South Bafgh lands are among the central Iran arid areas which have low rainfall and poor vegetation. Deflation of these bare lands has caused soil erosion and in addition to degradation of soil texture, damages to roads especially Tehran-Bandar Abbas and Tehran-Kerman railroads in South Bafgh and also agricultural lands and adjacent villages. Due to accumulation of transported sediments in southeast Bafgh, an Arg with 37Km2 area has been made. In order to control wind erosion in the region, first of all, the main Source regions needed to be identified. Therefore, for determination of Source area, the method of phase by phase Source-identification was used in the region. The results showed that the erosive winds blow from south-west and transport sediments towards sand dunes. Geomorphologic and sedimentological studies showed that the Source of sand dunes has been close and local and it is located on epandage plain of south west part of Arg, beds of Zarand Shoor-River and the other eolion erosive facies of this plain. In general, the main Source is adjacent regolite plain.

Recognition of Source areas of sand dunes is very important in wind erosion control projects. Due to the difficultes in application of traditional methods in recognition and determination of the share of sediment Sources in sediment production, fingerprinting methods, sediment tracery or in other wards, Source identification are alternative and suitable methods which are considered by different researches. In this method, by investigation of physical and chemical characteristics of sediments, rocks and soils of different Sources, the Source areas of sediment are determined.In this research, sediment tracery by geochemical elements, Source areas and the shares of these Sources in eolian sediment production in one of the most important eolian critical centers of sistan plain were identified. First, granolumetric and mineralogical studies were performed on eolian sediments of sistan province. then, step-wise Source identification method was performed on Niatak eolian sediments. Then. by using determination analysis method and suitable composition of geochemical elements, Organic carbon, Phosphorous and Nitrogen which can differentiate lithological, land use and geomorphological units in Niatak critical center and by using composite multivariate methods, the importantance and share of sediment Sources were determined. The results indicated that transport distances have been between 20-50 kilometers and the most important Sources were sediments of Hamon lake and then barren lands that 92% of particles have originate from Hamon lake and River bed and 69% of particles from barren lands.87% of particles have originate from NQts unit (upper Miocene– Pliocene fine – grained alluvial sediments of Hamon lake), having the highest share and QK unit (the youngest lucastrine sediments of Hamon) having the lowest share.

Large water distribution systems can be highly vulnerable to penetration of contaminant factors caused by different means including deliberate contamination injections. As contaminants quickly spread into a water distribution network, rapid characterization of the pollution Source has a high measure of importance for early warning assessment and disaster management. In this paper, a methodology based on Probabilistic Support Vector Machines (PSVMs) is proposed for identifying the contamination Source location in drinking water distribution systems. To obtain the required data for training the PSVMs, several computer simulations have been performed over multiple combinations of possible contamination Source locations and initial mass injections for a conservative solute. Then the trained probabilistic SVMs have been effectively utilized to identify the upstream zones that are more likely to have the positive detection results. In addition, the results of this method were compared and contrasted with Bayesian Networks (BNs) and Probabilistic Neural Networks (PNNs). The efficiency and versatility of the proposed methodology were examined using the available data and information from water distribution network of the City of Arak in the western part of Iran.

Background: The need for a rapid, noninvasive method for unwanted hair removal has led to the development of various light Sources for this purpose. Objective: To evaluate the efficacy of Intense Pulsed Light Source (IPLS) on unwanted hairs. Patients & Methods: In this open, uncontrolled clinical trial, 77 different anatomical areas in 34 referred patients to "Novin Didegan Clinic" in Tehran in 1380-1381, were treated by IPLS at fluencies ranging from 22 to 34 j/cm2 after their demographic data and the number of hairs were recorded. The 3 to 7 treatment sessions were accomplished 4 to 6 weeks apart. All of the patients were followed 6 months after the last therapy. The quantitative data were  analyzed by paired t test. Results: Six months after the last therapy, more than 50% reduction in number of hairs was noted in 80% of the treated areas; with mean reduction of 72±23 percent. Side effects were mild and reversible and occurred in ten patients. Conclusion: The IPLS appears to provide an effective and safe method for long term excess hair removal in various skin types.      

In this study, a certain characteristic of neural networks called Self Organizing Feature Maps (SOFM’s) was applied to pollution Source identification in the Kor and Sivand Rivers located in Fars Province, Iran. Wastewater quality data from significant industrial pollution Sources to these rivers (mainly factories located upstream) were given. Observed sets of water quality data in sampling stations, downstream from the pollution Sources, were utilized to identify the most probable pollution Source that may have contributed to pollution in these rivers. With the aid of partial semantic maps generated by SOFM’s, different patterns with different likelihoods were recognized in the pollution data. Certain patterns matched that of the pollution Sources very closely. In other words, the fingerprints of all pollution Sources (which were studied) were identified in the pollution data. Therefore, it is possible to use the maps as an aid to the management and decision support system of these rivers.