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Odor detection in insects is largely mediated by structures on antennae called sensilla, which feature a strongly conserved architecture and repertoire of olfactory sensory neurons (OSNs) and various support cell types. In Drosophila, OSNs are tightly apposed to supporting cells, whose connection with neurons and functional roles in odor detection remain unclear. Coupling mechanisms between these neuronal and non-neuronal cell types have been suggested based on morphological observations, concomitant physiological activity during odor stimulation, and known interactions that occur in other chemosensory systems. For instance, it is not known whether cell–cell coupling via gap junctions between OSNs and neighboring cells exists, or whether hemichannels interconnect cellular and extracellular sensillum compartments. Here, we show that innexins, which form hemichannels and gap junctions in invertebrates, are abundantly expressed in adult drosophilid antennae. By surveying antennal transcriptomes and performing various immunohistochemical stainings in antennal tissues, we discover innexin-specific patterns of expression and localization, with a majority of innexins strongly localizing to glial and non-neuronal cells, likely support and epithelial cells. Finally, by injecting gap junction-permeable dye into a pre-identified sensillum, we observe no dye coupling between neuronal and non-neuronal cells. Together with evidence of non-neuronal innexin localization, we conclude that innexins likely do not conjoin neurons to support cells, but that junctions and hemichannels may instead couple support cells among each other or to their shared sensillum lymph to achieve synchronous activity. We discuss how coupling of sensillum microenvironments or compartments may potentially contribute to facilitate chemosensory functions of odor sensing and sensillum homeostasis. [ABSTRACT FROM AUTHOR]

Research in the area of identifying the most influential users in social networks is currently regarded to be one of the most important areas of study. Through the examination of the most influential users on social networks, it is possible to analyze and, in some cases, manage the dissemination of information. A technique that is both quick and scalable is proposed in this research as a means of identifying the users with maximum diffusion capabilities in online social networks. This approach is suited for directed networks as well as undirected networks. The approach that has been suggested is comprised of four stages: (1) community detection, which involves the partial partitioning of the whole social network into communities that are connected to one another by the use of the Louvain algorithm; (2) the removal of communities that are not suitable; (3) selection of prominent nodes within the particular community; and (4) selection of the top k seed nodes. Experimental research was carried out on a number of datasets, each of which was of a different complexity. Using imperfect social networks, it has been demonstrated that the findings generate better outcomes for the diffusion of influence than the current related work models, and they do so with a much less amount of processing time being required. [ABSTRACT FROM AUTHOR]

Since cyberbullying impacts both individual victims and entire society, research on abusive language and its detection has attracted attention in recent years. Because social media sites like Facebook, Instagram, Twitter, and others are so widely accessible, hate speech, bullying, sexism, racism, aggressive material, harassment, poisonous comments, and other types of abuse have all substantially increased. Due to the critical requirement to detect, regulate, and limit the spread of harmful content on social networking sites, we conducted this study to automate the detection of offensive language or cyberbullying. We created a new Arabic balanced data set to be used in the offensive detection process because having a balanced data set for a model would result in improved accuracy models. Recently, the performance of single classifiers has been improved using ensemble machine learning. The purpose of this study is to examine the effectiveness of several single and ensemble machine learning algorithms in identifying Arabic text that contains foul language and cyberbullying. Applying them to three Arabic datasets, we have selected three machine learning classifiers and three ensemble models for this aim. Two of them are offensive datasets that are readily accessible in the public, while the third one was created. The results showed that the single learner machine learning strategy is inferior to the ensemble machine learning methodology. Voting performs is the best performing trained ensemble machine learning classifier, outperforming the best single learner classifier (65.1%, 76.2%, and 98%) for the same datasets with accuracy scores of (71.1%, 76.7%, and 98.5%) for each of the three datasets used. Finally, we improve the voting technique's performance through hyperparameter tuning on the Arabic cyberbullying data set. [ABSTRACT FROM AUTHOR]

This study focuses on the analysis of a Canva-based instructional textbook specifically designed for fifth-grade students preparing for the National Science Olympiad (OSN) in Mathematics. This study uses a qualitative approach with a literature review to develop a Canva-based Mathematics OSN textbook for fifth-grade elementary school students. Primary data is obtained from direct analysis of the textbook, while secondary data comes from articles, books, and papers that discuss digital book development, the use of Canva, and competitive math learning strategies. Data was collected through literature searches with relevant keywords and analyzed thematically to identify patterns and insights. The results are used to compile textbooks that suit the needs of teachers and students. The study's findings suggest that such a resource not only improves student readiness for the OSN competition but also promotes educational equity by mitigating the impact of the digital divide and other systemic barriers. This research contributes to the broader discourse on digital education, offering insights into the effective integration of technology in the development of competitive academic resources. Future research is recommended to evaluate the long-term impact of these resources on student performance and to explore further enhancements in digital educational tools.

Social media platforms have become inundated with offensive language. This issue must be addressed for the growth of online social networks (OSNs) and a healthy online environment. While significant research has been devoted to identifying toxic content in major languages like English, this remains an open area of research in the low-resource Pashto language. This study aims to develop an AI model for the automatic detection of offensive textual content in Pashto. To achieve this goal, we have developed a benchmark dataset called the Pashto Offensive Language Dataset (POLD), which comprises tweets collected from Twitter and manually classified into two categories: "offensive" and "not offensive". To discriminate these two categories, we investigated the classic deep learning classifiers based on neural networks, including CNNs and RNNs, using static word embeddings: Word2Vec, fastText, and GloVe as features. Furthermore, we examined two transfer learning approaches. In the first approach, we fine-tuned the pre-trained multilingual language model, XLM-R, using the POLD dataset, whereas, in the second approach, we trained a monolingual BERT model for Pashto from scratch using a custom-developed text corpus. Pashto BERT was then fine-tuned similarly to XLM-R. The performance of all the deep learning and transformer learning models was evaluated using the POLD dataset. The experimental results demonstrate that our pre-trained Pashto BERT model outperforms the other models, achieving an F1-score of 94.34% and an accuracy of 94.77%. [ABSTRACT FROM AUTHOR]

[Display omitted] • The GO membranes were fabricated with the intercalation of co-ordinated PhA-Fe 3 O 4. • An increase in GO d-spacing was achieved using co-ordinated PhA-Fe 3 O 4. • The membranes exhibited high solvent permeance and dyes rejection performance. • The optimum G 10(1) /P 1.5 -F 100µL membrane shows high Pendimethalin and Tetracycline rejection. • The optimum G 10(1) /P 1.5 -F 100µL membrane exhibited stable performance under harsh conditions. Graphene oxide (GO), a 2D nanomaterial known for its precise molecular sieving, holds potential for Organic Solvent Nanofiltration (OSN) and wastewater treatment. However, challenges such as thin interlayer spacing, extended diffusion pathways, and susceptibility to dehydration and negative charge-induced swelling limit its separation performance. To overcome these limitations, Herein, we have fabricated novel Fe 3 O 4 coordinated PhA intercalated GO nanocomposite membranes. Impressively, these nanocomposite membranes exhibited remarkable separation efficiency for dyes, pesticides, and pharmaceutical ingredients, addressing the drawbacks associated with pristine GO membranes and enhancing their overall performance. The optimum G 10(1) /P 1.5 -F 100µL nanocomposite membrane displayed impressive solvent permeance of 40.43, 33.45, 20.70, 16.24, and 5.6 L/m2 h bar for acetone, methanol, water, ethanol, and IPA, respectively, while maintaining a rejection rate of ≥98.97 % for the MO dye (MW = 327.33 g/mol). Furthermore, the membrane exhibited outstanding efficiency in separating tetracycline (TC) in ethanol (≥97.97 %) and Pendimethalin (PM) in water (≥99.88 %). Additionally, it demonstrated high rejection capabilities for MB (≥98 %) and CR (≥98.78 %) in harsh DMF solvent. Importantly, the nanocomposite membrane displayed outstanding durability throughout continuous separation of Pendimethalin (PM) in a water solution over a period of 168 h. The PM separation remained nearly unchanged for the entire 168-h duration, demonstrating high mechanical strength and chemical stability. Additionally, the single nanocomposite membrane showcased high efficiency in feed change separation, successfully separating CR in ethanol and PM in water. These results, compared to the other GO-based nanocomposite membranes, demonstrate the exceptional stability and immense potential for real OSN applications. [ABSTRACT FROM AUTHOR]

The topic of Czechoslovak scientific c-technical cooperation with the Western Bloc has recently enjoyed a revival of interest, which can be illustrated by the works of Doubravka Olšáková, Valentina Fava, or Emilie Těšínská. Nevertheless, there is still a range of specific aspects that have been omitted from the consideration of contemporary studies, the analysis of which seems to be a prerequisite for a comprehensive depiction of the history of the cross-Curtain flows of know-how, experts and capital. In this respect, it is appropriate to revisit some of the works of more technically oriented authors of the communist and early post-communist era (Miloslav Ernst, Jaroslav Nykryn, Karel Kaplan) and to objectivize their often ideologized conclusions with new findings from primary sources. To this end, both the fonds of industrial ministries in the Czech National Archives, as well as records of international organizations (UN, GATT, UNESCO) located in Geneva were consulted. As these newly accessible archival materials reveal, compared to the Gottwald era (1948--1953), the subsequent period of Antonín Novotný (1953--1968) saw a dynamic development of the forms and content of inter-bloc cooperation, which allowed for the exchange of competitive scientific-technical know-how and modern technologies that proved to have a significant positive impact on the further development of the Czechoslovak economy. [ABSTRACT FROM AUTHOR]

The problem of social network security has been well studied. Numbers of approaches are identified towards securing the profile of social network users and identifying the fake profiles. Still the methods suffer to produce efficient results on detecting fake profiles. To improve the performance, an efficient profile analysis and fake detection model is presented in this article. The proposed Real Time Profile Analysis and Fake Detection Model (RTPAFDM) works on three phases: first the Post Level Trust Analysis (PLTA) is performed, which monitors the post updated on every day and share the posts internally with other users to claim the trustworthy of the post which computes the value of User Post Trust Weight (UPTW); second the method performs Profile Level Trust Analysis (PrLTA) which extracts the profile information like user name, personal details, contact details and friends list. According to the information extracted, the method searches on the entire social network to measure the Profile Match Similarity (PSM). Based on the PSM value, the method would identify the originality of the profile; finally, the method performs Profile Historic Trust Analysis (PHTA) which works on the earlier posts made and their truth values. It computes the value of Historical Trusted Post Weight (HTPW). Using the result of all these trust analysis values, the profile security and fake detection is performed. The method is evaluated with Twitter data set and achieved fake detection accuracy up to 97% and supports the improvement profile security efficiently. [ABSTRACT FROM AUTHOR]

Steganography or data hiding is used to protect the privacy of information in the transit; it has been observed that the information that flows through Online Social Networks (OSN) is very much unsafe. Therefore, people hesitate to communicate their sensitive data on social media.. Most of the information on the online social network is not useful to users and appears to disregard such details. People’s actions provided a possibility for digital Steganography through the Internet.. TCPIP covert channels were used for steganography until the last decade. People began to utilize social media as a covert conduit to communicate hidden messages to targeted users as social media grew in popularity. There are numerous Online Social Networks accessible nowadays, ranging from Facebook to the more contemporary Twitter and Instagram. All of them may be utilized as covert channels without the general public noticing. The primary characteristic of steganography is the protection of information privacy; nonetheless, it has been utilized more for illicit message transmission, which is a source of concern. To make matters worse, adversaries are using steganalysis techniques to mess with the concealed data. In this article, we examine the different social media steganography techniques, such as those used on Facebook, WhatsApp, and Twitter, as well as the difficulties that these approaches raise. The positive and negative consequences of social media, as well as its current state, are discussed in this study. This paper discusses how the performances of Steganography methods may be assessed using the Entropy value of the Stego object. A look of the three features of steganography. It has been given with undetectability, robustness, and payload capacity. Finally, the paper’s concept’s future scope is explored.

BACKGROUND: Transition metal catalysis has, over recent decades, developed into one of the most important methods of constructing molecules. However, although efficient in the construction of complex molecules, these catalysts can be expensive as they are often based upon second‐ and third‐row transition metals which have, for the most part, low terrestrial abundance. Present removal and recovery techniques for homogeneous catalysts tend, at best, to focus on recovering the metal center and to consider ligands as single‐use components. Recovery of the whole catalyst would be more beneficial from economic and environmental points of view. RESULTS: The integration of a membrane separation protocol with gold‐catalyzed carboxylative cyclization of propargylamine was investigated. Filtration conditions were identified in membrane screening experiments with the dinuclear catalyst [Au2Cl2(L)] (Au‐1) and the mononuclear catalyst [Au(IPr)Cl] (Au‐2). Recovery of the whole catalyst was then investigated. Catalyst Au‐1 proved unstable and led to metal recovery for reprocessing upon completion of the process. However, the membrane methodology does allow recovery of the catalyst within the reaction mixture which increases its efficiency. On the other hand, catalyst Au‐2 proved more stable and can be recovered along with its ancillary ligands. CONCLUSIONS: For both catalysts a membrane‐based recovery protocol was successfully demonstrated. This protocol was either internal or external in that the Au complex along with its ligands can be recovered after reaction. This initial work shows the possibility that ligands do not always need to be considered as single‐use components. © 2021 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

he National Science Olympiad (OSN) is one of the government's strategies to improve the quality of education in Indonesia. The purpose of OSN is to develop critical, systematic, creative, innovative and productive thinking skills. Critical thinking skills is a present skill that must be developed by students. The results of the OSN which is not optimal, one of which is caused by not optimal preparation by the school in dealing with OSN selection. The purpose of this activity is to equip students of SMP Negeri 4 Tanjungpinang so they are ready Mathematics Olimpiade selection by giving questions based on critical thinking skills. The results of this activity are (1) students are very competitive and enthusiastic in participating in training, (2) students arealso motivated to participate in OSN 2020 selection, (3) the application of critical thinking skills makes it easier for students to analyze questions and solve them

Currently, online social networks (OSN) have become active collaborative learning and a popular e-learning platform for sharing knowledge. Informal learning is any form of learning that occurs outside the formal curricula of higher educational institutes. There is plenty of studies on the use of OSN for formal learning; however, the use of OSN for informal learning particularly by academic researchers got limited attention. This study attempts to fill this gap in the literature. This research aims to examine the factors which motivate academic researchers to accept OSN as a platform for informal learning. Also, a technology, interactivity, individuality, and behavior (TIIB) model is proposed based on feedback from academic researchers from research universities. To achieve this aim, a quantitative study based on the theory of constructivism and technology acceptance model 3 (TAM3) is done. With 786 valid questionnaires received, the proposed model is tested and statistically evaluated using partial least squares structural equation modelling (PLS-SEM) technique. The study results showed that collaborative, interactive, communication, quality of information, and subjective norms are the major factors of the behavioral intention of academic researchers to use OSN for informal learning. The study suggests instructional recommendations containing important technological, individual, and interactive features that could be considered by practitioners to encourage the acceptance of OSN for informal learning. The results of this research guide OSN for informal learning education providers in terms of developing an effective platform for academic researchers. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ternary Fe-MeIm/PA/PMIA membrane was synthesized by one-pot interfacial reactions. • Interfacial coordination and polymerization reactions have occurred simultaneously. • Effects of concentration and ratio of reagents on OSN performance were studied. • Various characterizations confirm the existence of nanofillers in the TFN membrane. • Optimal TFN membrane showed five times higher permeance than the TFC membrane. Thin-film nanocomposite (TFN) membranes have received immense research interest due to their high permeability and high selectivity in organic solvent nanofiltration (OSN). Herein, TFN membranes were fabricated by synchronizing interfacial coordination and polymerization reactions occurring at an ionic liquid/water interface. The one-pot interfacial reactions led to the simultaneous formation of Fe-MeIm nanofillers and polyamide thin layers with uniform dispersion and good compatibility. The resultant ternary membranes were denoted as Fe-MeIm/PA/PMIA, wherein the Fe-MeIm nanofillers were well-encapsulated in the ultrathin PA selective layer and both of them were located on top of the solvent-resistant PMIA substrate. The morphologies, microstructure, physicochemical properties, and OSN performance of the resultant TFN and traditional TFC membranes were systematically studied and compared. Moreover, the effects of the concentrations and ratios of reagents during the one-pot interfacial reactions were also elucidated. The optimal TFN membrane showed 5 times higher permeance than the TFC membrane without nanofillers while maintaining a high rejection for Congo red/ethanol separation. Moreover, it also showed good stability in various solvents and long-term stability and remarkably improved mechanical properties. This work provides a novel and time-saving fabrication strategy for TFN membranes for OSN application. [ABSTRACT FROM AUTHOR]

Online social networks (OSNs) are known to be vulnerable to Sybil Attack, where attackers leverage the openness to create multiple fake identities for launching many malicious activities. In this paper, we define a weighted-strong-social (WSS) graph that integrates the OSN structure and user behavior patterns and propose a novel hybrid graph-based sybil detection approach. The hybrid approach estimates the trustworthiness of users and user pairs based on user behaviors that can be obtained locally and add them to the OSN structure to construct a WSS graph for sybil detection. The evaluation results show that the AUC of the hybrid approach is 0.954, which is significantly higher than that of previous sybil detection methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Relations / Medzinarodne Vztahy is the property of University of Economics in Bratislava, Faculty of International Relations and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In online social networks, spam profiles represent one of the most serious security threats over the Internet; if they do not stop producing bad advertisements, they can be exploited by criminals for various purposes. This article addresses the nature and the characteristics of spam profiles in a social network like Twitter to improve spam detection, based on a number of publicly available language-independent features. In order to investigate the effectiveness of these features in spam detection, four datasets are extracted for four different language contexts (i.e. Arabic, English, Korean and Spanish), and a fifth is formed by combining them all. We conduct our experiments using a set of five well-known classification algorithms in spam detection field, k -Nearest Neighbours (k -NN), Random Forest (RF), Naive Bayes (NB), Decision Tree (DT) (J48) and Multilayer Perceptron (MLP) classifiers, along with five filter-based feature selection methods, namely, Information Gain, Chi-square, ReliefF, Correlation and Significance. The results show oscillating performance of each classifier across all datasets, but improved classification results with feature selection. In addition, detailed analysis and comparisons are carried out on two different levels: in the first level, we compare the selected features' importance among the feature selection methods, whereas in the second level, we observe the relations and the importance of the selected features across all datasets. The findings of this article lead to a better understanding of social spam and improving detection methods by considering the various important features resulting from the different lingual contexts. [ABSTRACT FROM AUTHOR]

Online social networks have practically a go-to source for information divulging, social exchanges and finding new friends. The popularity of such sites is so profound that they are widely used by people belonging to different age groups and various regions. Widespread use of such sites has given rise to privacy and security issues. This paper proposes a set of rules to be incorporated to safeguard the privacy policies of related users while sharing information and other forms of media online. The proposed access control network takes into account the content sensitivity and confidence level of the accessor to resolve the conflicting privacy policies of the co-owners. [ABSTRACT FROM AUTHOR]

Carbon nanotubes (CNT) are considered as promising materials for enhancing thin-film composite (TFC) membranes by introducing an intermediate layer. However, the specific function of the CNT intermediate layer in the formation of the selective polyamide (PA) layer using interfacial polymerization (IP) remains unclear. Consequently, in this study, a network of CNT was applied as a coating on a polyketone (PK) substrate to control its surface characteristics and act as an intermediate layer for PA formation. By adjusting the amount of CNT, the PK surface became less hydrophilic and had smaller surface pores. The CNT intermediate layer facilitated PA formation by expediting the release of IP monomers, reducing the interaction between PK and the monomers. Nevertheless, excessive CNT loading could lead to the retreat of large leaf-like structures, which are essential for achieving high performance in organic solvent nanofiltration (OSN). OSN tests revealed that the optimal TFC-0.5 membrane displayed a methanol permeance of 5.34 L m−2 h−1/bar with an extremely low molecular weight cut-off (MWCO) of 170–180 g/mol. This result was attributed to the reduced PA intrusion, enlarged PA filtration area, and the gutter layer effect brought about by the CNT intermediate layer. [Display omitted] • Carbon nanotubes (CNT) were introduced in thin-film composite (TFC) membranes. • The CNT interlayer successfully tailored the property of polyketone (PK) support. • Polyamide (PA) morphology transformed along with the CNT loading amount. • Monomer release of interfacial polymerization was facilitated by the CNT layer. • The CNT TFC membrane showed excellent organic nanofiltration (OSN) performances. [ABSTRACT FROM AUTHOR]

Solvent resistant nanofiltration (SRNF) has received increasing attention since the turn of the century within energy‐ and waste‐efficient unit processes. This work explores the synthesis and characterization of crosslinked polynorbornenes for SRNF. A series of polyvinylnorbornene thin‐film composite membranes with increasing degree of crosslinking were prepared and tested for their SRNF performance in heptane, chloroform, and toluene. Sudan Black B (457 g mol−1) was used as the solute. Then, 99% retention was achieved in heptane, 86% in chloroform, and 75% in toluene. It was further shown that the permeance and retention can be fine‐tuned in these solvents by controlling the extent of crosslinking. This work thus introduces the crosslinking of polynorbornenes as a promising material route for SRNF. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2019, 57, 1593–1600 [ABSTRACT FROM AUTHOR]

Copyright of Pravnik is the property of Czech Academy of Sciences, Institute of State & Law and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)