Financial Development and Poverty Alleviation in Developing and Emerging Economies

Poverty has remained one of the most significant challenges faced by emerging and developing countries. After witnessing the success of financial development in facilitating economic growth and improving social welfare in developed countries, emerging and developing countries have been striving to develop their financial sectors with the aim of replicating the success. However, the multidimensional nature of both the financial system and poverty, the quadrilateral relationship between finance, growth, crises and poverty, and the difference between country-specific characteristics altogether impeded researchers and policymakers from reaching a consensus on whether financial development is pro-poor. The literature on finance-poverty nexus received minimal attention over the last few decades, and existing literature has bifurcated into two main strands of views. One strand emphasises the positive effect of financial development through the direct and indirect growth channels, whereas the other focuses more on the negative indirect crisis channel and its associated costs for the poor during turbulent periods. The prior view tends to neglect the fact that crises are more likely to happen during economic booms being accompanied by financial development, and the effects of financial instability are exacerbated especially for countries with unsound financial system regulations and weak institutional performance. Once crises occur, the associated costs may pose a devastating effect on the poor. In contrast, the latter view overestimates the fact that crises are only occasional, and their adverse impacts on the poor are curable in the aftermaths if certain policies are appropriately tailored and implemented to minimise such effects. Thus, we are motivated to fill the gap by providing a comprehensive analysis that produces a unified approach for assessing the finance-poverty nexus. This approach is founded not only on a macroeconomic perspective that considers all channels, but also a microeconomic perspective to observe whether, on a household level, financial development is pro-poor

Bank financial innovation and SMEs lending: do we experience a transformation in a bank-SME relationship?

While sparked by financial technology firms, the digitalisation trend has also impacted a banking sector, providing greater incentives for traditional banks to become more financially inclusive. The advancements in financial technology development might create new financing opportunities for Small and Medium-sized enterprises (SMEs), which have typically been underserved by the traditional banks. In our paper we raise a question whether the technological advances have increased the interest of banks toward the SMEs lending. Using a sample of 179,921 SMEs, merged with the data from 54 largest European banks over the period of 2008-2019 at a firm level, we analyze the impact of bank digitalisation on the SMEs access to credit and its cost. Our results indicate that bank digitalisation has positively affected SMEs’ access to bank credit, though the effect is stronger for short-term lending rather than long-term one. However, our evidence also suggests that bank digitalisation increases the cost of credit to SMEs, though the effect is non-linear. Finally, we also show that the impact of financial innovation at banks manifest via different channels, and it is also conditional on credit market characteristics

Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation

Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable language sequences referred to as ``thoughts''. An effective thought design should consider three key perspectives: performance, efficiency, and flexibility. However, existing thought can at most exhibit two of these attributes. To address these limitations, we introduce a novel thought prompting approach called ``Everything of Thoughts'' (XoT) to defy the law of ``Penrose triangle of existing thought paradigms. XoT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge into thoughts, thereby enhancing LLMs' capabilities and enabling them to generalize to unseen problems efficiently. Through the utilization of the MCTS-LLM collaborative thought revision framework, this approach autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally, XoT empowers LLMs to engage in unconstrained thinking, allowing for flexible cognitive mappings for problems with multiple solutions. We evaluate XoT on several challenging multi-solution problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate that XoT significantly outperforms existing approaches. Notably, XoT can yield multiple solutions with just one LLM call, showcasing its remarkable proficiency in addressing complex problems across diverse domains.Comment: 17 pages, 5 figure

A Metabonomics Profiling Study on Phlegm Syndrome and Blood-Stasis Syndrome in Coronary Heart Disease Patients Using Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry

A metabonomics approach based on liquid chromatography/quadrupole time-of-flight mass spectrometry (LC-Q-TOF/MS) was utilized to obtain potential biomarkers of coronary heart disease (CHD) patients and investigate the ZHENG types differentiation in CHD patients. The plasma samples of 20 CHD patients with phlegm syndrome, 20 CHD patients with blood-stasis syndrome, and 16 healthy volunteers were collected in the study. 26 potential biomarkers were identified in the plasma of CHD patients and 19 differential metabolites contributed to the discrimination of phlegm syndrome and blood-stasis syndrome in CHD patients (VIP>1.5; P<0.05) which mainly involved purine metabolism, pyrimidine metabolism, amino acid metabolism, steroid biosynthesis, and arachidonic acid metabolism. This study demonstrated that metabonomics approach based on LC-MS was useful for studying pathologic changes of CHD patients and interpreting the differentiation of ZHENG types (phlegm and blood-stasis syndrome) in traditional Chinese medicine (TCM)

TraceDiag: Adaptive, Interpretable, and Efficient Root Cause Analysis on Large-Scale Microservice Systems

Root Cause Analysis (RCA) is becoming increasingly crucial for ensuring the reliability of microservice systems. However, performing RCA on modern microservice systems can be challenging due to their large scale, as they usually comprise hundreds of components, leading significant human effort. This paper proposes TraceDiag, an end-to-end RCA framework that addresses the challenges for large-scale microservice systems. It leverages reinforcement learning to learn a pruning policy for the service dependency graph to automatically eliminates redundant components, thereby significantly improving the RCA efficiency. The learned pruning policy is interpretable and fully adaptive to new RCA instances. With the pruned graph, a causal-based method can be executed with high accuracy and efficiency. The proposed TraceDiag framework is evaluated on real data traces collected from the Microsoft Exchange system, and demonstrates superior performance compared to state-of-the-art RCA approaches. Notably, TraceDiag has been integrated as a critical component in the Microsoft M365 Exchange, resulting in a significant improvement in the system's reliability and a considerable reduction in the human effort required for RCA

The Altered Reconfiguration Pattern of Brain Modular Architecture Regulates Cognitive Function in Cerebral Small Vessel Disease

Background: Cerebral small vessel disease (SVD) is a common cause of cognitive dysfunction. However, little is known whether the altered reconfiguration pattern of brain modular architecture regulates cognitive dysfunction in SVD.Methods: We recruited 25 cases of SVD without cognitive impairment (SVD-NCI) and 24 cases of SVD with mild cognitive impairment (SVD-MCI). According to the Framingham Stroke Risk Profile, healthy controls (HC) were divided into 17 subjects (HC-low risk) and 19 subjects (HC-high risk). All individuals underwent resting-state functional magnetic resonance imaging and cognitive assessments. Graph-theoretical analysis was used to explore alterations in the modular organization of functional brain networks. Multiple regression and mediation analyses were performed to investigate the relationship between MRI markers, network metrics and cognitive performance.Results: We identified four modules corresponding to the default mode network (DMN), executive control network (ECN), sensorimotor network and visual network. With increasing vascular risk factors, the inter- and intranetwork compensation of the ECN and a relatively reserved DMN itself were observed in individuals at high risk for SVD. With declining cognitive ability, SVD-MCI showed a disrupted ECN intranetwork and increased DMN connection. Furthermore, the intermodule connectivity of the right inferior frontal gyrus of the ECN mediated the relationship between periventricular white matter hyperintensities and visuospatial processing in SVD-MCI.Conclusions: The reconfiguration pattern of the modular architecture within/between the DMN and ECN advances our understanding of the neural underpinning in response to vascular risk and SVD burden. These observations may provide novel insight into the underlying neural mechanism of SVD-related cognitive impairment and may serve as a potential non-invasive biomarker to predict and monitor disease progression

Study on Effects of Energy Deposition and Thermal Shock Wave by Electron Beam Irradiation

The electron beam is an important way to effectively simulate the thermodynamic effects of an intense pulsed X-ray and the materials for electron beam irradiation are of great significance to study the effects of energy deposition and thermal shock waves. Based on the input conditions like the actual current, voltage, and energy spectrum of an electron beam device (REB), the analytic method and the Monte Carlo method were used to calculate the energy deposition of a multi-energy-spectrum electron beam in materials of hard aluminum and carbon phenolic, the differences of the two methods were analyzed, the energy deposition profiles of different incident angles and energies were obtained, and the energy deposition of electron beam irradiation of a multilayer target was calculated as well. Through the numerical simulation and experimental study of thermal shock waves of electron beam irradiation materials, the calculation error was less than 10% by comparing the stress changes of thermal shock waves with different energies. This is helpful for studying the protective structure of spacecraft