University of Thi-Qar Journal for Engineering Sciences

Digital modulators have become a hot research topic because of the progress in Radio Frequency (RF) frontend terminals and systems on chip industry. But with all the progress that has been made, the implementationcost is still a challenge. This research paper presents a new architecture of construction Quadrature PhaseShift Keying (QPSK) modulator in VHDL at the lowest possible implementation cost. Four different carriersignals were generated using two 8-bit accumulators and 64 values Look Up Table (LUT) depending on theconcept of Direct Digital Synthesizer (DDS) technique. The first accumulator operates on the rising edge ofthe clock, while the second one operates on the falling edge of the clock. The most significant bit in eachaccumulator was reversed using the XOR logic function. This process represents generating out-of-phasesignals (180-degree phase difference compared with the original ones). The whole implementation processwas done directly in Very high speed integrated circuit Hardware Descriptive Language (VHDL) using XILINXVivado without the assistance of any co- simulation tool like DSP Builder Tools or Xilinx System Generator.The presented fulfillment technique based on the 64 values LUT has reduced the utilization resources bymore than 70%.

The healthcare industry is the most widely used application of Wireless Body Area Network (WBAN). WBAN networks have been developed to provide a more flexible experience than traditional wired medical systems using low-power miniature sensors that monitor physiological signals. Some studies have addressed the problem of congestion in body networks and proposed new methodologies to deal with this problem. Recently, in WBAN networks, metaheuristics algorithms have been employed to improve performance and job execution efficiency, which has proven effective in finding optimal solutions, especially for congestion problems. This research applies a metaheuristic algorithm, the Firefly algorithm, to optimize node selection in WBAN. Additionally, the Random Early Detection system (RED) is employed to control network congestion. Two scenarios are applied, the first one represents a network with 20 nodes, and the second one represents a smaller network with 10 nodes. The results were compared statistically. The present paper defines an enhanced congestion handling method for WBANs. For this purpose, the fitness function of the nodes is evaluated based on essential factors: congestion probability, the variables are residual energy, average data rate, node distance, and sink distance. It also improves the routing strategy by introducing the firefly algorithm-based forward-looking node selection approach. This eventually results in improved quality of service

The aim of this study is to design a lithium bromide-water (LiBr-H2O) absorption cooling system with a rated capacity of about 1 kW of solar-powered cooling using lithium bromide as an adsorbent and water as a refrigerant. The proposed absorption cooling system consists of a rooftop evacuated tube solar collector, a LiBr-H2O single-effect absorption chiller (including a generator, a solution heat exchanger, an evaporator, a condenser, and an absorber), a fan coil unit, pumps, a flow choke, and control valves. All the governing thermal equations of the system were studied theoretically, and the system was designed according to the following assumptions (system capacity 1 kW, generator temperature 85 , condenser temperature 38 , evaporator temperature 5 , and absorber temperature 37 . To find out how different operational and design factors affect the thermal efficiency, a thermodynamic analysis of the absorption cooling cycle was performed. The dimensions of the components and the cooling capacity were explored in the results.

Designing and analyzing structures to mitigate the effects of earthquakes is considered important, especially in
recent times when earthquakes have increased in many places. This research includes analyzing a structure
consisting of a basement and (G+14) stories, which encompasses a plan area measuring 32.20 m * 30.70 m. The
building comprises a basement level serving as a car garage and 15 residential stories within coordinates
(34.19205N) (45.12537E). The response spectrum analysis method, a linear dynamic method, was used, and the
ETABS 21 program was adopted to perform this task. The conditions for the analysis in this way were done
according to the ASCE/SEI7-16 code. This method includes several steps, where, in the beginning, the response
spectrum function is defined, the earthquake is defined in the direction of both X and Y, and the source of the
mass is determined. The effect of the geometric nonlinearity is also considered, in addition to 30 mode shapes,
to reach the mass participation ratio equal to 100%. In order to perform the mentioned steps, several important
variables must be entered, which are the reduction factor (R=4), spectral acceleration in the short and long
direction (Ss=0.8, sl=0.2), and the damping value (5%). The analysis results included calculating the period, the
center of mass and rigidity, but the main objective of this research was to calculate the base shear, which
amounted to 2077 tons, and the story drift of the structure, which amounted to 0.0479. This value is considered
high, as it exceeded the permissible value specified by the ASCE/SEI7-16 code, and therefore, the structure is
considered unsafe in the event of an earthquake.

Enhancingthe thermal performance of a heat exchanger is a target for designers and researchers.This work uses numerical analysis to predict the thermal performance of a double-tube heat exchanger with a twist tape insert. Various twisting ratio (y/w) values of (2, 4, 6) wereexamined in theRe range of 5000 -30000. The aim of this workis to assess the thermal performance of a double-tube heat exchanger by inserting a twisted tape. To model this physical issue, in addition to the turbulence model, the Navier-Stokes and energy equations are taken into account.ANSYS Fluent was adopted to simulate the model equations. The findings show that the twisted tape has a significant effect on the thermal performance of a heat exchanger. An increasedcoefficient of overall heat transferand efficiency was observed when the twisted tape was inserted into the tube.The effectiveness of the heat exchanger improved by 34% compared with the double tubes without tape

This study investigates the key drivers influencing the implementation of land use policy (ILUP) in Iraq, with an emphasis on the role of social, cultural, and traditional moral values. The research examines how these factors shape ILUP outcomes, serving both as facilitators and obstacles in the tenure management process in Iraq. Through five distinct historical periods, starting from the Ottoman era through to the post-2003 period, the study identifies the predominant social norms and cultural practices that impact land use decisions within the ILUP framework. The research further explores how traditional morals contribute to community cohesion while also posing challenges to the adoption of modern land use practices. The findings underscore the necessity of integrating these deeply rooted social and cultural dynamics within ILUP to ensure the successful implementation of land use policies in Iraq, balancing the respect for tradition with the need for sustainable land management strategies. Results highlight the need for a comprehensive understanding of the rooted influence of local old-style morals and the need for legal transformations to shape ILUP structures in the Iraqi contemporary context.

Electroencephalogram (EEG) signals provide a window through which we can view brain activity, are of great importance for neurological diagnosis, developing Brain-Computer Interface (BCI), and cognitive neuroscience. Despite the importance of EEG data, they being complex and non-stationary make it difficult for analysis. With the development of machine learning (ML) and deep learning (DL) as state-of-the-art methods for decoding EEG signals, attempting to provide optimal performance in both accuracy and computational time costs when it comes to the problem of extreme complexity such as classification of huge dataset sizes. This paper presents a comprehensive review of the most recent ML and DL techniques in EEG signal analysis. We examine the latest methods — Convolutional Neural Networks (CNNs), Transformer models, Recurrent Neural Networks (RNNs), and both hybrid and traditional architectures with their individual undertakings in tasks of seizure detection, emotion classification, and motor imagery to evaluate each approach efficiency. Overall, the results validate the high transformative efficacy of ML and DL in the EEG signal domain which might provide a key towards optimizing our current knowledge of brain function, as well as serving to increase diagnostic accuracy in clinical environments.