In this paper, we focus to find desired node position in indoor environments using a sequence of observations and user movement records. For this purpose, we first record the user's movements in indoor environments by defining a set of states and several matrices, which are Viterbi inputs. To record the fingerprints of the environment, we move across the entire coordinates of the building to collect and record the fingerprints of different places. In the online phase, we use the Weighted K-Nearest Neighbors (WKNN) algorithm in parallel to check the accuracy of both WKNN and Viterbi algorithms and to correct the WKNN behavior by Viterbi. During this phase, an experimental node is inserted into the environment and moves in the desired direction by determining the destination. The proposed method calculates the current location of the node and its Most Probable location in the next step. The results of the implementation and testing of the proposed algorithm in the Faculty of Engineering, Arak University, show the optimal performance of the proposed idea for predicting the location and Path of the node.

The Most important purpose in location problems is usually to locate some facilities and allocate the demands of nodes so that the total transportation cost of the network is minimized. However, in real networks, there are some other influencing factors, aside from the transportation costs, for determin ing the allocation mode. In this paper, a minimum information approach is applied to the capacitated p-median problem to estimate the Most likely allo cation solution based on some prior probabilities. Indeed, the Most Probable solution is achieved through minimizing a log-based objective function, while the total transportation cost should be less than or equal to a predetermined budget. The problem is solved by using a decomposition method combined with the Karush–Kuhn–Tucker optimality conditions, and some numerical examples are provided to verify the added value of the proposed model and solution approach.

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.

The demand forecasting is essential for all production and non-production systems. However, nowadays there are only few researches on this area. Most of researches somehow benefited from simulation in the conditions of demand uncertainty. But this paper presents an iterative method to find Most Probable stochastic demand point with normally distributed and independent variables of n-dimensional space and the demand space is a nonlinear function. So this point is compatible with both external conditions and historical data and it is the shortest distance from origin to the approximated demand-state surface. Another advantage of this paper is considering n-dimensional and nonlinear (nth degree) demand function. Numerical results proved this procedure is convergent and running time is reasonable.

SummaryOpen pit design and production scheduling considering the commodity price uncertainty is one of the important issues in the field of open pit mining, so that mine design and planning regardless of price uncertainty leads to erroneous assessments and non-operational production scheduling, which itself increases the investment risk. The amount of reserve that can be extracted and the location of surface facilities directly depends on the final pit limit. In this paper, a mathematical algorithm based on Monte Carlo Simulation and Lerch and Grossman algorithm is presented, which is able to calculate the expected value of blocks based on the metal price history and estimating its distribution function and get the Most likely final pit limit. This pit can be the basis of long-term mine production planning as well as a criterion for locating surface facilities. IntroductionThe aim of the planning process for an open pit mine is usually to find optimum annual schedules that will give the highest Net Present Value (NPV). The primary input of this procedure is an economic block model, which includes a set of mining blocks representing the ore body and the surrounding rock. Net economic value is assigned to each block based on the revenue of recoverable metal content in a given block and subtracting all the operating costs, comprising mining, processing, refining, and selling costs. An economic evaluation of each block requires the estimation of ore tonnage and grade of mining blocks as well as some economic parameters such as metal prices and operation costs. In the current pit design approaches, the block economic values are calculated using a fixed known value. In this paper, using the metal prices in the past as well as the Monte Carlo simulation method, the Most Probable ultimate pit is obtained. Methodology and ApproachesIn this article, the price distribution function of the metal price (copper) was obtained using the metal price history. Then 100 prices were simulated using Monte Carlo simulation and the expected value of each block was obtained using these simulated values of other fixed technical and economic parameters. Finally, by using these values and using the NPV Scheduler software, a single optimal pit was obtained. Results and ConclusionsIn this article, the expected value of the blocks was obtained using the Monte Carlo simulation method, and then the optimal pit, which is actually the Most Probable pit, was obtained. Because the price history of the metal is considered in the design process, the obtained pit has little sensitivity to the changes in the price of the metal in the future.

Background and objectives: Acquired morphological traits of seedlings in nurseries are as influential as their originality in their performance and vigor. The evaluation of these traits, including the Dickson quality index (DQI) is considered an efficient method for considering the vigor of various kinds of seedlings, including fruit and forest seedlings. The magnitude of this index for seedlings is a function of the amount and the distribution of dry mass, height, and diameter. Therefore, it is a destructive and time-consuming method for predicting seedling quality in nurseries. Consequently, in order to rank the morphological traits that determine the quality of apricot seedlings, this project was conducted to evaluate the correlation relations between such traits of apricot seedlings of the Most important commercial varieties produced in different regions of Iran, based on Path analysis and multiple linear regression analysis.Materials and methods: To evaluate the relationship between the morphological indicators in apricot seedlings, bare-root seedlings of 12 commercial cultivars with three replicates from 8 nurseries in four provinces were lifted during the transplanting seasons of 2020-2021. Sapling height, diameter above grafting line (DAGL), root length, number of roots and branches, shoot and root fresh and dry weights, height to diameter ratio, and DQI were evaluated, and simple correlations between them were estimated. The correlation coefficients were broken down into direct and indirect effects through Path analysis, with DQI as the dependent variable.Results: The indices representing apricot seedling vigor, including DQI and some of its components containing DAGL, total dry mass, height to diameter ratio and shoot to root dry mass ratio were a function of the accumulated growth degree-days (AGDD) in different regions. So that by increasing AGDD from 2700 to about 4500 degree, the average DQI, DAGL and total dry mass were increased and height to diameter ratio was decreased. The shoot to root dry weight ratio of seedlings was also decreased in nurseries located in regions with more than 3400 growth degree days. Among the traits which can be evaluated at the nursery site through non-destructive methods, DAGL had a higher correlation with vigor and the other morphological traits, leading to being considered the Most important index in the qualitative evaluation of apricot seedlings, followed by shoot and root dry weights. Although later can be evaluated by destructive methods, they had the highest correlations with seedling vigor. The shoot number, height, sturdiness quotient had the weakest correlations with vigor and other morphological traits. Conclusion: DAGL is considered the Most efficient index in the quality assessment of the apricot seedlings due to having the highest correlation with the DQI and other morphological traits.

Listeria monocytogenes, as Gram-positive bacteria, is the cause of a opportunistic fatal infection in humans, which can be transmitted via a wide range of food products, including dairy, meat, and vegetables. Local white cheese in Iran is not produced under good manufacturing practices, however a comprehensive study about the prevalence of different serotypes of Listeria monocytogenes in this product has not been done yet. Babol, Qaem-shahr, Amol, Urmia, Hamedan, Tehran and Garmsar cities were selected as areas with high consumption of local white cheese, and 343 samples were collected from the dairy shops and delivered to the Food Safety and Hygiene Laboratory. Isolation and identification of Listeria monocytogenes, from cheese samples were carried out by pre-enrichment and cultivation in the selective media, the isolates were confirmed by biochemical methods, and then the serotype of each species was determined. Finally, the positive samples was enumerated using the five-tubes Most Probable number method. 9 cases of 343 samples were contaminated for Listeria monocytogenes. Most positive cases were isolated from Urmia city (3 cases). Considering the possibility of the survival of this organism during the processing of local white cheese, it seems that the monitoring of the manufacturing, storage and supply should be increased for this product and as much as possible, the production should be done in plants under the supervision of regulatory organizations.

Background: Long-term extraction and production of various oil derivatives causes contamination of soil adjacent to production and refining area. The main important issue is the ecological hazard of these pollutants. The main aim of the study was to identify and separate native bacteria that are responsible for degradation of oil contaminated fields.Methods: We prepared a compound sample of contaminated soil in the vicinity of oil wells drilled in Marun oil field of Ahwaz, Iran. Moreover, an uncontaminated sample was picked from the same area and deliberately contaminated with crude oil in a 5 wt% rate. The number of oil degrading bacteria was counted by MPN (Most Probable number) method. Then, the bacteria was cultured and isolated in a rich agar medium and late discrimination test was done by gram staining and biochemical tests at the level of family or genus.Findings: The number of bacteria in naturally contaminated and artificially soil was 6.8´105 and 3.5´107, respectively. The majority of bacteria responsible for oil degradation were Staphylococcus, Acinetobacter and Flavobacterium genus in naturally contaminated soil; however, Acinetobacter, Pseudomonas and Moraxella genus were the main bacteria in artificially-contaminated soil samples.Conclusion: It is recommended to be used for bioremediation of native organisms and bio-power and robust.

One of the obvious reasons for Most disorders in network service provisioning is network Path congestion. Congestion avoidance in today's networks is too costly and sometimes impossible. With the introduction of SDN, centralizing the equipment's control plane has become possible. This paper presents an enhanced method named ESV-DBRA to avoid congestion in multi-tenant SDN networks. At first, ESV-DBRA monitors the traffic load and delay of all network Paths for each tenant individually. Then, by merging the parameters obtained from the monitoring, the Service Level Agreements (SLA), and a novel proposed cost function, it calculates the cost of the network Paths per tenant. As a result, traffic for each tenant is routed through the Path/Paths at the lowest possible cost from the tenant's perspective. Next, the bandwidth quotas will be calculated and assigned to the tenants over their optimal routes. Afterward, whenever congestion is likely to occur in a Path, ESV-DBRA automatically changes the route or bandwidth of the tenants' traffic related to this Path to avoid congestion. Related algorithms are also proposed.Eventually, simulations show that the proposed method effectively increases bandwidth utilization by 10.76%.