Rectangular Hollow Flange (RHF) steel beams may have openings drilled in their top surface to pass some plumbing and electrical wiring inside the RHF cavity. These openings affect the behavior of these steel beams and may reduce their resistance to bending moment. To investigate this effect, Finite Element Modeling (FEM) was used to simulate RHF steel beams with and without flange openings with several variables in geometric di mensions. The FEM results were examined using 8 experimental test specimens of RHF steel beams obtained from previous studies without flange openings. The results showed high accuracy in modeling these RHF steel beams in structural behavior and ultimate bending moment capacity. Current design codes were applied to predict the capacity of these RHF steel beams without flange openings, both from FEM results and experimental tests. The prediction values were always less than the ultimate capacity of RHF steel beams without flange openings. After verifying the results, 98 RHF steel beams with different flange opening diameters were modeled. The reduction ratios in ultimate capacity in steel beams with hollow flange openings are directly proportional to the opening diameter, hollow flange height, and steel yield stress, while they are inversely proportional to the thickness and width of the hollow flange and the steel beam web height. The reduction ratios in the ultimate capacity for RHF steel beams with flange openings are small in the compact category, and these ratios increase with the non compact and slender categories.

External loads applied to a box-section steel column before it is filled with concrete to increase its efficiency due to modifications in structural systems or design errors may reduce its ultimate capacity and change its structural behavior. To examine this effect, finite element modeling (FEM) has been used to simulate these columns under preloading at different ratios with many variables in the geometric dimensions of the columns. The FEM results have been investigated using 38 experimental specimens obtained from previous studies without preloading. The results demonstrated high accuracy in modeling these columns in structural behavior and ultimate load capacity. After verifying the results, 84 Concrete-Filled Steel Columns (CFSC) were modeled under different preload ratios. The results indicated that some variables have directly affected the value of the decrease in column capacity in terms of its height, wall thickness, yield stress, and preload ratios, while others were inversely proportional in terms of the cross-section dimensions and concrete strength. The preload effect ratio had two separate limits, where when it reached 70%, the maximum value of the decrease in column capacity was 10.90%. The value increased sharply reaching 19.90% when there was a preload equal to 80%. New equations have been proposed to predict the ultimate capacity of CFSC under preloading with suitable accuracy with a correlation coefficient of no less than 0.949.

A combined multi-stage algorithm is proposed and built to handle thirteen challenges along with electrical
constraints that face electric-supply restoration in smart grids. The challenges include maximization of the
number of recovered out-of-service healthy loads, avoiding in-service load shedding, occurrence of multi-simultaneous faults, minimization of total power loss, sequence consideration of commanded switches, minimization of number of switches receiving order, reducing restoration time, independency on system size and
achieving self-healing. The electrical constraints include branch current capacity, voltage limits, load priority
and system radiality. These challenges are discussed in five stages, each stage represents a step in dealing with
these challenges and electrical constraints. The proposed algorithm is aimed at minimizing the energy-not-supplied by minimizing number of out-of-service healthy downstream loads in a tree exposed to a permanent
single fault or multi-simultaneous faults without violating the electrical constraints. The proposed algorithm is
independent on distribution system size and its restoration time lies within 190–199 ms. The proposed algorithm
is tested under IEEE 16-bus, IEEE 33-bus and IEEE 69-bus distribution systems for all maneuvering fault processes. The proposed algorithm achieves 100 % self-healing capability under a single fault maneuvering processes against 95.8 %, 98.3 % and 97. 8 % satisfaction of self-healing condition under exposure to two, three and four multi-simultaneous faults, respectively. The proposed algorithm showed better performance compared to
other algorithms reported in the literature as regards as capability of self-healing, efficiency, restoration time and
number of considered challenges.

Greenhouse gas emissions have become a significant concern for many countries due
to their effect on the global economy and environment. This work discusses a standalone hybrid
renewable generation system (HRGS) for use in isolated areas with different load demand profiles.
Three load profiles were studied in this work: educational, residential, and demand-side management
(DSM)-based residential load profiles. To investigate the economic and environmental aspects, a
proposed modified capuchin search algorithm (MCapSA) was implemented, and the obtained results
were compared with those of different conventional optimal procedures, such as the genetic algorithm
(GA), particle swarm optimization (PSO), and HOMER. The Levy flight distribution method, which
is based on random movement, enhances the capuchin algorithm’s search capabilities. The cost
of energy (CoE), electric source deficit (ESD), greenhouse gas (GHG) emissions, and renewable
factor (RF) indicators were all optimized and estimated to emphasize the robustness of the proposed
optimization technique. The results reveal that the shift in the residential load profile based on
individual-household DSM-scale techniques leads to significant sharing of renewable sources and
a reduction in the utilization of diesel generators, consequently diminishing GHG emissions. The
proposed MCapSA achieved optimal values of economic and environmental aspects that are equal
to or less than those achieved through PSO. From the overall results of the three scenarios, the
modified algorithm gives the best solution in terms of GHG, COE, and ESD compared to other
existing algorithms. The usage of MCapSA resulted in decreases in COE and GHG in three types of
loads. The robustness and effectiveness of MCapSA are demonstrated by the fact that the DSM-based
optimal configuration of the renewable energy sources produces the lowest CoE and GHG emissions
of 0.106 USD/kWh and 137.2 kg, respectively.

The global shift towards sustainable transportation has led to a surge in the adoption of electric and fuel cell
vehicles. This paper introduces a novel energy management approach for the charging stations that serve both
battery electric buses and fuel cell buses. Addressing the challenge of guaranteeing enough energy for all buses in the system, a novel priority charging approach is adopted. This approach involves a sophisticated control system that predicts which buses will receive the required energy to complete their designated routes. Minimizing the cost of the charging energy while ensuring adequate charging for buses across their entire routes without energy interruption is pursued. Six scenarios have been studied and compared, comprising a standalone and grid connected photovoltaic-based charging station, considering all buses are either battery electric buses, fuel cell
buses or a hybrid combination of both. The obtained results show that the grid-connected with battery electric
buses gives the lowest energy cost of $0.0162/kWh. Furthermore, the study highlights the potential of grid connected charging station accommodating both electric and fuel cell buses striking a balance between cost effectiveness and fleet diversity.

This study offers a comparative evaluation of the impact of carbon fibre reinforcement on polypropylene (PP) and polylactic acid (PLA) matrices, focusing on their application in fused deposition modelling (FDM). Composite filaments with varying micro carbon fibre (MCF) contents were fabricated for both matrices, with their mechanical, moisture absorption, and morphological properties thoroughly characterized. In PP composites, MCF addition significantly improved tensile and flexural strengths, achieving optimal enhancement at 9.09 wt%, where tensile and flexural strengths rose by 75% and 100%, respectively, compared to pure PP. Conversely, PLA composites showed slight strength increases at lower MCF contents (below 5 wt %) but experienced strength reductions as fibre content exceeded this threshold. However, both materials exhibited increased stiffness (elastic modulus) with rising MCF levels, though PLA achieved optimal strength at a lower fibre loading. Moisture absorption increased in both matrices as fibre content rose; PP showed a proportional increase, whereas PLA displayed more pronounced absorption due to inter- and intra-filament porosities. Optical microscopy (OM) highlighted further differences: PP retained fibre distribution and bonding over a wide range of MCF levels, while PLA showed strong fibre adhesion and ductile fracture behaviour at lower MCF, shifting to brittle fracture and void formation at higher levels. Gaussian Process Regression (GPR) modelling corroborated these trends, identifying optimal MCF content as 9.09 wt% for PP and around 2.5 wt% for PLA. These findings provide guidance on selecting material and fibre loading for FDM applications, with each material achieving a unique balance of mechanical performance and moisture resistance.