Research Title: N-dodecyl β-D-glucopyranoside micelles catalyzed reaction of ascorbic acid with azure A chloride salt dye in acidic aqueous solution: A kinetic, thermodynamic, and mechanism study

Author: Adnan Dahadha, Published Year: 2023

Colloid and Polymer Science, 301

Faculty: Science

Abstract: The current study has investigated the reaction of ascorbic acid with azure A chloride salt dye spectrophotometrically by following the absorbance decline of azure A chloride salt at 630 nm in the presence and absence of a non-ionic micelle of the natural surfactant (N-dodecyl β-D-glucopyranoside). Thus, the primary aim was to examine the influence of the non-ionic natural surfactant on the reaction rates of ascorbic acid with azure A chloride salt dye in acidic aqueous media, as well as using the Piszkiewicz, Arrhenius, and Eyring equations to clarify its catalytic effect in pre- and micellar phases. Hence, the kinetic experiments were achieved by adapting pseudo-first-order reaction conditions with respect to the azure A chloride salt dye. However, the reaction was found to be fractional order with respect to the oxidant, reductant, and H2SO4. The findings reveal the reaction rates have been enhanced in the presence of N-dodecyl β-D-glucopyranoside through the formation of several hydrogen bonds, which play a significant role in the binding N-dodecyl β-D-glucopyranoside monomers and micelles with reactant molecules in pre-micellar and micellar phases, in addition to determination of critical micelle concentration of N-dodecyl β-D-glucopyranoside at specific conditions. Thus, the aspects of kinetic features, thermodynamic parameters, the products at the end of the reactions, and their probable mechanism are estimated and discussed.

Keywords: Ascorbic acid, Azure A chloride salt, Nonionic micelles, N-dodecyl β-D glucopyranoside, Pre- and micellar phases

Research Title: An Ontology Learning Framework for unstructured Arabic Text

Author: Mohammad Taye, Published Year: 2023

7th International Symposium on Innovative Approaches in Smart Technologies (ISAS), istanbul -Turkey

Faculty: Information Technology

Abstract: Abstract— Ontologies are widely regarded as valuable sources of semantics and interoperability in all artificially intelligent systems. Due to the rapid growth of unstructured data on the web, studying how to automatically get ontology from unstructured text is important. Therefore, ontology learning (OL) is an important process in the business world. It involves finding and extracting concepts from the text so that these concepts can be used for things such as information retrieval. Unfortunately, learning ontology is not easy for some reasons, and there has not been much research on how to automatically learn a domain-specific ontology from data. Ontology Studying Arabic text is not as developed as learning Latin text. There is almost no automated support for using Arabic literary knowledge in semantically enabled systems. Machine learning (ML) has proven beneficial in numerous fields, including text mining. By employing neural language models such as AraBERT, it is possible to obtain word embeddings as distributed word representations from textual input using machine learning. However, the application of machine learning to aid the development of Arabic ontology is largely unexplored. This research examines the performance of AraBERT for ontology learning tasks in Arabic. Early performance results as an application of Arabic ontology learning are promising. In this research, we provide a method for populating an existing ontology with instance information extracted from the input natural language text. This prototype has achieved an information extraction accuracy of 91%.

Keywords: Arabic Ontology, Natural language Processing (NLP), Ontology, Ontology Learning (OL), Semantic Web, semantic representation.

Research Title: Enhancing flat slab design: machine learning and metaheuristic approaches to predict punching shear strength

Author: Sawsan Mohammad Alkhawaldeh, Published Year: 2023

Asian Journal of Civil Engineering, 2023

Faculty: Engineering and Technology

Abstract: This study aimed to enhance the design of flat slabs by utilizing the capabilities of machine learning and metaheuristic optimization. Besides, the main aim of this study was to develop a robust predictive model for estimating punching shear strength, which is a crucial factor in the design of flat slabs and an attempt to enhance the safety and efficiency of construction practices. The study employed a carefully selected dataset from The American Concrete Institute Committee 445C, encompassing experimental findings about flat slabs. The Light Gradient Boosting Machine (LGBM) was the fundamental prediction model. The Locust Swarm Algorithm (LSA) was integrated to optimize the model's parameters and configurations to improve its performance. The combination of LGBM and LSA substantially improved prediction precision, accompanied by a noteworthy decrease in RMSE, MAE, and MAPE metrics. The hybrid approach performed better than the standalone LGBM model and traditional approaches. The findings highlight the possibility of combining machine learning and metaheuristic optimization techniques in structural engineering. The convergence of these factors can fundamentally change the approach to decision-making in flat slab designs, focusing on utilizing data-driven methods. This can significantly transform safety protocols and enhance the overall efficiency of construction processes. Moreover, the results have significant implications for the broader domain of civil engineering, indicating the advent of a novel era characterized by evidence-based and highly efficient design practices. The culmination of the research involves promoting the utilization of the LGBM-LSA hybrid as a robust method for predicting punching shear strength in flat slabs. The integration of machine learning and metaheuristic optimization in this study establishes a foundation for potential advancements in structural engineering.

Keywords: Flat slab design · Punching shear strength · Machine learning · LGBM · Metaheuristic optimization · Locust swarm algorithm

Research Title: Hybrid RNN and metaheuristic approach for modeling and optimization of seismic behavior in thin‑walled rectangular hollow bridge piers

Author: Sawsan Mohammad Alkhawaldeh, Published Year: 2023

Asian Journal of Civil Engineering, 2023

Faculty: Engineering and Technology

Abstract: In seismic structural engineering, there is a significant issue in comprehending the behavior of thin-walled rectangular hollow bridge piers within the context of dynamic phenomena. This research aimed to investigate a complex behavior using recurrent neural networks (RNNs) in conjunction with metaheuristic algorithms, namely the charged system search (CSS) and the black hole algorithm (BHA), to optimize the analysis. The approach used in this study included a rigorous process of data preprocessing to enhance the quality of seismic datasets and the development of an RNN model optimized utilizing the metaheuristics above. The results of the study were significant. The combined use of the RNN-CSS and RNN-BHA models showed enhanced prediction capacities compared to solo RNNs, thereby emphasizing the effectiveness of integrating neural networks with global optimization approaches. In addition, the convergence, diversity, search space, and sensitivity studies provided further insights into the modeling technique’s stability, comprehensiveness, and dependability. In summary, our study signifies a novel shift in seismic structural modeling, emphasizing the prospects of using multidisciplinary approaches to forecast and comprehend the hysteresis characteristics of bridge piers subjected to seismic stresses.

Keywords: Seismic structural engineering · Recurrent neural networks (RNNs) · Charged system search (CSS) · Black hole algorithm (BHA) · Thin-walled rectangular hollow bridge piers · Metaheuristic optimization

Research Title: Design, Synthesis, Anticancer Screening and Molecular Modelling Studies of Novel Thiazoles

Author: Mohammad Jamal Abu Nuwr, Published Year: 2023

ChemistrySelect, 8

Faculty: Pharmacy

Abstract: A library of novel benzamide-thiazolyl-chalcone compounds was synthesized via Claisen-Schmidt reaction and spectroscopically characterized using FT-IR, 1H-NMR, 13C-NMR, LC–MS, and HR-MS. The synthesized compounds were biologically evaluated using in vitro MTT assay against different cancer cell lines (MCF-7, MDA-AMB-231, Caco-2, A549, and H1299). Amongst all compounds screened, (E)-N-(5-(3-(4-hydroxyphenyl)-3-oxoprop-1-en-1-yl)thiazol-2-yl)benzamide and (E)-N-(5-(3-(4-methoxyphenyl)-3-oxoprop-1-en-1-yl)thiazol-2-yl)benzamide showed potent anti-proliferation against breast (MCF-7) and colon cancer cell lines reaching 88.56 % and 84.36 % with IC50 values of 44.00 μM and 58.88 μM, respectively. All synthesized compounds exhibited no significant cytotoxicity to normal cells. Structure-activity relationship studies demonstrated the effect of electron-donating and electron-withdrawing groups on the anticancer activity of the molecules under investigation. This was also corroborated by theoretical DFT studies. Thus, these molecules may serve as potential lead candidates for further development of novel anticancer agents against breast and colon cancers.

Keywords: Anti-cancer · Chalcone · DFT · Molecular modelling · Thiazole

Research Title: Secure Protocols in VANETs: Availability Considerations

Author: Maram Bani Younes, Published Year: 2023

The 14th International Conference on Information and Communication Systems, Irbid

Faculty: Information Technology

Abstract: The vehicular network technology provides several intelligent applications over road networks. These applications are classified into safety, efficiency, and infotainment based on their functionalities. Several studies have been proposed recently in this field where advanced and intelligent applications are developed for connected vehicles. This field of research seeks intensive and dedicated studies to enhance the confidentiality, integrity, and availability of the connecting network. In this work, we investigate the availability feature for vehicular network applications. First, we define the denial of service (DoS) attack and its different types. Then, we explore several possible DoS scenarios in the vehicular network environment. After that, we study the previous secure protocols that have generally considered and tackled the DoS attack on the vehicular network. Finally, we remark on the performance and functionalities of previous studies illustrating the main limitations and required work in this field. Thus we recommend future required work and studies in this field.

Keywords: Availability, DoS, VANETs, Applications

Research Title: Design, Synthesis, Anticancer Screening and Molecular Modelling Studies of Novel Thiazoles

Author: Adnan Dahadha, Published Year: 2023

Faculty: Science

Abstract: A library of 25 novel thiazole-chalcone hybrids were designed and synthesized. Anticancer activity was conducted using breast (MCF-7 and MDA-AMB-231), colon (Caco-2) and lung (A549 and H1299) cancer cell lines. Compounds (E)-N-(5-(3-(4-hydroxyphenyl)-3-oxoprop-1-en-1-yl)thiazol-2-yl)benzamide and (E)-N-(5-(3-(4-methoxyphenyl)-3-oxoprop-1-en-1-yl)thiazol-2-yl)benzamide showed 88.56 % and 84.36 % inhibition and IC50 values of 44.00 μM, 58.88 μM against MCF-7 cell line, respectively. Compounds (E)-N-(5-(3-(4-hydroxyphenyl)-3-oxoprop-1-en-1-yl)thiazol-2-yl)benzamide and (E)-N-(5-(3-oxo-3-phenylprop-1-en-1-yl)thiazol-2-yl)benzamide exhibited 75.83 % and 72.91 % inhibition against MDA-AMB-231 cell line.

Keywords: Anticancer activity, Thiazoles

Research Title: Building Trust in Fintech: An Analysis of Ethical and Privacy Considerations in the Intersection of Big Data, AI, and Customer Trust

Author: Hassan Hamad Aldboush, Published Year: 2023

International Journal o f Financial Studies, 11: 90.

Faculty: Business

Abstract: Abstract: This research paper explores the ethical considerations in using financial technology (fintech), focusing on big data, artificial intelligence (AI), and privacy. Using a systematic literature review methodology, the study identifies ethical and privacy issues related to fintech, including bias, discrimination, privacy, transparency, justice, ownership, and control. The findings emphasize the importance of safeguarding customer data, complying with data protection laws, and promoting corporate digital responsibility. The study provides practical suggestions for companies, including the use of encryption techniques, transparency regarding data collection and usage, the provision of customer opt-out options, and the training of staff on data-protection policies. However, the study is limited by its exclusion of non-English-language studies and the need for additional resources to deepen the findings. To overcome these limitations, future research could expand existing knowledge and collect more comprehensive data to better understand the complex issues examined.

Keywords: Keywords: fintech; big-data analytics; artificial intelligence (AI); data security and privacy; corporate digital responsibility (CDR); customer trust; ethical considerations

Research Title: Towards Automated Goal Model Generation from UML Use Case and Swimlane Diagrams

Author: Said Ahmad Ammar Ghoul, Published Year: 2023

International Journal Computer Aapplications, , Vol. 30, No. 2,

Faculty: Information Technology

Abstract: The Goal Model of software is one of the important concepts in the goal-based requirements engineering. It helps in specifying the software goals and the relationships between them. Several research works were conducted to generate Goal Model of software from its requirements documents. However, the generated Goal Models merge behavior and soft goals into a single model unit. This merging leads to tangled and complex generated Goal Models. Therefore, the maintenance of these models is hard and costly. The work presented in this paper proposes an approach splitting the generated Goal Model into three separated concerns (aspects) models (behavior, soft, and constraints) that facilitate its evolution and maintenance. The proposed approach is semi-automated, taking UML use case and Swimlane diagrams as inputs and generating a separated aspects model GM as output. The separation of Goal Model aspects led to adding new required information in input requirements specification documents. The feasibility of the proposed approach was validated on a concrete business case (Philadelphia University Quality Assurance Agenda). Its implementation was demonstrated through processes programming with algorithms and UML. Its contribution was demonstrated through its comparison with similar works. According to the observed results, this approach could be valuable in any goal-oriented requirements engineering application.

Keywords: Goal model (GM), behavior goal, soft goal, unified modeling language (UML), UML use case diagram, UML swim lane diagram, goal model generation, goal model maintenance, separation of concerns, aspects programming

Research Title: Toward Automated Feature Model Generation from UML Use Case and Class Diagrams

Author: Said Ahmad Ammar Ghoul, Published Year: 2023

International Journal Computer Aapplications, , Vol. 30, No. 2,

Faculty: Information Technology

Abstract: The Feature Model is one of the most important concepts in the Software Product Line development process, as it helps to represent commonalities and variabilities between software products. Several research works are currently conducted to generate feature model of a software product line from its requirements document. However, the generated feature models do not include data and actor aspects which are essential parts of any software product line. The work presented in this paper proposes an approach handling this insufficiency. This approach defines a methodology toward automated feature model generation from requirement documents limited to Unified Modelling Language use case and class diagrams. The use case is specified with a use case description template enhanced with information required for feature model. The class diagram is specified in a textual form obtained by translating the diagram into java programming language. The target generated feature model is enhanced by the introduction of data and actor concepts. An evaluating real case study (quality assurance at Philadelphia University), was used to evaluate the feasibility of the proposed approach and obtained results show its practical benefits.

Keywords: Feature model (FM), software production line (SPL), unified modelling language (UML) use case, UML class diagram, feature model generation approach