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About half of the 16% adolescents in the world experience menstruation. Menstrual hygiene management (MHM) is a health concern and challenge especially in humanitarian situations as experienced by Myanmar Rohingya refugees living in Cox’s Bazar, Bangladesh. This study aims to assess knowledge, practice and influencing factors for MHM among Rohingya refugee adolescent girls of 14–18 years.

The study used both quantitative (a cross-sectional survey with 340 adolescent girls through a structured questionnaire) and qualitative (7 in-depth interviews with adolescent girls and 2 focus group discussions with the mothers) approaches. Quantitative data, analyzed using STATA version 13.0, were supported by qualitative data, grouped into themes and presented as matrix.

Around 51% adolescent girls learned about menstruation after menarche, at the mean age of 12 years, from their mothers and older sisters. About 75% used sanitary pads as absorbents which they got mostly as relief material or bought from local stores (83%); the rest used cloths and other materials (25%). About 57% of the respondents disposed of their absorbent by burying. Those who used reusable absorbents washed them with soap and water (40%) and mostly dried them indoors (17%). Factors influencing healthy MHM practice included the use of absorbent, privacy, disposal, washing and drying of clothes, physical activities, hygiene and pain management. Adolescents with secondary or higher education were four times more likely to have better MHM practice (odds ratio = 4.27; confidence interval = 1.19–15.31) than those with no formal schooling.

This paper is based on a research undertaken as part of academic requirement.

Zakat is an important Islamic economic instrument that plays significant role in Sustainable Development Goals. Accordingly, Zakat Institutions must manage zakat in a proper and efficient manner. This study aims to examine the efficiency of Zakat Institutions based on their clusters which are government, business and social organizations.

This study uses three quantitative methods: data envelopment analysis (DEA), free disposal hull and super-efficiency DEA. The analytical method is based on production approach, variable return to scale assumption and output orientation. The sample consists of 14 Zakat Institutions from three clusters: Zakat Institutions managed by government, Zakat Institutions managed by corporation and Zakat Institution managed by social organizations.

The results revealed that all of three techniques culminate the same ranking order of efficiency. Zakat Institution managed by the government is the most efficient Zakat Institution, with the average value of 0.87 by using three approaches combined. Meanwhile, Zakat Institutions owned by company and social institutions cluster are in second and third position, with the average value of 0.65 and 0.4, respectively, based on the results of the three approaches. This study contends that the level of efficiency of Zakat Institutions may be supported by clusters (affiliations) in their management.

This study’s limitation is the inadequacy of the required data. Nonetheless, this study provides insights to improve the efficiency of Zakat Institutions based on their clusters. Zakat Institutions in each cluster can improve their efficiency by optimizing inputs to produce multiple outputs.

This study enhances research on the efficiency of Zakat Institutions using three methods to assess the consistency and strength of Zakat Institutions’ efficiency values. In addition, this study examines the efficiency level of Zakat Institutions based on their clusters.

The purpose of the paper is to showcase the significant achievements of Egypt's scientists in the 20th century across various fields of study such as medicine, physics, chemistry, biology, math, geology, astronomy and engineering. The paper highlights the struggles and successes of these scientists, as well as the cultural, social and political factors that influenced their lives and work. The aim is to inspire young people to pursue careers in science and make their own contributions to society by presenting these scientists as role models for hard work and dedication. Ultimately, the paper seeks to promote the importance of science and its impact on society.

The purpose of this review is to present the scientific biographies of Egypt's most distinguished scientists, primarily in the field of Natural Sciences, in a balanced and comprehensive manner. The work is objective, honest and abstract, avoiding any bias or exaggeration. The author provides a clear and concise methodology, including a brief introduction to the scientist and their field of study, an explanation of their major contributions, the impact of their work on society, any challenges or obstacles faced during their career and their lasting legacy. The aim is to showcase the important achievements of these scientists, their impact on their respective fields and to inspire future generations to pursue scientific careers.

The group of outstanding scientists in 20th century Egypt were shaped by various factors, including familial upbringing, education, society, political and cultural atmosphere and state support for scientific research. These scientists made significant contributions to various academic disciplines, including medicine, physics, chemistry, biology, mathematics and engineering. Their impact on their communities and cultures has received international acclaim, making them role models for future generations of scientists and researchers. The history of these scientists highlights the importance of educational investments and supporting scientific research to foster innovation and social progress. The encyclopedia serves as a useful tool for students, instructors and education professionals, preserving Egypt's scientific heritage and honouring the scientists' outstanding accomplishments.

The encyclopedia preserves Egypt's scientific heritage, which has been overlooked for political or other reasons. It is a useful tool for a variety of readers, including students, instructors and education professionals, and it offers insights into universally relevant scientific success factors as well as scientific research methodologies. The encyclopedia honours the outstanding scientific accomplishments of Egyptian researchers and their contributions to the world's scientific community.

The practical implications of this paper are several. First, it highlights the importance of education, family upbringing and societal support for scientific research in fostering innovation and social progress. Second, it underscores the need for continued funding and support for scientific research to maintain and build upon the accomplishments of past generations of scientists. Third, it encourages young people to pursue scientific careers and make their own contributions to society. Fourth, it preserves the scientific heritage of Egypt and honors the contributions of its outstanding scientists. Finally, it serves as a useful tool for students, instructors and education professionals seeking to understand the factors underlying scientific success and research methodologies.

The social implications of the paper include promoting national pride and cultural identity, raising awareness of the importance of education and scientific research in driving social progress, inspiring future generations of scientists and researchers, reducing socioeconomic disparities and emphasizing the role of society, politics and culture in shaping scientific researchers' personalities and interests.

The paper's originality/value lies in its comprehensive documentation of the scientific biographies of Egypt's most prominent scientists in the 20th century, providing unique insights into the factors that contributed to their development and their impact across various academic disciplines. It preserves Egypt's scientific heritage and inspires future generations of scientists and researchers through the promotion of educational investments and scientific research. The encyclopedia serves as a useful tool for education professionals seeking to understand scientific success factors and research methodologies, emphasizing the importance of supportive and inclusive environments for scientific development.

This paper aims to propose an approach to automatically annotate a large corpus in Arabic dialect. This corpus is used in order to analyse sentiments of Arabic users on social medias. It focuses on the Algerian dialect, which is a sub-dialect of Maghrebi Arabic. Although Algerian is spoken by roughly 40 million speakers, few studies address the automated processing in general and the sentiment analysis in specific for Algerian.

The approach is based on the construction and use of a sentiment lexicon to automatically annotate a large corpus of Algerian text that is extracted from Facebook. Using this approach allow to significantly increase the size of the training corpus without calling the manual annotation. The annotated corpus is then vectorized using document embedding (doc2vec), which is an extension of word embeddings (word2vec). For sentiments classification, the authors used different classifiers such as support vector machines (SVM), Naive Bayes (NB) and logistic regression (LR).

The results suggest that NB and SVM classifiers generally led to the best results and MLP generally had the worst results. Further, the threshold that the authors use in selecting messages for the training set had a noticeable impact on recall and precision, with a threshold of 0.6 producing the best results. Using PV-DBOW led to slightly higher results than using PV-DM. Combining PV-DBOW and PV-DM representations led to slightly lower results than using PV-DBOW alone. The best results were obtained by the NB classifier with F1 up to 86.9 per cent.

The principal originality of this paper is to determine the right parameters for automatically annotating an Algerian dialect corpus. This annotation is based on a sentiment lexicon that was also constructed automatically.

The halal cosmetics industry continues to grow significantly. Furthermore, using halal cosmetics is a must for Muslims. This study aims to analyze the factors influencing the switching intention to halal cosmetics in Indonesia.

This quantitative study uses a Partial Least Square-Structural Equation Modelling (PLS-SEM) on 214 respondents. The variables include halal certification, halal awareness, product image, perceived behavioral control, subjective norm, attitude, advertisement and switching intention.

The product image plays the most influential role in deriving the attitude toward switching intention to halal cosmetics, following perceived behavioral control, halal awareness and subjective norm, but not halal certification and advertisement. The result indicates that the image of halal cosmetics influences customers’ attitudes toward switching to using halal cosmetics. Indonesian customers know the obligation to use halal products because they are Muslim. However, the existence of halal certification does not derive the switching intention to halal cosmetics.

This study conducts research only in Indonesia. As a recommendation, further studies might conduct a comparative test using multicultural respondents in several countries. Other studies also suggested examining factors of switching intention through different generational, especially in countries with high individualism traits.

This study will encourage the halal industry, especially the halal cosmetics industry, to pay more attention to the product image. Meanwhile, the government can provide incentives or rewards to promote industry participation in halal cosmetics. The findings provide a more detailed understanding of how product image can influence someone to switch to halal cosmetics.

Research on switching intention to halal cosmetics is still limited. This study uses halal variables, while previous studies only used religiosity. This study also introduced the product images motivating customers’ switching intention to use halal cosmetics.

This paper aims to investigate the relationship between financial development (FD) and monetary policy effectiveness (MPE) on output and inflation in ASEAN-3 countries (Singapore, Malaysia and the Philippines).

This study uses an open economy structural vector autoregressive model to generate MPE. Then, an autoregressive distributed lagged (ARDL) model is used to analyze the effect of FD on MPE across countries.

The findings revealed that FD plays a different role in MPE across countries. In Malaysia, a more developed financial system tends to reduce the MPE on output, whereas in Singapore, results show that the more developed financial system (stock market capitalization) tends to increase MPE on output. However, in the Philippines, the main results show that the effect of FD (liquid liabilities) upon MPE on output is depending on the policy variable (interest rates or money supply).

This paper fills this gap by providing the first study of ASEAN-3 countries in examining how effective is a monetary policy in response to the development of the financial market across the country. Second, this paper considers two FD indicators, namely, the banking sector and capital market development in investigating its effect on MPE on output and inflation. Third, the authors construct the MPE in each country using a structural (identified) VAR model by aggregating the response of output growth and inflation rate on monetary policy changes (interest rate and money supply) using impulse–response function. Regarding this, the results of this study provide new empirical evidence and insight into the long debate on the relationship between FD and the MPE.

The purpose of this study was to elucidate the association between helminth infections, dietary parameters and cognitive performance, as well as the predictors of undernutrition among school-age children (SAC) living in helminth-endemic fishing and farming communities in Ghana.

This was a cross sectional study involving 164 (9 to 12 years old) SAC from fishing (n = 84) and farming (n = 80) communities of the Kwahu Afram Plains South District of the Eastern Region of Ghana, using structured questionnaires and anthropometric and biochemical assessments.

Overall, 51.2% of the children were males, with no significant gender difference between the communities (p = 0.88). Average age of the children was 10.5 ± 1.25 years, with no significant difference between the farming and fishing communities (p = 0.90). About 53.1% of all children were anemic, with no significant differences between farming versus fishing communities (p = 0.87). Helminth-infected children were significantly anemic (p = 0.03). Mean serum zinc level of all children was 13.1 ± 4.57 µmol/L, with zinc deficiency being significantly higher in children in the farming community (p < 0.0001). About 7.5% of all the children were underweight, whilst 13.8% were stunted with a higher proportion of stunting occurring among older children (p = 0.001) and girls (p = 0.117). There was no significant difference in the Raven’s Colored Progressive Matrices cognitive test scores between the two communities (p = 0.79). Predictors of anemia were helminthiasis and pica behavior.

These findings are relevant and have the prospect of guiding the development of intervention programs in addressing the persistent problem of nutritional and cognitive deficits among SAC.

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.

Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.

The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.

The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.

This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.

This study aims to propose priority solutions for mitigating the impact of the Covid-19 pandemic through the Social Safety Net (SSN) based on the Islamic objectives.

The analytic network process method is used in this study. Focus group discussions and in-depth interviews with relevant stakeholders were used to collect data and supplemented by a literature review to explore comprehensive information.

The findings indicate different opinions among experts, including regulators, practitioners, associations and academics regarding the most important priority solutions to the impact of Covid-19. However, experts agree that the highest priority solution is the SSN program in the lineage sector, specifically the distribution of the Family Hope Program. A program in the field of protecting the mind is the second priority solution. The program in the field of soul and wealth is the third priority solution.

This study contributes to the development of government policy to deal with the socioeconomic impact of Covid-19 based on the Islamic objectives by mapping the SSN through the five elements of protection, namely, faith, soul, mind, lineage and wealth protection based on priority solution.