Analyzing relationships between air pollutants and Covid-19 cases during lockdowns in Iran using Sentinel-5 data

Introduction: Air quality improvement was an unparalleled environmental consequence of the Covid-19 global crisis in many regions. Numerous researches have been conducted on the influence of national quarantines on air pollution and the relationship between the abundance of infected cases and mortality caused by this pandemic with air pollutants; however, these investigations are limited in Iran. The present study aims to investigate the correlation between Covid-19 cases and air pollution from a statistical viewpoint in order to evaluate the performance of multiple national lockdowns from February 2020 to August 2021 through measuring changes in air pollutants in the 31 provinces of Iran. Materials and methods: We applied a remote sensing method by employing Sentinel-5P satellite data to analyze changes in PM2.5, CO, and O3 during the three public quarantine periods and their two months earlier. Results: We recognized a considerable positive correlation between PM2.5 and the infected cases (r=0.63, p=0.001) and victims (r=0.41, p=0.001). Moreover, we compared the efficiency of lockdowns and supposed lockdown 2 (November-December 2020) as an only effective quarantine due to a dramatic reduction in PM2.5 (21.2%), CO (0.8%), the infected cases (48.7%), and victims (66.9%) in comparison to the average of its next two months. Conclusion: Governments should handle the outbreak of Covid-19 by implementing efficient quarantines, as well as environmental conservation strategies.

Effect of inflation uncertainty on price dispersion in Iran

This paper studies the inflation relationship analysis and inflation uncertainty with relative price’ dispersion in Iran by using the ordinary minimum squares method, during monthly data 1991:4-2012:12. In this paper, we used the GARCH technique in order to modeling and measuring the inflation uncertainty variable. The results show that inflation uncertainty increasing leads to increased relative price dispersion. Also unexpected inflation regardless of being positive or negative increases the relative price dispersion considerably, but the unexpected inflation decomposition to two positive and negative components and lack of considering them in the equation showed that each component is in a high significant level and cannot be considered for symmetric effect of positive or negative unexpected inflation. Corporations change their price against the positive unexpected inflation alternatively in responding to the inflation shocks and consequently the price will be fluctuated for reaching the balance strictly, therefore positive unexpected inflation cases have been increasing in relative price dispersion. In the other hand, corporations have no tendency for changing the goods’ price against the negative unexpected inflation. Also according to the results, inflation variable coefficient is significant from the statistical viewpoint and this means that this variable increases the relative dispersion considerably.

Class II 6.7 GHz Methanol Maser Association with Young Massive Cores Revealed by ALMA

The association of 6.7 GHz class II methanol (CH3OH) masers with ATLASGAL/ ALMA 0.9 mm massive dense cores is presented in this work from a statistical viewpoint. 42 of the 112 cores (37.5%) detected with the Atacama Compact Array (ACA) excite 6.7 GHz CH3OH masers. ACA cores have offsets 0\rlap.″17 to 4\rlap.″79 from the methanol multibeam survey (MMB), with a median of 2.″19. Approximately 90% of the MMB-associated cores are of masses > 40 M⊙. Because all the cores show evidence of outflow activity, and only a fraction of the cores excited CH3OH masers, we suggest that outflows precede the emergence of maser emission. This first ALMA survey of massive dense cores combined with the MMB survey along with other maser specie surveys is a promising tool to trace the evolutionary sequence of high-mass stars.

When Does Label Propagation Fail? A View from a Network Generative Model

What kinds of data does Label Propagation (LP) work best on? Can we justify the solution of LP from a theoretical standpoint? LP is a semi-supervised learning algorithm that is widely used to predict unobserved node labels on a network (e.g., user's gender on an SNS). Despite its importance, its theoretical properties remain mostly unexplored. In this paper, we answer the above questions by interpreting LP from a statistical viewpoint. As our main result, we identify the network generative model behind the discretized version of LP (DLP), and we show that under specific conditions the solution of DLP is equal to the maximum {\it a posteriori} estimate of that generative model. Our main result reveals the critical limitations of LP. Specifically, we discover that LP would not work best on networks with (1) disassortative node labels, (2) clusters having different edge densities, (3) non-uniform label distributions, or (4) unreliable node labels provided. Our experiments under a variety of settings support our theoretical results.

Organizational Learning and Competitive Advantage in Banking Sector of Pakistan

Organizational learning is one of the major characteristic of high performing work systems. Organizations are depicted as intelligent organizations when they focus on constant organizational learning. In the dynamic era of digitalization, securing a competitive advantage over competitors has moved beyond the effective utilization of organizational resources to effective management of organizational knowledge. This research aims to study the impact of organizational learning as a competitive advantage in the banking sector of Pakistan. OLCA (Organizational learning and Competitive Advantage) model is applied and empirical evidence is collected from the banking sector of Pakistan. Reliability analysis, correlation, Mean, standard deviation, linear regression and step wise regression analysis are used to collect the statistical viewpoint. The results of the study show positive and reliable scores. The result of the study confirms the OLCA model comprehensions in the selected sector of study. The study concludes that rather focusing on increasing the resource efficiency to gain competitive advantage, organization must focus on organizational learning as a resource to gain a lasting competitive advantage.

Pain and Joy of a Panel Survey on Transport Studies

Over ten years ago, it was established that the most frequent reason that motivates a panel survey on transport studies is theevaluation of a change in the transportation system, or a specific transportation-planning project, especially when the projectinvolves novel elements. From a statistical viewpoint, a panel survey has the definite advantage to offer more accurate estimatesof changes than cross-sectional surveys for the same sample size. Observing travel patterns of individuals and households overseveral consecutive days, has offered insights into activity scheduling and travel planning. Variability in travel patterns hasimportant policy implications as well, but how much effort is worth to design a panel survey?To evaluate the effects of the transport policies introduced in Madrid during the last five years, a ‘short-long’ panel survey wasbuilt, based on a sample of a Madrid-worker subpopulation most affected by those recent changes in transport policy. The paperdescribes both the design and construction of the panel based on GPS technology, and presents some results based on an analysisof its two waves; for example, it registered an increment of public transport use and walking trips in 10%. The panel overcomesthe known attrition problem thanks to providing incentives, maintaining contact, using the same interviewer for the samerespondents, and conducting face-to-face interviews.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4056