Assessment of Biomass Burning and Mineral Dust Impacts on Air Quality and Regional Climate

East Asia is frequently influenced by dust storms and biomass burning. This study conducts a comprehensive investigation of its kind based on data analysis with surface measurements, satellite products, and model simulations. The objective of this study is to improve the understanding of the impacts of biomass burning and dust on air quality and regional climate. The study period covers March and April from 2006 to 2010. Biomass burning from Peninsular Southeast Asia (PSEA) has significant annual variations by up to 60% within the study period. The impact of biomass burning on air quality is mainly confined within the upper air due to the uplift motion driven by lee-side trough along eastern side of Tibet Plateau. The Weather Research and Forecasting and Community Multiscale Air Quality (WRF/CMAQ) system successfully reproduces the spatial distributions and temporal variations of air pollutants. Simulation bias falls in the range of 10%~50%, mainly due to the uncertainties within the emission inventory. This study reveals that the default WRF/CMAQ model has doubt counting of the soil moisture effect and subsequently underestimates dust emission by 55%. The microphysical parameterization and the speciation profile are revised to characterize the emission and mass contribution of dust better. Heterogeneous dust chemistry is also incorporated. These modifications substantially improve the model performance as indicated by the comparison between model simulations and observations. This study reveals that biomass burning has significant warming effect due to the presence of the underlying stratocumulus cloud. Biomass burning aerosol cools the near surface air by -0.2K, and significantly warms the upper air by up to +2K. Dust aerosol cools the near surface air by -0.9K and warms the upper air by +0.1K. This is the first investigation into the coexistence of biomass burning and dust over East Asia. This coexistence changes the aerosol direct radiative effect efficiencies of both biomass burning and dust by ±10%

A Hybrid Model for Object-Oriented Software Maintenance

An object-oriented software system is composed of a collection of communicating objects that co-operate with one another to achieve some desired goals. The object is the basic unit of abstraction in an OO program; objects may model real-world entities or internal abstractions of the system. Similar objects forms classes, which encapsulate the data and operations performed on the data. Therefore, extracting, analyzing, and modelling classes/objects and their relationships is of key importance in understanding and maintaining object-oriented software systems. However, when dealing with large and complex object-oriented systems, maintainers can easily be overwhelmed by the vast number of classes/objects and the high degree of interdependencies among them. In this thesis, we propose a new model, which we call the Hybrid Model, to represent object-oriented systems at a coarse-grained level of abstraction. To promote the comprehensibility of objects as independent units, we group the complete static description of software objects into aggregate components. Each aggregate component logically represents a set of objects, and the components interact with one other through explicitly defined ports. We present and discuss several applications of the Hybrid Model in reverse engineering and software evolution. The Hybrid Model can be used to support a divide-and-conquer comprehension strategy for program comprehension. At a low level of abstraction, maintainers can focus on one aggregate-component at a time, while at a higher level, each aggregate component can be understood as a whole and be mapped to coarse-grained design abstractions, such as subsystems. Based on the new model, we further propose a set of dependency analysis methods. The analysis results reveal the external properties of aggregate components, and lead to better understand the nature of their interdependencies. In addition, we apply the new model in software evolution analysis. We identify a collection of change patterns in terms of changes in aggregate components and their interrelationships. These patterns help to interpret how an evolving system changes at the architectural level, and provides valuable information to understand why the system is designed as the way it is

Corporate governance and executive compensation in Chinese e-commerce industry

This study examines the correlation between executive compensation and corporate governance in Chinese e-commerce listed companies. Chinese e-commerce listed companies listed on the Shanghai and Shenzhen stock exchanges between 2010 and 2020 are used as sample companies. With the help of fixed effects regression models and correlation matrices, this study finds that board size has a significantly active effect on executive compensation and ownership concentration is adversely correlated with executive compensation in Chinese e-commerce listed companies. Meanwhile, neither board independence nor remuneration committee meetings are significantly related to executive compensation in Chinese e-commerce listed companies. This study contributes to the understanding of the correlation between executive compensation and corporate governance and enriches the literature in this area

The Battle Between WeChat Pay and Alipay on Strategy Level

In the increasingly changing business world, solely adopting industry level analysis or firm level analysis might be inefficient to explain how certain company obtain sustainable competitive advantage and achieve success in market battle. In this case, to better understand the battle between Alibaba Group and Tencent in third-party payment industry, this dissertation conducts a conjunct analysis including both external and internal investigation. Results indicate that support infrastructure and specific organizational routine could be the most vital firm resources in generating Tencent’s sustainable competitive advantages, and the intrinsically high user stickiness of instant message industry provides Tencent with a protective barrier to maintain its advantages as well. Further quantitative researches could help generalize these findings and figure out the most decisive factors among all exterior and interior aspects, which could facilitate efficient strategy planning

STAR-RIS Aided Secure MIMO Communication Systems

This paper investigates simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided physical layer security (PLS) in multiple-input multiple-output (MIMO) systems, where the base station (BS) transmits secrecy information with the aid of STAR-RIS against multiple eavesdroppers equipped with multiple antennas. We aim to maximize the secrecy rate by jointly optimizing the active beamforming at the BS and passive beamforming at the STAR-RIS, subject to the hardware constraint for STAR-RIS. To handle the coupling variables, a minimum mean-square error (MMSE) based alternating optimization (AO) algorithm is applied. In particular, the amplitudes and phases of STAR-RIS are divided into two blocks to simplify the algorithm design. Besides, by applying the Majorization-Minimization (MM) method, we derive a closed-form expression of the STAR-RIS's phase shifts. Numerical results show that the proposed scheme significantly outperforms various benchmark schemes, especially as the number of STAR-RIS elements increases

Interpretable Sequence Clustering

Categorical sequence clustering plays a crucial role in various fields, but the lack of interpretability in cluster assignments poses significant challenges. Sequences inherently lack explicit features, and existing sequence clustering algorithms heavily rely on complex representations, making it difficult to explain their results. To address this issue, we propose a method called Interpretable Sequence Clustering Tree (ISCT), which combines sequential patterns with a concise and interpretable tree structure. ISCT leverages k-1 patterns to generate k leaf nodes, corresponding to k clusters, which provides an intuitive explanation on how each cluster is formed. More precisely, ISCT first projects sequences into random subspaces and then utilizes the k-means algorithm to obtain high-quality initial cluster assignments. Subsequently, it constructs a pattern-based decision tree using a boosting-based construction strategy in which sequences are re-projected and re-clustered at each node before mining the top-1 discriminative splitting pattern. Experimental results on 14 real-world data sets demonstrate that our proposed method provides an interpretable tree structure while delivering fast and accurate cluster assignments.Comment: 11 pages, 6 figure

Understanding the Long-term Trend of Organic Aerosol and the Influences from Anthropogenic Emission and Regional Climate Change in China

Organic aerosol (OA) is a major type of fine particulate matter. OA shows a large variability influenced by anthropogenic emissions, vegetation, and meteorological changes. Understanding OA trends is crucial for air quality and climate studies, yet changes in OA over time in China are poorly documented. This study applied the Community Atmosphere Model version 6 with comprehensive tropospheric and stratospheric chemistry (CAM6-Chem) to investigate long-term OA trends in China from 1990 to 2019 and identify the driving factors. The simulations agreed well with ground-based measurements of OA from 151 observational sites and the CAQRA reanalysis dataset. Although OA trends showed a modest 5.6 % increase, this resulted from a significant -8.1 % decrease in primary organic aerosols (POA) and a substantial 32.3 % increase in secondary organic aerosols (SOA). Anthropogenic emissions of POA and volatile organic compounds (VOCs) were the dominant contributors to these trends. While biogenic VOCs (BVOCs) played a secondary role in SOA formation, significant changes were observed in specific sub-species: isoprene-derived SOA decreased by -18.8 % due to anthropogenic sulfate reduction, while monoterpene-derived SOA increased by 12.3 % driven by enhanced emissions from rising temperatures. Our study found through sensitivity experiments a negligible response of monoterpene-derived SOA to changes in anthropogenic nitrogen oxides (NOx) emissions as a net effect of changes in multiple pathways. This study highlights the complex interplay between POA reduction and SOA growth, revealing notable OA trends in China and the varying roles of both anthropogenic and biogenic emissions