Al-Farahidi Expert Systems Journal

The Al-Farahidi Expert Systems Journal (FESJ) is an international, open-access publication established in 2024, dedicated to advancing research in expert systems and related computational fields. As a premier platform, FESJ focuses on cutting-edge developments in artificial intelligence, machine learning, decision-support systems, and their diverse applications across industries. The journal employs a rigorous double-blind peer review process and publishes two issues per volume annually, ensuring high-quality contributions from researchers worldwide. FESJ exclusively accepts manuscripts in English, reinforcing its commitment to global scholarly communication. As an open-access journal, it guarantees free accessibility to its content for researchers, academics, and professionals, fostering widespread knowledge dissemination. The journal prioritizes research that highlights advancements, challenges, and future directions in expert systems and their integration with real-world applications. It welcomes original research articles, comprehensive reviews, and concise communication pieces. With an efficient editorial workflow, FESJ ensures an average turnaround of 55 days from submission to acceptance, enabling the timely publication of impactful findings that contribute to the growing field of expert systems.

See the Aims and Scope for a complete coverage of the journal.

Current Issue

Volume 2, Issue 1 (2026)View issue

Featured Content

Article
23 June 2026
Assessment of Dihedral Angle Contribution to Roll-Stability Derivatives Using DATCOM and VLM Methods
Tarik Hassan Elsonni, Emad Qasem Hussein, Balsam H. Abed
This study investigates the lateral stability of a fixed-wing aircraft through the roll-moment stability derivative (C_lβ ), treated as the primary indicator of dihedral effect and overall roll stability. A coupled DATCOM-based analytical formulation and lifting-line/VLM modeling framework has been used to quantify the influence of the key geometric and aerodynamic parameters on the roll-stability derivative. Specifically, the influences of dihedral angle, angle of attack, and sideslip have been explored separately, followed by geometric factors such as aspect ratio, taper ratio, and sweep angle in a systematic way. It is observed that there is an increase in the dihedral angle from 5° to 15°; hence, there is an improvement of about 230–260% in (C_lβ ), which underlines the great stabilization role of dihedral geometry. Variations in the angle of attack from 0° to 15° produce a 150–190% change in the same derivative, demonstrating significant nonlinear sensitivity under increased aerodynamic loading. Three-dimensional stability maps further confirm that (C_lβ ) increases monotonically with both dihedral angle ($\Gamma $) and angle of attack (α ), while sideslip angle (β ) primarily scales its magnitude principally without changing its trend. Focusing on (C_lβ ), as the primary stability determinant, provides a clear physical interpretation of lateral-stability trends and strengthens the link between wing geometry and roll-stability performance across the operating envelope.
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Article
23 June 2026
AT-GPSR: Adaptive Trajectory to Support Geographic Routing Protocol in UAV Assisted VANET
Bassam Hasan
Connectivity and data delivery latency in vehicular networks are strongly affected by the high dynamism of vehicular traffic, which depend on many random factors. Under the inevitable constraints imposed by road paths and free-flowing vehicular traffic conditions (e.g., low to medium vehicle density, high mobility, high speeds), these networks suffer from fast topology variations that lead to frequent disconnections. Consequently, communications experience increased data delivery latency. Establishing a connection between source and destination vehicles depends heavily on vehicle density along the road. UAV assistance becomes more necessary as vehicle density along the road decreases. Moreover, when a road segment is low density or lacks roadside units (RSUs), reliance on UAVs increases. Here, the specific navigation model adopted by the UAV can significantly impact the performance of the routing protocol. Indeed, when UAVs operate as relay stations over vehicle networks (VANETs), several challenges arise, such as power management and determining the appropriate navigation models that will support message delivery within the routing route. In the context of UAV-assisted VANET networks, the mobility model has not been studied, considering what a routing protocol needs to function best. In order to reproduce the actual behavior of the UAV in combination with the needs of the routing protocol, so that the routing protocol works better. Our goal is to design a UAV path that takes into account the mechanism by which the routing protocol works. Thus, improving the performance of the network.
Read More

Current Articles

Article23 June 2026
Enhancement of Mechanical Properties in 7050 Aluminum Alloy Using Zirconia Nanoparticle Reinforcements
Ahmed A. Ebraheem, Tahseen F. Abbas, Talib A. Jasim
Aluminum metal matrix composites (AMMCs), particularly those reinforced with ceramic nanoparticles, are critical materials for aerospace, automotive, and defense applications due to their superior strength-to-weight ratio. This study investigates the effect of zirconia (ZrO2) nanoparticles on the mechanical properties of a 7050 aluminum alloy matrix. Composites were fabricated via the stir-casting technique with ZrO2 reinforcements incorporated at varying weight fractions (0, 1, 2, 3, and 4 wt.%). A comprehensive mechanical characterization was conducted, evaluating hardness, tensile strength, and surface roughness. The results demonstrate that the inclusion of ZrO2 nanoparticles significantly enhances all measured properties. Hardness increased from 66.4 to 80.1 HBN, ultimate tensile strength improved from 318 to 385 MPa, and surface roughness decreased from 0.92 to 0.57 μm, indicating a smoother surface. The composite with 4 wt.% ZrO2 exhibited the most superior performance, conclusively proving that ZrO2 nanoparticle reinforcement is an effective method for enhancing the mechanical characteristics of 7050 aluminum alloy.
Article23 June 2026
Computational Analysis of a Single Air Bubble Rising in Quiescent Water Under Temperature Variations
Mokdad Hayawi Rahman, Muna khalil Shehan, Emad Qasem Hussein, Farhan Lafta Rashid
Two-phase fluid flow is widely encountered in petroleum, chemical, nuclear, and energy applications. Computational fluid dynamics (CFD) by ANSYS Fluent 17.2 has been employed in this work to simulate the rise of a single air bubble through stagnant water at different temperatures (0 °C, 25 °C, and 50 °C). In addition, a user-defined function(UDF)was developed and implemented to incorporate phase-change behavior and temperature-dependent properties. This study investigates how the buoyancy-driven bubble dynamics will be responsive to changes in liquid viscosity, surface tension, and density. The results demonstrated that increasing the temperature of water enhances the bubble rise velocity significantly; the bubble rose approximately 18-30% faster at 50 °C compared with 0 °C due to the reduction in viscosity and interfacial tension. Additionally, the bubble behavior in 30% saline water was compared to pure water at ambient temperature, 25 °C. Salinity increased the rise velocity by 12-17% due to the reduction in the surface tension, while effective viscosity remained lower. All these results indicate that the variations in thermophysical properties dominate the bubble hydrodynamics, while the effects of boundary conditions remain almost the same for various temperature levels.
Article23 June 2026
Machine Learning Based Approaches to Survey Elderly Adults About the Perception of the Coronavirus Crisis
Ouban Wannous
The rapid digital transformation of society in recent years has resulted in the creation of vast databases available online, particularly benefiting businesses. The main challenge now is to effectively access and utilize this data to make informed business decisions. Artificial intelligence techniques, such as natural language processing and machine learning, have proven highly effective across various domains and present valuable opportunities for developing automated models to extract insights and uncover new information. This paper demonstrates the effectiveness of machine learning techniques in the field of social and economic sciences, focusing specifically on the task of classifying open-ended survey questions. We introduce a machine learning-based solution for verbatim classification, comparing a traditional and deep learning techniques. We use data from a survey conducted during the first lockdown in France to understand elderly adults' perceptions of the Coronavirus crisis.
Article23 June 2026
Agentic AI Approach for Online Financial Fraud Detection
Shadi Saleh, Kelechi Osuji, Wolfram Hardt
As digital payment systems facilitate billions of transactions every day, there is a high probability of fraudulent activities in these systems. Traditional fraud detection systems, including rule-based systems and machine learning-based systems, have three major limitations: lack of explainability in terms of regulatory requirements, lack of contextual reasoning in terms of rare behavioral patterns, and lack of interaction with human experts in fraud analysis. This paper proposes a novel agentic framework in fraud detection systems by integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) in context-aware reasoning based on historical transaction evidence. The framework is designed as a system of specialized agents working together in a coordinated manner in semantic retrieval, context-aware fraud analysis, and explanation generation. The vector-based retrieval system is designed to analyze transactions in behavioral context while minimizing hallucination risk associated with language models. The system is tested using a set of synthetic yet behaviorally realistic financial transactions that mimic class imbalance in real-world financial systems. The experimental results show better performance in terms of Precision = 0.985, Recall = 0.955, F1-score = 0.970, and AUCPR = 0.989 compared to traditional machine learning-based systems. The system is capable of generating structured explanations in a way that is useful for financial fraud analysis by humans. The results clearly show the effectiveness of retrieval-based agentic reasoning in financial fraud detection systems.
Article23 June 2026
Solving Some Heat Problems in the Sense of the Non-Conformable Fractional Derivative by Using Modified Fourier Transform
Sami Injrou, Wadii Ali
In this paper, we present two new concepts in Fourier analysis: the Fourier series and the Fourier transform in the sense of non-conformable fractional calculus. Some of their main properties are proven. Using this new Fourier series, new approximations of some functions are given, a frequency spectrum of a signal is plotted, and several cases of the heat equations involving the time non-conformable fractional derivative are solved and graphically presented. Furthermore, the introduced new Fourier transform is used to solve a homogeneous space-time non-conformable fractional heat equation.
Article23 June 2026
Smart City Utopia: Between Marketing and Humanization ``Epistemological Reading''
Fidaa Faez Zayna
The review offers a critical reading of the politicization of contemporary urban problems. Recently, numerous headlines and ideas have emerged regarding the ongoing quest to meet planning standards that keep pace with significant developments in the special needs of the population, but this quest has not been neutral with respect to the main goal of achieving the ``humanization'' of the city's constituent elements. The review discusses whether Urban Media Marketing of the ``fashion'' of smart cities, smart planning and other attractive titles ``within the framework of ``Urban Marketing'' has produced cities that respect human privacy. The review also touches on the problem of naturalization (normalization) of Urban Logic, which ``aims to get out of a political and institutional vision of the city (as proved in the Chicago school) ''with the aim of blind application of new urban policies (with its commercial - profit context) by the population (their daily lifestyle) to the detriment of the human dimension''. In this review, we present an epistemological critical reading of the works of both Zygmunt Bauman and Françoise Choay. Bauman, in his book Liquid Modernity, raises a central question: what is the nature of contemporary modernity, and how does it affect individuals' lives? In a context characterized by constant change and instability, how can individuals find meaning, achieve a sense of security, and construct a stable identity? This issue also opens up a broader inquiry into the extent to which the smart city, from an epistemological perspective, can embody a coherent urban image while simultaneously fostering spatial and morphological belonging among its inhabitants. On the other hand, the ideas of Françoise Choay are considered among the most significant references for understanding the problematics of the modern city, particularly in her well-known work Urbanism: Utopias and Realities. Her central question can be summarized as follows: is the city a social space that reflects human relationships, or a technical system governed by planning and modernization imperatives?
Article23 June 2026
Dynamic Analysis of the Rotary System with Elastically Coupled Drive and Working Rotors
Hassan Jamil Kouzy
The problems of designing high-speed textile rotary systems are related to the search for new design schemes and the development of effective methods. This paper examines the issues of reducing loads in the supports of a rotary system, the design contains a drive rotor and a technological working rotor connected to it by means of an elastic spherical hinge, which, depending on the purpose, can be made in the form of a spindle or spinning chamber. A new design scheme was proposed for a rotary system with elastically coupled drive and working rotors, and a dynamic analysis methodology for the rotary system was developed. The results demonstrate a reduction in loads on the rotary system supports and a reduction in the rotary system vibration level, thus increasing system lifespan and product quality, and reducing breakdowns and waste of materials.
Article23 June 2026
Artificial Intelligence for Predictive Maintenance in Renewable Energy Assets: Wind Turbines, Solar Farms, and Energy Storage Systems
Bassam Rafea Hani, Ayman Abdulmajeed Radhi
The integration of Artificial Intelligence (AI) into predictive maintenance for renewable energy assets represents a significant advancement in modern energy management. This study reviews and analyzes AI applications in predictive maintenance across wind turbines, solar farms, and energy storage systems. It emphasizes the role of machine learning and deep learning algorithms in predicting failures before they occur, enhancing operational efficiency, and reducing maintenance costs. The paper also discusses challenges related to data quality, scalability, and system integration within renewable energy environments. Findings indicate that AI-based predictive maintenance significantly improves equipment reliability and system performance, paving the way toward smarter, more sustainable, and cost-effective energy infrastructures. Purpose: This systematic review critically analyzes the integration of Artificial Intelligence (AI) for predictive maintenance in renewable energy assets... Methodology: Following PRISMA guidelines, this study synthesizes findings from [Number] peer-reviewed articles published between 2018–2024... Findings: The synthesis indicates that while Deep Learning models (e.g., LSTM) demonstrate superior accuracy in fault forecasting, their deployment is often hindered by... Originality: Unlike previous reviews, this paper provides a critical comparison of algorithmic performance and highlights the gap between simulated results and industrial applicability.
Article23 June 2026
Comparison of Textile-Reinforced Mortar and Ferrocement in Torsional Strengthening of RC Beams Using Finite Element Analysis
Roula Taha, Ihssan Tarsha, Muneeb AL-Allaf
Strengthening reinforced concrete (RC) elements is vital to rehabilitate aging infrastructure and adopting modern standards. While conventional designs focus on axial, shear, and bending forces, torsional moments become highly effective in specific beams. This study employs finite element analysis using ABAQUS to investigate effective strengthening strategies for enhancing the torsional performance of RC beams. Two strengthening systems, ferrocement and Textile Reinforced Mortar (TRM), were examined using two-sided and three-sided wrapping configurations. The numerical investigation evaluated torsional capacity, twist response, and crack propagation, with results compared to an unstrengthened control beam. The findings indicate that both strengthening techniques significantly improved stiffness and torsional resistance. Three-sided wrapping provided enhanced confinement, resulting in improved crack control and higher torsional capacity compared with two-sided strengthening. Among the investigated systems, ferrocement with three-sided wrapping achieved the best overall performance, increasing ultimate torque by approximately 50% while enhancing ductility and energy absorption due to the continuous wire mesh action that delayed crack initiation. TRM-strengthened beams exhibited increased stiffness but comparatively lower deformation capacity, achieving an ultimate torque capacity increase of about 41%. Overall, ferrocement demonstrated superior improvement in cracking and ultimate torsional moments relative to TRM. The results highlight ferrocement, particularly with three-sided application, as an efficient and practical solution for upgrading RC beams subjected to torsional loading.
Article23 June 2026
Utilizing BIM Technology to Model the Engineering Systems of the International Space Station
Lama Saoud, Fadi KBEILI
The usage of Building Information Modeling (BIM) technology significantly benefits the AEC industry due to the BIM's capability to solve many discrepancies and avoid risks during the design and execution stages. This has been made possible by BIM's ability to simulate any structure in the virtual environment. The International Space Station (ISS) is the largest engineering structure ever placed in space around the Earth. The ISS has faced many obstacles during the assembly and operation periods due to design discrepancies, as its various parts were designed by Russia, the United States, Europe, Japan, and Canada. This research aims to explore BIM's capability to create a virtual 3D model and simulate the engineering structure of the ISS. Many books and 2D designs have been reviewed to obtain accurate information for the space modules that have been used to assemble the ISS. A virtual 3D model has been created for 18 components from the ISS design by utilizing Revit-2024, and a simulation for the assembly has been done using Enscape. The results demonstrated a new and great ability of BIM tools in modeling and simulating all parts of the ISS. The resulting virtual model can be used to solve design discrepancies and serve as a base for operation and maintenance monitoring, risk analysis, solar energy, and sustainability studies on the ISS. It can also be used in the ISS decommissioning plan.
Article23 June 2026
The Effect of Heat Treatment on Mechanical and Physical Properties of HDPE Pipes for Drinking Water
Roya Noman
This research aims to investigate the effect of heat treatment on the physico-mechanical properties of HDPE pipes for drinking water. Pipe samples of varying thicknesses were subjected to heat treatment at temperatures of 60, 80, and 100°C for 2, 6, 24, and 48 hours. Experiments showed that regardless of the treatment temperature or time, some effect, however slight, will occur on the structure at the level of the amorphous transition regions. The longitudinal shrinkage values are clearly related to the heat treatment temperature. Treatments at 80°C showed an increase in some mechanical properties such as yield stress, necking stress, and fracture stress with the increasing of heat treatment time. In contrast, the heat treatment at 100°C for 6 hours stabilized this properties. Treatments at 80 and 60°C yielded similar values for longitudinal shrinkage, approximately 0.35%, whereas the 100°C treatment resulted about 1%.
Article23 June 2026
AT-GPSR: Adaptive Trajectory to Support Geographic Routing Protocol in UAV Assisted VANET
Bassam Hasan
Connectivity and data delivery latency in vehicular networks are strongly affected by the high dynamism of vehicular traffic, which depend on many random factors. Under the inevitable constraints imposed by road paths and free-flowing vehicular traffic conditions (e.g., low to medium vehicle density, high mobility, high speeds), these networks suffer from fast topology variations that lead to frequent disconnections. Consequently, communications experience increased data delivery latency. Establishing a connection between source and destination vehicles depends heavily on vehicle density along the road. UAV assistance becomes more necessary as vehicle density along the road decreases. Moreover, when a road segment is low density or lacks roadside units (RSUs), reliance on UAVs increases. Here, the specific navigation model adopted by the UAV can significantly impact the performance of the routing protocol. Indeed, when UAVs operate as relay stations over vehicle networks (VANETs), several challenges arise, such as power management and determining the appropriate navigation models that will support message delivery within the routing route. In the context of UAV-assisted VANET networks, the mobility model has not been studied, considering what a routing protocol needs to function best. In order to reproduce the actual behavior of the UAV in combination with the needs of the routing protocol, so that the routing protocol works better. Our goal is to design a UAV path that takes into account the mechanism by which the routing protocol works. Thus, improving the performance of the network.
Article23 June 2026
Assessment of Dihedral Angle Contribution to Roll-Stability Derivatives Using DATCOM and VLM Methods
Tarik Hassan Elsonni, Emad Qasem Hussein, Balsam H. Abed
This study investigates the lateral stability of a fixed-wing aircraft through the roll-moment stability derivative (C_lβ ), treated as the primary indicator of dihedral effect and overall roll stability. A coupled DATCOM-based analytical formulation and lifting-line/VLM modeling framework has been used to quantify the influence of the key geometric and aerodynamic parameters on the roll-stability derivative. Specifically, the influences of dihedral angle, angle of attack, and sideslip have been explored separately, followed by geometric factors such as aspect ratio, taper ratio, and sweep angle in a systematic way. It is observed that there is an increase in the dihedral angle from 5° to 15°; hence, there is an improvement of about 230–260% in (C_lβ ), which underlines the great stabilization role of dihedral geometry. Variations in the angle of attack from 0° to 15° produce a 150–190% change in the same derivative, demonstrating significant nonlinear sensitivity under increased aerodynamic loading. Three-dimensional stability maps further confirm that (C_lβ ) increases monotonically with both dihedral angle ($\Gamma $) and angle of attack (α ), while sideslip angle (β ) primarily scales its magnitude principally without changing its trend. Focusing on (C_lβ ), as the primary stability determinant, provides a clear physical interpretation of lateral-stability trends and strengthens the link between wing geometry and roll-stability performance across the operating envelope.

Most Popular Articles

Article
23 June 2026
Artificial Intelligence for Predictive Maintenance in Renewable Energy Assets: Wind Turbines, Solar Farms, and Energy Storage Systems
Bassam Rafea Hani, Ayman Abdulmajeed Radhi
The integration of Artificial Intelligence (AI) into predictive maintenance for renewable energy assets represents a significant advancement in modern energy management. This study reviews and analyzes AI applications in predictive maintenance across wind turbines, solar farms, and energy storage systems. It emphasizes the role of machine learning and deep learning algorithms in predicting failures before they occur, enhancing operational efficiency, and reducing maintenance costs. The paper also discusses challenges related to data quality, scalability, and system integration within renewable energy environments. Findings indicate that AI-based predictive maintenance significantly improves equipment reliability and system performance, paving the way toward smarter, more sustainable, and cost-effective energy infrastructures. Purpose: This systematic review critically analyzes the integration of Artificial Intelligence (AI) for predictive maintenance in renewable energy assets... Methodology: Following PRISMA guidelines, this study synthesizes findings from [Number] peer-reviewed articles published between 2018–2024... Findings: The synthesis indicates that while Deep Learning models (e.g., LSTM) demonstrate superior accuracy in fault forecasting, their deployment is often hindered by... Originality: Unlike previous reviews, this paper provides a critical comparison of algorithmic performance and highlights the gap between simulated results and industrial applicability.
Read More
Article
23 August 2025
Battery Energy Storage Overview : Applications, Potential Challenges and Future Perspective
haider jouma touma, Muhamadm Mansor, Ahmad Skaiky
In recent years, there has been a notable global increase in the adoption of Battery Energy Storage (BES). This trend is driven by significant reductions in battery manufacturing costs, improved battery performance, and the growing importance of sustainable energy generation systems, alongside global efforts to reduce harmful emissions. This article aims to provide a comprehensive and independent overview of BES, including battery classification. It also explores the diverse applications of BES across generation, transmission, and distribution levels. While emphasizing the significant benefits of BES, the article identifies a number of potential challenges, particularly those related to cost reduction for manufacturers, as well as administrative issues faced by stakeholders such as electric utilities. Additionally, it highlights technical challenges that affect both manufacturers and stakeholders. The article suggests potential future directions to address these challenges, including reducing electricity costs for consumers, fostering a competitive electricity market, and decreasing emissions from traditional generation units by optimizing battery usage. Finally, the article encourages further research into the integration of electric vehicles as mobile storage systems particularly with the advent of smart appliances which are used for energy management in power grids.
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Article
23 August 2025
Synthesis of some new sulfonamide derivatives from acetanilide and amino acids and confirmation of their structures by spectroscopic techniques
Ayman Ahmad Ramadan
A convenient, mild, eco-friendly, economical and efficient one-step synthesis of new sulfonamides derived from acetanilide by Hinsberg's reaction between p-acetamido benzene sulfonyl chloride (resulted by chlorosulfonation of acetanilide) and some amino acids (Glycine, Alanine, Phenylalanine, Valine) is described in the presence of sodium carbonate as an acid scavenger and water as a solvent at room temperature. The intermediate compound (of p-acetamido benzene sulfonyl chloride) was initially prepared in a yield of 83.1% by reacting chlorosulfonic acid with acetanilide. Four new sulfonamides were then prepared: the first compound was 2-(4′-acetamidophenyl sulfonamido) Acetic acid in a yield of 89.7% by reaction of the intermediate compound with Glycine, the second compound was 2-(4′-acetamidophenyl sulfonamido)-2-isopropyl Acetic acid in a yield of 93.6% by reaction of the intermediate compound with Alanine, the third compound was 2-(4′-acetamidophenyl sulfonamido) -2-benzyl Acetic acid in a yield of 94.1% by reaction of the intermediate compound with Phenylalanine, finally the fourth compound was 2-(4′-acetamidophenyl sulfonamido)-2-methyl Acetic acid in a yield of 82.6% by reaction of the intermediate compound with Valine. The structures of the new sulfonamides confirmed by spectroscopic analysis techniques: Fourier transform infrared spectroscopy (FT-IR) to identify different types of bonds and functional groups in the unknown compounds, and mass spectrometry with electrospray ionization (ESI-MS) to identify the unknown compounds by determining their molecular weights.
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Article
23 August 2025
Harmonizing Technological Advancement with Ethical and Safe AI Deployment by Bridging the Innovation–Regulation Divide
Majd A. Ali, Wadii Ali, Hussein Abbas Hameed
The development of technology, especially artificial intelligence (AI), has revolutionized various fields like business, healthcare, and even security. Unfortunately, examining the ethical and legal ramifications associated with such sophisticated technology is often left behind. This paper delves into the most pressing issues related to AI and its innovation; specifically, it covers issues such as governance frameworks, the legal gap of coordination, international relations, and ethical problems like discrimination by algorithms, lack of clarity on who is being held responsible, and transparency deficiency. The study focuses on the global strategies for AI governance, from the stringent AI regulations of the European Union to the sector-specific ones of the United States and the emerging ones in the Middle East, and notes the controversy between encouraging innovation and protecting against malfeasance. The findings indicate the need for governance frameworks that utilize interdisciplinary approaches with agile laws, collaboration between nations, and shifting within borders. With rapid development differing from ethical considerations, regulated guidelines alongside transparent algorithms and participating policies shift the balance. This study argues that the sustainable use of AI is achieved when there's a balance between innovations and controlling developments, allowing creation to flourish while steering it towards helping to uphold human rights and societal values.
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Article
23 August 2025
Study of the Phenolic Content of Methanolic Extracts of Eryngium maritimum Plant Parts in Lattakia City, Syria, Using HPLC Technique
Ahmad Kara Ali, Yaser Hammad, Hazem Krawi, Yamam mouhammd
This study aimed to determine the total content of phenolic compounds in different parts of the Eryngium maritimum plant (Sea Holly), which grows on sandy and rocky beaches of the Sports City shore in Lattakia, Syria, during the autumn season. The phenolic content of each plant part was quantified using High-Performance Liquid Chromatography (HPLC). The total percentage of polyphenols ranged from 3.44% to 17.69%. The results revealed that the roots contained the highest content of phenolic compounds in the methanol extracts compared to other parts of the Sea Holly plant, followed by fruits, leaves, seeds, and stems. Chlorogenic acid was found to have the highest percentage of phenolic content in the plant parts. This research highlights the medicinal importance of Eryngium maritimum due to its richness in various phenolic and flavonoid compounds of medicinal and pharmaceutical significance.
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Article
23 August 2025
Integrating Deep Learning and Expert Systems in Negotiation Support: A Framework for Body Language Recognition to Augment Decision-Making Via Non-Verbal Cues
Majd Ali
This study develops an AI-driven expert system to improve negotiation outcomes by analyzing body language, a key but often ignored aspect of human communication in decision-making. The proposed system integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to serve as a knowledge-driven inference engine. By analyzing skeletal keypoint data, it classifies ten negotiation-specific behavioral cues, including weak stance, strong stance, overconfidence, interest, and openness. This approach enhances traditional rule-based negotiation models by augmenting them with data-driven insights, demonstrating the system's potential to improve decision-making processes. Translating body movements into behavioral insights mimics human expertise to help resolve conflicts or biases during group discussions. A custom dataset, created with input from negotiation experts and using skeletal data, supports the model. Early tests show 99% accuracy, proving its potential to enhance traditional negotiation tools with real-time, adaptive advice. However, challenges like cultural differences in interpreting gestures and data biases need addressing. The framework integrates into Negotiation Support Systems (NSS) as a ``smart advisor'' that learns over time and explains its reasoning, building user trust. Blending technical precision with psychological understanding advances AI systems that better grasp human interaction. Future steps include adding voice or eye-tracking data and testing the system across diverse cultural settings to ensure broader applicability in high-stakes scenarios. This work bridges technology and human behavior, aiming to create AI tools that foster collaboration in complex negotiations.
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