Rondotia melanoleuca sp. nov., a new wild-mulberry silkworm from China (Lepidoptera, Bombycidae)

Several yellow larvae with black spots were discovered in the wild of Chinese Sichuan and Yunnan provinces, and were further raised in captivity. Reared adults exhibit a striking black and white wing pattern, and they represent unequivocally a new species, here described as Rondotia melanoleuca sp. nov. Molecular analyses suggest that this species could be sister to all previously known species of Rondotia

Load carrying capability of regional electricity-heat energy systems:Definitions, characteristics, and optimal value evaluation

Evaluating the load carrying capability of regional electricity-heat energy systems is of great significance to its planning and construction. Existing methods evaluate energy supply capability without considering load characteristics between various users. Besides, the impact of integrated demand response is not fully considered. To address these problems, this paper builds a load carrying capability interval model, which uses reliability as a security constraint and considers integrated demand response. An evaluation method for the optimal load carrying capability considering uncertainties of load growth is proposed. First, this paper defines energy supply capability, available capacity, and load carrying capability. Interval models are built to achieve the visualization display of these indices. Their characteristics are studied and the impact factors of interval boundary are analyzed. Secondly, a two-layer optimization model for the evaluation of optimal load carrying capability is constructed, considering the uncertainties of load growth. The upper-layer model aims at optimizing the sum of load carrying capability benefit, integrated demand response cost, and load curtailment penalty. The lower-layer model maximizes energy supply capability. Thereafter, the lower-layer model is linearized based on piecewise linearization and the least square method. The computation efficiency is greatly enhanced. In the case study, a real regional electricity-heat energy system is used to validate the proposed model and method.</p

A Scorpion Defensin BmKDfsin4 Inhibits Hepatitis B Virus Replication in Vitro

Hepatitis B virus (HBV) infection is a major worldwide health problem which can cause acute and chronic hepatitis and can significantly increase the risk of liver cirrhosis and primary hepatocellular carcinoma (HCC). Nowadays, clinical therapies of HBV infection still mainly rely on nucleotide analogs and interferons, the usage of which is limited by drug-resistant mutation or side effects. Defensins had been reported to effectively inhibit the proliferation of bacteria, fungi, parasites and viruses. Here, we screened the anti-HBV activity of 25 scorpion-derived peptides most recently characterized by our group. Through evaluating anti-HBV activity and cytotoxicity, we found that BmKDfsin4, a scorpion defensin with antibacterial and Kv1.3-blocking activities, has a comparable high inhibitory rate of both HBeAg and HBsAg in HepG2.2.15 culture medium and low cytotoxicity to HepG2.2.15. Then, our experimental results further showed that BmKDfsin4 can dose-dependently decrease the production of HBV DNA and HBV viral proteins in both culture medium and cell lysate. Interestingly, BmKDfsin4 exerted high serum stability. Together, this study indicates that the scorpion defensin BmKDfsin4 also has inhibitory activity against HBV replication along with its antibacterial and potassium ion channel Kv1.3-blocking activities, which shows that BmKDfsin4 is a uniquely multifunctional defensin molecule. Our work also provides a good molecule material which will be used to investigate the link or relationship of its antiviral, antibacterial and ion channel–modulating activities in the future

Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints

Unlike cloud-based deep learning models that are often large and uniform, edge-deployed models usually demand customization for domain-specific tasks and resource-limited environments. Such customization processes can be costly and time-consuming due to the diversity of edge scenarios and the training load for each scenario. Although various approaches have been proposed for rapid resource-oriented customization and task-oriented customization respectively, achieving both of them at the same time is challenging. Drawing inspiration from the generative AI and the modular composability of neural networks, we introduce NN-Factory, an one-for-all framework to generate customized lightweight models for diverse edge scenarios. The key idea is to use a generative model to directly produce the customized models, instead of training them. The main components of NN-Factory include a modular supernet with pretrained modules that can be conditionally activated to accomplish different tasks and a generative module assembler that manipulate the modules according to task and sparsity requirements. Given an edge scenario, NN-Factory can efficiently customize a compact model specialized in the edge task while satisfying the edge resource constraints by searching for the optimal strategy to assemble the modules. Based on experiments on image classification and object detection tasks with different edge devices, NN-Factory is able to generate high-quality task- and resource-specific models within few seconds, faster than conventional model customization approaches by orders of magnitude

Designing Artificial Two-Dimensional Landscapes via Room-Temperature Atomic-Layer Substitution

Manipulating materials with atomic-scale precision is essential for the development of next-generation material design toolbox. Tremendous efforts have been made to advance the compositional, structural, and spatial accuracy of material deposition and patterning. The family of 2D materials provides an ideal platform to realize atomic-level material architectures. The wide and rich physics of these materials have led to fabrication of heterostructures, superlattices, and twisted structures with breakthrough discoveries and applications. Here, we report a novel atomic-scale material design tool that selectively breaks and forms chemical bonds of 2D materials at room temperature, called atomic-layer substitution (ALS), through which we can substitute the top layer chalcogen atoms within the 3-atom-thick transition-metal dichalcogenides using arbitrary patterns. Flipping the layer via transfer allows us to perform the same procedure on the other side, yielding programmable in-plane multi-heterostructures with different out-of-plane crystal symmetry and electric polarization. First-principle calculations elucidate how the ALS process is overall exothermic in energy and only has a small reaction barrier, facilitating the reaction to occur at room temperature. Optical characterizations confirm the fidelity of this design approach, while TEM shows the direct evidence of Janus structure and suggests the atomic transition at the interface of designed heterostructure. Finally, transport and Kelvin probe measurements on MoXY (X,Y=S,Se; X and Y corresponding to the bottom and top layers) lateral multi-heterostructures reveal the surface potential and dipole orientation of each region, and the barrier height between them. Our approach for designing artificial 2D landscape down to a single layer of atoms can lead to unique electronic, photonic and mechanical properties previously not found in nature

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Numerical simulation on heat transfer and entropy generation of impingement cooling on boss shaped surface

Using impingement jet to cool the external cavity of the end wall of the gas turbine guide blade is very effective for prolonging the service life of the gas turbine and ensuring its safety operation. In this paper, the numerical simulation method is used to study the impingement cooling heat transfer performance of the boss shaped surface in the external cavity of the end wall of the gas turbine guide blade, and the entropy generation of the impingement heat transfer process is analyzed. The results show that the average Nusselt number on the impingement target surface and the impingement hole surface increase with the increase of the Reynolds number of the impingement jet. When the Reynolds number is constant, the average Nusselt number of impingement target surface and impingement hole surface decrease with the increase of impingement target distance, but the cooling range on the impingement target surface increases and the heat transfer is more uniform. With the increase of the width of the boss shaped upper surface, the cooling range on the impingement target surface relatively decreases, and the average Nusselt numbers of the impingement target surface decreases and that of the impingement hole surface increases respectively. The heat transfer of the upper surface of the boss is better than that of the lower surface on both sides. The entropy generation in the process of impingement cooling mainly comes from the entropy production caused by viscous dissipation and the entropy flow caused by heat transfer. The entropy production in the flow vortex region is the main reason for the entropy generation. The research conclusions can provide basis and reference for optimizing the structural and operating parameters of boss shaped impingement cavity and improving its impingement heat transfer effect

Load carrying capability of regional electricity-heat energy systems:Definitions, characteristics, and optimal value evaluation

Evaluating the load carrying capability of regional electricity-heat energy systems is of great significance to its planning and construction. Existing methods evaluate energy supply capability without considering load characteristics between various users. Besides, the impact of integrated demand response is not fully considered. To address these problems, this paper builds a load carrying capability interval model, which uses reliability as a security constraint and considers integrated demand response. An evaluation method for the optimal load carrying capability considering uncertainties of load growth is proposed. First, this paper defines energy supply capability, available capacity, and load carrying capability. Interval models are built to achieve the visualization display of these indices. Their characteristics are studied and the impact factors of interval boundary are analyzed. Secondly, a two-layer optimization model for the evaluation of optimal load carrying capability is constructed, considering the uncertainties of load growth. The upper-layer model aims at optimizing the sum of load carrying capability benefit, integrated demand response cost, and load curtailment penalty. The lower-layer model maximizes energy supply capability. Thereafter, the lower-layer model is linearized based on piecewise linearization and the least square method. The computation efficiency is greatly enhanced. In the case study, a real regional electricity-heat energy system is used to validate the proposed model and method.</p

Characteristics of Naturally Formed Nanoparticles in Various Media and Their Prospecting Significance in Chaihulanzi Deposit

In recent years, the exploration of concealed deposits has become extremely urgent as the shortage of surface resources worsens. In this study, naturally formed nanoparticles in five media (deep-seated fault gouge, ascending gas flow, soil, shallow groundwater and deep groundwater) in Chaihulanzi Au deposit, China, were analyzed by transmission electron microscopy. The characteristics of category, shape, lattice parameters, chemical component and association were obtained. The results show that deep media can carry natural nanoparticles to the surface media, resulting in an increased proportion of O and metal chemical valence such as Pb and Cu in nanoparticles. The metal elements Au, Ag, Cu, Zn and As in nanoparticles correspond to those of orebody minerals. Au-Ag-Cu, Fe-As, Cu-Sn and Pb-Zn element associations in nanoparticles are similar to those of mineral composition or orebody paragenesis in Chaihulanzi deposit. Compared with nanoparticle characteristics in deposit and background areas, it can be deduced that natural ore-bearing nanoparticles come from concealed orebodies. With the characteristics of more oxide forms and the dislocation of the crystal lattice, these nanoparticles are formed by faulting and oxidation. Nanoparticles produced in concealed orebodies that migrate from the deep to the surface media could be used for prospecting