Wasit Journal of Engineering Sciences

This study introduces a Double Deep Q-Network (DDQN) optimization framework to improve massive
MIMO-OFDM systems via reinforcement learning-driven adaptive parameter selection. It utilizes a dual
network architecture to mitigate overestimation bias and incorporates dynamic optimization for power
allocation, subcarrier fraction distribution, and modulation scheme selection across QAM-16, QAM-64, and
QAM-128 configurations. Extensive simulations performed across Signal-to-Noise Ratio ranges from -5 to 35
dBm reveal substantial performance enhancements, with DDQN-augmented systems attaining 5-6 dB SNR
savings for equivalent SE, a 50% increase in EE reaching 15.5-16 Gbps/W compared to conventional 10.5-11
Gbps/W implementations, and a 2.5 dB SNR reduction for a BER performance of 10⁻⁵. The optimization
framework ensures uniform parameter selection across diverse SNR conditions, facilitating a 40-50% increase
in coverage through enhanced low-SNR performance while delivering a 5 dB SNR improvement in low-power
operating scenarios. The study establishes a basis for intelligent communication systems that can autonomously
adapt to 6G wireless networks, supporting ultra-reliable low-power communications and mobile edge
computing applications.

Millimeter wave communications (mmWave) has been considered a key enabling technique for 5G systems
since it offers orders of magnitude more spectrum than current frequency bands. Unlike conventional
multiple input multiple output (MIMO) networks, beamforming in millimeter-wave systems cannot be
accomplished using digital techniques since only a limited number of analog-to-digital converters and mixers
can be handled due to their cost and power consumption. Recently, there has been a lot of interest in a hybrid
beamforming transceiver design, which includes an analog and digital beamformer, as a less expensive
alternative. However, the optimal design for these hybrid beamformers has not yet been fully established. In
order to approach the complex performance of the fully digital beamformer, this work will provide efficient
alternative minimization techniques for a hybrid beamforming structure, namely the manifold-based
geometric fully-connected antennas. According to simulation data on bandwidth efficiency, the proposed
iterative algorithm performs much better than the baseline hybrid beamforming approach, namely the
MMSE-MP algorithm (about 6.8% for a particular situation). Additionally, based on the recommended
techniques, comparing the simulation results of the two hybrid beamforming structures will yield vital design
information.

Wasit Governorate depends on the Tigris River for approximately 78% of its water supply. However,
upstream dam construction by neighboring countries has significantly reduced inflows leading to a severe
water shortage. Internal mismanagement has further intensified the problem, making the need for a balanced
water management strategy urgent. To address this challenge, the study applied the WEAP model and
CLIMWAT2 climate data, combined with FAO datasets, to simulate reference, current, and future scenarios
As a result of population growth and climate change. Real monitoring data, including weather patterns,
consumption rates, and distribution trends informed these. Results showed that the reference scenario covered
only 9.61% of demand, while the climate-based irrigation reduced the deficit by up to 53%. The deficit was
further reduced through the implementation of integrated and complementary strategies. Overall, the findings
suggest that resolving Wasit’s water crisis requires both technological solutions and improved administrative
practices

Climate change has significantly impacted the environment across various regions worldwide. The growing
interest in these impacts has raised the incentive of scholars to investigate and predict variations in critical
weather variables, such as precipitation, to supply helpful baseline results for future planning and control.
This research attempts to assess the effects of global warming on precipitation in Soran City in northern Iraq.
The daily rainfall is downscaled utilizing the LARS-WG (8.0) model with six General Circulation Models
(GCMs) and the SSP585 scenario for 2021-2040. These outcomes establish that the statistical analysis
confirmed the LARS-WG model's ability and reliability in downscaling precipitation.
Additionally, the average rainfall of six GCMs will likely fluctuate during the year. The highest increase is
anticipated in January and October, while the most decrease is expected in February, March, and November.
The findings can enhance the understanding of the effects of climate change on water availability and
motivate managers and stakeholders to develop the most effective techniques for mitigating these impacts.

Domestic wastewater is one of the biggest problems facing the environment, being one of the most important
sources of environmental pollution. It changes the physical and biological properties of the environment
when discharged without treatment. The removal of pollutants from industrial and domestic wastewater using
electrocoagulation (EC) technology was investigated. This study aimed to evaluate the effects of several
operating parameters on pollutant removal efficiency. These parameters included applied voltage
, flow rate,
electrode spacing, NH₄Cl concentration, and reaction time. A stainless-steel electrochemical cell, consisting
of both a cathode and an anode, was constructed for the experiments. The effect of each variable was
examined independently. The results showed that under optimal conditions of electrode spacing of 1 cm, a
flow rate of 0.05 L/min, an NH₄Cl concentration of 300 mg/L
, an applied voltage of 15 V, and a hydraulic
retention time of 45 minutes, the highest pollutant removal efficiencies were achieved: 83% for turbidity,
85% for TSS, 84% for COD, and 82% for BOD. Conversely, at non-ideal conditions (9 cm electrode spacing,
3 V voltage, and 15 min holding time), the removal effectiveness fell markedly to less than 50%. The findings
indicate that the electrocoagulation process is a sure method of removing pollutants in wastewater, provided
that it takes place under appropriate conditions. It improves the quality of the water by eliminating pollutants

Advanced composite materials are widely used in different applications because of their superior
performances. However, damage failures (i.e. micro- and meso-cracks) occurred in these materials after
subjecting to apply loading lead to reduce in their mechanical performances. In addition, repairing process of
these damaged composites to enhance their reliability and endurance is not possible at remote locations. In
this regards, self-healing composites are fabricated to heal cracks and damages, to restrict failure and enhance
the longevity of structures. It is widely employed in numerous applications, particularly in newly developed
vehicles and space applications. In this article review, the influence of adding nanomaterials on the mechanical
properties and the healing of damaged composite materials is discussed. Furthermore, the mechanical
performance of composite materials is influenced by the presence of nanoparticles. Additionally, the self-
healing processes and their mechanisms in composite materials were reviewed when subjected to impact,
flexural, and tensile forces.

A crucial objective within dermatology includes the identification of skin lesions, which can help with
highlighting diseases amongst the likes of melanoma early into the heart of the diagnosis process. With time,
professionals have depended on manually finding skin lesions on the body – a process which is inefficient
for time-management and is openly vulnerable to incorrect results. This paper suggests a deep learning-
dependent methodology utilizing You Only Look Once version 8 (DLBA- YOLOv8) in the case of skin
lesion detection with a quick and error-less methodology. Using the ISIC dataset, the model was effectively
trained with a collection of data of dermoscopic imagery of skin lesions. With state-of-the-art integration and
performance in lesion localization and classification, results from experiments have demonstrated YOLOv8’s
capability to achieve a mean Average Precision (mAP) of 98.8%, comparatively overcoming the efficiency
of YOLOv7 (89.3%) and Faster R-CNN (87.1%). In addition to that, when placing the results of YOLOv5
and Mask R-CNN in terms of Intersection over Union (IoU) scores side by side with YOLOv8, (85.2% and
84.6% respectively), YOLOv8 shows a great outperformance of both with a score of 88.7%. With the
utilization of the Ultralytics framework and an assessment through metrics along the likes of mAP and IoU,
the system shows a successful implementation. Handing the industry, a greatly developed perk in precision
and overall efficiency over current existing methodologies, this paper underscores the potential capabilities
of YOLOv8 in real clinical applications for skin lesion detection through an automated process.