An investigation into the socioeconomic aspects of two major earthquakes in Iran

An evaluation of the socioeconomic consequences of earthquakes is an essential part of the development of risk reduction and disaster management plans. However, these variables are not normally addressed sufficiently after strong earthquakes; researchers and relevant stakeholders focus primarily on the physical damage and casualties. The importance of the socioeconomic consequences of seismic events became clearer in Iran after the Bam earthquake on 26 December 2003, as demonstrated by the formulation and approval of various laws and ordinances. This paper reviews the country's regulatory framework in the light of the socioeconomic aspects of two major and destructive earthquakes: in Manjil–Rudbar in 1990, and in Bam in 2003. The results take the form of recommendations and practical strategies for incorporating the socioeconomic dimensions of earthquakes in disaster risk management planning. The results presented here can be applied in other countries with similar conditions to those of Iran in order to improve public preparedness and risk reduction.     

Improving the Antibacterial Activity of Curcumin Loaded Nanoparticles in Wastewater Treatment by Enhancing Permeability and Sustained Release

Journal of Polymers and the Environment - In this research, the antibacterial effect of curcumin entrapped in polymeric nanoparticles (mPEG-PCL/curcumin) on resistant bacteria were investigated....     

Investigation of long-term hazards of chemical weapon agents in the environment of Sardasht area,Iran

Environmental Science and Pollution Research - The present study aimed to investigate the persistence and existence of chemical warfare agents (CWAs) and related dissipation products in the...     

Multivariate modeling of agricultural river water abstraction via novel integrated-wavelet methods in various climatic conditions

Environment, Development and Sustainability - Water resource management in the agricultural sector is a necessity that profoundly impacts water resources conservation for future generations. With...     

Application of soft computing to predict water quality in wetland

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.

     

Predicting non-carcinogenic hazard quotients of heavy metals in pepper (Capsicum annum L.) utilizing electromagnetic waves

• There was significant absorption of heavy metals by the pepper in contaminated soils. • The target hazard quotient (THQ) indices followed the order of Pb>Zn>>Cd » Ni. • Relationships exist between contaminated plants and electromagnetic wave. • PCA and random search can select the main spectra and predict THQ for each element. Given the tendency of heavy metals to accumulate in soil and plants, the purpose of this study was to determine the contamination levels of Cd, Ni, Pb, and Zn on peppers (leaves and fruit) grown in contaminated soils in industrial centers. For this purpose, we measured the uptake of the four heavy metals by peppers grown in the heavy metal contaminated soils throughout the four growth stages: two-leaf, growth, flowering, and fruiting, and calculated various vegetation indices to evaluate the heavy metal contamination potentials. Electromagnetic waves were also applied for analyzing the responses of the target plants to various heavy metals. Based on the relevant spectral bands identified by principal component analysis (PCA) and random search methods, a regression method was then employed to determine the most optimal spectral bands for estimating the target hazard quotient (THQ). The THQ was found to be the highest in the plants contaminated by Pb (THQ= 62) and Zn (THQ= 5.07). The results of PCA and random search indicated that the spectra at the bands of b₅₇₀, b₆₅₀, and b₇₆₀ for Pb, b₄₀₀ and b₁₀₃₀ for Ni, b₄₀₀ and b₈₈₀ for Cd, and b₅₆₀, b₉₁₀, and b₁₀₅₀ for Zn were the most optimal spectra for assessing THQ. Therefore, in future studies, instead of examining the amount of heavy metals in plants by chemical analysis in the laboratory, the responses of the plants to the electromagnetic waves in the identified bands can be readily investigated in the field based on the established correlations.     

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.     

Evaluation of physico-chemical properties of biochar-based mixtures for soilless growth media

Journal of Material Cycles and Waste Management - This study was done to evaluate the suitability of biochar-based mixtures for growth media. Biochars from agricultural wastes were produced by slow...     

Characteristics of surface layer turbulence in coastal area of Qatar

A characterization of the atmospheric surface layer in coastal region of Qatar is presented in the present paper. The micrometeorological data is collected in the New Doha International Airport (25.25N, 51.62E), at a pier 500 m into the ocean by deploying tower based instruments during the month of June-2011. The normalized variance of the three components of velocity and temperature are studied within the framework of Monin–Obukhov similarity theory. The normalized variances of stream wise and transverse velocity at near-neutral condition are found to be consistent with the reported values from measurement sites around the world, but the normalized variance of vertical velocity at near-neutral condition is found to be consistently lower in the present case. The dependency of the normalized velocity variances with buoyancy parameter indicates a higher growth rate of variances, compared to other investigations. The non-dimensional variance of temperature matches well with the past investigations, but the correlation between vertical velocity and temperature indicates higher magnitude for the present location.     

Numerical study of scouring downstream of a stilling basin

Environmental Fluid Mechanics - The aim of the present study is to numerically investigate the scouring phenomenon downstream of a stilling basin for a wide range of Froude number and size of bed...