Associations Between Symptoms of Depression and Air Pollutant Exposure Among Older Adults: Results From the Taiwan Longitudinal Study on Aging (TLSA)

Background: Little epidemiological research has investigated the associations of air pollutant exposure over various time windows with older adults' symptoms of depression. This study aimed to analyze the relationships of long- and short-term ambient air pollution exposure (to coarse particulate matter, O3, SO2, CO, and NOx) with depressive symptoms in a sample of community-dwelling older adults.Methods: A sample of older adults (n = 1,956) was recruited from a nationally representative multiple-wave study (Taiwan Longitudinal Study on Aging). Between 1996 and 2007, four waves of surveys investigated depressive symptoms by using the 10-item Center for Epidemiologic Studies Depression questionnaire. We approximated air pollutant concentrations from 1995 to 2007 by using daily concentration data for five air pollutants at air quality monitoring stations in the administrative zone of participants' residences. after adjusting for covariates, we applied generalized linear mixed models to analyze associations for different exposure windows (7-, 14-, 21-, 30-, 60-, 90-, and 180-day and 1-year moving averages).Results: In a one-pollutant model, long- and short-term exposure to CO and NOx was associated with heightened risks of depressive symptoms; the odds ratio and corresponding 95% confidence interval for each interquartile range (IQR) increment in CO at 7-, 14-, 21-, 30-, 60-, 90-, and 180-day and 1-year moving averages were 1.232 (1.116, 1.361), 1.237 (1.136, 1.348), 1.216 (1.128, 1.311), 1.231 (1.133, 1.338), 1.224 (1.124, 1.332), 1.192 (1.106, 1.285), 1.228 (1.122, 1.344), and 1.180 (1.102, 1.265), respectively. Those for each IQR increment in NOx were 1.312 (1.158, 1.488), 1.274 (1.162, 1.398), 1.295 (1.178, 1.432), 1.310 (1.186, 1.447), 1.345 (1.209, 1.496), 1.348 (1.210, 1.501), 1.324 (1.192, 1.471), and 1.219 (1.130, 1.314), respectively. The exposure to PM10, O3, and SO2 over various windows were not significant. In the two-pollutant model, only the associations of NOx exposure with depressive symptoms remained robust after adjustment for any other pollutant.Conclusions: Exposure to traffic-associated air pollutants could increase depression risks among older adults.

Modeling the volatility of exchange rate and international trade in Ghana: empirical evidence from GARCH and EGARCH

PurposeThe volatility of exchange rate has generally been sighted as a primary cause for various shocks and instability in international trade of Ghana as witnessed over the years and most especially in recent times. Hence, owing to the increasing trade levels between Ghana and Ghana's global trading partners, the study aims to investigate if the trade–exchange rate volatility nexus in Ghana supports the positive, negative or ambiguous hypotheses?Design/methodology/approachThe study investigates the effects of Ghana's exchange rate volatility on international trade by designing import and export equations to estimate both short- and long-run specifications of the effect and employing the multivariate generalized autoregressive conditional heteroskedasticity (GARCH) with Baba, Engle, Kraft and Kroner (BEKK) specification developed by Engle and Kroner (1995) as a further check for the robustness of the findings. Monthly data between 1993 and 2017 on the real effective exchange rates of Ghana's trade with 143 trading partners were taken as the series for modeling the volatility using GARCH andexponential generalized autoregressive conditional heteroskedastic (EGARCH) models.FindingsThe empirical results show that the volatility of exchange rate negatively impact export performances in the Ghanian economy. On the other hand, there was no sufficient evidence to support the observed positive effect of exchange rate volatility on imports, as the effects were only significant at 10% level in the long run. Thus, it is concluded that the finding cannot confirm a relationship between volatility and import. Thus, the results present differences in the direction of the effect of exchange rate volatility on imports and exports in the context of the Ghanaian economy.Research limitations/implicationsConsidering the fragility of the Ghanaian economy and Ghana's macro-economic indicators, the study points at the crucial need for more integration of well-informed trade policies within the country's macro-economic policy framework to contain the impacts of exchange rate volatility on trade performances.Practical implicationsThe study contributes to literature by scope and method. More specifically, empirical studies have failed or provided little evidence uniquely on the Ghanaian economy's reaction to exchange rate volatility on the country's imports and exports. Additionally, most of the existing empirical studies measure exchange rate volatility using the standard deviation of the moving averages of the logarithmic transformation of exchange rates. This method is criticized because the method is unsuccessful in capturing the effects of potential booms and bursts of the exchange rate. The authors' study circumvents for these highlighted pitfalls.Social implicationsThe study contributes to literature by scope and method. More specifically, empirical studies have failed or provided little evidence uniquely on the Ghanaian economy's reaction to exchange rate volatility on the country's imports and exports. Thus, the study chat a course for socio-economic dynamic of Ghanaian economy.Originality/valueThe study contributes to literature by its scope and method, as extant empirical studies have provided little evidence specifically on the Ghanaian economy's reaction to exchange rate volatility. Additionally, most of the existing empirical studies measure exchange rate volatility using the standard deviation of the moving averages of the logarithmic transformation of exchange rates. This method is criticized because of the method's inadequacies in capturing the effects of potential booms and bursts of the exchange rate. The study thereby essentially circumvents for these highlighted pitfalls.

Application of Representation Learning based Chronological Modeling for Network Intrusion Detection

An autoencoder has the potential to overcome the limitations of current intrusion detection methods by recognizing benign user activity rather than differentiating between benign and malicious activity. However, the line separating them is quite blurry with a significant overlap. The first part of this study aims to investigate the rationale behind this overlap. The results suggest that although a subset of traffic cannot be separated without labels, timestamps have the potential to be leveraged for identification of activity that does not conform to the normal or expected behavior of the network. The second part aims to eliminate dependence on visual-inspections by exploring automation. The trend of errors for HTTP traffic was modeled chronologically using resampled data and moving averages. This model successfully identified attacks that had orchestrated over HTTP within their respective time slots. These results support the hypothesis that it is technically feasible to build an anomaly-based intrusion detection system where each individual observation need not be categorized.

Postfundamentals Price Drift in Capital Markets: A Regression Regularization Perspective

Regression regularization techniques show that deviations of accounting fundamentals from their preceding moving averages forecast drifts in equity market prices. Deviations-based predictability survives a comprehensive set of prominent anomalies. The profitability applies strongly to the long leg and survives value weighting and excluding microcaps. We provide evidence that the predictability arises because investors anchor to recent means of fundamentals. A factor based on our fundamentals-based index yields economically significant intercepts after controlling for a comprehensive set of other factors, including those based on profit margins and earnings drift. This paper was accepted by Gustavo Manso, finance.

EEG Monitoring Is Feasible and Reliable during Simultaneous Transcutaneous Electrical Spinal Cord Stimulation

Transcutaneous electrical spinal cord stimulation (tSCS) is a non-invasive neuromodulatory technique that has in recent years been linked to improved volitional limb control in spinal-cord injured individuals. Although the technique is growing in popularity there is still uncertainty regarding the neural mechanisms underpinning sensory and motor recovery. Brain monitoring techniques such as electroencephalography (EEG) may provide further insights to the changes in coritcospinal excitability that have already been demonstrated using other techniques. It is unknown, however, whether intelligible EEG can be extracted while tSCS is being applied, owing to substantial high-amplitude artifacts associated with stimulation-based therapies. Here, for the first time, we characterise the artifacts that manifest in EEG when recorded simultaneously with tSCS. We recorded multi-channel EEG from 21 healthy volunteers as they took part in a resting state and movement task across two sessions: One with tSCS delivered to the cervical region of the neck, and one without tSCS. An offline analysis in the time and frequency domain showed that tSCS manifested as narrow, high-amplitude peaks with a spectral density contained at the stimulation frequency. We quantified the altered signals with descriptive statistics—kurtosis, root-mean-square, complexity, and zero crossings—and applied artifact-suppression techniques—superposition of moving averages, adaptive, median, and notch filtering—to explore whether the effects of tSCS could be suppressed. We found that the superposition of moving averages filter was the most successful technique at returning contaminated EEG to levels statistically similar to that of normal EEG. In the frequency domain, however, notch filtering was more effective at reducing the spectral power contribution of stimulation from frontal and central electrodes. An adaptive filter was more appropriate for channels closer to the stimulation site. Lastly, we found that tSCS posed no detriment the binary classification of upper-limb movements from sensorimotor rhythms, and that adaptive filtering resulted in poorer classification performance. Overall, we showed that, depending on the analysis, EEG monitoring during transcutaneous electrical spinal cord stimulation is feasible. This study supports future investigations using EEG to study the activity of the sensorimotor cortex during tSCS, and potentially paves the way to brain–computer interfaces operating in the presence of spinal stimulation.

Towards the global equilibrium of COVID-19: statistical analysis of country-level data

Objectives: In our study, we explore the COVID-19 dynamics to test whether the virus has reached its equilibrium point and to identify the main factors explaining R and CFR variability across countries. Design: A retrospective study of publicly available country-level data. Setting: Fifty countries having the highest number of confirmed COVID--19 cases at the end of July 2021. Participants: Aggregated data including 182 085 182 COVID-19 cases confirmed in the selected fifty countries from the start of the epidemic to July 31, 2021. Primary and secondary outcome measures: The daily values of COVID-19 R and CFR measures were estimated using country-level data from the Our World in Data website. Results: The mean values of country-level moving averages of R and CFR went down from 1.114 and 5.51%, respectively, on July 31, 2020, to 1.059 and 2.35% on January 31, 2021, and to 1.010 and 2.17% by July 31, 2021. In parallel, the cross-country variance of R and CFR moving averages decreased from 0.015 and 0.19%, respectively, on July 31, 2020, to 0.004 and 0.02% on January 31, 2021, and stayed on a similar level by July 31, 2021. Conclusions: The continuous decrease in the country-level moving averages of R, down to the level of 1.0, accompanied by repeated outbreaks ("waves") in various countries, may indicate that COVID-19 has reached its point of a stable endemic equilibrium. Only a prohibitively high level of herd immunity (about 70%) can stop the endemic by reaching a stable disease-free equilibrium. Also, the average percentage of the fully vaccinated population appears to be the only statistically significant factor associated with country-specific CFR, bringing it close to the level of seasonal flu (about 0.1%) after vaccinating more than half of a country's population. Thus, while the currently available vaccines prove to be effective in reducing mortality from the existing COVID-19 variants, they are unlikely to stop the spread of the virus in the foreseeable future. It is noteworthy that no statistically significant effects of government measures restricting the people's behavior (such as lockdowns) were found in the analyzed data.