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Scientists from a CAAS institute in Shenzhen developed a new error correction algorithm for DNA digital storage (DDS). Their proof-of-concept DDS system based on this method can push the storage limit to the "brontobyte-scale".
Paper:
Error-correcting codes (ECCs) employed in the state-of-the-art DNA digital storage (DDS) systems suffer from a trade-off between error-correcting capability and the proportion of redundancy. To address this issue, in this study, we introduce soft-decision decoding approach into DDS by proposing a DNA-specific error prediction model and a series of novel strategies. We demonstrate the effectiveness of our approach through a proof-of-concept DDS system based on Reed-Solomon (RS) code, named as Derrick. Derrick shows significant improvement of error-correcting capability without involving additional redundancy in both in vitro and in silico experiments, using various sequencing technologies such as Illumina, PacBio and Oxford Nanopore Technology (ONT). Notably, in vitro experiments using ONT sequencing at a depth of 7 × reveal that Derrick, compared with the traditional hard-decision decoding strategy, doubles the error-correcting capability of RS code, decreases the proportion of matrices with decoding-failure by 229 folds, and amplifies the potential maximum storage volume by impressive 32 388 folds. Also, Derrick surpasses the ‘state-of-the-art’ DDS systems by comprehensively considering the information density and the minimum sequencing depth required for complete information recovery. Crucially, the soft-decision decoding strategy and key steps of Derrick are generalizable to other ECCs’ decoding algorithms.
News release:
近日，中国农业科学院农业基因组学技术研发与应用创新团队提出DNA数字存储纠错新算法，成功突破了冗余对纠错能力的限制，将大幅提升DNA存储纠错能力。相关研究成果发表在《国家科学评论（National Science Review）》上。
DNA数字存储以其存储密度高、存储寿命长且维护成本低的优势，被视为高潜力的新兴存储技术。然而，DNA数字存储过程中的合成错误、保存错误以及测序错误，给数据的准确恢复带来了挑战。为解决这一问题，研究人员基于DNA数字存储的错误偏好性，构建出了错误预测模型，在此基础上首次融入纠错码解码技术，开发出可将纠错数量提高至硬判决2倍的软判决译码软件Derrick，预计可达到千亿亿亿字节规模的无损存储容量。
该研究得到了国家重点研发计划、国家自然科学基金、深圳市科技创新委员会和中国农业科学院科技创新工程的资助与支持。
Paper:
Improving Error-correcting Capability in DNA Digital Storage via Soft-decision Decoding
Abstract
Error-correcting codes (ECCs) employed in the state-of-the-art DNA digital storage (DDS) systems suffer from a trade-off between error-correcting capability and the proportion of redundancy. To address this issue, in this study, we introduce soft-decision decoding approach into DDS by proposing a DNA-specific error prediction model and a series of novel strategies. We demonstrate the effectiveness of our approach through a proof-of-concept DDS system based on Reed-Solomon (RS) code, named as Derrick. Derrick shows significant improvement of error-correcting capability without involving additional redundancy in both in vitro and in silico experiments, using various sequencing technologies such as Illumina, PacBio and Oxford Nanopore Technology (ONT). Notably, in vitro experiments using ONT sequencing at a depth of 7 × reveal that Derrick, compared with the traditional hard-decision decoding strategy, doubles the error-correcting capability of RS code, decreases the proportion of matrices with decoding-failure by 229 folds, and amplifies the potential maximum storage volume by impressive 32 388 folds. Also, Derrick surpasses the ‘state-of-the-art’ DDS systems by comprehensively considering the information density and the minimum sequencing depth required for complete information recovery. Crucially, the soft-decision decoding strategy and key steps of Derrick are generalizable to other ECCs’ decoding algorithms.
News release:
近日，中国农业科学院农业基因组学技术研发与应用创新团队提出DNA数字存储纠错新算法，成功突破了冗余对纠错能力的限制，将大幅提升DNA存储纠错能力。相关研究成果发表在《国家科学评论（National Science Review）》上。
DNA数字存储以其存储密度高、存储寿命长且维护成本低的优势，被视为高潜力的新兴存储技术。然而，DNA数字存储过程中的合成错误、保存错误以及测序错误，给数据的准确恢复带来了挑战。为解决这一问题，研究人员基于DNA数字存储的错误偏好性，构建出了错误预测模型，在此基础上首次融入纠错码解码技术，开发出可将纠错数量提高至硬判决2倍的软判决译码软件Derrick，预计可达到千亿亿亿字节规模的无损存储容量。
该研究得到了国家重点研发计划、国家自然科学基金、深圳市科技创新委员会和中国农业科学院科技创新工程的资助与支持。
keep in mind NearLink was developed with over 300 major companies in China in association including BYD, Vivo, Oppo, Honor, Xiaomi, Tencent, SAIC, Geely & such (maybe i'm over including someone here).
But yeah, this is a national effort. Huawei is just the 1st to use it. That and this new audio standard.
But the key here is that in the past, Western companies developed the standard first and then ROW adopted to them.
Now, China developed certain tech & standard. Will obviously move to use this superior tech. We will see what western countries do
New excuse for the US to sanction Chinese EV's
Yang: I used a Wahl hair clipper set for a haircut, sanction me
broadsword
Brigadier
What new audio standard?
East China University of Science and Technology created interpretable deep learning framework to predict life of lithium-ion batteries.
Paper:
News story:
在锂电池寿命预测领域，建立全面的电池老化模型是项艰巨任务。因此，数据驱动方法受到越来越多的关注。深度学习已被证明是电池应用领域中一种强大的数据驱动拟合方法。然而，可解释性仍然是该领域面临的挑战，限制了深度学习方法的实际应用。
随着可解释技术的发展，深度学习不仅可以作为黑盒工具，还可以用于探索外部电池数据与内部电化学变化之间的关系。研究团队提出了一种可解释的深度学习框架，利用梯度加权类激活映射来解释训练好的卷积神经网络模型的输入和输出之间的联系。
研究团队通过锂电池容量衰退拐点识别任务对可解释的深度学习框架进行了演示。结果发现，该深度学习模型在有效预测电池容量衰退拐点的基础上，可以敏锐捕捉与电池老化机制相关的特征，其中包括人类尚未完全理解的关键特征。此外，通过在不同预测任务，如考虑多种电池体系、实际工况和数据集中验证该方法，展现了该框架优秀的可迁移性。在无先验知识的情况下，该可解释的深度学习框架可以为研究者理解复杂电池老化机理提供新见解。该可解释性深度学习方法的提出为电池相关领域的数据驱动研究提供了新的思路，将积极推动人工智能技术在先进电池设计开发及安全使用方面的广泛应用。
Paper:
Interpretable deep learning for accelerated fading recognition of lithium-ion batteries
Abstract
Data-driven approaches have gained increasing attention in the field of battery life-related prediction, as building a comprehensive mechanistic remains a challenge. has emerged as a powerful data-driven fitting method for battery-related applications. However, interpretability remains an issue in this field, hindering the practical utilization of deep learning methods. With the development of interpretable techniques, deep learning methods not only can be conducted as black box tools for fitting, but also for exploring the relationship between external battery data and internal electrochemical changes. In this paper, an interpretable deep learning procedure is proposed and exemplified by accelerated fading point (knee-point) recognition based on an open battery dataset. The Gradient-weighted Class Activation Mapping (Grad-CAM) is conducted to explain the link between the input and output of the trained (CNN) model. The trained CNN model possesses deep insight into battery degradation, giving the very first warning when accelerated fading occurs. Through interpretability analysis, it is confirmed that the well-trained model can spontaneously focus on features associated with internal battery degradation and identify some additional features beyond existing human experience. The proposed method can be used to discover the relationship between battery data and by artificial intelligence in the electric vehicles (EVs) field.News story:
深度学习框架可预测锂电池寿命
近日，华东理工大学机械与动力工程学院、先进电池系统与安全重点实验室教授栾伟玲课题组与国家级高层次人才、华东理工大学讲席教授陈浩峰合作，在全球交通科学与技术领域期刊《交通电动化》发表论文，首次提出用于锂电池寿命预测相关的可解释性深度学习框架。在锂电池寿命预测领域，建立全面的电池老化模型是项艰巨任务。因此，数据驱动方法受到越来越多的关注。深度学习已被证明是电池应用领域中一种强大的数据驱动拟合方法。然而，可解释性仍然是该领域面临的挑战，限制了深度学习方法的实际应用。
随着可解释技术的发展，深度学习不仅可以作为黑盒工具，还可以用于探索外部电池数据与内部电化学变化之间的关系。研究团队提出了一种可解释的深度学习框架，利用梯度加权类激活映射来解释训练好的卷积神经网络模型的输入和输出之间的联系。
研究团队通过锂电池容量衰退拐点识别任务对可解释的深度学习框架进行了演示。结果发现，该深度学习模型在有效预测电池容量衰退拐点的基础上，可以敏锐捕捉与电池老化机制相关的特征，其中包括人类尚未完全理解的关键特征。此外，通过在不同预测任务，如考虑多种电池体系、实际工况和数据集中验证该方法，展现了该框架优秀的可迁移性。在无先验知识的情况下，该可解释的深度学习框架可以为研究者理解复杂电池老化机理提供新见解。该可解释性深度学习方法的提出为电池相关领域的数据驱动研究提供了新的思路，将积极推动人工智能技术在先进电池设计开发及安全使用方面的广泛应用。
After more than 20 years of hard working, scientists from CAAS developed a new technology to increase peanut crop production and reduce concentration of aflatoxin at the same time.
In 2022, based on data collected from 40 test sites in 16 provincial regions across the country, peanut fields with their microbial product applied had crop production increase by 19.67% in average. Aflatoxin concentration in the harvested peanuts dropped by more than 60%.
澎湃新闻（www.thepaper.cn）从中国农业科学院油料所获悉，该院李培武院士团队历经20多年持续研究，通过从土壤源头开展黄曲霉毒素阻控和结瘤固氮诱导耦合探索，首创了花生提质固氮减损增产ARC耦合技术。近日举行的示范田现场测产结果显示，通过采用该技术，示范田花生亩产达375.2公斤，相比对照增产20.37%。
黄曲霉毒素是迄今发现毒性最大、致癌力最强的一类真菌毒素，花生、玉米等粮油产品易受黄曲霉毒素污染，威胁产业发展和生命健康，其污染阻控一直是世界性难题。同时，花生属于豆科作物，能够与土壤中根瘤菌共生结瘤固氮，将空气中氮气转化为作物养分氨，但自然状态下花生根瘤数量少，固氮效率差，难以满足高质高效绿色生产需求，如何提高花生结瘤固氮效率同样是世界热点前沿难题。
李培武院士团队历经20多年持续研究，通过从土壤源头开展黄曲霉毒素阻控和结瘤固氮诱导耦合探索，首创了花生提质固氮减损增产ARC耦合技术，发明出ARC微生物菌剂，实现了源头阻控黄曲霉毒素与同步提高生物固氮。2020年至2022年连续3年在全国花生主产区示范应用，普遍出现结瘤时间提前、结瘤数量增多、结瘤及固氮时间延长、固氮酶活性提高，示范点普遍显著增产。
2022年全国农业技术推广服务中心等组织对全国花生主产区16省40个花生示范点现场测产结果：高、中、低产田及盐碱地四类产田普遍显著增产，平均增产19.67%。同时，花生果黄曲霉产毒菌丰度降低60%以上，花生仁黄曲霉毒素污染水平下降了 80%，黄曲霉毒素阻控效果十分显著，应用ARC微生物菌剂试验示范田均表现出显著的提质固氮减损增产效果。
近日在河南正阳县召开的花生提质固氮减损增产ARC耦合技术千亩连片应用现场观摩与交流研讨会上，专家组公布了当日在正阳县兰青乡吴庄村程旭东家庭农场示范田现场测产结果，通过采用该技术，示范田花生亩产达375.2公斤，相比对照增产20.37%。在正阳县黄磊家庭农场2800亩花生连片示范现场考察结果显示，技术示范区花生正处于饱果期，植株叶色浓绿，生长健壮，根系发达，根瘤数量显著增加，果实更饱满。
据种植大户黄磊介绍，应用ARC微生物菌剂后能明显减轻死苗、烂果，还能明显提高花生的品相，花生果变得又多又白又饱满，预计每亩增收200到300多元不成问题。
In 2022, based on data collected from 40 test sites in 16 provincial regions across the country, peanut fields with their microbial product applied had crop production increase by 19.67% in average. Aflatoxin concentration in the harvested peanuts dropped by more than 60%.
澎湃新闻（www.thepaper.cn）从中国农业科学院油料所获悉，该院李培武院士团队历经20多年持续研究，通过从土壤源头开展黄曲霉毒素阻控和结瘤固氮诱导耦合探索，首创了花生提质固氮减损增产ARC耦合技术。近日举行的示范田现场测产结果显示，通过采用该技术，示范田花生亩产达375.2公斤，相比对照增产20.37%。
黄曲霉毒素是迄今发现毒性最大、致癌力最强的一类真菌毒素，花生、玉米等粮油产品易受黄曲霉毒素污染，威胁产业发展和生命健康，其污染阻控一直是世界性难题。同时，花生属于豆科作物，能够与土壤中根瘤菌共生结瘤固氮，将空气中氮气转化为作物养分氨，但自然状态下花生根瘤数量少，固氮效率差，难以满足高质高效绿色生产需求，如何提高花生结瘤固氮效率同样是世界热点前沿难题。
李培武院士团队历经20多年持续研究，通过从土壤源头开展黄曲霉毒素阻控和结瘤固氮诱导耦合探索，首创了花生提质固氮减损增产ARC耦合技术，发明出ARC微生物菌剂，实现了源头阻控黄曲霉毒素与同步提高生物固氮。2020年至2022年连续3年在全国花生主产区示范应用，普遍出现结瘤时间提前、结瘤数量增多、结瘤及固氮时间延长、固氮酶活性提高，示范点普遍显著增产。
2022年全国农业技术推广服务中心等组织对全国花生主产区16省40个花生示范点现场测产结果：高、中、低产田及盐碱地四类产田普遍显著增产，平均增产19.67%。同时，花生果黄曲霉产毒菌丰度降低60%以上，花生仁黄曲霉毒素污染水平下降了 80%，黄曲霉毒素阻控效果十分显著，应用ARC微生物菌剂试验示范田均表现出显著的提质固氮减损增产效果。
近日在河南正阳县召开的花生提质固氮减损增产ARC耦合技术千亩连片应用现场观摩与交流研讨会上，专家组公布了当日在正阳县兰青乡吴庄村程旭东家庭农场示范田现场测产结果，通过采用该技术，示范田花生亩产达375.2公斤，相比对照增产20.37%。在正阳县黄磊家庭农场2800亩花生连片示范现场考察结果显示，技术示范区花生正处于饱果期，植株叶色浓绿，生长健壮，根系发达，根瘤数量显著增加，果实更饱满。
据种植大户黄磊介绍，应用ARC微生物菌剂后能明显减轻死苗、烂果，还能明显提高花生的品相，花生果变得又多又白又饱满，预计每亩增收200到300多元不成问题。
Westlake University created water-based organic flow battery that captures CO2 during charge.
Paper:
News release:
Paper:
A phenazine-based high-capacity and high-stability electrochemical CO2 capture cell with coupled electricity storage
Abstract
Carbon dioxide capture technologies will be important for counteracting difficult-to-abate greenhouse gas emissions if humanity is to limit global warming to acceptable levels. Electrochemically mediated CO2 capture has emerged as a promising alternative to conventional amine scrubbing, offering a potentially cost effective, environmentally friendly and energy efficient approach. Here we report an electrochemical cell for CO2 capture based on pH swing cycles driven through proton-coupled electron transfer of a developed phenazine derivative, 2,2′-(phenazine-1,8-diyl)bis(ethane-1-sulfonate) (1,8-ESP), with high aqueous solubility (>1.35 M) over pH range 0.00–14.90. The system operates with a high capture capacity of 0.86–1.41 mol l−1, a low energetic cost of 36–55 kJ mol−1 and an extremely low capacity fade rate of <0.01% per day, depending on organic concentration. The system charge–discharge cycle provides an electrical energy storage function that could be run only for storage when called for by electricity market conditions.News release:
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Excerpt from wikipedia:
"Light: Science & Applications is a peer-reviewed open-access scientific journal published by Nature Portfolio on behalf of the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences and the Chinese Optical Society."
Established in 2012, this journal, with impact factor of 19.4 by June 2023, has been in the top 3 journals on optics in the world for the past 9 years.
"Light: Science & Applications is a peer-reviewed open-access scientific journal published by Nature Portfolio on behalf of the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences and the Chinese Optical Society."
Established in 2012, this journal, with impact factor of 19.4 by June 2023, has been in the top 3 journals on optics in the world for the past 9 years.
Only if Chinese EVs get tariffed.