Journal of Engineering

This work studied the textural properties of three adsorbents using nitrogen adsorption
desorption at a constant low temperature (77 K). The adsorbents are (a) mesoporous silica
(MS), (b) mesoporous silica modified with Fe3O4 (MSF), and (c) mesoporous silica modified
with Fe3O4 and coated with deacetylated chitin (MSFDAC). The adsorption–desorption
isotherm curve demonstrated that the studied adsorbents have a mesoporous structure of
type IV, according to the International Union of Pure and Applied Chemistry (IUPAC)
classification. The obtained results show that the Brunauer–Emmett–Teller (BET) isotherm
fits the data better than the Langmuir and the Freundlich isotherms. The surface area of MS
was at its maximum value as compared to MSF and MSFDAC, which were 1000.55, 168.05,
and 163.63 m2/g, respectively. The analysis of particle size distribution for all adsorbent
materials was in a narrow distribution, suggesting that the structures were mesoporous and
well-defined. Despite slight variations in pore diameter distribution due to loading Fe3O4
and modification with deacetylated chitin, the MSFDAC adsorbent results in heterogeneity
and unequal particle diameters. The average pore diameter increased from MS (4.6 nm) to
MSF (7.32 nm) and MSFDAC (11.69 nm). Conversely, the total pore volume decreased from
MS (1.15 cm3/g) to MSF (0.89 cm3/g) and MSFDAC (0.48 cm3/g). A comparative study was
carried out with the previous studies, which demonstrated that the textual properties of the
modified magnetic silica adsorbent biomaterial agreed with those of other studies.

Groundwater depletion, climate change, and rising water demand are the primary
challenges to the sustainable management of water resources in arid/semi-arid areas, such
as Iraq. This study evaluates current models for groundwater and their performance and
assesses the recent advancements in groundwater modeling techniques. It addresses that
currently, numerical models, Artificial intelligence (AI), and remote sensing techniques are
being implemented separately. Physically based models are widely used in groundwater
modelling due to their reliable performance in simulating groundwater flow and transport
processes. It was widely used in Iraqi and international groundwater studies, constituting
62% and 60% of groundwater studies in those regions. In contrast, the application of AI
based tools offers great potential in terms of predicting groundwater dynamics' nonlinear
behavior, improved predictive results, and a data-driven assessment of hydrogeological
conditions. The use of remote sensing-based techniques such as GRACE and GLDAS has
become more popular among researchers as a means of estimating changes in groundwater
storage volumes. However, its application in studies is rather limited and amounts to around
28% of studies conducted in Iraq and 23% of international research. It is evident from the
review that the use of hybrid approaches can greatly improve predictions and groundwater
management.

Cell-Free Massive MIMO is believed to be one of the most promising designs of future
wireless networks due to its ability to provide more coverage, high reliability and improved
spectral efficiency, while also supporting secure communication through distributed access
point (AP) cooperation. However, secure downlink transmission in cell-free massive
Multiple-Input Multiple-Output (CF-mMIMO) systems becomes challenging when an active
eavesdropper (Eve) is located near the targeted user. To achieve high secrecy spectral
efficiency (SSE) with a limited number of active APs and a restricted power budget under
Nakagami-m fading and imperfect channel state information (CSI), a secrecy-oriented AP
selection algorithm is proposed with maximum-ratio transmission (MRT) precoding. Two
power allocation schemes are considered: equal power allocation (EPA) and water-filling
(WF). Simulation results indicate that the proposed joint-optimization AP activation method
enhances the secrecy rate for the targeted user compared with non-optimized selection
strategies. For CSI estimation error ???????? = 0.1 , the proposed method achieves SSE
improvements of 149% with WF and 317% with EPA. At a higher CSI estimation error ???????? =
0.4, the improvements are 144.5% with WF and 280.6% with EPA.

Yet little is known about the synergism of quaternary bio-based nanocomposite systems on
cement mortar performance. For the first time, this research examines a quaternary
nanocomposite comprising nano-chitosan, polyvinyl alcohol (PVA), polyacrylic acid (PAA),
and silver nanoparticles (AgNPs) synthesized via ultrasonication and added to Ordinary
Portland Cement mortar at 1.0 wt. % of cement. To study the effects of PVA and PAA on
compressive strength, four systems (Za1–Za4) were fixed at nano-chitosan = 1.50 g and
AgNO₃ = 10 mL while changing PVA content (0.25- 1.50 g) and PAA content (0.125-0.750 g)
to a cement: sand ratio of 1:2.75 and w/c = 0.485. We compared these variables of
compressive strength, water absorption, bulk density, and setting times. One-way ANOVA
and Tukey's HSD confirmed that all the mixes were significantly different (p < 0.001).
Results: The optimal mixture Za1 reported compressive strength 34.33 ± 0.058 MPa (4.7%
increase), water absorption 2.77 ± 0.058% (31.7% reduction), bulk density of 2259 ± 1·0
kg/m³, and setting time prolonged by initial and final, respectively, with values of 12.9% and
7·7%. In situ formation of AgNPs was confirmed by X-ray Diffraction (XRD), and Za1
exhibited a dense and homogeneous microstructure as revealed by Scanning Electron
Microscopy (SEM) micrographs. C-S-H nucleation, pore-filling, crack-bridging, and nano
crosslinking mechanisms serve the process of improvement.

Path planning in autonomous robotic systems (ARS) is challenging, especially in dynamic
or uncertain environments. Many classical methods are computationally expensive and lack
adaptability to real-world scenarios. In order to improve the overall path-planning
capabilities of robots; this paper introduces a new smart robotic navigation system which
uses Software Defined Network (SDN) and Multi-Spike Elman Neural Network (MS-ENN).
The introduced system includes an innovative way to encode temporal information using
multiple spikes which can capture much greater amounts of detail about changing
environmental characteristics than conventional artificial neural networks. Additionally, it
includes a spiking wave-front planner (SWP) to produce a preliminary set of paths and an
MS-ENN that produces decisions on how to make changes to those paths based upon the
environment. Results indicate that the proposed method was able to increase path
efficiency, decrease planning-time, and improve the success-rate within static environments.
The proposed model implementation demonstrates the strengths of coupling SDN with more
sophisticated spiking neural architectures for smart robotic navigation systems.

The advances in computer technologies have caused new architectural formations in
contemporary buildings. They were characterized by free forms with diverse formal
relationships that appear at different levels within the building, especially the surface
formation level and the mass formation level. Also, the methods of embodiment of these
formations and their mechanisms varied. Difficulties in distinguishing these new formations
within the main structure have led to the need to find new structural solutions that embody
these formations. In contemporary architecture, the relationship between the main and the
secondary structure is defined by a hierarchy of the load-bearing roles. The research
problem arises from the absence of clarification of the secondary structural role in
embodying these formations. The research assumes that the secondary structural systems
play an active role in embodying the architectural formations of the building envelope.
Therefore, this study aims to uncover the potentialities of the secondary structures in the
process of embodying the architectural formations, according to mechanisms of form
generation. The research relies on the analytical approach to extract the indicators of the
theoretical framework and then adopts the comparative analytical approach for five selected
case studies of contemporary buildings and shows a variety in their use of secondary
structure. The study finds that the secondary structure acts as a flexible structural medium,
allowing it to produce complex forms according to the diversity in the mechanisms,
depending on the methods of architectural formation. Therefore, the surface and mass
formations transform from a static to a dynamic state.

Increasing carbon emissions pose a serious issue affecting the environment. Several
methods have been proposed to reduce this phenomenon. Carbon capture and storage (CCS)
and enhanced oil recovery EOR are among these methods, which typically involve injecting
CO₂ into geological formations to store CO₂ or to enhance oil production. The effectiveness
of these methods relies heavily on the interaction between CO₂, rock, and water. This review
summarizes most of the important studies that focused on the alteration of mineralogy,
porosity, permeability, and wettability of the host rock, employing different methods such
as core flooding and static soaking. Despite extensive research on CO₂–water–rock
interactions, significant uncertainties remain regarding the comparative effects of
supercritical CO₂ and carbonated brine injection on petrophysical, geochemical, and geo
mechanical properties under representative reservoir conditions. This review addresses
these gaps by synthesizing recent experimental findings and identifying key challenges and
research needs related to injectivity, low-permeability formations, and long-term reservoir
integrity. This review focuses on the effects of various factors, including the CO₂ phase
during injection and the presence of impurities. An analytical comparison of the supercritical
and dissolved CO₂ phases was also conducted, which revealed that supercritical CO₂
generally promotes stronger mineral dissolution, wettability alteration, and salt
precipitation near the injection zone. In contrast, carbonated brine produces more uniform
geochemical reactions and tends to preserve or improve permeability while maintaining
water-wet conditions. This review provides a comprehensive understanding of the effect of
CO₂ injection on the geochemical and petrophysical properties of various rock types,
integrating recent discoveries and identifying knowledge gaps.

This paper proposes a hybrid artificial intelligence (AI) model that combines an Artificial
Neural Network (ANN) and Particle Swarm Optimization (PSO) to predict the Quality of
Service (QoS) in 5G networks. The model utilizes radio channel indicators such as Reference
Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal
Strength Indicator (RSSI), and Channel Quality Indicator (CQI) to forecast throughput and
latency levels. These indicators are critical factors affecting network performance; however,
the nonlinear relationship among them makes traditional analytical models inadequate for
accurate QoS prediction. The importance of the current study lies in estimating QoS in 5G
networks by combining radio-level physical-layer indicators into a neural network based on
the PSO algorithm. The proposed approach allows for better modeling of QoS dynamics and
improvement in prediction accuracy. The hybrid ANN-PSO model that integrates an Artificial
Neural Network (ANN) with a Particle Swarm Optimization (PSO) algorithm is compared
with traditional methods such as Linear Regression and Random Forest to evaluate
prediction accuracy. Experimental results demonstrate that the proposed model achieves
higher accuracy and lower prediction error, making it a promising tool for predictive QoS
optimization in next-generation 5G systems.

Waste polyester fibers (WPFs) were used to prepare a range of high-density polyethylene
(HDPE) composites with maleic anhydride-grafted polyethylene (PE-g-MA) as
compatibilizer, and these were characterized. The objective of the research was to enhance
the mechanical properties of HDPE and to encourage the use of plastic and textile waste. The
composite specimens were made using single-screw extrusion, and tensile, flexural, impact,
differential scanning calorimetry (DSC), density, porosity, and water absorption were
measured. A systematic evaluation of fiber orientation and recycled HDPE (r-HDPE) content
was made. The results showed that the fiber orientation had a significant influence on the
stress transfer and failure mechanisms. The mesh-reinforced composite configuration had
the best overall performance with tensile strength of 52 MPa and impact strength of 487.8
J/m, while the aligned fiber configuration had the highest flexural strength of 104.7 MPa. PE
g-MA was added, which increased the adhesion between the fiber and the matrix and
decreased the void formation. With the gradual incorporation of r-HDPE, the mechanical
properties were gradually reduced because of the degradation of the polymer and reduction
in interfacial interactions, but the composites with up to 15 wt.% r-HDPE still performed
well. The results show that the use of polyester fiber-reinforced HDPE waste composite
represents a viable approach to producing high-quality engineering materials with high
recycled content and improved mechanical properties.

The evolution of shell structure and nuclear deformation in neutron-rich nuclei remains
one of the major challenges in nuclear structure physics, particularly near regions associated
with shell erosion and shape coexistence. In the present work, neutron-rich isotones with
neutron numbers N=50 and N=34 are investigated within the Hartree–Fock (HF) plus
Bardeen–Cooper–Schrieffer (BCS) framework using the Skyrme SLy5 parameterization. The
potential energy surfaces (PES) are calculated as functions of the quadrupole deformation
parameter β2 in order to determine the equilibrium nuclear shapes and examine the
evolution of shell stability in neutron-rich systems. The calculated results for 70Ca show a
nearly spherical minimum at β2 = 0.001 together with a weak oblate configuration, while 72Ti
and 74Cr exhibit multiple competing minima corresponding to spherical, oblate, and prolate
shapes, indicating pronounced shape coexistence and weakening of the traditional N=50
shell closure. For the N=34 isotones, 54Ca displays weak deformation with β2 = −0.052,
supporting enhanced spherical stability, whereas 56Ti and 58Cr exhibit increasing prolate
deformation with β2 = 0.101 and β2 = 0.151, respectively, reflecting stronger collective
effects and shell evolution. These results indicate that proton-neutron correlations, pairing
interactions, and deformation-driving orbitals play a significant role in the evolution of shell
structure and the emergence of shape coexistence phenomena in neutron-rich nuclei.

Urban environments face serious climate challenges due to rapid urban expansion,
exacerbating the surface urban heat island (SUHI) phenomenon. This study proposes a
hybrid framework based on Multi-sensor Fusion technology within the cloud computing
environment of the Google Earth Engine platform to overcome the limitations of single data
sources. Focusing on Baghdad from 2018 to 2023, Multi-spectral, radar, and atmospheric
satellite datasets (Landsat 8/9, Sentinel-1, Sentinel-2, Sentinel-5P) were integrated to
accurately extract morphological and environmental variables at the standardized
residential neighborhood scale. A spatiotemporal model was constructed based on the
random forest algorithm to evaluate the relative impact of urban drivers. The model showed
high predictive efficiency, achieving a coefficient of determination (R2 = 0.842) and a root
mean square error (RMSE = 1.007°C). The results revealed that urban morphology (NDBI
contribution of 29.43%) and air quality degradation (NO2 contribution of 25.38%) are the
main drivers of surface warming. This study provides empirical evidence of the synergistic
polluted-heat-dome effect, offering a geospatial tool for urban planners to adopt sustainable,
climate-resilient mitigation strategies in arid metropolitan areas.