An underwater image enhancement model for domain adaptation

Underwater imaging has been suffering from color imbalance, low contrast, and low-light environment due to strong spectral attenuation of light in the water. Owing to its complex physical imaging mechanism, enhancing the underwater imaging quality based on the deep learning method has been well-developed recently. However, individual studies use different underwater image datasets, leading to low generalization ability in other water conditions. To solve this domain adaptation problem, this paper proposes an underwater image enhancement scheme that combines individually degraded images and publicly available datasets for domain adaptation. Firstly, an underwater dataset fitting model (UDFM) is proposed to merge the individual localized and publicly available degraded datasets into a combined degraded one. Then an underwater image enhancement model (UIEM) is developed base on the combined degraded and open available clear image pairs dataset. The experiment proves that clear images can be recovered by only collecting the degraded images at some specific sea area. Thus, by use of the scheme in this study, the domain adaptation problem could be solved with the increase of underwater images collected at various sea areas. Also, the generalization ability of the underwater image enhancement model is supposed to become more robust. The code is available at https://github.com/fanren5599/UIEM

Genome‑wide association analyses of leaf rust resistance in cultivated emmer wheat

Leaf rust, caused by Puccinia triticina (Pt), constantly threatens durum (Triticum turgidum ssp. durum) and bread wheat (Triticum aestivum) production worldwide. A Pt race BBBQD detected in California in 2009 poses a potential threat to durum production in North America because resistance source to this race is rare in durum germplasm. To find new resistance sources, we assessed a panel of 180 cultivated emmer wheat (Triticum turgidum ssp. dicoccum) accessions for seedling resistance to BBBQD and for adult resistance to a mixture of durum-specific races BBBQJ, CCMSS, and MCDSS in the field, and genotyped the panel using genotype-by-sequencing (GBS) and the 9 K SNP (Single Nucleotide Polymorphism) Infinium array. The results showed 24 and nine accessions consistently exhibited seedling and adult resistance, respectively, with two accessions providing resistance at both stages. We performed genome-wide association studies using 46,383 GBS and 4,331 9 K SNP markers and identified 15 quantitative trait loci (QTL) for seedling resistance located mostly on chromosomes 2B and 6B, and 11 QTL for adult resistance on 2B, 3B and 6A. Of these QTL, one might be associated with leaf rust resistance (Lr) gene Lr53, and two with the QTL previously reported in durum or hexaploid wheat. The remaining QTL are potentially associated with new Lr genes. Further linkage analysis and gene cloning are necessary to identify the causal genes underlying these QTL. The emmer accessions with high levels of resistance will be useful for developing mapping populations and adapted durum germplasm and varieties with resistance to the durum-specific races

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

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

Model-informed Charging Policymaking: How does modeling evidence influence EV charging infrastructure policymaking in the UK and the Netherlands?

The establishment of a well-developed charging infrastructure is imperative for the broader adoption of Electric Vehicle (EV) and necessitates the formulation of an effective charging infrastructure policy. To navigate the intricacies involved in the policymaking process, the incorporation of EV charging models can be advantageous. Existing research indicates that models have a significant impact on facilitating policymaking in the broader energy sector. Nevertheless, it remains unclear whether computer-based models exert a similar influence on the EV charging policies. Previous studies lack comprehensive insights into the practical application of models in EV charging policy processes and the resultant policy modifications due to the unique attributes of both EV charging models and policies. Furthermore, there exists a lack of systematic understanding regarding the utilization of charging infrastructure models. Given these gaps in knowledge, this research aims to investigate the following question: How does modeling evidence influence EV charging infrastructure policymaking in the UK and the Netherlands?This study finds that EV charging models have exerted substantial influence across various stages of local policy cycles, significantly shaping decision-making processes. Such impact has pre- dominantly concentrated on the practical and operational aspects of the models, primarily concerning the optimal number and spatial distribution of charging points. However, there remains a noticeable lack of attention to strategic considerations pertaining to broader energy transition and green transport initiatives. This oversight is particularly evident in the insufficient exploration of how EV charging in- frastructure can be effectively integrated into a more extensive and long-term blueprint. This research highlights the need for a strategic-level approach to comprehend the interplay between EV charging networks and the larger energy transition agenda, encompassing themes such as renewable energy integration, smart grid compatibility and urban planning synergies. Consequently, policymakers and modelers should expand their planning of charging infrastructure to encompass the broader landscape and envision how EV charging models can harmonize with sustainable urban development, ensuring a cohesive and effective implementation within the overarching framework of environmental conservation and sustainable mobility.Management of Technology (MoT

Solenophrya: portrait

Apresenta imagem de Solenophrya um suctoriano que ocorre presa ao substrato, com os ramos estendidos em todas as direções. Cada ramo é uma boca, da qual é expandem extrusomes que ajudam-na a conquistar o seu alimento. Esta imagem foi tirado no Lago Donghu, ChinaComponente Curricular::Educação Superior::Ciências Biológicas::Microbiologi

Solenophrya: portrait

Apresenta imagem de Solenophrya um suctoriano que ocorre presa ao substrato, com os ramos estendidos em todas as direções. Cada ramo é uma boca, da qual é expandem extrusomes que ajudam-na a conquistar o seu alimento. Esta imagem foi tirado no Lago Donghu, ChinaComponente Curricular::Educação Superior::Ciências Biológicas::Microbiologi