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This study analyzes the intra- and international collaboration of 11 member states of the Organization of Islamic Cooperation (OIC) in science and technology (S&T) disciplines in the period 1996-2010, by applying various bibliometric indicators along with publication and citation counts and our proposed average collaboration strength index, that measures the intra-collaboration strength of a region. The quantitative and qualitative findings indicate that the OIC states are making impressive progress in all S&T disciplines, but it is still low compared to the EU and Association of Southeast Asian Nations regions. The OIC states must come up with short- and long-term plans to become more progressive and competitive with the rest of the world. Moreover, special attention needs to be given to improving the status of international collaboration with the outside world. This study provides useful information for the scientific community, as well as for technology and innovation policy-makers. [ABSTRACT FROM AUTHOR]

Foreign Exchange or Forex is the sale purchase market point of foreign currency pairs. Due to the high volatility in the forex market, it is difficult to predict the future price of any currency pair. This study shows that a significant enhancement in the prediction of forex price can be achieved by incorporating domain knowledge in the process of training machine learning models. The proposed system integrates the Forex Loss Function (FLF) into a Long Short-Term Memory model called FLF-LSTM — that minimizes the difference between the actual and predictive average of Forex candles. Using the data of 10,078 four-hour candles of EURUSD pair, it is found that compared to the classic LSTM model, the proposed FLF-LSTM system shows a decrease in overall mean absolute error rate by 10.96%. It is also reported that the error in forecasting the high and low prices is reduced by 10% and 9%, respectively. The proposed model, in comparison to the Recurrent Neural Network-based prediction system, shows an overall reduction of 73.57% in mean absolute error, by exhibiting up to 68.71% and 72.31% error reduction in high and low prices, respectively. In comparison to Auto-Regressive Integrated Moving Average, our proposed model shows a 13% reduced error. Specifically, in the open, high, and low prices, the error is reduced by 28.5%, 14.2%, 9.3%, respectively. Finally, we compare our model with another well-known time series forecasting model, i.e., FB Prophet — where FLF-LSTM demonstrates 31.8%, 47.7%, 23.6%, 47.7% error reduction in open, high, low, and close prices, respectively. The data and the code used in this study can be accessed at the following URL: https://github.com/slab-itu/forex_flf_lstm. • A novel function has been designed for the Forex Prediction System called FLF-LSTM. • The FLF-LSTM shows a decrease in the prediction error rate by 14%. • The FLF-LSTM outperforms FB Prophet, ARIMA, and RNN-based models of Forex prediction. • The proposed implementation opens a new gateway to predict Forex market trends. [ABSTRACT FROM AUTHOR]

Traditional bibliometric techniques gauge the impact of research through quantitative indices based on the citations data. However, due to the lag time involved in the citation-based indices, it may take years to comprehend the full impact of an article. This paper seeks to measure the early impact of research articles through the sentiments expressed in tweets about them. We claim that cited articles in either positive or neutral tweets have a more significant impact than those not cited at all or cited in negative tweets. We used the SentiStrength tool and improved it by incorporating new opinion-bearing words into its sentiment lexicon pertaining to scientific domains. Then, we classified the sentiment of 6,482,260 tweets linked to 1,083,535 publications covered by Altmetric.com. Using positive and negative tweets as an independent variable, and the citation count as the dependent variable, linear regression analysis showed a weak positive prediction of high citation counts across 16 broad disciplines in Scopus. Introducing an additional indicator to the regression model, i.e. 'number of unique Twitter users', improved the adjusted R-squared value of regression analysis in several disciplines. Overall, an encouraging positive correlation between tweet sentiments and citation counts showed that Twitter-based opinion may be exploited as a complementary predictor of literature's early impact. [ABSTRACT FROM AUTHOR]

Malaria microscopy, microscopic examination of stained blood slides to detect parasite Plasmodium, is considered to be a gold standard for detecting life-threatening disease malaria. Detecting the plasmodium parasite requires a skilled examiner and may take up to 10 to 15 minutes to completely go through the whole slide. Due to a lack of skilled medical professionals in the underdeveloped or resource-deficient regions, many cases go misdiagnosed, which results in unavoidable medical complications. We propose to complement the medical professionals by creating a deep learning-based method to automatically detect (localize) the plasmodium parasites in the photograph of stained film. To handle the unbalanced nature of the dataset, we adopt a two-stage approach. Where the first stage is trained to classify cells into just healthy or infected. The second stage is trained to classify each detected cell further into the malaria life-cycle stage. To facilitate the research in machine learning-based malaria microscopy, we introduce a new large-scale microscopic image malaria dataset. Thirty-eight thousand cells are tagged from the 345 microscopic images of different Giemsa-stained slides of blood samples. Extensive experimentation is performed using different Convolutional Neural Networks on this dataset. Our experiments and analysis reveal that the two-stage approach works better than the one-stage approach for malaria detection. To ensure the usability of our approach, we have also developed a mobile app that will be used by local hospitals for investigation and educational purposes. The dataset, its annotations, and implementation codes will be released upon publication of the paper. [ABSTRACT FROM AUTHOR]

Background and Objective • Overactive bladder (OAB) is a medical condition characterized by an increase frequency and urgency, with or without urge incontinence. 80% of the world’s population presently uses herbal medicine for some aspect of primary health care according to a survey conducted by World Health Organization. The objective of present review was to present an updated, comprehensive and categorized information about medicinal plants used or with potential for the treatment of overactive bladder. Methods • All relevant literature databases were searched up. The sources of data included Google Scholar, Science Direct, Web of Science and PubMed. The search terms were plant, herb, herbal therapy, phytotherapy, overactive bladder, cystitis and urinary incontinence. Results • Studies on a number of medicinal plants revealed that phytochemials extracted from different plant’s part showed protective effect against increased contractile activity of urinary bladder and increased urination frequency, the characteristic manifestations of OAB. Medicinal plant extracts also reduced urinary oxidative stress markers,inflammation and agonist induced bladder contractile response. Conclusion • A variety of medicinal plants promise their use in overactive bladder diseases. In addition to the standardization of these medicinal plants, screening plants as anti-overactive bladder agents may help to find new sources of therapy for overactive bladder. [ABSTRACT FROM AUTHOR]

In the context of smart cities, it is crucial to filter out falsified information spread on social media channels through paid campaigns or bot-user accounts that significantly influence communication networks across the social communities and may affect smart decision-making by the citizens. In this paper, we focus on two major aspects of the Twitter social network associated with altmetrics: (a) to analyze the properties of bots on Twitter networks and (b) to distinguish between bots and human accounts. Firstly, we employed state-of-the-art social network analysis techniques that exploit Twitter's social network properties in novel altmetrics data. We found that 87% of tweets are affected by bots that are involved in the network's dominant communities. We also found that, to some extent, community size and the degree of distribution in Twitter's altmetrics network follow a power-law distribution. Furthermore, we applied a deep learning model, graph convolutional networks, to distinguish between organic (human) and bot Twitter accounts. The deployed model achieved the promising results, providing up to 71% classification accuracy over 200 epochs. Overall, the study concludes that bot presence in altmetrics-associated social media platforms can artificially inflate the number of social usage counts. As a result, special attention is required to eliminate such discrepancies when using altmetrics data for smart decision-making, such as research assessment either independently or complementary along with traditional bibliometric indices. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the anticonvulsant, antimicrobial and cytotoxic activities of Berberis calliobotrys. Methods: The powdered plant material (10 kg) was extracted thrice with methanol (3 × 12 L) by dipping for seven days. The methanol extract was concentrated to dryness under reduced pressure, and then successively fractionated with solvents of different polarity, including n-hexane, chloroform, ethyl acetate and n-butanol. The anticonvulsant effect of the extract and fractions (at oral doses 500 and 1000 mg/kg) was studied against picrotoxin-, pentylenetetrazole (PTZ)- and strychnine-induced seizures in Swiss albino mice of either sex divided into 12 groups (n = 6). Diazepam was used as standard drug. Antimicrobial activity of the extract against Bacillus subtilis, Pseudomonas aeruginosa, Staphylococcus aureus as well as against Candida albicans, Penicillium notatum was conducted by disc diffusion method and minimum inhibitory concentration (MIC). Cytotoxicity of the extract/fractions was analyzed by haemolytic method while the phenolic compounds present in the ethyl acetate fraction of the plant were determined by high performance liquid chromatography (HPLC). Results: The extract and its ethyl acetate and n-butanol fractions showed maximum response against drug-induced convulsions and provided 100 % protection to animals at both doses. They also showed zones of inhibition of 27.00 ± 2.51, and 22.00 ± 2.51 mm against all bacterial and fungal strains, respectively, especially Staphylococcus aureus. The methanol extract and ethyl acetate fraction also showed high MIC against all bacterial and fungal strains. Cytotoxicity data from hemolytic assay indicate that the extract/fractions are safe. The highest amount of phenolic found was chlorogenic acid (84.44 ± 0.06 ppm). Conclusion: The plant is thus a potential source of new lead compounds for the development of new clinically effective anticonvulsant and antimicrobial compounds. [ABSTRACT FROM AUTHOR]

• This study presents a novel adversarial-based approach to up-sample the sequential data in an educational setting. • The proposed method generates new student sequences such that the past behavior of each student is encapsulated in its next sequence. • The data from the Open University (UK) is transformed into a sequential format and used as a case study to eliminate the imbalance in students' academic performances. • The proposed approach outperforms the conventional state-of-the-art Random Over-sampling and Synthetic Minority Over-sampling techniques with an improved AUC of 7.07% and 6.53%, respectively. Class imbalance is a challenging problem especially in a supervised learning setup, as most classification algorithms are designed for balanced class distributions. Although various up-sampling approaches exist for eliminating the class imbalance, however, they do not handle the complexities of sequential data. In this study, using the data of over 30,000 students from the Open University (UK), we implement a deep-learning-based approach using adversarial networks, Sequential Conditional Generative Adversarial Network (SC-GAN) that encapsulates the past behavior of each student for its previous sequences and generates synthetic student records for the next timestamp. The proposed approach is devised to generate instances, which are augmented with the actual data to eliminate class imbalance. A performance comparison of the proposed SC-GAN with the standard up-sampling methods is also presented and the results validate the proposed method with an improved AUC of 7.07% and 6.53%, respectively, when compared with conventional Random Over-sampling and Sythetic Minority Oversampling techniques. [Display omitted] [ABSTRACT FROM AUTHOR]

One of the major advances in artificial intelligence nowadays is to understand, process and utilize the humans' natural language. This has been achieved by employing the different natural language processing (NLP) techniques along with the aid of the various deep learning approaches and architectures. Using the distributed word representations to substitute the traditional bag-of-words approach has been utilized very efficiently in the last years for many NLP tasks. In this paper, we present the detailed steps of building a set of efficient word embedding models called ArWordVec that are generated from a huge repository of Arabic tweets. In addition, a new method for measuring Arabic word similarity is introduced that has been used in evaluating the performance of the generated ArWordVec models. The experimental results show that the performance of the ArWordVec models overcomes the recently available models on Arabic Twitter data for the word similarity task. In addition, two of the large Arabic tweets datasets are used to examine the performance of the proposed models in the multi-class sentiment analysis task. The results show that the proposed models are very efficient and help in achieving a classification accuracy ratio exceeding 73.86% with a high average F1 value of 74.15. [ABSTRACT FROM AUTHOR]

Shape matching is a long-studied problem and lies at the core of many applications in statistical shape analysis, virtual reality and human–computer interaction. This paper presents an automatic dense correspondence method to match the mesh vertices of two 3D shapes under near-isometric and non-rigid deformations. The goal is achieved by combining three types of graphic structure information. The method includes three major steps: first, we describe the vertices based on three types of graphical information, Euclidean structure information, Riemannian structure information, and conformal structure information; second, the match between two shapes is formulated as an optimization problem and a novel objective function is proposed; third, we resolve the optimal solution by using the projected descent optimization procedure to solve the objective function. The method is tested on various shape pairs with different poses, surface details, and topological noises. We demonstrate the performance of our approach through an extensive quantitative and qualitative evaluation on several challenging 3D shape matching datasets where we achieve superior performance to existing methods. [ABSTRACT FROM AUTHOR]

Simplifying summaries of scholarly publications has been a popular method for conveying scientific discoveries to a broader audience. While text summarization aims to shorten long documents, simplification seeks to reduce the complexity of a document. To accomplish these tasks collectively, there is a need to develop machine learning methods to shorten and simplify longer texts. This study presents a new Simplification Aware Text Summarization model (SATS) based on future n-gram prediction. The proposed SATS model extends ProphetNet, a text summarization model, by enhancing the objective function using a word frequency lexicon for simplification tasks. We have evaluated the performance of SATS on a recently published text summarization and simplification corpus consisting of 5,400 scientific article pairs. Our results in terms of automatic evaluation demonstrate that SATS outperforms state-of-the-art models for simplification, summarization, and joint simplification-summarization across two datasets on ROUGE, SARI, and CSS 1 . We also provide human evaluation of summaries generated by the SATS model. We evaluated 100 summaries from eight annotators for grammar, coherence, consistency, fluency, and simplicity. The average human judgment for all evaluated dimensions lies between 4.0 and 4.5 on a scale from 1 to 5 where 1 means low and 5 means high., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zaman, Kamiran, Shardlow, Hassan, Karim and Aljohani.)

Type 2 diabetes mellitus is a complicated metabolic disorder with no definite treatment. Cyperus iria (Cyperaceae) possess several traditional therapeutic uses. According to the folklore tales, the whole plant of Cyperus iria possesses antihyperglycemic activity. The present study was undertaken to investigate whether aqueous-ethanol extract of Cyperus iria can ameliorate the altered activities of carbohydrate metabolism in streptozotocin (STZ)-induced diabetic rats along with appraisal of inflammatory and stress markers involved in endocrine dysfunction. Presence of biophenolics and flavonoids might be responsible for the antidiabetic potential. STZ-induced diabetic rats were treated orally with Cyperus iria extract (125, 250, and 500 mg/kg) for 15 days. Blood samples were collected. Metformin was used as positive control. Significantly higher quantities of phenolic (82.79±0.003 mg/g GAE) and flavonoid (13.61±0.002 mg/g QE) contents were present. Inhibitory concentration (IC50) exhibited an excellent potential for both antioxidant (IC50= 3.22 μg/mL) and alpha amylase (IC50=36.29 μg/mL) inhibitory assays. High-performance liquid chromatography (HPLC) confirmed the existence of myercetin, quercetin, kaempferol, and ferulic acid. Cyperus iria aqueous-ethanol extract exhibits good tolerance against glucose at 90 min in normal rats. Streptozotocin-induced hyperglycemia declined significantly at day 9 (265 mg/dL) along with improvement in inflammatory (TNF-α=15.6± 0.2 g/l, COX-2=357±0.396 U/l, IL-6= 572±0.99 pg/l) and oxidative stress markers (SOD= 163±0.616 and GSH-ST= 95.8±0.44 U/mL) along with biochemical parameters in a dose-dependent manner. Present study suggests that Cyperus iria aqueous-ethanol extract possesses hypoglycemic potential which might be attributed to the decrease in oxidative stress and inflammatory markers., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)