Al-Khwarizmi Engineering Journal

In amputation surgery, osseointegration is the placement of a metal implant in the residual bone and is conducted with
an external prosthesis. In this study, the prospective application of Ti–13Nb–13Zr alloy in a transtibial osseointegrated
prosthesis was carefully examined. The alloy demonstrated favourable mechanical properties, such as strong mechanical
strength with an average yield strength of 482 MPa, ultimate tensile strength of 551.843 MPa and modulus of elasticity
of 74 GPa. In compression testing, the material showed its resilience to compressive loads, exhibiting 700 MPa yield
strength and 1010 MPa compressive strength. The elastic modulus of Ti–13Nb–13Zr alloy is approximately 55–65 GPa,
which is much closer to that of human bone (10–30 GPa) compared with that of Ti–6Al–4V alloy (110 GPa). This
proximity reduces stress shielding, a common issue in implants that a mismatch in stiffness between the implant and bone
leads to bone resorption. Analyses using the finite element method demonstrated uniform stress distribution, safety factors
and minimal deformation for a range of implant sizes, guaranteeing structural integrity and functionality. The maximal
von Mises stress on the tibia bone and implant did not surpass the yield tensile stress of the titanium alloy and bone, which
is 470 and 175 MPa, respectively; the maximum safety factor at L=120 and D=3 and the stress levels are not expected to
induce material failure. These findings bring up new possibilities for enhanced prosthetic design and use in the field of
biomedical engineering by demonstrating the alloy’s potential suitability for osseointegrated prosthetic applications.

This research aims to assess the behavior and effectiveness of flash graphene (FG) in the remediation of pH, EC, total dissolved solids (TDS), cations, and Fe+3 from real groundwater (GW) of the wells of the Al-Raeed Research Station, which is used in reclamation and irrigation. enclose that contaminant levels in remediated real GW abide criterion of Iraqis and Food and Agriculture (FAO) standards. Adsorption technology in the batch adsorption mode experiment was achieved by FG, a carbon-based, highly porous structure that has strong adsorption capacity due to its beneficial surface area (71.7 m2/gm). A locally designed and manufactured electroflash reactor was used to synthesize FG by converting activated carbon derived from banana peels. In this process, banana-peel activated carbon (BPAc) was exposed to a mild spark and 8–10 circuit break shocks per reactor run. The FG morphology and functional adsorption groups were characterized via scanning electron microscopy, Fourier transform infrared, and X-ray diffraction tests. The remediation efficiencies in the batch experiments were 69%, 69%, 61%, and 100% for Ca, Mg, TDS, and Fe, respectively, at an optimum FG dosage of 1.5 g, contact time of 4 hours, 150 rpm, and pH of 7. The adsorption capacity, which followed the Freundlich isotherm model, was Ca+2 = 14.6 mg/g, Mg+2 = 16.4 mg/g, Fe+6 = 0.074 mg/g, TDS = 7.3 mg/g. Finally, with the synthesized FG used and the parameters (dose, agitation speed, pH, and contact time) adjusted, the batch mode experiments on real GW samples yielded pH, EC, T.D.S., Mg+2, Ca+2, and Fe+3 values, which satisfied FAO restrictions and Iraqi standards.

This study presents the preparation method and characterisation of activated carbon (AC) from pomegranate waste by using chemical with microwave activation. Pomegranate peels in powder form were impregnated in 40–60 %wt sulphuric acid with different ratios (1:3 of pomegranate to sulphuric acid) and times (2–6 h) for chemical activation. The impregnated samples were activated in microwave under inert atmosphere at different powers (450–900 W) and activation times (7–21 min) to produce AC. Design-Expert software (version 13) was selected with the I-optimal method to analyse the effects of AC preparation variables. The preparation variables, including acid concentration, impregnation time, impregnation ratio, microwave power and activation time, with their effects on the adsorption capacity of methylene blue dye, were investigated. On the basis of the obtained maximum methylene blue number, the AC and raw material were characterised via Brunauer–Emmett–Teller test, field emission scanning electron microscopy with energy-dispersive X-ray analysis, Fourier transform infrared spectroscopy and X-ray diffraction analysis. Results indicate that the AC produced from pomegranate peels achieves a sufficient surface area of 1431.8 m2/g and a total pore volume of 1.6236 cm3/g with a spongy and amorphous nature with minor crystal characteristics. It can be used as a low-cost product with desirable surface characteristics. These results promote the use of AC prepared from pomegranate waste as an obtainable and low-cost adsorbent.

The recent trend towards the use of a blockchain as a means to guarantee the security of health data has raised concerns with regard to its applicability in Internet of Things (IoT) scenarios due to computationally heavy primitives (e.g. hashing functions) and lack of scalability. As a solution to this problem, this article introduces Rabbit-256: an addition–rotation–XOR-based sponge construction derived from the Rabbit stream cipher that is twisted and adapted to a lightweight hash function, suitably adapted for distributed solutions in healthcare systems with a blockchain nature. Rabbit-256 is a lightweight encryption cipher that wears the mask of a hash function but with better diffusion and avalanche through an official buildup in Merkle trees. The presented system is evaluated using common cryptographic measures against SHA-256, i.e. grid operators of 100, 500, and 1000 inputs for the avalanche effect, Hamming distance, and mean standard deviation. We observe that Rabbit-256 exhibits a higher security margin and lower computational overhead, and thus, it is an optimal alternative to resource-constrained IoT systems given its resistance against attacks. Although the current work is developed in simulation, Rabbit-256 can be utilised for actual deployment to ensure the privacy of e-health records and medical sensor data in IoT and clinical services over a blockchain. In the future, we will focus on hardware design, energy efficiency, and integration (i.e. to be compliant with the Health Insurance Portability and Accountability Act in the U.S. and the General Data Protection Regulation in Europe).

Traffic lights operating on a fixed schedule are mostly time-consuming; for example, running green signals in the absence of vehicles, leading to a buildup of long queues at red lights. This inefficiency results in congestion in cities, contributes to delays and economic losses and intensifies pollution levels. In this study, a deep learning-based adaptive image processing traffic light control system for real-time dynamic regulation of signals was proposed. Different from typical sensor-based solutions, the proposed method uses established surveillance cameras, enabling cost-efficient deployment and easy installation. A YOLOv10-based detection model identifies and classifies vehicles by type, applying weight factors to effectively estimate traffic demand. A dynamic timing algorithm enables continuous redistribution of green-light durations due to existing unbalances in the flow for any or all intersection phases. A practical microcontroller-based system might be integrated directly into the existing infrastructure. For assessment, the model used data from 12,500 images labelled accordingly and divided into the following: 70% for training, 15% for validation and 15% for testing. The model was assessed in a SUMO-based simulation of a very busy four-way intersection and actual deployment in Baghdad, Iraq. Compared with fixed time control, this adaptive system reduced vehicle wait time by up to 91.7%. Furthermore, results indicate reduced fuel consumption and CO2 emissions, thereby leading to considerable economic and environmental benefits. Overall, the proposed framework represents a practical and scalable implementation for modern traffic management, overlooking possible implementations of enhancements such as prioritisation of emergency vehicles and multi-intersection coordination.

Whilst wireless communication technologies proliferate, putting extra demand on the finite radio frequency spectrum and leading to issues of congestion, underutilisation and interference, this dissertation presents a modern spectrum management model on the binary genetic algorithm (BGA) capable of improving detection accuracy and adaptive spectrum access in cognitive radio networks (CRNs). BGA follows binary encoding to determine optimum weighting factors for secondary users in a CRN scenario with a much faster performance and better reliability than conventional genetic approaches. In the cooperative spectrum-sensing scheme proposed in this paper, multiple secondary users will forward their local sensing outcomes to a fusion centre in which BGA optimisation will fine-tune the weighting coefficients throughout the soft decision fusion mechanism. The algorithm then evolves from one generation to the next through the application of selection, crossover and mutation operations to discover the best configuration. Extensive simulation experiments were conducted to study the effects of the critical genetic parameters of mutation probability, crossover rate and population size on detection capability. The results indicate that the optimised BGA framework can achieve detection probability close to 96%, false alarm rate of 0.1, mutation rate of 0.12 and bit error rate of around 7 × 10⁻⁵ even when the signal-to-noise ratio is extremely low at –15 dB. In addition, the comparative evaluation showed the definite superiority of the proposed algorithm when tested against conventional algorithms, such as energy detection, matched filtering and neural network-based convolutions, when subjected to challenging and noise-prone conditions. The work further affirms the applicability of evolutionary algorithms in enhancing the cognitive intelligence of CRs and presents a scalable solution for spectrum management in existing 5G systems and future 6G frameworks.

This study examines the thermal and electrical characteristics of insulated gate bipolar transistors (IGBTs) in boost direct current (DC)–DC converters used in photovoltaic systems with respect to how switching and conduction losses vary under different operating conditions. The behaviour of IKWH70N65WR6 IGBT was measured (PLECS software) with load resistances of 5, 10 and 20 Ω at switching frequencies of 1–100 kHz. Important results are as follows: conduction losses prevail at low frequencies and load currents and switching losses increase towards high frequencies. Thermal stress is the highest at mid-range frequencies (50–60 kHz) and IGBT junction temperatures peak to 123 °C. Reduced load resistance leads to increased power consumption and total losses, and the value of load matching must be optimised. This study offers critical data regarding the choice and optimisation of IGBTs to improve efficiency and reliability within the uses of renewable energies.

A‍‌‍‍‌ gantry robot is one of the most common types of industrial robots with linear movement. This type of robot is also known as a Cartesian or linear robot. It is an automated industrial system that moves along linear paths, enabling it to create a 3D envelope of the space in which it operates. A robot of this type has a standardised configuration process because it can have several sets of axes, such as X, Y and Z. The gantry robot picks up products from several places, so it can search through various locations. Afterwards, it carefully deposits the products on a conveyor belt for the next stage of the procedure or final ‍‌‍‍‌shipment. This integration enables continuous and automated material flow, increasing overall productivity and efficiency in manufacturing operations. Dimension measuring and object placement are critical tasks in robot and conveyor systems that depend substantially on the types of sensors. Progress in sensors and camera technologies has markedly enhanced precision, efficacy, productivity and adaptability. Sensors are commonly classified as range and vision sensors on the basis of their mode of measurement. This review article offers useful knowledge on gantry robots and conveyor systems and their historical background. It examines recent research that has reported on the advantages and disadvantages of gantry robots. Moreover, literature on object dimensioning and positioning via sensors and cameras in gantry robots is presented. This review elucidates the advantages and disadvantages of several sensing types, including cameras and conventional sensors. Comparisons of robotic systems in terms of accuracy, speed, cost, energy efficiency and other aspects are also performed. Many studies have been conducted on body detection via vision technology and sensors, but the use of laser sensors has received minimal attention and needs further focus in the future.

To assist decision-makers in selecting the best solutions and arriving at better solutions for temporary shelter and to identify key issues related to temporary shelter methods and provide some principles and recommendations, this study presented a theoretical investigation into the measurement of thermal comfort in standard relief tents in cold climates. The results were analysed and compared using the CBE MRT and CBE Thermal Comfort tools based on field-measured values inside a tent from December 2024 to February 2025. The results on 11, 13, 14 and 18 December 2024, were selected to avoid repetition. Thermal comfort is a key factor in the design and use of tents in cold regions because of its direct impact on the safety and well-being of occupants. Factors affecting the thermal comfort of tents in cold weather include insulation, ventilation, clothing and heating. This study was applied in nature and was conducted using a descriptive analytical approach. Data were collected using library and documentation methods. This study provides important indicators in the field of thermal comfort, particularly in the design of heating, ventilation and air conditioning systems. These environmental and individual factors influence the relationship between the predicted population mean vote and the predicted dissatisfaction rate. On the basis of these factors, the study helps measure human indoor thermal comfort and determine the influencing factors in each case to reach the desired conclusions while emphasising that the tents did not provide suitable thermal conditions for their inhabitants until they were treated using an experimental model.

Interest in biodegradable and sustainable is steadily rising which has made the investigation into natural fibre reinforced biopolymers. Among these, poly(3-hyroxybutyrate-co-3-hydroxyvalerate) (PHBV) stands out for its biodegradability and biocompatibility, but its inherent brittle and has few or no properties conducive to application to wider engineering fields. Additionally. The natural variability of fibres can lead to inconsistent mechanical performance, yet only a few studies have examined this variability from a statistical perspective. In this study, the influence of adding pineapple leaf fibres (PALF) at different loading (10-40 wt.%) was investigated on the mechanical behaviour of PHBV. The tensile strength was analysed using a linearized two-parameter Weibull method to determine the scale (η) and shape (β) parameters, providing insight into both the typical strength and the variability of the composite’s performance. The results show that tensile strength increases with fibre loading, with highest of 46.54 MPa (±4.12) at 30 wt.% PALF, corresponding to a 73% rise in scale parameter compare to other loadings. A strong correlation was established between the experimental data and the Weibull model, with (R2 > 0.9) and the differences are below 1 % and 7 % for neat PHBV and PALF/PHBV respectively. Scanning electron microscopy (SEM) analysis revealed signs of fibre rupture and fibre extraction, indicating that the composite’s failure was a result of poor interfacial bonding between the matrix and fibre. The Weibull analysis able to provide more comprehensive evaluation of the composite mechanical reliability as it taken account the statistical distribution of all measured data rather than just the means value. The two-parameter Weibull model demonstrated that the composite containing 30 wt.% PALF attained the maximum tensile strength with satisfactory reliability. This finding signifies an ideal equilibrium between performance and variability, essential for the dependable design of sustainable PHBV-based composites.