Average Rank Research Articles

Weighted mean of vectors optimization algorithm and its application in designing the power system stabilizer

Accurate design of the power system stabilizer (PSS) models is a crucial issue due to their significant impact on the stability of power system operation. However, identifying the parameters of a PSS model is a challenging task owing to its nonlinearity and multi-modality characteristics. Due to such characteristics, handling algorithms may be prone to stagnation in local optima. Therefore, this paper proposes a potent integrated optimization algorithm by comprising the weIghted meaN oF vectOrs (INFO) optimizer with chaotic-orthogonal based learning (COBL) and Gaussian bare-bones (GBB) strategies, named INFO-GBB, for achieving the optimal parameters of a PSS model used in a single-machine infinite-bus (SMIB) system. In the INFO-GBB, the COBL aims to enhance the searching capability to explore new regions using the orthogonal design aspect and thus improving the diversity of solutions. Also, the GBB is adopted to assist the algorithm to perform an immediate vicinity of the best solution and thus enhances the exploitation capabilities. The effectiveness and efficacy of the INFO-GBB algorithm is validated on CEC 2020 benchmark suits and the designing task of the PSS model. The achieved results by the INFO-GBB are compared with eighteen well-known algorithms. The statistical verifications along with the Friedman test have ascertained that the INFO-GBB is capable of achieving promising performances compared to the other counterparts. The results obtained based on the Friedman test illustrate that the INFO-GBB offers superior performance over the state-of-the-art algorithms as it outperforms fifteen out of eighteen algorithms by an average rank greater than 61% for benchmark problems while outperforming O-LSHADE, LSHADE, and TSA algorithms by 25%,33%, and 58%, respectively. Furthermore, the applicability of the INFO-GBB is realized through designing the PSS model used in a SMIB system. The obtained results indicate that the INFO-GBB algorithm exhibits accurate and superior performance compared to other peers as it provides the lowest value for the integral of time multiplied absolute error (ITAE) performance index which is used as an objective function. For example, the achieved results of the mean ITAE found by INFO-GBB is 1.36E−03 with improvement percentages of 24.93%, 19.78%, 13.04%, 26.64%, and 24.86%, over the LSHADE, GWO, EO, RSA, and original INFO algorithms, respectively. Therefore, the INFO-GBB can efficiently affirm its superiority and stability to deal with the function optimization task and parameters’ estimation of the PSS model.

Flexible species distribution modelling methods perform well on spatially separated testing data

AbstractAimTo assess whether flexible species distribution models that perform well at nearby testing locations still perform strongly when evaluated on spatially separated testing data.LocationAustralian Wet Tropics (AWT), Ontario, Canada (CAN), north‐east New South Wales, Australia (NSW), New Zealand (NZ), five countries of South America (SA), and Switzerland (SWI).Time periodMost species data were collected between 1950 and 2000.Major taxa studiedBirds, mammals, plants and reptiles.MethodsWe compared 10 species distribution modelling methods with varying flexibility in terms of the allowed complexity of their fitted functions [boosted regression trees (BRT), generalized additive model (GAM), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt), support vector machine (SVM), variants of generalized linear model (GLM) and random forest (RF), and an Ensemble model]. We used established practices for model selection to avoid overfitting, including parameter tuning in learning methods. Models were trained on presence–background data for 171 species and tested on presence–absence data. Training and testing data were separated using both random and spatial partitioning, the latter based on 75‐km blocks. We calculated the average performance and mean rank of the methods (focussing on the area under the receiver operating characteristic and precision‐recall gain curves, and correlation) and assessed the statistical significance of the differences between them.ResultsThe ranking of methods did not change when evaluated on spatially separated testing data. Methods with the strongest predictive performance were nonparametric methods known to be flexible. An ensemble formed by averaging predictions of five pre‐selected modelling methods was the best model in both random and spatial partitioning, followed by MaxEnt and a variant of random forest.Main conclusionsWhilst some modellers expect methods limited to simple smooth functions to predict better spatially separated data, we found no evidence of that using blocks of 75 km. We conclude that flexible models that are tuned well enough to avoid overfitting are effective at predicting to spatially distinct areas.

Analytic ranks of elliptic curves over number fields

Let E E be an elliptic curve over Q \mathbb {Q} . Then, we show that the average analytic rank of E E over cyclic extensions of degree l l over Q \mathbb {Q} with l l a prime not equal to 2 2 , is at most 2 + r Q ( E ) 2+r_\mathbb {Q}(E) , where r Q ( E ) r_\mathbb {Q}(E) is the analytic rank of the elliptic curve E E over Q \mathbb {Q} . This bound is independent of the degree l l . Using a recent result of Bhargava, Taniguchi and Thorne [Improved error estimates for the Davenport–Heilbronn theorems, arxiv.org/abs/2107.12819, 2021], we obtain a non-trivial upper bound on the average analytic rank of E E over S 3 S_3 -fields.

A comparative assessment of Conventional and Rough-Based Multi-Criteria methods for failure mode and effects analysis of Root canal treatment

This paper presents failure mode and effects analysis (FMEA) of a dentistry procedure i.e. root canal treatment (RCT) The aims of the present study are as follows: (i) to identify the potential failure modes (FMs) associated with RCT and (ii) to determine the ranks of the identified FMs to represent their degree of severity for the failure of the RCT procedure. Sixteen potential FMs of RCT are identified with the help of five members of the FMEA team. The FMs are evaluated with respect to three risk factors (RFs) i.e. severity (S), occurrence (O), and detection (D). Opinions regarding the importance of RFs on a scale of 1-9 and importance of each RF for each FM on a scale of 1-10 are gathered from FMEA team members. Subsequently, the obtained information is transformed into rough numbers in order to take care of the vagueness and inconsistency present in the human judgement. Further, weights of the RFs, in terms of rough numbers, are determined. Subsequently, four rough-based multi-criteria decision making (MCDM) methods i.e. rough weighted aggregated sum product assessment (R-WASPAS), rough additive ratio assessment (R-ARAS), rough TOPSIS (R-TOPSIS), and rough VIKOR (R-VIKOR) are used to rank the FMs. The final ranking of the FMs is derived using the grade average integrated ranking method. The results reveal that “Missed canal” and “Root canal under fillings” are the most and the least critical FMs, respectively. Further, the top 20%, the next 30%, and the last 50% FMs are categorized into highly, fairly, and the least critical groups. A few important suggestions are proposed to reduce the risks of the top 20% FMs. The results of the study may be useful for the professionals involved in the RCT and may help them in properly planning and successfully executing the RCT procedure.

Relative Validity and Reproducibility of Dietary Measurements Assessed by a Semiquantitative Food Frequency Questionnaire among Chinese Healthy Adults.

This study aimed to evaluate the relative validity and reproducibility of a semiquantitative food frequency questionnaire (SFFQ) in adult populations in China. Among the 49 recruited healthy participants (age range: 20-60 years), the relative validity of a 79-item SFFQ was assessed in two ways: (1) by comparing its dietary intake estimates with those from the average measurements of three inconsecutive 24 h dietary records (24-HDRs); and (2) by comparing its estimates of dietary fatty acids with the measured plasma levels of fatty acids. The reproducibility of the SFFQ was evaluated by a comparison of two SFFQ measurements from the same participants collected one year apart. In the relative validity study, the average Spearman correlation coefficient (r) was 0.27 among 18 prespecified food group intakes estimated from the SFFQ and the 24-HDRs; nevertheless, that of five food group intakes (e.g., red meat and seafood) was higher (all rs > 0.40, p < 0.05). In addition, a moderate correlation between the SFFQ estimate of polyunsaturated fatty acid intakes (energy-adjusted percentage of total fatty acids) and its plasma level was observed (r = 0.42, p < 0.05). Regarding the one-year reproducibility of the SFFQ-assessed intakes, the average rank intraclass correlation coefficient (ICC) was 0.35 for the 18 food group estimates. In particular, moderately reproducible estimates of seven food group intakes (e.g., refined grains and red meat, all ICCs ≥ 0.40, p < 0.05) were observed. In conclusion, the SFFQ provides valid and reproducible estimates of dietary intakes for various food groups in general and performs well as a potential tool for estimating habitual dietary intakes of some unsaturated fatty acids.

One size does not fit all: Preferences for HIV care delivery among out-of-care people living with HIV in the Southeastern United States.

Approximately half of the people with HIV (PWH) in the United States are retained in HIV care and only 57% have achieved viral suppression, due to barriers including transportation access, stigma, poor mental health, substance use, and medical mistrust. Community-based HIV care models have potential to address the diverse needs of patients and to improve retention in care, but their success is contingent on acceptance by patients and key community stakeholders. Recognizing that the preferences of PWH who are out-of-care (PWH-OOC) likely differ from those retained in care, we conducted a mixed-methods study from June 2019 to May 2021 composed of surveys with PWH-OOC (n = 50) and in-depth interviews with key clinic and community stakeholders (n = 41) to examine the relative preference and perceived advantages and disadvantages for six different community-based HIV care models versus the traditional fixed-clinic model. Survey data was analyzed to assess average rank preference for each care model and interview transcripts were thematically coded to examine factors influencing model acceptance. The highest preference for care delivery was via a mobile clinic, followed by community-based peer navigation, primary care clinics, telemedicine, traditional HIV subspeciality clinic, homeless shelter, and drug treatment center. Common factors influencing preference included convenience, accessibility, potential to preserve confidentiality, quality of care assurance, opportunity to develop rapport with their HIV care provider, access to a smart device, and potential to alleviate versus exacerbate HIV stigma. Participants discussed need for integration of care models and for individuals to choose different care models at different times. Providers and patients differed in preference for care model and weighting of relative advantages and disadvantages of each. Findings highlight the need to integrate alternative, community-based care models into the national plan to end the HIV epidemic and to allow for PWH-OOC to choose the model most fitting based on individual circumstances.

Multilayer Perceptron and Their Comparison with Two Nature-Inspired Hybrid Techniques of Biogeography-Based Optimization (BBO) and Backtracking Search Algorithm (BSA) for Assessment of Landslide Susceptibility

Regarding evaluating disaster risks in Iran’s West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, NDVI (land use), slope (degree), stream power index (SPI), topographic wetness index (TWI), rainfall, and sediment transport index (STI), and 504 landslides as target variables, a large geographic database is constructed. Applying the techniques mentioned above to the synthesis of the MLP results in the suggested BBO-MLP and BSA-MLP ensembles. As accuracy standards, we benefit from mean absolute error, mean square error, and area under the receiving operating characteristic curve to assess the utilized models, we have also designed a scoring system. The MLP’s accuracy increases thanks to the application of the BBO and BSA algorithms. Comparing the BBO with the BSA, we find that the former achieves higher average MLP optimization ranks (20, 15, and 14). A further finding showed that the BBO is superior to the BSA at maximizing the MLP.

Analyzing Physics-Inspired Metaheuristic Algorithms in Feature Selection with K-Nearest-Neighbor

In recent years, feature selection has emerged as a major challenge in machine learning. In this paper, considering the promising performance of metaheuristics on different types of applications, six physics-inspired metaphor algorithms are employed for this problem. To evaluate the capability of dimensionality reduction in these algorithms, six diverse-natured datasets are used. The performance is compared in terms of the average number of features selected (AFS), accuracy, fitness, convergence capabilities, and computational cost. It is found through experiments that the accuracy and fitness of the Equilibrium Optimizer (EO) are comparatively better than the others. Finally, the average rank from the perspective of average fitness, average accuracy, and AFS shows that EO outperforms all other algorithms.

The role of diverse leadership styles in teaching to sustain academic excellence at secondary level.

Educational leadership is a multifaceted area of study. Unquestionably, leadership is the most deliberate field within the social sciences. Still, administrators have evaded the notions of leadership concept like a haunting tune. This study has focused particularly on the significance of varied leadership styles in teaching to sustain academic excellence at the secondary school level. The quantitative research method was used. Data was collected through the scale of diverse leadership styles (strategic, cultural, instructional leaderships and sustaining academic excellence) from 103 secondary schools in Punjab, Pakistan. The sample consisted of 540 teachers who were enacting as teachers presently. Based on research objectives and questions, two hypotheses were formed and tested using mean analysis to determine the average ranking of leadership styles. Pearson correlation to know the statistically significant relationship between each leadership styles, and overall scales with sustaining academic excellence. The results revealed that most teachers give preference strategic Leadership, then instructional leadership, and finally cultural leadership in their teaching to sustain academic excellence. Moreover, the findings also indicated that a statistically strong positive relationship among diverse leadership styles in teaching and sustaining academic excellence with the value (r = 0.752). Based on the findings, it has been concluded that when teachers increase their effort in the use of strategic, instructional, and cultural leadership styles, academic excellence may also sustain and improve.

Genome-wide imputed differential expression enrichment analysis identifies trait-relevant tissues.

The identification of pathogenically-relevant genes and tissues for complex traits can be a difficult task. We developed an approach named genome-wide imputed differential expression enrichment (GIDEE), to prioritise trait-relevant tissues by combining genome-wide association study (GWAS) summary statistic data with tissue-specific expression quantitative trait loci (eQTL) data from 49 GTEx tissues. Our GIDEE approach analyses robustly imputed gene expression and tests for enrichment of differentially expressed genes in each tissue. Two tests (mean squared z-score and empirical Brown's method) utilise the full distribution of differential expression p-values across all genes, while two binomial tests assess the proportion of genes with tissue-wide significant differential expression. GIDEE was applied to nine training datasets with known trait-relevant tissues and ranked 49 GTEx tissues using the individual and combined enrichment tests. The best-performing enrichment test produced an average rank of 1.55 out of 49 for the known trait-relevant tissue across the nine training datasets-ranking the correct tissue first five times, second three times, and third once. Subsequent application of the GIDEE approach to 20 test datasets-whose pathogenic tissues or cell types are uncertain or unknown-provided important prioritisation of tissues relevant to the trait's regulatory architecture. GIDEE prioritisation may thus help identify both pathogenic tissues and suitable proxy tissue/cell models (e.g., using enriched tissues/cells that are more easily accessible). The application of our GIDEE approach to GWAS datasets will facilitate follow-up in silico and in vitro research to determine the functional consequence(s) of their risk loci.

КАЧЕСТВО ЭЛЕКТРОННЫХ ГОСУДАРСТВЕННЫХ УСЛУГ: ПРОБЛЕМА, ПОДХОДЫ К ОЦЕНКЕ И ИЗМЕРИТЕЛИ

Assessing the quality of public services in electronic form in Russia is carried out by the state represented by the Federal State Statistics Service, the Ministry of Digital Development, Communications and Mass Communications and state development institutions, as well as a wide expert community represented by university research teams and rating agencies. All the many methods used indicate that the quality of electronic government services (EGS) can be assessed using various approaches and criteria, which do not always confirm each other’s results. The study, the results of which are presented in these materials, compared conclusions drawn from the interpretation of open data on the e-Government Development Index (EGDI), as well as those obtained on the basis of European statistics, Russian state statistics, VСIOM data and data from independent web analytical services. The study showed that international experts from a special UN department rank the Russian Federation in the seventh ten countries in terms of EGS quality (above average rank), while national government agencies, while monitoring the use of EGS over the past years, have recorded a consistently high and growing level. Awareness and satisfaction with the quality of EGS among the Russian population is confirmed by independent web analytics data on traffic and its quality characteristics (depth and time) of the main state online service.

An investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for TOPSIS

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a popular multi-criteria decision-making method that ranks the available alternatives by examining the ideal-positive and ideal-negative solutions for each decision criterion. The first step of using TOPSIS is to normalize the presence of incommensurable data in the decision matrix. There are several normalization methods, and the choice of these methods does affect TOPSIS results. As such, some efforts were made in the past to compare and recommend suitable normalization methods for TOPSIS. However, such studies merely compared a limited collection of normalization methods or used a noncomprehensive procedure to evaluate each method's suitability, leading to equivocal recommendations. This study, therefore, employed an alternate, comprehensive procedure to evaluate and recommend suitable benefit/cost criteria-based normalization methods for TOPSIS (out of ten methods extracted from past literature). The procedure was devised based on three evaluation metrics: the average Spearman's rank correlation, average Pearson correlation, and standard deviation metrics, combined with the Borda count technique.•The first study examined the suitability of ten benefit/cost criteria-based normalization methods over TOPSIS.•Users should combine the sum-based method and vector method into the TOPSIS application for safer decision-making.•The maximum method (version I) or Jüttler's-Körth's method has an identical effect on TOPSIS results.

Computational procedures for quality assessment of latent concepts

The estimation of a latent construct is a crucial issue when a phenomenon is a consequence of the joint occurrence of multiple features. Several techniques for estimating latent phenomena are proposed in literature, but since the variable under study is unobservable, and the law by which the features combine together is unknown, comparison of results obtained with different methods is achieved through simulation‐based analyses. The latent construct is measured by a multidimensional index (MI), which determines a ranking among the observations. It is therefore relevant to assess the reliability of the methods by which such rankings are produced. In this paper, we propose two procedures evaluating the assessment of latent constructs and the corresponding rankings. The first one is a simulation‐based mean square error, which evaluates the MI accuracy, while the second one uses a simulation average split ranking index, which assesses its reliability. Both methodologies are illustrated with an application to the assessment of the urban sustainability, and three methodologies of building MIs are compared: the first one based on structural equation models; the second one based on principal component analysis; and the third one that uses an equal weight method.

The Determinants of Insider Trading in the Credit Default Swap Market-A Network Perspective

Compared to the corporate bond and stock markets, investors prefer the CDS market to implement insider trading since the CDS market is less regulated and trading in this market on their private information is relatively easy without generating great transaction costs. Social networks, board networks in particular, are one of the central features of most economic activities and play an important role in information transmission. Therefore, investors are motivated to make use of the informational advantage gained from well-connected directors in the CDS market. In this paper, we investigate whether and how insider trading in the credit default swap (CDS) market is influenced by the corporate board network, formed by interlocking boards. We use CDS innovation as a proxy for the intensity of insider trading in the CDS market. To estimate the centrality of a firm position in the board network, we use the average quintile ranks of the four commonly used normalized board network centrality measures. We test our hypothesis based on a large sample of U.S. firms from 2004-2014 and find strong evidence that firms with a more centralized position in the board network experience a higher degree of insider trading in the CDS market. Our results suggest that the board network facilitates private information leakage to investors, resulting in more active informed trading in the CDS market. Moreover, such an association is more pronounced for firms with negative earnings news and for firms with weak corporate governance. In addition, using the enactment of the Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank Act) as a quasi-natural experiment, we document that insider trading becomes less active after the passage of the Dodd-Frank Act. Our results still hold when we repeat the baseline analysis using four centrality measures separately or when controlling for the number of banking relationships. Our results add both to the insider trading literature and the social networks literature by examining the relationship between board network centrality and insider trading behavior in the CDS market. The findings indicate that board networks could serve as a relationship network that transmits private information to outside investors.

Hyoid body morphology of thyroglossal duct cyst: a comparative study based on imaging

Objective:To provide reference for preoperative diagnosis and treatment of thyroglossal duct cyst by studying the morphological changes of hyoid body. Methods:The CT data（midsagittal image） of congenital thyroglossal duct cyst（TGDC group） diagnosed by pathology and the control group（C group） were collected from January 2016 to October 2021. The differences of hyoid body height（HBH）, hyoid body width（HBW）, hyoid bone thickness（HBT）, HBW/HBH, HBT/HBH between the two groups were compared. HBH, HBW and HBT were analyzed by t-test; The HBW/HBH and HBT/HBH were analyzed by Mann-Whitney U test. Results:Twenty-nine cases were included in the TGDC group and 58 in the C group. The HBH in the TGDC group was（8.93 ±0.22） mm and that in the C group was（8.94±0.12） mm, there was no significant difference between the two groups（P>0.05）. The HBW in the TGDC group and the C group were（5.09±0.21） mm and（4.48±0.11） mm, and the HBT were（3.84±0.12） mm and（3.13±0.08） mm, respectively, the difference between the two groups was statistically significant（P<0.05）. The average rank sum of HBW/HBH in the TGDC group and the C group was 53.95 and 39.03, respectively, and the average rank sum of HBT/HBH was 59.90 and 36.05, respectively, the difference between the two groups was statistically significant（P<0.05）. Conclusion:The morphological changes of hyoid body of thyroglossal duct cyst may be helpful for preoperative diagnosis, and it also suggests that hyoid body resection may reduce the possibility of postoperative recurrence.

A Factor Based Multiple Imputation Approach to Handle Class Imbalance

Class imbalance and incompleteness are the two most serious problems faced in data science and machine learning when working on real-life datasets. Both of these cases have severe implications on the ability of classification algorithms to make accurate predictions. When a dataset used for training classifiers is both imbalanced as well as incomplete, the traditional approach is to address the missing data first and then handle class imbalance but it could lead to some issues such as overfitting as well as amplification of some errors due to random duplication. In this paper, an alternate factor-based multiple imputation oversampling method (FB-MIO) is proposed to handle class imbalance as well as missing values in the training dataset at the same time. First, a new factor is presented to evaluate the density of missing values belonging to the majority class with respect to the minority class in a particular region. With the help of this factor, an oscillator is developed to guide how imputation based oversampling should be carried out. Then the training set is divided into multiple smaller subsets and used the oscillator to determine whether missing values for the majority class belonging to that subsets should be imputed or not. This would help in preventing exaggerated duplication when not needed. Experiments were carried out on 27 imbalanced datasets after random addition of missing value and the F1 and AUROC scores of FB-MIO were compared to other dataset level resampling methods such as SMOTE, ADASYN, B-SMOTE etc. The effectiveness of the proposed method has been validated after experiments on benchmark datasets and the comparative results are presented in the form of average rank and number of wins.

Optimal Recommendation Models Based on Knowledge Representation Learning and Graph Attention Networks

Knowledge representation learning techniques process the knowledge graph, embedding entities and relationships into a continuous dense low-dimensional vector space, and providing the rich semantic association information between entities embedded in the knowledge graph to the recommendation module, improving recommendation performance and providing better interpretability. However, the knowledge representation learning techniques based on the translation model TransD with too many parameters and no association between entity representations are difficult to apply to large knowledge graph, as well as the problem is that most existing knowledge graph which based on recommendation systems ignore the different levels of importance that users attach to different relationships of items. To address these shortcomings, we propose an improved knowledge representation learning model Cluster TransD and a recommendation model Cluster TransD-GAT based on knowledge graph and graph attention networks, where the Cluster TransD model reduces the number of entity projections, makes the association between entity representations, reduces the computational pressure, and makes it better to be applied to the large knowledge graph, and the Cluster TransD-GAT model can capture the attention of different users to different relationships of items. Extensive comparison and ablation experiments on three real datasets show that the model proposed in this paper has significant performance improvements in terms of average ranking, accuracy and recall of the scoring function compared to other state-of-the-art models.

A study of the evaluation metrics for generative images containing combinational creativity

AbstractIn the field of content generation by machine, the state-of-the-art text-to-image model, DALL⋅E, has advanced and diverse capacities for the combinational image generation with specific textual prompts. The images generated by DALL⋅E seem to exhibit an appreciable level of combinational creativity close to that of humans in terms of visualizing a combinational idea. Although there are several common metrics which can be applied to assess the quality of the images generated by generative models, such as IS, FID, GIQA, and CLIP, it is unclear whether these metrics are equally applicable to assessing images containing combinational creativity. In this study, we collected the generated image data from machine (DALL⋅E) and human designers, respectively. The results of group ranking in the Consensual Assessment Technique (CAT) and the Turing Test (TT) were used as the benchmarks to assess the combinational creativity. Considering the metrics’ mathematical principles and different starting points in evaluating image quality, we introduced coincident rate (CR) and average rank variation (ARV) which are two comparable spaces. An experiment to calculate the consistency of group ranking of each metric by comparing the benchmarks then was conducted. By comparing the consistency results of CR and ARV on group ranking, we summarized the applicability of the existing evaluation metrics in assessing generative images containing combinational creativity. In the four metrics, GIQA performed the closest consistency to the CAT and TT. It shows the potential as an automated assessment for images containing combinational creativity, which can be used to evaluate the images containing combinational creativity in the relevant task of design and engineering such as conceptual sketch, digital design image, and prototyping image.

Frequency analysis of absolute maximum air temperatures in Serbia

This paper describes the frequency analysis of absolute maximum air temperatures, using annual maximum series (AMS) in the period 1961-2010 from 40 climatological stations in Serbia with maximum likelihood estimation of distribution parameters. For the goodness of fit testing of General Extreme Value (GEV), Normal, Log-Normal, Pearson 3 (three parameters), and Log-Pearson 3 distribution, three different tests were used (Kolmogorov-Smirnov, Anderson-Darling, chi-square). Based on the results of these tests (best average rank of certain distribution), the appropriate distribution is selected. GEV distribution proved to be the most appropriate one in most cases. The probability of exceedance of absolute maximum air temperatures on 1%, 0.5%, 0.2%, and 0.1% levels are calculated. A spatial analysis of the observed and modeled values of absolute maximum air temperatures in Serbia is given. The absolute maximum air temperature of 44.9?C was recorded at Smederevska Palanka station, and the lowest value of maximum air temperature 35.8?C was recorded at Zlatibor station, one of the stations with the highest altitude. The modeled absolute maximum air temperatures are the highest at Zajecar station with 44.5?C, 45.6?C, 47.0?C, and 48.0?C and the lowest values are calculated for Sjenica station with 35.5?C, 35.8?C, 36.1?C, and 36.2?C for the return periods of 100, 200, 500, and 1000 years, respectively. Our findings indicate the possible occurrence of much higher absolute maximum air temperatures in the future than the ones recorded on almost all of the analyzed stations.

Designing a model for using basic mobile social networks in identifying adolescents

Background and Aim: The most important issue of adolescence is gaining identity. Identity is considered as a process of coherence of individual changes, social demands and expectations for the future. The purpose of this study is to design a model for the use of virtual social networks in identifying adolescents. Methods: This research has been done in the framework of a quantitative approach and using the survey research method. For this purpose, 385 adolescents aged 15 to 18 years in Tehran in 2020 were selected by multi-stage cluster sampling. A questionnaire was used to collect data. Results: According to the findings of the present study, the most use of social networks of adolescents is for the purpose of acquiring knowledge and in the second place for the purpose of entertainment. The level of trust and influence of male students from social networks is higher according to the average rankings. The stability and strength of the family is more than other dimensions of identity referred. The degree of influence of social networks has a significant relationship with family structure. The influence of artists and actors and social networks is more after choosing a job and activity. Conclusion: The results of the present study indicate that there is a relationship between the use of mobile social networks and the identification of adolescents.