A MULTIBLOCK PARTIAL LEAST SQUARES ALGORITHM FOR INVESTIGATING COMPLEX CHEMICAL SYSTEMS

The details of a general multiblock partial least squares(PLS)algorithm based on one originallypresented by Wold et al.have been developed and are completely presented.The algorithm can handlemost types of relationships between the blocks and constitutes a significant advancement in the modelingof complex chemical systems.The algorithm has been programmed in FORTRAN and has been testedon two simulated multiblock problems,a three-block and a five-block problem.The algorithm combinesthe score vectors for all blocks predicting a particular block into a new block.This new block is used topredict the predicted block in a manner analogous to the two-block PLS.In a similar manner if one blockpredicts more than one other block,the score vectors of all predicted blocks are combined to form a newblock,which is then predicted by the predictor block as in the two-block PLS.Blocks that both predictand are predicted are treated in such a way that both of these roles can be taken into account whencalculating interblock relationships.The results of numerical simulations indicate that the computerprogram is operating properly and that the multiblock PLS produces meaningful and consistent results.     

Natural radionuclides and trace elements in rice field soils in relation to fertilizer application: study of a chronic kidney disease area in Sri Lanka

The rising number of chronic kidney disease patients with no identifiable cause (CKD of uncertain aetiology), prevalent in some areas of the dry zone of Sri Lanka is suspected to be related to the environmental exposure to heavy metals. Agricultural soils are well recognized as being contaminated with potentially toxic metals from various forms of fertilizers and agro-chemicals, which could easily enter the human body through the food chain. The objective of this paper is to determine the content of heavy metals and activity concentration of background radionuclides such as K-40, Ra-226 and Th-232, in rice field soils. Rice farming is the most common agricultural practice in the affected region and possible heavy metal sources such as fertilizers are applied in abundance in the rice fields. Soils collected from a rice field in a non-CKD region was used for the comparison. In dry zone soils, Ca, K, Ba, Pb and Zr contents were higher and Fe, Mn, Cr, Ni and Zn contents were lower compared to that of soils from the wet zone non-CKD region. However, the activity concentration of soils was mostly the same in all samples, except for the K-40 contents of the soils, which were higher in the rice field soils compared to the undisturbed forest soils and also to the world averages. The mean U content was 3.6 mg/kg in the studied soils, although extremely high uranium contents were found in some fertilizer samples particularly in the triple superphosphates. Most uranium applied via fertilizer could contaminate the drinking water sources and even low uranium concentrations in drinking water may cause nephrotoxic effects.     

Multi-technique approach to characterise the effects of cryopreservation on larval development of the Pacific oyster (Crassostrea gigas)

Cryopreservation experiments were conducted on D-stage larvae of the Pacific oyster (Crassostrea gigas) to investigate the effects of two cryoprotectant solutions and three cooling rates on larval development from 1 to 22 days post-fertilisation. Cryoprotectant solutions were made up to final concentrations (after 1:1 dilution with larvae) of 10% ethylene glycol, 1% polyvinylpyrrolidone and either 0.2 or 0.4 M trehalose. Three cooling rates (0.5, 1 and 2 °C min^?1 between ?10 and ?35 °C post-holding) were tested in an orthogonal design with the two cryoprotectants. Results indicate that control larvae out-performed all cryopreservation treatments for survival, feeding consumption and shell length parameters. However, larvae exposed to 0.4 M trehalose did considerably better than those exposed to 0.2 M trehalose, regardless of cooling rate conditions. Scanning electron and light microscopy observations were used to assess larval morphology and organogenesis, indicating that treatments with surviving larvae were morphologically and developmentally similar to control larvae.     

Roadheader performance prediction using genetic programming (GP) and gene expression programming (GEP) techniques

Roadheading machines play a vital role in excavation operation in tunneling and mining industries notably when selective mining is required. Roadheaders are more effective in soft to medium rock formations due to a higher cutting rate in such strata. A precise prediction of machine’s performance is a crucial issue, as it has considerable effects on excavation planning, project’s cost estimation, machine specification selection as well as safety of the project. In this research, a database of machine performance and some geomechanical parameters of rock formations from Tabas coal mine project, the largest and fully mechanized coal mine in Iran, has been established, including instantaneous cutting rate (ICR), uniaxial compressive strength, Brazilian tensile strength, rock quality designation, influence of discontinuity orientation (Alpha angle) and specific energy. Afterward, the parameters were analyzed through genetic programming (GP) and gene expression programming (GEP) approaches to yield more accurate models to predict the performance of roadheaders. As statistical indices, coefficient of determination, root mean square error and variance account were used to evaluate the efficiency of the developed models. According to the obtained results, it was observed that developed models can effectively be implemented for prediction of roadheader performance. Moreover, it was concluded that performance of the GEP model is better than the GP model. A high conformity was observed between predicted and measured roadheader ICR for GEP model.     

Application of prediction–area plot in the assessment of MCDM methods through VIKOR,PROMETHEE II,and permutation

Natural Hazards - One of the most important natural hazards is landslides that after the earthquake and floods cause the highest damage to humans. Nowadays, landslide events are taken into...     

Observation of Conditions Preceding Peak Indoor Air Volatile Organic Compound Concentrations in Vapor Intrusion Studies

Temporal and spatial variability of indoor air volatile organic compound (VOC) concentrations can complicate vapor intrusion (VI) assessment and decision-making. Indicators and tracers (I&T) of VI, such as differential temperature, differential pressure, and indoor radon concentration, are low-cost lines of evidence to support sampling scheduling and interpretation of indoor air VOC sampling results. This study compares peak indoor air chlorinated VOC concentrations and I&T conditions before and during those peak events at five VI sites. The sites differ geographically and in their VI conceptual site models (CSM). Relative to site-specific baseline values, the results show that cold or falling outdoor temperatures, rising cross slab differential pressures, and increasing indoor radon concentrations can predict peak VOC concentrations. However, cold outdoor air temperature was not useful at one site where elevated shallow soil temperature was a better predictor. Correlations of peak VOC concentrations to elevated or rising barometric pressure and low wind speed were also observed with some exceptions. This study shows how the independent variables that control or predict peak indoor air VOC concentrations are specific to building types, climates, and VI CSMs. More I&T measurements at VI sites are needed to identify scenario-specific baseline and peak related I&T conditions to improve decision-making.     

Investigation of liquid extraction and thermodynamic studies on uranium from sulfate solution by Alamine 336 as an extractant

This paper presents a comprehensive study of the liquid–liquid extraction of uranium from 0.2 M sulfuric acid solution by tertiary amine (Alamine 336) and kerosene as an extractant and a diluent, respectively. For this purpose, the effects of operating parameters on the uranium extraction efficiency such as Alamine 336 concentration, contact time, initial aqueous phase pH, sulfate ion concentration, temperature and stripping process by different agents are investigated. The Gibbs energy change (ΔG), enthalpy change (ΔH) and entropy change (ΔS) have been determined as thermodynamic parameters to prove exothermic and spontaneous reaction. Also, equilibrium constants, reaction equation and stoichiometric coefficients for uranium extraction with Alamine 336 in sulfate medium are specified. The proposed procedure can be applied to the recovery of uranium from low-grade ore processing and from wastewater in the different stages of the nuclear fuel cycle.     

Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models,Sarkhoun basin,Southwestern Iran

Landslides are natural disasters often activated by interaction of different controlling environmental factors, especially in mountainous terrains. In this research, the landslide susceptibility map was developed for the Sarkhoun catchment using Index of Entropy (IoE) and Dempster–Shafer (DS) models. For this purpose, 344 landslides were mapped in GIS environment. 241 (70%) out of the landslides were selected for the modeling and the remaining (30%) were employed for validation of the models. Afterward, 10 landslide conditioning factor layers were prepared including land use, distance to drainage, slope gradient, altitude, lithology, distance to roads, distance to faults, slope aspect, Topography Wetness Index, and Stream Power Index. The relationship between the landslide conditioning factors and landslide inventory maps was determined using the IoE and DS models. In order to verify the models, the results were compared with validation landslide data not employed in training process of the models. Accordingly, Receiver Operating Characteristic (ROC) curves were applied, and Area Under the Curve (AUC) was calculated for the obtained susceptibility maps using the success (training data) and prediction (validation data) rate curves. The land use was found to be the most important factor in the study area. The AUC are 0.82, and 0.81 for success rates of the IoE, and DS models, respectively, while the prediction rates are 0.76 and 0.75. Therefore, the results of the IoE model are more accurate than the DS model. Furthermore, a satisfactory agreement is observed between the generated susceptibility maps by the models and true location of the landslides.