Al-Qadisiyah Journal for Engineering Sciences

In this review article, various cutting-edge strategies are addressed that aim to enhance the mechanical and
functional properties of Biocompatibility metal, which are manufactured using additive manufacturing techniques,
in particular for biomedical applications such as implants and prostheses. Additive manufacturing has several
advantages, including time saving and cost reduction, especially in small product manufacturing processes and
prototypes, freedom of design for complex shapes that are difficult to achieve by traditional methods, the advantage
of reducing waste and material waste, the possibility of customizing products to order, enhancing sustainability and
reducing environmental impact, and others. It can be said that most of the previous studies focused either on the
biological properties of biometallics that were manufactured using addition techniques or improving mechanical
properties, while comprehensive strategies that integrate the two aspects together have not been reviewed, and this
article shows how to achieve synergistic improvements between the two performances. Any modern integrative
revision combines various strategies (alloying, microscopic, surface, computational).

This paper presents a comprehensive study on the structural synthesis and classification of planetary gear mechanisms (PGCMs), which combine the advantages of planetary gear trains (PGTs) and cam mechanisms to produce precise, intermittent, or variable-speed motions. These mechanisms are particularly suitable for high-performance industrial applications, such as indexing tables, path generation systems, and rigid body guidance. The work begins by detailing the kinematic and structural characteristics of PGCMs and identifying possible configurations based on degrees of freedom, link types, and joint arrangements. Using graph theory, rooted graph representations are developed to systematically enumerate two-degree-of-freedom PGCMs, up to seven links. A spanning-tree-based synthesis method is used to generate candidate structures, followed by genetic compatibility analysis to identify viable PGCM configurations. The proposed method produces a set of functionally and structurally distinct PGCMs, including a novel five-link mechanism that achieves precise path generation with minimal complexity. This study not only enhances the theoretical framework for PGCM design, but also provides a practical basis for its application in modern mechanical systems.

This article delves into the intricate dynamics of groundwater flow within duct systems, examining both critical and sub-critical flow conditions. Employing mathematical models, sophisticated potential methodologies, numerical simulations, and flow net analysis, the research investigates the behaviour of the phreatic surface under varying flow coefficients m and slope angles θ. Noteworthy discoveries include the significant influence of the flow coefficient on the curvature and deflection of the phreatic surface, with higher m values resulting in steeper slopes. Additionally, the study emphasizes that changes in slope angle θ impact the interface's shape, leading to variations in flow depth. Innovative visualizations incorporating streamlines and velocity potential contours offer insights into flow patterns, recirculation zones, and potential turbulence areas. These critical findings supply essential insights for enhancing environmental strategies, optimizing water resource management, and improving the efficiency of fluid systems. The study emphasises how important it is to use flow net analysis and thoroughly investigate critical and sub-critical flow scenarios in order to handle issues related to groundwater management and sustainability. Stakeholder can enhance their capacity for fluid system optimization by applying these analytical tools, leading to improved environmental outcomes and informed decision-making.

Keywords

Generally, the production processes of citronella (Cymbopogon Nardus) oil refining in Aceh are traditionally carried out, making them less efficient in resource usage. Every activity along the citronella supply chain has an environmental impact due to raw materials, water, and energy resources. Therefore, this study aimed to assess the environmental impacts caused by activities along the citronella oil supply chain based on a life cycle assessment framework. We compared two production process methods, namely a distillation system with one boiler for one kettle and one boiler for three kettles. The environmental impacts measured include greenhouse gas emissions, global warming potential, land use, and air acidification. Research stages involved determining goal and scope, data collection, environmental impact measurement, interpretation, and proposals for improving the production process. The results showed that the production process of a single boiler system for three kettles had an environmental impact lower than the other method. Furthermore, the distillation process and solid waste combustion have a significant impact on the environment, hence they need to be improved. This life cycle assessment of the citronella oil supply chain is the basis for enhancing the production process to reduce environmental impact.

Climate change, workforce shortages, and sustainability requirements create serious obstacles for oil palm farms, which contribute $65 billion annually. Automated monitoring solutions are very important because traditional manual tree counting techniques, which are widely used in the sector, have counting error rates of 15-25% and require significant human resources. In this comprehensive review, deep learning applications, specifically, convolutional neural networks (CNNs) for oil palm tree detection and counting are evaluated. Performance, constraints, and realistic deployment pathways are examined. Publications from 2016 to 2025 that focused on oil palm recognition using deep learning with quantitative measurements were found using a literature search across Scopus, Web of Science, IEEE Xplore, and Google Scholar. Architectures, dataset properties, and performance metrics were recorded using data extraction. Malaysia is at the forefront of cooperative networks that span 22 nations, according to an analysis of 47 datasets. With mature trees, modern CNN architectures improved with YOLO frameworks achieve >95% detection accuracy; nevertheless, for young trees, they show notable degradation (87.2% vs. 96.8% mAP). Cross-regional generalization (21.9 percentage point accuracy degradation), processing demands (450-650 ms inference), and financial obstacles are important obstacles. Real-time viability is demonstrated by edge-optimized models, which achieve 98.6\% accuracy with 80 ms inference. Geographic bias (68% Malaysian, 23% Indonesian) and restricted public availability (8%) are revealed by dataset analysis. Deep learning can significantly improve oil palm management by 15-20% compared to conventional techniques. Widespread adoption requires standardized benchmark datasets (10,000+ images), transfer learning techniques (<500 images per region), edge-optimized architectures (<100 ms inference), and phased deployment (10-50 hectare pilots). CNN's convergence with precision agriculture positions the industry for comprehensive digitalization while addressing sustainability and labor challenges.

his work explores the use of photoelectrocatalysis (PEC) as one of the most advanced oxidation processes (AOPs) for petroleum refinery wastewater (PRW) treatment. A photoelectrochemical reactor equipped with a Gr/Bi-Ni-Sb-SnO2 photoanode and UVC irradiation was designed to enhance the degradation of pollutants. The effects of key operational parameters, including current density, pH, and anode rotation speed, on the treatment efficiency were systematically evaluated. The results confirmed that increasing the current density provided better chemical oxygen demand (COD) removal, while increasing pH above 5 results in a decrease in COD removal. In addition, increasing the anode rotation speed improved mass transfer and pollutant decomposition up to 200 rpm; after that, no significant improvement was observed, with a slight decrease in COD removal. According to the results, the best performance was achieved at a pH of 5, a current density of 6 mA/cm2, and a rotation speed of 200 rpm, achieving a COD removal efficiency of 86.2% in 90 min with an energy consumption of 110.34 kWh/kg-1 COD. Kinetic studies confirmed that the degradation of COD over time exhibited pseudo-first-order kinetics, with an R2 of at least 0.999. Considering all factors, the results demonstrate that PEC technology, using Gr/Bi-Ni-Sb-SnO2 Photoanode, offers a low-energy, sustainable, and facilitating method for the degradation of complex petroleum-derived pollutants. This study highlights the potential of PEC as a practical alternative to conventional industrial wastewater treatment methods.

The current work investigates the application of deep learning methodologies for detecting and classifying tumors in mammograms. A comprehensive analysis was conducted by evaluating various pre-trained neural network architectures to identify the model that delivers optimal performance. Among the tested architectures, Residual Neural Networks and Densely Connected Convolutional Networks were explored extensively. Data preprocessing, including data augmentation, was a critical step due to the limited availability of public medical imaging datasets. This process ensured diversity in the data and improved model robustness. This paper evaluates deep-learning models for mammogram tumor detection. Experiments without the AdamW optimizer and horizontal flip showed overfitting and low precision (below 40\%). Densely Connected Convolutional Networks achieved high precision but exhibited overfitting with noisy validation loss curves. Adding AdamW and horizontal flip reduced overfitting but lowered overall performance. In classification, the model detected tumors in 9 out of 16 images, showing potential but requiring improvement. The model's ability to detect calcification tumors enhances robustness. YOLO network metrics were modest, reflecting the task's complexity, but results were acceptable for tumor classification challenges.

AR technique is one of the innovative techniques of our time, which is increasingly used in the field of architecture at several levels and for multiple purposes. One of these is to enhance public participation in architectural design in an easy and understandable way. Due to limited attention to this emerging technique in our local context, including the academic one in Iraqi universities, and the scarcity of research contributions addressing it, this paper explores the concept of AR, its utilizations in architecture, and its role in promoting public participation in design. Also, it involved developing a mobile AR app " BUMAR " and testing it in real-world settings, all with the goal of introducing this technology and exploring its potential to facilitate and achieve public participation in design. To achieve this, a proposed virtual model was created as a hypothetical building for the Petroleum Engineering Department, intended to be built. The model was exported to the app, which was shared on social media for the target audiences. The app was tested in displaying and evaluating the model, with experiments conducted over several days by students, faculty, and others. This was accompanied by a questionnaire to gather opinions on BUMAR 's effectiveness, specifically, and the importance of the AR technique in achieving understanding and interaction with the proposed design and its role in facilitating participation and expressing opinions. BUMAR achieved good results, as indicated by the questionnaire results showing acceptance, satisfaction, interaction, interest, requests for further development of the app, and willingness to participate in future augmented experiments. This supports the claim of the importance of AR technique and the success of BUMAR in explaining it to users, suggesting further development of the app and its use in evaluating real construction projects in the future.

Keywords

Assessment of bone age, which represents the development and maturity of bones. It helps treat various pediatric conditions and address legal issues. Conventional bone age assessment is a complex and laborious procedure that is susceptible to inconsistencies between different reviewers and within the same reviewer. Artificial intelligence is a new automatic, accurate, and fast method used to evaluate bone age from X-ray images. In this work, a new network design based on AI methods is proposed. This network is based on the dataset obtained from the Paediatric Bone Age Challenge organized by the Radiological Society of North America. The collection comprises 12,600 radiological pictures of left hands, each labeled with the patient's bone age and sex. The design involves two steps: first, using a Faster R-CNN mask by training the ResNet50 model to select regions of interest and then entering the selected regions into three models (Inception v3, GoogleNet, and ResNet50) for regression-based bone age estimation. The results from these models vary based on their internal structure. ResNet50 yields a mean absolute error (MAE) of 10.6 for males and 9.5 for females. Inception v3 has an MAE of 7.5 and 8.3 for Males and females, and GoogleNet has an MAE of 8.4 for males and 9.2 for females. These models can enhance the precision and effectiveness of bone age prediction.

The current study's objective is to use Diesel-RK simulation software to numerically assess the effects of employing waste plastic oil blends on the thermal characteristics of diesel engines. Each autonomous zone's governing equations are solved using the multi-zone combustion model. Six distinct volumetric mixes were used to analyze the engine's characteristics of waste plastic oil (10%, 20%, 30%, 50%, 70% and 100%) as a comparison to the standard diesel case. The data collected showed a slight decrease in pressure and heat release for all waste plastic oil blends compared to pure diesel fuel. The Sauter mean diameter of droplets increased by 0.91%, 1.85%, 2.68%, 4.35%,5.74% and 7.1% for 10%, 20%,30%, 50%, 70%, and 100\% waste plastic oil (WPO), respectively. The slightly lower cetane number of WPO compared to fossil diesel resulted in a longer ignition delay, resulting in a slightly later combustion start. Brake specific fuel consumption (BSFC) increased by 2.2%, 4.1%, 5.86%, 9.23%, 15.14%, and 15.960% for 10%, 20%,30%, 50%, 70%, and 100% WPO, respectively, because of differences in density, viscosity, and heat content, respectively. A significant drop in the Bosch Smoke Number (BSN) was observed with 30% and 50% WPO, reporting reductions by 2.86% and 4.55%, respectively. The lowest increase in particulate matter was 1.96% and 2.87% for 30% and 50% WPO biodiesel blends. A higher biodiesel content resulted in lower NOx emissions compared to diesel. The findings suggest that 30% WPO is the optimal blend recommended for use in a diesel engine without alteration, which aligns with the results from other studies.

This study presents a simulation of electricity generation systems utilizing solar energy, employing TRNSYS and EES software to address the energy needs of isolated areas in Algeria. Two solar energy systems were compared: the CSP-ORC and the Photovoltaic, analyzed across three regions: Adrar, Illizi, and El-Bayadh. The CSP-ORC system was enhanced by incorporating components for optimizing the operation of the pump within the ORC, as well as integrating a solar photovoltaic system with battery storage at a capacity of 50 kW. The estimated electrical power generated by the studied systems is approximately 1 MW. We compared the technical performance of a 1 MW s-ORC system with thermal energy storage against that of a solar photovoltaic system of the same capacity. This analysis underscores the viability of both CSP-ORC and PV systems for electricity generation in isolated arid regions of Algeria. However, the superior performance of the PV system, particularly during the winter months, suggests that while CSP-ORC systems are promising, they may require further enhancements or integrated solutions (such as hybrid systems) to improve output and reliability across all seasons. The economic analysis highlights the cost-effectiveness of PV systems, which have lower investment, maintenance costs, and LCOE than CSP-ORC. The lowest LCOE (0.0311 €/kWh) and fastest payback (3.62 years) were observed in Illizi for PV, while CSP-ORC had the highest LCOE in El-Bayadh. Environmentally, PV reduces 55.2 tons of CO2 emissions in Illizi, whereas CSP-ORC, generating more electricity, prevents 84 tons. Both systems significantly cut emissions compared to diesel generators.

In Oman's hot-arid climate, school buildings often experience elevated indoor temperatures due to inefficient building envelope designs, resulting in a heavy reliance on air conditioning systems and high energy consumption. To address this issue, this study aims to evaluate indoor air temperature in typical Omani school buildings and determine how modifications to key envelope components, such as walls, roofs, and glazing, can improve indoor temperature conditions and enhance thermal comfort. A mixed-method approach was adopted, combining field measurements with computer simulation. Indoor temperature and relative humidity were monitored in selected classrooms, while DesignBuilder software was used to model existing conditions and test alternative envelope configurations. This integrated method is appropriate for quantifying the thermal performance of building envelopes under realistic climatic and operational settings. Results showed that baseline classrooms reached average indoor temperatures of 33 °C in summer and 24.5 °C in winter, exceeding recommended comfort limits during hot periods. The most effective design alternative reduced indoor temperature by up to 2.3 °C and increased the number of hours within the comfort range by 29%. The study concludes that optimizing wall insulation, roof composition, and glazing selection can significantly improve indoor thermal comfort and energy efficiency in Omani schools, providing practical design guidance for future educational buildings in hot-arid climates.

n today's complex and rapidly evolving world, organizations are increasingly confronted with a diverse workforce in terms of culture, language, generation, religion, gender, and ethnicity. While such diversity presents certain challenges, it also offers valuable opportunities to enhance organizational performance. The present study focuses on the construction industry in Iraq and aims to analyze the impact of HRDM on its consequences, with particular emphasis on the mediating role of HRMD strategies. This research adopts an applied and sequential exploratory mixed-methods approach (qualitative–quantitative). In the qualitative phase, key themes were identified through interviews and thematic analysis using MAXQDA software, leading to the development of an initial conceptual model. This model was then tested in the quantitative phase using a structured questionnaire and data analysis through SmartPLS, applying structural equation modeling (SEM). The findings revealed that HRMD strategies serve as significant mediators between HRMD practices and their consequences, and that transformational approaches can foster improved performance and team cohesion. The proposed conceptual model offers a strategic framework for policymaking and implementation of HRMD programs in the construction sector and similar industries across developing countries.

The sixth generation (6G) networks represent the revolutionary processes in the field of wireless networks, such as ultra-dense network (UDN) frameworks, multi-dimensional connectivity, and network automation procured by AI. Nevertheless, the high rate of small cell and heterogeneous network environment proliferation poses severe challenges in handover management that result in higher signalling overhead, latency, and service interruptions. This review paper investigates the latest handover management solutions in 6G UDNs with some of the most significant challenges being mobility prediction, resource, and security constraints. We especially examine the new solutions, such as machine learning (ML)-based mobility prediction models, Long short-term memory (LSTM) and gated recurrent unit (GRU), reinforcement learning (RL)-based handover decision models, and split federated learning (SFL) of privacy-preserving optimization. Moreover, we will look at network-slicing integration and blockchain-based security solutions as an effort to ensure an efficient and dynamic handover procedure. The paper gives a methodological future study roadmap to optimisation of handover in ultra-dense 6G networks, which synthesizes existing approaches with research gaps identified. These results point to the necessity to optimise the interactions between layers and coordinate network efforts by using artificial intelligence and the proactive handover paradigm to provide seamless, low-latency, and energy-efficient mobility management in future next-generation wireless networks.

This work examines the two-dimensional continuous flow of a Casson-Williamson fluid over a stretched surface under a Darcy-Forchheimer permeable medium. Several elements can affect the flow, including Joule heating, radiation, chemical reactions, thermal sources, electric field influences, and magnetic field influences. Nonlinear partial differential equations articulate the fundamental equations governing the system's dynamics in this physical model. We simplify these equations to a system of nonlinear ordinary differential equations by applying requisite changes. The Keller Box technique is utilized to simplify this collection of ordinary differential equations. Velocity, temperature and concentration graphs are plotted. The velocity profiles decline with a rise in the Casson parameter, magnetic parameter, porous parameter, Weissenberg number, and velocity slip parameter. Still, the electric field parameter diminishes when the speed slip constraint is enhanced. This study primarily examines several local properties, including the skin resistance coefficient, the Nusselt number, and the Sherwood numbers. We compare our results with the current literature by computing the skin friction coefficient for different inputs of the Casson factor. Prior studies have yielded results that are fairly congruent with this one.

With the advances in technology, sustainable renewable energy implementation in many applications is becoming
important, such as in wireless sensors. Vibration energy harvesting can convert environmental mechanical
vibration into useful energy. Honeycomb core structure has shown promising results in cantilever dynamic
vibration. However, there is a limited study on its structural variation. This research focuses on different beam
thickness and angle orientation of 3D printed hexagonal core structure to improve vibrational cantilever energy
harvesting. The beam thickness was studied at 4 mm, 6 mm, and 8 mm with re-oriented honeycomb core at
0°and 30°. The test rig was modelled as a mass-spring system. The model was simulated for modal analysis and
power generation with piezoelectric. The natural frequencies and vibration amplitudes were compared between
simulation and experimentation with a vibrational shaker machine and a data acquisition device. The results
showed that the 30°honeycomb core has better dynamical amplitudes and energy harvesting as compared to the
conventional 0°honeycomb core beam. The different beam thicknesses affect the beam stiffness and, therefore,
vary the vibrational amplitude and generated power. The 30°core with lower thickness was found to have the
highest power generation due to the better distribution of stress along the beam

In this search article, an integrated hybrid treatment system combines adsorption activated carbon (AC) and
electrocoagulation (EC) to remove lead metal from produced water. The novel used a batch reactor design
that facilitates synergistic connections between the two treatment processes. The process was optimized under
optimized conditions using Response Surface Methodology (RSM) with a Box-Behnken (BBD. The optimization
process conditions include the electrolysis time, pH, activated carbon dose, and current. Lead removal efficiencies
reached 88.4% for activated carbon alone, 95.4% for electrocoagulation alone, and 99.8% for the hybrid method
at 45 min electrolysis time, pH 3.0, 1 gm of activated carbon dose, and 1 Amp. The results indicate that the
combined scheme significantly enhances the lead metal removal rates from the produced water compared to the
individual process methods, providing an active solution for the production of clean water. The hybrid process
offers an eco-friendly, cost-effective, and sustainable approach to removing metal pollutants from water resources.

This study investigates the impact of side friction (SF) on traffic movements for selected urban roads in Hilla,
focusing on how static and dynamic friction elements influence traffic flow and speed. The primary SF elements
observed include parked vehicles and pedestrian movements with more dynamic interactions, such as erratic
road crossings. Traffic data collection was conducted on four distinct Hilla streets using elevated video cameras
that recorded traffic volume, vehicle speed, and SF events. Data recording occurred on Sundays, Mondays, and
Wednesdays, both in the morning and in the evening. An analysis of the recorded data revealed significant
variations in traffic behavior due to SF elements. On streets with high commercial activity and educational centers,
a notable reduction in average vehicle speed was found to be correlated with periods of high SF activity. During
the busiest hours, the speed reduction reached 51% in comparison to periods of low SF activity. This substantial
impact underscores the sensitivity of urban traffic flow to side friction factors, particularly during peak hours.
Dynamic SF events, such as pedestrians crossing roads erratically, had the highest frequency during evening
hours, aligning with the end of business and school activities

Recently, software cost estimation has become a more important issue in the software project development cycle,
software quality, and decision-making management. In view of the common problem of inaccurate and difficult
cost estimation in the software industry, in this article, the proposed bagging method is one of the ensemble
learning methods to estimate the cost of software development. Five algorithms were used as the basic models:
Random Forest, Decision Tree, AdaBoost, K-Nearest Neighbor, and Gradient Boosting, compiled using the
bagging method. The proposed method was applied to a data set (ISBSG). The contribution of the paper suggests
a more accurate method compared to previous studies, and applying it to high-quality data, which was prepared to
obtain more accurate results when applying the proposed model, which showed its superiority over individual
models in estimating the cost of software development. The results showed high accuracy in R
2 prediction in ratio
(97%) and gave a lower error rate (MMRE: Mean Magnitude of Relative Error) compared to previous studies in
ratio (0.1). This indicates its accuracy in prediction is closer to the real cost, where the RF model was the basic
estimator model in this method, because it surpasses the main models that were used in the proposed method. The
KNN model gave the lowest accuracy ratio (73%) among the main models when trained on the ISBSG datase

In this study, the effects of ground granulated blast furnace slag (GGBFS) stabilization of subgrade soil on flexible
pavement layer thicknesses and initial construction costs were investigated. For this purpose, Proctor compaction
tests were conducted on bentonite (BT) soil samples prepared by adding GGBFS at rates of 5%, 10%, 15%, and
20% by weight. Following these tests, unconfined compressive strength specimens were prepared and tested
after curing periods of 3 and 7 days. The results indicated that the highest compressive strength was obtained
in mixtures containing 15% GGBFS. In addition, untreated soil and soil samples stabilized with 15% GGBFS
were subjected to the California Bearing Ratio (CBR) test after 3 and 7 days of curing. It was determined that the
CBR values of the samples containing 15% GGBFS increased by 2.09 and 2.85 times, respectively. Using the
CBR values obtained, pavement layer thicknesses were calculated based on the AASHTO 1993 flexible pavement
design guidelines, and cost analyses were performed. The results showed that stabilization with 15% GGBFS
reduced the initial construction costs by 23.03%.

When reliable crash data is unavailable, Surrogate Safety Measures (SSMs) become valuable tools for traffic safety
research. This study assesses traffic safety at signalized intersections, explicitly examining how traffic operations
such as stopped delay time and Level of Service affect hourly traffic conflicts using surrogate measures. Three
four-leg signalized junctions in Karbala City were selected. Data on traffic volume and conflicts were collected
using video cameras managed by the Karbala Traffic Police. The VISSIM software was utilized for traffic operation
analysis, which then served as input for the Surrogate Safety Assessment Model (SSAM) software. The results
show that simulation-based hourly traffic conflicts increase as stopped delay time increases, with a correlation
coefficient (R
2
) ranging from 0.7533 − 0.9127. Specifically, stopped delay values ranged from 117sec. at the
Sayed Jawda intersection (considered high-risk) to 79sec. at the Al-Sofaraa intersection (considered low-risk).
Rear-end conflicts accounted for the highest percentage of total conflicts, reaching 63%, 58%, and 66% at the
respective intersections. This high percentage is attributed to rear-end conflicts predominantly occurring within
the same approach in the traffic queue. This research provides valuable insights into evaluating safety at signalized
intersections in urban areas and is an additional reference for future road safety studies. This study differs from
previous research by emphasizing the correlation between stopped delay time and the frequency of hourly traffic
conflicts using the SSAM software. This study provides a more nuanced understanding of the relationship between
operational characteristics and safety at signalized intersections

As global energy consumption increases, there is an increasing desire to investigate nanotechnology-based
approaches for enhancing oil recovery. Integrating nanostructures has shown an augmentation in the rate of
crude oil recovery. This study used Iraqi crude oil obtained from well-known fields in Basra, such as Majnoon,
Zubair1, North Rumaila, Nahran Omar, and West Qurna1, which feature unique attributes. Titanate nanoparticles
(TiNP) and titanate nanotubes (TiNT) were employed as additives to enhance various properties of Iraqi heavy
crude oil, including viscosity, API, specific gravity, surface tension, and interfacial tension. These additives were
used either as enhancers or for advanced treatment purposes. The research examined the influence of various
concentrations of nanoparticles (ranging from 0.01 to 0.1 wt%) on the efficiency of systems developed for crude
oil characteristics. TiNT exhibited outstanding performance in the face of a 37% fall in the viscosity of Majnoon
crude oil, surpassing the performance of other fields to a greater extent. The surface tension of Zubair1, North
Rumaila, Nahran Omar, and West Qurna1 fields fell by 15.5%, 14.5%, 15.1%, 18.6%, and 15.7%, respectively.
Similarly, the addition of 0.1 wt% of TiNT resulted in reductions of 26.07%, 19.7%, 23.4%, 23.6%, and 19.6% for
the aforementioned areas. The impact of TiNT on IFT was reduced by up to 22.3%. However, TiNP demonstrated
more efficacy than TiNT in decreasing the specific gravity by 4.3% in crude oil from the Majnoon field. The
inclusion of titanate nanostructures resulted in the following API values: 23.5%, 19.5%, 17%, 22.4%, and 21.2%
with TiNP; and 15.15%, 14.8, 15.4%, 15.6%, and 8.2% with TiNT at a concentration of 0.1 wt%.

Diagnosing attacks in wireless sensor networks (WSN) is a crucial concern in cyber security that impacts diagnosis
models’ accuracy because of data imbalance and outliers existence. The present paper aims to design and perform
a novel strategy for efficient attack diagnosis in WSN that applies DL models’ integration (DNN and CNN+LSTM)
and Bayesian optimization. The present study checks concerns on data analysis such as various attack complexities
and instances’ imbalance. Such concerns have been mentioned by applying techniques such as MinMax scaling,
data balancing with ADASYN, polynomial feature engineering, and outlier elimination with DBSCAN. The
outcomes of the experiments illustrate that the DNN model obtained an F1-Score of 85.71%, and appeared an
accuracy of 99.14% which is an important development across conventional techniques. Such results illustrate
that was presented technique could develop WSN security and possesses high capability for various attacks’ kinds’
diagnosis

Understanding the near-field distribution of antennas has become more significant since it directly effects radiation
performance, energy absorption, and safety in current wireless applications, especially when antennas operate
close to the human body. This research explores a Meander Line Antenna (MLA)’s electric field patterns within
in-body contexts, emphasizing propagation loss, energy conservation, and electric field deterioration in different
physiological tissues. This investigation examines the process of signal weakening in embedded antenna use
by assessing the variations in electric field strengths among Muscular tissue, skin, and fatty layers at diverse
conductivity levels. By utilizing results from FEKO simulations, the research demonstrates the absorption losses
associated with the electric field distribution across tissue surfaces. The findings indicate these tissue components
exhibiting higher electrical conductance, such as muscle and skin, experience faster degradation when subjected
to electric fields compared to fat, which possess lower dielectric constant. These results provide crucial insights
into effectively optimizing MLA structure for medical purposes, ensuring reliable wireless signal transfer in
physiological environments.

The stability analysis of Marangoni magneto-convection (MMC) is investigated in a two-layer system consisting
of an electrically conductive fluid-saturated porous layer overlain by an identical fluid layer, incorporating variable
heat sources and a uniform magnetic field. The upper fluid surface is free, allowing surface-tension-driven
convection, while the lower porous boundary is rigid. Two thermal boundary conditions are examined: (i)
adiabatic–adiabatic (A–A) and (ii) adiabatic–isothermal (A–I). The governing equations are solved analytically
using an exact method to obtain the thermal Marangoni number, an eigenvalue, as a function of depth ratio,
Darcy number, Chandrasekhar number, internal Rayleigh numbers, wave number, and thermal diffusivity ratio.
Graphical results show that the onset of MMC can be either advanced or delayed by appropriate choices of
depth ratio and thermal boundary conditions. The novelty of the present work lies in deriving closed-form
expressions for a composite fluid–porous system with simultaneous consideration of variable internal heat sources
and magnetic effects under dual thermal boundary conditions, and demonstrating how depth ratio and thermal
boundary conditions can be strategically tuned to either advance or delay the onset of instability.

Creating sustainable environments has become an urgent goal for most communities all over the world. This is
because creating a sustainable environment would lead to improving a community’s life socially, environmentally,
and economically. To do so, urban sustainability indicators need to be incorporated into the urban design process,
beginning with the architecture teaching syllabus. This study aims to find out how students’ projects in the urban
design course could consider sustainability indicators when developing built environments, including campuses,
and how architecture students’ projects can consider the role of physical character in achieving sustainability when
developing campuses. Therefore, students’ projects designated for developing the main campus of the University
of Mosul were the case study of this research. Explorative graphical analysis was adopted as the study method.
Findings highlighted key characteristics of sustainable urban design that are incorporated into these projects and
the way to do so

This work creates a simulation-based scheme to optimize Proton Exchange Membrane Fuel Cells (PEMFCs) in the
maritime sector, focusing on the interim functions of thermal, water, and hydrogen pathways in defining efficiency
and sustainability. Findings indicate that although peak efficiencies are close to 0.90, dependable operation is
limited to 0.84 -0.87, better than the hydration limit of 0.82 and worse than the thermal instability threshold of
0.88. The best hydrogen usage is between 70 -82 because lower percentages were tiger and higher percentages
were almost 2 times higher rates of degradation when 0.006 V/1000h (harbour) and 0.012 V/1000h (sprint) were
used in respectively. Mode comparison proves cruise operation at 91% net efficiency, 13% auxiliary demand,
and 27% performance 27% Excellent, 36% Good, 3% Fail, and sprinting is 86% efficiency and 16% auxiliaries
with 27% Fail/Poor results. This study outsmarts the other studies by being the first to establish quantitative
safe operation envelopes of maritime PEMFCs and provide a workable blueprint of sustainable deployment by
integrating radar, multi-panel, and 3D threshold-based analysis

This study proposes a conceptual urban policy framework for planning and designing urban streets that promotes
people-oriented development. We utilized a scoping review and content analysis to map the key factors, indicators,
methodological approaches, and tools within research on people-oriented development. The search was conducted
across the Scopus and Google Scholar databases to ensure broader coverage of relevant peer-reviewed literature
published between 2013 and 2023, resulting in 210 manuscripts. After three rounds of inclusion and exclusion
criteria, we ended with 83 manuscripts. We also conducted a content analysis of a selection of 24 manuscripts
for deep qualitative analysis of insights about people-oriented development. The findings highlight a strong
relationship between people-oriented street design and improved community satisfaction, social interaction,
and overall well-being. The review identified twenty-four methodological tools, which were synthesized into
a five-step action plan: pre-assessment, data collection, analysis, evaluation, and the development of a priority
map. This framework offers urban planners and policymakers an evidence-based pathway for implementing
integrated, people-focused street design strategies. The study’s findings strengthen the conceptual understanding of
people-oriented development. The insight gained from the scoping review supports the creation of more inclusive,
livable, and human-centered urban streets

Recently, a proliferation of techniques capable of replicating sounds has emerged, encompassing manipulated
recordings, synthesized audio, and deepfake technologies. These advanced methods for generating artificial sounds
have inadvertently given rise to a multitude of issues, prominently including the dissemination of misinformation,
propaganda, and significant reputational damage. This article addresses this critical problem by proposing the
application of a Convolutional Neural Network (CNN) model designed to predict the authenticity of a given sound.
For feature extraction from the audio, Mel Frequency Cepstral Coefficients (MFCC) are utilized. To rigorously
assess the robustness of the proposed model, the intricate combinations of these extracted features were further
analyzed using derivatives of the MFCC features. The Fake-or-Real (FoR) dataset was used to train and test the
proposed model. The Accuracy, F1-score, Precision, Recall, and Loss are used as metrics to evaluate the model’s
performance. The model performed well, with an accuracy of 99.64%. The proposed model was comprehensively
evaluated by systematically varying the extracted features through different derivative orders, where the accuracy
decreased to 94.34%. The experimental results demonstrate the effectiveness of the model in accurately detecting
fabricated audio.

Distance measuring equipment (DME), which gauges the distance between an aircraft and a ground station, is
an essential navigational tool in aviation. However, because of instrument constraints, multipath interference,
and ambient conditions, DME measurements are frequently noisy and prone to errors. This study introduces a
framework that integrates machine learning (ML), sensor fusion, neural networks (NNs), and real-time processing
with the aim at enhancing the accuracy and reliability of distance estimation, with particular emphasis on regression
models. To boost robustness, the suggested system uses sensor fusion to combine DME data with inputs from
additional sensors, such as GPS and Inertial Navigation Systems (INS). The intricate correlations between sensor
inputs and actual distance are modelled by NNs because of their ability to produce precise predictions even in
the presence of noise and they provide a very accurate distance calculation with minimal latency. ML based
regression models further improve system reliability by detecting and correcting anomalies in the sensor data.
When tested in MATLAB and compared with standalone DME measurements, the proposed system shows higher
accuracy of distance estimation. In addition, the real-time sensor fusion ensures precise and timely outputs
for essential aviation applications. Using this method not only improves the DME system, but also provides a
scalable and flexible solution for different navigation and positioning systems in dynamic scenarios. The system
is measured based on significant metrics including mean squared error (MSE), peak signal-to-noise ratio (PSNR)
and signal-to-noise ratio (SNR).

The authors sincerely apologize for this oversight and for any inconvenience it may have caused. To address this matter, the authors wish to revise the Funding
Source section as follows:
Funding source:
This study was supported by Faculty of Architecture and Planning, Thammasat University Research Fund, Contract No. TDS 16/2566.