255 research outputs found
Democracy and economic growth: a perspective of cooperation
Does democracy cause higher economic growth? We build a model taking culture and interpersonal cooperation into account and find that democracy increases economic productivity through giving people more equal rights, which allows people to build a larger interpersonal network so that they can reduce investment risk and employ high-productivity (high-risk) methods in production
Investor Sentiment, Venture Capital, R&D Investment, and IPO Underpricing: An Empirical Analysis of Chinese Star Market
The Shanghai Science and Technology Innovation Board (STAR) led the Chinese capital market revolution with a registration system and showed a high Initial Public Offering (IPO) underpricing rate. This study investigates the impact of the research & development ratio, venture capital, and investor sentiment, on the IPO underpricing rate. The study utilizes STAR data from July 2019 to Dec 2022 listing 457 companies and employing the Ordinary Least Squares (OLS) model to conduct the analysis. The results indicate that venture capital and investor sentiment are positively related to IPO underpricing, while R&D investment negatively influences IPO underpricing. The examination of the determinants shaping IPO underpricing within the Chinese STAR market constitutes a valuable endeavor that facilitates a comprehensive and methodical comprehension of this specific facet of the capital market
Optimization of the Front-end Logistics Routes of Agricultural Products Based on Network Platform
Aiming to promote the effective connection between the individual farmers and the modern “big market” and improve the logistics efficiency of agricultural products, this paper offers a logistics model for decentralized production to achieve organized information and large-scaled transportation. Based on the in-depth analysis of the traditional agricultural product logistics chain, this paper originates a network logistics model for agricultural products. It constructs a two-stage framework (grouping first and then scheduling), analyzes the “First Mile” logistics routes, and then uses the improved loop routes optimization algorithm to obtain the approximate optimal solution to the model. Through example verification, it is found that the model can help improve the efficiency of logistics distribution and save the logistics costs of small agricultural products from fragmented land. Moreover, the results show that the agricultural product logistics method based on overall transportation and information management is obviously superior to the traditional logistics methods
Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks
The IoT (Internet of Things) technology has been widely adopted in recent
years and has profoundly changed the people's daily lives. However, in the
meantime, such a fast-growing technology has also introduced new privacy
issues, which need to be better understood and measured. In this work, we look
into how private information can be leaked from network traffic generated in
the smart home network. Although researchers have proposed techniques to infer
IoT device types or user behaviors under clean experiment setup, the
effectiveness of such approaches become questionable in the complex but
realistic network environment, where common techniques like Network Address and
Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic
analysis using traditional methods (e.g., through classical machine-learning
models) is much less effective under those settings, as the features picked
manually are not distinctive any more. In this work, we propose a traffic
analysis framework based on sequence-learning techniques like LSTM and
leveraged the temporal relations between packets for the attack of device
identification. We evaluated it under different environment settings (e.g.,
pure-IoT and noisy environment with multiple non-IoT devices). The results
showed our framework was able to differentiate device types with a high
accuracy. This result suggests IoT network communications pose prominent
challenges to users' privacy, even when they are protected by encryption and
morphed by the network gateway. As such, new privacy protection methods on IoT
traffic need to be developed towards mitigating this new issue
Quantum Gaussian process regression
In this paper, a quantum algorithm based on gaussian process regression model
is proposed. The proposed quantum algorithm consists of three sub-algorithms.
One is the first quantum subalgorithm to efficiently generate mean predictor.
The improved HHL algorithm is proposed to obtain the sign of outcomes.
Therefore, the terrible situation that results is ambiguous in terms of
original HHL algorithm is avoided, which makes whole algorithm more clear and
exact. The other is to product covariance predictor with same method. Thirdly,
the squared exponential covariance matrices are prepared that annihilation
operator and generation operator are simulated by the unitary linear
decomposition Hamiltonian simulation and kernel function vectors is generated
with blocking coding techniques on covariance matrices. In addition, it is
shown that the proposed quantum gaussian process regression algorithm can
achieve quadratic faster over the classical counterpart
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Five-S-isotope evidence of two distinct mass-independent sulfur isotope effects and implications for the modern and Archean atmospheres.
The signature of mass-independent fractionation of quadruple sulfur stable isotopes (S-MIF) in Archean rocks, ice cores, and Martian meteorites provides a unique probe of the oxygen and sulfur cycles in the terrestrial and Martian paleoatmospheres. Its mechanistic origin, however, contains some uncertainties. Even for the modern atmosphere, the primary mechanism responsible for the S-MIF observed in nearly all tropospheric sulfates has not been identified. Here we present high-sensitivity measurements of a fifth sulfur isotope, stratospherically produced radiosulfur, along with all four stable sulfur isotopes in the same sulfate aerosols and a suite of chemical species to define sources and mechanisms on a field observational basis. The five-sulfur-isotope and multiple chemical species analysis approach provides strong evidence that S-MIF signatures in tropospheric sulfates are concomitantly affected by two distinct processes: an altitude-dependent positive 33S anomaly, likely linked to stratospheric SO2 photolysis, and a negative 36S anomaly mainly associated with combustion. Our quadruple stable sulfur isotopic measurements in varying coal samples (formed in the Carboniferous, Permian, and Triassic periods) and in SO2 emitted from combustion display normal 33S and 36S, indicating that the observed negative 36S anomalies originate from a previously unknown S-MIF mechanism during combustion (likely recombination reactions) instead of coal itself. The basic chemical physics of S-MIF in both photolytic and thermal reactions and their interplay, which were not explored together in the past, may be another ingredient for providing deeper understanding of the evolution of Earth's atmosphere and life's origin
Experimental and Theoretical Exploration of Terahertz Channel Performance through Glass Doors
In the evolving landscape of terahertz communication, the behavior of
channels within indoor environments, particularly through glass doors, has
garnered significant attention. This paper comprehensively investigates
terahertz channel performance under such conditions, employing a measurement
setup operational between 113 and 170 GHz. Analyzing scenarios frequently
induced by human activity and environmental factors, like door movements, we
established a comprehensive theoretical model. This model seamlessly integrates
transmission, reflection, absorption, and diffraction mechanisms, leveraging
the Fresnel formula, multi-layer transmission paradigm, and knife-edge
diffraction theory. Our experimental results and theoretical predictions
harmoniously align, revealing intricate dependencies, such as increased power
loss at higher frequencies and larger incident angles. Furthermore, door
interactions, whether opening or oscillations, significantly impact the
terahertz channel. Notably, door edges lead to a power blockage surpassing the
transmission loss of the glass itself but remaining inferior to metallic handle
interferences. This paper's insights are pivotal for the design and fabrication
of terahertz communication systems within indoor settings, pushing the
boundaries of efficient and reliable communication.Comment: Scheduled to publish in Nano Communication Network
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