Diffusion in Networks and the Unexpected Virtue of Burstiness

Whether an idea, information, infection, or innovation diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions. Recent studies have shown that diffusion can fail on a network in which people are only active in "bursts", active for a while and then silent for a while, but diffusion could succeed on the same network if people were active in a more random Poisson manner. Those studies generally consider models in which nodes are active according to the same random timing process and then ask which timing is optimal. In reality, people differ widely in their activity patterns -- some are bursty and others are not. Here we show that, if people differ in their activity patterns, bursty behavior does not always hurt the diffusion, and in fact having some (but not all) of the population be bursty significantly helps diffusion. We prove that maximizing diffusion requires heterogeneous activity patterns across agents, and the overall maximizing pattern of agents' activity times does not involve any Poisson behavior

Dynamic Matching Market Design

We introduce a simple benchmark model of dynamic matching in networked markets, where agents arrive and depart stochastically and the network of acceptable transactions among agents forms a random graph. We analyze our model from three perspectives: waiting, optimization, and information. The main insight of our analysis is that waiting to thicken the market can be substantially more important than increasing the speed of transactions, and this is quite robust to the presence of waiting costs. From an optimization perspective, naive local algorithms, that choose the right time to match agents but do not exploit global network structure, can perform very close to optimal algorithms. From an information perspective, algorithms that employ even partial information on agents' departure times perform substantially better than those that lack such information. To elicit agents' departure times, we design an incentive-compatible continuous-time dynamic mechanism without transfers

Epidemioclinical Feature of Early-Onset Colorectal Cancer at-Risk for Lynch Syndrome in Central Iran.

BACKGROUND Colorectal cancer (CRC) is becoming one of the most complicated challenges of human health, particularly in developing countries like Iran. In this paper, we try to characterize CRC cases diagnosed < age 50 at-risk for Lynch syndrome within central Iran. MATERIALS AND METHODS We designed a descriptive retrospective study to screen all registered CRC patients within 2000-2013 in Poursina Hakim Research Center (PHRC), a referral gastroenterology clinic in central Iran, based on being early-onset (age at diagnosis ≤50 years) and Amsterdam II criteria. We calculated frequencies and percentages by SPSS 19 software to describe clinical and family history characteristics of patients with early-onset CRC. RESULTS Overall 1,659 CRC patients were included in our study of which 413 (24.9%) were ≤50 years at diagnosis. Of 219/413 successful calls 67 persons (30.6%) were reported deceased. Family history was positive for 72/219 probands (32.9%) and 53 families (24.2%) were identified as familial colorectal cancer (FCC), with a history of at-least three affected members with any type of cancer in the family, of which 85% fulfilled the Amsterdam II Criteria as hereditary non-polyposis colorectal cancer (HNPCC) families (45/219 or 20.5%). Finally, 14 families were excluded due to proband tumor tissues being unavailable or unwillingness for incorporation. The most common HNPCC-associated extracolonic- cancer among both males and females of the families was stomach, at respectively 31.8 and 32.7 percent. The most common tumor locations among the 31 probands were rectum (32.3%), sigmoid (29.0%), and ascending colon (12.9%). CONCLUSIONS Given the high prevalence of FCC (~1/4 of early-onset Iranian CRC patients), it is necessary to establish a comprehensive cancer genetic counseling and systematic screening program for early detection and to improve cancer prognosis among high risk families

Energy inputs – yield relationship and sensitivity analysis for tomato greenhouse production in Iran

This paper studies the energy balance between the input and the output energies per unit area for greenhouse tomato production.  For this purpose, the data on 30 tomato production greenhouses in Isfahan province, Iran were collected and analyzed.  The results indicated that a total specific input energy of 116,768.4 MJ ha-1 was consumed for tomato production.  Diesel fuel (with 40%) and chemical fertilizers and manure (with 30%) were amongst the highest input energies for tomato production.  The energy productivity was estimated to be 1.16 kg MJ-1.  The ratio of output energy to input energy was approximately 0.92. 19% and 81% of total energy input was in renewable and non-renewable forms, respectively.  The regression results revealed that the contribution of input energies on crop yield for human power, machinery, pesticides and electricity inputs was significant.  The human power energy had the highest impact (1.45) among the other inputs in greenhouse tomato production.  The marginal physical productivity of diesel fuel, seed and total chemical fertilizer with manure was negative.  It can be because of applying the inputs more than required or improperly applying.  The highest shares of expenses were found to be 34% and 21% for human power and total diesel fuel and machinery, respectively.  Cost analysis revealed that total cost of production for 1 ha greenhouse tomato production was around US$34939.  Accordingly, the benefit-cost ratio was estimated as 2.74.  Results of greenhouse gas emission indicated that tomato production is mostly depended on diesel fuel sources.  Diesel fuel had the highest share (2,719.98 kg CO2eq.ha-1) followed by electricity (729.6 kg CO2eq.ha-1) and nitrogen fertilizer (409.5 kg CO2eq.ha-1).   Keywords: tomato, greenhouse, energy productivity, economic analysis, Cobb-Douglas functio

Mathematical Formulation for Determining Lateral Displacement of Tubular Frame and Outriggers Equipped with Viscous Dampers

Viscous Dampers are mainly used to mitigate and control vibration, acceleration and lateral displacement in buildings. A hybrid system comprised of framed tube, shear core and outriggers equipped with passive linear viscous dampers is investigated, and the mathematical formulation is developed to analyze the hybrid system as a beam-like structure. A mathematical model, based on Euler-Bernoulli flexural beam theory, is developed to build a simple, yet accurate model for calculating the lateral displacement profile of the hybrid system, under lateral load patterns varying against time. The properties of the hybrid system may vary in arbitrary segments, and any number of outrigger systems may be considered through the height of the structure in the proposed method. Kinetic and potential energies and non-conservative works due to the velocity dependent viscous dampers force and the lateral load exerted on the hybrid system are obtained. Next, Hamilton’s principle is implemented, and the governing equation of motion and boundary conditions are derived. As the aforementioned partial differential equations are dependent on both time and space, the central finite difference method is chosen as a numerical method to find the answer to the equation of motion

Persian topic detection based on Human Word association and graph embedding

In this paper, we propose a framework to detect topics in social media based on Human Word Association. Identifying topics discussed in these media has become a critical and significant challenge. Most of the work done in this area is in English, but much has been done in the Persian language, especially microblogs written in Persian. Also, the existing works focused more on exploring frequent patterns or semantic relationships and ignored the structural methods of language. In this paper, a topic detection framework using HWA, a method for Human Word Association, is proposed. This method uses the concept of imitation of mental ability for word association. This method also calculates the Associative Gravity Force that shows how words are related. Using this parameter, a graph can be generated. The topics can be extracted by embedding this graph and using clustering methods. This approach has been applied to a Persian language dataset collected from Telegram. Several experimental studies have been performed to evaluate the proposed framework's performance. Experimental results show that this approach works better than other topic detection methods

A Human Word Association based model for topic detection in social networks

With the widespread use of social networks, detecting the topics discussed in these networks has become a significant challenge. The current works are mainly based on frequent pattern mining or semantic relations, and the language structure is not considered. The meaning of language structural methods is to discover the relationship between words and how humans understand them. Therefore, this paper uses the Concept of the Imitation of the Mental Ability of Word Association to propose a topic detection framework in social networks. This framework is based on the Human Word Association method. A special extraction algorithm has also been designed for this purpose. The performance of this method is evaluated on the FA-CUP dataset. It is a benchmark dataset in the field of topic detection. The results show that the proposed method is a good improvement compared to other methods, based on the Topic-recall and the keyword F1 measure. Also, most of the previous works in the field of topic detection are limited to the English language, and the Persian language, especially microblogs written in this language, is considered a low-resource language. Therefore, a data set of Telegram posts in the Farsi language has been collected. Applying the proposed method to this dataset also shows that this method works better than other topic detection methods

Effect of hydroxyethyl starch on acidosis in patients with aluminum phosphide poisoning

Background: Given the fact that various studies have reported the positive effects of hydroxyethyl starch therapy in controlling shock, this study aimed to compare the effects of hydroxyethyl starch on modifying acidosis and treating patients with aluminum phosphide poisoning. Methods: This was a randomized clinical trial that was conducted on 60 patients with aluminum phosphide poisoning. We compared the two groups of patients treated with hydroxyethyl starch and normal saline. Base excess and serum pH of arterial blood gases (ABG) were measured before and after the treatment and compared by t-test. Results: Results showed that arterial pH in the starch group and normal saline group increased by 0.13 and 0.18, respectively, and the difference between the two groups was not statistically significant. The difference in the base excess before and after treatment in the starch group and normal saline group was 6.41 and 5.39, respectively, and the difference between the two groups was not statistically significant. Changes in mean values of arterial pH after the intervention in comparison with before treatment were statistically significant (p&#60;0.05). Conclusion: Overall, the results of the present study show that starch is at least as effective as normal saline in treating acidosis in patients poisoned with aluminum phosphide and can be used instead of normal saline, and both of the two&#160;treatments&#160;could be equally effective

Association between diabetes mellitus and rs2868371; a polymorphism of HSPB1

Introduction: Diabetes (DM) is a type of metabolic disorder that its types are generated by collectingof genetic and environmental risk agents. Here, the association between HSPB1 polymorphism as a genetic risk factor and DM was investigated. Methods: Total 690 participants from MASHAD cohort study population were recruited into the study.Anti-HSP27-level was assessed followed by genotyping using Taqman®-probes-based assay. Anthropometric, demographic and hematological/biochemical characteristics were evaluated. Kaplan-Meier curves were utilized, while logistic regression models were used to assess the association of the genetic variant with clinical characteristics of population. Results: Finds was shown there are meaningful differences among groups of age, height, waist circumference, systolic blood pressure, FBG,TG, HDL-C, and hs-CRP, and was no big -significant difference between theexists in different HSP27 SNP in the two studied groups (with and without DM), also was no remarkable relation between genetic forms of HSPB1and T2DM. This investigation was the first research that analyzed the relationship between the genetic type of the HSPB1 gene (rs2868371) and Type 2 diabetes (DM2). In our population, the CC genotype (68.1%) had a higher prevalence versus GC (26.6%) and GG (5.3%) genotypes and the data shown that no genetic difference of HSPB1 gene polymorphism (rs2868371) was related with DM2. Conclusion: HSPB1 polymorphism, rs2868371, was not associated with type 2 diabetes mellitus