The Bank of Japan's exchange traded fund purchases: a help or hindrance to market efficiency?

We examine the impact of the Bank of Japan's exchange traded fund (ETF) purchases on two aspects of market efficiency—long-range dependence and price delay—of the TOPIX and Nikkei 225 indices. An increase in ETF purchases results in lower long-range dependence for both indices while the impact on the price delay varies according to index and measure. A sub-period analysis shows that the impact on market efficiency varies over time, with the dominant pattern being a delayed harmful effect, followed by a positive impact and thereafter a negative effect. The implications of these findings are discussed.

Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting.

With the growing popularity of digital currencies known as cryptocurrencies, there is a need to develop models capable of robustly analyzing and predicting the value of future returns in these markets. In this article, we extract behavior rules to predict the values of future returns in the Bitcoin, Ethereum, Litecoin, and Ripple closing series. We used categorical data in the analyses and Markov chain models from the first to the tenth order to propose a new way of establishing possible future scenarios, in which we analyze the dependence of memory on the dynamics of the process. We used the measurements of accuracy Mean Quadratic Error, Absolute Error Mean Percentage, and Absolute Standard Deviation for the choice of the best models. Our findings reveal that cryptocurrencies have long-range memory. Bitcoin, Ethereum, and Ripple exposed seven steps of memory, while Litecoin displayed nine memory steps. From the transitions between states that happened the most, we defined decision rules that assisted in the definition of future returns in the series. Our results can support the decisions of traders, investors, crypto-traders, and policy-makers.

Two years of COVID-19. Impacts on accessibility of a mental health service for immigrants and individuals in socio-economic difficulties

We extend our previous investigation of the effects of prehydrodynamic evolution on final-state observables in heavy-ion collisions [38] to smaller systems. We use a state-of-the-art hybrid model for the numerical simulations with optimal parameters obtained from a previous Bayesian study. By studying p-Pb collisions, we find that the effects due to the assumption of a conformal evolution in the prehydrodynamical stage are even more important in small systems. We also show that this effect depends on the time duration of the pre-equilibrium stage, which is further enhanced in small systems. Finally, we show that the recent proposal of a free-streaming with subluminal velocity for the pre-equilibrium stage, thus effectively breaking conformal invariance, can alleviate the contamination of final-state observables. Our study further reinforces the need for moving beyond conformal approaches in pre-equilibrium dynamics modeling, especially when extracting transport coefficients from hybrid models in the high-precision era of heavy-ion collisions.

The influence of COVID-19 pandemic on PM2.5 air quality in Northern Taiwan from Q1 2020 to Q2 2021.

The study aimed to investigate the PM2.5 variations in different periods of COVID-19 control measures in Northern Taiwan from Quarter 1 (Q1) 2020 to Quarter 2 (Q2) 2021. PM2.5 sources were classified based on long-range transport (LRT) or local pollution (LP) in three study periods: one China lockdown (P1), and two restrictions in Taiwan (P2 and P3). During P1 the average PM2.5 concentrations from LRT (LRT-PM2.5-P1) were higher at Fuguei background station by 27.9% and in the range of 4.9-24.3% at other inland stations compared to before P1. The PM2.5 from LRT/LP mix or pure LP (Mix/LP-PM2.5-P1) was also higher by 14.2-39.9%. This increase was due to higher secondary particle formation represented by the increase in secondary ions (SI) and organic matter in PM2.5-P1 with the largest proportion of 42.17% in PM2.5 from positive matrix factorization (PMF) analysis. A similar increasing trend of Mix/LP-PM2.5 was found in P2 when China was still locked down and Taiwan was under an early control period but the rapidly increasing infected cases were confirmed. The shift of transportation patterns from public to private to avoid virus infection explicated the high correlation of the increasing infected cases with the increasing PM2.5. In contrast, the decreasing trend of LP-PM2.5-P3 was observed in P3 with the PM2.5 biases of ∼45% at all the stations when China was not locked down but Taiwan implemented a semi-lockdown. The contribution of gasoline vehicle sources in PM2.5 was reduced from 20.3% before P3 to 10% in P3 by chemical signatures and source identification using PMF implying the strong impact of strict control measures on vehicle emissions. In summary, PM2.5 concentrations in Northern Taiwan were either increased (P1 and P2) or decreased (P3) during the COVID-19 pandemic depending on control measures, source patterns and meteorological conditions.

Characterization of Genetic Mutations Using DNA Walks and Multifractal Detrended Fluctuation Analysis

Monitoring genetic mutations in DNA sequences and their subsequent characterisation provide the possibility for rapid development of diagnostics and therapeutic tools. Here, it is shown that the "DNA walk" (DNAW) representation together with multifractal detrended fluctuation analysis (MFDFA), i.e. DNAW/MFDFA, form a reliable characterization method for studying local and global properties of similar DNA sequences. The DNAW/MFDFA approach allows to study the stochastic properties of genetic sequences by constructing a one-to-one map of the sequence onto a walk, and is able to uncover the self-similarity properties of DNA walks. These features are illustrated on a set of similar DNA sequences of SARS-CoV-2 virus, in which the differences in nucleotide bases arise due to genetic mutations. The results show that DNAW/MFDFA can be used to extract long-range correlation information and type and degree of fractal complexity.

Characterization of size-fractionated carbonaceous particles in the small to nano-size range in Batam city, Indonesia.

A cascade impactor type sampler equipped with an inertial filter was used to collect size-segregated particles down to ultrafine particles (UFPs or PM0.1) on Batam Island in Sumatra, Indonesia, bordered by Singapore and Malaysia during a wet and the COVID-19 pandemic season in 2021. Carbonaceous species, including organic carbon (OC) and elemental carbon (EC), were analyzed by a thermal/optical carbon analyzer to determine the carbon species and their indices. The average UFP was 3.1 ± 0.9 µg/m3, which was 2-4 times lower than in other cities in Sumatra during the same season in the normal condition. The PMs mass concentration was largely affected by local emissions but long-range transportation of particles from Singapore and Malaysia was also not negligible. The air mass arrived at the sampling site passed the ocean, which introduced out clean air with a low level of PMs. The backward trajectory of the air mass and the largest fraction of OC2 and OC3 in all sizes was identified as being transported from the 2 above countries. OC is the dominant fraction in TC and the ratio of carbonaceous components indicated that origin of all particle sizes was predominantly vehicle emissions. UFPs were dominantly emitted from vehicles exhaust emission, while coarser particles (>10 µm) were influenced by the non-exhaust emissions, such as tire wear. Other particles (0.5-1.0; 1.0-2.5; and 2.5-10 µm) were slightly affected by biomass burning. The effective carbon ratio (ECR) and inhalation dose (ID) related EC indicated that finer particles or UFPs and PM0.5-1 contributed more to human health and global warming.

A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images.

The COVID-19 pandemic has extremely threatened human health, and automated algorithms are needed to segment infected regions in the lung using computed tomography (CT). Although several deep convolutional neural networks (DCNNs) have proposed for this purpose, their performance on this task is suppressed due to the limited local receptive field and deficient global reasoning ability. To address these issues, we propose a segmentation network with a novel pixel-wise sparse graph reasoning (PSGR) module for the segmentation of COVID-19 infected regions in CT images. The PSGR module, which is inserted between the encoder and decoder of the network, can improve the modeling of global contextual information. In the PSGR module, a graph is first constructed by projecting each pixel on a node based on the features produced by the encoder. Then, we convert the graph into a sparsely-connected one by keeping K strongest connections to each uncertainly segmented pixel. Finally, the global reasoning is performed on the sparsely-connected graph. Our segmentation network was evaluated on three publicly available datasets and compared with a variety of widely-used segmentation models. Our results demonstrate that (1) the proposed PSGR module can capture the long-range dependencies effectively and (2) the segmentation model equipped with this PSGR module can accurately segment COVID-19 infected regions in CT images and outperform all other competing models.

Ventilation Methods against Indoor Aerosol Infection of COVID-19 in Japan

The importance of effective ventilation as one of the measures against COVID-19 is widely recognized worldwide. In Japan, at the early stage of the pandemic, in March 2020, an official announcement was made about basic ventilation measures against COVID-19. WHO also used the term "long-range aerosol or long-range airborne transmission” for the first time in December 2021. Based on the aerosol infection control measures before 2021 by the Japanese government, we conducted experiments on methods related to partition placement as an element of effective ventilation methods. In July 2022, the governmental subcommittee on Novel Coronavirus Disease Control provided an emergent proposal about effective ventilation methods to prevent two types of aerosol infection;infection by large aerosol on the air current and infection by small floating aerosol diffusion in a room. They also showed the way of setting droplet prevention partitions, which do not block off ventilation based on this investigation's results. © 2023 by the authors.

Fractal correlations in the Covid-19 genome sequence via multivariate rescaled range analysis

An approach based on fractal scaling analysis to characterize the organization of the Covid-19 genome sequences is presented in this work. The method is based on a multivariate version of the fractal rescaled range analysis implemented on a sliding window scheme to detect variations of long-range correlations over the genome sequence domains. As a preliminary step, the nucleotide sequence is mapped in a numerical sequence by following a Voss rule, resulting in a multichannel sequence represented as a binary matrix. Fractal correlations, quantified in terms of the Hurst exponent, depending on the region of the sequence, where the Covid-19 genome sequences are predominantly random, with some patches of weak long-range correlations. The analysis shows that the regions of randomness are more abundant in the Covid-19 sequences than in the primitive SARS sequence, which suggests that the Covid-19 virus possesses a more diverse genomic structure for replication and infection. The analysis constrained to the surface glycoprotein region shows that the Covid-19 sequence is less random as compared to the SARS sequence, which indicates that the Covid-19 virus can undergo more ordered replications of the spike protein. The Omicron variation exhibits an interesting pattern with some randomness similarities with the other SARS and the Covid-19 genome sequences. Overall, the results show that the multivariate rescaled range analysis provides a suitable framework to assess long-term correlations hidden in the internal organization of the Covid-19 genome sequence. © 2023

Asymmetric Multifractal Cross-Correlation Dynamics between Fiat Currencies and Cryptocurrencies

This paper performs the asymmetric multifractal cross-correlation analysis to examine the COVID-19 effects on three relevant high-frequency fiat currencies, namely euro (EUR), yen (YEN) and the Great Britain pound (GBP), and two cryptocurrencies with the highest market capitalization and traded volume (Bitcoin and Ethereum) considering two periods (Pre-COVID-19 and during COVID-19). For both periods, we find that all pairs of these financial assets are characterized by overall persistent cross-correlation behavior (alpha xy(0) > 0.5). Moreover, COVID-19 promoted an increase in the multifractal spectrum's width, which implies an increase in the complexity for all pairs considered here. We also studied the Generalized Cross-correlation Exponent, which allows us to verify that there is no asymmetric behavior between Bitcoin and fiat currencies and between Ethereum and fiat currencies. We conclude that investing simultaneously in major fiat currencies and leading cryptocurrencies can reduce the portfolio risk, leading to improvement in the investment results.

Characterization of Genetic Mutations Using DNA Walks and Multifractal Detrended Fluctuation Analysis

Monitoring genetic mutations in DNA sequences and their subsequent characterisation provide the possibility for rapid development of diagnostics and therapeutic tools. Here, it is shown that the "DNA walk" (DNAW) representation together with multifractal detrended fluctuation analysis (MFDFA), i.e. DNAW/MFDFA, form a reliable characterization method for studying local and global properties of similar DNA sequences. The DNAW/MFDFA approach allows to study the stochastic properties of genetic sequences by constructing a one-to-one map of the sequence onto a walk, and is able to uncover the self-similarity properties of DNA walks. These features are illustrated on a set of similar DNA sequences of SARS-CoV-2 virus, in which the differences in nucleotide bases arise due to genetic mutations. The results show that DNAW/MFDFA can be used to extract long-range correlation information and type and degree of fractal complexity.

Concentration and size distribution of atmospheric particles in southern Italy during COVID-19 lockdown period.

Many countries imposed lockdown (LD) to limit the spread of COVID-19, which led to a reduction in the emission of anthropogenic atmospheric pollutants. Several studies have investigated the effects of LD on air quality, mostly in urban settings and criteria pollutants. However, less information is available on background sites, and virtually no information is available on particle number size distribution (PNSD). This study investigated the effect of LD on air quality at an urban background site representing a near coast area in the central Mediterranean. The analysis focused on equivalent black carbon (eBC), particle mass concentrations in different size fractions: PM2.5 (aerodynamic diameter Da < 2.5 µm), PM10 (Da < 10 µm), PM10-2.5 (2.5 < Da < 10 µm); and PNSD in a wide range of diameters (0.01-10 µm). Measurements in 2020 during the national LD in Italy and period immediately after LD (POST-LD period) were compared with those in the corresponding periods from 2015 to 2019. The results showed that LD reduced the frequency and intensity of high-pollution events. Reductions were more relevant during POST-LD than during LD period for all variables, except quasi-ultrafine particles and PM10-2.5. Two events of long-range transport of dust were observed, which need to be identified and removed to determine the effect of LD. The decreases in the quasi-ultrafine particles and eBC concentrations were 20%, and 15-22%, respectively. PM2.5 concentration was reduced by 13-44% whereas PM10-2.5 concentration was unaffected. The concentration of accumulation mode particles followed the behaviour of PM2.5, with reductions of 19-57%. The results obtained could be relevant for future strategies aimed at improving air quality and understanding the processes that influence the number and mass particle size distributions.

Abundance, Source Apportionment and Health Risk Assessment of Polycyclic Aromatic Hydrocarbons and Nitro-Polycyclic Aromatic Hydrocarbons in PM2.5 in the Urban Atmosphere of Singapore

In this study, the levels of fine particulate matter (PM2.5), polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs (NPAHs) in PM2.5 samples were determined from 2020 to 2021 in Singapore. For analysis convenience, the sampling period was classified according to two monsoon periods and the inter-monsoon period. Considering Singapore's typically tropical monsoon climate, the four seasons were divided into the northeast monsoon season (NE), southwest monsoon season (SW), presouthwest monsoon season (PSW) and prenortheast monsoon season (PNE)). The PM2.5 concentration reached 17.1 +/- 8.38 mu g/m(3), which was slightly higher than that in 2015, and the average PAH concentration continuously declined during the sampling period compared to that reported in previous studies in 2006 and 2015. This is the first report of NPAHs in Singapore indicating a concentration of 13.1 +/- 10.7 pg/m(3). The seasonal variation in the PAH and NPAH concentrations in PM2.5 did not obviously differ owing to the unique geographical location and almost uniform climate changes in Singapore. Diagnostic ratios revealed that PAHs and NPAHs mainly originated from local vehicle emissions during all seasons. 2-Nitropyrene (2-NP) and 2-nitrofluoranthene (2-NFR) in Singapore were mainly formed under the daytime OH-initiated reaction pathway. Combined with airmass backward trajectory analysis, the Indonesia air mass could have influenced Singapore's air pollution levels in PSW. However, these survey results showed that no effect was found on the concentrations of PAHs and NPAHs in PM2.5 in Indonesia during SW because of Indonesia's efforts in the environment. It is worth noting that air masses from southern China could impact the PAH and NPAH concentrations according to long-range transportation during the NE. The results of the total incremental lifetime cancer risk (ILCR) via three exposure routes (ingestion, inhalation and dermal absorption) for males and females during the four seasons indicated a low long-term potential carcinogenic risk, with values ranging from 10(-10) to 10(-7). This study systematically explains the latest pollution conditions, sources, and potential health risks in Singapore, and comprehensively analyses the impact of the tropical monsoon system on air pollution in Singapore, providing a new perspective on the transmission mechanism of global air pollution.

A post-occupancy study of ventilation effectiveness from high-resolution CO2 monitoring at live theatre events to mitigate airborne transmission of SARS-CoV-2.

Mass-gathering events were closed around the world in 2020 to minimise the spread of the SARS-CoV-2 virus. Emerging research on the transmission of SARS-CoV-2 emphasised the importance of sufficient ventilation. This paper presents the results of an indoor air quality (IAQ) monitoring study over 82 events in seven mechanically ventilated auditoria to support the UK government Events Research Programme. Indoor carbon dioxide concentration was measured at high resolution before, during, and after occupancy to allow for assessment of the ventilation systems. Generally, good indoor air quality was measured in all auditoria, with average IAQ found to be excellent or very good for 70% of spaces. In some auditoria, spatial variation in IAQ was identified, indicating poor mixing of the air. In addition, surface and air samples were taken and analysed for the presence of bacteria by culture and SARS-CoV-2 using RT-qPCR in one venue. SARS-CoV-2 RNA was detected on a small number of surfaces at very low copy numbers, which are unlikely to pose an infection risk. Under the ventilation strategies and occupancy levels investigated, it is likely that most theatres pose a low risk of long-range transmission of COVID-19.

Inactivation of porcine epidemic diarrhea virus with electron beam irradiation under cold chain conditions.

The many instances of COVID-19 outbreaks suggest that cold chains are a possible route for the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, owing to the low temperatures of cold chains, which are normally below 0 °C, there are limited options for virus inactivation. Here, high-energy electron beam (E-beam) irradiation was used to inactivate porcine epidemic diarrhea virus (PEDV) under simulated cold chain conditions. This coronavirus was used as a surrogate for SARS-CoV-2. The possible mechanism by which high-energy E-beam irradiation inactivates PEDV was also explored. An irradiation dose of 10 kGy reduced the PEDV infectious viral titer by 1.68-1.76 log10TCID 50 / 100 µ L in the cold chain environment, suggesting that greater than 98.1% of PEDV was inactivated. E-beam irradiation at 5-30 kGy damaged the viral genomic RNA with an efficiency of 46.25%-92.11%. The integrity of the viral capsid was disrupted at 20 kGy. The rapid and effective inactivation of PEDV at temperatures below freezing indicates high-energy E-beam irradiation as a promising technology for disinfecting SARS-CoV-2 in cold chain logistics to limit the transmission of COVID-19.

WBMS: Waste Bin Management System for densely populated urban areas

Management of solid waste is a major challenge in densely populated urban areas. Such areas also have other predominant health management issues and improper waste management contributes to that. With the rise of COVID in the last two years hygiene has become the first priority of the Government to control the pandemic. Traditionally, municipality vehicles come as their routine to collect solid waste from commonly collected garbage bins placed by local authorities in residential and commercial areas. These bins are usually over-full and the waste roll-out of these bins is due to botching actions of the local occupants. These openly overflowing waste bins add to the contaminated environmental conditions leading to the spread of numerous diseases. Waste-bin management is a challenge in highly populated areas for municipal corporations. Internet of Things (IoT) can be used to monitor such waste bins and notify the municipal corporations about the waste level so that the bins can be cleared on time avoiding the spilling of garbage around them. In this paper, we have proposed an approach to waste bin management using IoT in urban areas. The model is built using LoRa waste-bin surveil units that will monitor and locate waste-bins in residential/commercial areas and notify the authority about their level. © 2022 IEEE.

Direct Numerical Simulation of a Moist Cough Flow using Eulerian Approximation for Liquid Droplets

The COVID-19 pandemic has inspired several studies on the fluid dynamics of respiratory events. Here, we propose a computational approach in which respiratory droplets are coarse-grained into an Eulerian liquid field advected by the fluid streamlines. A direct numerical simulation is carried out for a moist cough using a closure model for space-time dependence of the evaporation time scale. Stokes-number estimates are provided, for the initial droplet size of 10 mu m, which are found to be MUCH LESS-THAN1, thereby justifying the neglect of droplet inertia, over the duration of the simulation. Several important features of the moist-cough flow reported in the literature using Lagrangian tracking methods have been accurately captured using our scheme. Some new results are presented, including the evaporation time for a 'mild' cough, a saturation-temperature diagram and a favourable correlation between the vorticity and liquid fields. The present approach can be extended for studying the long-range transmission of virus-laden droplets.

Lightweight Local Transformer for COVID-19 Detection Using Chest CT Scans

Convolutional Neural Networks (CNNs) have mainly failed to explicitly model long-range dependencies, primarily because of their intrinsic locality. To address this issue, Transformers have drawn increasing interest in exploiting long-range dependencies among input data. In this study, we aim to enjoy the merits of both local and global feature extractions in CNN and Transformer architectures. To this end, we go beyond the conventional Transformer frameworks and introduce a highly efficient Transformer architecture for early diagnosis and treatment of COVID-19 patients using CT images. Unlike conventional data-hungry Transformers, our model relaxes the requirement of large-scale training data in vision Transformers and also outperforms the state-of-the-art studies. This flexibility empowers our Transformer architecture to be exploited in data-scarce domains. Moreover, we tailor our Transformer architecture in two ways to embody the principle of locality, which once belonged to CNNs. First, we minimally inject convolutional inductive bias into the early blocks of our Transformer architecture and eliminate standard image patching in the vanilla Transformers. Second, unlike typical patch integration in the standard Transformers, we benefit from a deformable convolution in our architecture to adaptively attend to a small set of key features corresponding to nearby patches. Extensive evaluations verify that our Transformer surpasses its counterparts, alleviates the computational complexity of Transformers, and deals with the lack of large-scale training dataset for COVID-19 diagnosis. © 2021 IEEE.

Advances in air quality research – current and emerging challenges

This review provides a community's perspective on air quality research focusing mainly on developments over the past decade. The article provides perspectives on current and future challenges as well as research needs for selected key topics. While this paper is not an exhaustive review of all research areas in the field of air quality, we have selected key topics that we feel are important from air quality research and policy perspectives. After providing a short historical overview, this review focuses on improvements in characterizing sources and emissions of air pollution, new air quality observations and instrumentation, advances in air quality prediction and forecasting, understanding interactions of air quality with meteorology and climate, exposure and health assessment, and air quality management and policy. In conducting the review, specific objectives were (i) to address current developments that push the boundaries of air quality research forward, (ii) to highlight the emerging prominent gaps of knowledge in air quality research, and (iii) to make recommendations to guide the direction for future research within the wider community. This review also identifies areas of particular importance for air quality policy. The original concept of this review was borne at the International Conference on Air Quality 2020 (held online due to the COVID 19 restrictions during 18–26 May 2020), but the article incorporates a wider landscape of research literature within the field of air quality science. On air pollution emissions the review highlights, in particular, the need to reduce uncertainties in emissions from diffuse sources, particulate matter chemical components, shipping emissions, and the importance of considering both indoor and outdoor sources. There is a growing need to have integrated air pollution and related observations from both ground-based and remote sensing instruments, including in particular those on satellites. The research should also capitalize on the growing area of low-cost sensors, while ensuring a quality of the measurements which are regulated by guidelines. Connecting various physical scales in air quality modelling is still a continual issue, with cities being affected by air pollution gradients at local scales and by long-range transport. At the same time, one should allow for the impacts from climate change on a longer timescale. Earth system modelling offers considerable potential by providing a consistent framework for treating scales and processes, especially where there are significant feedbacks, such as those related to aerosols, chemistry, and meteorology. Assessment of exposure to air pollution should consider the impacts of both indoor and outdoor emissions, as well as application of more sophisticated, dynamic modelling approaches to predict concentrations of air pollutants in both environments. With particulate matter being one of the most important pollutants for health, research is indicating the urgent need to understand, in particular, the role of particle number and chemical components in terms of health impact, which in turn requires improved emission inventories and models for predicting high-resolution distributions of these metrics over cities. The review also examines how air pollution management needs to adapt to the above-mentioned new challenges and briefly considers the implications from the COVID-19 pandemic for air quality. Finally, we provide recommendations for air quality research and support for policy.

Reasons for the observed tropospheric ozone weakening over south-western Europe during COVID-19: Strict lockdown versus the new normal.

In this work we investigate the variation in tropospheric ozone concentrations in south-western Europe in March and April 2020 in the context of COVID-19 disease, and to what extent the former situation was recovered one year after the pandemic outbreak. To carry this study, data from 15 regional background sites in Spain, from 2010 onwards, are used. Historic (2010-2019) and most recent tropospheric ozone concentrations are compared. March and April 2020 ozone concentrations declined over 15% in most cases, rising to 23-28% at sites facing the Mediterranean. Most of the decay was related to the reduction of hemispheric background concentrations, but those sites downwind continental emissions from the Iberian Peninsula and neighbouring countries experienced an additional lessening. By exploring O3 concentrations one year after, March and April 2021, the general decline with respect to 2010-2019 persist but its magnitude was substantially lessened with respect to the strict lockdown period. The pandemic situation unveiled that air pollution is not an endemic matter but it should be tackle with adequate actions. Ozone abatement plans for Mediterranean countries should need a pan-regional covenant in order to drop precursor emissions.