The decision-making trial and evaluation laboratory (DEMATEL) method is one of the most significant multi-criteria techniques for defining the relationships among criteria and for defining the weight coefficients of criteria. Since multi-criteria models are very often used in management and decision-making under conditions of uncertainty, the fuzzy DEMATEL model has been extended in this paper by D numbers (fuzzy DEMATEL-D). The aim of this research was to develop a multi-criteria methodology that enables the objective processing of fuzzy linguistic information in the pairwise comparison of criteria. This aim was achieved through the development of the fuzzy DEMATEL-D method. Combining D numbers with trapezoidal fuzzy linguistic variables (LVs) allows for the additional processing of uncertainties and ambiguities that exist in experts’ preferences when comparing criteria with each other. In addition, the fuzzy DEMATEL-D methodology has a unique reasoning algorithm that allows for the rational processing of uncertainties when using fuzzy linguistic expressions for pairwise comparisons of criteria. The fuzzy DEMATEL-D methodology provides an original uncertainty management framework that is rational and concise. In order to illustrate the effectiveness of the proposed methodology, a case study with the application of the proposed multi-criteria methodology is presented.

The aim of this paper is to compare air quality in Sarajevo in March 2019 and March 2020 with outbreak of the novel coronavirus SARS-CoV-2 in Sarajevo and Bosnia and Herzegovina. First preventive and protective measures were issued at the end of second week of March, while on 21 March 2020 an order imposing complete ban of movement of citizens from late afternoon until early in the morning next day was issued. This was rare opportunity to compare air quality in Sarajevo having same causes of air pollution for one part of March 2019 and March 2020 and different causes of air pollution during the lockdown and ban of movement caused by SARS-CoV-2. Statistical hypothesis testing is used to compare values during March 2019 and March 2020 before the lockdown (the first phase) and during the lockdown (the second phase). Complete and comprehensive analysis is performed for both phases of March 2019 and March 2020, before the lockdown and during the lockdown. It is shown that there are statistical evidences that during the lockdown period mean concentration values of O3 and NO2 are smaller than mean values during the same period in March 2019, while mean concentration value of PM10 is greater than mean value during the same period in March 2019. Also, statistical hypothesis testing is used to compare concentration of air pollutants before and during lockdown period in March 2020. It is shown that mean concentration values of PM10 and O3 are greater during lockdown period, while mean concentration value of NO2 before the lockdown in March 2020 is greater than during the lockdown period. Coefficients of correlation as the measure of the strength of linear association between air pollutants PM10, O3 and NO2 and meteorological parameters air temperature, humidity and pressure, wind speed and wind direction are calculated as well.

The objective of this study was to determine the minimum inhibitory concentrations (MICs) of nine antimicrobials (enrofloxacin, ciprofloxacin, norfloxacin, gentamicin, spectinomycin, oxytetracycline, tylosin, florfenicol, and tiamulin) against 24 Mycoplasma ovipneumoniae isolates obtained from sheep and goats and to compare the resulting antimicrobial profiles. Enrofloxacin and ciprofloxacin had the lowest MIC50 values (<0.03 μg/mL) and MIC90 values (0.25 μg/mL) for all tested isolates. The highest MIC50 value (2 μg/mL) was obtained for florfenicol, while oxytetracycline and tylosin exhibited the highest MIC90 values (16 μg/mL). The MIC values for all fluoroquinolones and oxytetracycline were significantly lower for sheep isolates. Sheep isolates were considerably more susceptible to norfloxacin and tylosin than were goat isolates. This study demonstrated differences in antimicrobial susceptibilities between sheep and goat isolates, revealing M. ovipneumoniae in goat isolates to be less susceptible. The results suggest a possible link between antimicrobial profiles of M. ovipneumoniae isolates and their host ruminant species.

Abstract Walls represent globally distributed, locally extensive, artificial ecosystems. Wall vegetation is still poorly known in the Mediterranean and Temperate regions of southeastern Europe. The aim of this study is to classify and describe chasmophytic vegetation of walls, covering southeastern Europe from Slovenia to North Macedonia. From a total 463 phytosociological relevés, we identify and describe 12 distinct species – poor to moderately rich vegetation units using TWINSPAN evaluated by NMDS, and indicator values. The vegetation units are included within three alliances from two macroclimate regions: (1) vegetation of cool-temperate Europe of the Cymbalario-Asplenion alliance, and (2) Mediterranean vegetation of the Galio valantiae-Parietarion judaicae and Artemisio arborescentis-Capparidion spinosae alliances. The southernmost limit of the Cymbalario-Asplenion was determined in Central Bosnia. The presence of the Artemisio arborescentis-Capparidion spinosae in the eastern Adriatic is highlighted.

The aim of the research is to determine the effects on the recognition and naming of certain sentence structures through a verbal and non-verbal approach, or through the use of a verbal and non-verbal communication channel. The study was conducted on a sample of 30 deaf pupils at age from 11 to 14 years. As a measurement instrument, a modified image description test was used. The test consisted of six complexes, illustrated sentences, in which the actions and subjects performing certain actions are shown in the picture sequence in a way that deaf children understand. The descriptive analysis method was used for data processing. Measures of central tendencies and variations have been made. Testing the difference between verbal and non-verbal approach was performed by t-test. The correlation between the chronological age and the recognition and naming of the spoken structures were verified through the non-verbal approach. The results of the research have shown that respondents have achieved better results in recognizing and naming spoken content using the non-verbal communication channel, that there is a statistically significant difference in the use of verbal and non-verbal communication approaches in recognizing and naming spoken content, and that there is a high correlation between the chronological age of respondents and recognizing and naming of spoken content through a non-verbal approach.

BACKGROUND AND OBJECTIVE The SOX2OT lcnRNA has been recognized as positive regulator in transcription regulation of SOX2 gene. Recent studies have approved the dysregulation of SOX2OT lncRNA expression patterns in some common caner types including esophageal, lung, and breast cancer. The objective of present study was to investigate correlation between overexpression of SOX2OT lcnRNA and susceptibility to breast cancer. METHOD SOX2OT lncRNA expression profiling in 15 breast cancer and normal tumour-adjacent breast tissue samples was performed by using qRT-PCR. To evaluating the diagnostic potential of the SOX2OT lncRNA we performed ROC curve analyses. RESULTS The expression of SOX2OT lncRNA in patients suffering from breast cancer revealed a significant overexpression in comparison with the healthy group (P<0.001). Significantly, the elevated circulating SOX2OT lncRNA was found specific to breast cancer and could differentiate breast cancer from controls with 100% of both sensitivity of and specificity. Based on the Kaplan-Meier analysis, there were no significant correlation between SOX2OT lcnRNA expression and overall survival. CONCLUSION The results confirmed the association between breast cancer and higher SOX2OT lncRNA expression. According to the ROC curve results, SOX2OT lcnRNA could be a new measurable indicator of the breast cancer and a potential therapeutic target for breast cancer patients.

PurposeThis paper aims to investigate the impacts of social media on the Pakistani consumers' buying behavior, which could be reflected in either complex buying, variety seeking, dissonance reducing or habitual buying. Entrepreneurs need to know how their loyal and prospective customers feel, think and how do they decide on purchasing certain products and services.Design/methodology/approachThe self-administered online questionnaire is used to collect feedback from consumers in order to analyze the data and come up with the findings. A sample size of 396 respondents was used to analyze and find a relationship between social media and consumer buying behavior.FindingsSocial media is found to have a partially significant impact on Pakistani consumers' buying behavior; word of mouth and content credibility are the two factors that influence Pakistani consumers' buying behavior. Pakistani consumers, below the age of 40, possess more complex buying behavior, which alerts entrepreneurs to consider it for their future marketing strategies.Practical implicationsEntrepreneurs should make an effort to be differentiated from others while keeping customers aware of the products they provide. In addition, customers should not spend too much time when comparing brands; rather, businesses should make it more captive.Originality/valueThis paper provides different results in comparison to the previous studies, in terms of the factors influencing consumers' buying behavior.

Tramadol hydrochloride/paracetamol is an opioid that is composed of two different analgesics, tramadol (opioid, 37.5 mg) and paracetamol (non opioid, 325 mg). The study presents first data of tramadol hydrochloride/paracetamol effects on haematological parameters and glucose levels in Wistar rats. Oral administration ad libitum of tramadol hydrochloride/paracetamol was administered during a twenty days period. This research includes two groups of animals. Group I include animals that were administered tramadol hydrochloride/paracetamol daily at dosage of 1.12 g/300 ml and group II that were administered daily at dosage of 1.68 g/300 ml of water. We analayzed 14 haematological parameters and glucose concentration. Significant changes were established for all analyzed parameters. Significantly low numbers of erythrocytes, leukocytes and lymphocytes were observed. Tramadol hydrochloride/paracetamol has an extremely negative effect on haematological parameters in Wistar rats, particularily on the blood coagulation due to the thrombocitopenia, anaemia and weakened immunity. If properly administered, tramadol hydrochloride/paracetamol can be an effective analgesic but at high dosage and over a prolonged period it may cause adverse effects in Wistar rats.

Automatic modulation classification (AMC) is an essential technology for the non-cooperative communication systems, and it is widely applied into various communications scenarios. In the recent years, deep learning (DL) has been introduced into AMC due to its outstanding identification performance. However, it is almost impossible to implement previously proposed DL-based AMC algorithms without large number of labeled samples, while there are generally few labeled sample and large unlabel samples in the realistic communication scenarios. In this paper, we propose a transfer learning (TL)-based semi-supervised AMC (TL-AMC) in a zero-forcing aided multiple-input and multiple-output (ZF-MIMO) system. TL-AMC has a novel deep reconstruction and classification network (DRCN) structure that consists of convolutional auto-encoder (CAE) and convolutional neural network (CNN). Unlabeled samples flow from CAE for modulation signal reconstruction, while labeled samples are fed into CNN for AMC. Knowledge is transferred from the encoder layer of CAE to the feature layer of CNN by sharing their weights, in order to avoid the ineffective feature extraction of CNN under the limited labeled samples. Simulation results demonstrated the effectiveness of TL-AMC. In detail, TL-AMC performs better than CNN-based AMC under the limited samples. What’s more, when compared with CNN-based AMC trained on massive labeled samples, TL-AMC also achieved the similar classification accuracy at the relative high SNR regime.