Bidirectional AC-DC Converter for Vehicle-to-Grid (V2G) Applications

Electric vehicles are growing at a rapid pace in the internal combustion engine dominated transportation sector, and bring environmental and economic benefits to society. Electric vehicles produce nearly zero carbon emission, provided that they are charged through renewable energy sources. Electric vehicles reduce our dependency on foreign oil and also offer additional benefits like Vehicle-to-grid (V2G). V2G is a technology that allows electric energy stored in the electric vehicle batteries to be returned to the grid during peak demand. V2G can also provide voltage regulation, voltage shaving, reactive power compensation and distributed generation. This necessitates that an electric vehicle battery charger be bi-directional, capable of sinking or sourcing real and reactive power. The state of the art battery charging converter is unidirectional and has multiple stages of power conversion. In this thesis, a single phase, single stage, isolated, bi-directional Silicon Carbide (SiC) AC-DC converter based on Dual Active Bridge (DAB) topology is proposed and analyzed. Direct-quadrature axis (DQ) current control of the DABbased topology is implemented with phase shift modulation. Simulation results are presented with various operating conditions showing the converter’s ability to sink or source real and reactive power in the AC grid. Hardware and firmware implementation of a single phase bi-directional AC-DC converter operating at 100 kHz utilizing Silicon Carbide (SiC) MOSFETs are discussed in detail. Experimental results are shown confirming simulation results. A single phase bi-directional AC-DC converter uses large electrolytic capacitors to filter ripple currents in the DC bus. Electrolytic capacitors are bulky and are prone to failure. These electrolytic capacitors can be eliminated by rejecting the ripple current in the DC bus. The ripple current is rejected by injecting a current of same magnitude and opposite phase to the ripple current. A rigorous analysis is performed on the ripple rejection technique used in single phase bi-directional AC-DC converters. Simulation results are presented to verify the analysis. A three phase bi-directional AC-DC converter improves the charging time of the electric vehicles by charging the batteries at a higher power level. A three phase, single stage, isolated, bi-directional AC-DC converter is analyzed. DQ current control of the three phase AC-DC converter is implemented in simulation to verify the analysis

LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search

Search spaces hallmark the advancement of Neural Architecture Search (NAS). Large and complex search spaces with versatile building operators and structures provide more opportunities to brew promising architectures, yet pose severe challenges on efficient exploration and exploitation. Subsequently, several search space shrinkage methods optimize by selecting a single sub-region that contains some well-performing networks. Small performance and efficiency gains are observed with these methods but such techniques leave room for significantly improved search performance and are ineffective at retaining architectural diversity. We propose LISSNAS, an automated algorithm that shrinks a large space into a diverse, small search space with SOTA search performance. Our approach leverages locality, the relationship between structural and performance similarity, to efficiently extract many pockets of well-performing networks. We showcase our method on an array of search spaces spanning various sizes and datasets. We accentuate the effectiveness of our shrunk spaces when used in one-shot search by achieving the best Top-1 accuracy in two different search spaces. Our method achieves a SOTA Top-1 accuracy of 77.6\% in ImageNet under mobile constraints, best-in-class Kendal-Tau, architectural diversity, and search space size

GNM: A General Navigation Model to Drive Any Robot

Learning provides a powerful tool for vision-based navigation, but the capabilities of learning-based policies are constrained by limited training data. If we could combine data from all available sources, including multiple kinds of robots, we could train more powerful navigation models. In this paper, we study how a general goal-conditioned model for vision-based navigation can be trained on data obtained from many distinct but structurally similar robots, and enable broad generalization across environments and embodiments. We analyze the necessary design decisions for effective data sharing across robots, including the use of temporal context and standardized action spaces, and demonstrate that an omnipolicy trained from heterogeneous datasets outperforms policies trained on any single dataset. We curate 60 hours of navigation trajectories from 6 distinct robots, and deploy the trained GNM on a range of new robots, including an underactuated quadrotor. We find that training on diverse data leads to robustness against degradation in sensing and actuation. Using a pre-trained navigation model with broad generalization capabilities can bootstrap applications on novel robots going forward, and we hope that the GNM represents a step in that direction. For more information on the datasets, code, and videos, please check out http://sites.google.com/view/drive-any-robot

The Dynamic Characteristics of a non-linear main landing gear system of an aircraft during landing

The landing gear plays a very important role during landing by absorbing the high impact energy of the aircraft. The main landing gear absorbs the bulk of the load to reduce the load experienced by both the aircraft fuselage and the nose landing gear. In this paper, a mathematical approach is used to extract the dynamic characteristics of the system. A two-degree of freedom mathematical model of the main landing gear is developed. This model is used to derive the dynamic equations of the landing gear system and to study the behaviour of main landing gear during main gear and nose gear touchdown conditions. The non-linear stiffness and damping co-efficient in an Oleo-Pneumatic shock absorber are integrated into the system to achieve a more accurate response of the system. The response of this system is established by adopting a complex modal analysis approach to account for the non-classical damping exhibited by the system. The obtained spring force, damping force and responses are reported. This work provides an alternative approach using complex modal analysis to obtain results for complex systems exhibiting non-linear characteristics

Virtual classroom proficiency-based progression for robotic surgery training (VROBOT): a randomised, prospective, cross-over, effectiveness study

Robotic surgery training has lacked evidence-based standardisation. We aimed to determine the effectiveness of adjunctive interactive virtual classroom training (VCT) in concordance with the self-directed Fundamentals of Robotic Surgery (FRS) curriculum. The virtual classroom is comprised of a studio with multiple audio-visual inputs to which participants can connect remotely via the BARCO weConnect platform. Eleven novice surgical trainees were randomly allocated to two training groups (A and B). In week 1, both groups completed a robotic skills induction. In week 2, Group A received training with the FRS curriculum and adjunctive VCT; Group B only received access to the FRS curriculum. In week 3, the groups received the alternate intervention. The primary outcome was measured using the validated robotic-objective structured assessment of technical skills (R-OSAT) at the end of week 2 (time-point 1) and 3 (time-point 2). All participants completed the training curriculum and were included in the final analyses. At time-point 1, Group A achieved a statistically significant greater mean proficiency score compared to Group B (44.80 vs 35.33 points, p = 0.006). At time-point 2, there was no significant difference in mean proficiency score in Group A from time-point 1. In contrast, Group B, who received further adjunctive VCT showed significant improvement in mean proficiency by 9.67 points from time-point 1 (95% CI 5.18-14.15, p = 0.003). VCT is an effective, accessible training adjunct to self-directed robotic skills training. With the steep learning curve in robotic surgery training, VCT offers interactive, expert-led learning and can increase training effectiveness and accessibility

LncRNA VEAL2 regulates PRKCB2 to modulate endothelial permeability in diabetic retinopathy

Long non‐coding RNAs (lncRNAs) are emerging as key regulators of endothelial cell function. Here, we investigated the role of a novel vascular endothelial‐associated lncRNA (VEAL2) in regulating endothelial permeability. Precise editing of veal2 loci in zebrafish (veal2 (gib005Δ8/+)) induced cranial hemorrhage. In vitro and in vivo studies revealed that veal2 competes with diacylglycerol for interaction with protein kinase C beta‐b (Prkcbb) and regulates its kinase activity. Using PRKCB2 as bait, we identified functional ortholog of veal2 in humans from HUVECs and named it as VEAL2. Overexpression and knockdown of VEAL2 affected tubulogenesis and permeability in HUVECs. VEAL2 was differentially expressed in choroid tissue in eye and blood from patients with diabetic retinopathy, a disease where PRKCB2 is known to be hyperactivated. Further, VEAL2 could rescue the effects of PRKCB2‐mediated turnover of endothelial junctional proteins thus reducing hyperpermeability in hyperglycemic HUVEC model of diabetic retinopathy. Based on evidence from zebrafish and hyperglycemic HUVEC models and diabetic retinopathy patients, we report a hitherto unknown VEAL2 lncRNA‐mediated regulation of PRKCB2, for modulating junctional dynamics and maintenance of endothelial permeability

New recommendations to reduce unnecessary blood tests after robot-assisted radical prostatectomy.

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The evolution of lung cancer and impact of subclonal selection in TRACERx

Lung cancer is the leading cause of cancer-associated mortality worldwide. Here we analysed 1,644 tumour regions sampled at surgery or during follow-up from the first 421 patients with non-small cell lung cancer prospectively enrolled into the TRACERx study. This project aims to decipher lung cancer evolution and address the primary study endpoint: determining the relationship between intratumour heterogeneity and clinical outcome. In lung adenocarcinoma, mutations in 22 out of 40 common cancer genes were under significant subclonal selection, including classical tumour initiators such as TP53 and KRAS. We defined evolutionary dependencies between drivers, mutational processes and whole genome doubling (WGD) events. Despite patients having a history of smoking, 8% of lung adenocarcinomas lacked evidence of tobacco-induced mutagenesis. These tumours also had similar detection rates for EGFR mutations and for RET, ROS1, ALK and MET oncogenic isoforms compared with tumours in never-smokers, which suggests that they have a similar aetiology and pathogenesis. Large subclonal expansions were associated with positive subclonal selection. Patients with tumours harbouring recent subclonal expansions, on the terminus of a phylogenetic branch, had significantly shorter disease-free survival. Subclonal WGD was detected in 19% of tumours, and 10% of tumours harboured multiple subclonal WGDs in parallel. Subclonal, but not truncal, WGD was associated with shorter disease-free survival. Copy number heterogeneity was associated with extrathoracic relapse within 1 year after surgery. These data demonstrate the importance of clonal expansion, WGD and copy number instability in determining the timing and patterns of relapse in non-small cell lung cancer and provide a comprehensive clinical cancer evolutionary data resource

The evolution of non-small cell lung cancer metastases in TRACERx

Metastatic disease is responsible for the majority of cancer-related deaths. We report the longitudinal evolutionary analysis of 126 non-small cell lung cancer (NSCLC) tumours from 421 prospectively recruited patients in TRACERx who developed metastatic disease, compared with a control cohort of 144 non-metastatic tumours. In 25% of cases, metastases diverged early, before the last clonal sweep in the primary tumour, and early divergence was enriched for patients who were smokers at the time of initial diagnosis. Simulations suggested that early metastatic divergence more frequently occurred at smaller tumour diameters (less than 8 mm). Single-region primary tumour sampling resulted in 83% of late divergence cases being misclassified as early, highlighting the importance of extensive primary tumour sampling. Polyclonal dissemination, which was associated with extrathoracic disease recurrence, was found in 32% of cases. Primary lymph node disease contributed to metastatic relapse in less than 20% of cases, representing a hallmark of metastatic potential rather than a route to subsequent recurrences/disease progression. Metastasis-seeding subclones exhibited subclonal expansions within primary tumours, probably reflecting positive selection. Our findings highlight the importance of selection in metastatic clone evolution within untreated primary tumours, the distinction between monoclonal versus polyclonal seeding in dictating site of recurrence, the limitations of current radiological screening approaches for early diverging tumours and the need to develop strategies to target metastasis-seeding subclones before relapse