DIYALA JOURNAL OF ENGINEERING SCIENCES

Many types of e-health applications support smart cities in terms of health care. Every application is subject to requirements that must be met to be considered successful. In terms of system performance, the communication networks are responsible for meeting the requirements of e-health applications to transfer the data of applications successfully to their destinations with a focus on reliability, latency and packet loss. In addition, data protection against expected attacks is essential to maintain the data integrity of such applications. The integration of the healthcare system with promising information and communication technologies (ICTs) can lead to effective, personalised and accessible healthcare solutions, thus ultimately improving patient outcomes and the overall healthcare ecosystem. Meanwhile, sustainability has become one of the most vital conditions for any application in a smart city. In this context, this study explores the modern related literature to discover the types and the requirements of e-health applications and the available ICTs that meet these requirements. In addition, this study analyses the cybersecurity requirements related to e-health applications. Furthermore, this work engages the sustainability concept with e-health and ICTs to investigate the expected benefit of this approach. Finally, the work provides as energy consumption analysis for internet of medical things (MIoT) sensors to demonstrate the impact of energy-efficient methods used in e-health systems.

Recently, generative models, such as ChatGPT, have gained considerable attention because of their capacity to generate text almost identical to that produced by humans. However, ChatGPT raises several concerns, particularly regarding the integrity of academic work, the protection of personal information and security, the reliance on artificial intelligence (AI), the evaluation of learning, and the precision of information. Distinguishing between writing generated by machines and text that humans wrote is one of the most critical issues at present. The purpose of this literature review is to provide a comprehensive, up-to-date analysis of the most recent methods for identifying text that ChatGPT created. It examines more than 60 academic papers, especially research articles published after the model’s release in 2022, and analyzes state-of-the-art machine learning, deep learning, and hybrid approaches for detecting AI-generated text. The review categorizes detection methods into statistical models, transformer-based architectures, perplexity-based techniques, and human-assisted evaluation. The findings indicate that deep learning models, particularly the Robustly Optimized BERT Pretraining Approach (RoBERTa) and Cross-lingual Language Model with RoBERTa Architecture, have high detection accuracy (up to 99%), whereas traditional statistical methods exhibit limitations in distinguishing complex AI-generated content. This work recommends the use of machine and deep learning techniques and human reviewers in ongoing efforts to distinguish between AI-generated and human-written text. However, given the increasing sophistication and complexity of models, such as ChatGPT, detection techniques have to be continuously improved and innovated to ensure reliability and maintain the integrity of content across various sectors.

The Public Works Department (PWD) Strategic Plan 2021-2025 outlines ambitious targets for Building Information Modelling (BIM) adoption, aiming for 50% by 2021 and 80% by 2025. Reflecting this trend, the Malaysian construction industry is progressively integrating BIM, which offers quantity surveyors a more reliable method, particularly for developing cost estimates. However, BIM in Malaysian quantity surveying practices (QSP) has been slow, leading to limited overall use of BIM in their professional practices. Given the urgent need to meet national BIM adoption targets and improve construction project outcomes, there was a pressing need to explore BIM capabilities, challenges, and strategic solutions to bridge the adoption gap. Using a quantitative approach, questionnaires were distributed to quantity surveyors in Klang Valley, Malaysia, with 120 respondents participating. The findings showed high reliability based on Cronbach’s alpha and identified that BIM capabilities in refining cost plans with evolving design detail receive the highest ranking. The most significant challenge in conventional QSP was the absence of standardised documentation formats. Furthermore, early comprehension of BIM by top management was found to be the most effective strategy for BIM implementation in quantity surveying. A comparative analysis with other developing countries further supported these findings. Based on the results, a conceptual framework was developed to guide BIM adoption in the Malaysian quantity surveying profession. Overall, the research successfully meets its objectives and provides valuable insights for stakeholders on the potential benefits and challenges associated with BIM implementation in the future.

Traffic congestion remains a major challenge in urban areas due to the high cost, scalability issues, and inefficiencies of traditional monitoring systems. This study proposes an innovative, cost-effective traffic monitoring system utilizing Android-based Smart Roadside Units (RSUs) to detect vehicles and analyze real-time traffic data. The system leverages the You Only Look Once, version 8 (YOLOv8) model, enhanced with the Slicing Aided Hyper Inference (SAHI) algorithm to improve detection accuracy for small and distant objects. Field experiments were conducted using three Android device categories high, medium, and low-cost to assess detection accuracy across different distances. Results indicated that high-cost devices could accurately detect vehicles up to 500 meters away, whereas medium and low-cost devices exhibited reduced detection accuracy and range. The findings highlight the impact of hardware specifications and environmental conditions on system performance. The proposed approach addresses limitations of conventional traffic monitoring by providing an adaptable, open-source infrastructure that reduces hardware costs while ensuring real-time processing. Utilizing mobile devices enhances scalability and cost-effectiveness compared to traditional RSUs, which are expensive and hard to deploy at scale. Future research will integrate functionalities like pedestrian detection and vehicle tracking to further enhance smart transportation systems. This study demonstrates the feasibility of Android-based RSUs, offering a practical alternative to conventional methods and advancing intelligent traffic management solutions.

Cancer is a deadly disorder, which affected by a mixture of genetic illnesses and a diversity of organic anomalies. Colon and lung cancer has been arisen as dual of the foremost origins of disability and death in humans. The histopathological recognition of such diseases is generally the most significant module in defining the finest progress of action. Prompt disease recognition on both front significantly reduces the probability of death. Machine learning (ML) and deep learning (DL) approaches are employed to haste up cancer recognition, letting researchers to analysis a great amount of patients in a short period of time and at a low cost. This research presents an Optimizing Cancer Detection using whale optimizer with Deep Learning in Colon and Lung Cancer (OCDWO-DLCLC) model on Biomedical Images. The presented OCDWO-DLCLC technique makes use of biomedical images for the recognition of colon and lung cancer. To achieve this, the OCDWO-DLCLC system uses wiener filtering (WF) technique for noise elimination process. In addition, the OCDWO-DLCLC technique uses NASNet Mobile model for learning complex feature patterns. Also, the hybrid of convolutional bidirectional gated recurrent unit (CNN‐BiGRU) model was applied for classifying the existence of colon and lung cancer in the biomedical images. Eventually, the whale optimization algorithm (WOA) is used to optimally choose the hyperparameters of the CNN‐BiGRU model. To confirm the improved analytical outcomes of the OCDWO-DLCLC approach, extensive simulations are executed on benchmark dataset. The comparative outcome analysis displays the promising performance of the OCDWO-DLCLC method on the recent models.

Modern living and technological advancements demand high-power microchips, leading to an increase in chip surface temperatures. To mitigate this heat, porous metallic heat sinks are employed. This study presents a simulation of a high-power microprocessor (>10⁴ W/m²) encased in a porous metallic heat sink. The microchip measures 4 cm in diameter with a thickness of 5 mm. The governing equations are solved numerically using the finite element method, implemented in COMSOL Multiphysics software. This paper investigates the cooling performance of a microchip through natural convection, focusing on three key parameters: aspect ratio, porosity, and heat sink material. The study considers heat sink porosities of 0.2, 0.4, and 0.8; aspect ratios of 0.2, 0.5, 1, and 2; and heat sink materials including copper, aluminium, and steel. Results are presented as temperature and Nusselt number contours on the microchip's surface. The findings indicate that copper achieves the lowest surface temperature (41.58℃) and the highest Nusselt number (243.81) at an aspect ratio of 2. Increasing the aspect ratio enhances the heat transfer rate and, consequently, the Nusselt number. Conversely, steel exhibits the highest surface temperature (89.35℃), the lowest heat transfer coefficient (164.89 W/m·K), and the lowest Nusselt number (62.84).

Conventional hardening, sometimes, is not economically viable due to consuming additional costs and energy. This makes applying selective hardening on specific regions an attractive alternative for members subjected to local friction and wear. This work is devoted for applying cold working hardening on discrete values on regions of aluminum 6061-O alloy using nanosecond fiber laser of 100 W average power and pulse duration of 81 ns. An exaggerated plasma pressure resulted from ablation a thin coat layer on the metal surface was built in order to cause cold plastic deformation and increase the surface hardness of the alloy. The power density (Pd) and pulse overlap percentage (OV) were employed as working parameters. Due to the sensitivity of aluminium alloys to excessive heating, the criteria of the best outcomes were considered according to the objective of the study in supporting the hardness by strain hardening. The higher attained a significant increase in the hardness associated with less consumed optical energy, the most clean and flawless surface, observed by the high-resolution SEM images, which indicates hardening with cold work. The best hardness value is conducted at a Pd of 4.46 GW/cm2 reaching a hardness of 57.6 HV at an OV of 43% and 53.8 VHD at an OV of 77.3%. The X-Ray diffraction analysis (XRD) revealed a reduction of 56% in grain size compared to the original alloy and increase in the number of dislocations density by 378%.

A modelling of three interconnected areas based on Great Britain's (GB) power system for frequency control offered a wide range of stability analyses for both under and postgraduate studies. The system inertia was counted according to the generation amount for the current system and the year 2035. The areas were assigned according to the GB transmission boundaries. This includes the north zone which is above the B7a boundary, the South is below the B9 boundary, and the Midland is in between. Each area has an aggregated model of each generation for Gas, Coal, Hydro, Nuclear, wind, and others as well as an aggregated load. The wind farms were divided into offshore and onshore and did not participate in frequency regulation or ancillary services. Automatic Generation Control (AGC) was used in each area to regulate the area frequency according to the system frequency. Area Control Error (ACE) was used in the proposed model as the total summation of the area frequency error alongside the power deviation of the transmission lines (tie-lines) with other areas. The main goal was to evaluate the effect of power system stabilizers (PSS) on system stability under disturbances, such as three-phase faults and resonance conditions. Results showed that wide-band PSSs offer superior stability by effectually damping low-frequency oscillations, while Delta PSS established better performance in mitigating the impact of generator resonance. The outcomes highlight the importance of integrating modern PSSs in large generators to improve dynamic stability and reduce the risks associated with resonance.

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Southern Iraq, specifically Basrah, has a warm and arid climate. August has the greatest average temperatures in Basrah, with highs of 48°C. To overcome obstacles resulting from such major temperatures, Phase Change Materials (PCMs) are increasingly being used in building construction. This paper performed an experimental study to examine the energy efficiency of building walls that integrated with PCM under Iraq conditions. Additionally, the thermal performance of two different building walls was compared. The parameters investigated include the influence of heat gain, PCM temperature, and thermal load levelling (TLL) for both PCM and cement cases. Two test cases were exanimations. In the first case, the wall is made of blocks with cement filled holes. In the second case, the wall is made of blocks with PCM filling the holes. The outside conditions are controlled by using the hot water tank, and the cold-water tank represents the inside conditions for air conditioning space. The experimental analysis indicated that building blocks filled with PCM case achieve lower interior temperature and heat gain than cement case blocks. Furthermore, the peak heat flow was decreased by 75% when PCM-filled blocks were used instead of cement-filled blocks. The results revealed that PCM significantly minimize TLL by 42.6% as compared to cement. In conclusion, when the PCM is used to replace the cement, the interior thermal comfort improved and cooling energy use reduced.

This paper presents the design and performance evaluation of a Substrate-Integrated Waveguide (SIW) antenna fabricated using 3D printing technology, with a focus on optimizing its performance for future free-space applications. The proposed antenna integrates an SIW structure with a horn antenna concept, effectively combining the transmitter and receiver functions into a compact, monolithic substrate. This approach provides a miniaturized alternative to conventional free-space material characterization setups, leveraging SIW technology to replace bulky horn antennas with integrated structures. The study demonstrates the feasibility of compact free-space techniques for non-destructive testing, sensing, and electromagnetic material characterization applications. The study involves modeling and simulation using Computer Simulation Technology (CST) software, focusing on antenna performance in the G-band range (4–6 GHz). The fabricated prototype demonstrates resonant frequencies at 5.02 GHz and 6.0 GHz, with a scattering parameter below -10 dB and a well-defined radiation pattern, exhibiting sidelobes at -2.7 dB. The antenna is fabricated using biodegradable Polylactic Acid (PLA) material, reinforcing the potential for sustainable electronics while maintaining structural integrity and electromagnetic compatibility. While this study does not yet validate the antenna for material characterization, the results confirm its feasibility as a compact, cost-effective alternative to conventional free-space setups. This work lays the foundation for further optimization and experimental validation, advancing the role of 3D-printed SIW antennas in free-space applications.

This paper presents an optimal new method to design infinite impulse response (IIR) digital filters through implementing and comparing various multiplier architectures in order to opt the best approach among them. The reason behind focussing on the multiplier unit belongs to its essential role in the performance of the IIR filters. Direct form II is the realization form opted to characterize the suggested IIR filter which is coded, using Quartus II software and subdued to gate level simulation on field programmable gate array (FPGA) kit board. The proposed IIR filter, whose architecture includes the binary coded decimal (BCD) involved in it, has gained the best outcomes. The BCD multiplier is compared to other multipliers such as: array multiplier, parallel multiplier with two-splitting of Booth and parallel circuits, Vedic multiplier and Mux-Multiplier which are implemented with the Cyclone IV GX FPGA kit platform. It was observed that BCD-multiplier has recorded the smallest logic elements (188) along with the fastest operation speed (282.8 MHz) and the lowest delay time (7.89 ŋSec) compared to other approaches. The IIR filter circuit designed with the BCD multiplier is programmed and implemented on the Cyclone IV GX FPGA kit platform. Depending on the obtained results, the IIR filter based BCD-multiplier has promising features that make it more attractive than IIR systems based on other multipliers. Moreover, these features make the IIR filter widely exploited in diverse electronic systems and devices such as: images and biomedical signal processing applications, communications and radar systems.

Air pollution is a significant global concern that is continually increasing and threatening both the environment and human health. Air pollution is the principal factor leading to the deterioration of Indoor Air Quality (IAQ) in buildings. Carbon dioxide (CO2) significantly contributes to indoor pollution intensifying, primarily from human activities. The demand for effective IAQ systems has increased due to the necessity for sustainable building development. The artificial intelligence (AI) models presented in this work utilized Machine Learning (ML) and Deep Learning (DL) methodologies to train the available dataset. This dataset was collected by the indoor sensors in Shanghai from November 2016 to March 2017 to predict CO2 concentration and obtain pertinent information. The accuracy and the result of Ml and DL algorithms may differ depending on the datasets used and the algorithms' suitability for the specific data and application domain. Therefore, a significant benefit would be achieved by finding the best-fitted ML and DL models concerning the actual datasets and the application area. This necessity was fulfilled through an intensive review of the already existing DL and ML models. This analysis aims to implement the specified models and assess the efficiency of their prediction by computing several performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Median Absolute Error (Median AE), and Coefficient of determination (R2). Among implemented models, the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have performed better results in forecasting IAQ.

Traditional soap-based greases such as lithium and calcium are no longer able to meet the requirements of high-performance lubrication due to their high cost and environmentally harmful nature, thus there is a need for new solid, non-organic and non-soapy greases that can act as effective and inexpensive greases. This paper presents a comparative study and analysis of the effect of adding polytetrafluoroethylene (PTFE) and graphite to Iraqi Bentonite Lubricating Greases Activated with Nitric Acid (AIBLG-HNO3) of which 24-types were synthesized in laboratory using two types of local base oil stock 60 and 150, Iraqi bentonite as thickener, and two types of soft and hard paraffin waxes. The tribological characteristics such as corrosion resistance and diameter of wear scars (WSDs) that take place due to friction between metal surfaces inside the equipment were studied, as WSDs are directly proportional to friction. The results showed that the manufactured Iraqi bentonite greases have a high ability to resist corrosion or anti-wear, especially when adding PTFE to all samples. It was concluded that adding either PTFE or graphite nanocomposites should be preferably at a weight percentage of not less than 15 wt%. Moreover, in comparison with the parent Iraqi Bentonite Lubricating Greases (IBLG) samples free of additives, the results also indicate a decrease in WSDs by 90% to 93% using base oil 150 for four samples and in the presence of either soft and/or hard wax.

The denture base is a critical component of complete or partial dentures, significantly influencing functionality, comfort, and aesthetics. Traditional manufacturing processes for denture bases face challenges such as limited dimensional accuracy, mechanical integrity, and surface quality. To address these limitations, this study aims to evaluate and optimize the effect of digital light processing (DLP) 3D printing parameters on the mechanical properties and surface quality of acrylic resin denture bases. Specifically, the research investigates how layer height and printing orientation influence hardness and surface roughness. Using the L9 orthogonal array of the Taguchi experimental design, nine specimens were fabricated with varying parameter levels (50 mm, 100 mm, and 150 mm for layer height; 0°, 45°, and 90° for printing orientation). Shore hardness (scale D) and surface roughness were measured for each specimen. ANOVA analysis revealed that both parameters significantly impacted the hardness and surface roughness. Maximum hardness (85 HD) and minimum surface roughness (<0.2 µm) were achieved at 50 mm layer height and 0° printing orientation. The study identified comparable contributions of layer height and printing orientation to hardness (51% and 49%, respectively), while layer height exhibited a threefold greater influence on surface roughness (74.25% vs. 25.75%). These findings provide valuable observations for enhancing the parameters of DLP 3D to improve the performance of denture bases.

Suicide bomb vests represent one of the most devastating and indiscriminate forms of violence, often employed by extremist groups to instill fear and terror within communities. Computational techniques can be used to simulate blast wave propagation to gain a better knowledge of pressure dynamics, threat of explosions on human health and life, and the effects of explosions on infrastructure. The current study utilizes a coupled Eulerian-lagrangian (CEL) approach in ABAQUS to numerically simulate blast wave propagation generated from a suicide bombing in free-air. A virtual scene of targeted area is proposed to assess the lethality of a fully vented burst. The peak-overpressure (Pso) in free-air is predicted and compared to empirical prediction by Kingery-Bulmash model. The CEL model prediction of peak-overpressure in free-air demonstrated good convergence with Kingery-Bulmash measurements. The CEL analysis results of incident peak-overpressure showed an excellent agreement with empirical model measurements with maximum difference of 9%. Furthermore, the reflected peak-overpressure value is double the incident peak-overpressure magnitude in front of the walls. However, blast walls attenuated blast wave energy and peak-overpressure dropped by 53% at a distance 1.5h behind the walls. The measurements clarified high risk zone around the explosion source due to severity of the detonation, and further studies are essential to suggest protection methods to mitigate blasts and minimize losses in areas at risk of explosion.