On the index of length four minimal zero-sum sequences

Let $G$ be a finite cyclic group. Every sequence $S$ over $G$ can be written in the form $S=(n_1g)\cdot\ldots\cdot(n_lg)$ where $g\in G$ and n_1, \ldots, n_l\in[1, \ord(g)], and the index \ind(S) of $S$ is defined to be the minimum of (n_1+\cdots+n_l)/\ord(g) over all possible $g\in G$ such that $\langle g \rangle =G$. A conjecture on the index of length four sequences says that every minimal zero-sum sequence of length 4 over a finite cyclic group $G$ with $\gcd(|G|, 6)=1$ has index 1. The conjecture was confirmed recently for the case when $|G|$ is a product of at most two prime powers. However, the general case is still open. In this paper, we make some progress towards solving the general case. Based on earlier work on this problem, we show that if $G=\langle g\rangle$ is a finite cyclic group of order $|G|=n$ such that $\gcd(n,6)=1$ and $S=(x_1g)(x_2g)(x_3g)(x_4g)$ is a minimal zero-sum sequence over $G$ such that $x_1,\cdots,x_4\in[1,n-1]$ with $\gcd(n,x_1,x_2,x_3,x_4)=1$, and $\gcd(n,x_i)>1$ for some $i\in[1,4]$, then \ind(S)=1. By using an innovative method developed in this paper, we are able to give a new (and much shorter) proof to the index conjecture for the case when $|G|$ is a product of two prime powers

Acute and chronic health impacts of PM2.5 in China and the influence of interannual meteorological variability

High concentrations of PM2.5 in China have an adverse impact on human health and present a major problem for air quality control. Here we evaluate premature deaths attributable to chronic and acute exposure to ambient PM2.5 at different scales in China over 2013-2017 with an air quality model at 5 km resolution and integrated exposure-response methods. We estimate that 1,210,000 (95% Confidence Interval: 720,000-1,750,000) premature deaths annually are attributable to chronic exposure to PM2.5 pollution. Chongqing exhibits the largest chronic per capita mortality (1.4‰) among all provinces. A total of 116,000 (64,000-170,000) deaths annually are attributable to acute exposure during pollution episodes over the period, with Hubei province showing the highest acute per capita mortality (0.15‰). We also find that in urban areas premature deaths are 520,000 (320,000-760,000) due to chronic and 55,000 (3,000-81,000) due to acute exposure, respectively. At a provincial level, the annual mean PM2.5 concentration varies by ±20% due to interannual variability in meteorology, and PM2.5-attributable chronic mortality varies by ±8%, and by >±5% and ±1% at a national level. Meteorological variability shows larger impacts on interannual variations in acute risks than that in chronic exposure at both provincial (>±20%) and national (±4%) levels. These findings emphasize that tighter controls of PM2.5 and precursor emissions are urgently needed, particularly under unfavorable meteorological conditions in China

Hybrid interpretable predictive machine learning model for air pollution prediction

Air pollution prediction is a burning issue, as pollutants can harm human health. Traditional machine learning models usually aim to improve the overall prediction accuracy but neglect the accuracy for peak values. Moreover, these models are not interpretable. They fail to explain the interactions between various determining factors and their impacts on air pollution. In this paper, we propose a new Hybrid Interpretable Predictive Machine Learning model for the Particulate Matter 2.5 prediction, which carries two novelties. First, a hybrid model structure is constructed with deep neural network and Nonlinear Auto Regressive Moving Average with Exogenous Input model. Second, automatic feature generation and feature selection procedures are integrated into this hybrid model. The experimental results demonstrate the superiority of our model over other models in prediction accuracy for peak values and model interpretability. The proposed model reveals how PM2.5 prediction is estimated by historical PM2.5, weather, and season. The accuracies (measured by correlation coefficients) of 1, 3 and 6-hour-ahead prediction are 0.9870, 0.9332 and 0.8587, respectively. More importantly, the proposed approach presents a new interpretable machine learning framework for time series data, enabling to explain complex dependence of multimode inputs, and to build reliable predictive models

High-Efficiency Closed-Loop Control of a Robotic Fish via Virtual Musculoskeletal Methodology

Improving propulsion efficiency holds the promise of enabling the robotic fish to work for a long time with a limited battery in its small body. In this paper, for the swimming of a bionic robotic fish, we present a virtual musculoskeletal control method from the bionic model of the joint driven by agonist muscle and antagonist muscle. A closed-loop method composed of two loops is proposed as a rule of thumb for the speed control of the robotic fish. The outer loop adjusts the swimming speed using the speed deviation; the inner loop regulates the stiffness according to the virtual muscle spindle feedback to fit the water environment. Compared with the proportion control, the evaluation results show that the virtual musculoskeletal methodology increases the efficiency by 3.4% in the steady flow and 7% in the Karman-vortex flow. This algorithm provides a new idea for the joint-space control of the bionic robots that need to reduce the energy consumption of movements

Multi-task deep neural network for air pollution prediction and spatial effect analysis

The deterioration of air quality is a crucial issue because of the negative effect it brings to human health and environment. Many data-driven models have been established to predict air pollution of one single location from its local weather and pollutant factors. However, an existing challenge is to develop a single model that can simultaneously predict air pollution of multiple locations and investigate the spatial effect. In response to this problem, this paper proposes a multi-task deep neural network (MT-DNN) model. The proposed model can identify the shared key factors that affect air pollution of multiple locations, build a shared model structure with these factors, and produce predictions for multiple locations. The proposed model is applied to predict PM2.5 of six locations in Beijing area. The experiment results show that high prediction accuracies have been achieved by the model. More importantly, the model reveals the spatiotemporal correlations between factors of multiple locations, and how these factors interact with each other. These findings present a new framework for time series prediction problem with spatial effect analysis.</p

Hybrid interpretable predictive machine learning model for air pollution prediction

Air pollution prediction is a burning issue, as pollutants can harm human health. Traditional machine learning models usually aim to improve the overall prediction accuracy but neglect the accuracy for peak values. Moreover, these models are not interpretable. They fail to explain the interactions between various determining factors and their impacts on air pollution. In this paper, we propose a new Hybrid Interpretable Predictive Machine Learning model for the Particulate Matter 2.5 prediction, which carries two novelties. First, a hybrid model structure is constructed with deep neural network and Nonlinear Auto Regressive Moving Average with Exogenous Input model. Second, automatic feature generation and feature selection procedures are integrated into this hybrid model. The experimental results demonstrate the superiority of our model over other models in prediction accuracy for peak values and model interpretability. The proposed model reveals how PM2.5 prediction is estimated by historical PM2.5, weather, and season. The accuracies (measured by correlation coefficients) of 1, 3 and 6-hour-ahead prediction are 0.9870, 0.9332 and 0.8587, respectively. More importantly, the proposed approach presents a new interpretable machine learning framework for time series data, enabling to explain complex dependence of multimode inputs, and to build reliable predictive models

SAA1 regulated by S1P/S1PR1 promotes the progression of ESCC via β-catenin activation

Abstract Serum amyloid A1 (SAA1), an inflammation-related molecule, is associated with the malignant progression of many tumors. This study aimed to investigate the role of SAA1 in the progression of esophageal squamous cell carcinoma (ESCC) and its molecular mechanisms. The expression of SAA1 in ESCC tissues and cell lines was analyzed using bioinformatics analysis, western blotting, and reverse transcription-quantitative PCR (RT‒qPCR). SAA1-overexpressing or SAA1-knockdown ESCC cells were used to assess the effects of SAA1 on the proliferation, migration, apoptosis of cancer cells and the growth of xenograft tumors in nude mice. Western blotting, immunofluorescence and RT‒qPCR were used to investigate the relationship between SAA1 and β-catenin and SAA1 and sphingosine 1-phosphate (S1P)/sphingosine 1-phosphate receptor 1 (S1PR1). SAA1 was highly expressed in ESCC tissues and cell lines. Overexpression of SAA1 significantly promoted the proliferation, migration and the growth of tumors in nude mice. Knockdown of SAA1 had the opposite effects and promoted the apoptosis of ESCC cells. Moreover, SAA1 overexpression promoted the phosphorylation of β-catenin at Ser675 and increased the expression levels of the β-catenin target genes MYC and MMP9. Knockdown of SAA1 had the opposite effects. S1P/S1PR1 upregulated SAA1 expression and β-catenin phosphorylation at Ser675 in ESCC cells. In conclusion, SAA1 promotes the progression of ESCC by increasing β-catenin phosphorylation at Ser675, and the S1P/S1PR1 pathway plays an important role in its upstream regulation

Diagenetic characteristics under abnormally low pressure: A case from the Paleogene of southern Western Sag, Liaohe Depression, Bohai Bay Basin

The effects of low pressure and abnormally low pressure on reservoir diagenesis and physical property of the Paleocene in southern part of Western Sag of Liaohe Depression, Bohai Bay Basin have been analyzed using large amounts of pressure, physical property and formation testing data. When formation pressure is low or abnormally low, the pore fluid has lower pressure, the overburden litho-static pressure is largely born by the sandstone framework, sometimes over compaction occurs, leading to densification of reservoir and stronger mechanical compaction; residual formation pressure has a negative correlation with carbonate cement content, low pressure or abnormally low pressure tight sandstone formations have higher carbonate cement content than sandstone formations with hydrostatic pressure or weak overpressure; pore fluid in sandstones with low pressure or abnormally low pressure has higher Si4+, conducive to the siliceous cementation; when dissolution happens, reservoirs with low pressure or abnormally low pressure, poor in original physical properties, are not favorable for the injection of dissolution fluid and the expulsion of dissolution products, so they have weaker dissolution. In summary, reservoirs with low pressure or abnormally low pressure have poorer physical properties. Key words: abnormally low pressure, diagenesis, reservoir physical property, tight sandstone, Western Sag, Liaohe Depressio

Experimental Study of the Feasibility of Air Flooding in an Ultra-Low Permeability Reservoir

The development effect of water flooding in an ultra-low permeability reservoir is poor due to its poor physical properties and high shale content, so an experimental study of air flooding which helps to complement energy production was carried out. Based on the Accelerating Rate Calorimeter experimental results, the crude oil of N block in L oilfield can undergo low-temperature oxidation reactions, which are the basic condition for air flooding. Three groups of experimental natural cylinder cores designed for oil displacement, water flooding and air flooding were used respectively, and the relationship between differential pressure, oil recovery, injection capacity with injection volume was investigated. It is observed that the recovery efficiency increased 2.58%, the injection-production pressure difference dropped 60% and the injection capability increased 60% in the experiment of shifting air flooding after water flooding to 75% moisture content, compared with water flooding alone. It has been shown in the results that the recovery efficiency improved sharply more than water flooding, the effect of depressurization and augmented injection was obvious, and the air displacement was thus validated. We suggest that other science and technology workers should perform further tests and verify this result through numerical simulation