New insights on survivability in multi-domain optical networks

With the development of intelligent optical networks and the general multi-protocol label switching (GMPLS) technique, the seamless convergence between IP network and optical network is no longer be a dream but a practical reality. Similar to the Internet, current optical networks have been divided into multiple domains each of which has its own network provider and management policy. Therefore, the development of multi-domain optical networks will be the trend of new-generation intelligent optical networks, and GMPLS-based survivability for multi-domain optical networks will become a hot topic of research in the future. This paper provides a comprehensive review of the existing survivable schemes in multi-domain optical networks and analyzes the shortcomings of current research. Based on previous studies, we present possible challenges and propose new ideas to design efficient survivable schemes to guide the future work of researchers in multi-domain optical networks.     

Single-Atom Metal Sites Anchored Hydrogen-Bonded Organic Frameworks for Superior “Two-In-One” Photocatalytic Reaction

Photoredox catalysis is a green solution for organics transformation and CO₂ conversion into valuable fuels, meeting the challenges of sustainable energy and environmental concerns. However, the regulation of single-atomic active sites in organic framework not only influences the photoredox performance, but also limits the understanding of the relationship for photocatalytic selective organic conversion with CO₂ valorization into one reaction system. As a prototype, different single-atomic metal (M) sites (M²⁺ = Fe²⁺, Co²⁺, Ni²⁺, Cu²⁺, and Zn²⁺) in hydrogen-bonded organic frameworks (M-HOF) backbone with bridging structure of metal-nitrogen are constructed by a typical “two-in-one” strategy for superior photocatalytic C N coupling reactions integrated with CO₂ valorization. Remarkably, Zn-HOF achieves 100% conversion of benzylamine oxidative coupling reactions, 91% selectivity of N-benzylidenebenzylamine and CO₂ conversion in one photoredox cycle. From X-ray absorption fine structure analysis and density functional theory calculations, the superior photocatalytic performance is attributed to synergic effect of atomically dispersed metal sites and HOF host, decreasing the reaction energy barriers, enhancing CO₂ adsorption and forming benzylcarbamic acid intermediate to promote the redox recycle. This work not only affords the rational design strategy of single-atom active sites in functional HOF, but also facilitates the fundamental insights upon the mechanism of versatile photoredox coupling reaction systems.     

In Situ Polymerizing Internal Encapsulation Strategy Enables Stable Perovskite Solar Cells toward Lead Leakage Suppression

Despite the outstanding power conversion efficiency (PCE) of perovskite solar cells (PSCs) achieved over the years, unsatisfactory stability and lead toxicity remain obstacles that limit their competitiveness and large-scale practical deployment. In this study, in situ polymerizing internal encapsulation (IPIE) is developed as a holistic approach to overcome these challenges. The uniform polymer internal package layer constructed by thermally triggered cross-linkable monomers not only solidifies the ionic perovskite crystalline by strong electron-withdrawing/donating chemical sites, but also acts as a water penetration and ion migration barrier to prolong shelf life under harsh environments. The optimized MAPbI₃ and FAPbI₃ devices with IPIE treatment yield impressive efficiencies of 22.29% and 24.12%, respectively, accompanied by remarkably enhanced environmental and mechanical stabilities. In addition, toxic water-soluble lead leakage is minimized by the synergetic effect of the physical encapsulation wall and chemical chelation conferred by the IPIE. Hence, this strategy provides a feasible route for preparing efficient, stable, and eco-friendly PSCs.     

Thermally Responsive Fibers for Versatile Thermoactivated Protective Fabrics

Smart textiles with good mechanical adaptability play an important role in personal protection, health monitoring, and aerospace applications. However, most of the reported thermally responsive polymers has long response time and poor processability, comfort, and wearability. Skin-core structures of thermally responsive fibers with multiple commercial fiber cores and temperature-responsive hydrogel skins are designed and fabricated, which exhibit rapid mechanical adaptability, good thermohardening, and thermal insulation. This universal method enables tight bonding between various commercial fiber cores and hydrogel skins via specific covalently anchored networks. At room temperature, prepared fibers show softness, flexibility, and skin compatibility similar to those of ordinary fibers. As temperature rises, smart fibers become hard, rigid, and self-supporting. The modulus of hydrogel skin increases from 304% to 30883%, showing good mechanoadaptability and impact resistance owing to the synergy between hydrophobic interactions and ionic bonding. Moreover, this synergistic effect leads to an increase in heat absorption, and fibers exhibit good thermal insulation, which reduces the contact temperature of the body surface by ≈25 °C under the external temperature of 95 °C, effectively preventing thermal burns. Notably, the active mechanoadaptability of these smart fibers using conductive fibers as cores is demonstrated. This study provides feasibility for fabricating environmentally adaptive intelligent textiles.     

Auxiliary tumour diagnosis image with deep learning technology

The Journal of Supercomputing - In order to analyse the application value of U-Net neural network in reconstruction and diagnosis of computed tomography (CT) scanning image of lung cancer and...     

Distributed algorithm for mixed equilibrium problems with event-triggered strategy

Neural Computing and Applications - A new iterative method based on the event-triggered strategy for finding a solution to a mixed equilibrium problem (MEP) is introduced in this paper. The target...     

Prediction of NSCLC recurrence from microarray data with GEP

Lung cancer is one of the deadliest diseases in the world. Non‐small cell lung cancer (NSCLC) is the most common and dangerous type of lung cancer. Despite the fact that NSCLC is preventable and curable for some cases if diagnosed at early stages, the vast majority of patients are diagnosed very late. Furthermore, NSCLC usually recurs sometime after treatment. Therefore, it is of paramount importance to predict NSCLC recurrence, so that specific and suitable treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology data and predictions are not reliable in many cases. The microarray gene expression (GE) technology provides a promising and reliable way to predict NSCLC recurrence by analysing the GE of sample cells. This study proposes a new model from GE programming to use microarray datasets for NSCLC recurrence prediction. To this end, the authors also propose a hybrid method to rank and select relevant prognostic genes that are related to NSCLC recurrence prediction. The proposed model was evaluated on real NSCLC microarray datasets and compared with other representational models. The results demonstrated the effectiveness of the proposed model.Inspec keywords: lung, cancer, lab‐on‐a‐chip, genetics, patient diagnosisOther keywords: NSCLC recurrence prediction, microarray data, GE programming, nonsmall cell lung cancer, cancer recurrence, histopathology data, microarray gene expression, prognostic genes