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.

Aligning thousands of images from serial imaging techniques can be a cumbersome task. Methods ([2, 11, 21]) and programs for automation exist (e.g. [1, 4, 10]) but often need case-specific tuning of many meta-parameters (e.g. mask, pyramid-scales, denoise, transform-type, method/metric, optimizer and its parameters). Other programs, that apparently only depend on a few parameter often just hide many of the remaining ones (initialized with default values), often cannot handle challenging cases satisfactorily. Instead of spending much time on the search for suitable meta-parameters that yield a usable result for the complete image series, the described approach allows to intervene by manually aligning problematic image pairs. The manually found transform is then used by the automatic alignment as an initial transformation that is then optimized as in the pure automatic case. Therefore the manual alignment does not have to be very precise. This way the worst case time consumption is limited and can be estimated (manual alignment of the whole series) in contrast to tuning of meta-parameters of pure auto-alignment of complete series which can hardly be guessed.

In this paper, we presented a logistic regression model that we applied for assessment of the users’ quality of experience with web browsing service over mobile network. With this regard, we chose the Average-Time-to-Connect-TCP network service quality parameter as an independent predictor, obtained by passive monitoring of live traffic data, captured by a passive probe on the mobile network Gn interface, and related to detailed records of the Transport Control Protocol. In parallel with in-service measuring the selected network parameter, we conducted simultaneous subjective tests of the quality of experience acceptability to users, specifically for web browsing service. Particularly, it was found that the model provided correct acceptability classification in 84.5% of cases, while reducing the chosen independent predictor for 100 ms implied increasing the chance of the service acceptability by factor of 1.65. Based on the obtained results, it comes out that the applied logistic regression model provides satisfactory estimation of the web browsing service quality experience acceptability.

Management literature proposes several broad categories of business motives behind Knowledge Management (KM) initiatives: minimising risk, improving efficiency and effectiveness and enabling innovation. While risk minimisation and efficiency and effectiveness improvement are fundamental for organisational survival, innovation is the key for organisational advancement and long-term economic success. Choosing the right KM strategy is of utmost importance for organisational performance. On the one hand, two popular Knowledge Management strategies termed codification and personalisation differ in their reliance on technology or people. On the other hand, two strategies referred to as exploitation and exploration differ in their focus on transferring existing or developing new knowledge. This study aims to examine the main business forces in KM adoption and to identify preferred KM strategies responsively. More specifically, it aims to provide a metric in determining (1) applied and realised KM strategies, (2) business focus, (3) knowledge processes and (4) knowledge focus. Data, collected from 372 surveyed employees of Turkish organisations across different industries, were analysed in terms of the two key classes of KM drivers and strategies. The findings reveal a widespread tendency for simultaneous pursuance of dual survival and advancement business goals and widespread integration of codification and personalisation as well as exploitation and exploration strategies. The findings validate the ability of Turkish firms in the organisation of KM activities through the combination of somewhat contradictory drivers and strategies implying their ambidexterity regarding considered KM strategies.