Herbivory and leaf expansion of Cyathea phalerata Mart. (Cyatheaceae) in subtropical Atlantic Forest, southern Brazil

Herbivory is an interaction with great impact on plant communities since relationships between herbivores and plants are fundamental to the distribution and abundance of species over time and space. The aim of this study was to monitor the rate of leaf expansion in the tree fern Cyathea phalerata and evaluate the damage caused by herbivores to leaves of different ages and whether such damage is related to temperature and precipitation. The study was performed in a subtropical Atlantic Forest fragment located in the municipality of Caraá, in the northeast hillside of Rio Grande do Sul state, in southern Brazil. We monitored 24 mature individuals of C. phalerata with croziers in a population of approximately 50 plants. Leaf expansion rate, percentage of damaged leaves and leaf blade consumption rate by herbivory were calculated. Monthly means for temperature and accumulated rainfall were calculated from daily data. Croziers of C. phalerata were found to expand rapidly during the first and second months after emergence (3.98 cm day-1; 2.91 cm day-1, respectively). Damage caused by herbivory was observed in all of the monitored leaves, but none of the plants experienced complete defoliation. The highest percentage (57%) of damaged leaves was recorded at 60 days of monitoring, and also the highest monthly consumption rate of the blade (6.04%) occurred with young, newly-expanded leaves, while this rate remained between 1.50 and 2.21% for mature leaves. Rates of monthly leaf consumption and damaged leaves showed positive and strong relationship with each other and with temperature. The rapid leaf expansion observed for C. phalerata can be considered a phenological strategy to reduce damage to young leaves by shortening the developmental period and accelerating the increase of defenses in mature leaves.

The statistical analysis of daily data associated with different parameters of the New Coronavirus COVID-19 pandemic in Georgia and their monthly interval prediction from September 1, 2021 to December 31, 2021

The lockdown introduced in Georgia on November 28, 2020 contributed to positive trends in the spread of COVID-19 until February - the first half of March 2021. Then, in April-May 2021, the epidemiological situation worsened significantly, and from June to the end of December COVID - situation in Georgia was very difficult. In this work results of the next statistical analysis of the daily data associated with New Coronavirus COVID-19 infection of confirmed (C), recovered (R), deaths (D) and infection rate (I) cases of the population of Georgia in the period from September 01, 2021 to December 31, 2021 are presented. It also presents the results of the analysis of monthly forecasting of the values of C, D and I. As earlier, the information was regularly sent to the National Center for Disease Control & Public Health of Georgia and posted on the Facebook page https://www.facebook.com/Avtandil1948/. The analysis of data is carried out with the use of the standard statistical analysis methods of random events and methods of mathematical statistics for the non-accidental time-series of observations. In particular, the following results were obtained. Georgia's ranking in the world for Covid-19 monthly mean values of infection and deaths cases in investigation period (per 1 million population) was determined. Among 157 countries with population ≥ 1 million inhabitants in October 2021 Georgia was in the 4 place on new infection cases, and in September - in the 1 place on death. Georgia took the best place in terms of confirmed cases of diseases (thirteenth) in December, and in mortality (fifth) - in October. A comparison between the daily mortality from Covid-19 in Georgia from September 01, 2021 to December 31, 2021with the average daily mortality rate in 2015-2019 shows, that the largest share value of D from mean death in 2015-2019 was 76.8 % (September 03, 2021), the smallest 18.7 % (November 10, 2021). As in previous work [9,10] the statistical analysis of the daily and decade data associated with coronavirus COVID-19 pandemic of confirmed, recovered, deaths cases and infection rate of the population of Georgia are carried out. Maximum daily values of investigation parameters are following: C = 6024 (November 3, 2021), R = 6017 (November 15, 2021), D = 86 (September 3, 2021), I = 12.04 % (November 24, 2021). Maximum mean decade values of investigation parameters are following: C = 4757 (1 Decade of November 2021), R = 4427 (3 Decade of November 2021), D = 76 (2 Decade of November 2021), I = 10.55 % (1 Decade of November 2021). It was found that as in spring and summer 2021 [9,10], from September to December 2021 the regression equations for the time variability of the daily values of C, R, D and I have the form of a tenth order polynomial. Mean values of speed of change of confirmed -V(C), recovered - V(R), deaths - V(D) and infection rate V(I) coronavirus-related cases in different decades of months for the indicated period of time were determined. Maximum mean decade values of investigation parameters are following: V(C) = +139 cases/day (1 Decade of October 2021), V(R) = +124 cases/day (3 Decade of October 2021), V(D) = +1.7 cases/day (3 Decade of October 2021), V(I) = + 0.20 %/ day (1 decades of October 2021). Cross-correlations analysis between confirmed COVID-19 cases with recovered and deaths cases shows, that from September 1, 2021 to November 30, 2021 the maximum effect of recovery is observed on 12 and 14 days after infection (CR=0.77 and 0.78 respectively), and deaths - after 7, 9, 11, 13 and 14 days (0.70≤CR≤0.72); from October 1, 2021 to December 31, 2021 - the maximum effect of recovery is observed on 14 days after infection (RC=0.71), and deaths - after 9 days (CR=0.43). In Georgia from September 1, 2021 to November 30, 2021 the duration of the impact of the delta variant of the coronavirus on people (recovery, mortality) could be up to 28 and 35 days respectively; from October 1, 2021 to December 31, 2021 - up to 21 and 29 days respectively. Comparison of daily real and calculated monthly predictions data of C, D and I in Georgia are carried out. It was found that in investigation period of time daily and mean monthly real values of C, D and I practically fall into the 67% - 99.99% confidence interval of these predicted values. Traditionally, the comparison of data about C and D in Georgia (GEO) with similar data in Armenia (ARM), Azerbaijan (AZE), Russia (RUS), Turkey (TUR) and in the World (WRL) is also carried out.

Can Industrial Restructuring Improve Urban Air Quality?—A Quasi-Experiment in Beijing during the COVID-19 Pandemic

The conflict between economic growth and environmental pollution has become a considerable bottleneck to future development throughout the world. The industrial structure may become the possible key factor in resolving the contradiction. Using the daily data of air quality from January to April in 2019 and 2020, we used the DID model to identify the effects of industrial structure on air quality by taking the COVID-19 pandemic as a quasi-experiment. The results show that, first, the impact of profit of the secondary industry on air quality is ten times higher than that of the tertiary industry. Therefore, the secondary industry is the main factor causing air pollution. Second, the effect of the reduction in the secondary industry on the improvement of air quality is better than that of the tertiary industry in Beijing. Therefore, the implementation of Beijing’s non-capital function relief policy is timely and reasonable, and the adjustment of the industrial structure is effective in the improvement of air quality. Third, PM2.5, NO2, and CO are affected by the secondary and tertiary industries, where PM2.5 is affected most seriously by the second industry. Therefore, the transformation from the secondary industry to the tertiary industry can not only solve the problem of unemployment but also relieve the haze. Fourth, the result of O3 is in opposition to other pollutants. The probable reason is that the decrease of PM2.5 would lead to an increase in the O3 concentration. Therefore, it is difficult to reduce O3 concentrationby production limitation and it is urgent to formulate scientific methods to deal with O3 pollution. Fifth, the air quality in the surrounding areas can also influence Beijing. As Hebei is a key area to undertake Beijing’s industry, the deterioration of its air quality would also bring pressure to Beijing’s atmospheric environment. Therefore, in the process of industrial adjustment, the selection of appropriate regions for undertaking industries is very essential, which is worth our further discussion.

Comparison of Early Twentieth Century Arctic Warming and Contemporary Arctic Warming in the light of daily and sub-daily data

A review of many studies published since the late 1920s reveals that the main driving mechanisms responsible for the Early Twentieth Century Arctic Warming (ETCAW) are not fully recognized. The main obstacle seems to be our limited knowledge about the climate of this period and some forcings. A deeper knowledge based on greater spatial and temporal resolution data is needed. The article provides new (or improved) knowledge about surface air temperature (SAT) conditions (including their extreme states) in the Arctic during the ETCAW. Daily and sub-daily data have been used (mean daily air temperature, maximum and minimum daily temperature, and diurnal temperature range). These were taken from ten individual years (selected from the period 1934–50) for six meteorological stations representing parts of five Arctic climatic regions. Standard SAT characteristics were analyzed (monthly, seasonal, and yearly means), as were rarely investigated aspects of SAT characteristics (e.g., number of characteristic days; day-to-day temperature variability; and onset, end, and duration of thermal seasons). The results were compared with analogical calculations done for data taken from the Contemporary Arctic Warming (CAW) period (2007–16). The Arctic experienced warming between the ETCAW and the CAW. The magnitude of warming was greatest in the Pacific (2.7 °C) and Canadian Arctic (1.9 °C) regions. A shortening of winter and lengthening of summer were registered. Furthermore, the climate was also a little more continental (except the Russian Arctic) and less stable (greater day-to-day variability and diurnal temperature range) during the ETCAW than during the CAW.

Estimation of Underreported Cases of Infections and Deaths from COVID-19 for Countries with Limited and Scarce Data: Examples from Nepal

COVID-19 pandemic has overburdened the public healthcare system around the world. Further, lockdown imposed to curb the spread of pandemic has shown to have an adverse effect on economic and health status of an individual. It has also compelled us to switch from the physical world to virtual world, thus depriving us of benefits of person-to-person direct contact. People from developing countries are specially affected. An average person here lacks basic skills needed to survive in the digital world. Due to limited COVID-19 testing capacities in such countries, there is also less testing. Less testing means less contact tracing, underreported cases, and rapid spread of disease. In this paper, the underreported cases of daily infections and daily deaths are predicted using mathematical models. This is based on daily data published by the Government of Nepal. Here, Kathmandu valley is taken as a model area for estimation of underreporting. The behavior of probability of infection, probability of recovery, and probability of deaths is also mathematically analyzed. A time-dependent susceptible infected and recovered model is also proposed. Here, the second wave of COVID-19 is analyzed in detail from 1 Feb 2021 to 1 June 2021. The effect of lockdown on the psychology of people is also modeled with principal components analysis. The inherent and latent factors affecting the people in lockdown are identified. This is based on detailed primary data collected from a survey of 277 households.

Conducting Causal Analysis by Means of Approximating Probabilistic Truths

The current paper develops a probabilistic theory of causation using measure-theoretical concepts and suggests practical routines for conducting causal inference. The theory is applicable to both linear and high-dimensional nonlinear models. An example is provided using random forest regressions and daily data on yield spreads. The application tests how uncertainty in short- and long-term inflation expectations interacts with spreads in the daily Bitcoin price. The results are contrasted with those obtained by standard linear Granger causality tests. It is shown that the suggested measure-theoretic approaches do not only lead to better predictive models, but also to more plausible parsimonious descriptions of possible causal flows. The paper concludes that researchers interested in causal analysis should be more aspirational in terms of developing predictive capabilities, even if the interest is in inference and not in prediction per se. The theory developed in the paper provides practitioners guidance for developing causal models using new machine learning methods that have, so far, remained relatively underutilized in this context.

Prolonged exposure for pain and comorbid PTSD: a single-case experimental study of a treatment supplement to multiprofessional pain rehabilitation

Objectives It is unclear how to address PTSD in the context of chronic pain management. Here we examine the potential benefits of an addition of prolonged exposure (PE) therapy for PTSD for adults attending multidisciplinary CBT for chronic pain. Methods Four adults seeking treatment for chronic pain from a specialized pain rehabilitation service were offered PE for PTSD using a replicated, randomized, single-case experimental phase design, prior to commencing a 5-week multidisciplinary CBT program for chronic pain. Pre-, post-, follow-up, and daily measures allowed examination of PTSD and pain outcomes, potential mediators, and the trajectory of these outcomes and potential mediators during the subsequent pain-focused CBT program. Results Visual inspection of the daily data demonstrated changes in all outcome variables and potential mediators during the PE phase. Changes came at different times and at different rates for the four participants, highlighting the individual nature of putative change mechanisms. Consistent with expectation, PE produced reliable change in the severity of PTSD symptoms and trauma-related beliefs for all four participants, either by the end of the PE phase or the PE follow-up, with these gains maintained by the end of the 5-week pain-focused CBT program. However, few reductions in pain intensity or pain interference were seen either during the PE phase or after. Conclusions Although “disorder specific” approaches have dominated the conceptualising, study, and treatment of conditions like PTSD and chronic pain, such approaches may not be optimal. It may be better instead to approach cases in an individual and process-focused fashion. Ethical committee number 2013/381.

Modelling and Forecasting Portfolio Inflows

This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution.

Analysis of Precipitation Variability using Memory Based Artificial Neural Networks

This article analyzes the variability in precipitation of the Barak river basin using memory-based ANN models called Gamma Memory Neural Network(GMNN) and genetically optimized GMNN called GMNN-GA for precipitation downscaling precipitation. GMNN having adaptive memory depth is capable techniques in modeling time varying inputs with unknown input characteristics, while an integration of the model with GA can further improve its performances. NCEP reanalysis and HadCM3A2 (a) scenario data are used for downscaling and forecasting precipitation series for Barak river basin. Model performances are analyzed by using statistical criteria, RMSE and mean error and are compared with the standard SDSM model. Results obtained by using 24 years of daily data sets show that GMNN-GA is efficient in downscaling daily precipitation series with maximum daily annual mean error of 6.78%. The outcomes of the study demonstrate that execution of the GMNN-GA model is superior to the GMNN and similar with that of the standard SDSM.

A simple approach towards estimating solar irradiance

Solar radiation is or vital interest in characterizing an area with respect to its agricultural potential. However, these are not readily available for a large network. An attempt. has been made to deduce solar irradiance from climatic data, such as temperature range. Based on daily data of Pune for 1986-90, a relationship has been developed between atmospheric transmittance and the daily range of air temperature. The model developed has been tested on independent data and found to give fairly accurate estimation of solar irradiance. Nearly 70% of the variation in daily solar radiation could be explained by this simple method. The effect of solar irradiance on microclimate has also been discussed. The model developed has been tested for Hyderabad and Calcutta and found to give encouraging results.