Steam boilers are widely used in power generation and industrial processes. Improving their efficiency is crucial for enhancing sustainability and reducing operating costs. This study conducts a comprehensive bibliometric analysis to map the evolving research landscape on steam boiler efficiency improvement from 2014 to 2023. A literature search in the Scopus database retrieved 3574 publications. This study employs bibliometric analysis using Bibliometrix R packages and VOSViewer software to examine research trends, focusing on publication growth, key journals, influential authors, and emerging themes in steam boiler efficiency improvement. The results indicate a significant increase in annual research output, reflecting sustained global efforts in the field. China leads in both the volume and impact of contributions. Key research themes include materials development, innovative designs, heat recovery, and sustainable solutions. Notable publications emphasize eco-friendly approaches such as solar and organic thermoelectrics. Prolific authors from China, the United States, and Europe have shaped the discourse through influential collaborations. Emerging trends highlight a growing focus on renewable energy integration, advanced thermal management, and computational methodologies. This study consolidates knowledge on enhancing steam boiler efficiency through both quantitative and qualitative analyses, showcasing remarkable progress driven by dedicated international efforts. These insights can inform future strategies and inspire innovation in optimizing this critical energy conversion process.

In highly populated cities, the close spacing of buildings often results in interaction effects, including structure–soil–structure interaction (SSSI) and seismic pounding (SP), which occur because of the limited gaps left between adjacent structures. In most seismic design and structural analyses, the soil–structure interaction effects are frequently ignored, even though the foundation–soil system can significantly alter the structural behaviour and response demands. Moreover, the vibration characteristics and seismic performance of a building cannot be considered independent from those of neighbouring structures. This study aims to examine SSI, SP and their combined effects (SSSI) on the performance of adjacent buildings. Three-dimensional finite element models were developed using ETABS program of single and three adjacent multi-story buildings with identical floor levels, supported on shallow raft foundations over soft soil. These models were analyzed and compared with fixed-base models to evaluate the influence of SSI. Furthermore, the seismic responses of the adjacent buildings under both fixed-base and soft soil conditions were compared with those of single-building models to assess the effect of SP. The combined influence of SSI and pounding (SSSI) was investigated by comparing the adjacent building model with SSI to the corresponding single-building fixed-base model. Seismic demands are evaluated using nonlinear time history (TH) analyses with nine earthquake records. Variations in period of vibration, story lateral displacement, story drift ratio, story shear force, story moment, story acceleration and pounding force are 

Seismic pounding between adjacent buildings has been frequently observed during past earthquakes. This phenomenon can significantly increase seismic demands, often leading to severe global or local damage and even partial or total collapse. This study investigates the seismic response and both inter-story and story-story pounding behavior of adjacent RC buildings with different dynamic characteristics by ETABS software using nonlinear time-history analyses. Eleven recorded earthquake ground motions are employed to capture comprehensive excitation characteristics. The numerical model includes nonlinear flexural and shear plastic hinges and contact elements to simulate impact interaction between the structures. Global response parameters including inter-story drift ratios, displacement demands, and acceleration demands, and local response parameters including impact force, column shear force demands and plastic hinges behavior are obtained and analyzed to identify the critical stories and gap separation levels associated with pounding occurrence. The results indicate that increasing the gap distance does not necessarily guarantee a reduction in localized damage resulting from story-column pounding. This is due to non-monotonic relationship between separation gaps and impact velocity while intermediate gaps producing high relative velocities at impact which induced and may lead to greater contact forces and a more rapid degradation in stiffness.

Urban road networks in megacities face increasing vulnerability to disruptions that can severely impair mobility and accessibility. Assessing the resilience of such systems requires identifying critical links whose congestion/failures disproportionately degrade network performance. However, conventional link-criticality analysis (LCA) approaches often require exhaustive traffic reassignment, making them computationally impractical for large-scale applications. This study introduces a scalable, data-driven framework for LCA that integrates deep learning (DL) and global sensitivity analysis (GSA) to efficiently quantify link-level vulnerability in metropolitan road networks. The framework formulates LCA as a mapping from network topology, geometric attributes, and traffic-demand features to a composite criticality index that captures both operational degradation and accessibility loss. A DL model is trained on simulation-based disruption scenarios to approximate the impact of network performance, thereby replacing computationally intensive traffic assignment procedures. Subsequently, GSA is applied to interpret the influence and interactions of the underlying factors that drive criticality predictions. The proposed approach is demonstrated on the Greater Cairo road network using a detailed, multi-class model calibrated with observed traffic data. Results show that the DL surrogate accurately reproduces network efficiency and travel-time loss metrics while achieving orders-of-magnitude reductions in computation time compared with traditional LCA. GSA results highlight a limited subset of topologically central and highly loaded corridors as primary drivers of system vulnerability, revealing non-linear interactions among capacity, redundancy, and demand patterns. These findings underscore the potential of combining DL with GSA for interpretable, scalable, and policy-relevant LCA supporting resilient transport planning, investment prioritization, and emergency response in complex urban systems.

Improving the operational effectiveness of water structures requires effective hydraulic design. Significant changes in water levels, velocity distribution, heading up, and energy loss occur at the entrance zone of these structures because of the interaction between the canal inside slope and upstream wing walls geometry. However, prior research has not adequately assessed the integrated hydraulic effects of these characteristics. This study experimentally examines the interplay between wing wall type and canal inside slope on the hydraulic performance of water structures. A total of 360 laboratory experiments were conducted using four upstream wing wall configurations (box, broken, curved, and splayed) and three canal inside slopes (Z = H:V = 1:1, 3:2, and 2:1), representing common conditions in Egyptian irrigation canals. The findings show that hydraulic behavior is strongly influenced by both parameters. When compared to the box type, the splayed configuration exhibited the lowest energy losses and afflux, with reductions of up to 84.12% and 30.01%, respectively. Furthermore, steeper slopes were linked to higher afflux and energy losses, while the 1:1 canal inside slope continuously exhibited maximum hydraulic efficiency. New empirical relationships were developed to enable the prediction and optimization of irrigation structure performance. The outcomes support sustainable water management by increasing operational dependability, lowering maintenance requirements, and increasing hydraulic efficiency. For both new construction and rehabilitation projects, the study suggests giving priority to splayed wing walls in conjunction with a 1:1 canal inside slope, considering the soil conditions.

Powder Bed Fusion (PBF) is a growing and expanding technology for Additive Manufacturing (AM) of metallic materials. PBF has numerous advantages, including near-net-shaped parts, low tooling costs, and the ability to tailor microstructures, which make it an ideal candidate for manufacturing extremely complex designs that can be used in a variety of high-tech industries. Despite these enormous benefits, residual stress, and distortion, arising from a substantial cooling rate, temperature gradient in the melt pool and the layer-by-layer nature of the process, represent a challenge for the implementation of PBF technology in metal AM more broadly. Residual stress has a significant impact on the dimensional accuracy of printed parts and leads to severe defects such as cracks, delamination, and distortion. Conducting trial-and-error real AM build iterations to predict the developed residual stress and finding a strategy to mitigate its adverse effect is a time consuming, costly, and challenging process. Computational-based models offer an effective means to accurately predict residual stress, while reducing costs and saving time. This article discusses the fundamental principles underlying the numerical models used to predict residual stress. It investigates and presents their principles in a systematic manner, facilitating an in-depth understanding of these various numerical models. This deep investigation not only establishes a solid foundation for the improvement of existing models but also highlights the potential for the development of novel, more efficient numerical models for predicting residual stress in PBF. The review examines the experimental techniques used to validate these numerical approaches and clarifies how they can be used to validate residual stress prediction models. Furthermore, the potential application and integration of machine learning in conjunction with numerical models to predict residual stress and suggest effective mitigation strategies are also discussed. The review demonstrates how simulation can be used as an effective tool to efficiently identify suitable residual stress mitigation strategies in the PBF AM process.

SiC is susceptible to decomposition into Silicon and Carbon during. D-LPBF when exposed to temperatures above its decomposition threshold. This study investigates the impact of various laser scanning strategies; linear, concentric in-out, zigzag, and hexagonal patterns. The temperature history effects of these scanning strategies were investigated to observe the SiC degradation and its impact on the phase composition. A thermal simulation model was developed to analyse temperature evolution under each scanning pattern. Results showed that concentric in-out, zigzag, and hexagonal strategies involving frequent changes in laser direction generated localised high-temperature peaks. These peaks enhanced layer bonding but also triggered partial decomposition of SiC. However, the linear strategy lacked sufficient thermal peaks, resulting in poor interlayer adhesion and overlapping defects. Quantitative phase …

Evaluating line criticality in public transport networks is computationally challenging due to overlapping routes, frequency-based operations, and the combinatorial growth of multi-line disruption scenarios. Traditional approaches, whether topology-based or reliant on repeated equilibrium assignments, do not scale to realistic networks, where full-scan or multi-failure enumeration can require thousands of computationally intensive assignments. This study presents an innovative, computationally efficient framework that integrates a section-based transit assignment model with Mutual Information–based Global Sensitivity Analysis (MISA) to rank individual line criticality without simulating any failure scenarios. Stochastic O–D demand samples are generated using Monte Carlo methods, and equilibrium flows are computed once per scenario using a variational inequality formulation on an augmented network representation. The resulting dataset is analyzed using information-theoretic sensitivity indices, capturing nonlinear and interaction effects while reducing the computational burden by several orders of magnitude compared to brute-force multi-line failure analysis. Application to a benchmark network demonstrates that the proposed method reliably identifies lines whose operational roles and network embeddings make them most influential for system-wide performance. The framework offers a practical, scalable solution for infrastructure planners seeking data-driven tools for resilient public transport design and prioritization under uncertainty.