Agent-Based Modelling and Disease: Demonstrating the Role of Human Remains in Epidemic Outbreaks

Hemorrhagic fever viruses present a high risk to humans, given their associated high fatality rates, extensive care requirements, and few relevant vaccines. One of the most famous such viruses is the Ebola virus, which first came to international attention during an outbreak in 1976. Another is Marburg virus, cases of which are being reported in Equatorial Guinea at the time of writing. Researchers and governments all over the world share a goal in seeking effective ways to reduce or prevent the influence or spreading of such diseases. This study introduces a prototype agent-based model to explore the epidemic infectious progression of a simulated fever virus. More specifically, this work seeks to recreate the role of human remains in the progression of such an epidemic, and to help gauge the influence of different environmental conditions on this dynamic

1D simulation of Electron acceleration by Inertial Alfven wave pulse

第3回極域科学シンポジウム/第36回極域宙空圏シンポジウム 11月27日（火） 国立極地研究所 2階大会議

Detection of Microcystin-Producing Cyanobacteria in a Reservoir by Whole Cell Quantitative PCR

AbstractCyanobacterial blooms are of increasing concern due to their negative impacts on environment and cyanobacteria-producing cyanotoxins, which create serious threats to animal and human health. Early detection of the harmful cyanotoxin-producing cyanobacteria is vital for the water management. In this study, we compared the amplification specificity for microcystin-producing cyanobacteria with primers targeting 16S rRNA genes, phycocyanin operon, and regions of mcy gene clusters. To detect presence of microcystin-producing cyanobacteria in a drinking water sourced reservoir, whole cell PCR assay was performed to amplify partial mcyA, mcyE, and the results were compared with that of direct microscopic counts based on morphologic identification. The positive liner correlation of the Microcystis colony by microscopic counts with mcyA containing cell, which was quantified by whole cell quantitative RT-PCR assay, was further confirmed. The results indicated that the microcystin-producer in the reservoir was mainly Microcystis. Therefore, we provided a simple, rapid, sensitive and applicable method for early detection of toxic cyanobacteria

Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes

Network structure evolves with time in the real world, and the discovery of changing communities in dynamic networks is an important research topic that poses challenging tasks. Most existing methods assume that no significant change in the network occurs; namely, the difference between adjacent snapshots is slight. However, great change exists in the real world usually. The great change in the network will result in the community detection algorithms are difficulty obtaining valuable information from the previous snapshot, leading to negative transfer for the next time steps. This paper focuses on dynamic community detection with substantial changes by integrating higher-order knowledge from the previous snapshots to aid the subsequent snapshots. Moreover, to improve search efficiency, a higher-order knowledge transfer strategy is designed to determine first-order and higher-order knowledge by detecting the similarity of the adjacency matrix of snapshots. In this way, our proposal can better keep the advantages of previous community detection results and transfer them to the next task. We conduct the experiments on four real-world networks, including the networks with great or minor changes. Experimental results in the low-similarity datasets demonstrate that higher-order knowledge is more valuable than first-order knowledge when the network changes significantly and keeps the advantage even if handling the high-similarity datasets. Our proposal can also guide other dynamic optimization problems with great changes.Comment: Submitted to IEEE TEV

トウナン アジア チイキ ニオケル セキドウ ジェット デンリュウ ノ タイヨウ カツドウ イゾンセイ

赤道ジェット電流(equatorial electrojet: EEJ)は,昼側磁気赤道直下における電離層電気伝導度の局所的増強に起因する電流系である.我々は,波長10.7 cmの太陽電波強度(F10.7)を太陽活動度の指標として用いて,EEJ強度の太陽活動度依存性を調査した.本研究で我々は,MAGDAS/CPMN 観測網のデータを用いて新しく構築されたEEJ指数の一つの成分であるEUEL指数のうち,2011 年の東南アジア地域のデータから算出されたEUEL 指数を解析に用いた.磁気赤道から±3°の狭い緯度帯に集中して流れるEEJ 帯の内側と外側にそれぞれ位置する2 観測点で得られたEUEL 指数の差からEEJ 強度を算出し(2 観測点法),F10.7 強度とEEJ強度に関してパワースペクトル解析と相関解析を行った.その結果,F10.7変動と正相で同期した約24 日と28 日周期を持つEEJ 強度の変動成分の存在を見いだした.一方でEEJ強度の日変化は,解析を行った期間,F10.7 の日変化と,低い相関を示していたことが明らかとなった.A:The equatorial electrojet (EEJ) is a current system caused by the enhanced ionospheric conductivity near the dayside magnetic dip equator. We examined the dependence of the EEJ on solar activity, represented by the 10.7 cm solar radio flux (F10.7). For this analysis, we used a new equatorial electrojet index, EUEL, provided by the MAGDAS/CPMN network in the Southeast Asia sector for the year 2011. Using a two-station method, the EEJ strength was calculated as the difference between the EUEL index of the dip equator station and the EUEL index of the off-dip equator station located outside the narrow channel (±3°in latitudinal range) of the EEJ band. The relationship between the EEJ component and the F10.7 index was then examined using power spectrum and correlation analyses. We found approximate 24-day and 28-day periodicities in the EEJ component, which are in phase with F10.7 variations. On the other hand, the daily values of EEJ showed low correlation with the daily F10.7 variations during the study period

Q-YOLO: Efficient Inference for Real-time Object Detection

Real-time object detection plays a vital role in various computer vision applications. However, deploying real-time object detectors on resource-constrained platforms poses challenges due to high computational and memory requirements. This paper describes a low-bit quantization method to build a highly efficient one-stage detector, dubbed as Q-YOLO, which can effectively address the performance degradation problem caused by activation distribution imbalance in traditional quantized YOLO models. Q-YOLO introduces a fully end-to-end Post-Training Quantization (PTQ) pipeline with a well-designed Unilateral Histogram-based (UH) activation quantization scheme, which determines the maximum truncation values through histogram analysis by minimizing the Mean Squared Error (MSE) quantization errors. Extensive experiments on the COCO dataset demonstrate the effectiveness of Q-YOLO, outperforming other PTQ methods while achieving a more favorable balance between accuracy and computational cost. This research contributes to advancing the efficient deployment of object detection models on resource-limited edge devices, enabling real-time detection with reduced computational and memory overhead

A Pareto-Based Adaptive Variable Neighborhood Search for Biobjective Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Different from most researches focused on the single objective hybrid flowshop scheduling (HFS) problem, this paper investigates a biobjective HFS problem with sequence dependent setup time. The two objectives are the minimization of total weighted tardiness and the total setup time. To efficiently solve this problem, a Pareto-based adaptive biobjective variable neighborhood search (PABOVNS) is developed. In the proposed PABOVNS, a solution is denoted as a sequence of all jobs and a decoding procedure is presented to obtain the corresponding complete schedule. In addition, the proposed PABOVNS has three major features that can guarantee a good balance of exploration and exploitation. First, an adaptive selection strategy of neighborhoods is proposed to automatically select the most promising neighborhood instead of the sequential selection strategy of canonical VNS. Second, a two phase multiobjective local search based on neighborhood search and path relinking is designed for each selected neighborhood. Third, an external archive with diversity maintenance is adopted to store the nondominated solutions and at the same time provide initial solutions for the local search. Computational results based on randomly generated instances show that the PABOVNS is efficient and even superior to some other powerful multiobjective algorithms in the literature