Joint pricing and ordering decisions for perishable products

Ph.DDOCTOR OF PHILOSOPH

RIS-aided Real-time Beam Tracking for a Mobile User via Bayesian Optimization

The conventional beam management procedure mandates that the user equipment (UE) periodically measure the received signal reference power (RSRP) and transmit these measurements to the base station (BS). The challenge lies in balancing the number of beams used: it should be large enough to identify high-RSRP beams but small enough to minimize reporting overhead. This paper investigates this essential performance-versus-overhead trade-off using Bayesian optimization. The proposed approach represents the first application of real-time beam tracking via Bayesian optimization in RIS-assisted communication systems. Simulation results validate the effectiveness of this scheme

A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models

Passive sensing techniques based on Wi-Fi signals have emerged as a promising technology in advanced wireless communication systems due to their widespread application and cost-effectiveness. However, the proliferation of low-cost Internet of Things (IoT) devices has led to dense network deployments, resulting in increased levels of noise and interference in Wi-Fi environments. This, in turn, leads to noisy and redundant Channel State Information (CSI) data. As a consequence, the accuracy of human activity recognition based on Wi-Fi signals is compromised. To address this issue, we propose a novel CSI data signal extraction method. We established a human activity recognition system based on the Intel 5300 network interface cards (NICs) and collected a dataset containing six categories of human activities. Using our approach, signals extracted from the CSI data serve as inputs to machine learning (ML) classification algorithms to evaluate classification performance. In comparison to ML methods based on Principal Component Analysis (PCA), our proposed High-Dimensional Factor Model (HDFM) method improves recognition accuracy by 6.8%

Design of Reconfigurable Intelligent Surfaces for Wireless Communication: A Review

Existing literature reviews predominantly focus on the theoretical aspects of reconfigurable intelligent surfaces (RISs), such as algorithms and models, while neglecting a thorough examination of the associated hardware components. To bridge this gap, this research paper presents a comprehensive overview of the hardware structure of RISs. The paper provides a classification of RIS cell designs and prototype systems, offering insights into the diverse configurations and functionalities. Moreover, the study explores potential future directions for RIS development. Notably, a novel RIS prototype design is introduced, which integrates seamlessly with a communication system for performance evaluation through signal gain and image formation experiments. The results demonstrate the significant potential of RISs in enhancing communication quality within signal blind zones and facilitating effective radio wave imaging

The double Ringel-Hall algebra on a hereditary abelian finitary length category

In this paper, we study the category $\mathscr{H}^{(\rho)}$ of semi-stable coherent sheaves of a fixed slope $\rho$ over a weighted projective curve. This category has nice properties: it is a hereditary abelian finitary length category. We will define the Ringel-Hall algebra of $\mathscr{H}^{(\rho)}$ and relate it to generalized Kac-Moody Lie algebras. Finally we obtain the Kac type theorem to describe the indecomposable objects in this category, i.e. the indecomposable semi-stable sheaves.Comment: 29 page

Precursor apportionment of atmospheric oxygenated organic molecules using a machine learning method

Publisher Copyright: © 2023 The Author(s). Published by the Royal Society of Chemistry.Gas-phase oxygenated organic molecules (OOMs) can contribute significantly to both atmospheric new particle growth and secondary organic aerosol formation. Precursor apportionment of atmospheric OOMs connects them with volatile organic compounds (VOCs). Since atmospheric OOMs are often highly functionalized products of multistep reactions, it is challenging to reveal the complete mapping relationships between OOMs and their precursors. In this study, we demonstrate that the machine learning method is useful in attributing atmospheric OOMs to their precursors using several chemical indicators, such as O/C ratio and H/C ratio. The model is trained and tested using data acquired in controlled laboratory experiments, covering the oxidation products of four main types of VOCs (isoprene, monoterpenes, aliphatics, and aromatics). Then, the model is used for analyzing atmospheric OOMs measured in both urban Beijing and a boreal forest environment in southern Finland. The results suggest that atmospheric OOMs in these two environments can be reasonably assigned to their precursors. Beijing is an anthropogenic VOC dominated environment with ~64% aromatic and aliphatic OOMs, and the other boreal forested area has ~76% monoterpene OOMs. This pilot study shows that machine learning can be a promising tool in atmospheric chemistry for connecting the dots.Peer reviewe

Acid-Base Clusters during Atmospheric New Particle Formation in Urban Beijing

Molecular clustering is the initial step of atmospheric new particle formation (NPF) that generates numerous secondary particles. Using two online mass spectrometers with and without a chemical ionization inlet, we characterized the neutral clusters and the naturally charged ion clusters during NPF periods in urban Beijing. In ion clusters, we observed pure sulfuric acid (SA) clusters, SA-amine clusters, SA-ammonia (NH3) clusters, and SA-amine-NH3 clusters. However, only SA clusters and SA-amine clusters were observed in the neutral form. Meanwhile, oxygenated organic molecule (OOM) clusters charged by a nitrate ion and a bisulfate ion were observed in ion clusters. Acid-base clusters correlate well with the occurrence of sub-3 nm particles, whereas OOM clusters do not. Moreover, with the increasing cluster size, amine fractions in ion acid-base clusters decrease, while NH3 fractions increase. This variation results from the reduced stability differences between SA-amine clusters and SA-NH3 clusters, which is supported by both quantum chemistry calculations and chamber experiments. The lower average number of dimethylamine (DMA) molecules in atmospheric ion clusters than the saturated value from controlled SA-DMA nucleation experiments suggests that there is insufficient DMA in urban Beijing to fully stabilize large SA clusters, and therefore, other basic molecules such as NH3 play an important role.Peer reviewe

Contribution of Atmospheric Oxygenated Organic Compounds to Particle Growth in an Urban Environment

Gas-phase oxygenated organic molecules (OOMs) can contribute substantially to the growth of newly formed particles. However, the characteristics of OOMs and their contributions to particle growth rate are not well understood in urban areas, which have complex anthropogenic emissions and atmospheric conditions. We performed long-term measurement of gas-phase OOMs in urban Beijing during 2018-2019 using nitrate-based chemical ionization mass spectrometry. OOM concentrations showed clear seasonal variations, with the highest in the summer and the lowest in the winter. Correspondingly, calculated particle growth rates due to OOM condensation were highest in summer, followed by spring, autumn, and winter. One prominent feature of OOMs in this urban environment was a high fraction (similar to 75%) of nitrogen-containing OOMs. These nitrogen-containing OOMs contributed only 50-60% of the total growth rate led by OOM condensation, owing to their slightly higher volatility than non-nitrate OOMs. By comparing the calculated condensation growth rates and the observed particle growth rates, we showed that sulfuric acid and its clusters are the main contributors to the growth of sub-3 nm particles, with OOMs significantly promoting the growth of 3-25 nm particles. In wintertime Beijing, however, there are missing contributors to the growth of particles above 3 nm, which remain to be further investigated.Peer reviewe

Responses of gaseous sulfuric acid and particulate sulfate to reduced SO2 concentration : A perspective from long-term measurements in Beijing

SO2 concentration decreased rapidly in recent years in China due to the implementation of strict control policies by the government. Particulate sulfate (pSO(4)(2-)) and gaseous H2SO4 (SA) are two major products of SO2 and they play important roles in the haze formation and new particle formation (NPF), respectively. We examined the change in pSO(4)(2-) and SA concentrations in response to reduced SO2 concentration using long-term measurement data in Beijing. Simulations from the Community Multiscale Air Quality model with a 2-D Volatility Basis Set (CMAQ/2D-VBS) were used for comparison. From 2013 to 2018, SO2 concentration in Beijing decreased by similar to 81% (from 9.1 ppb to 1.7 ppb). pSO(4)(2-) concentration in submicrometer particles decreased by similar to 60% from 2012-2013 (monthly average of similar to 10 mu g.m(-3)) to 2018-2019 (monthly average of similar to 4 mu g.m(-3)). Accordingly, the fraction of pSO(4)(2-) in these particles decreased from20-30% to b10%. Increased sulfur oxidation ratio was observed both in the measurements and the CMAQ/2D-VBS simulations. Despite the reduction in SO2 concentration, there was no obvious decrease in SA concentration based on data from several measuring periods from 2008 to 2019. This was supported by the increased SA:SO2 ratio with reduced SO2 concentration and condensation sink. NPF frequency in Beijing between 2004 and 2019 remains relatively constant. This constant NPF frequency is consistent with the relatively stable SA concentration in Beijing, while different from some other cities where NPF frequency was reported to decrease with decreased SO2 concentrations. (C) 2020 Elsevier B.V. All rights reserved.Peer reviewe

Sulfuric acid-amine nucleation in urban Beijing

New particle formation (NPF) is one of the major sources of atmospheric ultrafine particles. Due to the high aerosol and trace gas concentrations, the mechanism and governing factors for NPF in the polluted atmospheric boundary layer may be quite different from those in clean environments, which is however less understood. Herein, based on long-term atmospheric measurements from January 2018 to March 2019 in Beijing, the nucleation mechanism and the influences of H2SO4 concentration, amine concentrations, and aerosol concentration on NPF are quantified. The collision of H2SO4-amine clusters is found to be the dominating mechanism to initialize NPF in urban Beijing. The coagulation scavenging due to the high aerosol concentration is a governing factor as it limits the concentration of H2SO4-amine clusters and new particle formation rates. The formation of H2SO4-amine clusters in Beijing is sometimes limited by low amine concentrations. Summarizing the synergistic effects of H2SO4 concentration, amine concentrations, and aerosol concentration, we elucidate the governing factors for H2SO4-amine nucleation for various conditions.Peer reviewe