Large Periods Of Time Research Articles

Reconciling evidence of oxidative weathering and atmospheric anoxia on Archean Earth.

Evidence continues to emerge for the production and low-level accumulation of molecular oxygen (O2) at Earth’s surface before the Great Oxidation Event. Quantifying this early O2 has proven difficult. Here, we use the distribution and isotopic composition of molybdenum in the ancient sedimentary record to quantify Archean Mo cycling, which allows us to calculate lower limits for atmospheric O2 partial pressures (PO2) and O2 production fluxes during the Archean. We consider two end-member scenarios. First, if O2 was evenly distributed throughout the atmosphere, then PO2 > 10–6.9 present atmospheric level was required for large periods of time during the Archean eon. Alternatively, if O2 accumulation was instead spatially restricted (e.g., occurring only near the sites of O2 production), then O2 production fluxes >0.01 Tmol O2/year were required. Archean O2 levels were vanishingly low according to our calculations but substantially above those predicted for an abiotic Earth system.

Analysis of the post-stress recovery of reverse leakage current in GaN HEMTs

The recovery of high reverse gate leakage current in AlGaN/GaN HEMTs has been analyzed in this study. To demonstrate the recovery after a thermal stress for large time periods (8–12 h), two mechanisms: phonon assisted tunnelling (PAT) and trap assisted tunnelling (TAT) have been utilized. Charge-based analytical models from literature have been used to match experimental leakage currents in reverse bias conditions. The degradation in the devices has been activated through thermal stress and the effect of high electric field induced inverse piezo electric effect has been eliminated. PAT component shows dominance in higher temperature regime (T > 500 K) and the slow recovery at 300 K with characteristic τ values around 5–13 h has been attributed to the TAT component through a deep trap level within the AlGaN barrier.

Recent trends in the timing of the growing season in New Zealand’s natural and semi-natural grasslands

ABSTRACT We investigate the temporal dynamics of shifts in phenological responses of a range of key stages of the growing season in New Zealand’s three indigenous grassland types over the last 16 years (2001–2016). A near-daily Normalized Difference Vegetation Index (NDVI) time series from MODerate Resolution Imaging Spectroradiometer (MODIS) was used to extract five annual growth phenology indices, namely the Start, End, Length, Peak and Peak NDVI of a growing season. The start of the growing season advanced (i.e. happened earlier) by a median of 7.2, 6.0 and 8.8 days per decade in Alpine, Tall Tussock and Low Producing grassland, whereas the end of the season advanced by a median of 4.5, 0.4 and 0.4 days in the three types respectively. The length of growing season was extended by 3.2, 5.2 and 7.1 days per decade in these three grassland types. Over 86% of the investigated grassland areas showed an advancing (earlier) start of the growing season, and 74% of Alpine grassland showed a trend toward an earlier end of season. Over 63% of all grassland types showed an increase in growing season length. A trend toward earlier growing season peak and overall increasing NDVI in the three grassland types indicate a tendency for increasing vegetation vitality in grassland ecosystems in recent years. The start of growing season was correlated with atmospheric pressure (negatively) and precipitation (positively) changes in winter–spring months, while the timing of the season end is positively correlated with air temperature and solar radiation in summer–autumn months. Our study shows that different grassland types differ in magnitude – but not in direction – of their recent shifts in timing of key growing season stages with high-alpine grasslands showing the strongest response. This study highlights the usefulness of remote sensing for monitoring ecosystem-level phenological shifts over large areas and long time periods.

Adaptive fault detection framework for recipe transition in semiconductor manufacturing

The fault detection and classification (FDC) model is a prediction model that utilizes the sensor data of equipment to predict whether each wafer is faulty or not in the future, which is important to achieve a high yield and reduce the cost. To construct a high-performance FDC model with deep learning, a large amount of labeled training data is required. However, in real-world semiconductor manufacturing processes, the transition of recipe leads to a change in the distribution of input sensor data, which causes performance degradation for the existing FDC model. Model retraining for the new recipe is required, but a large time period is required to acquire a large amount of labeled data for the new recipe. In this study, an adaptive fault detection framework is proposed to minimize the performance degradation caused by the transition of recipe. In this framework, immediately after the recipe transition occurs, unsupervised adaptation is employed to reduce the performance degradation. After inspection results for some new recipe wafers are acquired, semi-supervised adaptation is employed to quickly recover the performance with a small amount of labeled data. Through experiments using real-world data, we demonstrate that the proposed framework can adapt to the new recipe with a reduced performance degradation.

Propagation of radical ideas in societies with memory: Competition between radical strength and social cohesion

Based on a dynamical model employing fractional derivatives we discuss the evolution of radical groups in a society with memory in terms of the active proselytism of radical groups and the level of cohesion in social networks. We find the conditions that determine that the radical group either disappears or, on the contrary, achieves a permanent significant expression. We also discuss the possible intervention strategies on the susceptible population and show the conditions that lead to the disappearance of radical groups. Regarding the application of this policy, we find that the higher the level of memory, the maximum proportion of radicals decreases, but those groups manage to maintain their presence in society during a larger period of time.

Movements, Habitat Use, and Diving Behavior of Shortfin Mako in the Atlantic Ocean

The shortfin mako is one of the most important shark species caught in Atlantic Ocean pelagic fisheries. Given increasing concerns for the stock status of the species, the present study was designed to fill gaps in the knowledge of habitat use and movement patterns of shortfin mako in the Atlantic Ocean. From 2015 to 2019, 53 shortfin makos were tagged with pop-up satellite archival tags within the North, Central, and Southwest Atlantic Ocean, with successful transmissions received from 34 tags. Generally, sharks tagged in the Northwest and Central Atlantic moved away from tagging sites showing low to no apparent residency patterns, whereas sharks tagged in the Northeast and Southwest Atlantic spent large periods of time near the Canary Archipelago and Northwest Africa, and over shelf and oceanic waters off southern Brazil and Uruguay, respectively. These areas showed evidence of site fidelity and were identified as possible key areas for shortfin mako. Sharks spent most of their time in temperate waters (18–22°C) above 90 m; however, data indicated the depth range extended from the surface down to 979 m, in water temperatures ranging between 7.4 and 29.9°C. Vertical behavior of sharks seemed to be influenced by oceanographic features, and ranged from marked diel vertical movements, characterized by shallower mean depths during the night, to yo-yo diving behavior with no clear diel pattern observed. These results may aid in the development of more informed and efficient management measures for this species.

Practical geospatial and sociodemographic predictors of human mobility

Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

The Role of Gulls as Reservoirs of Antibiotic Resistance in Aquatic Environments: A Scoping Review.

The role of wildlife with long-range dispersal such as gulls in the global dissemination of antimicrobial resistance (AMR) across natural and anthropogenic aquatic environments remains poorly understood. Antibiotic-resistant bacteria have been detected in resident and migratory gulls worldwide for more than a decade, suggesting gulls as either sentinels of AMR pollution from anthropogenic sources or independent reservoirs that could maintain and disperse AMR across aquatic environments. However, confirming either of these roles remains challenging and incomplete. In this review, we present current knowledge on the geographic regions where AMR has been detected in gulls, the molecular characterization of resistance genes, and the evidence supporting the capacity of gulls to disperse AMR across regions or countries. We identify several limitations of current research to assess the role of gulls in the spread of AMR including most studies not identifying the source of AMR, few studies comparing bacteria isolated in gulls with other wild or domestic species, and almost no study performing longitudinal sampling over a large period of time to assess the maintenance and dispersion of AMR by gulls within and across regions. We suggest future research required to confirm the role of gulls in the global dispersion of AMR including the standardization of sampling protocols, longitudinal sampling using advanced satellite tracking, and whole-genome sequencing typing. Finally, we discuss the public health implications of the spread of AMR by gulls and potential solutions to limit its spread in aquatic environments.

Lower Kolyma multilingualism: Historical setting and sociolinguistic trends

Aims and objectives/purpose/research questions: The region of Lower Kolyma (LK), Russia, exhibits a unique multilingual situation which involves five languages from five different families: Even (Tungusic), Yakut (Turkic), Chukchi (Chukotko-Kamchatkan), Tundra Yukaghir (Yukaghir), and Russian (Slavic, Indo-European). Our research aims to show the ongoing changes in this setting and to explain the historical background of these trends. Design/methodology/approach: Our approach combines the results of a sociolinguistic survey, published data about the history and ethnography of the region, and observed linguistic effects of language contact. Data and analysis: The sample for our sociolinguistic study includes 196 individuals, born between 1878 and 1996. To assess the patterns of multilingualism we use two parameters: the number of acquired languages and the composition of the linguistic repertoires. To model the linguistic situation in the 20th century, an average life span parameter is used. Findings/conclusions: We demonstrate that in 1940 to 1980 it was most common for LK residents to use all languages of the region. Partly, at least, this was a consequence of the introduction of boarding schools, which became an additional domain for interethnic communication. In recent decades we observe a tendency for a shift to Russian with a concomitant decrease in speakers of the minority languages. Originality: We use an approach of retrospective interviews which allows us to cover a large time period and to provide an overview of changes in the multilingual setting during the 20th century. Thus, even though multilingualism in this area has already been discussed by Vakhtin, the methodological approach is different. Significance/implications: The paper contributes to the field of sociolinguistics, presenting a new case study of multilingualism from an area of Siberia which is understudied in this regard.

General Models for Handwritten Text Recognition: Feasibility and State-of-the Art. German Kurrent as an Example

Existing text recognition engines enables to train general models to recognize not only one specific hand but a multitude of historical hands within a particular script, and from a rather large time period (more than 100 years). This paper compares different text recognition engines and their performance on a test set independent of the training and validation sets. We argue that both, test set and ground truth, should be made available by researchers as part of a shared task to allow for the comparison of engines. This will give insight into the range of possible options for institutions in need of recognition models. As a test set, we provide a data set consisting of 2,426 lines which have been randomly selected from meeting minutes of the Swiss Federal Council from 1848 to 1903. To our knowledge, neither the aforementioned text lines, which we take as ground truth, nor the multitude of different hands within this corpus have ever been used to train handwritten text recognition models. In addition, the data set used is perfect for making comparisons involving recognition engines and large training sets due to its variability and the time frame it spans. Consequently, this paper argues that both the tested engines, HTR+ and PyLaia, can handle large training sets. The resulting models have yielded very good results on a test set consisting of unknown but stylistically similar hands.

Weak Supervision and Black-Litterman for Automated ESG Portfolio Construction

The authors propose an approach that combines modern machine learning techniques in natural language processing with portfolio optimization to incorporate views of companies’ environment, social, and governance (ESG) performance. This is automatically done through curating and subsequently converting large-scale news data into portfolio management decisions. They train a machine learning news data classifier to automatically identify several key ESG issues in news data over time. They then aggregate these issues over time to generate a views vector under the Black–Litterman portfolio framework and finally compare the performance of an ESG-tilted portfolio against a standard Black–Litterman portfolio. They also show how this can be achieved at scale, in a fully automated manner, and with consistency over large periods of time. Their methodology thus demonstrates a reasonable and agile method for asset managers to incorporate ESG considerations into their portfolios free of any exclusionary frameworks and without sacrificing performance. <b>TOPICS:</b>ESG investing, quantitative methods, statistical methods, big data/machine learning, portfolio construction <b>Key Findings</b> ▪ The authors describe a theoretical framework for using an automated NLP system to incorporate ESG criteria into portfolio optimization decisions. ▪ The authors demonstrate the technical implementation details for incorporating ESG signals into augmented portfolio weights. ▪ The authors demonstrate the competitive performance of such a portfolio through a long-term historical backtest with the S&amp;P 500 Index.

Assessment of the exclusion potential of suspects by using microbial signature in sexual assault cases: A scenario-based experimental study

Sexual assault offences are one of the most serious crimes committed against a person, typically rank only second to homicide, and represent one of the major challenges in forensic sciences. In some cases of sexual assault, there may be more than one suspect and the analysis of the biological evidence with currently available methods such as human DNA analysis may not yield results. In this study using the designed experimental model (with different experimental scenarios that can be designed), it was aimed to investigate the effectiveness of the microbiome profile for the identification of the offender by comparing the microbiome structures of the suspects' saliva samples with the mixed samples on the victim (saliva transmitted on breast skin) within the first 48 h after a sexual assault. For this purpose, a total of 44 samples was collected from four healthy females and four healthy males aged 20–50 years. Microbiome profiles of 44 samples in four groups containing saliva, breast skin and mixed samples were determined with the IIlumina HiSeq platform. Differentiation between samples were calculated by beta-diversity analysis methods by using QIIME software (v1.80). To compare the differentiation among samples and groups, unweighted UniFrac distance values were applied. Eight dominant microbial genera accounted for 86.15% of the total bacterial population in male saliva samples and were composed of Fusobacterium, Haemophilus, Neisseria, Porphyromonas, Prevotella, Rothia, Streptococcus and Veillonella. These genera constituted 76.72% of the bacterial population in mixed samples, whereas they constituted 34.40% of the bacterial population in the breast skin samples. Results of this study show that bacterial DNA in saliva can be recovered from saliva transmitted breast skin within at least 48 h. In conclusion, it has been found that examination of the microbiota of the saliva transmitted to breast skin of a sexual assault victim as a forensic tool may have the potential to determine the offender of the incident among the suspects or to reduce the number of suspects. Supporting the results of this study with further studies using parameters such as different case models, different body regions, larger time periods and a higher number of participants will be beneficial to draw accurate conclusion of the judicial case.

Bibliographia Dystopia, Volume 1: John Steinbeck's The Moon Is Downx

<i>Bibliographia Dystopia, Volume 1: John Steinbeck's</i> The Moon Is Downx

High incidence of mislabeling and a hint of fraud in the ceviche and sushi business

Peru is a major fishing country and has a high consumption of fish per capita of approximately 25.4 kg/person. The gastronomic boom of the Peruvian seafood business has been increasing steadily, mostly led by seafood restaurants called cevicherias for the prevalence of their main dish “ceviche”. We investigated the incidence of seafood fraud (i.e. mislabeling and substitution) in restaurants, supermarkets, and fish retail markets from September 2017 and October 2018, in Lima, the capital of Peru. We conducted DNA barcode sequencing of 364 samples collected from fresh and frozen fish fillets and ready-to-eat dishes (i.e. ceviche, sushi, and sashimi) and compared the expected species (from sale name) to observed species (from genetic identification). We found incidences of seafood fraud in 43% of samples collected. Restaurants exhibited a significantly higher incidence (61%) compared to supermarkets. Within restaurants, 71% of samples collected in cevicherias and 47% from sushi-bars exhibited mislabeling/substitution. For cevicherias, mislabeling/substitution was inversely associated to the cost of the dish. A small number of species were commonly used as substitutes, including species without fishery management plans. Also found in the samples were threatened species, notably: Anguilla anguilla and Thunnus thynnus. Our results revealed that seafood fraud, whether as mislabeling or as substitution, occurs in Lima and increases along the supply chain, it is influenced by product price, and includes threatened species. But this is a first snapshot. We recommend the implementation of an official and regulated list of commercial fish products and continuous monitoring at points of sale to understand the prevalence of seafood fraud over large periods of time and in other cities with high consumption of seafood products.

THEORY OF THE WAVE HISTORY OF ETHNOS IN THE NORTHERN HEMISPHERE

This article is devoted to the discovery of long-term cyclic psychophysical pendulum processes in the history of the ethnic groups of the northern hemisphere for the period from about 5500 BC to the 21st century. The discovery was made based on the analysis of related disciplines: DNA genealogy, linguistics, history, mythology, and climatology. And, in particular, my empirical conclusions. The "Theory of wave history " (hereinafter – TWH) is relevant for the study of deep and large-scale processes of history in large periods of time. TWH reveals the cyclical nature of history with a period of approximately 1850 years, in similar phases of which similar or similar psychophysical phenomena occur, which form the general vector of history. On the basis of the TWH, it is possible to resolve issues of white spots of history. TWH is based on the psychophysical feature of a person to create within the current and popular framework of society, because history itself puts its heroes on the lists. This shows its immanence and its worldliness. TWH does not aim to describe history as a collection of accumulated progress, it does not concern the economic and political spheres. TWH is a method of historical research.

Simultaneous Optimization of Size and Operation for Seawater Reverse Osmosis Network with Permeate Split and Interstage Permeate Split Designs

Simultaneous optimization of size and operation for the seawater reverse osmosis (RO) system is proposed through a superstructure-based mixed-integer differential-algebraic programming approach. The influences of operating conditions on the performance of pumps, RO membrane, and the properties of seawater are considered. The permeate split (PS) design, interstage permeate split (ISPS) design, and on–off model could give more operability and cost saving. For constant feed conditions, nearly constant profiles of control variables are obtained with a larger water tank. The maximum level in a water tank is an indicator to measure its buffer capacity. For the variation of temperature and time-of-use electricity price, although energy consumption can be effectively saved by one time shut off at a large water demand time period with high electricity price, not much water cost is reduced due to the increase of the plant size. Low load operation is an alternative solution. The ISPS design could balance the flux distributions inside the pressure vessel and extract more permeates with high quality; thus, the water cost and energy consumption could be reduced, compared with the interstage design (ISD) and the PS design. The uncertainties of the maximum concentration of product water and the membrane fouling factor are considered in a two-stage stochastic program. It is useful for identifying a robust optimal design and operation solutions for membrane-based seawater desalination.

Analysis of Time-Series Trends and ARIMA models to Forecast COVID – 19 cases

COVID-19 a novel corona virus originated from Wuhan China. It turned into a pandemic resulting in a large number of deaths and loss of livelihood. It is vital to determine the manner in which the number of cases propagates so that future pandemics can be tackled scientifically. However the pandemic can be controlled systematically using efficient health care systems. It is difficult to predict the pandemic propagation over a large period of time due to various factors. In this paper an analysis is made for short periods using statistical tools like predicting the probability curve, probability density function. Forecasting of Covid-19 cases is done using time series trend analysis and ARIMA models. The test of hypothesis for difference of means and standard deviations of the actual and forecasted values with 99% CI showed no significant difference between them.

Earthquake Early Warning Systems as an Asset Risk Management Tool

Losses due to strong seismic events can amount to millions or billions of US dollars and can affect regions for large periods of time, even severely undermining the economy. Earthquake early warning systems have proven to be helpful tools to mitigate the social and economic impact on communities and businesses. Recent case studies are briefly described, followed by examples of proactive measures for assets, infrastructure, citizens education and empowerment, complementary to earthquake early warning systems.

Analytical construction of the projectile motion trajectory in midair

A classic problem of the motion of a projectile thrown at an angle to the horizon is studied. Air resistance force is taken into account with the use of the quadratic resistance law. An analytic approach is mainly applied for the investigation. Equations of the projectile motion are solved analytically for an arbitrarily large period of time. The constructed analytical solutions are universal, that is, they can be used for any initial conditions of throwing. As a limit case of motion, the vertical asymptote formula is obtained. The value of the vertical asymptote is calculated directly from the initial conditions of motion. There is no need to study the problem numerically. The found analytical solutions are highly accurate over a wide range of parameters. The motion of a baseball, a tennis ball, and a shuttlecock of badminton are presented as examples.

Modeling of Photovoltaic Systems Using Matlab.(Dept.E)

This paper presents the modeling of photovoltaic power system elements. The solar cell is modeled mathematically for determining its electrical behavior. Current voltage and power voltage characteristics of each solar cell are determined by aiding of the model at different isolation levels. The solar cell model is then extended to obtain the module along with the array model. Experimental verification of the model indicates a good fitting of the model. The battery plays an important role in photovoltaic power system. Hence, the paper illustrates the modeling of one cell of a lead acid battery. The cell model is built up for discharging and charging processes. The two models are a function of the state of charge of the cell during charging and discharging conditions. These models give the cell voltage at any desired value of state of charge and hence against the operating time. The cell model can be extended for obtaining the whole battery model depending upon the number of battery cells. The battery models are very important for determining the battery electrical performance. For obtaining the battery performance experimentally, this needs a very large period of time for the experiments need. The models exhibit a good fitting with the battery experimental performance. The inverter model gives the relationship between its output and input. The inverter output is time dependence. Conversely its input is independent of time. The inverter model depends upon the pulse width modulation used. The pulse width modulation can be obtained by using rather triangular carrier or ramp carrier wave. The technique of pulse width modulation is used for improving the inverter output waveform and for reducing the harmonic contents. The importance of inverter model is for giving good strategy of pulse signals used for triggering the inverter switches. The model exhibits good conforms between the theoretical pulse strategy and the experimental strategy obtained by the inverter triggering circuit constructed in the laboratory.