Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on Customer Purchase Behavior Analysis Based on Hidden Topic Markov Models

1. はじめに 近年,ECサイトを通じたオンラインでの商品購買は広く 一般消費者に受け入れられるようになり,市場規模が拡大し ている [1]. ECサイト上では,ユーザの各ページの閲覧行動 の詳細なログが取得可能であるため,これらのデータを活用 したWebマーケティング技術の重要性が高まっている. 一 方,ECサイトにおける購買に至る割合(Conversion Rate ; CVR)は通常,高々数%であることが知られている [2]. そのため,多くの ECサイトで CVRを改善するための施 策が必要とされている. 例えば,ユーザの購買意欲を把握す ることで効果的なタイミングで施策を打ったり,購買につな がりやすいページを把握することでそのページにユーザを誘 導したりすることにより,CVRの向上が見込める. 一般に,ユーザは商品を購入する前に ECサイト上の様々 なページを閲覧することが多い. したがって,ユーザの思考 状態(購買意欲の有無や嗜好,ニーズ等)の変化は ECサ イトのページ遷移傾向に反映されていると考えられる. その ため,閲覧履歴データを分析することで,ユーザの購買意欲 を抽出できれば,適切な施策の一助になると考えられる. こ の様な分析においては,ユーザの思考状態に依存して各閲覧 ページが生成されたと仮定することは極めて自然であり,観 測不可能な思考状態を観測可能な閲覧ページから推測できる と考えられる. そこで本研究では,ユーザのページ遷移の背 後に潜在トピック(潜在クラス)を仮定するモデルを提案 する. 従来の潜在クラスモデルをページ遷移データに適用すると, 連続的に閲覧した一連のページ全体に同じ潜在トピックを仮 定するか,または各ページに対し毎回異なる潜在トピック を仮定するかのどちらかである. しかし,閲覧中にユーザの 思考状態が変化する可能性は十分に考えられるため,連続的 に閲覧した一連のページ全体に同じ潜在トピックを仮定する ことは好ましくない. 一方で,ページごとにユーザの思考状 態が頻繁に変わることもほとんどないと考えられるため,毎 ページ異なる潜在トピックを仮定したトピックモデルの適用 も好ましくない. そこで本研究では, Hidden Topic Markov Models (HTMM)[3]をベースとし,リアルタイムに閲覧履歴データ の分析を可能にした購買行動分析モデルを提案する. HTMM は,前後関係を考慮しない Latent Dirichlet Allocation (LDA)[4]に隠れマルコフモデル(Hidden Markov Chain model ; HMM)の考え方を援用したモデルである. HTMM は文書に対して複数の潜在トピックを想定するが,LDAと は異なり,文書中の文単位での潜在トピックを考える. す なわち,各文中の単語は同じ潜在トピックに存し,かつ連続 する文は同じ潜在トピックを持つ可能性が高いことを仮定す る. このため,文書を同じ統計的特徴を持つ複数の文群に分 割することができる. したがって HTMMを閲覧履歴デー タに援用し,ページ単位での潜在トピックの推定を行うこと で,閲覧履歴を同じ特徴を持つ複数の群に分割することがで きる. そのうえで実際の閲覧履歴データから得られる購買実 績を併用することで,各潜在トピックの購買確率を求めるこ とができる. 提案モデルにより,ユーザの購買意思をリアル タイムで予測することが可能になると考えられる. 加えて, 各ユーザの潜在トピックの変化点を検出することが可能にな り,ユーザの思考が変化する際にどのようなページ遷移が起 こっているかを理解することができる. 本研究では,実閲覧 履歴データを用いて分析し,提案手法の有効性を検証する. 2. 関連研究 2.1. 隠れマルコフモデル Hidden Markov Model(HMM)は,観測不可能なマ ルコフ過程とその各状態に依存して生成されるシンボルの組 み合わせによって,シンボルの系列を表現するモデルである. HMMの対象となる系列データは,複数の状態を持ち,それ らの状態がある遷移確率により遷移するマルコフモデルと仮 定される. これを一次マルコフ性という. 2.2. Hidden Topic Markov Model Hidden Topic Markov Model(HTMM)は,LDAに HMMの考え方を援用した,文書生成モデルである. 図 1に HTMMのグラフィカルモデルを示す.
The Use of Systematic Review in EPA's Toxic Substances Control Act Risk Evaluations

Exposures to industrial chemicals in food, water, air, and in consumer products can cause harm to human health and the environment. Risk assessment is a key public-policy tool to inform decision-making to protect public health and ecological receptors from unsafe environmental exposures to chemicals. The U.S. Environmental Protection Agency’s Office of Pollution Prevention and Toxics (OPPT) conducts chemical risk assessments to inform federal policymaking in accordance with the Toxic Substances Control Act (TSCA) of 1976. Growing calls for reform of TSCA—which has not been updated to reflect both changes in chemical production and use or advances in understanding of exposure to and effects of chemicals in the environment—culminated in passage of the Frank R. Lautenberg Chemical Safety for the 21st Century Act in June 2016. The Lautenberg Act provides the OPPT increased authority to regulate chemicals existing before the original 1976 TSCA was amended. The Lautenberg Act gave EPA new authority to regulate chemicals. Stakeholders called for the adoption of systematic review within these important risk evaluations. That new authority came with a timetable that imposed tight deadlines on OPPT as it assembled teams, promulgated rules, and drafted the guidance documents, and operating procedures that prescribe how OPPT exerts its new authority. FEBRUARY 2021
International Spillover Effects of Quantitative Easing Policy: A Case Study on the U.S. and Japan

ABSTRACT This study analyses the influence of the changing nature of the U.S. quantitative easing (QE) policy’s international spillover on the Japanese economy following the global financial crisis. Based on existing studies, we construct a two-country vector autoregression model for the U.S. and Japan and estimate the time-varying parameters to comprehend the changing macroeconomic and financial structures of both countries. We not only confirmed the existence of spillover effects but also found significant changes in them over time. The spillover effects on Japan are larger for the real economy in the earlier phase and for financial markets in the later phase of the U.S. QE policy.
How interactions between tropical depressions and western disturbances affect heavy precipitation in South Asia

Interactions over South Asia between tropical depressions (TDs) and extratropical storms known as western disturbances (WDs) are known to cause extreme precipitation events, including those responsible for the 2013 floods over northern India. In this study, existing databases of WD and TD tracks are used to identify potential WD–TD interactions from 1979–2015; these are filtered according to proximity and intensity, leaving 59 cases which form the basis of this paper. Synoptic charts, vorticity budgets, and moisture trajectory analyses are employed to identify and elucidate common interaction types among these cases. Two broad families of interaction emerge. Firstly, a dynamical coupling of the WD and TD, whereby either the upper- and lower-level vortices superpose (a vortex merger), or the TD is intensified as it passes into the entrance region of a jet streak associated with the WD (a jet-streak excitation). Secondly, a moisture exchange between the WD and TD, whereby either anomalous moisture is advected from the TD to the WD, resulting in anomalous precipitation near the WD (a TD-to-WD moisture exchange), or anomalous moisture is advected from the WD to the TD (a WD-to-TD moisture exchange). Interactions are most common in the post-monsoon period as the subtropical jet, which brings WDs to the subcontinent, returns south; there is a smaller peak in May and June, driven by monsoon onset vortices. Precipitation is heaviest in dynamically-coupled interactions, particularly jet-streak excitations. Criteria for automated identification of interaction types are proposed, and schematics for each type are presented to highlight key mechanisms.
Associations between Meteorological Factors and Reported Mumps Cases from 1999 to 2020 in Japan

The present study investigated associations between epidemiological mumps patterns and meteorological factors in Japan. We used mumps surveillance data and meteorological data from all 47 prefectures of Japan from 1999 to 2020. A time-series analysis incorporating spectral analysis and the least-squares method was adopted. In all power spectral densities for the 47 prefectures, spectral lines were observed at frequency positions corresponding to 1-year and 6-month cycles. Optimum least-squares fitting (LSF) curves calculated with the 1-year and 6-month cycles explained the underlying variation in the mumps data. The LSF curves reproduced bimodal and unimodal cycles that are clearly observed in northern and southern Japan, respectively. In investigating factors associated with the seasonality of mumps epidemics, we defined the contribution ratios of a 1-year cycle (Q1) and 6-month cycle (Q2) as the contributions of amplitudes of 1-year and 6-month cycles, respectively, to the entire amplitude of the time series data. Q1 and Q2 were significantly correlated with annual mean temperature. The vaccine coverage rate of a measles–mumps–rubella vaccine might not have affected the 1-year and 6-month modes of the time series data. The results of the study suggest an association between mean temperature and mumps epidemics in Japan.
Making Low-Resolution Satellite Images Reborn: A Deep Learning Approach for Super-Resolution Building Extraction

Existing methods for building extraction from remotely sensed images strongly rely on aerial or satellite-based images with very high resolution, which are usually limited by spatiotemporally accessibility and cost. In contrast, relatively low-resolution images have better spatial and temporal availability but cannot directly contribute to fine- and/or high-resolution building extraction. In this paper, based on image super-resolution and segmentation techniques, we propose a two-stage framework (SRBuildingSeg) for achieving super-resolution (SR) building extraction using relatively low-resolution remotely sensed images. SRBuildingSeg can fully utilize inherent information from the given low-resolution images to achieve high-resolution building extraction. In contrast to the existing building extraction methods, we first utilize an internal pairs generation module (IPG) to obtain SR training datasets from the given low-resolution images and an edge-aware super-resolution module (EASR) to improve the perceptional features, following the dual-encoder building segmentation module (DES). Both qualitative and quantitative experimental results demonstrate that our proposed approach is capable of achieving high-resolution (e.g. 0.5 m) building extraction results at 2×, 4× and 8× SR. Our approach outperforms 8 other methods with respect to the extraction result of mean Intersection over Union (mIoU) values by a ratio of 9.38%, 8.20% and 7.89% with SR ratio factors of 2, 4, and 8, respectively. The results indicate that the edges and borders reconstructed in super-resolved images serve a pivotal role in subsequent building extraction and reveal the potential of the proposed approach to achieve super-resolution building extraction. Our code is available at https://github.com/xian1234/SRBuildSeg.
The Nexus Between Tourism, Environmental Degradation and Economic Growth

This study analyzes the impact of tourism on environmental degradation for 32 OECD countries employing panel estimation techniques taking into consideration cross-sectional dependence. The test results demonstrate that tourism and economic growth enhance CO2 emissions in these countries. The Emirmahmutoglu-Kose panel Granger causality test show that unidirectional association running from tourism to CO2 emissions exists in OECD countries. For individual countries, bidirectional association between tourism and CO2 emissions for Canada, a unidirectional association from tourism to CO2 emissions for Chile, Germany, Ireland, Latvia, and United States exists. Bidirectional relationship is also confirmed between tourist arrivals and GDP for Austria, Germany, and Slovak Republic. Moreover, unidirectional causality is found from tourist arrivals to GDP for Colombia, Latvia, Netherlands, Poland, and Spain.
The Importance of Self-Compassion in Terms of Self Psychology

Self-compassion has fostered a new way to build connectedness with oneself and affirmed the importance of offering compassion to oneself. However, there have been numerous arguments about the similarity with other self-related concepts, yet they have neglected its importance in self-psychology. For instance, self-compassion could be seen as an important element in Kohut’s concept of self in illustrating the self-object and self-experience theory, thus leading to a healthy self. Additionally, the self-schemas (self-esteem and self-efficacy) have been sufficiently shown to be associating with and predicting positive psychological outcomes and have further demonstrated the positive relations with self-compassion. Self-compassion thus demonstrated that similar features have been shared among the self-schemas. Moreover, with the claim that self-compassion was more precise than self-esteem, self-compassion should be emphasized by self-psychology then to develop a new era of “true self-esteem”.
Characterization of the effect of hemicellulose on the deposition of calcium sopas of tall oil using factorial design analysis

Le taux de depot des resines dans les tamis de pâte ecrue des usines kraft est affecte par de multiples variables telles que le pH, la temperature et la concentration de certaines matieres dans l'eau blanche. Grâce a l'analyse factorielle, on a etudie en laboratoire a l'aide d'un Vibromixer l'effet de l'hemicellulose sur le depot des savons de calcium du tall oil. A des concentrations d'hemicellulose superieures a 0.5 g/l, les depots etaient fortement reduits. On a pu deriver le rapport entre le depot des savons de calcium du tall oil et la teneur en hemicellulose, la temperature et le pH
Removal of Cu(II) and Zn(II) ions on corn cobs

Chemical modeling of metal biosorption requires the characterization of biomass used as sorbent The (local) structural environment of Cu(II) and Zn(II) sorbed on corn cobs biomass has been investigated by secondary electron microscopy (SEM) and energy-dispersive X-ray analysis (EDX). The IR Spectrum obtained for the above said biomass render a complex nature of the corn cobs biomass. Despite this complexity some characteristic peaks may be identified and assigned, revealing the presence of hydroxyl, carboxyl, carbonyl and amino functional groups in the structure of investigated biomass. The paper also deals with studies of physical and biochemical properties of the sorbent: bulk density, apparent density, surface area, iodine number, CEC. From biomass characterization a possible mechanism of biosorption is suggested. The sorption experiments were carried out at a pH of 5.5 and low and high ionic strength in a 0.01M Na 2 SO 4 solution. The results showed a high capacity of corn cobs with respect to the removal of the investigated metals cations.
Scatter-Air Ratios

Scatter-air ratios are empirical quantities derived from t issm-alr ratios for use in calcnlating the dose from scattered radiation at a point in an irrarliated phantom. Like tissue-air ratios, for each radiation quality, thcy tlcpcn(i o~dy on depth and beam cross-section hut are intlrpentlent of the dlstance from the wurcv. Their use for calculations within uniform and non-uniform radiation tx.am3 is outlinecl and the extension to account for tissue-heterogen~itiFs is tliscuwctl.
Acute effects of ambient air pollution on lower respiratory infections in Guangzhou children of China

Background Daily concentrations of air pollution are associated with lower respiratory diseases. This study investigated the short-term association of ambient air pollution with daily hospital admissions due to pneumonia among children aged 0–17 in Guangzhou city of China.Methods Daily ambient air pollution concentrations were extracted from the databases of the Chinese Environmental Monitoring Center. Children hospital admission counts for pneumonia during 2013–2018 were sourced from the Guangdong Maternal and Child Healthcare Hospital. Associations between outdoor air pollution levels and hospital admissions were estimated for time lags of zero up to seven days using Quasi-Poisson regression models, adjusted for seasonal variations, meteorological variables, day of week and holidays. The associations between clinical pathogenic microorganism inspection results for pneumonia and air pollutants were calculated using Lasso regression models.Results Ambient air pollutants were all positively associated with children hospital admissions due to pneumonia of all ages. Significant associations were found for air pollutants except for inhalable particulate matter (PM) \(\le\)10 µm in aerodynamic diameter (PM10) in children aged 0–17 years. Increments of an interquartile range (IQR) (279.10µg/m3 and 28.42µg/m3, respectively) in the 7-day-average level of carbon monoxide (CO) and nitrogen dioxide (NO2) were associated with a 26.17% (95% confidence interval (CI) 1.40%-56.98%) and 25.09% (95%CI 0.54%-55.64%) increase in pneumonia hospitalizations for children aged 6–17, respectively. An IQR increase in CO concentrations (279.10µg/m3) was associated with a 15.15% (95%CI 4.34%-27.08%) increase in pneumonia hospitalizations for children aged 1–5. Estimates for CO were statistically significant among children aged 1–5 years in summer. The associations remained stable in two-pollutant models. Daily cases of microbial detection for pneumonia were positively associated with daily NO2 concentration. The pneumonia hospitalizations due to Mycoplasma pneumonia, Flu A virus and Flu B virus, the predominant pathogenic microorganisms detected in children aged 0–5 are apparently associated with levels of PMs, CO, NO2 and O3.Conclusions Strong associations among hospital admissions for lower respiratory infections, pathogenic microorganisms and daily levels of air pollution confirm the urgent need to adopt sustainable improving ambient air quality policies in Guangzhou city to protect children's health.
Semantic segmentation sample augmentation based on simulated scene generation—case study on dock extraction from high spatial resolution imagery

ABSTRACT Deep learning-based semantic segmentation methods, such as fully convolutional networks (FCNs), are state-of-the-art techniques for object extraction from high spatial resolution images. However, collecting massive scene-formed training samples typically required in FCNs is time-consuming and labour-intensive. A suit of automatic sample augmentation schemes based on simulated scene generation is proposed in this study to reduce the manual workload. Proposed schemes include style transfer, target embedding, and mixed modes by utilizing techniques, such as texture transfer, image inpainting, and region-line primitive association framework, which automatically expand the sample set on the basis of a small number of real samples. Dock extraction experiments using UNet are conducted on China’s GaoFen-2 imagery with expanded sample sets. Results showed that the proposed schemes can successfully generate sufficient simulation samples, increase sample diversity, and subsequently improve semantic segmentation accuracy. Compared with the results that use the original real sample set, measures of F 1-score (F 1) and intersection over union (IoU) of dock extraction accuracy demonstrate a maximum improvement of 20.53% and 23.01%, respectively, after sample augmentation.
Development and Characterization of Mitoxantrone-Loaded Glutaraldehyde Crosslinked Sodium Alginate Nanoparticles for the Delivery of Anticancer Drugs

Every year millions of new cases of various types of cancers are diagnosed, leading to an alarming rate of fatalities. Mitoxantrone is an anthracenedione antineoplastic agent which is used in the treatment of various types of cancer, mostly acute myeloid leukemia and prostate cancer. In spite of its therapeutic applications, it possesses numerous limitations and side effects including specific targeting and systemic toxicity. Sodium alginate is a biodegradable, mucoadhesive and biocompatible polymer commonly used in drug delivery applications. Glutaraldehyde is a saturated dialdehyde and is used as a polymer cross linker. In this study, mitoxantrone-loaded glutaraldehyde-sodium alginate nanoparticles were developed by ionic gelation method and characterized (determination of particle size, drug entrapment efficiency, drug release and its kinetics) for the delivery of anticancer drugs. The nanoparticles mean particle size was found to be within the acceptable range. The entrapment efficiency was also on the higher side with sustained drug release. The findings of this study reveal promising potential of delivery system and project the way forward for further in vitro and in vivo investigations. Original Research Article Amjad and Raja; JPRI, 33(19A): 18-24, 2021; Article no.JPRI. 66919 19
Influence of the Indicators of Carbohydrate Metabolism on the Elemental Status

This study examined the elemental status of the hair and blood biochemistry of young men in the Orenburg region (n=38) with different glucose levels. Estimation of the element status was carried out through the study of the chemical composition of the hair by ICP-AES and ICP-MS. It was found that the studied serum parameters (Ca, Mg, Fe, P cholesterol, thyroxine, TSH and testosterone) were within normal values, and no statistically significant differences were obtained when comparing the groups. The concentration of chemical elements in the hair of the men in each group was in the range of physiologically acceptable values for this region. In the group with elevated glucose levels, there was a tendency for the men to have lower values of Ca andMg, and higher levels of K, Na, P and a number of toxic elements (Al, Cd, Sn, Hg, Pb).
Utilização de Grafite Funcionalizado em Eletrodos de Pasta de Carbono para Detecção Simultânea de Ácido Ascórbico e Ácido Fólico em Amostra de Urina

This work describes the development of a sensor, using functionalized graphite in basic medium, to simultaneously detect ascorbic acid and folic acid. The graphite was functionalized in solution of NaOH 4 mol L -1 , before its use in the preparation of the carbon paste electrode. The electrochemical techniques of cyclic voltammetry and differential pulse voltammetry were used to characterize the redox process, which occurs in the working electrode’s surface. The electrochemical impedance spectroscopy was used to characterize the influence of the functionalization in the electrical properties of the electrode-solution interface. The obtained results showed that the functionalization increases the electrocatalytic activity of the graphite, verified by an increase of about 1.8x in the peak of oxidation current of both ascorbic and folic acid. The increase in the electrocatalytic activity of the functionalized graphite was due to a decrease in the resistance to charge transfer and an increase of the standard rate constant of reaction . The obtained results showed that carbon paste electrodes utilizing functionalized graphite can be used in simultaneous detection of ascorbic and folic acid. The sensor has been used successfully in the simultaneous detection of ascorbic acid and folic acid in human urine samples contaminated artificially. The results showed a recovery of 96-101% and a relative standard deviation less than 3%. DOI: http://dx.doi. org/10.17807/orbital.v8i3. 820
Risk Cloud Model for Evaluating Nautical Navigational Environments

Uncertainty makes the risk evaluation of complex water transportation systems (WTSs) a difficult task. To achieve reasonable results while accounting for uncertainty, the risk evaluation of nautical navigational environments (NNEts) is often based on classical cloud model theory. This study proposes the concept of a risk cloud model (RCM) for NNEt evaluation and uses a fuzzy statistics-based computational approach to obtain the RCM parameters. As a case study, the proposed RCM method was applied to the risk evaluation of the Qiongzhou Strait. The performance of the proposed method was compared to those of a fuzzy theory-based method and an earlier proposed simplified algorithm. The results of the case study demonstrated the effectiveness of the proposed method along with several key advantages. First, the method could deal with uncertainty, take advantage of multichannel information, and evaluate risk features. Second, the RCM droplets intuitively displayed the qualitative and quantitative characteristics of risk levels, which facilitated understanding and analysis. Third, it showed a good sensitivity to ensure the refinement of evaluation results. The proposed method offered an improved approach to NNEt risk evaluation under uncertain conditions.