Advanced methods for offshore windfarm planning

There have been increasing interests and projects of Offshore wind farm (OWF) development across the world given the rich wind resources in order to achieve carbon neutral objectives. Appropriate electrical system design of OWF is of key importance in terms of cost saving and improving system efficiency. Two novel electric system layout optimization models for OWF planning are proposed to optimize the topology of collector system and connected transmission system simultaneously in OWFs with single and multiple substations. For OWF with single-substation, a novel mathematical model to represent the system topology is proposed to reduce the number of variables so as to effectively decrease the search space of the optimisation problem, where the continuous substation sitting problem is discretized by a 2-step rasterization method. For large-scale OWFs, the overall electric system optimization problem has been classified into 3 levels: substation optimization, feeder selection, and cable determination. Fuzzy clustering technique and wind turbine allocation method has been proposed to effectively divide the large offshore windfarms into partitions. Both HVDC and HVAC cables are considered as alternatives used in the associated transmission system, which can be optimized at the substation level. The concept of clustering is further applied in feeder level to cluster wind turbines into appropriate feeders. The proposed model and the optimization algorithms are tested and validated in two large-scale offshore winds. A comprehensive decision support model is proposed which covers three key factors that characterize OWF integration: investment cost, system stability and the interactions between MTDC and local AC system, all of which are concerned to characterize the optimal integration location of wind turbines into AC bus location and appropriate converter size installed at the corresponding MTDC terminals. To better fit into the real-world situation, various wind speed and load scenarios have been considered. Validity and effectiveness of the proposed model has been tested to integrate two wind farms to a benchmark AC system via a MTDC grid. The research methodologies presented in the thesis form a rather comprehensive approach for OWF design and planning. With the rapid development in OWF technologies, future research needs are also identified and presented in the thesis

DAMO-YOLO : A Report on Real-Time Object Detection Design

In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO, which achieves higher performance than the state-of-the-art YOLO series. DAMO-YOLO is extended from YOLO with some new technologies, including Neural Architecture Search (NAS), efficient Reparameterized Generalized-FPN (RepGFPN), a lightweight head with AlignedOTA label assignment, and distillation enhancement. In particular, we use MAE-NAS, a method guided by the principle of maximum entropy, to search our detection backbone under the constraints of low latency and high performance, producing ResNet/CSP-like structures with spatial pyramid pooling and focus modules. In the design of necks and heads, we follow the rule of ``large neck, small head''.We import Generalized-FPN with accelerated queen-fusion to build the detector neck and upgrade its CSPNet with efficient layer aggregation networks (ELAN) and reparameterization. Then we investigate how detector head size affects detection performance and find that a heavy neck with only one task projection layer would yield better results.In addition, AlignedOTA is proposed to solve the misalignment problem in label assignment. And a distillation schema is introduced to improve performance to a higher level. Based on these new techs, we build a suite of models at various scales to meet the needs of different scenarios. For general industry requirements, we propose DAMO-YOLO-T/S/M/L. They can achieve 43.6/47.7/50.2/51.9 mAPs on COCO with the latency of 2.78/3.83/5.62/7.95 ms on T4 GPUs respectively. Additionally, for edge devices with limited computing power, we have also proposed DAMO-YOLO-Ns/Nm/Nl lightweight models. They can achieve 32.3/38.2/40.5 mAPs on COCO with the latency of 4.08/5.05/6.69 ms on X86-CPU. Our proposed general and lightweight models have outperformed other YOLO series models in their respective application scenarios.Comment: Project Website: https://github.com/tinyvision/damo-yol

On Maximal Distance Energy

Let G be a graph of order n. If the maximal connected subgraph of G has no cut vertex then it is called a block. If each block of graph G is a clique then G is called clique tree. The distance energy ED(G) of graph G is the sum of the absolute values of the eigenvalues of the distance matrix D(G). In this paper, we study the properties on the eigencomponents corresponding to the distance spectral radius of some special class of clique trees. Using this result we characterize a graph which gives the maximum distance spectral radius among all clique trees of order n with k cliques. From this result, we confirm a conjecture on the maximum distance energy, which was given in Lin et al. Linear Algebra Appl 467(2015) 29-39

DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network

The rapid advances in Vision Transformer (ViT) refresh the state-of-the-art performances in various vision tasks, overshadowing the conventional CNN-based models. This ignites a few recent striking-back research in the CNN world showing that pure CNN models can achieve as good performance as ViT models when carefully tuned. While encouraging, designing such high-performance CNN models is challenging, requiring non-trivial prior knowledge of network design. To this end, a novel framework termed Mathematical Architecture Design for Deep CNN (DeepMAD) is proposed to design high-performance CNN models in a principled way. In DeepMAD, a CNN network is modeled as an information processing system whose expressiveness and effectiveness can be analytically formulated by their structural parameters. Then a constrained mathematical programming (MP) problem is proposed to optimize these structural parameters. The MP problem can be easily solved by off-the-shelf MP solvers on CPUs with a small memory footprint. In addition, DeepMAD is a pure mathematical framework: no GPU or training data is required during network design. The superiority of DeepMAD is validated on multiple large-scale computer vision benchmark datasets. Notably on ImageNet-1k, only using conventional convolutional layers, DeepMAD achieves 0.7% and 1.5% higher top-1 accuracy than ConvNeXt and Swin on Tiny level, and 0.8% and 0.9% higher on Small level.Comment: Accepted by CVPR 202

Spine Patient Optimal Radiosurgery Treatment for Symptomatic Metastatic Neoplasms (SPORTSMEN): a randomized phase II study protocol

Background: Approximately 40% of patients with metastatic cancer will have spinal metastatic disease. Historically treated with external beam radiation therapy (EBRT) with limited durability in pain control, the increased lifespan of this patient population has necessitated more durable treatment results via spine radiosurgery/stereotactic body radiation therapy (SBRT). The goal of this study is to assess three-month pain freedom rates via the Spine Patient Optimal Radiosurgery Treatment for Symptomatic Metastatic Neoplasms (SPORTSMEN) randomized trial. Materials and methods: This study is a prospective randomized three-arm phase II trial which will recruit patients with symptomatic spine metastases. All patients will be randomized to standard-of care SBRT (24 Gy in 2 fractions), single-fraction SBRT (19 Gy in 1 fraction), or EBRT (8 Gy in 1 fraction), with the primary endpoint of three-month pain freedom (using the Brief Pain Inventory). We expect that SPORTSMEN will help definitively answer the efficacy of spine SBRT versus EBRT for achieving pain freedom, while defining the safety and efficacy of 19 Gy single-fraction spine SBRT. Local control will be defined according to Spine Response Assessment in Neuro-Oncology (SPINO) criteria. Discussion: This is the first phase II trial to objectively assess optimal spine SBRT dosing in the treatment of symptomatic spine metastatic disease, while assessing spine SBRT versus EBRT. Findings should allow for better determination of the efficacy of two-fraction spine SBRT versus EBRT in the United States, as well as for the novel single-fraction 19 Gy spine SBRT regimen in patients with symptomatic spine metastases. Trial Registration: Clinicaltrials.gov identifier: NCT05617716 (registration date: November 14, 2022)

Reversible Interlayer Sliding and Conductivity Changes in Adaptive Tetrathiafulvalene-Based Covalent Organic Frameworks.

Ordered interlayer stacking is intrinsic in two-dimensional covalent organic frameworks (2D COFs) and has strong implications on COF's optoelectronic properties. Reversible interlayer sliding, corresponding to shearing of 2D layers along their basal plane, is an appealing dynamic control of both structures and properties, yet it remains unexplored in the 2D COF field. Herein, we demonstrate that the reversible interlayer sliding can be realized in an imine-linked tetrathiafulvalene (TTF)-based COF TTF-DMTA. The solvent treatment induces crystalline phase changes between the proposed staircase-like sql net structure and a slightly slipped eclipsed sql net structure. The solvation-induced crystallinity changes correlate well with reversible spectroscopic and electrical conductivity changes as demonstrated in oriented COF thin films. In contrast, no reversible switching is observed in a related TTF-TA COF, which differs from TTF-DMTA in terms of the absence of methoxy groups on the phenylene linkers. This work represents the first 2D COF example of which eclipsed and staircase-like aggregated states are interchangeably accessed via interlayer sliding, an uncharted structural feature that may enable applications such as chemiresistive sensors

Sequencing of Androgen-Deprivation Therapy of Short Duration With Radiotherapy for Nonmetastatic Prostate Cancer (SANDSTORM): A Pooled Analysis of 12 Randomized Trials

Càncer de pròstata; Teràpia de privació d'andrògensCáncer de próstata; Terapia de privación de andrógenosProstate cancer; Androgen-deprivation therapyPURPOSE The sequencing of androgen-deprivation therapy (ADT) with radiotherapy (RT) may affect outcomes for prostate cancer in an RT-field size-dependent manner. Herein, we investigate the impact of ADT sequencing for men receiving ADT with prostate-only RT (PORT) or whole-pelvis RT (WPRT). MATERIALS AND METHODS Individual patient data from 12 randomized trials that included patients receiving neoadjuvant/concurrent or concurrent/adjuvant short-term ADT (4-6 months) with RT for localized disease were obtained from the Meta-Analysis of Randomized trials in Cancer of the Prostate consortium. Inverse probability of treatment weighting (IPTW) was performed with propensity scores derived from age, initial prostate-specific antigen, Gleason score, T stage, RT dose, and mid-trial enrollment year. Metastasis-free survival (primary end point) and overall survival (OS) were assessed by IPTW-adjusted Cox regression models, analyzed independently for men receiving PORT versus WPRT. IPTW-adjusted Fine and Gray competing risk models were built to evaluate distant metastasis (DM) and prostate cancer–specific mortality. RESULTS Overall, 7,409 patients were included (6,325 neoadjuvant/concurrent and 1,084 concurrent/adjuvant) with a median follow-up of 10.2 years (interquartile range, 7.2-14.9 years). A significant interaction between ADT sequencing and RT field size was observed for all end points (P interaction < .02 for all) except OS. With PORT (n = 4,355), compared with neoadjuvant/concurrent ADT, concurrent/adjuvant ADT was associated with improved metastasis-free survival (10-year benefit 8.0%, hazard ratio [HR], 0.65; 95% CI, 0.54 to 0.79; P < .0001), DM (subdistribution HR, 0.52; 95% CI, 0.33 to 0.82; P = .0046), prostate cancer–specific mortality (subdistribution HR, 0.30; 95% CI, 0.16 to 0.54; P < .0001), and OS (HR, 0.69; 95% CI, 0.57 to 0.83; P = .0001). However, in patients receiving WPRT (n = 3,049), no significant difference in any end point was observed in regard to ADT sequencing except for worse DM (HR, 1.57; 95% CI, 1.20 to 2.05; P = .0009) with concurrent/adjuvant ADT. CONCLUSION ADT sequencing exhibits a significant impact on clinical outcomes with a significant interaction with field size. Concurrent/adjuvant ADT should be the standard of care where short-term ADT is indicated in combination with PORT.Funding support for this study comes from the Prostate Cancer Foundation and ASTRO to AUK. AUK also thanks generous donations from the DeSilva, McCarrick, and Bershad families. A.T. acknowledges support from Cancer Research UK (C33589/A28284 and C7224/A28724) the National Institute for Health Research (NIHR) Cancer Research Network. This project represents independent research supported by the National Institute for Health research (NIHR) Biomedical Research Centre at The Royal Marsden NHS Foundation Trust and the Institute of Cancer Research, London. N.G.Z. is supported by the American Cancer Society – Tri State CEOs Against Cancer Clinician Scientist Development Grant, CSDG‐20‐013‐01‐CCE (2020)

Individualized outcome prognostication for patients with laryngeal cancer

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142424/1/cncr31087.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142424/2/cncr31087_am.pd