Expected reliability of communication protocols

We consider the problem of sending a message from a sender $s$ to a receiver $r$ through an unreliable network by specifying in a protocol what each vertex is supposed to do if it receives the message from one of its neighbors. A protocol for routing a message in such a graph is finite if it never floods $r$ with an infinite number of copies of the message. The expected reliability of a given protocol is the probability that a message sent from $s$ reaches $r$ when the edges of the network fail independently with probability $1?p$.We discuss, for given networks, the properties of finite protocols with maximum expected reliability in the case when $p$ is close to 0 or 1, and we describe networks for which no one protocol is optimal for all values of $p$. In general, finding an optimal protocol for a given network and fixed probability is challenging and many open problems remain.     

Compressibility of random walker trajectories on growing networks

We find that the simple coupling of network growth to the position of a random walker on the network generates a traveling wave in the probability distribution of nodes visited by the walker. We argue that the entropy of this probability distribution is bounded as the network size tends to infinity. This means that the growth of a space coupled to a random walker situated in it constrains its dynamics to a set of typical random walker trajectories, and walker trajectories inside the growing space are compressible.     

Classify the Higgs decays with the PFN and ParticleNet at electron–positron colliders

Various Higgs factories are proposed to study the Higgs boson precisely and systematically in a model- independent way. In this study, the Particle Flow Network and ParticleNet techniques are used to classify the Higgs decays into multicategories, and the ultimate goal is to realize an "end-to-end" analysis. A Monte Carlo simulation study is performed to demonstrate the feasibility, and the performance looks rather promising. This result could be the basis of a "one-stop" analysis to measure all the branching fractions of the Higgs decays simultaneously.     

Triple-function Hydrated Eutectic Electrolyte for Enhanced Aqueous Zinc Batteries

Aqueous rechargeable zinc-ion batteries (ARZBs) are impeded by the mutual problems of unstable cathode, electrolyte parasitic reactions, and dendritic growth of zinc (Zn) anode. Herein, a triple-functional strategy by introducing the tetramethylene sulfone (TMS) to form a hydrated eutectic electrolyte is reported to ameliorate these issues. The activity of H₂O is inhibited by reconstructing hydrogen bonds due to the strong interaction between TMS and H₂O. Meanwhile, the preferentially adsorbed TMS on the Zn surface increases the thickness of double electric layer (EDL) structure, which provides a shielding buffer layer to suppress dendrite growth. Interestingly, TMS modulates the primary solvation shell of Zn²⁺ ultimately to achieve a novel solvent co-intercalation ((Zn-TMS)²⁺) mechanism, and the intercalated TMS works as a “pillar” that provides more zincophilic sites and stabilizes the structure of cathode (NH₄V₄O₁₀, (NVO)). Consequently, the Zn||NVO battery exhibits a remarkably high specific capacity of 515.6 mAh g⁻¹ at a low current density of 0.2 A g⁻¹ for over 40 days. This multi-functional electrolytes and solvent co-intercalation mechanism will significantly propel the practical development of aqueous batteries.     

Boosting Hydrogen Peroxide Electrosynthesis via Modulating the Interfacial Hydrogen-Bond Environment

Designing highly efficient and stable electrode-electrolyte interface for hydrogen peroxide (H₂O₂) electrosynthesis remains challenging. Inhibiting the competitive side reaction, 4 e⁻ oxygen reduction to H₂O, is essential for highly selective H₂O₂ electrosynthesis. Instead of hindering excessive hydrogenation of H₂O₂ via catalyst modification, we discover that adding a hydrogen-bond acceptor, dimethyl sulfoxide (DMSO), to the KOH electrolyte enables simultaneous improvement of the selectivity and activity of H₂O₂ electrosynthesis. Spectral characterization and molecular simulation confirm that the formation of hydrogen bonds between DMSO and water molecules at the electrode-electrolyte interface can reduce the activity of water dissociation into active H* species. The suitable H* supply environment hinders excessive hydrogenation of the oxygen reduction reaction (ORR), thus improving the selectivity of 2 e⁻ ORR and achieving over 90 % selectivity of H₂O₂. This work highlights the importance of regulating the interfacial hydrogen-bond environment by organic molecules as a means of boosting electrochemical performance in aqueous electrosynthesis and beyond.     

Highly Recyclable and Tough Elastic Vitrimers from a Defined Polydimethylsiloxane Network

Despite intensive research on sustainable elastomers, achieving elastic vitrimers with significantly improved mechanical properties and recyclability remains a scientific challenge. Herein, inspired by the classical elasticity theory, we present a design principle for ultra-tough and highly recyclable elastic vitrimers with a defined network constructed by chemically crosslinking the pre-synthesized disulfide-containing polydimethylsiloxane (PDMS) chains with tetra-arm polyethylene glycol (PEG). The defined network is achieved by the reduced dangling short chains and the relatively uniform molecular weight of network strands. Such elastic vitrimers with the defined network, i.e., PDMS-disulfide-D, exhibit significantly improved mechanical performance than random analogous, previously reported PDMS vitrimers, and even commercial silicone-based thermosets. Moreover, unlike the vitrimers with random network that show obvious loss in mechanical properties after recycling, those with the defined network enable excellent thermal recyclability. The PDMS-disulfide-D also deliver comparable electrochemical signals if utilized as substrates for electromyography sensors after the recycling. The multiple relaxation processes are revealed via a unique physical approach. Multiple techniques are also applied to unravel the microscopic mechanism of the excellent mechanical performance and recyclability of such defined network.     

Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer

In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease–gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git.     

结合区域提取和改进卷积神经网络的水下小目标检测

针对水下小目标信息量有限而难以提取有效特征导致的检测性能不佳问题,提出了一种结合区域提取和融合Hu矩特征的改进卷积神经网络水下小目标检测方法。该方法包含区域提取和分类两个步骤。首先以马尔可夫随机场分割算法为基础进行区域提取,对潜在目标定位的同时降低伪目标对后续分类的干扰;然后提取潜在目标区域的Hu矩特征并融入卷积神经网络,形成一种形状特征表征能力更强的改进卷积神经网络用于分类。声呐实测数据处理结果表明,该方法可以有效提升对水下小目标的发现概率和正确报警率,与其他目标检测方法相比,该方法具有更好的检测性能和泛化性。