The 2024 Nobel Prize in Chemistry was recently granted to David Baker, Demis Hassabis and John M. Jumper, renowned for their pioneering works in protein design.

Network security has always been the focus of Internet research. In order to better detect these attacks, it is necessary to more accurately evaluate the state performance of the network. The existing measurement methods often only focus on a single or very few indicators.

Existing swirling combustion technology, which relies on faulty coal, is unable to meet deep peak shaving demands without auxiliary methods. This paper developed a deep peak regulation burner (DPRB) to achieve stable combustion at 15%–30% of the boiler’s rated load without auxiliary support. Gas-particle tests, industrial trials, and transient numerical simulations were conducted to evaluate the burner’s performance. At full rated load, the DPRB formed a central recirculation zone (RZ) with a length of 1.5d and a diameter of 0.58d (where d represents the outlet diameter). At 40%, 20%, and 15% rated loads, the RZ became annular, with diameters of 0.30d, 0.40d, and 0.39d, respectively, with a length of 1.0d. At 20% and 15% rated loads, the recirculation peak and the range of particle volume flux were comparable to those at 40% rated load. The prototype burner demonstrated that, without oil support, the gas temperature within 0 to 1.8 m from the primary air outlet remained below 609 °C, insufficient to ignite faulty coal. As the load rate increased from 20% to 30%, the prototype’s central region temperature remained low, with a maximum of 750 °C between 0 and 2.0 m. In contrast, the DPRB’s central region temperature reached 750 °C at around 0.65–0.70 m. At a 3%·min−1 load-up rate, when the load increased from 20% to 30%, the prototype burner extinguished after 30 s. However, the DPRB maintained stable combustion throughout the process.

This feature explores how European education systems negotiate tensions between collective ideals and growing competition. Drawing on studies from Denmark, Sweden, Norway, and Belarus, it examines shadow education, policy debates over equity, culturally grounded early childhood learning, and enduring post-Soviet public institutions. Together, these perspectives reveal education as a social mirror, continuously balancing public good, cultural identity, historical legacy, and individual ambition across diverse European contexts and shared societal values.

Small interfering RNA (siRNA) has shown promising therapeutic prospects in many major diseases. However, two main reasons limit the application of siRNA: poor endocytosis efficiency and weak endosomal escape ability. Therefore, the development of efficient and safe delivery vectors has always been an important study aspect of RNAi technology. Herein, we designed a self-assembled nanoparticle based on functionalized peptides to deliver siRNA to the down-regulated polo-like kinase 1 (PLK1) gene, which can inhibit tumor cells in the G2 phase. The functional polypeptide consists of cell membrane-penetrating peptide (CPP44) and p16 minimal inhibitory sequence (p16MIS). CPP44 can effectively mediate endocytosis, while p16MIS can inhibit tumor growth in the G1 phase and synergistically promote the apoptosis of tumor cells with siPLK1. In vitro and in vivo studies demonstrate that the developed nanoparticle exhibits high levels of silencing efficiency, antitumor activity, and therapeutic efficacy. Consequently, this study provides a novel approach to cancer treatment by simultaneously disrupting two stages of tumor cell division.

Knowing the correct phase connectivity information plays a significant role in maintaining high-quality power and reliable electricity supply to end-consumers. However, managing the consumer-phase connectivity of a low-voltage distribution network is often costly, prone to human errors, and time-intensive, as it involves either installing expensive high-precision devices or employing field-based methods. Besides, the ever-increasing electricity demand and the proliferation of behind-the-meter resources have also increased the complexity of leveraging the phase connectivity problem. To overcome the above challenges, this paper develops a data-driven model to identify the phase connectivity of end-consumers using advanced metering infrastructure voltage and current measurements. Initially, a preprocessing method that employs linear interpolation and singular value decomposition is adopted to improve the quality of the smart meter data. Then, using Kirchoff’s current law and correlation analysis, a discrete convolution optimization model is built to uniquely identify the phase to which each end-consumer is connected. The data sets utilized are obtained by performing power flow simulations on a modified IEEE-906 test system using OpenDSS software. The robustness of the model is tested against data set size, missing smart meter data, measurement errors, and the influence of prosumers. The results show that the method proposed correctly identifies the phase connections of end-consumers with an accuracy of about 98%.