Cryptanalysis and Improvement of an Efficient CCA Secure PKE Scheme

Recently in Chinese Journal of Computers, Kang et al. [12] proposed an efficient CCA secure public key encryption (PKE) scheme, and claimed that it is more efficient in the public/private keys than the famous CS98 and BMW05 CCA secure public key encryption scheme. However, in this paper we will show that their proposal is not secure at all. Furthermore, we improve their scheme to be a secure one and prove its security

Freestanding Ammonium Vanadate Composite Cathodes with Lattice Self-Regulation and Ion Exchange for Long-Lasting Ca-Ion Batteries

Calcium-ion batteries (CIBs) have emerged as a promising alternative for electrochemical energy storage. The lack of high-performance cathode materials severely limits the development of CIBs. Vanadium oxides are particularly attractive as cathode materials for CIBs, and preinsertion chemistry is often used to improve their calcium storage performance. However, the room temperature cycling lifespan of vanadium oxides in organic electrolytes still falls short of 1000 cycles. Here, based on preinsertion chemistry, the cycling life of vanadium oxides is further improved by integrated electrode and electrolyte engineering. Utilizing a tailored Ca electrolyte, the constructed freestanding (NH4)2V6O16·1.35H2O@graphene oxide@carbon nanotube (NHVO-H@GO@CNT) composite cathode achieves a 305 mAh g−1 high capacity and 10 000 cycles record-long life. Additionally, for the first time, a Ca-ion hybrid capacitor full cell is assembled and delivers a capacity of 62.8 mAh g−1. The calcium storage mechanism of NHVO-H@GO@CNT based on a two-phase reaction and the exchange of NH4+ and Ca2+ during cycling are revealed. The lattice self-regulation of V─O layers is observed and the layered vanadium oxides with Ca2+ pillars formed by ion exchange exhibit higher capacity. This work provides novel strategies to enhance the calcium storage performance of vanadium oxides via integrated structural design of electrodes and electrolyte modification

Reversible Zn metal anodes enabled by trace amounts of underpotential deposition initiators

Routine electrolyte additives are not effective enough for uniform zinc (Zn) deposition, because they are hard to proactively guide atomic-level Zn deposition. Here, based on underpotential deposition (UPD), we propose an "escort effect" of electrolyte additives for uniform Zn deposition at the atomic level. With nickel ion (Ni2+) additives, we found that metallic Ni deposits preferentially and triggers the UPD of Zn on Ni. This facilitates firm nucleation and uniform growth of Zn while suppressing side reactions. Besides, Ni dissolves back into the electrolyte after Zn stripping with no influence on interfacial charge transfer resistance. Consequently, the optimized cell operates for over 900 h at 1 mA cm-2 (more than 4 times longer than the blank one). Moreover, the universality of "escort effect" is identified by using Cr3+ and Co2+ additives. This work would inspire a wide range of atomic-level principles by controlling interfacial electrochemistry for various metal batteries

Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing reagents. This work was supported by the Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In addition, L.L. was supported by the National Natural Science Foundation of China (31900466), Y. Hou was supported by the Natural Science Foundation of Guangdong Province (2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award (419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science joint research project (GJHZ2093), the National Natural Science Foundation of China (92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075). M.L. was supported by the National Key Research and Development Program of China (2021YFC2600200).S

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001.Peer reviewe

SiPM-matrix readout of two-phase argon detectors using electroluminescence in the visible and near infrared range

Proportional electroluminescence (EL) in noble gases is used in two-phase detectors for dark matter searches to record (in the gas phase) the ionization signal induced by particle scattering in the liquid phase. The “standard” EL mechanism is considered to be due to noble gas excimer emission in the vacuum ultraviolet (VUV). In addition, there are two alternative mechanisms, producing light in the visible and near infrared (NIR) ranges. The first is due to bremsstrahlung of electrons scattered on neutral atoms (“neutral bremsstrahlung”, NBrS). The second, responsible for electron avalanche scintillation in the NIR at higher electric fields, is due to transitions between excited atomic states. In this work, we have for the first time demonstrated two alternative techniques of the optical readout of two-phase argon detectors, in the visible and NIR range, using a silicon photomultiplier matrix and electroluminescence due to either neutral bremsstrahlung or avalanche scintillation. The amplitude yield and position resolution were measured for these readout techniques, which allowed to assess the detection threshold for electron and nuclear recoils in two-phase argon detectors for dark matter searches. To the best of our knowledge, this is the first practical application of the NBrS effect in detection science

Design and construction of a new detector to measure ultra-low radioactive-isotope contamination of argon

Large liquid argon detectors offer one of the best avenues for the detection of galactic weakly interacting massive particles (WIMPs) via their scattering on atomic nuclei. The liquid argon target allows exquisite discrimination between nuclear and electron recoil signals via pulse-shape discrimination of the scintillation signals. Atmospheric argon (AAr), however, has a naturally occurring radioactive isotope, 39Ar, a β emitter of cosmogenic origin. For large detectors, the atmospheric 39Ar activity poses pile-up concerns. The use of argon extracted from underground wells, deprived of 39Ar, is key to the physics potential of these experiments. The DarkSide-20k dark matter search experiment will operate a dual-phase time projection chamber with 50 tonnes of radio-pure underground argon (UAr), that was shown to be depleted of 39Ar with respect to AAr by a factor larger than 1400. Assessing the 39Ar content of the UAr during extraction is crucial for the success of DarkSide-20k, as well as for future experiments of the Global Argon Dark Matter Collaboration (GADMC). This will be carried out by the DArT in ArDM experiment, a small chamber made with extremely radio-pure materials that will be placed at the centre of the ArDM detector, in the Canfranc Underground Laboratory (LSC) in Spain. The ArDM LAr volume acts as an active veto for background radioactivity, mostly γ-rays from the ArDM detector materials and the surrounding rock. This article describes the DArT in ArDM project, including the chamber design and construction, and reviews the background required to achieve the expected performance of the detector

Impact of Urbanization on Seismic Risk: A Study Based on Remote Sensing Data

The management of seismic risk is an important aspect of social development. However, urbanization has led to an increase in disaster-bearing bodies, making it more difficult to reduce seismic risk. To understand the changes in seismic risk associated with urbanization and then adjust the risk management strategy, remote-sensing technology is necessary. By identifying the types of earthquake-bearing bodies, it is possible to estimate the seismic risk and then determine the changes. For this purpose, this study proposes a set of algorithms that combine deep-learning models with object-oriented image classification and extract building information using multisource remote sensing data. Following this, the area of the building is estimated, the vulnerability is determined, and, lastly, the economic and social impacts of an earthquake are determined based on the corresponding ground motion level and fragility function. Our study contributes to the understanding of changes in seismic risk caused by urbanization processes and offers a practical reference for updating seismic risk management, as well as a methodological framework to evaluate the effectiveness of seismic policies. Experimental results indicate that the proposed model is capable of effectively capturing buildings’ information. Through verification, the overall accuracy of the classification of vulnerability types reaches 86.77%. Furthermore, this study calculates social and economic losses of the core area of Tianjin Baodi District in 2011, 2012, 2014, 2016, 2018, 2020, and 2021, obtaining changes in seismic risk in the study area. The result shows that for rare earthquakes at night, although the death rate decreased from 2.29% to 0.66%, the possible death toll seems unchanged, due to the increase in population