Large stars with few colors

A recent question in generalized Ramsey theory is that for fixed positive integers $s\leq t$, at least how many vertices can be covered by the vertices of no more than $s$ monochromatic members of the family $\cal F$ in every edge coloring of $K_n$ with $t$ colors. This is related to an old problem of Chung and Liu: for graph $G$ and integers $1\leq s<t$ what is the smallest positive integer $n=R_{s,t}(G)$ such that every coloring of the edges of $K_n$ with $t$ colors contains a copy of $G$ with at most $s$ colors. We answer this question when $G$ is a star and $s$ is either $t-1$ or $t-2$ generalizing the well-known result of Burr and Roberts

Holographic Mutual Information for Singular Surfaces

We study corner contributions to holographic mutual information for entangling regions composed of a set of disjoint sectors of a single infinite circle in three-dimensional conformal field theories. In spite of the UV divergence of holographic mutual information, it exhibits a first order phase transition. We show that tripartite information is also divergent for disjoint sectors, which is in contrast with the well-known feature of tripartite information being finite even when entangling regions share boundaries. We also verify the locality of corner effects by studying mutual information between regions separated by a sharp annular region. Possible extensions to higher dimensions and hyperscaling violating geometries is also considered for disjoint sectors.Comment: 35 pages, 25 Figures, v2: presentation improved, v3: matches published version in JHE

Critical point approaches to second-order differential systems generated by impulses

Using variational methods and critical point theory, we establish multiplicity results of solutions for second-order differential systems generated by impulses. Indeed, employing two sorts of three critical points theorems, we establish the multiplicity results for weak solutions of the problem and verify that these solutions are generated by impulses.Publisher's Versio

Forecasting the Number of Injured in Traffic Accidents Referred to Forensic Medicine in Hamadan Province using Multi-layered Artificial Neural Network

سابقه و هدف: حوادث ترافیک جاده ای یک مشکل جدید بهداشت عمومی در سراسر جهان است به گونه ای که ﺗﺼﺎدﻓﺎت راﻧﻨﺪﮔﯽ یکی از ﻣﻬﻢﺗﺮﯾﻦ دﻟﯿﻞ ﻣﺮگ، ﻧﺎﺗﻮاﻧﯽ و ﺑﺴﺘﺮي در ﺑﯿﻤﺎرﺳﺘﺎن را ﺗﺸﮑﯿﻞ ﻣﯽدﻫﻨﺪ . پیش بینی روند تعداد مصدومین حوادث ترافیکی ارجاعی به پزشکی قانونی استان همدان با استفاده از شبکه عصبی مصنوعی چند لایه هدف این تحقیق بود. &nbsp;روش بررسی: تحقیق حاضر توصیفی و تحلیلی از نوع مقایسه ای بود که با استفاده از اطلاعات گذشته به پیش بینی آینده پرداخت. در این تحقیق با استفاده از آمار مصدومان ترافیکی ارجاعی به پزشکی قانونی استان همدان&nbsp; بین فروردین 1368 تا اسفند 1398 با استفاده از شبکه عصبی مصنوعی به پیش بینی تعداد مصدومین برای 12 ماهه منتهی به سال 1399 پرداخته شد. ایتدا شبکه عصبی مناسب با داده های مصدومین طراحی گردید و سپس با استفاده از بهترین شبکه طراحی شده، شبکه شروع به آموزش نمود و&nbsp; شبکه مورد اعتبار سنجی با شاخص درصد قدر مطلق میانگین خطا&nbsp; قرار گرفت. ملاحضات اخلاق در &nbsp;پژوهش در تحقیق حاضر رعایت شد و تحقیق دارای کد اخلاق به شماره&nbsp; IR.MEDILAM.REC.1398.213&nbsp; &nbsp;می باشد. نتایج:&nbsp; شبکه عصبی مصنوعی با 12 ورودی ، یک خروجی و 5 لایه پنهان&nbsp; مناسب ترین شبکه برای پیش بینی مصدومین ارجاعی به پزشکی قانونی همدان&nbsp; بود، شبکه عصبی&nbsp; توانست که با دقت 90 درصد و خطای 10 درصد مقادیر 12 ماهه&nbsp; مصدومان همدان در سال 1399 را&nbsp; به خوبی پیش بینی کند. نتیجه گیری: مقادیر پیش بینی شده نشان داد&nbsp; تعداد مصدومان ترافیکی در استان&nbsp; همدان در حال کاهش است.با توجه به دقت بالا&nbsp; شبکه عصبی مصوعی&nbsp; در این تحقیق می توان این روش را به عنوان مبنایی برای آینده پژوهی در تصادفات قرار داد. روند نزولی تعداد مصدومان ترافیکی استان همدان نشان از موثر بودن برنامه های کاهش تصادفات در این استان است. How to cite this article: Omidi MR, Omidi N. Forecasting the Number of Injured in Traffic Accidents Referred to Forensic Medicine in Hamadan Province using Multi-layered Artificial Neural Network. J Saf Promot Inj Prev. 2020; 8(1):24-9.Background and Objectives: Road traffic accidents are a new public health problem around the world, and "roadblocks" are one of the main causes. Materials and Methods: In this study, using the statistics of traffic injured people referred to forensic medicine in Hamadan province between April 1989 and March 2017, using an artificial neural network, the number of injured for the 12 months leading to 1399 has been predicted. In this study, the appropriate neural network was designed with the data of the injured and then, using the best designed network, the network began to be trained and the network was validated with the absolute percentage of mean error. The authors observe all the ethical considerations of the research in this research and the present research has the code of ethics with the number IR.MEDILAM.REC.1398.213. Results: The artificial neural network with 12 inputs of one output and 5 hidden layers is suitable for predicting the injured referred to Hamedan forensic medicine. Predict well. Conclusion: The predicted values showed that the number of traffic injured in Hamadan province is decreasing. Due to the high accuracy of the artificial neural network in this research, this method can be used as a basis for future research in accidents. The downward trend in the number of traffic injured in Hamadan province shows the effectiveness of accident reduction programs in this province. How to cite this article: Omidi MR, Omidi N. Forecasting the Number of Injured in Traffic Accidents Referred to Forensic Medicine in Hamadan Province using Multi-layered Artificial Neural Network. J Saf Promot Inj Prev. 2020; 8(1):24-9

SYNDEEP: a deep learning approach for the prediction of cancer drugs synergy.

Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examination of all the possible combinations might not be plausible. This study presented a novel computational approach to predicting synergistic drug combinations. Specifically, the deep neural network-based binary classification was utilized to develop the model. Various physicochemical, genomic, protein-protein interaction and protein-metabolite interaction information were used to predict the synergy effects of the combinations of different drugs. The performance of the constructed model was compared with shallow neural network (SNN), k-nearest neighbors (KNN), random forest (RF), support vector machines (SVMs), and gradient boosting classifiers (GBC). Based on our findings, the proposed deep neural network model was found to be capable of predicting synergistic drug combinations with high accuracy. The prediction accuracy and AUC metrics for this model were 92.21% and 97.32% in tenfold cross-validation. According to the results, the integration of different types of physicochemical and genomics features leads to more accurate prediction of synergy in cancer drugs

A Survey on the Best Choice for Modulus of Residue Code

Nowadays, the development of technology and the growing need for dense and complex chips have led chip industries to increase their attention on the circuit testability. Also, using the electronic chips in certain industries, such as the space industry, makes the design of fault tolerant circuits a challenging issue. Coding is one of the most suitable methods for error detection and correction. The residue code, as one of the best choices for error detection aims, is wildly used in large arithmetic circuits such as multiplier and also finds a wide range of applications in processors and digital filters. The modulus value in this technique directly effect on the area overhead parameter. A large area overhead is one of the most important disadvantages especially for testing the small circuits. The purpose of this paper is to study and investigate the best choice for residue code check base that is used for simple and small circuits such as a simple ripple carry adder. The performances are evaluated by applying stuck-at-faults and transition-faults by simulators. The efficiency is defined based on fault coverage and normalized area overhead. The results show that the modulus 3 with 95% efficiency provided the best result. Residue code with this modulus for checking a ripple carry adder, in comparison with duplex circuit, 30% improves the efficiency

Genome-Wide Association Study (GWAS) and genome prediction of seedling salt tolerance in bread wheat (Triticum aestivum L.)

Background: Salinity tolerance in wheat is imperative for improving crop genetic capacity in response to the expanding phenomenon of soil salinization. However, little is known about the genetic foundation underlying salinity tolerance at the seedling growth stage of wheat. Herein, a GWAS analysis was carried out by the random-SNP-effect mixed linear model (mrMLM) multi-locus model to uncover candidate genes responsible for salt tolerance at the seedling stage in 298 Iranian bread wheat accessions, including 208 landraces and 90 cultivars.Results: A total of 29 functional marker-trait associations (MTAs) were detected under salinity, 100 mM NaCl (sodium chloride). Of these, seven single nucleotide polymorphisms (SNPs) including rs54146, rs257, rs37983, rs18682, rs55629, rs15183, and rs63185 with R-2 >= 10% were found to be linked with relative water content, root fresh weight, root dry weight, root volume, shoot high, proline, and shoot potassium (K+), respectively. Further, a total of 27 candidate genes were functionally annotated to be involved in response to the saline environment. Most of these genes have key roles in photosynthesis, response to abscisic acid, cell redox homeostasis, sucrose and carbohydrate metabolism, ubiquitination, transmembrane transport, chromatin silencing, and some genes harbored unknown functions that all together may respond to salinity as a complex network. For genomic prediction (GP), the genomic best linear unbiased prediction (GBLUP) model reflected genetic effects better than both bayesian ridge regression (BRR) and ridge regression-best linear unbiased prediction (RRBLUP), suggesting GBLUP as a favorable tool for wheat genomic selection.Conclusion: The SNPs and candidate genes identified in the current work can be used potentially for developing salt-tolerant varieties at the seedling growth stage by marker-assisted selection

Kinetic analysis of drug release from nanoparticles

PURPOSE. Comparative drug release kinetics from nanoparticles was carried out using conventional and our novel models with the aim of finding a general model applicable to multi mechanistic release. Theoretical justification for the two best general models was also provided for the first time. METHODS. Ten conventional models and three models developed in our laboratory were applied to release data of 32 drugs from 106 nanoparticle formulations collected from literature. The accuracy of the models was assessed employing mean percent error (E) of each data set, overall mean percent error (OE) and number of Es less than 10 percent. RESULTS. Among the models the novel reciprocal powered time (RPT), Weibull (W) and log-probability (LP) ones produced OE values of 6.47, 6.39 and 6.77, respectively. The OEs of other models were higher than 10%. Also the number of errors less than 10% for the models was 84.9, 80.2 and 78.3 percents of total number of data sets. CONCLUSIONS. Considering the accuracy criteria the reciprocal powered time model could be suggested as a general model for analysis of multi mechanistic drug release from nanoparticles. Also W and LP models were the closest to the suggested model RPT

Nicotine Dependence Severity and Revised Reinforcement Sensitivity Theory: Assessing the Mediating Role of Risky Decision Making Using Path Analysis

Background: Gray’s revised Reinforcement Sensitivity Theory (r-RST) is a neuropsychologicalexplanation of personality that has been broadly used in substance use disorders. Although theBehavioral Approach System (BAS) is strongly related to nicotine dependence, findings inBehavioral Inhibition System (BIS) are controversial and there is little information about therole of the Fight/Flight/Freeze System (FFFS) in nicotine dependence. The purpose of thepresent study was to evaluate the mediating role of Risky Decision Making (RDM) in thisrelationship to clarify the controversy and fill the gap.Methods: The final sample of this correlation study comprised of 347 university students (age,Mean±SD 23.2±6.7) who completed two self-report measures, including the Fagerstrom TestFor Nicotine Dependence (FTND), Jackson-5 questionnaire of r-RST, and a computerizedIowa Gambling Task (IGT) to measure RDM. Pearson correlation and a path analysisframework were used to determine the simple, direct, and indirect effects of r-RST systems onnicotine dependence severity through RDM.Results: Using Amos, path analyses demonstrated significant direct and indirect effects ofBAS and FFFS/BIS on tobacco use. Also, the relationship between BAS/FFFS/BIS andtobacco use was shown to be mediated by RDM.Conclusion: It was demonstrated that the relationship among BAS /FFFS/BIS and tobaccouse can be partly explained using maladaptive RDM strategies, suggesting that decreasingreliance on decision-making in risky situations, while increasing the use of decision-makingskills educations in risky situations, and increasing the use of alternative sensation and funseeking by smoking and emotion regulation and mindfulness-based therapie