Defining heatwave thresholds using an inductive machine learning approach

Establishing appropriate heatwave thresholds is important in reducing adverse human health consequences as it enables a more effective heatwave warning system and response plan. This paper defined such thresholds by focusing on the non-linear relationship between heatwave outcomes and meteorological variables as part of an inductive approach. Daily data on emergency department visitors who were diagnosed with heat illnesses and information on 19 meteorological variables were obtained for the years 2011 to 2016 from relevant government agencies. A Multivariate Adaptive Regression Splines (MARS) analysis was performed to explore points (referred to as "knots") where the behaviour of the variables rapidly changed. For all emergency department visitors, two thresholds (a maximum daily temperature >= 32.58 degrees C for 2 consecutive days and a heat index >= 79.64) were selected based on the dramatic rise of morbidity at these points. Nonetheless, visitors, who included children and outside workers diagnosed in the early summer season, were reported as being sensitive to heatwaves at lower thresholds. The average daytime temperature (from noon to 6 PM) was determined to represent an alternative threshold for heatwaves. The findings have implications for exploring complex heatwave-morbidity relationships and for developing appropriate intervention strategies to prevent and mitigate the health impact of heatwave

Classical-to-quantum convolutional neural network transfer learning

Machine learning using quantum convolutional neural networks (QCNNs) has demonstrated success in both quantum and classical data classification. In previous studies, QCNNs attained a higher classification accuracy than their classical counterparts under the same training conditions in the few-parameter regime. However, the general performance of large-scale quantum models is difficult to examine because of the limited size of quantum circuits, which can be reliably implemented in the near future. We propose transfer learning as an effective strategy for utilizing small QCNNs in the noisy intermediate-scale quantum era to the full extent. In the classical-to-quantum transfer learning framework, a QCNN can solve complex classification problems without requiring a large-scale quantum circuit by utilizing a pre-trained classical convolutional neural network (CNN). We perform numerical simulations of QCNN models with various sets of quantum convolution and pooling operations for MNIST data classification under transfer learning, in which a classical CNN is trained with Fashion-MNIST data. The results show that transfer learning from classical to quantum CNN performs considerably better than purely classical transfer learning models under similar training conditions.Comment: 16 pages, 7 figure

A study on the consumer's perception of front-of-pack nutrition labeling

The goal of this research is to investigate the present situation for front of pack labeling in Korea and the perception of consumers for the new system of labeling, front of pack labeling, based on the consumer survey. We investigated the number of processed foods with front of pack labeling in one retailer in Youngin-si. And we also surveyed 1,019 participants nationwide whose ages were from 20 to 49; the knowledge of nutrition labeling, the knowledge of 'front of pack labeling', and the opinion about the labeling system. The data were analyzed using SAS statistics program. The results were as follows: 13.4% of processed foods had front of pack labeling, and 16.8% of the consumers always checked the nutrition labeling, while 32.7% of the consumers seldom checked it. In addition, 44.3% of the consumers think that 'front of pack labeling' is necessary, and 58.3% of the consumers think it is important to show the percentage of daily value as a way of 'front of pack labeling'. However, 32% of the consumer think the possibility of 'front of pack labeling' is slim. Meanwhile, 58.3% of the consumers think that it is important to have the color difference according to contents. The number of favorite nutrients in the front of pack was four or five. It seems that the recognition of current nutrition labeling has the influence on the willingness of using the future 'front of pack labeling'. Along with our study, the policy for 'front of pack labeling' has to be updated and improved constantly since 'front of pack labeling' helps consumer understand nutrition facts

Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting

Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencies and intricate interactions--both direct and indirect. To confront these complexities, we introduce a method of representing multivariate signals as nodes in a graph with edges indicating interdependency between them. Specifically, we leverage graph neural networks (GNN) and attention mechanisms to efficiently learn the underlying relationships within the time series data. Moreover, we suggest employing hierarchical signal decompositions running over the graphs to capture multiple spatial dependencies. The effectiveness of our proposed model is evaluated across various real-world benchmark datasets designed for long-term forecasting tasks. The results consistently showcase the superiority of our model, achieving an average 23\% reduction in mean squared error (MSE) compared to existing models.Comment: Temporal Graph Learning Workshop @ NeurIPS 2023, New Orleans, United State

Fate of Iprobenfos and Tricyclazole at Paddy Cultivation Environment

Objectives This study aimed to identify the fate of iprobenfos and tricyclazole in the soil and paddy water during the rice cultivation process and to identify their exposure pathways into surface water. Methods Both iprobenfos and tricyclazole were sprayed onto two sample sites following the pesticide safety usage guidelines. Residues in the samples. Soil, paddy water, and drainage water samples were collected for 28 days after post-application. Residues were subsequently analyzed using LC-MS/MS. Results and Discussion The fate of the two pesticides, iprobenfos and tricyclazole, in the rice cultivation environment was influenced by factors such as soil half-life, Koc, water solubility, formulation, and precipitation patterns. Initial concentrations of iprobenfos and tricyclazole in the paddy field near the drainage were 5,774 μg/L and 307 μg/L, respectively, while concentrations in the drain water were 1,850 μg/L and 182 μg/L. Four days after application, the residual concentrations of iprobenfos and tricyclazole in both paddy and drain water ranged from N.D. (Not Detected) to 5.6 μg/L and N.D. to 56 μg/L, respectively, indicating a rapid decline. During the experimental period, the average concentration reduction ratios in drain water near the drainage compared to the drain water were higher for tricyclazole (90%) than for iprobenfos (52%). Conclusion The soil and water half-life of tricyclazole exceeded that of iprobenfos, leading to a slower rate of concentration reduction. The lower Koc value for tricyclazole suggests enhanced soil desorption due to rainfall, increasing its concentration in paddy fields. The presence of iprobenfos and tricyclazole in surface water is likely due to dispersion during pesticide application. While concentrations diminish owing to the dilution effect when water moves from paddy fields to surface water, it's posited that runoff could affect nearby stream water within seven days post-application

Long-term variation study of fine-mode particle size and regional characteristics using AERONET data

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).To identify the long-term trend of particle size variation, we analyzed aerosol optical depth (AOD, τ) separated as dust (τD) and coarse-(τPC) and fine-pollution particles (τPF) depending on emission sources and size. Ångström exponent values are also identified separately as total and fine-mode particles (αT and αPF). We checked these trends in various ways; (1) first-order linear regression analysis of the annual average values, (2) percent variation using the slope of linear regression method, and (3) a reliability analysis using the Mann–Kendall (MK) test. We selected 17 AERONET sun/sky radiometer sites classified into six regions, i.e., Europe, North Africa, the Middle East, India, Southeast Asia, and Northeast Asia. Although there were regional differences, τ decreased in Europe and Asian regions and increased in the Middle East, India, and North Africa. Values of τPC and τPF, show that aerosol loading caused by non-dust aerosols decreased in Europe and Asia and increased in India. In particular, τPF considerably decreased in Europe and Northeast Asia (95% confidential levels in MK-test), and τPC decreased in Northeast Asia (Z-values for Seoul and Osaka are −2.955 and −2.306, respectively, statistically significant if |z| ≥ 1.96). The decrease in τPC seems to be because of the reduction of primary and anthropogenic emissions from regulation by air quality policies. The meaningful result in this paper is that the particle size became smaller, as seen by values of αT that decreased by −3.30 to −30.47% in Europe, North Africa, and the Middle East because αT provides information on the particle size. Particle size on average became smaller over India and Asian regions considered in our study due to the decrease in coarse particles. In particular, an increase of αPF in most areas shows the probability that the average particle size of fine-mode aerosols became smaller in recent years. We presumed the cause of the increase in αT is because relatively large-sized fine-mode particles were eliminated due to air quality policies.Peer reviewe

The Role of Light and Circadian Clock in Regulation of Leaf Senescence

Leaf senescence is an integrated response of the cells to develop age information and various environmental signals. Thus, some of the genes involved in the response to environmental changes are expected to regulate leaf senescence. Light acts not only as the primary source of energy for photosynthesis but also as an essential environmental cue that directly control plant growth and development including leaf senescence. The molecular mechanisms linking light signaling to leaf senescence have recently emerged, exploring the role of Phytochrome-Interacting Factors (PIFs) as a central player leading to diverse senescence responses, senescence-promoting gene regulatory networks (GRNs) involving PIFs, and structural features of transcription modules in GRNs. The circadian clock is an endogenous time-keeping system for the adaptation of organisms to changing environmental signals and coordinates developmental events throughout the life of the plant. Circadian rhythms can be reset by environmental signals, such as light-dark or temperature cycles, to match the environmental cycle. Research advances have led to the discovery of the role of core clock components as senescence regulators and their underlying signaling pathways, as well as the age-dependent shortening of the circadian clock period. These discoveries highlight the close relationship between the circadian system and leaf senescence. Key issues remain to be elucidated, including the effect of light on leaf senescence in relation to the circadian clock, and the identification of key molecules linking aging, light, and the circadian clock, and integration mechanisms of various senescence-affecting signals at the multi-regulation levels in dynamics point of view.1

Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?

Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning. Causal reasoning of PLM relies solely on text-based descriptions, in contrast to causal discovery which aims to determine the causal relationships between variables utilizing data. Recently, there has been current research regarding a method that mimics causal discovery by aggregating the outcomes of repetitive causal reasoning, achieved through specifically designed prompts. It highlights the usefulness of PLMs in discovering cause and effect, which is often limited by a lack of data, especially when dealing with multiple variables. Conversely, the characteristics of PLMs which are that PLMs do not analyze data and they are highly dependent on prompt design leads to a crucial limitation for directly using PLMs in causal discovery. Accordingly, PLM-based causal reasoning deeply depends on the prompt design and carries out the risk of overconfidence and false predictions in determining causal relationships. In this paper, we empirically demonstrate the aforementioned limitations of PLM-based causal reasoning through experiments on physics-inspired synthetic data. Then, we propose a new framework that integrates prior knowledge obtained from PLM with a causal discovery algorithm. This is accomplished by initializing an adjacency matrix for causal discovery and incorporating regularization using prior knowledge. Our proposed framework not only demonstrates improved performance through the integration of PLM and causal discovery but also suggests how to leverage PLM-extracted prior knowledge with existing causal discovery algorithms

ICONE14-89044 APERIODIC INSTABILITY OF A ONCE-THROUGH STEAM GENERATOR WITH A FEEDWATER LINE

ABSTRACT Aperiodic (static) flow instability is an instability related to the change of a flow direction in individual steam generating U-shaped channels operating at given pressure difference. The nature of an aperiodic instability is close to a Ledinegg instability INTRODUCTION The hydrodynamic stability of OTSG, in particular OTSG in nuclear power plant, is one of the most important conditions ensuring their reliable operation. The operation of a OTSG under unstable conditions can damage the heating surface as a result of overheating or temperature fluctuations, and lead to a decrease of the heat reception It is known that two types of instability are possible for the OTSG [3]: a parallel-channel instability in the system of the channels connected in parallel and an aperiodic instability in the system of the U-shaped channels: Hydrodynamic instability of the OTSGs in terms of steamwater flow fluctuations occurs in the system of parallel channels and operating at a permanent pressure difference. It should be noted that it is typical for the OTSG to operate in low flow and low pressure conditions. The main disturbance source in a steam-water channel, finally leading to flowrat

INVESTIGATION ON EFFECTS OF ENLARGED PIPE RUPTURE SIZE AND AIR PENETRATION TIMING IN REALSCALE EXPERIMENT OF SIPHON BREAKER

To ensure the safety of research reactors, the water level must be maintained above the required height. When a pipe ruptures, the siphon phenomenon causes continuous loss of coolant until the hydraulic head is removed. To protect the reactor core from this kind of accident, a siphon breaker has been suggested as a passive safety device. This study mainly focused on two variables: the size of the pipe rupture and the timing of air entrainment. In this study, the size of the pipe rupture was increased to the guillotine break case. There was a region in which a larger pipe rupture did not need a larger siphon breaker, and the water flow rate was related to the size of the pipe rupture and affected the residual water quantity. The timing of air entrainment was predicted to influence residual water level. However, the residual water level was not affected by the timing of air entrainment. The experimental cases, which showed the characteristic of partical sweep-out mode in the separation of siphon breaking phenomenon [2], showed almost same trend of physical properties.ungraded1111Ysciescopu