Availability-aware provisioning in P-Cycle-Based mesh networks

Survivability of high-capacity optical wavelength-division multiplexing (WDM) mesh networks has received much research attention for many years now. These networks are typically designed to survive single component failures. The method of pre-configured protection cycles ( p -cycles), recently proposed by W. Grover's research group, promises to achieve ring-like high speed protection with mesh-like high efficiency in use of spare capacity. In such networks, which are designed to withstand only single failures, service availability comes to depend on dual-failure (or more) considerations. Hence, availability-aware service provisioning emerged as a topic of great importance in the past few years. In this thesis, we first revisit the problem of availability analysis in p -cycle based networks and present an accurate model for availability-aware provisioning after highlighting major flaws in prior work. Our model provides a technique for allocating p -cycles to restore single link failures such that the unavailability of all the demands in the network is bounded by an upper limit. We then provide some heuristics for restricting the number of variables and constraints in an integer linear programming formulation in order to solve our problem in a reasonable amount of time. Failure-Independent Path Protecting (FIPP) p -cycle recently has been proposed as an extension of the basic p -cycle to provide a pre-connected, failure independent, path-protecting network design. We present in this thesis the first model for availability-aware provisioning in FIPP based networks. Our study focuses on determining whether FIPP will maintain its resource efficiency advantages over span p -cycles when the network design is based on limiting the service unavailability

ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages

Building multi-modal language models has been a trend in the recent years, where additional modalities such as image, video, speech, etc. are jointly learned along with natural languages (i.e., textual information). Despite the success of these multi-modal language models with different modalities, there is no existing solution for neural network architectures and natural languages. Providing neural architectural information as a new modality allows us to provide fast architecture-2-text and text-2-architecture retrieval/generation services on the cloud with a single inference. Such solution is valuable in terms of helping beginner and intermediate ML users to come up with better neural architectures or AutoML approaches with a simple text query. In this paper, we propose ArchBERT, a bi-modal model for joint learning and understanding of neural architectures and natural languages, which opens up new avenues for research in this area. We also introduce a pre-training strategy named Masked Architecture Modeling (MAM) for a more generalized joint learning. Moreover, we introduce and publicly release two new bi-modal datasets for training and validating our methods. The ArchBERT's performance is verified through a set of numerical experiments on different downstream tasks such as architecture-oriented reasoning, question answering, and captioning (summarization). Datasets, codes, and demos are available supplementary materials.Comment: CoNLL 202

Effects of Chlorhexidine and Sodium Hypochlorite on the Setting Time of Calcium-Enriched Mixture Cement

Introduction: The aim of the present study was to evaluate whether adding 2% chlorhexidine (CHX) and 2.6% sodium hypochlorite (NaOCl) to calcium-enriched mixture (CEM) cement would affect its setting time (ST), or not. Methods and Materials: In this study, the setting time of CEM cement was evaluated in three groups (n=9) as follows: group 1; CEM cement, group 2; CEM cement+2% CHX and group 3; CEM cement+2.6% NaOCl. Then the mean values of ST were calculated and the Kolmogorov-Smirnov test was used to evaluate the normal distribution of data. The Kruskal-Wallis and Mann-Whitney U tests were used for statistical analysis. Statistical significance was set at 0.05. Results: The mean ST for groups 1, 2 and 3 were 105, 120 and 220 min, respectively. There was a significant increase in the duration of ST in group 3 (NaOCl) in comparison with the two other groups (P<0.05). Conclusion: NaOCl significantly increased the ST of CEM cement, whereas chlorhexidine did not alter the ST.Keywords: Calcium-Enriched Mixture; CEM Cement; Chlorhexidine; Setting Time; Sodium Hypochlorit

Condition Monitoring of an Industrial Oil Pump Using a Learning Based Technique

This paper proposes an efficient learning based approach to detect the faults of an industrial oil pump. The proposed method uses the wavelet transform and genetic algorithm (GA) ensemble for an optimal feature extraction procedure. Optimal features, which are dominated through this method, can remarkably represent the mechanical faults in the damaged machine. For the aim of condition monitoring, we considered five common types of malfunctions such as casing distortion, cavitation, looseness, misalignment, and unbalanced mass that occur during the machine operation. The proposed technique can determine optimal wavelet parameters and suitable statistical functions to exploit excellent features via an appropriate distance criterion function. Moreover, our optimization algorithm chooses the most appropriate feature submatrix to improve the final accuracy in an iterative method. As a case study, the proposed algorithms are applied to experimental data gathered from an industrial heavy-duty oil pump installed in Arak Oil Refinery Company. The experimental results are very promising

Investigating the Trend of Dust Changes in The Eastern Half of Iran

IntroductionThe impact of dust storms on the ecosystem, human health, and economy is significant in the affected areas. In arid and semi-arid regions, dust is a natural occurrence that covers about one third of the earth's surface. This phenomenon is caused by wind erosion and factors such as the size of soil particles and their adhesion force, surface roughness, weather conditions such as long-term droughts cause it to intensify. To understand the impact of the dust phenomenon on humans and the environment, it is essential to evaluate the spatial and temporal distribution of dust and its changes and effects. Numerous studies have been carried out in the field of evaluating dust changes using different methods. For example, using CMIP5 models, synoptic fog systems are predicted to increase during the 21st century in the Middle East. In addition, using AOD obtained from MODIS and MERRA-2 sensors, researchers showed a significant upward trend in dust changes from 2000 to 2010. A significant upward trend was shown in Iran's winter AOD values during the period 2000 to 2010, and a decreasing trend during the period 2010 to 2018. A point can be spotted by examining various studies in the Middle East and Iran that evaluate the spatio-temporal changes of dust. Statistical tests of time series study, such as Mann-Kendall spatially and pixel by pixel, have been used in limited research to evaluate the trend of dust changes. In Iran, there is a research gap in not using spatial and pixel-by-pixel statistical tests to evaluate the trend of dust changes, as stated. This research aimed to provide a solution and address the problem by analyzing the spatial and temporal changes of dust using the AOD index in the eastern half of the country. Material and Methods In this research, in order to evaluate the temporal-spatial changes of dust, the AOD data of the blue band (470 nm) of the MCD19A2 product of the MODIS sensor was used. AOD parameter is known as one of the most key factors in studying the climatic effects of aerosols and atmospheric pollution. In order to extract AOD data, monthly data from 2001 to 2022 were obtained in the Google Earth Engine system by averaging the daily AOD data. Over a 22-year period, the average of each month was calculated. The months that had the highest average AOD values were chosen and their changes were evaluated. In this research, the Mann-Kendall test was used to evaluate the change process. Menkendall's ZM coefficient was calculated for months in the Earth Trend Modeler (ETM) of the TerrSet software to achieve this. In the next step, the intensity of monthly AOD changes per time unit was calculated for 22 years in selected months. To simulate the process of changes, linear regression analysis can be utilized for this purpose. This method is used to determine the linear relationship between all the data of a dependent variable and the corresponding data of the independent index. If the slope is higher than zero, the dependent variable will change in the same direction as the independent variable. The dependent variable changes in the opposite direction of the independent variable if the slope is smaller than zero. The steeper the slope of changes, the greater the impact of the independent variable on the dependent variable. The Earth Trend Modeler (ETM) of TerrSet software also carried out this step. Results and DiscussionBased on the evaluation of the monthly average AOD changes in the studied area, the trend and intensity of AOD changes from 2001 to 2022 were assessed in April, May, June, and July. In most areas of the studied area, AOD is increasing with a probability of more than 70%, and the intensity of changes is mostly high and very high in April. It can be concluded that AOD is experiencing a strong increase in April. This is despite the fact that in May, June and July, respectively, a considerable part of the western half, northern half and eastern half is increasing with different intensities with a probability of more than 70%. It can be concluded that the trend and intensity of AOD changes in the above-mentioned months follow a different spatial pattern. The dispersion of dust production centers inside and outside Iran, and the local and regional synoptic conditions governing dust production centers is the cause of changes in the spatio-temporal patterns of dust storms. The unprincipled extraction of water resources by humans, land degradation, soil moisture reduction, and the loss of vegetation due to climate change all affect these factors in turn. The results showed that the monitoring of monthly average AOD changes can help to identify new hotspots and evaluate the results of wind and dust erosion control and management activities. Therefore, it can be suggested that a system based on remote sensing must be designed and presented to monitor dust changes, so that the management of the dust phenomenon in Iran becomes more. We need to pay attention to the factors that influence these changes and evaluate their impact on the dust phenomenon.  On the other hand, by modeling the environmental factors affecting on the trend of dust changes in each region by using methods such as dust evaluation, it is possible to determine the role of each factor and the most important factor affecting the trend of dust changes in each region

Experimental investigation of sandy soil stabilization using chitosan biopolymer

The performance of an environmentally friendly biopolymer synthesised from secondary resources to overcome the wind erosion of sandy soil was investigated in this study. The study employed a multi-scale approach to investigate the mechanical, erosional, and hydraulic properties of sandy soil. At the macroscale, experimental techniques such as unconfined and triaxial compression tests, permeability measurements, contact angle assessments, and wind tunnel experiments were utilized to characterize the bulk behavior of the soil. Concurrently, molecular dynamics (MD) simulations were conducted at the nanoscale to predict surface mechanical characteristics and elucidate chemical interactions at the molecular level. Results show that when the outer surface of the sandy particles is coated with a sparse concentration of biopolymer, the sandy aerosol inhibitory performance is significant even under extreme storm conditions reaching speeds of 140 km/h of storms. The study on the impact of biopolymer content, curing time, and curing conditions revealed that the addition of chitosan biopolymer has the ability to enhance the bonding between particles and significantly enhance the mechanical properties of sandy soil. The atomic insight from molecular dynamics reveals huge entanglement between sandy particles and biopolymer by Van der Waals interaction. The results of the Unconfined Compressive Strength test indicate that chitosan enhances the compressive strength of sand by up to 320 kPa. Additionally, the triaxial test demonstrated that the application of chitosan led to a 34.2 kPa improvement in the cohesion of sand. Furthermore, analysis of the permeability test results revealed a decrease in the hydraulic conductivity coefficient from 1.6 × 10^-6 m/s to 5.7 × 10^-7 m/s, representing a reduction of approximately 35 %

Propagation and stock enhancement of silver pomfret (Pamus argenteus) in the north west of Persian Gulf

This study was conducted in the Persian Gulf (Coastal waters of Khuzestan province) from March 2009 to April 2010. The 237 specimen of Silver pomfert P. argenteus collected. Maximum of mature brooders at Jun and maximum of hatched eggs obtained at same month. Artificial fertilization was successful. Absolut fecundity was between 19000 to 38000.maximum of hatch was 51 percent. Maximum of fertilization was 32 percent and continued development of larvae to 35 days. Average of fertilized eggs was 1.1 mm. newly hatched larvae were 2.2 to 2.4 mm. The all Silver Pomfret larvae were dead at prude 35 days gently. Suppose n enhancement phase wasn't performed