Synthesis and characterization of atmospheric pressure chemically vapor deposited aluminum

This study investigates the use of atmospheric pressure chemical vapor deposition (APCVD) to produce high quality aluminum coatings for corrosion protection of steel. The coatings were produced through thermal decomposition of tri-isobutyl-aluminum (TIBAL) over the 275 to 300°C temperature range. Under optimal deposition conditions, growth rates as high as 1.2 um/min were achieved. X-ray photoelectron spectroscopy, auger electron spectroscopy, glow discharge optical emission spectroscopy and nuclear reaction analysis revealed that the coatings consisted essentially of pure aluminum (~99 at.%) with oxygen and carbon present as minor constituents. The coatings were characterized in terms of their morphological, structural, electrical, and mechanical properties, and corrosion performance. The coatings were found to be continuous with a rough surface topography typical of CVD metal deposits. The Al coatings showed x-ray diffraction patterns that were similar to the typical polycrystalline aluminum powder pattern regardless of deposition conditions. Cross-sectional SEM micrographs confirmed that the APCVD process can offer excellent step coverage and throwing power. Corrosion testing revealed that APCVD Al coatings exhibit excellent corrosion resistance. With such correlations, this study offers an environmentally benign alternative to cadmium plating, as well as promises to provide high production throughput, low cost, and coatings with desirable properties and performance

No-frills Temporal Video Grounding: Multi-Scale Neighboring Attention and Zoom-in Boundary Detection

Temporal video grounding (TVG) aims to retrieve the time interval of a language query from an untrimmed video. A significant challenge in TVG is the low "Semantic Noise Ratio (SNR)", which results in worse performance with lower SNR. Prior works have addressed this challenge using sophisticated techniques. In this paper, we propose a no-frills TVG model that consists of two core modules, namely multi-scale neighboring attention and zoom-in boundary detection. The multi-scale neighboring attention restricts each video token to only aggregate visual contexts from its neighbor, enabling the extraction of the most distinguishing information with multi-scale feature hierarchies from high-ratio noises. The zoom-in boundary detection then focuses on local-wise discrimination of the selected top candidates for fine-grained grounding adjustment. With an end-to-end training strategy, our model achieves competitive performance on different TVG benchmarks, while also having the advantage of faster inference speed and lighter model parameters, thanks to its lightweight architecture

Multi-objective optimal longitudinal flight control system design for a large flexible transport aircraft.

This thesis presents a multi-objective evolutionary algorithm design of a longitudinal optimal controller for a large exible transport aircraft. The algorithm uses a mixed optimization approach based on a combination of Linear Quadratic Regulator(LQR) control and a Multi-Objective Genetic Algorithm (MOGA) to search over a set of possible weighting function structures and parameter values in order to satisfy a number of conflicting design criteria. The proposed approach offers a number of potential optimal solutions lying on or near the Pareto optimal front of competing objectives. The approach is explained in this thesis and some results are presented.PhD in Aerospac

The reactions of ruthenium (ii) polypyridyl complexes

Ruthenium (II) polypyridine complexes in general have been extensively studied because of their unique redox and photochemical properties. A typical example of such complexes is tris(2,2’-bipyridyl) ruthenium (II). In this study, this complex was synthesized and then characterized using electronic spectroscopy and cyclic voltammetry. It was also shown that the ruthenium concentration could be accurately determined using ICP-MS. It was found that the complex is very stable in various chemical environments. It was observed from spectrophotometric investigations that persulphate and lead dioxide easily oxidize Ru(bpy)3 2+ to Ru(bpy)3 3+ in the presence of heat and H2SO4, respectively. It was also observed that the oxidation between Ru(bpy)3 2+ and cerium (IV) occurred at approximately 3:2 [Ce(IV)]/[Ru(II)] mole ratio. The resultant Ru(bpy)3 3+ solution was unstable in the presence of light and recovery of Ru(bpy)3 2+ occurred gradually. The regeneration of Ru(bpy)3 2+ from Ru(bpy)3 3+ was found to be a multistep process, which appears to involve the formation of an intermediate species. The following reaction model was found to best explain the kinetic data obtained: Ru(bpy)3 2+ + Ce(IV) → Ru(bpy)3 3+ Ru(bpy)3 3+ → Ru(bpy)3 2+ Ru(bpy)3 3+ → Ru* intermediate Ru* intermediate → Ru(bpy)3 2+ Theoretical rate constants were also calculated for the same process under the experimental conditions. The comparison between the experimental and theoretical results gave good agreement. In addition, the factors that influence the rate of the regeneration of Ru(bpy)3 2+ from Ru(bpy)3 3+ were also discussed

Human-Object Interaction Detection:A Quick Survey and Examination of Methods

Human-object interaction detection is a relatively new task in the world of computer vision and visual semantic information extraction. With the goal of machines identifying interactions that humans perform on objects, there are many real-world use cases for the research in this field. To our knowledge, this is the first general survey of the state-of-the-art and milestone works in this field. We provide a basic survey of the developments in the field of human-object interaction detection. Many works in this field use multi-stream convolutional neural network architectures, which combine features from multiple sources in the input image. Most commonly these are the humans and objects in question, as well as the spatial quality of the two. As far as we are aware, there have not been in-depth studies performed that look into the performance of each component individually. In order to provide insight to future researchers, we perform an individualized study that examines the performance of each component of a multi-stream convolutional neural network architecture for human-object interaction detection. Specifically, we examine the HORCNN architecture as it is a foundational work in the field. In addition, we provide an in-depth look at the HICO-DET dataset, a popular benchmark in the field of human-object interaction detection. Code and papers can be found at https://github.com/SHI-Labs/Human-Object-Interaction-Detection.Comment: Published at The 1st International Workshop On Human-Centric Multimedia Analysis, at ACM Multimedia Conference 202

A review of the pre-chamber ignition system applied on future low-carbon spark ignition engines

Legislations for greenhouse gas and pollutant emissions from light-duty vehicles are pushing the spark ignition engine to be cleaner and more efficient. As one of the promising solutions, enhancing the ignition energy shows great potential in simultaneously mitigating combustion knock and enabling lean-burn operation. Featured with distributed ignition sites, pre-chamber ignition systems with large or small pre-chamber volumes, auxiliary or no auxiliary fueling, and large or small orifices have gained a surge of interest in decreasing the fuel consumption and pollutant emissions. This paper aims at presenting a comprehensive review of recent progress and development trends of pre-chamber ignition systems adopted on future low-carbon and low-emission spark ignition engines. First, mechanisms behind this technology are discussed from the perspectives of the pre-chamber scavenging and combustion, jet ejection, main chamber combustion, and emission formations. Second, the design criteria of pre-chamber geometries are presented in detail, followed by a discussion on the fuel and air management for the main chamber. Next, recent numerical and experimental studies on the pre-chamber ignition system and its applications in conjunction with other complementary technologies are summarized. Finally, critical issues for commercialization and future research directions are discussed.</p

Multi-objective optimal longitudinal flight control system design for large flexible transport aircraft

This paper presents a multi-objective evolutionary algorithm design of a longitudinal optimal controller for a large flexible transport aircraft. The algorithm uses a mixed optimization approach based on a combination of Linear Quadratic Regulator (LQR) control and a Multi-Objective Genetic Algorithm (MOGA) to search over a set of possible weighting function structures and parameter values in order to satisfy a number of conflicting design criteria. The proposed approach offers a number of potential optimal solutions lying on or near the Pareto optimal front of competing objectives. The approach is explained in this paper and some results are presented

Socio-Economic Predictors and Distribution of Tuberculosis Incidence in Beijing, China: A StudyUsing a Combination of Spatial Statistics and GIS Technology

Evidence shows that multiple factors, such as socio-economic status and access to health care facilities, affect tuberculosis (TB) incidence. However, there is limited literature available with respect to the correlation between socio-economic/health facility factors and tuberculosis incidence. This study aimed to explore the relationship between TB incidence and socio-economic/health service predictors in the study settings. A retrospective spatial regression analysis was carried out based on new sputum smear-positive pulmonary TB cases in Beijing districts. Global Moran’s I analysis was adopted to detect the spatial dependency followed by spatial regression models (spatial lag model, and spatial error model) along with the ordinary least square model were applied to examine the correlation between TB incidence and predictors. A high incidence of TB was seen in densely populated districts in Beijing, e.g., Haidian, Mentougou, and Xicheng. After comparing the R2, log-likelihood, and Akaike information criterion (AIC) values among three models, the spatial error model (R2 = 0.413; Log Likelihood = −591; AIC = 1199.76) identified the best model fit for the spatial regression model. The study showed that the number of beds in health institutes (p \u3c 0.001) and per capita gross domestic product (GDP) (p = 0.025) had a positive effect on TB incidence, whereas population density (p \u3c 0.001) and migrated population (p \u3c 0.001) had an adverse impact on TB incidence in the study settings. High TB incidence districts were detected in urban and densely populated districts in Beijing. Our findings suggested that socio-economic predictors influence TB incidence. These findings may help to guide TB control programs and promote targeted intervention

LLaMA Rider: Spurring Large Language Models to Explore the Open World

Recently, various studies have leveraged Large Language Models (LLMs) to help decision-making and planning in environments, and try to align the LLMs' knowledge with the world conditions. Nonetheless, the capacity of LLMs to continuously acquire environmental knowledge and adapt in an open world remains uncertain. In this paper, we propose an approach to spur LLMs to explore the open world, gather experiences, and learn to improve their task-solving capabilities. In this approach, a multi-round feedback-revision mechanism is utilized to encourage LLMs to actively select appropriate revision actions guided by feedback information from the environment. This facilitates exploration and enhances the model's performance. Besides, we integrate sub-task relabeling to assist LLMs in maintaining consistency in sub-task planning and help the model learn the combinatorial nature between tasks, enabling it to complete a wider range of tasks through training based on the acquired exploration experiences. By evaluation in Minecraft, an open-ended sandbox world, we demonstrate that our approach LLaMA-Rider enhances the efficiency of the LLM in exploring the environment, and effectively improves the LLM's ability to accomplish more tasks through fine-tuning with merely 1.3k instances of collected data, showing minimal training costs compared to the baseline using reinforcement learning.Comment: 18 page