Floating wind farms (FOWF) are one of the main forms of wind energy utilization in the deep-sea areas. This study proposes a multi-objective reposition control approach for floating wind farms. Firstly, an imperial wake model of floating wind turbines is constructed considering the effects of wind and wave conditions. A nonlinear model of a catenary mooring line is subsequently constructed. Furthermore, a multi-objective location optimization method is proposed that allows for the tradeoff between the maximum power of the farm and the minimum drift distance of the turbines accounting for the time-varying wind speed and direction. The results of the proposed approach are then compared with those of traditional methods. The findings indicate that time-varying changes in wind have a significant influence on the optimal position of turbines. It can decrease the maximum drift distance by approximately 7 % when considering temporal variations in wind. Furthermore, the proposed reposition control maintains almost the same power output of the wind farm while reducing the total offset distance from the equilibrium point of turbines by approximately 11 %. The impact of mooring orientation, natural length, turbine spacing, and wave speed on the control performance are also elucidated.

Fluxless tin soldering eliminates flux residues but introduces tin fog, which affects the reliability of electronic packaging. The influence of tin fog was first analyzed through the shear strength of the Al wires bonded on DBC substrates. After aging at 300 °C for 4 h, shear strength increased by 4.5 % on pure copper surfaces but decreased by 22.6 % on tin fog-covered surfaces. Further SEM revealed tin fog created interfacial porosity, reducing strength. Secondly, the impact of tin fog formation during the formic acid reflow process was studied under different formic acid concentrations and different vacuum frequencies during the reflow's preheating stage. Through SEM, EDS, Micro-IR, and XPS tests, the principal components of tin fog were identified as elemental tin, tin dioxide, and organic compounds such as amides and esters. It is found that tin fog forms through a process wherein tin formate produced during formic acid reflow adheres to the substrate surface, which is already adsorbed with organic substances such as esters and amides, and subsequently decomposes at high temperatures to generate derivative products creating the fog. Therefore, to mitigate tin fog formation, tin fog was effectively controlled under a tripled vacuum frequency, resulting in reduced organic residues on substrates. Under such a mitigation strategy, further reliability tests showed that the shear strength of Al wire bonding after aging at 300 °C was comparable to that on clean DBC substrates. This research provides a valuable reference for enhancing the reliability of fluxless soldering in electronic packaging.

HTTP/3 (H3) has experienced significant growth and extensive adoption in various scenarios, especially in Content Delivery Networks (CDNs). Over the past few years, there have been numerous insightful studies on its deployment in industrial CDNs. However, these studies often separately analyze H3 and CDN, overlooking their synergistic integration. In this work, we explore the applicability of H3 in CDN from a holistic perspective. We analyze 325 websites hosted by seven CDN providers and identify three key characteristics where CDN align perfectly with H3's strengths. Firstly, CDN resources dominate the composition of webpages, where enabling H3 can amplify H3's benefits in connection acceleration. Secondly, CDN providers also exhibit a dominant characteristic, with the majority of CDN resources hosted by a few large providers. This phenomenon makes different webpages share the same provider. When browsing consecutively, H3 helps to skip the connection phase by resuming the connections to the same CDN provider across pages. Thirdly, H3 mitigates the congestion problem on webpages serving multiple CDN resources. This work provides a deeper insight into the applicability of H3 in large-scale distributed systems like CDNs, holding promise for informing the development and optimization of industrial H3.

Outstanding users (OUs) denote the influential, 'core' or 'bridge' users in online social networks. How to accurately detect and rank them is an important problem for third-party online service providers and researchers. Conventional efforts, ranging from early graph-based algorithms to recent machine learning-based approaches, typically rely on an entire social network's information. However, for privacy-conscious users or newly-registered users, such information is not easily accessible. To address this issue, we present DeepPick, a novel framework that considers both the generalization and specialization in the detection task of OUs. For generalization, we introduce deep neural networks to capture dynamic features of the users. For specialization, we leverage the traditional descriptive features to make use of public information about users. Extensive experiments based on real-world datasets demonstrate that our approach achieves a high efficacy of detection performance against the state-of-the-art.

We identified three temporal patterns shown in commit activities among Chinese and American companies and found that Chinese businesses are more likely to follow long work hours than American ones. We also conducted a survey on the trends of, reasons for, and results of overtime work. Our study could provide references for developers to choose workplaces and for companies to make regulations.

Superior thermal quenching and degradation of phosphors are required for long lifetime lighting devices, such as light-emitting diodes, which can be realized through composition modification. Here, Al-N bonds in AlON: Eu²⁺ phosphors are substituted by higher bond order of Si-C. Photoluminescence (PL) results show thermal quenching (at 150 °C) and thermal degradation (after 600 °C treatment in air) are improved by 5% and 8% with a small decrease of PL intensity in 5% SiC doped AlON: Eu²⁺ phosphor. To explain these observations, first-principles computational study was performed to understand the Si and C configuration in AlON:Eu²⁺. The calculations reveal that Si and C elements are not randomly distributed in AlON lattice. It was found that Si prefers occupying tetrahedral sites (Td-Si) and the insertion of C in Td-Si is always energetically favorable, which results in the formation of SiC₄ and SiNC₃ clusters. Thus, the Al-N substitution by Si-C induces a stronger local structure, which accounts for the emission redshift and better thermal stability.