Gotowe bibliografie tematyczne / Low light enhancement

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Low light enhancement”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Low light enhancement"

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Hao, Shijie, Xu Han, Yanrong Guo, and Meng Wang. "Decoupled Low-Light Image Enhancement." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 4 (2022): 1–19. http://dx.doi.org/10.1145/3498341.

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The visual quality of photographs taken under imperfect lightness conditions can be degenerated by multiple factors, e.g., low lightness, imaging noise, color distortion, and so on. Current low-light image enhancement models focus on the improvement of low lightness only, or simply deal with all the degeneration factors as a whole, therefore leading to sub-optimal results. In this article, we propose to decouple the enhancement model into two sequential stages. The first stage focuses on improving the scene visibility based on a pixel-wise non-linear mapping. The second stage focuses on improv
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SANTHIYA, S., S. NANDHINI, M. MOGANA PRIYA, and K. SELVA BHUVANESWARI. "LOW-LIGHT IMAGE ENHANCEMENT USING INVERTED ATMOSPHERIC LIGHT." i-manager’s Journal on Software Engineering 15, no. 4 (2021): 8. http://dx.doi.org/10.26634/jse.15.4.18142.

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Park, Seonhee, Kiyeon Kim, Soohwan Yu, and Joonki Paik. "Contrast Enhancement for Low-light Image Enhancement: A Survey." IEIE Transactions on Smart Processing & Computing 7, no. 1 (2018): 36–48. http://dx.doi.org/10.5573/ieiespc.2018.7.1.036.

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Liu, Kang, Zhihao Xv, Zhe Yang, Lian Liu, Xinyu Li, and Xiaopeng Hu. "Continuous detail enhancement framework for low-light image enhancement." Displays 88 (July 2025): 103040. https://doi.org/10.1016/j.displa.2025.103040.

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Dabas, Megha. "Low Light Image Enhancement Using Python." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–8. https://doi.org/10.55041/ijsrem39588.

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ABSTRACT----The poor signal-to-noise ratio (SNR) in low-light photos frequently results in significant sensor noise. Moreover, the noise is non-Gaussian and signal-dependent. We propose a novel denoising technique to tackle the issue by combining weighted total variation (TV) regularization with a Poisson noise model. The weighted Total Variation (T V) regularization effectively eliminates noise while preserving details, whereas the Poisson noise model retains the nature of the noise. Our suggested strategy performs better on NIQE scores than the most advanced techniques. KEYWORDS----COOPERATI
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Journal, IJSREM, Dr S. Babu, Dr R. Rajmohan, et al. "MONOCHROME AUGMENTED LOW-LIGHT IMAGE ENHANCEMENT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–8. http://dx.doi.org/10.55041/ijsrem37853.

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Low light short exposure photography is challenging, but an important factor in capturing images in temporarily dynamic scenes avoiding unwanted effects such as ghosting, motion blur, camera shakes, image artifacts, etc. Monochrome augmented low-light image enhancement aims to get improved low-light short-exposure images by using an additional monochrome sensor and its data. Monochrome images typically possess a higher SNR (Signal-to-Noise Ratio) and better luma information, since it avoids the attenuation by the Bayer Filter. The objective here is to develop a deep learning based approach to
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Xie, Junyi, Hao Bian, Yuanhang Wu, Yu Zhao, Linmin Shan, and Shijie Hao. "Semantically-guided low-light image enhancement." Pattern Recognition Letters 138 (October 2020): 308–14. http://dx.doi.org/10.1016/j.patrec.2020.07.041.

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Zhou, Chu, Minggui Teng, Youwei Lyu, Si Li, Chao Xu, and Boxin Shi. "Polarization-Aware Low-Light Image Enhancement." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3742–50. http://dx.doi.org/10.1609/aaai.v37i3.25486.

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Polarization-based vision algorithms have found uses in various applications since polarization provides additional physical constraints. However, in low-light conditions, their performance would be severely degenerated since the captured polarized images could be noisy, leading to noticeable degradation in the degree of polarization (DoP) and the angle of polarization (AoP). Existing low-light image enhancement methods cannot handle the polarized images well since they operate in the intensity domain, without effectively exploiting the information provided by polarization. In this paper, we p
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Liang, Xiwen, and Xiaoyan Chen. "Enhancement methodology for low light image." Proceedings of International Conference on Artificial Life and Robotics 28 (February 9, 2023): 12–19. http://dx.doi.org/10.5954/icarob.2023.ps3.

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Zhai, Guangtao, Wei Sun, Xiongkuo Min, and Jiantao Zhou. "Perceptual Quality Assessment of Low-light Image Enhancement." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 4 (2021): 1–24. http://dx.doi.org/10.1145/3457905.

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Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low
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Rozprawy doktorskie na temat "Low light enhancement"

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Dalasari, Venkata Gopi Krishna, and Sri Krishna Jayanty. "Low Light Video Enhancement along with Objective and Subjective Quality Assessment." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13500.

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Enhancing low light videos has been quite a challenge over the years. A video taken in low light always has the issues of low dynamic range and high noise. This master thesis presents contribution within the field of low light video enhancement. Three models are proposed with different tone mapping algorithms for extremely low light low quality video enhancement. For temporal noise removal, a motion compensated kalman structure is presented. Dynamic range of the low light video is stretched using three different methods. In Model 1, dynamic range is increased by adjustment of RGB histograms us
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CASULA, ESTER ANNA RITA. "Low mass dimuon production with the ALICE muon spectrometer." Doctoral thesis, Università degli Studi di Cagliari, 2014. http://hdl.handle.net/11584/266451.

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Low mass vector meson (ρ, ω,Φ ) production provides key information on the hot and dense state of strongly interacting matter produced in high-energy heavy ion collisions (called Quark Gluon Plasma). Strangeness enhancement is one of the possible signatures of the Quark Gluon Plasma formation and can be accessed through the measurement of Φ meson production with respect to ρ and Φ mesons, while the measurement of the Φ nuclear modification factor provides a powerful tool to probe the production dynamics and hadronization process in relativistic heavy ion collisions. Vector mesons can be dete
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Landin, Roman. "Object Detection with Deep Convolutional Neural Networks in Images with Various Lighting Conditions and Limited Resolution." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300055.

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Computer vision is a key component of any autonomous system. Real world computer vision applications rely on a proper and accurate detection and classification of objects. A detection algorithm that doesn’t guarantee reasonable detection accuracy is not applicable in real time scenarios where safety is the main objective. Factors that impact detection accuracy are illumination conditions and image resolution. Both contribute to degradation of objects and lead to low classifications and detection accuracy. Recent development of Convolutional Neural Networks (CNNs) based algorithms offers possib
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Wang, Szu-Chieh, and 王思傑. "Extreme Low Light Image Enhancement with Generative Adversarial Networks." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/cz8pqb.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>107<br>Taking photos under low light environments is always a challenge for current imaging pipelines. Image noise and artifacts corrupt the image. Tak- ing the great success of deep learning into consideration recently, it may be straightforward to train a deep convolutional network to perform enhance- ment on such images to restore the underlying clean image. However, the large number of parameters in deep models may require a large amount of data to train. For the low light image enhancement task, paired data requires a short exposure image and a long exposure im
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Chen, Hsueh-I., and 陳學儀. "Deep Burst Low Light Image Enhancement with Alignment, Denoising and Blending." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/sfk685.

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碩士<br>國立臺灣大學<br>資訊網路與多媒體研究所<br>106<br>Taking photos under low light environment is always a challenge for most camera. In this thesis, we propose a neural network pipeline for processing burst short-exposure raw data. Our method contains alignment, denoising and blending. First, we use FlowNet2.0 to predict the optical flow between burst images and align these burst images. And then, we feed the aligned burst raw data into a DenoiseUNet, which includes denoise-part and color-part, to generate an RGB image. Finally, we use a MaskUNet to generate a mask that can distinguish misalignment. We blen
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Chen, Chih-Ming, and 陳知名. "FPGA-based Real-time Low-Light Image Enhancement for Side-Mirror System." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/75th2h.

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碩士<br>國立臺北科技大學<br>電子工程系<br>106<br>In recent years, camera and display are widely used on vehicle. Because the camera wide angle is greater than the field of the lens-view, traditional side-mirror gradually replaced by camera and display. When driving at night, the images always suffer from low visibility when captures in low-light conditions, so driver and pedestrians are in danger. In this paper, design of PCB circuit to connect two motor control modules and side-mirror control lines to integrate FPGA, and presents a high-speed method to enhanced low-light image. The proposed brightnss enhanc
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Chen, Yi-Jun, and 陳怡均. "The Study on Video Enhancement in the Low-Light Environment by Spatio-Temporal Filtering." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/75377479563860423821.

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碩士<br>國立高雄應用科技大學<br>電子與資訊工程研究所碩士班<br>96<br>As science and technology developing, the digital media appearance increasingly presents diversified structure and form. People more and more depend on the digital media to acquire the information and knowledge. In practical video systems, the source image is easily interfered by noise during acquisition process, especially in a low light environment. Noise causes the significant degradation of video quality because the noise remains as large residual errors and results in poor compression efficiency. Therefore noise reduction is an important research
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Malik, Sameer. "Low Light Image Restoration: Models, Algorithms and Learning with Limited Data." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6120.

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The ability to capture high quality images under low-light conditions is an important feature of most hand-held devices and surveillance cameras. Images captured under such conditions often suffer from multiple distortions such as poor contrast, low brightness, color-cast and severe noise. While adjusting camera hardware settings such as aperture width, ISO level and exposure time can improve the contrast and brightness levels in the captured image, they often introduce artifacts including shallow depth-of-field, noise and motion blur. Thus, it is important to study image processing approaches
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Książki na temat "Low light enhancement"

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Hong, M. H. Laser applications in nanotechnology. Edited by A. V. Narlikar and Y. Y. Fu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533060.013.24.

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This article discusses a variety of laser applications in nanotechnology. The laser has proven to be one of many mature and reliable manufacturing tools, with applications in modern industries, from surface cleaning to thin-film deposition. Laser nanoengineering has several advantages over electron-beam and focused ion beam processing. For example, it is a low-cost, high-speed process in air, vacuum or chemical environments and also has the capability to fulfill flexible integration control. This article considers laser nanotechnology in the following areas: pulsed laser ablation for nanomater
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Części książek na temat "Low light enhancement"

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Wang, Haodian, Yang Wang, Yang Cao, and Zheng-Jun Zha. "Fusion-Based Low-Light Image Enhancement." In MultiMedia Modeling. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27077-2_10.

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Wang, William Y., Lisa Liu, and Pingping Cai. "Adversarially Regularized Low-Light Image Enhancement." In MultiMedia Modeling. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53305-1_18.

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Liu, Deyang, Zhengqu Li, Xin Zheng, Jian Ma, and Yuming Fang. "Low-Light Light-Field Image Enhancement With Geometry Consistency." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8685-5_32.

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Fotiadou, Konstantina, Grigorios Tsagkatakis, and Panagiotis Tsakalides. "Low Light Image Enhancement via Sparse Representations." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11758-4_10.

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Hershkovitch Neiterman, Evgeny, Michael Klyuchka, and Gil Ben-Artzi. "Adaptive Enhancement of Extreme Low-Light Images." In Advanced Concepts for Intelligent Vision Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45382-3_2.

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Vinh, Truong Quang, Tran Quang Duy, and Nguyen Quang Luc. "Low-Light Image Enhancement Using Quaternion CNN." In Intelligence of Things: Technologies and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46573-4_23.

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He, Wenchao, and Yutao Liu. "Low-Light Image Enhancement via Unsupervised Learning." In Artificial Intelligence. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8850-1_19.

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Kavya, Avvaru Greeshma, Uruguti Aparna, and Pallikonda Sarah Suhasini. "Enhancement of Low-Light Images Using CNN." In Emerging Research in Computing, Information, Communication and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1342-5_1.

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Zhang, Zhixiang, and Shan Jiang. "Curve Enhancement: A No-Reference Method for Low-Light Image Enhancement." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8148-9_3.

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Song, Juan, Liang Zhang, Peiyi Shen, Xilu Peng, and Guangming Zhu. "Single Low-Light Image Enhancement Using Luminance Map." In Communications in Computer and Information Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3005-5_9.

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Streszczenia konferencji na temat "Low light enhancement"

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Florin, Toadere. "Low light images enhancement." In 2024 Advanced Topics on Measurement and Simulation (ATOMS). IEEE, 2024. https://doi.org/10.1109/atoms60779.2024.10921532.

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Zhang, Gengchen, Yulun Zhang, Xin Yuan, and Ying Fu. "Binarized Low-Light Raw Video Enhancement." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02433.

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Saini, Saurabh, and P. J. Narayanan. "Specularity Factorization for Low-Light Enhancement." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00009.

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Kapoor, Shalini, Yojna Arora, Nidhi Bansal, Khushwant Virdi, Firdous Sadaf M. Ismail, and Suraj Malik. "Low Light Image Enhancement: A Special Click." In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2025. https://doi.org/10.1109/cictn64563.2025.10932613.

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Zheng, Zhihao, and Mooi Choo Chuah. "Latent Disentanglement for Low Light Image Enhancement." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802761.

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Chen, Ethan, Robail Yasrab, and Pramit Saha. "Using MIRNet for Low Light Image Enhancement." In 12th International Conference on Bioimaging. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013099600003911.

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Sun, Yao, and Qingfeng Hu. "Low-Light Image Enhancement Based on Light Effect Region Suppression." In 2025 7th International Conference on Software Engineering and Computer Science (CSECS). IEEE, 2025. https://doi.org/10.1109/csecs64665.2025.11009235.

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Liu, Ziyang, Tianjiao Zeng, Qifan He, Xu Zhan, Wensi Zhang, and XiaoLing Zhang. "Low-light lensless image enhancement via diffusion model." In Holography, Diffractive Optics, and Applications XIV, edited by Changhe Zhou, Liangcai Cao, Ting-Chung Poon, and Hiroshi Yoshikawa. SPIE, 2024. http://dx.doi.org/10.1117/12.3035654.

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Gao, Yin, Hao Li, Chao Yan, et al. "Low-Light Image Enhancement via Camera Response Function." In 2024 9th International Conference on Robotics and Automation Engineering (ICRAE). IEEE, 2024. https://doi.org/10.1109/icrae64368.2024.10851690.

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Wang, Zeyu, and Yan Qi. "Low-light Image Enhancement Algorithm Based on Transformer." In 2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC). IEEE, 2024. http://dx.doi.org/10.1109/spic62469.2024.10691514.

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Raporty organizacyjne na temat "Low light enhancement"

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Birkmire, Robert, Juejun Hu, and Kathleen Richardson. Beyond the Lambertian limit: Novel low-symmetry gratings for ultimate light trapping enhancement in next-generation photovoltaics. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1419008.

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Norelli, John L., Moshe Flaishman, Herb Aldwinckle, and David Gidoni. Regulated expression of site-specific DNA recombination for precision genetic engineering of apple. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7587214.bard.

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Objectives: The original objectives of this project were to: 1) evaluate inducible promoters for the expression of recombinase in apple (USDA-ARS); 2) develop alternative selectable markers for use in apple to facilitate the positive selection of gene excision by recombinase (Cornell University); 3) compare the activity of three different recombinase systems (Cre/lox, FLP/FRT, and R/RS)in apple using a rapid transient assay (ARO); and 4) evaluate the use of recombinase systems in apple using the best promoters, selectable markers and recombinase systems identified in 1, 2 and 3 above (Collabor
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Technical Guidelines to Facilitate the Implementation of Security Council Resolution 2370 (2017) and Related International Standards and good Practices on Preventing Terrorists from Acquiring Weapons. UNIDIR, 2022. http://dx.doi.org/10.37559/caap/22/pacav/03.

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Terrorist acquisition of different types of weapons, including Small Arms and Light Weapons (SALW), their corresponding ammunition, improvised explosive device (IED) components, and uncrewed aerial systems (UAS) and components, poses a global threat to international peace and security. Preventing such acquisitions by terrorists presents States and the international community as well as communities of practitioners with a set of complex and multifaceted challenges. In March 2022, the UN Counter-Terrorism Committee Executive Directorate (CTED), United Nations Counter-Terrorism Centre (UNCCT) of
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