The Financing Principles of High Carbon Enterprises Green Development Based on Double Carbon Target in China—Take MYSE as an Example

The “double carbon target” puts forward new requirements for China’s energy structure adjustment. As a leader in offshore wind power equipment, MYSE is an active practitioner of the concept of “carbon neutrality”. The company has established a green growth path and is actively experimenting with various green financial instruments to meet its rapidly growing capital needs. This paper describes in detail the “double carbon goal” to promote the intelligent green development of MYSE, as well as the diversified financing strategy based on green development. Taking the company’s experience of issuing green notes, green bonds, listing on London Stock Exchange and obtaining the “Green economy mark” as examples, this paper analyzes the conditions and strategies of green financing for high-carbon enterprises, and deeply understands the mutual support and promotion relationship between green development and green finance of companies. Deeply understand the positive significance of innovative financing channels and the use of green financing tools for the company’s green development, and provide reference and inspiration for local high-carbon enterprises to actively use green financing tools

Analysis of radial oscillations of gas bubbles in Newtonian or viscoelastic mediums under acoustic excitation

Acoustic cavitation plays an important role in a broad range of biomedical, chemical and oceanic engineering problems. For example, kidney stone can be crushed into the powder (being discharged naturally) by the acoustic cavitation generated by carefully controlled focused ultrasonic beams. Therefore, the prediction of generation of acoustic cavitation is essential to the aforementioned emerging non-invasive technique for kidney stone crushing. The objective of this PhD program is to study the generation of acoustic cavitation (e.g. through rectified mass diffusion across bubble interface) theoretically in the Newtonian fluids (e.g. water) or viscoelastic mediums (e.g. human soft tissue) under acoustic excitation of single or dual frequency. The compressibility and the viscosity of the liquid, heat and mass transfer across bubble-medium interface are all considered in this study. During this PhD program, the established works in the literature on the above topic have been re-examined. More physically general formulas of natural frequency and damping of gas bubble oscillations in Newtonian or viscoelastic mediums has been derived and further employed for solving the problem of bubble growth under acoustic field (i.e. rectified mass diffusion). For rectified mass diffusion of gas bubbles in Newtonian liquids, the predictions have been improved for high-frequency region of megahertz and above. Effects of medium viscoelasticity and dual-frequency acoustic excitation on rectified mass diffusion have also been studied. To facilitate the fast growth of bubble under acoustic field, dynamic-frequency and dual-frequency techniques have been proposed and demonstrated

Development of nanoscale delivery systems for breast cancer treatment

Nanoparticle (NP) assisted diagnosis and drug delivery for antitumor applications have been widely investigated in the past few decades. To date, some of them have been approved for clinical applications and many more of them are under clinical trials. Although some progress has been achieved, it is still necessary to explore novel materials for antitumor applications. The work summarized in this thesis focused on organic NPs, and evaluated engineered polymer NPs and protein-lipid NPs as antitumor drug delivery systems in vitro. And a multifunctional fluorinated NP system was also assessed as theranostic (the combination of therapy and diagnosis) platform. In paper I, two types of 2,2 bis(hydroxymethyl) propionic acid (bis-MPA) based dendritic- linear (DL) polymers were synthesized. One type has the hyperbranched (HB) dendritic structure while the other has dendrons (perfectly branched structures). HBDL and DL materials were compared as drug delivery systems in respect to their synthesis difficulty, quality of micelle formation and efficiency in drug delivery. It was found that HBDL can be synthesized in large scales and drug loaded HBDL tended to have stronger efficacy compared to DL, therefore it is a promising alterative to DL in anticancer drug delivery. Further, in paper II, a detailed study regarding the uptake profile of a bis-MPA based hyperbranched copolymer micelle was conducted. The NP consisted of a Boltorn-H30 core (hyperbranched polyester) and PEG10k hydrophilic tails. It was found that the hyperbranched NP can be internalized into breast cancer cells via clathrin-dependent and macropinocytosis-mediated pathway through a time, concentration and energy dependent process. In paper III, fluorinated copolymers micelles were synthesized and evaluated as theranostic system, which has both diagnostic and therapeutic functions. The consequent micelles were able to load and release doxorubicin (DOX) and demonstrated similar efficacy compared to free (non- formulated) DOX. Also these NPs could generate a detectable signal for 19F-MRI in vitro. In paper IV, unimolecular NPs were developed from polyester based hyperbranched dendritic- linear polymers (HBDLPs). Such micelles were homogenous and did not have critical micelle concentration (CMC). And they were able to load DOX and delivery the drug into breast cancer cells. One HBDLP based NP containing a fluorinated polymer fragment was also synthesized to prove that these unimolecular systems are potentially useful as theranostic platforms. In paper V, histamine functionalized copolymer micelles were developed in order to introduce pH responsive property to NPs and achieve endo-lysosomal escape. These NPs were non-toxic and capable of loading and release DOX. Drug loaded NPs exhibited significant enhanced inhibition of mitochondria function in breast cancer cells during short periods (12 h) compared to free DOX. Although the expected pH responsive behaviour was not observed for the in vitro drug release model, NPs with histamine functionalization demonstrated partly endo-lysosomal escape property, in particular for those with 50% histamine modification. Intracellular tracking of NPs revealed that they could escape from endo-lysosomes and relocate DOX into mitochondria and the nuclei. In paper VI, lipoprotein like NP systems were developed by incorporating Saposin A, phospholipids and selected hydrophobic cargos. Such systems were shown to have promise as drug delivery platforms and to serve as NP based vaccine stabilizers

Characterization of the interaction of EEN and its domains with Ca2+ and proline rich domain

Master'sMASTER OF SCIENC

Integrated Transformers Inference Framework for Multiple Tenants on GPU

In recent years, Transformer models have gained prominence in the deep learning domain, serving as the foundation for a wide array of applications, including Natural Language Processing (NLP) and Computer Vision (CV). These models have become essential for numerous inference tasks, but their implementation often faces challenges related to GPU utilization and system throughput. Typically, current GPU-based inference frameworks treat each model individually, which results in suboptimal resource management and decreased performance. To address these limitations, we introduce ITIF: Integrated Transformers Inference Framework for multiple tenants with a shared backbone. ITIF allows multiple tenants to share a single backbone Transformer model on a single GPU, consolidating operators from various multi-tenant inference models. This approach significantly optimizes GPU utilization and system throughput. Our proposed framework, ITIF, marks a considerable advancement towards enhancing the efficiency of deep learning, particularly for large-scale cloud providers hosting numerous models with a shared backbone. In our experiments, we extensively evaluated the performance of ITIF in comparison with traditional baselines. We conducted tests on a variety of deep learning tasks, including NLP and CV tasks. We found that ITIF consistently outperformed the baselines, with improvements in performance by up to 2.40 times. In conclusion, our research highlights the potential benefits of adopting the ITIF framework for improving the efficiency and scalability of Transformer-based deep learning systems. By enabling multiple tenants to share a single backbone model, ITIF provides an innovative solution to address the challenges faced by large-scale cloud providers in optimizing GPU utilization and system throughput. As such, ITIF presents a promising direction for further research and development in the field of deep learning

Uncovering multifunctional mechano-intelligence in and through phononic metastructures harnessing physical reservoir computing

The recent advances in autonomous systems have prompted a strong demand for the next generation of adaptive structures and materials to possess more built-in intelligence in their mechanical domain, the so-called mechano-intelligence (MI). Previous MI attempts mainly focused on specific designs and case studies to realize limited aspects of MI, and there is a lack of a systematic foundation in constructing and integrating the different elements of intelligence in an effective and efficient manner. Here, we propose a new approach to create the needed foundation in realizing integrated multifunctional MI via a physical reservoir computing (PRC) framework. That is, to concurrently embody computing power and the various elements of intelligence, namely perception, decision-making, and commanding, directly in the mechanical domain, advancing from conventional adaptive structures that rely solely on add-on digital computers and massive electronics to achieve intelligence. As an exemplar platform, we construct a mechanically intelligent phononic metastructure with the integrated elements of MI by harnessing the PRC power hidden in their high-degree-of-freedom nonlinear dynamics. Through analyses and experimental investigations, we uncover multiple adaptive structural functions ranging from self-tuning wave controls to wave-based logic gates. This research will provide the basis for creating future new structures that would greatly surpass the state of the art - such as lower power consumption, more direct interactions, and much better survivability in harsh environment or under cyberattacks. Moreover, it will enable the addition of new functions and autonomy to systems without overburdening the onboard computers.Comment: 15 pages, 4 figure

MegDet: A Large Mini-Batch Object Detector

The improvements in recent CNN-based object detection works, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from new network, new framework, or novel loss design. But mini-batch size, a key factor in the training, has not been well studied. In this paper, we propose a Large MiniBatch Object Detector (MegDet) to enable the training with much larger mini-batch size than before (e.g. from 16 to 256), so that we can effectively utilize multiple GPUs (up to 128 in our experiments) to significantly shorten the training time. Technically, we suggest a learning rate policy and Cross-GPU Batch Normalization, which together allow us to successfully train a large mini-batch detector in much less time (e.g., from 33 hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st place of Detection task

FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction

Predicting the future trajectories of the traffic agents is a gordian technique in autonomous driving. However, trajectory prediction suffers from data imbalance in the prevalent datasets, and the tailed data is often more complicated and safety-critical. In this paper, we focus on dealing with the long-tail phenomenon in trajectory prediction. Previous methods dealing with long-tail data did not take into account the variety of motion patterns in the tailed data. In this paper, we put forward a future enhanced contrastive learning framework to recognize tail trajectory patterns and form a feature space with separate pattern clusters. Furthermore, a distribution aware hyper predictor is brought up to better utilize the shaped feature space. Our method is a model-agnostic framework and can be plugged into many well-known baselines. Experimental results show that our framework outperforms the state-of-the-art long-tail prediction method on tailed samples by 9.5% on ADE and 8.5% on FDE, while maintaining or slightly improving the averaged performance. Our method also surpasses many long-tail techniques on trajectory prediction task.Comment: Accepted for publication at the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (CVPR 2023