Assessing Multi-site Drought Connections in Iran Using Empirical Copula

Drought is a multi-dimensional natural hazard with stochastic characteristics usually related to each other. Separate univariate statistical models cannot capture the important relationships among drought characteristics, that is, severity and duration. In this study, an empirical copula is employed to construct a bivariate model of droughts, where droughts are defined as continuously negative standardized precipitation index (SPI) periods with one SPI value reaching ?1 or less. Bivariate frequency analyses in terms of recurrence intervals are performed using the established empirical copula-based bivariate drought model. The inter-connection among different regions of droughts is explored by a lower tail dependence coefficient. A nonparametric estimation based on an empirical copula is employed pairwisely to calculate the lower tail dependence coefficient among stations. The proposed method is applied to six rainfall gauge stations in Iran to explore drought properties of single sites as well as the inter-connection among multi-sites. The results show that greater mean drought severity and duration are associated with the least arrival rate of drought events, which occurs at the Ahwaz station. The tail dependence analysis reveals that distance between stations is not a key parameter. Generally, the Ahwaz and Isfahan stations have the highest probability of simultaneous droughts among the six stations.     

Design of on-line river water quality monitoring systems using the entropy theory: a case study

The design of a water quality monitoring network is considered as the main component of water quality management including selection of the water quality variables, location of sampling stations and determination of sampling frequencies. In this study, an entropy-based approach is presented for design of an on-line water quality monitoring network for the Karoon River, which is the largest and the most important river in Iran. In the proposed algorithm of design, the number and location of sampling sites and sampling frequencies are determined by minimizing the redundant information, which is quantified using the entropy theory. A water quality simulation model is also used to generate the time series of the concentration of water quality variables at some potential sites along the river. As several water quality variables are usually considered in the design of water quality monitoring networks, the pair-wise comparison is used to combine the spatial and temporal frequencies calculated for each water quality variable. After selecting the sampling frequencies, different components of a comprehensive monitoring system such as data acquisition, transmission and processing are designed for the study area, and technical characteristics of the on-line and off-line monitoring equipment are presented. Finally, the assessment for the human resources needs, as well as training and quality assurance programs are presented considering the existing resources in the study area. The results show that the proposed approach can be effectively used for the optimal design of the river monitoring systems.     

Biodegradable Composite Foams of PLA and PHBV Using Subcritical CO<Subscript>2</Subscript>

One key strategy for increasing the application potential for biodegradable plastics lies in improving the physical and mechanical characteristics, which can be attained by inducing a cellular morphology in the pure polymer with the aid of a blowing agent, as well as by blending two or more polymers with the desirable properties. This paper examines the effect that blending two biodegradable polymers has on the thermal properties and morphology of the resultant foams blown with carbon dioxide (CO₂). Polylactic acid (PLA), polyhydroxybutyrate-co-valerate (PHBV) and blends of both were foamed and characterized in terms of thermal characteristics, relative density, cell size, and foam morphology. The results indicate that although PLA and PHBV are immiscible, the presence of small quantities of PHBV (25 wt%) could lead to low density foams with finer, more uniform cells. Furthermore, the crystallinity of PHBV was found to be unaffected by the presence of PLA in the composite, which supports the immiscibility of PLA and PHBV.     

Organochlorine pesticide and polychlorinated biphenyl residues in feathers of birds from different trophic levels of South-West Iran

Persistent organochlorine pesticides (OCPs), such as dichlorodiphenyltrichloroethane (DDT) and its metabolites, hexachlorobenzene (HCB), alpha, beta and gamma-hexachlorocyclohexane (HCH) isomers, together with polychlorinated biphenyl (PCB) congeners (IUPAC Nos. 28, 52, 101, 138, 153 and 180), were determined in tail feathers from 37 birds belonging to 18 species, all originating from the South-West of Iran (Khuzestan, coast of the Persian Gulf). This is the first report on organochlorine contaminants in feathers from museum collections and it is an indication of the concentrations of OCPs and PCBs in the past (1991-1996). Median concentrations of HCHs, DDTs, PCBs and HCB were 22, 14, 11 and 10 ng/g feather, respectively. Significant correlations (p<0.05) were calculated between OCPs (except HCB) and PCBs in the bird feathers. p,p'-DDE and gamma-HCH were the most abundant OCPs, while CB 180, CB 138 and CB 101 were the predominant PCB congeners in almost all species. Significant differences (p<0.05) in the mean concentrations of DDTs and PCBs were detected among species grouped according to their feeding habits. Levels of DDTs and PCBs were highest in the carnivorous species and lowest in the herbivorous species. Levels of OCPs and PCBs in feathers of bids in the 1990s were generally below the thresholds reported to affect reproduction.     

An improved optimization model for predicting Pb recovery efficiency from residual of liberator cells: a hybrid of support vector regression and modified tunicate swarm algorithm

Journal of Material Cycles and Waste Management - In this study, a hybrid of support vector regression and a modified tunicate swarm algorithm (SVR-MTSA) strategy is developed to optimize the...     

The detection of drowsiness using a driver monitoring system

Abstract

Objective: Drowsiness is a major cause of driver impairment leading to crashes and fatalities. Research has established the ability to detect drowsiness with various kinds of sensors. We studied drowsy driving in a high-fidelity driving simulator and evaluated the ability of an automotive production-ready driver monitoring system (DMS) to detect drowsy driving. Additionally, this feature was compared to and combined with signals from vehicle-based sensors.

Methods: The National Advanced Driving Simulator was used to expose drivers to long, monotonous drives. Twenty participants drove for about 4?h in the simulator between 10 p.m. and 2 a.m. They were allowed to use cruise control and traffic was sparse and semirandom, with both slower- and faster-moving vehicles. Observational ratings of drowsiness (ORDs) were used as the ground truth for drowsiness, and several dependent measures were calculated from vehicle and DMS signals. Drowsiness classification models were created that used only vehicle signals, only driver monitoring signals, and a combination of the 2 sources.

Results: The model that used DMS signals performed better than the one that used only vehicle signals; however, the combination of the two performed the best. The models were effective at discriminating low levels of drowsiness from moderate to severe drowsiness; however, they were not effective at telling the difference between moderate and severe levels. A binary model that lumped drowsiness into 2 classes had an area under the receiver operating characteristic (ROC) curve of 0.897.

Conclusions: Blinks and saccades have been shown to be predictive of microsleeps; however, it may be that detection of microsleeps and lane departures occurs too late. Therefore, it is encouraging that the model was able to distinguish mild from moderate drowsy driving. The use of automation may make vehicle-based signals useless for characterizing driver states, providing further motivation for a DMS. Future improvements in impairment detection systems may be expected through a combination of improved hardware, physiological measures from unobtrusive sensors and wearables, and the intelligent integration of environmental variables like time of day and time on task.     

Impact of roof rain water harvesting of runoff capture and household consumption

Environmental Science and Pollution Research - In recent years, the occurrence of floods is one of the most important challenges facing in Hamadan city. In the absence/inefficiency of urban...     

Leaching kinetics of valuable metals from waste Li-ion batteries using neural network approach

Journal of Material Cycles and Waste Management - The kinetic study of valuable metals recovery from waste lithium-ion batteries (LIBs) using the artificial neural network (ANN) was investigated. A...     

A charge model for household waste management services: a case study of Tehran municipality

In practice, it is difficult to implement Pay-As-You-Throw charge systems based on accurate estimates of waste generation. In many cities, this is made impossible, due to lack of technical and administrative infrastructure and cultural considerations, especially in developing countries. This paper presents an alternative two-component charge model that is the combination of cost-accounting technique of Waste Management Services (WMSs) and econometric functions of waste generation. Practical and computation steps considered by the model are presented as a proposal to reform the current system of waste charge in Tehran municipality. The presented model is simple to implement and resolves some of the disadvantages of the traditional methods of waste charge systems, including insufficient revenue to cover the cost of waste management and unfairness related to social inequality.     

The association between bisphenol A exposure and type-2 diabetes: a world systematic review

The present study was conducted to systematically review, analyze, and interpret all the relevant evidence in the literature on the possible link between exposure to bisphenol A (BPA) and the risk of type-2 diabetes mellitus (T2DM). We developed a comprehensive search strategy and used it to search Web of Science, Scopus, PubMed, and Google Scholar up to March 31, 2016, producing 3108 hits, of which 13 original papers were included. Findings of these studies were quite controversial; few studies indicated a significant positive association between BPA exposure and T2DM, while some other failed to detect such a relationship. Overall, it can be suggested that chance is unlikely the plausible explanation for the observed association between BPA exposure and T2DM. This was mainly because even in the negative studies some clues could be found in favor of a statistically significant relationship between BPA and T2DM. Additionally, some of the studies had shortcomings in defining the exposure and outcome measures, which, if present, might have led to underestimating the relationship between BPA exposure and T2DM. The theoretical plausibility of such a relationship found earlier in animal studies also supports this point. However, more definitive answer requires the conduct of future longitudinal studies, in which the possible association between BPA exposure and T2DM is assessed over much longer periods of time with more temporally robust BPA measurements. In addition, it would be quite beneficial if future studies be conducted in areas where data is still lacking (e.g., South America, Australia/Oceania, and Europe).

Open image in new window

Graphical abstract ?