Research Title: The effect of malnutrition on students' academic performance: Roy’s model application

Author: Mariam Mofleh Kawafha, Published Year: 2024

Nutrition and Food Science, 54

Faculty: Nursing

Abstract: Purpose This study aims to enhance understanding of malnutrition's effect on academic achievement of primary school students. Design/methodology/approach This is a descriptive, cross-sectional design built on Roy's adaptation model (RAM). This study uses a random cluster sample, consisting of 453 primary school students. Contextual stimuli (mother's educational level, income and child’s breakfast eating) and focal stimuli (wasting, thinness, body mass index and stunting) were examined regarding adaptive responses to student’s academic achievement. Findings The investigation revealed that Model 1, which took into account factors of age, gender, the frequency of breakfast, income, the number of family members and the education of mothers, explained 12% (R2 = 0.12) of the variance in academic achievement. Stuntedness (β = −3.2 and p < 0.01), BMI (β = 0.94 and p < 0.001), family income per month (β = 5.60 and p < 0.001) and mother's education (β = 2.79 and p < 0.001) were the significant predictors in Model 2. Practical implications This study provides evidence that malnutrition is associated with ineffective academic achievement. Moreover, variables such as the mother's level of education, family income and the child’s breakfast consumption have a significant impact on academic achievements. Originality/value RAM is a useful framework for determining factors affecting people's reactions to difficult circumstances.

Keywords: malnutrition's

Research Title: Deep Learning-based Arabic Optical Character Recognition: A New Comprehensive Dataset at Character and Word Levels.

Author: Maram Bani Younes, Published Year: 2024

International Conference on Information and Communication Systems, Jordan

Faculty: Information Technology

Abstract: Optical character recognition (OCR) technology aims mainly to transform printed or handwritten text into machine-readable text. Recently, OCR technology has improved text scanning for various languages. It is widely used for digitizing physical documents, making them searchable and editable digitally. It aids in language learning and boosts business efficiency. However, attempts to apply OCR technology to Arabic texts face several challenges. This is because of the high complexity of its script, the cursive nature of Arabic characters, and contextual variations. Many Arabic OCR mechanisms have been developed in the literature. However, they suffer high error rates and issues of low accuracy. Then, an accurate OCR benefits the visually impaired, making content accessible via text-to-speech and braille displays is required. This work introduces a new dataset, MFSRHRD (Multiple Fonts, Sizes, Resources, High Records Dataset), containing sufficient records to ensure adequate training and correct learning of the model. This dataset gathers two types of words; the first includes words with diacritics, and the second includes words without diacritics. Besides, the new dataset contains images of the words collected from different specialized websites to ensure the diversity of the words and separate character images with and without diacritics. Therefore, the proposed dataset is classified at word and character levels. All these words and letters were drawn in different font styles, sizes, and image quality. The diacritical marks are dhamma, fatha, kasra, shade, and sukun. This work describes the detailed specifications of the proposed dataset intended for the research community.

Keywords: Arabic Optical Character Recognition (OCR), Arabic OCR datasets, Deep learning, MFRSHD, and Neural Networks.

Research Title: modelling and synthesis use of linear programming to generate tool paths for milling

Author: Hasan Abdel-Razzaq Al Dabbas, Published Year: 2024

Third International conference of Reliability of Engineering and Smart Energy (ICRESE), Istanbul

Faculty: Engineering and Technology

Abstract: The motion transmitted by a machine to both a tool and a workpiece being shaped, can be expressed by means of the fundamental kinematic cutting schemes [1, 8, 9 ]. As per the conventional fundamental kinematic cutting scheme, the movement of cutting elements of the tool relative to the surfaces of the workpiece being cut follows a path relative to working motion at speeds, predetermined by relations: the tool (T)–the workpiece (D). When cutting the workpieces of any form in the simplest and shortest way possible the kinematics of cutting are represented as a combination of two basic motions: linear (straight line) and rotary [1]. In this case, both can be either primary or feeding, which is factored into different classifications of fundamental kinematic cutting schemes for cutting simple and complex shapes [1, 2, 8, 9].

Keywords: Modelling , Linear programming ,Milling ,kinematic cutting

Research Title: Novel anthraquinone amide derivatives as potential glyoxalase-I inhibitors

Author: Soha Telfah, Published Year: 2024

JOURNAL of MEDICINE and LIFE, 17

Faculty: Pharmacy

Abstract: This study aimed to identify novel Glyoxalase-I (Glo-I) inhibitors with potential anticancer properties, focusing on anthraquinone amide-based derivatives. We synthesized a series of these derivatives and conducted in silico docking studies to predict their binding interactions with Glo-I. In vitro assessments were performed to evaluate the anti-Glo-I activity of the synthesized compounds. A comprehensive structure-activity relationship (SAR) analysis identified key features responsible for specific binding affinities of anthraquinone amide-based derivatives to Glo-I. Additionally, a 100 ns molecular dynamics simulation assessed the stability of the most potent compound compared to a co-crystallized ligand. Compound MQ3 demonstrated a remarkable inhibitory effect against Glo-I, with an IC50 concentration of 1.45 μM. The inhibitory potency of MQ3 may be attributed to the catechol ring, amide functional group, and anthraquinone moiety, collectively contributing to a strong binding affinity with Glo-I. Anthraquinone amide-based derivatives exhibit substantial potential as Glo-I inhibitors with prospective anticancer activity. The exceptional inhibitory efficacy of compound MQ3 indicates its potential as an effective anticancer agent. These findings underscore the significance of anthraquinone amide-based derivatives as a novel class of compounds for cancer therapy, supporting further research and advancements in targeting the Glo-I enzyme to combat cancer.

Keywords: Glyoxalase-I, anthraquinone amide derivatives, zinc-binding group, molecular docking, CDOCKER, molecular dynamics, pharmacokinetics

Research Title: Lower Respiratory Infections in Children With Febrile Neutropenic Leukemia: A Case in a Jordanian Hospital

Author: Nabil Nimer, Published Year: 2023

Clinical Pediatrics, 62 (11) 1342-1349

Faculty: Pharmacy

Abstract: The study aimed to examine the prevalence of pneumonia in pediatric children diagnosed with leukemia at King Hussein Medical Center, Royal Medical Services, Amman, Jordan. The study was conducted from January 2019 to March 2020. A total of 100 hospitalized leukemia patients with febrile neutropenic episodes were evaluated for the presence of pneumonia. Samples were collected from all patients and tested for microbial growth. Univariate analysis revealed that age (P = .033) and packed cell volume (P = .006) were statistically significant risk factors, associated with the prevalence of pneumonia in leukemia patients with febrile neutropenia episodes. Similarly, as the absolute neutrophil count counts increased with an odds ratio and a 95% confidence interval of 2.386 (0.859-6.625), the odds of pneumonia in febrile neutropenic patients were more prevalent. The study reported the prevalence of pneumonia in immunocompromised febrile neutropenic patients with leukemia, which could lead to the development of evidence-based febrile neutropenic treatment protocol development. It will assure more responsive patient management and treatment.

Keywords: febrile neutropenia, chemotherapy, pediatric, leukemia, Jordan

Research Title: Lower Respiratory Infections in Children With Febrile Neutropenic Leukemia: A Case in a Jordanian Hospital

Author: Nabil Nimer, Published Year: 2023

Clinical Pediatrics, 62 (11) 1342-1349

Faculty: Pharmacy

Abstract: The study aimed to examine the prevalence of pneumonia in pediatric children diagnosed with leukemia at King Hussein Medical Center, Royal Medical Services, Amman, Jordan. The study was conducted from January 2019 to March 2020. A total of 100 hospitalized leukemia patients with febrile neutropenic episodes were evaluated for the presence of pneumonia. Samples were collected from all patients and tested for microbial growth. Univariate analysis revealed that age (P = .033) and packed cell volume (P = .006) were statistically significant risk factors, associated with the prevalence of pneumonia in leukemia patients with febrile neutropenia episodes. Similarly, as the absolute neutrophil count counts increased with an odds ratio and a 95% confidence interval of 2.386 (0.859-6.625), the odds of pneumonia in febrile neutropenic patients were more prevalent. The study reported the prevalence of pneumonia in immunocompromised febrile neutropenic patients with leukemia, which could lead to the development of evidence-based febrile neutropenic treatment protocol development. It will assure more responsive patient management and treatment.

Keywords: febrile neutropenia, chemotherapy, pediatric, leukemia, Jordan

Research Title: Scoping Review of Behavioral Changes in Pregnant Mothers

Author: Muhmar Odeh Aldalaeen, Published Year: 2023

Journal of Chemical Health Risks (JCHR), Vol. 13 No. 6 (2023

Faculty: Nursing

Abstract: During pregnancy, a woman's body, mind, and social life all change drastically. For healthcare providers to provide effective assistance and guidance, they need to understand what factors affect behavior change during pregnancy. Aim: The aim of this study was to provide an overview of the effectiveness of behavior modification programs currently used during pregnancy. Methods: A scoping review was carried out. This covered Intervention studies, RCT studies , Systematic studies published from (May 2020) to (May 2023) in English which included behavioral change programs during pregnancy .A bibliographic search was made A search of three databases (PubMed, Google Scholar, and Cochrane) in order to locate relevant papers for the investigation. Each article's author(s), publication year, study location, study design, study objective, number of participants in the intervention and control groups, results, and conclusions were extracted and analyzed. Findings: As a result, the scoping review comprised ten investigations. According to the studies being considered, treatments emphasizing changes in a pregnant woman's lifestyle and behavior have the potential to improve her health. More research is needed to assess the efficacy of these interventions and to develop comprehensive, fact-based programs to encourage healthy behaviors during pregnancy. Conclusions: According to the studies being considered, treatments emphasizing changes in a pregnant woman's lifestyle and behavior have the potential to improve her health. More research is needed to assess the efficacy of these interventions and to develop comprehensive, fact-based programs to encourage healthy behaviors during pregnancy

Keywords: Behavioral, Changes, Pregnancy, Mothers

Research Title: A Class of Bi-Univalent Functions in a Leaf-Like Domain Defined through Subordination via q-Calculus

Author: Abdullah Mohammed Khalid Al Soboh, Published Year: 2024

Mathematics MDPI, 12

Faculty: Science

Abstract: Bi-univalent functions associated with the leaf-like domain within open unit disk are investigated, and a new subclass is introduced and studied in the research presented here. This is achieved by applying the subordination principle for analytic functions in conjunction with q-calculus. The class is proved to not be empty. By proving its existence, generalizations can be given to other sets of functions. In addition, coefficient bounds are examined with a particular focus on |α2| and |α3| coefficients, and Fekete–Szegö inequalities are estimated for the functions in this new class. To support the conclusions, previous works are cited for confirmation.

Keywords: Analytic functions; Taylor–Maclaurin coefficients; univalent functions; bi-univalent functions; starlike class; q¸ -calculus; leaf-like domain; Fekete–Szegö problem; subordination

Research Title: Applications of q−Ultraspherical polynomials to bi-univalent functions defined by q−Saigo’s fractional integral operators

Author: Abdullah Mohammed Khalid Al Soboh, Published Year: 2024

Aim Mathematics, 9

Faculty: Science

Abstract: This study established upper bounds for the second and third coefficients of analytical and bi-univalent functions belonging to a family of particular classes of analytic functions utilizing q−Ultraspherical polynomials under q−Saigo’s fractional integral operator. We also discussed the Fekete-Szego family function problem. As a result of the specialization of the parameters used in our main results, numerous novel outcomes were demonstrated.

Keywords: : q−Ultraspherical polynomials; univalent functions; bi-univalent functions; q−calculus; analytic functions

Research Title: Towards optimal tuned machine learning techniques based vehicular traffic prediction for real roads scenarios

Author: Maram Bani Younes, Published Year: 2024

Ad hoc Networks, 161

Faculty: Information Technology

Abstract: Smart cities have been widely investigated, and several algorithms, techniques, and protocols have recently been developed to serve smart city environments. Intelligent traffic management is one of the main research fields in this area. It aims to control and monitor the traffic over downtown and highway scenarios. The real-time traffic characteristics on road networks highly affect safety conditions and driving behaviors. Detecting hazardous traffic conditions and congested areas early helps drivers make the best decision to avoid dangerous situations. After extensive training, machine learning algorithms are mainly used for prediction in this field. In the intelligent traffic control target, trained machines are beneficial in obtaining instant and accurate predictions regarding traffic congestion levels and their distributions. Several parameters control the efficiency of the selected machines. This includes the dataset quality, the regression algorithm’s parameters, and how they have been tuned. In this work, we first create high-quality datasets representing the moving vehicles for different main roads in various countries, including Bank St in Canada, North Rodeo Drive (NRD) in Los Angeles, USA, Queen Rania Al Abdullah (QRA) road in Jordan, and Sheikh Zayed Road (SZR) in Saudi Arabia. Then, we test the most popular regression algorithms and evaluate their performance in terms of temporal prediction of traffic characteristics and traffic congestion in different geographical road scenarios. We optimized these regression algorithms and tuned the used parameters using the grid search approach. After that, more advanced metaheuristic optimization algorithms were used to improve the prediction accuracy of the selected machine. Experimentally, we have proved the enhancements in predicting traffic characteristics for the tested road scenarios. Both approaches show enhanced results compared to using the regression algorithm without tuning the parameters. It shows advanced results for both approaches for the coefficient of determination measure, named R squared ( ), with approximately 0.07, 0.04, and 0.08 enhanced values for Bank St., NRD, and SZR roads, respectively. In contrast, it shows approximately 0.95, 0.49, and 3.62 enhanced values for Bank St., NRD, and SZR roads, respectively, for the Root Mean Squared Error ( ) measure.

Keywords: Machine learning Traffic characteristics Traffic prediction SUMO KNN Grid search Evolutionary algorithms Swarm intelligence Optimization algorithms Metaheuristic algorithms