Exploring the Correlation between Hα\rm{H}\alpha-to-UV Ratio and Burstiness for Typical Star-forming Galaxies at z∼2z\sim2

The $\rm{H}\alpha$-to-UV luminosity ratio ($L(\rm H\alpha)/L(\rm UV)$) is often used to probe SFHs of star-forming galaxies and it is important to validate it against other proxies for burstiness. To address this issue, we present a statistical analysis of the resolved distribution of $\Sigma_{\rm{SFR}}$ as well as stellar age and their correlations with the globally measured $L(\rm H\alpha)/L(\rm UV)$ for a sample of 310 star-forming galaxies in two redshift bins of $1.37 < z < 1.70$ and $2.09 < z < 2.61$ observed by the MOSDEF survey. We use the multi-waveband CANDELS/3D-HST imaging of MOSDEF galaxies to construct $\Sigma_{\rm{SFR}}$ and stellar age maps. We analyze the composite rest-frame far-UV spectra of a subsample of MOSDEF targets obtained by the Keck/LRIS, which includes 124 star-forming galaxies (MOSDEF-LRIS) at redshifts $1.4 < z < 2.6$, to examine the average stellar population properties, and the strength of age-sensitive FUV spectral features in bins of $L(\rm H\alpha)/L(\rm UV)$. Our results show no significant evidence that individual galaxies with higher $L(\rm H\alpha)/L(\rm UV)$ are undergoing a burst of star formation based on the resolved distribution of $\Sigma_{\rm{SFR}}$ of individual star-forming galaxies. We segregate the sample into subsets with low and high $L(\rm H\alpha)/L(\rm UV)$. The high-$L(\rm H\alpha)/L(\rm UV)$ subset exhibits, on average, an age of $\log[\rm{Age/yr}]$ = 8.0, compared to $\log[\rm{Age/yr}]$ = 8.4 for the low-$L(\rm H\alpha)/L(\rm UV)$ galaxies, though the difference in age is significant at only the $2\sigma$ level. Furthermore, we find no variation in the strengths of Siiv$\lambda\lambda1393, 1402$ and Civ$\lambda\lambda1548, 1550$ P-Cygni features from massive stars between the two subsamples.Comment: 16 pages, 10 figures, published by the Monthly Notices of the Royal Astronomical Societ

Developing a Based-on-play Cognitive-behavioral Educational Package and Determining Its Effectiveness in Improving the Language Disorders and Social Adjustment in Bilingual Children

Background: The present study was conducted to develop a game-based cognitive-behavioral educational package and determine its effectiveness in improving the receptive language disorders and social adjustment of bilingual children.  Methods: The current study was applied objectively and in terms of the nature of the data, it was quasi-experimental with a pretest post-test design and follow-up with experimental, control, and pseudo-control groups. The statistical population of the research includes all bilingual children of Bojnord City, Iran who were studying in preschool centers in the academic year 2018-2019. A sample consisting of 60 male and female students was selected using convenience sampling and according to the inclusion and exclusion criteria, and they were randomly assigned to three experimental, control, and pseudo-control groups (each group with 20 people). In the pretest stages, the participants completed the Nikamer and Hamill language development test and the Dokhanchi social adjustment scale, and then during the intervention process of the experimental group, they received a game-based cognitive-behavioral training program for 12 sessions. The pseudo-control group received a program except for play therapy, and the control group received no intervention. After the end of the intervention, all three groups responded to both scales again in the post-test stages and were re-evaluated after two months. The data were analyzed using the analysis of variance test with repeated measurements and using SPSS software, versian 24. Results: The results showed that the game-based cognitive-behavioral intervention is significantly effective in improving the receptive language disorders and social adjustment of bilingual children compared to the control and quasi-control groups. Conclusion: It seems that game-based cognitive-behavioral interventions can play a significant role in improving the language skills and social adaptation of bilingual children

Auraptene consolidates memory, reverses scopolamine-disrupted memory in passive avoidance task, and ameliorates retention deficits in mice

Objective(s): Auraptene (7-geranyloxycoumarin) (AUR), from Citrus species has shown anti-inflammatory, neuroprotective, and acetylcholinesterase (AChE) and beta-secretase inhibitory effects. Scopolamine is a nonselective muscarinic receptor antagonist which causes short-term memory impairments and is used for inducing animal model of Alzheimer’s disease (AD). This research aimed to investigate the effect of AUR on scopolamine-induced avoidance memory retention deficits in step-through task in mice. Materials and Methods: The effect of four-day pre-training injections of AUR (50, 75, and 100 mg/kg, subcutaneous (SC)) and scopolamine (1 mg/kg, IP), and their co-administration on avoidance memory retention in step-through passive avoidance task, was investigated by measuring the latency to enter to the dark chamber. Results:Pre-training administration of AUR caused significant increase in step-through latency in comparison with control group, 48, 96, and 168 hr after training trial. The findings of this study showed that scopolamine (1 mg/kg, IP, for four consecutive days) impaired passive avoidance memory retention compared to saline-treated animals. Step-through passive avoidance task results showed that AUR markedly reversed scopolamine-induced avoidance memory retention impairments, 24 and 168 hr after training trial in step-through task. Conclusion: Results from co-administration of AUR and scopolamine showed that AUR reversed scopolamine-induced passive avoidance memory retention impairments

Evaluation of the Performance of ClimGen and LARS-WG models in generating rainfall and temperature time series in rainfed research station of Sisab, Northern Khorasan

Introduction:Many existing results on water and agriculture researches require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. Therefore, the required daily climate data should be generated based on the limited available data. For this purpose, weather generators can be used to enlarge the data length. Among the common weather generators, two models are more common: LARS-WG and ClimGen. Different studies have shown that these two models have different results in different regions and climates. Therefore, the output results of these two methods should be validated based on the climate and weather conditions of the study region. Materials and Methods:The Sisab station is 35 KM away from Bojnord city in Northern Khorasan. This station was established in 1366 and afterwards, the meteorological data including precipitation data are regularly collected. Geographical coordination of this station is 37º 25׳ N and 57º 38׳ E, and the elevation is 1359 meter. The climate in this region is dry and cold under Emberge and semi-dry under Demarton Methods. In this research, LARG-WG model, version 5.5, and ClimGen model, version 4.4, were used to generate 500 data sample for precipitation and temperature time series. The performance of these two models, were evaluated using RMSE, MAE, and CD over the 30 years collected data and their corresponding generated data. Also, to compare the statistical similarity of the generated data with the collected data, t-student, F, and X2 tests were used. With these tests, the similarity of 16 statistical characteristics of the generated data and the collected data has been investigated in the level of confidence 95%. Results and Discussion:This study showed that LARS-WG model can better generate precipitation data in terms of statistical error criteria. RMSE and MAE for the generated data by LAR-WG were less than ClimGen model while the CD value of LARS-WG was close to one. For the minimum and maximum temperature data there was no significant difference between the RMSE and CD values for the generated and collected data by these two methods, but the ClimGen was slightly more successful in generating temperature data. The X2 test results over seasonal distributions for length of dry and wet series showed that LARS-WG was more accurate than ClimGen.The comparison of LARS-WG and ClimGen models showed that LARS-WG model has a better performance in generating daily rainfall data in terms of frequency distribution. For monthly precipitation, generated data with ClimGen model were acceptable in level of confidence 95%, but even for monthly precipitation data, the LARS-WG model was more accurate. In terms of variance of daily and monthly precipitation data, both models had a poor performance.In terms of generating minimum and maximum daily and monthly temperature data, ClimGen model showed a better performance compared to the LARS-WG model. Again, both models showed a poor performance in terms of variance of daily and monthly temperature data, though LAR-WG was slightly better than ClimGen. For lengths of hot and frost spells, ClimGen was a better choice compared to LARS-WG. Conclusion:In this research, the performances of LARS-WG and ClimGen models were compared in terms of their capability of generating daily and monthly precipitation and temperature data for Sisab Station in Northern Khorasan. The results showed that for this station, LARS-WG model can better simulate precipitation data while ClimGen is a better choice for simulating temperature data. This research also showed that both models were not very successful in the sense of variances of the generated data compared to the other statistical characteristics such as the mean values, though the variance for monthly data was more acceptable than daily data

Thermoluminescent dosimetry properties of double doped calcium tetraborate (CaB4O7:Cu-Mn) nanophosphor exposed to gamma radiation

This study reports the dosimeteric properties of double Cu–Mn doped calcium tetraborate nanophosphor prepared by co-precipitation technique. The structure and the morphology of the synthesized nanocrystals were characterized by X-ray diffraction and transmission electron microscope. The presented XRD patterns showed the monoclinic structure and transmission electron microscopy revealed the formation of spherical shape nanoparticles with an average particle size of 8 nm. The results demonstrated that the synthesized calcium borate nanophosphor has the highest dosimetric sensitivity at combined concentration of 2% and 1%, copper and manganese molar ratio, respectively. TL glow curve of this material showed two well resolved peaks located at around 124 and 256 °C. The most striking dosimeteric feature of this nanomaterial is the linearity response, which has a long range of 0.05–3000 Gy for both temperature peaks