Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remain...Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.展开更多
Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and interact.However,alongside its benefits,social media has also given rise to signi...Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and interact.However,alongside its benefits,social media has also given rise to significant challenges,one of the most pressing being cyberbullying.This issue has become a major concern in modern society,particularly due to its profound negative impacts on the mental health and well-being of its victims.In the Arab world,where social media usage is exceptionblly high,cyberbullying has become increasingly prevalent,necessitating urgent attention.Early detection of harmful online behavior is critical to fostering safer digital environments and mitigating the adverse efcts of cyberbullying.This underscores the importance of developing advanced tools and systems to identify and address such behavior efectively.This paper investigates the development of a robust cyberbullying detection and classifcation system tailored for Arabic comments on YouTube.The study explores the efectiveness of various deep learning models,including Bi-LSTM(Bidirectional Long Short Term Memory),LSTM(Long Short-Term Memory),CNN(Convolutional Neural Networks),and a hybrid CNN-LSTM,in classifying Arabic comments into binary classes(bullying or not)and multiclass categories.A comprehensive dataset of 20,000 Arabic YouTube comments was collected,preprocessed,and labeled to support these tasks.The results revealed that the CNN and hybrid CNN-LSTM models achieved the highest accuracy in binary classification,reaching an impressive 91.9%.For multiclass dlassification,the LSTM and Bi-LSTM models outperformed others,achieving an accuracy of 89.5%.These findings highlight the efctiveness of deep learning approaches in the mitigation of cyberbullying within Arabic online communities.展开更多
The objective of the study is to examine the moderating influence of gender on the relationship between cultural values and Islamic work ethics(IWE)among Palestinian Arab high school teachers in Israel who represent a...The objective of the study is to examine the moderating influence of gender on the relationship between cultural values and Islamic work ethics(IWE)among Palestinian Arab high school teachers in Israel who represent an ethnic and religious minority within a Western-oriented framework.The study sample comprised 1,245 Arab teachers(759 females and 476 males).Data analysis was conducted using structural equation modeling with AMOS,focusing on path analysis.The research findings highlight a substantial relationship between cultural values and Islamic work ethics,with gender as a moderating variable.Additionally,the results indicate a significant positive relationship between the cultural value dimension of uncertainty avoidance and both dimensions of Islamic work ethics-dedication and social responsibility in the workplace,along with independence,diligence,and achievement.In contrast,a pronounced and significant negative relationship was identified between the cultural dimension of femininity/masculinity and these two dimensions of Islamic work ethics.展开更多
BACKGROUND Cardiac arrest is a critical condition characterized by abrupt cessation of cardiac function,resulting in reduced oxygen delivery to vital organs and rapid progression to death if not timely treated.Despite...BACKGROUND Cardiac arrest is a critical condition characterized by abrupt cessation of cardiac function,resulting in reduced oxygen delivery to vital organs and rapid progression to death if not timely treated.Despite advances in medical science and resuscitation techniques,cardiac arrest remains a significant burden globally,with survival rates remaining low.Comprehensive research on cardiac arrest,particularly comparisons between in-hospital cardiac arrest(IHCA)and out-of-hospital cardiac arrest(OHCA),is limited.AIM To compare the survival rates,return of spontaneous circulation(ROSC),survival to discharge,and neurological outcomes after IHCA and OHCA in Arab Asian countries.METHODS We systematically searched PubMed,Medline,EMBASE and Google Scholar(2000-2024)using keywords(“IHCA”,“OHCA”,“cardiac arrest”,“Middle East”,“Arab”,“Asian”)in titles/abstracts.The inclusion criterion was observational studies on adults(≥18 years)in Arab Asian countries reporting relevant outcomes.The exclusion criteria were narrative reviews,non-Arab Asian studies,non-English publications,inaccessible full texts,pediatric-only populations,and studies lacking outcome data.RESULTS In total,44 observational studies from nine Arab Asian countries comprising 32535 participants were included.This review highlights the substantial variability in cardiac arrest outcomes in Asian countries.OHCA mortality rates were alarmingly high in several nations,with Kuwait(99%),Bahrain(98.8%),and Qatar(97.6%)reporting the highest figures.In contrast,the Kingdom of Saudi Arabia(KSA)had a markedly lower OHCA mortality rate(8.2%).The rates of ROSC also varied,with Qatar achieving the highest(34.4%)and Kuwait the lowest(3.3%).Survival to hospital discharge ranged from 1.2%in Bahrain to 18.7%in Kuwait,with Qatar also reporting favorable rates(17.5%).For IHCA,mortality was 73.6%in the United Arab Emirates(UAE)and 72.8%in KSA,whereas Lebanon and Iraq reported higher rates of 94.6%and 88%,respectively.ROSC rates were the highest in Lebanon(55.9%)and the UAE(51.3%).Neurological outcome reporting has been inconsistent,although Qatar reported a high rate(68.6%)for OHCA survivors.Comparative data showed generally better survival and neurological outcomes with IHCA than with OHCA.CONCLUSION This systematic review underscores the clear disparity in survival outcomes between IHCA and OHCA in Arab Asian countries,with IHCA demonstrating superior outcomes.Despite progress in some countries,outcomes remain suboptimal compared with international standards.Future multicenter studies with standardized methodologies are required to generate high-quality evidence and provide region-specific interventions for cardiac arrest management.展开更多
Liver transplantation is a vital intervention for patients with end-stage liver disease;however,the Arab world faces significant barriers that hinder access to this life-saving procedure in terms of both practice and ...Liver transplantation is a vital intervention for patients with end-stage liver disease;however,the Arab world faces significant barriers that hinder access to this life-saving procedure in terms of both practice and research.This narrative review explores the multifaceted challenges,including financial constraints,limited healthcare infrastructure,cultural factors,and the prevalence of infectious diseases.In the Arab countries,both culture and religion were found to play major roles in the acceptability of liver transplantation.High rates of misconceptions and financial strain on patients and healthcare systems necessitate more transplantation programs and improved financial coverage and insurance policies.Enhancing healthcare facilities and improving access to innovative technologies through research is essential for optimizing transplantation outcomes,considering that common diseases in the region decrease the donor pool and increase complication risks.Public health initiatives to prevent and control prevalent liver diseases,particularly hepatitis,and to manage infection risk are also critical.Stricter regulations should be enforced in less developed countries in the region along with early screening practices to address inherited blood disorders and infectious diseases.Additionally,targeted research on liver diseases specific to the Arab context is crucial,along with fostering dialogue about cultural,religious,economic,and health-related factors affecting donor and recipient eligibility.By tackling these complex barriers through targeted comprehensive strategies,the Arab world can advance to a more equitable and effective liver transplantation system,ultimately improving patient outcomes and quality of life.展开更多
Protein-energy malnutrition (PEM) as a result of poor nutrition, especially for deprived resourced households, is a big health concern in the world. According to the World Health Organisation, PEM accounts for 49% of ...Protein-energy malnutrition (PEM) as a result of poor nutrition, especially for deprived resourced households, is a big health concern in the world. According to the World Health Organisation, PEM accounts for 49% of the 10.4 million deaths of children under five that take place in developing countries. The aim of this study was to evaluate the influence of gum Arabic (GA) and texturized soy protein (TSP) and their interactive effect on proximate, functional, and textural properties of the protein-rich snack stick produced from ground green maize, GA powder, and ground TSP. GA varied at 0%, 4%, 8%, and 12%, while TSP varied at 0%, 12%, 24% and 36%. The 5 cm long protein-rich snack sticks were made using a sausage stuffer and baked in an oven at 110˚C for 1 hr 30 minutes. The snack sticks were subjected to proximate, functional and textural analysis using the standard methods. Increasing GA resulted in a significant (p p < 0.05) increased the protein content (32.46%), Ash content (3.6%), fat (11.96%), and moisture content (16.25%) of protein-rich snack sticks. The interactive effect between GA and TSP led to a decrease in fibre and carbohydrates. Results from this study show GA and TSP significantly enhanced the physico-chemical properties of protein-rich snack sticks. A sample with 4% GA and 36% TSP is recommended for the best physico-chemical attributes of the protein-rich snack stick.展开更多
Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)...Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)and deep learning(DL)approaches utilizing natural language processing(NLP)methods and by proposing relevant datasets.However,most of these endeavors focused predominantly on the English language,leaving a substantial gap in addressing Arabic cyberbullying.Given the complexities of the Arabic language,transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained models that use a large dataset.Therefore,this study proposes a hybrid model using transformers trained on extensive Arabic datasets.It then fine-tunes the hybrid model on a newly curated Arabic cyberbullying dataset collected from social media platforms,in particular Twitter.Additionally,the following two hybrid transformer models are introduced:the first combines CAmelid Morphologically-aware pretrained Bidirectional Encoder Representations from Transformers(CAMeLBERT)with Arabic Generative Pre-trained Transformer 2(AraGPT2)and the second combines Arabic BERT(AraBERT)with Cross-lingual Language Model-RoBERTa(XLM-R).Two strategies,namely,feature fusion and ensemble voting,are employed to improve the model performance accuracy.Experimental results,measured through precision,recall,F1-score,accuracy,and AreaUnder the Curve-Receiver Operating Characteristic(AUC-ROC),demonstrate that the combined CAMeLBERT and AraGPT2 models using feature fusion outperformed traditional DL models,such as Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM),as well as other independent Arabic-based transformer models.展开更多
The environment-friendly and efficient selective separation of chalcopyrite and molybdenite poses a challenge in mineral pro-cessing.In this study,gum Arabic(GA)was initially proposed as a novel depressant for the sel...The environment-friendly and efficient selective separation of chalcopyrite and molybdenite poses a challenge in mineral pro-cessing.In this study,gum Arabic(GA)was initially proposed as a novel depressant for the selective separation of molybdenite from chalcopyrite during flotation.Microflotation results indicated that the inhibitory capacity of GA was stronger toward molybdenite than chalcopyrite.At pH 8.0 with 20 mg/L GA addition,the recovery rate of chalcopyrite in the concentrate obtained from mixed mineral flota-tion was 67.49%higher than that of molybdenite.Furthermore,the mechanism of GA was systematically investigated by various surface characterization techniques.Contact angle tests indicated that after GA treatment,the hydrophobicity of the molybdenite surface signifi-cantly decreased,but that of the chalcopyrite surface showed no apparent change.Fourier transform-infrared spectroscopy and X-ray photoelectron spectroscopy revealed a weak interaction force between GA and chalcopyrite.By contrast,GA was primarily adsorbed onto the molybdenite surface through chemical chelation,with possible contributions from hydrogen bonding and hydrophobic interactions.Pre-adsorbed GA could prevent butyl xanthate from being adsorbed onto molybdenite.Scanning electron microscopy–energy-dispersive spectrometry further indicated that GA was primarily adsorbed onto the“face”of molybdenite rather than the“edge.”Therefore,GA could be a promising molybdenite depressant for the flotation separation of Cu–Mo.展开更多
Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk o...Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.展开更多
BACKGROUND Arabic-speaking patients are underrepresented in orthopedic clinical studies,particularly in foot and ankle trauma research.The lack of validated Arabic language tools hinders their inclusion,creating a nee...BACKGROUND Arabic-speaking patients are underrepresented in orthopedic clinical studies,particularly in foot and ankle trauma research.The lack of validated Arabic language tools hinders their inclusion,creating a need for culturally and linguistically adapted instruments.The American Academy of Orthopedic Surgeons Foot and Ankle Outcomes Questionnaire(AAOS-FAOQ)is a widely used tool but has not been adapted for Arabic-speaking patients.AIM To translate,cross-culturally adapt,and validate the AAOS-FAOQ for Arabicspeaking patients with traumatic foot and ankle injuries.METHODS The cross-cultural adaptation followed established guidelines,involving forward and backward translations,expert review,and pre-testing.The final Arabic version was administered alongside the Arabic Short-Form 36(SF-36)to 100 patients for validity testing.Reliability was assessed through test-retest methods with 20 patients completing the questionnaire twice within 48 hours.Pearson correlation coefficients measured convergent and divergent validity with SF-36 subscales,while Cronbach's alpha and intraclass correlation coefficients(ICC)determined internal consistency and reliability.RESULTS Out of 100 patients,92 completed the first set of questionnaires.The Arabic AAOS-FAOQ showed strong correlations with the SF-36 subscales,particularly in physical function and bodily pain(r>0.6).Test-retest reliability was robust,with ICCs of 0.69 and 0.66 for the Global Foot and Ankle Scale and Shoe Comfort Scale,respectively.Cronbach's alpha for internal consistency ranged from 0.7 to 0.9.CONCLUSION The Arabic version of the AAOS-FAOQ demonstrated validity and reliability for use in Arabic-speaking patients with traumatic foot and ankle injuries.This adaptation will enhance the inclusion of this population in orthopedic clinical studies,improving the generalizability of research findings and patient care.展开更多
Egypt's ready made garment exports increased by 22 per cent year-on-year during the first four months this year,reaching$1.028 billion,according to the Apparel Export Council of Egypt(AECE).In terms of regional ma...Egypt's ready made garment exports increased by 22 per cent year-on-year during the first four months this year,reaching$1.028 billion,according to the Apparel Export Council of Egypt(AECE).In terms of regional market performance,the United States remains the largest export destination for Egyptian garments,with exports amounting to$384 million in the first four months,followed by the European market,with exports amounting to$259 million,and the Arab countries,which showed a moderate growth trend,with an increase of 6%yearon-year,and exports amounting to$190 million.展开更多
Dingzhou eye ointment gained significant popularity from the late Qing dynasty to the Republican era.Its legacy persists through three prominent families:Bai Jingyu(白敬宇),Ma Yinglong(马应龙)and Zhang Qizhu(张齐珠).E...Dingzhou eye ointment gained significant popularity from the late Qing dynasty to the Republican era.Its legacy persists through three prominent families:Bai Jingyu(白敬宇),Ma Yinglong(马应龙)and Zhang Qizhu(张齐珠).Each family’s Dingzhou eye ointment is similar,with comparable ingredients,dosages,and usage methods,suggesting a common origin.While many well-established Dingzhou eye ointment brands claim their origins date back to the late Ming dynasty,the origins of its formulation are likely much older.In ancient Chinese medical texts,there were proto-formulations of similar eye ointments recorded during the Jin and Yuan dynasties,with comparable external and standardized eye medicine formulations appearing in the Ming and Qing dynasties.By the late Qing dynasty,Dingzhou eye ointment had become a popular mainstream topical eye treatment in traditional Chinese medicine.From a broader geographically perspective,the composition aligns with that of similar treatments used in Greco-Roman medicine,which combined minerals such as calamine with aromatic ingredients to create eye medicines.The principles behind the composition of Dingzhou eye ointment are in accord with the concept of the four humours in Arabian medicine.It is possible that the Hui ethnic group was responsible for the introduction of Dingzhou eye ointment to China,indicating cultural exchange between Chinese and foreign medicine practices.展开更多
CHINESE Cloisonneis the centuries-old handicraft of creating designs on copper bodies with colored-enamel placed within divisions made of copper wires,which are bent to follow the outline of decorative patterns.This f...CHINESE Cloisonneis the centuries-old handicraft of creating designs on copper bodies with colored-enamel placed within divisions made of copper wires,which are bent to follow the outline of decorative patterns.This form of art originated in the Arab world and was introduced to China during the late Yuan Dynasty(1271-1368),and since then it has become a favorite of the country’s emperors.展开更多
The second edition of the United Arab Emirates inflammatory bowel disease(IBD)consensus guidance provides updated recommendations for diagnosing,treating,and monitoring IBD.Significant therapeutic advances and evolvin...The second edition of the United Arab Emirates inflammatory bowel disease(IBD)consensus guidance provides updated recommendations for diagnosing,treating,and monitoring IBD.Significant therapeutic advances and evolving treatment paradigms since 2020(including risk stratification and treat-to-target approaches)necessitated this comprehensive update to standardize care across the United Arab Emirates.Developed via Delphi consensus methodology,this guidance incorporates a systematic literature review and key international gui-delines.It presents 188 summary statements covering the full spectrum of IBD care,including complex scenarios like perianal disease and pregnancy.Key updates feature guidance on newer pharmacologic therapies-interleukin-23,Janus kinase,and sphingosine-1-phosphate receptor inhibitors-with refined therapeutic positioning informed by recent head-to-head trials.The consensus emphasizes early,effective treatment to prevent irreversible bowel damage,optimization strategies like therapeutic drug monitoring,and achieving objective treat-to-target goals to improve long-term outcomes.Recognizing local healthcare system challenges,it offers practical recommendations on reducing variability and enhancing equitable access to IBD care.By integrating current clinical evidence with United Arab Emirates-specific considerations,the second edition United Arab Emirates IBD consensus guidance aims to standardize care across sectors,provide a benchmark for payers and policymakers,optimize treatment outcomes,and improve IBD outcomes,aligning national practice with international standards.展开更多
Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep ...Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.展开更多
Arabic Sign Language(ArSL)recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing(DHH)community.Researchers have proposed multiple methods for automated recognition of ArSL;howev...Arabic Sign Language(ArSL)recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing(DHH)community.Researchers have proposed multiple methods for automated recognition of ArSL;however,these methods face multiple challenges that include high gesture variability,occlusions,limited signer diversity,and the scarcity of large annotated datasets.Existing methods,often relying solely on either skeletal data or video-based features,struggle with generalization and robustness,especially in dynamic and real-world conditions.This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint distances and angles with temporal features extracted from RGB videos using the Inflated 3D ConvNet(I3D).By fusing these complementary modalities at the feature level and applying a majority-voting ensemble of XGBoost,Random Forest,and Support Vector Machine classifiers,the framework robustly captures both spatial configurations and motion dynamics of sign gestures.Feature selection using the Pearson Correlation Coefficient further enhances efficiency by reducing redundancy.Extensive experiments on the ArabSign dataset,which includes RGB videos and corresponding skeletal data,demonstrate that the proposed approach significantly outperforms state-of-the-art methods,achieving an average F1-score of 97%using a majority-voting ensemble of XGBoost,Random Forest,and SVM classifiers,and improving recognition accuracy by more than 7%over previous best methods.This work not only advances the technical stateof-the-art in ArSL recognition but also provides a scalable,real-time solution for practical deployment in educational,social,and assistive communication technologies.Even though this study is about Arabic Sign Language,the framework proposed here can be extended to different sign languages,creating possibilities for potentially worldwide applicability in sign language recognition tasks.展开更多
Finely divided silver nanoparticles were synthesized via the hydrothermal method. Arabic gum （AG） was used as both the reductant and steric stabilizer without any other surfactant. By adjusting the reaction temperat...Finely divided silver nanoparticles were synthesized via the hydrothermal method. Arabic gum （AG） was used as both the reductant and steric stabilizer without any other surfactant. By adjusting the reaction temperature, mass ratio of AG to AgNO3, and reaction time, silver nanoparticles with different morphological characteristics could be obtained. The products were characterized by UV-Vis, FTIR, TEM, SEM, and XRD measurements. It was found that temperature and AG played an important role in the synthesis of mono-disperse silver nanoparticles. Well dispersed and quasispherical silver nanoparticles were obtained under the optimal synthesis conditions of 10 mmol/L AgNO3, m（AG）/m（AgN03）= l：1, 160 ℃ and 3 h.展开更多
基金This study is financed by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001.
文摘Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.
基金financed by the European Union-NextGenerationEU,through the National Recowery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001-C01.
文摘Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and interact.However,alongside its benefits,social media has also given rise to significant challenges,one of the most pressing being cyberbullying.This issue has become a major concern in modern society,particularly due to its profound negative impacts on the mental health and well-being of its victims.In the Arab world,where social media usage is exceptionblly high,cyberbullying has become increasingly prevalent,necessitating urgent attention.Early detection of harmful online behavior is critical to fostering safer digital environments and mitigating the adverse efcts of cyberbullying.This underscores the importance of developing advanced tools and systems to identify and address such behavior efectively.This paper investigates the development of a robust cyberbullying detection and classifcation system tailored for Arabic comments on YouTube.The study explores the efectiveness of various deep learning models,including Bi-LSTM(Bidirectional Long Short Term Memory),LSTM(Long Short-Term Memory),CNN(Convolutional Neural Networks),and a hybrid CNN-LSTM,in classifying Arabic comments into binary classes(bullying or not)and multiclass categories.A comprehensive dataset of 20,000 Arabic YouTube comments was collected,preprocessed,and labeled to support these tasks.The results revealed that the CNN and hybrid CNN-LSTM models achieved the highest accuracy in binary classification,reaching an impressive 91.9%.For multiclass dlassification,the LSTM and Bi-LSTM models outperformed others,achieving an accuracy of 89.5%.These findings highlight the efctiveness of deep learning approaches in the mitigation of cyberbullying within Arabic online communities.
文摘The objective of the study is to examine the moderating influence of gender on the relationship between cultural values and Islamic work ethics(IWE)among Palestinian Arab high school teachers in Israel who represent an ethnic and religious minority within a Western-oriented framework.The study sample comprised 1,245 Arab teachers(759 females and 476 males).Data analysis was conducted using structural equation modeling with AMOS,focusing on path analysis.The research findings highlight a substantial relationship between cultural values and Islamic work ethics,with gender as a moderating variable.Additionally,the results indicate a significant positive relationship between the cultural value dimension of uncertainty avoidance and both dimensions of Islamic work ethics-dedication and social responsibility in the workplace,along with independence,diligence,and achievement.In contrast,a pronounced and significant negative relationship was identified between the cultural dimension of femininity/masculinity and these two dimensions of Islamic work ethics.
文摘BACKGROUND Cardiac arrest is a critical condition characterized by abrupt cessation of cardiac function,resulting in reduced oxygen delivery to vital organs and rapid progression to death if not timely treated.Despite advances in medical science and resuscitation techniques,cardiac arrest remains a significant burden globally,with survival rates remaining low.Comprehensive research on cardiac arrest,particularly comparisons between in-hospital cardiac arrest(IHCA)and out-of-hospital cardiac arrest(OHCA),is limited.AIM To compare the survival rates,return of spontaneous circulation(ROSC),survival to discharge,and neurological outcomes after IHCA and OHCA in Arab Asian countries.METHODS We systematically searched PubMed,Medline,EMBASE and Google Scholar(2000-2024)using keywords(“IHCA”,“OHCA”,“cardiac arrest”,“Middle East”,“Arab”,“Asian”)in titles/abstracts.The inclusion criterion was observational studies on adults(≥18 years)in Arab Asian countries reporting relevant outcomes.The exclusion criteria were narrative reviews,non-Arab Asian studies,non-English publications,inaccessible full texts,pediatric-only populations,and studies lacking outcome data.RESULTS In total,44 observational studies from nine Arab Asian countries comprising 32535 participants were included.This review highlights the substantial variability in cardiac arrest outcomes in Asian countries.OHCA mortality rates were alarmingly high in several nations,with Kuwait(99%),Bahrain(98.8%),and Qatar(97.6%)reporting the highest figures.In contrast,the Kingdom of Saudi Arabia(KSA)had a markedly lower OHCA mortality rate(8.2%).The rates of ROSC also varied,with Qatar achieving the highest(34.4%)and Kuwait the lowest(3.3%).Survival to hospital discharge ranged from 1.2%in Bahrain to 18.7%in Kuwait,with Qatar also reporting favorable rates(17.5%).For IHCA,mortality was 73.6%in the United Arab Emirates(UAE)and 72.8%in KSA,whereas Lebanon and Iraq reported higher rates of 94.6%and 88%,respectively.ROSC rates were the highest in Lebanon(55.9%)and the UAE(51.3%).Neurological outcome reporting has been inconsistent,although Qatar reported a high rate(68.6%)for OHCA survivors.Comparative data showed generally better survival and neurological outcomes with IHCA than with OHCA.CONCLUSION This systematic review underscores the clear disparity in survival outcomes between IHCA and OHCA in Arab Asian countries,with IHCA demonstrating superior outcomes.Despite progress in some countries,outcomes remain suboptimal compared with international standards.Future multicenter studies with standardized methodologies are required to generate high-quality evidence and provide region-specific interventions for cardiac arrest management.
文摘Liver transplantation is a vital intervention for patients with end-stage liver disease;however,the Arab world faces significant barriers that hinder access to this life-saving procedure in terms of both practice and research.This narrative review explores the multifaceted challenges,including financial constraints,limited healthcare infrastructure,cultural factors,and the prevalence of infectious diseases.In the Arab countries,both culture and religion were found to play major roles in the acceptability of liver transplantation.High rates of misconceptions and financial strain on patients and healthcare systems necessitate more transplantation programs and improved financial coverage and insurance policies.Enhancing healthcare facilities and improving access to innovative technologies through research is essential for optimizing transplantation outcomes,considering that common diseases in the region decrease the donor pool and increase complication risks.Public health initiatives to prevent and control prevalent liver diseases,particularly hepatitis,and to manage infection risk are also critical.Stricter regulations should be enforced in less developed countries in the region along with early screening practices to address inherited blood disorders and infectious diseases.Additionally,targeted research on liver diseases specific to the Arab context is crucial,along with fostering dialogue about cultural,religious,economic,and health-related factors affecting donor and recipient eligibility.By tackling these complex barriers through targeted comprehensive strategies,the Arab world can advance to a more equitable and effective liver transplantation system,ultimately improving patient outcomes and quality of life.
文摘Protein-energy malnutrition (PEM) as a result of poor nutrition, especially for deprived resourced households, is a big health concern in the world. According to the World Health Organisation, PEM accounts for 49% of the 10.4 million deaths of children under five that take place in developing countries. The aim of this study was to evaluate the influence of gum Arabic (GA) and texturized soy protein (TSP) and their interactive effect on proximate, functional, and textural properties of the protein-rich snack stick produced from ground green maize, GA powder, and ground TSP. GA varied at 0%, 4%, 8%, and 12%, while TSP varied at 0%, 12%, 24% and 36%. The 5 cm long protein-rich snack sticks were made using a sausage stuffer and baked in an oven at 110˚C for 1 hr 30 minutes. The snack sticks were subjected to proximate, functional and textural analysis using the standard methods. Increasing GA resulted in a significant (p p < 0.05) increased the protein content (32.46%), Ash content (3.6%), fat (11.96%), and moisture content (16.25%) of protein-rich snack sticks. The interactive effect between GA and TSP led to a decrease in fibre and carbohydrates. Results from this study show GA and TSP significantly enhanced the physico-chemical properties of protein-rich snack sticks. A sample with 4% GA and 36% TSP is recommended for the best physico-chemical attributes of the protein-rich snack stick.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,through the project number“NBU-FFR-2025-1197-01”.
文摘Cyberbullying is a remarkable issue in the Arabic-speaking world,affecting children,organizations,and businesses.Various efforts have been made to combat this problem through proposed models using machine learning(ML)and deep learning(DL)approaches utilizing natural language processing(NLP)methods and by proposing relevant datasets.However,most of these endeavors focused predominantly on the English language,leaving a substantial gap in addressing Arabic cyberbullying.Given the complexities of the Arabic language,transfer learning techniques and transformers present a promising approach to enhance the detection and classification of abusive content by leveraging large and pretrained models that use a large dataset.Therefore,this study proposes a hybrid model using transformers trained on extensive Arabic datasets.It then fine-tunes the hybrid model on a newly curated Arabic cyberbullying dataset collected from social media platforms,in particular Twitter.Additionally,the following two hybrid transformer models are introduced:the first combines CAmelid Morphologically-aware pretrained Bidirectional Encoder Representations from Transformers(CAMeLBERT)with Arabic Generative Pre-trained Transformer 2(AraGPT2)and the second combines Arabic BERT(AraBERT)with Cross-lingual Language Model-RoBERTa(XLM-R).Two strategies,namely,feature fusion and ensemble voting,are employed to improve the model performance accuracy.Experimental results,measured through precision,recall,F1-score,accuracy,and AreaUnder the Curve-Receiver Operating Characteristic(AUC-ROC),demonstrate that the combined CAMeLBERT and AraGPT2 models using feature fusion outperformed traditional DL models,such as Long Short-Term Memory(LSTM)and Bidirectional Long Short-Term Memory(BiLSTM),as well as other independent Arabic-based transformer models.
基金supported by the National Key Research and Development Program of China(Nos.2022YFC2904502 and 2022YFC2904501)the Major Science and Technology Projects in Yunnan Province,China(No.202202AB080012).
文摘The environment-friendly and efficient selective separation of chalcopyrite and molybdenite poses a challenge in mineral pro-cessing.In this study,gum Arabic(GA)was initially proposed as a novel depressant for the selective separation of molybdenite from chalcopyrite during flotation.Microflotation results indicated that the inhibitory capacity of GA was stronger toward molybdenite than chalcopyrite.At pH 8.0 with 20 mg/L GA addition,the recovery rate of chalcopyrite in the concentrate obtained from mixed mineral flota-tion was 67.49%higher than that of molybdenite.Furthermore,the mechanism of GA was systematically investigated by various surface characterization techniques.Contact angle tests indicated that after GA treatment,the hydrophobicity of the molybdenite surface signifi-cantly decreased,but that of the chalcopyrite surface showed no apparent change.Fourier transform-infrared spectroscopy and X-ray photoelectron spectroscopy revealed a weak interaction force between GA and chalcopyrite.By contrast,GA was primarily adsorbed onto the molybdenite surface through chemical chelation,with possible contributions from hydrogen bonding and hydrophobic interactions.Pre-adsorbed GA could prevent butyl xanthate from being adsorbed onto molybdenite.Scanning electron microscopy–energy-dispersive spectrometry further indicated that GA was primarily adsorbed onto the“face”of molybdenite rather than the“edge.”Therefore,GA could be a promising molybdenite depressant for the flotation separation of Cu–Mo.
文摘Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the movie.However,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic content.Tis study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie reviews.SGD allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language data.Tis adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better performance.Two distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for analysis.Te proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and efciency.Te SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both datasets.Tis indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English datasets.Tis study helps deepen the understanding of sentiments across various linguistic datasets.Unlike many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.
文摘BACKGROUND Arabic-speaking patients are underrepresented in orthopedic clinical studies,particularly in foot and ankle trauma research.The lack of validated Arabic language tools hinders their inclusion,creating a need for culturally and linguistically adapted instruments.The American Academy of Orthopedic Surgeons Foot and Ankle Outcomes Questionnaire(AAOS-FAOQ)is a widely used tool but has not been adapted for Arabic-speaking patients.AIM To translate,cross-culturally adapt,and validate the AAOS-FAOQ for Arabicspeaking patients with traumatic foot and ankle injuries.METHODS The cross-cultural adaptation followed established guidelines,involving forward and backward translations,expert review,and pre-testing.The final Arabic version was administered alongside the Arabic Short-Form 36(SF-36)to 100 patients for validity testing.Reliability was assessed through test-retest methods with 20 patients completing the questionnaire twice within 48 hours.Pearson correlation coefficients measured convergent and divergent validity with SF-36 subscales,while Cronbach's alpha and intraclass correlation coefficients(ICC)determined internal consistency and reliability.RESULTS Out of 100 patients,92 completed the first set of questionnaires.The Arabic AAOS-FAOQ showed strong correlations with the SF-36 subscales,particularly in physical function and bodily pain(r>0.6).Test-retest reliability was robust,with ICCs of 0.69 and 0.66 for the Global Foot and Ankle Scale and Shoe Comfort Scale,respectively.Cronbach's alpha for internal consistency ranged from 0.7 to 0.9.CONCLUSION The Arabic version of the AAOS-FAOQ demonstrated validity and reliability for use in Arabic-speaking patients with traumatic foot and ankle injuries.This adaptation will enhance the inclusion of this population in orthopedic clinical studies,improving the generalizability of research findings and patient care.
文摘Egypt's ready made garment exports increased by 22 per cent year-on-year during the first four months this year,reaching$1.028 billion,according to the Apparel Export Council of Egypt(AECE).In terms of regional market performance,the United States remains the largest export destination for Egyptian garments,with exports amounting to$384 million in the first four months,followed by the European market,with exports amounting to$259 million,and the Arab countries,which showed a moderate growth trend,with an increase of 6%yearon-year,and exports amounting to$190 million.
基金financed by the grant from the National Social Science Fund of China(No.22VJXG040).
文摘Dingzhou eye ointment gained significant popularity from the late Qing dynasty to the Republican era.Its legacy persists through three prominent families:Bai Jingyu(白敬宇),Ma Yinglong(马应龙)and Zhang Qizhu(张齐珠).Each family’s Dingzhou eye ointment is similar,with comparable ingredients,dosages,and usage methods,suggesting a common origin.While many well-established Dingzhou eye ointment brands claim their origins date back to the late Ming dynasty,the origins of its formulation are likely much older.In ancient Chinese medical texts,there were proto-formulations of similar eye ointments recorded during the Jin and Yuan dynasties,with comparable external and standardized eye medicine formulations appearing in the Ming and Qing dynasties.By the late Qing dynasty,Dingzhou eye ointment had become a popular mainstream topical eye treatment in traditional Chinese medicine.From a broader geographically perspective,the composition aligns with that of similar treatments used in Greco-Roman medicine,which combined minerals such as calamine with aromatic ingredients to create eye medicines.The principles behind the composition of Dingzhou eye ointment are in accord with the concept of the four humours in Arabian medicine.It is possible that the Hui ethnic group was responsible for the introduction of Dingzhou eye ointment to China,indicating cultural exchange between Chinese and foreign medicine practices.
文摘CHINESE Cloisonneis the centuries-old handicraft of creating designs on copper bodies with colored-enamel placed within divisions made of copper wires,which are bent to follow the outline of decorative patterns.This form of art originated in the Arab world and was introduced to China during the late Yuan Dynasty(1271-1368),and since then it has become a favorite of the country’s emperors.
文摘The second edition of the United Arab Emirates inflammatory bowel disease(IBD)consensus guidance provides updated recommendations for diagnosing,treating,and monitoring IBD.Significant therapeutic advances and evolving treatment paradigms since 2020(including risk stratification and treat-to-target approaches)necessitated this comprehensive update to standardize care across the United Arab Emirates.Developed via Delphi consensus methodology,this guidance incorporates a systematic literature review and key international gui-delines.It presents 188 summary statements covering the full spectrum of IBD care,including complex scenarios like perianal disease and pregnancy.Key updates feature guidance on newer pharmacologic therapies-interleukin-23,Janus kinase,and sphingosine-1-phosphate receptor inhibitors-with refined therapeutic positioning informed by recent head-to-head trials.The consensus emphasizes early,effective treatment to prevent irreversible bowel damage,optimization strategies like therapeutic drug monitoring,and achieving objective treat-to-target goals to improve long-term outcomes.Recognizing local healthcare system challenges,it offers practical recommendations on reducing variability and enhancing equitable access to IBD care.By integrating current clinical evidence with United Arab Emirates-specific considerations,the second edition United Arab Emirates IBD consensus guidance aims to standardize care across sectors,provide a benchmark for payers and policymakers,optimize treatment outcomes,and improve IBD outcomes,aligning national practice with international standards.
基金supported by the Deanship of Scientific Research at King Khalid University through Small Groups funding(Project Grant No.RGP1/243/45)The funding was awarded to Dr.Mohammed Abker.And Natural Science Foundation of China under Grant 61901388.
文摘Arabic Dialect Identification(DID)is a task in Natural Language Processing(NLP)that involves determining the dialect of a given piece of text in Arabic.The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect.Despite the effectiveness of these solutions,the performance heavily relies on the amount of labeled examples,which is labor-intensive to atain and may not be readily available in real-world scenarios.To alleviate the burden of labeling data,this paper introduces a novel solution that leverages unlabeled corpora to boost performance on the DID task.Specifically,we design an architecture that enables learning the shared information between labeled and unlabeled texts through a gradient reversal layer.The key idea is to penalize the model for learning source dataset specific features and thus enable it to capture common knowledge regardless of the label.Finally,we evaluate the proposed solution on benchmark datasets for DID.Our extensive experiments show that it performs signifcantly better,especially,with sparse labeled data.By comparing our approach with existing Pre-trained Language Models(PLMs),we achieve a new state-of-the-art performance in the DID field.The code will be available on GitHub upon the paper's acceptance.
基金funding this work through Research Group No.KS-2024-376.
文摘Arabic Sign Language(ArSL)recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing(DHH)community.Researchers have proposed multiple methods for automated recognition of ArSL;however,these methods face multiple challenges that include high gesture variability,occlusions,limited signer diversity,and the scarcity of large annotated datasets.Existing methods,often relying solely on either skeletal data or video-based features,struggle with generalization and robustness,especially in dynamic and real-world conditions.This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint distances and angles with temporal features extracted from RGB videos using the Inflated 3D ConvNet(I3D).By fusing these complementary modalities at the feature level and applying a majority-voting ensemble of XGBoost,Random Forest,and Support Vector Machine classifiers,the framework robustly captures both spatial configurations and motion dynamics of sign gestures.Feature selection using the Pearson Correlation Coefficient further enhances efficiency by reducing redundancy.Extensive experiments on the ArabSign dataset,which includes RGB videos and corresponding skeletal data,demonstrate that the proposed approach significantly outperforms state-of-the-art methods,achieving an average F1-score of 97%using a majority-voting ensemble of XGBoost,Random Forest,and SVM classifiers,and improving recognition accuracy by more than 7%over previous best methods.This work not only advances the technical stateof-the-art in ArSL recognition but also provides a scalable,real-time solution for practical deployment in educational,social,and assistive communication technologies.Even though this study is about Arabic Sign Language,the framework proposed here can be extended to different sign languages,creating possibilities for potentially worldwide applicability in sign language recognition tasks.
文摘Finely divided silver nanoparticles were synthesized via the hydrothermal method. Arabic gum （AG） was used as both the reductant and steric stabilizer without any other surfactant. By adjusting the reaction temperature, mass ratio of AG to AgNO3, and reaction time, silver nanoparticles with different morphological characteristics could be obtained. The products were characterized by UV-Vis, FTIR, TEM, SEM, and XRD measurements. It was found that temperature and AG played an important role in the synthesis of mono-disperse silver nanoparticles. Well dispersed and quasispherical silver nanoparticles were obtained under the optimal synthesis conditions of 10 mmol/L AgNO3, m（AG）/m（AgN03）= l：1, 160 ℃ and 3 h.
