Experimental investigation of gasoline – Dimethyl Ether dual fuel CAI combustion with internal EGR

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new dual fuel Controlled Auto-Ignition (CAI) combustion concept was proposed and researched for lower exhaust emissions and better fuel economy. The concept takes the advantage of the complementary physical and chemical properties of high octane number gasoline and high cetane number Di-Methyl Ether (DME) to organize the combustion process. Homogeneous gasoline/air mixture is utilized as the main combustible charge, which is realised by a low-cost Port Fuel Injection (PFI) system. Pressurised DME is directly injected into cylinder via a commercial Gasoline Direct Injection (GDI) injector. Flexible DME injection strategies are employed to realise the controlled auto ignition of the premixed charge. The engine is operated at Wide Open Throttle (WOT) in the entire operating region in order to minimize the intake pumping loss. Engine load is controlled by varing the amount of internal Exhaust Gas Recirculation (iEGR) which is achieved and adjusted by Positive Valve Overlap (PVO) and/or exhaust back pressure, and exhaust rebreathing method. The premixed mixture can be of either stoichiometric air/fuel ratio or fuel lean mixture and is heated and diluted by recycled exhaust gases. The use of internal EGR is considered as a very effective method to initiate CAI combustion due to its heating effect and moderation of the heat release rate by its dilution effect. In addition, the new combustion concept is compared to conventional SI combustion. The results indicate that the new combustion concept has potential for high efficiency, low emissions, enlargement of the engine operational region and flexible control of CAI combustion

Spectral Ranking and Unsupervised Feature Selection for Point, Collective and Contextual Anomaly Detection

Anomaly detection problems can be classified into three categories: point anomaly detection, collective anomaly detection and contextual anomaly detection. Many algorithms have been devised to address anomaly detection of a specific type from various application domains. Nevertheless, the exact type of anomalies to be detected in practice is generally unknown under unsupervised setting, and most of the methods exist in literature usually favor one kind of anomalies over the others. Applying an algorithm with an incorrect assumption is unlikely to produce reasonable results. This thesis thereby investigates the possibility of applying a uniform approach that can automatically discover different kinds of anomalies. Specifically, we are primarily interested in Spectral Ranking for Anomalies (SRA) for its potential in detecting point anomalies and collective anomalies simultaneously. We show that the spectral optimization in SRA can be viewed as a relaxation of an unsupervised SVM problem under some assumptions. SRA thereby results in a bi-class classification strength measure that can be used to rank the point anomalies, along with a normal vs. abnormal classification for identifying collective anomalies. However, in dealing with contextual anomaly problems with different contexts defined by different feature subsets, SRA and other popular methods are still not sufficient on their own. Accordingly, we propose an unsupervised backward elimination feature selection algorithm BAHSIC-AD, utilizing Hilbert-Schmidt Independence Critirion (HSIC) in identifying the data instances present as anomalies in the subset of features that have strong dependence with each other. Finally, we demonstrate the effectiveness of SRA combined with BAHSIC-AD by comparing their performance with other popular anomaly detection methods on a few benchmarks, including both synthetic datasets and real world datasets. Our computational results jusitify that, in practice, SRA combined with BAHSIC-AD can be a generally applicable method for detecting different kinds of anomalies

Increasing climate change changes household medical expenditures

Climate change is exacerbating global disease risks, which will change household medical expenditures. Employing machine learning techniques and fine-scale bank transaction data, this study explores the changing household medical expenditures in 290 Chinese cities under four SSP scenarios (SSP1-2.6、SSP2-4.5、SSP3-7.0、SSP5-8.5) and further evaluates the adaptive impacts from socio-economic and physiological adaptations. The results show that the increasing temperature is projected to decrease future medical expenses in China by 5.24% (SSP1-2.6) to 5.60% (SSP5-8.5) in 2060. Cities exhibit differentiated sensitivity to increasing temperatures. Richer cities have enhanced resilience to high temperatures, and cold regions demonstrate less vulnerability to extreme cold weather. Physiological adaptation to climate change can significantly reduce medical expenditures by 27.6% by 2060. Meanwhile, socio-economic adaptation is expected to amplify national total medical expenses by 22.5% in 2060 under the SSP5-8.5 scenario. Our study incorporates adaptation into the prediction of future medical expenditures in China, aiming to assist cities in devising tailored climate adaptation strategies to alleviate the household economic strain induced by climate change

Synthesizing Physically Plausible Human Motions in 3D Scenes

Synthesizing physically plausible human motions in 3D scenes is a challenging problem. Kinematics-based methods cannot avoid inherent artifacts (e.g., penetration and foot skating) due to the lack of physical constraints. Meanwhile, existing physics-based methods cannot generalize to multi-object scenarios since the policy trained with reinforcement learning has limited modeling capacity. In this work, we present a framework that enables physically simulated characters to perform long-term interaction tasks in diverse, cluttered, and unseen scenes. The key idea is to decompose human-scene interactions into two fundamental processes, Interacting and Navigating, which motivates us to construct two reusable Controller, i.e., InterCon and NavCon. Specifically, InterCon contains two complementary policies that enable characters to enter and leave the interacting state (e.g., sitting on a chair and getting up). To generate interaction with objects at different places, we further design NavCon, a trajectory following policy, to keep characters' locomotion in the free space of 3D scenes. Benefiting from the divide and conquer strategy, we can train the policies in simple environments and generalize to complex multi-object scenes. Experimental results demonstrate that our framework can synthesize physically plausible long-term human motions in complex 3D scenes. Code will be publicly released at https://github.com/liangpan99/InterScene

One-shot Implicit Animatable Avatars with Model-based Priors

Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large-scale specific 3D human datasets such that reconstruction can be performed with sparse-view inputs. Most of these methods fail to achieve realistic reconstruction when only a single image is available. To enable the data-efficient creation of realistic animatable 3D humans, we propose ELICIT, a novel method for learning human-specific neural radiance fields from a single image. Inspired by the fact that humans can effortlessly estimate the body geometry and imagine full-body clothing from a single image, we leverage two priors in ELICIT: 3D geometry prior and visual semantic prior. Specifically, ELICIT utilizes the 3D body shape geometry prior from a skinned vertex-based template model (i.e., SMPL) and implements the visual clothing semantic prior with the CLIP-based pretrained models. Both priors are used to jointly guide the optimization for creating plausible content in the invisible areas. Taking advantage of the CLIP models, ELICIT can use text descriptions to generate text-conditioned unseen regions. In order to further improve visual details, we propose a segmentation-based sampling strategy that locally refines different parts of the avatar. Comprehensive evaluations on multiple popular benchmarks, including ZJU-MoCAP, Human3.6M, and DeepFashion, show that ELICIT has outperformed strong baseline methods of avatar creation when only a single image is available. The code is public for research purposes at https://huangyangyi.github.io/ELICIT/.Comment: To appear at ICCV 2023. Project website: https://huangyangyi.github.io/ELICIT

Demethyleneberberine alleviated the inflammatory response by targeting MD-2 to inhibit the TLR4 signaling

IntroductionThe colitis induced by trinitrobenzenesulfonic acid (TNBS) is a chronic and systemic inflammatory disease that leads to intestinal barrier dysfunction and autoimmunedisorders. However, the existing treatments of colitis are associated with poor outcomes, and the current strategies remain deep and long-time remission and the prevention of complications. Recently, demethyleneberberine (DMB) has been reported to be a potential candidate for the treatment of inflammatory response that relied on multiple pharmacological activities, including anti-oxidation and antiinflammation. However, the target and potential mechanism of DMB in inflammatory response have not been fully elucidated.MethodsThis study employed a TNBS-induced colitis model and acute sepsis mice to screen and identify the potential targets and molecular mechanisms of DMB in vitro and in vivo. The purity and structure of DMB were quantitatively analyzed by high-performance liquid chromatography (HPLC), mass spectrometry (MS), Hydrogen nuclear magnetic resonance spectroscopy (1H-NMR), and infrared spectroscopy (IR), respectively. The rats were induced by a rubber hose inserted approximately 8 cm through their anus to be injected with TNBS. Acute sepsis was induced by injection with LPS via the tail vein for 60 h. These animals with inflammation were orally administrated with DMB, berberine (BBR), or curcumin (Curc), respectively. The eukaryotic and prokaryotic expression system of myeloid differentiation protein-2 (MD-2) and its mutants were used to evaluate the target of DMB in inflammatory response.ReslutsDMB had two free phenolic hydroxyl groups, and the purity exceeded 99% in HPLC. DMB alleviated colitis and suppressed the activation of TLR4 signaling in TNBS-induced colitis rats and LPS-induced RAW264.7 cells. DMB significantly blocked TLR4 signaling in both an MyD88-dependent and an MyD88-independent manner by embedding into the hydrophobic pocket of the MD-2 protein with non-covalent bonding to phenylalanine at position 76 in a pi–pi T-shaped interaction. DMB rescued mice from sepsis shock induced by LPS through targeting the TLR4–MD-2 complex.ConclusionTaken together, DMB is a promising inhibitor of the MD-2 protein to suppress the hyperactivated TLR4 signaling in inflammatory response