a system of nonlinear equations. It is proved that if the underlying graph is strongly connected and the intersection of constraint intervals is empty, then there exists a unique equilibrium point; and if the intersection is non-empty, then there exist multiple equilibrium points. We also establish several conditions for the local stability of the unique equilibrium point or local constraint consensus by invoking Lyapunov's indirect method. In addition, we show in two special cases that global convergence to the unique equilibrium point or state consensus can be guaranteed by employing Lyapunov stability theory and robust analysis techniques.

Pairwise comparison matrix (PCM) has been widely employed in the multi-criteria decision-making (MCDM) problems to rank the criteria and alternatives according to the considered criteria in Analytic Hierarchy Process (AHP). The PCM should have the acceptable consistency before deriving a priority vector from it. Approximate thresholds of geometric consistency index (GCI) and consistency ratio (CR) have been proposed to test whether the PCM has the acceptable consistency. However, approximate thresholds of GCI and CR always suffer from some criticisms and disagreements in existing literature. In this paper, we try to induce dynamic thresholds of GCI by combining hypothesis testing and random index (RI), which vary with the order of the PCM, significance level and assessment level of decision maker. The induced dynamic thresholds of GCI may explain different (or conflicting) results obtained by approximate thresholds of GCI and CR and avoid the unnecessary revisions of some judgments of the PCM for the desired consistency. Finally, several numerical examples and real-world decision-making problems are examined and compared with existing decision-making methods to illustrate the performance of dynamic thresholds of GCI.

Permanent magnet synchronous motor (PMSM) is widely used in electric vehicles due to its high power density. New requirements are constantly proposed with the development of PMSM, one of which is torque ripple. Current harmonic injection is a common method to minimize torque ripple. Designing effective controller to realize current harmonic injection is the key technology. In this paper, the transient voltage-current coupling equation considering magnetic saturation is derived by introducing incremental inductance. After appropriately simplifying, it is shown in form of the harmonic state space (HSS) equation. Considering that PMSM parameters varies with current due to magnetic saturation, robust controller is adopted to ensure stability. In order to eliminate the influence of motor speed change on control stability, the HSS is transformed into a spatial form. Then the speed adaptive robust (SAR) controller is designed. At last, the SAR controller is used in simulation and test bench experiment to verify dynamic response and stability. Compared with traditional PI and PR controller, the SAR controller can ensure good dynamic response, steady-state error and robust stability when speed and PMSM parameters vary. Moreover, SAR controller can well resist the harmonic coupling effect and system lag.

Chun-hui Huang,1,2,* Peng-yi Hu,2,3,* Qiu-yan Wu,2,3 Ming-yan Xia,2,3 Wen-liu Zhang,3 Zhi-qiang Lei,3 Dong-xun Li,3 Guo-song Zhang,2,3 Jian-fang Feng1,2 1School of Pharmacy, Guangxi University of Chinese Medicine, Nanning, Guangxi, 530200, People’s Republic of China; 2National Engineering Research Center of Chinese Medicine Solid Preparation Manufacturing Technology, Nanchang, 330006, People’s Republic of China; 3Jiangxi University of Traditional Chinese Medicine, Nanchang, 330004, People’s Republic of China*These authors contributed equally to this workCorrespondence: Guo-song Zhang, National Engineering Research Center of Chinese Medicine Solid Preparation Manufacturing Technology, Nanchang, 330006, People’s Republic of China, Email zhgs81411@aliyun.com Jian-fang Feng, School of Pharmacy, Guangxi University of Chinese Medicine, Nanning, 530200, People’s Republic of China, Email fengjianfang@vip.163.comBackground: Ibuprofen (IBU), a nonsteroidal anti-inflammatory drug, shows poor gastrointestinal absorption due to its low solubility, which limits its clinical application.Objective: In the present study, we aimed to develop thermosensitive gel-mediated ibuprofen-solid lipid nanoparticles (IBU-SLN-ISG) to improve the dissolution and bioavailability of IBU after rectal delivery.Methods: IBU-loaded SLNs (IBU-SLNs) were developed and optimized applying Box-Behnken design. The optimized IBU-SLNs were characterized by physicochemical parameters and morphology. Then, the optimized IBU-SLNs was incorporated into the gel and characterized for gel properties and rheology and investigated its release in vitro, pharmacokinetics in vivo, rectal irritation and rectal retention time.Results: The optimized SLNs had an EE of 90.74 ± 1.40%, DL of 11.36 ± 1.20%, MPS of 166.77 ± 2.26 nm, PDI of 0.27 ± 0.08, and ZP of − 21.00 ± 0.59 mV. The FTIR spectra confirmed successful encapsulation of the drug inside the nanoparticle as only peaks responsible for the lipid could be identified. This corroborated well with XRD spectra, which showed a completely amorphous state of the IBU-SLNs as compared to the crystalline nature of the pure drug. The gelation temperature of the prepared IBU-SLN-ISG was 33.30 ± 0.78°C, the gelation time was 14.67 ± 2.52 s, the gel strength was 54.00 ± 1.41 s, and the mucoadhesion was (11.54± 0.37) × 102dyne/cm2. The in vitro results of IBU-SLNs and IBU-SLN-ISG showed a biphasic release pattern with initial burst release followed by sustained release. More importantly, IBU-SLN-ISG produced much better absorption of IBU and improved bioavailability in rats. In addition, IBU-SLN-ISG caused no irritation or damage to rectal tissues, and could be retained in the rectum for a long time.Conclusion: Thermosensitive in situ gel loaded with IBU-solid lipid nanoparticles might be further developed as a more convenient and effective rectal dosage form.Keywords: thermosensitive gel, ibuprofen, solid lipid nanoparticle, rectal delivery, bioavailability

Type 1 diabetes (T1D) is caused by the immune-mediated loss of pancreatic beta cells that produce insulin. The latest advances in stem cell (SC)-beta cell differentiation methods have made a cell replacement therapy for T1D feasible. However, recurring autoimmunity would rapidly destroy transplanted SC-beta cells. A promising strategy to overcome immune rejection is to genetically engineer SC-beta cells. We previously identified Renalase (Rnls) as a novel target for beta cell protection. Here we show that Rnls deletion endows beta cells with the capacity to modulate the metabolism and function of immune cells within the local graft microenvironment. We used flow cytometry and single-cell RNA sequencing to characterize beta cell graft-infiltrating immune cells in a mouse model for T1D. Loss of Rnls within transplanted beta cells affected both the composition and the transcriptional profile of infiltrating immune cells in favor of an anti-inflammatory profile with decreased antigen presenting capacity. We propose that changes in beta cell metabolism mediate local immune regulation and that this feature could be exploited for therapeutic goals.

Type 1 diabetes (T1D) is caused by the immune-mediated loss of pancreatic beta cells that produce insulin. The latest advances in stem cell (SC)-beta cell differentiation methods have made a cell replacement therapy for T1D feasible. However, recurring autoimmunity would rapidly destroy transplanted SC-beta cells. A promising strategy to overcome immune rejection is to genetically engineer SC-beta cells. We previously identified Renalase (Rnls) as a novel target for beta cell protection. Here we show that Rnls deletion endows beta cells with the capacity to modulate the metabolism and function of immune cells within the local graft microenvironment. We used flow cytometry and single-cell RNA sequencing to characterize beta cell graft-infiltrating immune cells in a mouse model for T1D. Loss of Rnls within transplanted beta cells affected both the composition and the transcriptional profile of infiltrating immune cells in favor of an anti-inflammatory profile with decreased antigen presenting capacity. We propose that changes in beta cell metabolism mediate local immune regulation and that this feature could be exploited for therapeutic goals.

Osteosarcoma is a primary bone tumor with a high mortality rate. The event-free survival rate has not improved significantly in the past 30 years, which brings a heavy burden to patients and society. The high heterogeneity of osteosarcoma leads to the lack of specific targets and poor therapeutic effect. Tumor microenvironment is the focus of current research, and osteosarcoma is closely related to bone microenvironment. Many soluble factors and extracellular matrix secreted by many cells in the bone microenvironment have been shown to affect the occurrence, proliferation, invasion and metastasis of osteosarcoma through a variety of signaling pathways. Therefore, targeting other cells in the bone microenvironment may improve the prognosis of osteosarcoma. The mechanism by which osteosarcoma interacts with other cells in the bone microenvironment has been extensively investigated, but currently developed drugs targeting the bone microenvironment have poor efficacy. Therefore, we review the regulatory effects of major cells and physical and chemical properties in the bone microenvironment on osteosarcoma, focusing on their complex interactions, potential therapeutic strategies and clinical applications, to deepen our understanding of osteosarcoma and the bone microenvironment and provide reference for future treatment. Targeting other cells in the bone microenvironment may provide potential targets for the development of clinical drugs for osteosarcoma and may improve the prognosis of osteosarcoma.

Modern fermentation processes include a variety of fermentation methods, such as fed batch fermentation and continuous fermentation. This paper will focus on the principles and case studies of these two methods. Fed batch fermentation originates from fractionation fermentation with closed culture and adjustment of the pH value of the carbon source, to which the process of feeding the carbon source to the cell culture in a controlled manner has been added. This type of fermentation is more commonly used compared to the other. Continuous fermentation is also a closed fermentation system, which can operate without restrictions by continuous or intermittent addition of fresh nutrient media to the fermenter; however, it is susceptible to contamination by stray bacteria and metabolic inconvenience.

Light field imaging has drawn broad attention since the advent of practical light field capturing systems that facilitate a wide range of applications in computer vision. However, existing learning-based methods for improving the spatial resolution of light field images neglect the shifts in the sub-pixel domain that are widely used by super-resolution techniques, thus, fail in recovering rich high-frequency information. To fully exploit the shift information, our method attempts to learn an epipolar shift compensation for light field image super-resolution that allows the restored light field image to be angular coherent with the enhancement of spatial resolution. The proposed method first utilizes the rich surrounding views along some typical epipolar directions to explore the inter-view correlations. We then implement feature-level registration to capture accurate sub-pixel shifts of central view, which is constructed by the compensation module equipped with dynamic deformable convolution. Finally, the complementary information from different spatial directions is fused to provide high-frequency details for the target view. By taking each sub-aperture image as a central view, our method could be applied for light field images with any angular resolution. Extensive experiments on both synthetic and real scene datasets demonstrate the superiority of our method over the state-of-the-art qualitatively and quantitatively. Moreover, the proposed method shows good performance in preserving the inherent epipolar structures in light field images. Specifically, our LFESCN method outperforms the state-of-the-art method with about 0.7 dB (PSNR) on average.

Measures implemented to prevent transmission of severe acute respiratory syndrome coronavirus 2 in outpatient dialysis facilities may also help to prevent catheter-associated bloodstream infections in patients receiving hemodialysis. We used United States Renal Data System data to examine rates of antibiotic administration within dialysis facilities and rates of hospital admission for catheter-associated bloodstream infection from March 2018 through November 2020, and rates of hospitalization for sepsis, to address overall changes in hospitalization during the coronavirus disease 2019 (COVID-19) pandemic. Using logistic regression, we estimated year-over-year adjusted odds ratios of these events in 3-month intervals. During the first 6 months of the pandemic, rates of antibiotic administration were between 20% and 21% lower, and rates of hospitalization for catheter-associated bloodstream infection were between 17% and 24% lower than during corresponding periods in 2019, without significant changes in rates of hospitalization for sepsis. However, rates of catheter-associated events also decreased between 2018 and 2019, driven by reductions in facilities operated by a large dialysis provider. These data suggest that significant reductions in catheter-associated infections occurred during the pandemic, superimposed on nonpandemic-related reductions in some facilities before the pandemic. Even after the pandemic, it may be prudent to continue some COVID-19 mitigation measures to prevent catheter-associated bloodstream infections.

Purpose This paper aims to investigate the influence of sinusoidal texture (ST) with different morphology parameters on the corresponding tribological effects. Design/methodology/approach The STs with different amplitudes, ranging from 0.05 to 0.3 mm, and frequencies, ranging from 5 to 17.5, are fabricated using nanosecond pulsed laser equipment. The friction experiments and the finite element analysis method are combined to investigate the tribological properties, under dry friction conditions. Findings Test results show that when the amplitude is 0.15 mm and frequency is 10, ST surface has the lowest friction coefficient of 0.373, and exhibits great anti-friction effect. It also possesses a complete texture edge after friction. The friction reduction effect of ST with larger or smaller amplitude and frequency is worse. Originality/value The results of this study can provide a guidance for the design optimization of ST of reciprocating sliding contact surfaces, under dry friction conditions.

Along with the performance improvement of deep-learning-based face hallucination methods, various face priors (facial shape, facial landmark heatmaps, or parsing maps) have been used to describe holistic and partial facial features, making the cost of generating super-resolved face images expensive and laborious. To deal with this problem, we present a simple yet effective dual-path deep fusion network (DPDFN) for face image super-resolution (SR) without requiring additional face prior, which learns the global facial shape and local facial components through two individual branches. The proposed DPDFN is composed of three components: a global memory subnetwork (GMN), a local reinforcement subnetwork (LRN), and a fusion and reconstruction module (FRM). In particular, GMN characterize the holistic facial shape by employing recurrent dense residual learning to excavate wide-range context across spatial series. Meanwhile, LRN is committed to learning local facial components, which focuses on the patch-wise mapping relations between low-resolution (LR) and high-resolution (HR) space on local regions rather than the entire image. Furthermore, by aggregating the global and local facial information from the preceding dual-path subnetworks, FRM can generate the corresponding high-quality face image. Experimental results of face hallucination on public face data sets and face recognition on real-world data sets (VGGface and SCFace) show the superiority both on visual effect and objective indicators over the previous state-of-the-art methods.

The deposition of CeO2 nanoparticles on supported lipid bilayers was governed by DLVO interactions at various pH values and electrolyte concentrations.

Hypothesis. Organic co-solvents, which are universally employed in adsorption studies of hydrophobic organic chemicals (HOCs), can inhibit HOC adsorption by competing for active sites on the adsorbent. The adsorbent structure can influence co-solvent interference of HOC adsorption; however, this effect remains unclear, leading to an incomplete understanding of the adsorption mechanism. Experiments. In this study, dioctyl phthalate (DOP) was used to investigate competitive adsorption on functionalized graphene sheet in a water–methanol co-solvent system through molecular dynamics simulations and quantum chemical calculations. Findings. The simulations showed that the functional groups in the graphene defects had a strong adsorption affinity for methanol. The adsorbed methanol occupied a large number of active sites at the graphene center, thereby weakening DOP adsorption. However, the methanol adsorbed at the graphene edges could not compete with DOP for the active sites. –COOH had the strongest binding affinity for methanol among the functional groups and thus predominantly controlled the interaction between graphene and methanol. This study makes an innovative contribution toward understanding the competitive adsorption of methanol and DOP on functionalized graphene sheet, especially in visualizing the competition for active sites, and provides theoretical guidance for the removal of HOCs and practical application of graphene.

Objective: Wiedemann-Steiner syndrome (WSS) is a rare autosomal dominant disorder caused by deleterious heterozygous variants of the KMT2A gene. This study aims to describe the phenotypic and genotypic features of Chinese WSS patients, and assess therapeutic effects of recombinant human growth hormone (rhGH).Methods: Eleven Chinese children with WSS were enrolled in our cohort. Their clinical, imaging, biochemical and molecular findings were analyzed retrospectively. Moreover, the phenotypic features of 41 previously reported Chinese WSS patients were reviewed and included in our analysis.Results: In our cohort, the 11 WSS patients presented with classic clinical manifestations, but with different frequencies. The most common clinical features were short stature (90.9%) and developmental delay (90.9%), followed by intellectual disability (72.7%). The most frequent imaging features were patent ductus arteriosus (57.1%) and patent foramen ovale (42.9%) in cardiovascular system, and abnormal corpus callosum (50.0%) in the brain. In the set comprising 52 Chinese WSS patients, the most common clinical and imaging manifestations were developmental delay (84.6%), intellectual disability (84.6%), short stature (80.8%) and delayed bone age (68.0%), respectively. Eleven different variants, including three known and eight novel variants, of the KMT2A gene were identified in our 11 WSS patients without a hotspot variant. Two patients were treated with rhGH and yielded satisfactory height gains, but one developed acceleration of bone age.Conclusion: Our study adds 11 new patients with WSS, reveals different clinical characteristics in Chinese WSS patients, and extends the mutational spectrum of the KMT2A gene. Our study also shares the therapeutic effects of rhGH in two WSS patients without GH deficiency.

To retrospectively explore the characteristics of plasma amino acids (PAAs) in children with autism spectrum disorder and their clinical association via case-control study. A total of 110 autistic and 55 healthy children were recruited from 2014 to 2018. The clinical phenotypes included severity of autism, cognition, adaptability, and regression. Compared with the control group, autistic children had significantly elevated glutamate, γ-Amino-n-butyric acid, glutamine, sarcosine, δ-aminolevulinic acid, glycine and citrulline. In contrast, their plasma level of ethanolamine, phenylalanine, tryptophan, homocysteine, pyroglutamic acid, hydroxyproline, ornithine, histidine, lysine, and glutathione were significantly lower. Elevated neuroactive amino acids (glutamate) and decreased essential amino acids were mostly distinct characteristics of PAAs of autistic children. Increased level of tryptophan might be associated with severity of autism.