Search Results (56)

Modal decomposition, introduced by Fourier, expresses complex functions, such as sums of symmetric basis modes. However, convergence alone does not ensure structural fidelity. Discontinuities, sharp gradients, and localized features often lie outside the chosen basis’s symmetry class, producing artifacts such as the Gibbs overshoot. This study introduces a unified geometric framework for assessing when modal representations remain faithful by defining three symbolic invariants—curvature (κ), strain (τ), and compressibility (σ)—and their diagnostic ratio Γ = κ/τ. Together, these quantities measure how closely the geometry of a function aligns with the symmetry of its modal basis. The condition Γ < σ identifies the domain of structural closure: this is the region in which expansion preserves both accuracy and symmetry. Analytical demonstrations for Fourier, polynomial, and wavelet systems show that overshoot and ringing arise precisely where this inequality fails. Numerical illustrations confirm the predictive value of the invariants across discontinuous and continuous test functions. The framework reframes modal analysis as a problem of geometric compatibility rather than convergence alone, establishing quantitative criteria for closure-preserving transforms in mathematics, physics, and applied computation. It provides a general diagnostic for detecting when symmetry, curvature, and representation fall out of alignment, offering a new foundation for adaptive and structure-aware transform design. In practical terms, the invariants (κ, τ, σ) offer a diagnostic for identifying where modal systems preserve geometric structure and where they fail. Their link to symmetry arises because curvature measures structural deviation, strain measures representational effort within a given symmetry class, and compressibility quantifies efficiency. This geometric viewpoint complements classical convergence theory and clarifies why adaptive spectral methods, edge-aware transforms, multiscale PDE solvers, and learned operators benefit from locally increasing strain to restore the closure condition Γ < σ. These applications highlight the broader analytical and computational relevance of the closure framework. Full article

Underground gas storage (UGS), encompassing hydrogen, natural gas, and compressed air, is a cornerstone of large-scale energy transition strategies, offering seasonal balancing, security of supply, and integration with renewable energy systems. However, the complexity of geological conditions, multiphysics coupling, and operational uncertainties pose significant challenges for UGS design, monitoring, and optimization. Artificial intelligence (AI)—particularly machine learning and deep learning—has emerged as a powerful tool to overcome these challenges. This review systematically examines AI applications in underground storage types such as salt caverns, depleted hydrocarbon reservoirs, abandoned mines, and lined rock caverns using bibliometric and knowledge-graph analysis of 176 publications retrieved from the Web of Science Core Collection. The study revealed a rapid surge in AI-related research on UGS since 2017, with underground hydrogen storage emerging as the most dynamic and rapidly expanding research frontier. The results reveal six dominant research frontiers: (i) AI-assisted geological characterization and property prediction; (ii) physics-informed proxy modeling and multi-physics simulation; (iii) gas–rock–fluid interaction, wettability, and interfacial behavior prediction; (iv) injection-production process optimization; (v) intelligent design and construction of underground storage, especially salt caverns; and (vi) intelligent monitoring, optimization, and risk management. Despite these advances, challenges persist in data scarcity, physical consistency, and generalization. Future efforts should focus on hybrid physics-informed AI, digital twin-enabled operation, and multi-gas comparative frameworks to achieve safe, efficient, and intelligent underground storage systems aligned with global carbon neutrality. Full article

Heavy-duty vehicles cause a significant percentage of the harmful gas emissions from the automotive industry. This article presents the development of a compression system for hydrogen as part of the H2REF-DEMO hydrogen refueling station, joining the European efforts to promote hydrogen (H₂) as a fuel that can play a key role in the energy transition of these types of vehicles. The H2REF-DEMO project, co-funded by the European Union’s “Horizon. Europe” programme under the “Clean Hydrogen Partnership” (grant agreement no. 101101517), involves a partnership between companies and research centers that aims to investigate the possibility of compressing hydrogen through hydraulic power to handle large vehicle refueling applications, such as bus fleet depots, trucks, or trains. The basic principle is the exploitation of hydraulic power to compress hydrogen through hydro-pneumatic bladder accumulators. The hydraulic power units, in fact, pump oil into the accumulators, causing a deformation of the bladder containing H₂ and thus a consequent gas compression. In this article, we focus on the development of the compression system, from the theoretical starting point to the core final layout of the refueling station for large vehicles. We also exploit a lumped parameter numerical model to both support the system design and virtually test its first control logic. The latter, in particular, allows the system to operate in three modes—Bypass, Parallel, and Serial modes—thus leaving room for testing basic and more complex control strategies. The results of numerical simulations demonstrate the effectiveness of this innovative compression technology and its considerable efficiency in terms of refueling time and energy consumption, especially when compared to the standard systems used for this application. These are thus encouraging results that can support the development of an actual H2REF-DEMO hydraulic test rig for hydrogen compression. Full article

Magnesium (Mg) alloys are the lightest metals used in engineering structures, making them highly valuable for lightweight designs in aerospace, automotives, and related industries. Their low density offers clear advantages for reducing product weight and improving energy efficiency–key priorities in modern manufacturing. However, their unique crystal structure leads to notable drawbacks: low plasticity at room temperature, uneven performance across different directions, and inconsistent strength under tension versus compression. These issues have severely limited their broader application beyond specialized use cases. Shear deformation methods address this challenge by creating high strain variations and complex stress conditions. This approach provides an effective way to regulate the internal structure of Mg alloys and enhance their overall performance, overcoming the inherent limitations of their crystal structure. This paper systematically summarizes current research on using shear deformation to process Mg alloys. It focuses on analyzing key structural changes induced by shear, including the formation and evolution of shear–related features, real–time grain reorganization, crystal twinning processes, the distribution of additional material phases, and reduced directional performance bias. The review also clarifies how these structural changes improve critical mechanical traits: strength, plasticity, formability, and the balance between tensile and compressive strength. Additionally, the paper introduces advanced shear–based processes and their derivative technologies, such as equal–channel angular extrusion, continuous shear extrusion, and ultrasonic vibration–assisted shearing. It also discusses strategies for constructing materials with gradient or mixed internal structures, which further expand the performance potential of Mg alloys. Finally, the review outlines future development directions to advance this field: developing shear processes that combine multiple physical fields, conducting real–time studies of microscale mechanisms, designing tailored shear paths for high–performance Mg alloys, and evaluating long–term service performance. These efforts aim to promote both theoretical innovation and industrial application of shear deformation technology for Mg alloys. Full article

In the context of global efforts toward energy conservation and emission reduction, accurate short-term electric load forecasting plays a crucial role in improving energy efficiency, enabling low-carbon dispatching, and supporting sustainable power system operations. To address the growing demand for accuracy and stability in this domain, this paper proposes a novel prediction model tailored for power systems. The proposed method combines Spearman correlation analysis with modal decomposition techniques to compress redundant features while preserving key information, resulting in more informative and cleaner input representations. In terms of model architecture, this study integrates Bidirectional Gated Recurrent Units (BiGRUs) with dilated convolution. This design improves the model’s capacity to capture long-range dependencies and complex relationships. For parameter optimization, an Improved Beluga Whale Optimization (IBWO) algorithm is introduced, incorporating dynamic population initialization, adaptive Lévy flight mechanisms, and refined convergence procedures to enhance search efficiency and robustness. Experiments on real-world datasets demonstrate that the proposed model achieves excellent forecasting performance (RMSE = 26.1706, MAE = 18.5462, R² = 0.9812), combining high predictive accuracy with strong generalization. These advancements contribute to more efficient energy scheduling and reduced environmental impact, making the model well-suited for intelligent and sustainable load forecasting applications in environmentally conscious power systems. Full article

This paper derives and modifies the structural quantitative indicator of loess—the structural index—based on the theory of limit equilibrium, thereby obtaining structural evolution parameters and structural modification parameters. However, since the structural index is defined for uniaxial stress states, this paper also introduces a structural parameter applicable to complex stress states—the complex stress structural parameter $m_{\tau }$. This parameter is not only applicable to complex stress states, but also enables the study of soils such as soft clay that are difficult to test under uniaxial compression. Additionally, it offers the advantages of requiring less testing effort, featuring simpler expression formulas, and possessing clearer physical meaning. Finally, the relationship between complex stress structure parameters and soil strength indicators was analyzed. We defined a quantitative indicator—the comprehensive structural state parameter $P_{cs}$—that reflects the combined effects of soil structural factors. By combining comprehensive structural state parameters with the physical properties of undisturbed loess (such as moisture content and dry density), it is possible to determine the strength parameters of loess under a given structural state over a wide range. Full article

Artificial intelligence (AI) often suffers from high energy consumption and complex deployment in resource-constrained environments, leading to a structural mismatch between capability and deployability. This review takes two representative scenarios—energy-first and performance-first—as the main thread, systematically comparing cloud, edge, and fog/cloudlet/mobile edge computing (MEC)/micro data center (MDC) architectures. Based on a standardized literature search and screening process, three categories of miniaturization strategies are distilled: redundancy compression (e.g., pruning, quantization, and distillation), knowledge transfer (e.g., distillation and parameter-efficient fine-tuning), and hardware–software co-design (e.g., neural architecture search (NAS), compiler-level, and operator-level optimization). The purposes of this review are threefold: (1) to unify the “architecture–strategy–implementation pathway” from a system-level perspective; (2) to establish technology–budget mapping with verifiable quantitative indicators; and (3) to summarize representative pathways for energy- and performance-prioritized scenarios, while highlighting current deficiencies in data disclosure and device-side validation. The findings indicate that, compared with single techniques, cross-layer combined optimization better balances accuracy, latency, and power consumption. Therefore, AI miniaturization should be regarded as a proactive method of structural reconfiguration for large-scale deployment. Future efforts should advance cross-scenario empirical validation and standardized benchmarking, while reinforcing hardware–software co-design. Compared with existing reviews that mostly focus on a single dimension, this review proposes a cross-level framework and design checklist, systematizing scattered optimization methods into reusable engineering pathways. Full article

Learning and implementing algorithms is a fundamental but challenging aspect of Computer Science education. One of the key tools used in teaching algorithms is pseudocode, which serves as an abstract representation of the logic behind a given algorithm. This study explores the educational value of the FLoCIC (Few Lines of Code for Image Compression) algorithm, which is designed to teach lossless image compression through algorithmic implementation, particularly within the context of multimedia data. Image compression represents a typical multimedia task that combines algorithmic thinking with practical problem-solving. By analysing questionnaire responses (N = 121) from undergraduate and graduate students, this study identifies critical challenges in pseudocode-based learning, including understanding complex algorithmic components and debugging recursive functions. This paper highlights the influence of prior knowledge in areas such as data structures, compression, and algorithms in general on the success of students in completing the task, with graduate students demonstrating stronger results compared to undergraduates. The study analyses the role of external resources and online code repositories, further revealing their utility in supporting implementation efforts but highlighting the need for a fundamental understanding of the algorithm for successful implementation. The findings highlight the importance of promoting conceptual understanding and practical problem-solving skills to improve student learning in algorithmic tasks. Full article

In multi-project environments characterized by resource constraints and high uncertainty, traditional scheduling approaches often fail to respond effectively to dynamic project conditions. Fixed activity durations and rigid resource allocations limit adaptability, leading to inefficiencies and delays. To address this, the paper proposes a novel heuristic-based scheduling method that compresses and decompresses activity durations dynamically within the context of multi-project scheduling under uncertainty and resource flexibility—while preserving resource and precedence feasibility. The technique integrates Critical Path Method (CPM) calculations with heuristic rules to identify candidate activities whose durations can be reduced or extended based on slack availability and resource effort profiles. The objective is to enhance scheduling flexibility, improve resource utilization, and better align project execution with organizational priorities and sustainability goals. Validated through a case study at an automotive company in Portugal, the method demonstrates its practical effectiveness in recalibrating schedules and balancing resource loads. This contribution offers a timely and necessary innovation for companies aiming to enhance responsiveness and competitiveness in increasingly complex project landscapes. It provides an actionable framework for dynamic schedule adjustment in multi-project environments, helping companies to respond more effectively to uncertainty and resource fluctuations. Importantly, the proposed approach also supports sustainability objectives in new product development and supply chain operations. For practitioners, the method offers a responsive and sustainable planning tool that supports real-time adjustments in project portfolios, enhancing resource visibility and execution resilience. For researchers, the study contributes a reproducible, Python-based implementation grounded in Design Science Research (DSR), addressing gaps in stochastic multi-project scheduling and sustainability-aware planning. Full article

The concept of “material effort” appears in continuum mechanics wherever the response of a material to the currently existing state of loads and boundary conditions loses its previous, predictable character. However, within the material, which still descriptively remains a continuous medium, new physical structures appear and new previously unused physical features of the continuum are activated. The literature is dominated by a simplified way of thinking, which assumes that all these states can be characterized and described by one and the same measure of effort—for metals it is the Huber–Mises–Hencky equivalent stress. Quantitatively, perhaps 90% of the literature is dedicated to this equivalent stress. The remaining authors, as well as the author of this paper, assume that there is no single universal measure of effort that would “fit” all operating conditions of materials. Each state of the structure’s operation may have its own autonomous measure of effort, which expresses the degree of threat from a specific destruction mechanism. In the current energy sector, we are increasingly dealing with “low-cycle thermal fatigue states”. This is related to the fact that large, difficult-to-predict renewable energy sources have been added. Professional energy based on coal and gas units must perform many (even about 100 per year) starts and stops, and this applies not only to the hot state, but often also to the cold state. The question arises as to the allowable shortening of start and stop times that would not to lead to dangerous material effort, and whether there are necessary data and strength characteristics for heat-resistant steels that allow their effort to be determined not only in simple states, but also in complex stress states. Do these data allow for the description of the material’s yield surface? In a previous publication, the author presented the results of tension and compression tests at elevated temperatures for two heat-resistant steels: St12T and 26H2MF. The aim of the current work is to determine the properties and strength characteristics of these steels in a pure torsion test at elevated temperatures. This allows for the analysis of the strength of power turbine components operating primarily on torsion and for determining which of the two tested steels is more resistant to high temperatures. In addition, the properties determined in all three tests (tension, compression, torsion) will allow the determination of the yield surface of these steels at elevated temperatures. They are necessary for the strength analysis of turbine elements in start-up and shutdown cycles, in states changing from cold to hot and vice versa. A modified testing machine was used for pure torsion tests. It allowed for the determination of the sample’s torsion moment as a function of its torsion angle. The experiments were carried out at temperatures of 20 °C, 200 °C, 400 °C, 600 °C, and 800 °C for St12T steel and at temperatures of 20 °C, 200 °C, 400 °C, 550 °C, and 800 °C for 26H2MF steel. Characteristics were drawn up for each sample and compared on a common graph corresponding to the given steel. Based on the methods and relationships from the theory of strength, the yield stress and torsional strength were determined. The yield stress of St12T steel at 600 °C was 319.3 MPa and the torsional strength was 394.4 MPa. For 26H2MH steel at 550 °C, the yield stress was 311.4 and the torsional strength was 382.8 MPa. St12T steel was therefore more resistant to high temperatures than 26H2MF. The combined data from the tension, compression, and torsion tests allowed us to determine the asymmetry and plasticity coefficients, which allowed us to model the yield surface according to the Burzyński criterion as a function of temperature. The obtained results also allowed us to determine the parameters of the Drucker-Prager model and two of the three parameters of the Willam-Warnke and Menetrey-Willam models. The research results are a valuable contribution to the design and diagnostics of power turbine components. Full article

Nano-modified concrete has attracted wide attention due to its improved mechanical properties. Among them, compressive strength is the most critical indicator. However, testing nano-concrete is costly and complex because it requires control over many factors, such as nanoparticle content and dispersion. Machine learning offers a data-driven way to predict compressive strength more efficiently. It reduces trial-and-error efforts and supports mix design optimization. Currently, machine learning is more adept at handling complicated datasets than experimental and traditional statistical models. In this article, the development of machine learning research in predicting the strength of concrete enhanced by nanoparticles is reviewed. First, we systematically outline a three-phase ML framework encompassing data curation, model development, and validation protocols; next, popular algorithms and their uses in predicting the strength of nano-modified concrete are evaluated, such as Artificial Neural Networks, K-Nearest Neighbor, Random Forest, etc. Ultimately, the article offers a forward-looking perspective on how future machine learning advancements can foster and accelerate the development of nano-modified concrete. Full article

The transport of deformable particles (TDPs) through porous media has been of considerable interest due to the multiple applications found in industrial and medical processes. The adequate design of these applications has been mainly achieved through experimental efforts, since TDPs through porous media are challenging to model because of the mechanical blockage of the pore throat due to size exclusion, deformation in order to pass through the pore throat under the driven pressure, and breakage under strong extrusion. In this work, based on the diffusivity equation and considering the TDP as a complex fluid whose viscosity and density depend on the local pressure, a simple but accurate theoretical model is proposed to describe the pressure behavior under steady- and unsteady-state flow conditions. Assuming a linear pressure dependence of the viscosity and density of the TDPs, valid for moderate pressure changes, the solution of the mathematical model yields a quantitative correlation between the pressure evolution and the parameters compressibility, viscosity coefficient, elastic modulus, particle size, and friction factor. The predictions of the model agree with experiments and allow the understanding of transport of deformable particles through a porous media. Full article

This paper presents a detailed analytical approach for the bending analysis of reinforced concrete beams, integrating both structural mechanics principles and Eurocode 2 provisions. The general analytical expressions derived for the curvature were applied for the transverse displacement analysis of a simply supported reinforced concrete beam under four-point loading, focusing on key limit states: the initiation of cracking, the yielding of tensile reinforcement and the compressive failure of concrete. The displacement’s results were validated through experimental testing, showing a high degree of accuracy in the elastic and crack propagation phases. Deviations in the yielding phase were attributed to the conservative material assumptions within the Eurocode 2 framework, though the analytical model remained reliable overall. To streamline the computational process for more complex structures, a simplified model utilising a non-linear rotational spring was further developed. This model effectively captures the influence of cracking with significantly reduced computational effort, making it suitable for serviceability limit state analyses in complex loading scenarios, such as seismic impacts. The results demonstrate that combining detailed analytical methods with this simplified model provides an efficient and practical solution for the analysis of reinforced concrete beams, balancing precision with computational efficiency. Full article

Tailing storage facilities are very complex structures whose failure generally leads to catastrophic consequences in terms of casualties, serious environmental impacts on local biodiversity, and disruptions in the mineral supply. For this reason, structures at risk must be reinforced or decommissioned. One possible option is its reinforcement with compacted filtered tailings stabilized with binders. Alkali-activated binders provide a more sustainable solution than ordinary Portland cement but require an optimization of the tailing–binder mixture, which, in some cases, can lead to a substantial experimental effort. Statistical models have been used to reduce the number of those experiments, but a rational design methodology is still lacking. This methodology to define the right mixture for a required strength should consider both the mixture components and in situ conditions. In this paper, response surface methods were used to plan and interpret unconfined compression strength test results on an iron tailing stabilized with alkali-activated binders. It was concluded that the fly ash content was the most important parameter, followed by the liquid content and sodium hydroxide concentration. From the obtained results, several statistical models were defined and compared according to the definition of a strength prediction model based on a mixture index parameter. It was interesting to observe that models with the porosity cement index still provide reasonable adjustment even when different tailings’ water contents are considered. Full article

Introduction: Lumbar foraminal stenosis (LFS) occurs primarily due to degenerative changes in older adults, affecting the spinal foramina and leading to nerve compression. Characterized by pain, numbness, and muscle weakness, LFS arises from structural changes in discs, joints, and ligaments, further complicated by factors like inflammation and spondylolisthesis. Diagnosis combines patient history, physical examination, and imaging, while management ranges from conservative treatment to surgical intervention, underscoring the need for a tailored approach. Materials and Methods: This multicenter study, conducted over six years at a tertiary hospital, analyzed the volumetric dimensions of lumbar foramina and their correlation with nerve structures in 500 patients without lumbar pathology. Utilizing high-resolution MRI with a standardized imaging protocol, eight experienced researchers independently reviewed the images for accurate measurements. The study emphasized quality control through the calibration of measurement tools, double data entry, validation checks, and comprehensive training for researchers. To ensure reliability, interobserver and intraobserver agreements were analyzed, with statistical significance determined by kappa statistics and the Student’s t-test. Efforts to minimize bias included blinding observers to patient information and employing broad inclusion criteria to mitigate referral and selection biases. The methodology and findings aim to enhance the understanding of normal lumbar foramina anatomy and its implications for diagnosing and treating lumbar conditions. Results: The study’s volumetric analysis of lumbar foramina in 500 patients showed a progressive increase in foraminal volume from the L1/L2 to the L5/S1 levels, with significant enlargement at L5/S1 indicating anatomical and biomechanical complexity in the lumbar spine. Lateral asymmetry suggested further exploration. High interobserver and intraobserver agreement levels (ICC values of 0.91 and 0.95, respectively) demonstrated the reliability and reproducibility of measurements. The patient cohort comprised 58% males and 42% females, highlighting a balanced gender distribution. These findings underscore the importance of understanding foraminal volume variations for lumbar spinal health and pathology. Conclusion: Our study significantly advances spinal research by quantifying lumbar foraminal volumes, revealing a clear increase from the L1/L2 to the L5/S1 levels, indicative of the spine’s adaptation to biomechanical stresses. This provides clinicians with a precise tool to differentiate between pathological narrowing and normal variations, enhancing the detection and treatment of lumbar foraminal stenosis. Despite limitations like its cross-sectional design, the strong agreement in measurements underscores the method’s reliability, encouraging future research to further explore these findings’ clinical implications. Full article