Performance analysis of sustainable supply networks with bounded,discrete, and joint factors

Environment, Development and Sustainability - The majority of data envelopment analysis (DEA) research studies evaluate the sustainability of processes with real-valued factors and individual role,...     

Effects of cerium oxide nanoparticles on biochemical and oxidative parameters in marigold leaves

Abstract

The effects of suspensions of cerium oxide nanoparticles sprayed onto marigold seedlings (Calendula officinalis L.) at concentrations between 50 and 3200 mg L^?1 were studied. At concentrations of 50 and 100 mg L^?1, the nanoparticles had a slight positive effect on some growth parameters, but at higher concentrations they caused severe oxidative damage. Activities of phenylalanine ammonia lyase and lipoxygenase increased from 400 mg L^?1 upward, tyrosine ammonia lyase from 1600 mg L^?1. Ascorbate and glutathione contents decreased from 400 mg L^?1 upwards.     

The role of drug and alcohol use and the risk of motor vehicle crashes in Shiraz,Iran, 2014: A case–crossover study

Background: Traffic accidents and traffic-related injuries and mortality have become a major public health concern in Iran. This study aimed to examine the role of drug and alcohol use in motor vehicle accidents in Iran.

Methods: This case–crossover study was conducted on 441 drivers who survived a road traffic crash and were taken to the emergency department of Shahid Rajaee trauma hospital in Shiraz, southern Iran. Data were collected using checklists that included demographic characteristics and drug and alcohol use prior to driving. Alcohol and drug use was identified through self-report, and cannabis, morphine, and methamphetamine urine tests were used to confirm drug abuse among drivers.

Results: In total 17.9% of drivers reported using drugs (cannabis, opium, or metamphetamine) and 8.84% of drivers reported consuming alcohol prior to the collision. The crude odds ratios (ORs) for having a crash for opium, cannabis, and metamphetamine were 1.94 (95% interval confidence [CI], 1.11–3.38), 2.37 (95% CI, 1.03–5.42), 5.5 (95% CI, 1.21–24.81), respectively, and for all drugs was 3.83 (95% CI, 2.28–6.43). The OR for alcohol was 3.5 (95% CI, 1.73–7.06) based on self-report.

Conclusion: Drug and alcohol use are increasing the risk of traffic crashes in Iran. Risk-reducing programs must be designed and implemented.     

Effects of Compatibilizer and Thermoplastic Starch (TPS) Concentration on Morphological,Rheological, Tensile,Thermal and Moisture Sorption Properties of Plasticized Polylactic Acid/TPS Blends

The blends of polylactic acid plasticized with acetyl tributyl citrate (P-PLA) and thermoplastic wheat starch (TPS) were prepared by a co-rotating twin screw extruder and the effect of maleic anhydride grafted PLA (PLA-g-MA) content as reactive compatibilizer on blends compatibility through morphological, rheological and tensile properties of the blends was investigated. Considerable improvement in properties of P-PLA/TPS (70/30 w/w) blend with incorporating the optimum PLA-g-MA content of 4 phr was achieved as this blend exhibited better morphological and rheological properties with an increase by 158 and 276% in tensile strength and elongation at break, respectively, compared to the uncompatibilized blend. Also the thermal stability and moisture sorption properties of the blends as effected by TPS content were studied. Decreasing in thermal stability and increasing in equilibrium moisture content of the blends were observed with progressively increasing of TPS content. For prediction the moisture sorption behaviour of blends with various TPS contents at different relative humidity, the moisture sorption isotherm data were modeled by GAB (Guggenheim–Anderson–de Boer) model.     

Modeling of nitrate concentration in groundwater using artificial intelligence approach—a case study of Gaza coastal aquifer

Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making     

Optimized forest degradation model (OFDM): an environmental decision support system for environmental impact assessment using an artificial neural network

The purpose of this article is Artificial Neural Network (ANN) modeling using ecological and associated factors with forest degradation to predict the degradation of ecosystem, thereby enabling us to assess the environmental impacts of forest projects as an Environmental Decision Support System (EDSS). Results of the Multi-Layer Feed-Forward Network (MLFN), trained for Optimized Forest Degradation Model (OFDM), indicate that the performance of OFDM is more than other degradation models. Changes in forest management activities with higher value in sensitivity analysis help forest managers to decrease OFDM entity and environment impacts. The system is an intelligent EDSS, which allows the decision-maker to model criteria in forest degradation in order to reach and employ the optimal allocation plan. Considering results, multi criteria decision analysis (MCDA) approaches based on ANN, is an encouraging and robust method for solving MCDA problems.     

Urban landscape services planning in an urban river-valley corridor system case study: Tehran’s Farahzad River-valley landscape system

Environment, Development and Sustainability - Undesirable changes in structural-functional relation of urban landscape structures’ ecological processes lead to energy-information fluxes of...