Search Results (6,430)

Perforation, a common well completion method in oil and gas exploitation, introduces structural defects in casings that alter their mechanical properties. Based on engineering specifications, this study calculates critical loads (i.e., collapse pressure and yield pressure) and the triaxial equivalent stress for casings. Four load cases were selected for analysis: uniform external pressure, uniform internal pressure, external pressure with axial compression, and internal pressure with axial tension. The equivalent stresses around circular, elliptical, pentagonal, and hexagonal perforation defects were computed. A self-defined perforation influence coefficient was used to evaluate changes in mechanical performance. Results show that circular defects have the least effect on the mechanical properties of the casing. Maximum equivalent stress occurs along the hole centerline parallel to the casing axis and increases with greater disparity between ellipse axes or smaller polygon angles. High shot density (>24 holes/m) and large phase angle (60°) generally enhance safety, but an optimal combination exists. Under tensile loads near cracked defects, crack propagation may lead to fracture. For elliptical defects with cracks, the Mode I stress intensity factor grows faster with greater axis disparity, accelerating crack tip stress and deformation, and raising fracture risk. Cracks perpendicular to tensile stress influence the stress intensity factor more significantly than parallel ones. Full article

A critical aspect in the design of power electronics packages is the prediction of their mechanical response under severe thermomechanical loads and the consequent structural damage. For this purpose, finite element (FE) simulations are used to estimate the mechanical performance and reliability under operational conditions, typically alternate high voltages/currents resulting in thermal gradients. When simulations are performed, it is common practice to consider the as-received package to be in a stress-free state. Namely, residual stresses and plastic deformation induced by the manufacturing processes are neglected. In this study, an advanced FE modeling approach is proposed to assess the structural consequences of the encapsulating resin curing, typical in the production of silicon carbide (SiC)-based power electronics modules for electric vehicles. This work offers a general modeling framework that can be further employed to simulate the effects of thermal gradients induced by the production process on the effective shape and residual stresses of the as-received package for other manufacturing stages, such as metal brazing, soldering processes joining copper and SiC, and, to lower extents, the application of polyimide on top of passivation layers. The obtained results have been indirectly validated with experimental data from literature. Full article

Growing interest in sustainable hydrogen production has brought renewed attention to photoelectrochemical (PEC) water splitting as a promising route for direct solar-to-chemical energy conversion. This study explores how integrating hematite (α-Fe₂O₃) and cupric oxide (CuO) photoelectrodes with a series of nickel-based co-catalysts can improve photoelectrochemical activity. Photoanodic (NiO_x, NiFeO_x, NiWO₄) and photocathodic (Ni, NiCu, NiMo) co-catalysts were synthesized via co-precipitation and mechanochemical methods and characterized through X-ray Diffraction (XRD), X-ray Fluorescence (XRF), Transmission Electron Microscopy–Energy Dispersive X-ray Spectroscopy (TEM-EDX), Scanning Electron Microscopy–Energy Dispersive X-ray Spectroscopy (SEM-EDX), X-ray photoelectron spectroscopy (XPS) and Brunauer–Emmett–Teller (BET) gas-adsorption analyses to clarify their crystallographic, morphological, and compositional properties, as well as their surface chemistry and textural properties (surface area and porosity). Electrochemical tests under 1 SUN illumination showed that NiO_x significantly improves the photocurrent of hematite photoanodes. Among the cathodic co-catalysts, NiMo demonstrated the best performance when combined with CuO photocathodes. For both photoelectrodes, an optimal co-catalyst loading was identified, beyond which performance declined due to potential charge transfer limitations and light attenuation. These findings highlight the critical role of co-catalyst composition and loading in optimizing the efficiency of PEC systems based on earth-abundant materials, offering a pathway toward scalable and cost-effective hydrogen production. Full article

The static nature of traditional military symbology, such as MIL-STD-2525D, hinders effective real-time threat detection and response in modern cybersecurity operations. This research introduces the Dynamic Adaptive Symbol System (DASS), a novel framework enhancing cyber situational awareness in military and enterprise environments. The DASS addresses static symbology limitations by employing a modular Python 3.10 architecture that uses machine learning-driven threat detection to dynamically adapt symbol visualization based on threat severity and context. Empirical testing assessed the DASS against a MIL-STD-2525D baseline using active cybersecurity professionals. Results show that the DASS significantly improves threat identification rates by 30% and reduces response times by 25%, while achieving 90% accuracy in symbol interpretation. Although the current implementation focuses on virus-based scenarios, the DASS successfully prioritizes critical threats and reduces operator cognitive load. Full article

Additive Manufacturing (AM) has attracted considerable interest over the past three decades, driven by growing industrial demand. Among metal AM techniques, Wire and Arc Additive Manufacturing (WAAM), a Directed Energy Deposition (DED) variant, has emerged as a prominent method for producing large-scale components with high deposition rates and cost efficiency. However, WAAM parts typically exhibit rough surface profiles, which can induce stress concentrations and promote fatigue crack initiation under cyclic loading. This study presents an integrated experimental and numerical investigation into the fatigue performance of as-built WAAM steel. Fatigue specimens extracted from a WAAM-fabricated wall were tested under cyclic loading, followed by fractography to assess the influence of surface irregularities and subsurface defects on fatigue behaviour. Surface topography analysis identified critical stress-concentration regions and key surface roughness parameters. Additionally, 3D scanning was used to reconstruct the specimen topography, enabling detailed 2D and 3D finite element (FE) modelling to analyze stress distribution along the as-built surface and predict fatigue life. A Smith-Watson-Topper (SWT) critical plane-based approach was applied for multiaxial fatigue life estimation. The results reveal a good correlation between experimental fatigue data and numerically predicted results, validating the proposed combined methodology for assessing durability of as-built WAAM components. Full article

Enhancing indoor environmental quality while reducing building energy consumption represents a critical challenge for sustainable building design, particularly in hot arid climates where cooling loads dominate energy use. Despite extensive research on green wall systems (GWSs), robust quantitative data on their combined impact on air quality and thermal performance in real-world office environments remains limited. This research quantified the synergistic effects of an active indoor green wall system on key indoor air quality indicators and cooling energy consumption in a contemporary office environment. A comparative field study was conducted over 12 months in two identical office rooms in Dhahran, Saudi Arabia, with one room serving as a control while the other was retrofitted with a modular hydroponic green wall system. High-resolution sensors continuously monitored indoor CO₂, volatile organic compounds via photoionization detection (VOC_PID; isobutylene-equivalent), and PM2.5 concentrations, alongside dedicated sub-metering of cooling energy consumption. The green wall system achieved statistically significant improvements across all parameters: 14.1% reduction in CO₂ concentrations during occupied hours, 28.1% reduction in volatile organic compounds, 20.9% reduction in PM2.5, and 13.5% reduction in cooling energy consumption (574.5 kWh annually). Economic analysis indicated financial viability (2.0-year payback; benefit–cost ratio 3.0; 15-year net present value SAR 31,865). Productivity-related benefits were valued from published relationships rather than measured in this study; base-case viability remained strictly positive in energy-only and conservative sensitivity scenarios. Strong correlations were established between evapotranspiration rates and cooling benefits (r = 0.734), with peak performance during summer months reaching 17.1% energy savings. Active indoor GWSs effectively function as multifunctional strategies, delivering simultaneous air quality improvements and measurable cooling energy reductions through evapotranspiration-mediated mechanisms, supporting their integration into sustainable building design practices. Full article

Microgrids with high renewable energy penetration face critical challenges in frequency stability due to reduced system inertia and the presence of parameter uncertainties. This study introduces a novel adaptive virtual inertia control strategy utilizing a combination of the Walrus Optimization Algorithm (WaOA), a recent metaheuristic optimization technique, and Proportional–Integral–Derivative (PID) controllers (WaOA-PID) to improve frequency regulation in islanded microgrids under diverse operating conditions. The proposed method is evaluated across three scenarios: medium inertia, low inertia, and parametric uncertainty. Comparative analyses with conventional, IMC-tuned PID and H∞ Vector Internal Controllers (VIC) reveal that the WaOA-PID controller achieves the lowest overshoot, undershoot, and rate of change of frequency (RoCoF), while maintaining acceptable settling times in all cases. At an estimated load deviation of 0.18, the demand is varied from 200 MW to 250 MW to evaluate the system’s performance. The proposed technique yields an Integral Time Absolute Error (ITAE) of 0.000576, with PID gains of Ki = 0.9994, Kd = 0.185, and Kp = 0.774. Compared to traditional methods, the proposed controller demonstrates high reliability and efficiency in maintaining load frequency control and enhancing power system management, validating its suitability for real-time renewable energy-integrated microgrid applications. Full article

Underreamed anchor bolts, as an emerging anchoring element in geotechnical engineering, operate via a fundamentally distinct load transfer mechanism compared with conventional friction type anchors. The accurate and reliable prediction of their ultimate bearing capacity constitutes a pivotal technological impediment to their broader engineering adoption. Firstly, this paper systematically elucidates the constituent mechanisms of underreamed anchor resistance and their progressive load transfer trajectory. Subsequently, in situ full-scale pull-out experiments are leveraged to decompose the load–displacement response throughout its entire evolution. The multi-stage development law and the underlying mechanisms governing the evolution of anchorage characteristics are thereby elucidated. Based on the experimental dataset, a three-dimensional elasto-plastic numerical model is rigorously established. The model delineates, at high resolution, the failure mechanism of surrounding soil mass and the spatiotemporal evolution of its three-dimensional displacement field. A definitive critical displacement criterion for the attainment of the ultimate bearing capacity of underreamed anchors is established. Consequently, analytical models for the ultimate side frictional stress and end-bearing capacity at the limit state are advanced, effectively circumventing the parametric uncertainties inherent in extant empirical formulations. Ultimately, characteristic parameters of the elasto-plastic branch of the load–displacement curve are extracted. An ultimate bearing capacity prognostic framework, founded on an optimized hyperbolic model, is established. Its superior calibration fidelity to the evolving load–displacement response and its demonstrable engineering applicability are rigorously substantiated. Full article

In urban redevelopment, adding basements beneath existing buildings often requires specialized retaining structures, such as existing and newly added double-row piles, yet their complex load-sharing mechanism is not yet fully understood. This study addresses this gap through a series of physical model tests, systematically investigating the influence of two key variables: the row spacing and the newly added/existing pile length ratio. The results reveal that row spacing is a critical factor governing the system’s stability and cooperative behavior. The newly added piles bear the majority of the earth pressure, effectively shielding the existing piles. A distinct, layered pressure distribution was observed in the inter-row soil, a phenomenon that classical earth pressure theories cannot adequately predict. Based on a comprehensive evaluation of structural performance, deformation control, and stability, this study proposes an optimized configuration with a row spacing of 4D and a newly added/existing pile length ratio of 9/6. This configuration achieves an effective balance between structural performance and economic efficiency, offering valuable practical guidance for the design of supplementary retaining systems in basement addition projects. Full article

Efficient resource utilization and low response times are critical challenges in storage area network (SAN) systems, especially as data-intensive applications like those driven by the Internet of Things and Artificial Intelligence place increasing demands on reliable, high-performance data storage solutions. Addressing these challenges, this paper contributes by proposing a proactive, utilization-driven traffic redistribution strategy to achieve balanced load distribution across switches, thereby improving the overall SAN performance and alleviating the risk of overload-incurred cascading failures. The proposed approach incorporates a Jackson Queueing Network-based method to evaluate both utilization and response time of individual switches, as well as the overall system response time. Based on a comprehensive case study of a mesh SAN system, two key parameters—the transition probability adjustment step size and the node selection window size—are analyzed for their impact on the effectiveness of the proposed strategy, revealing several valuable insights into fine-tuning traffic redistribution parameters. Full article

The agro-industry is among the largest methane emitters, posing a critical challenge for sustainability. In rural areas, producers lack effective technologies to manage daily organic waste. Anaerobic digestion (AD) offers a circular pathway by converting waste into biogas and biofertilizers; however, its adoption is limited by inappropriate designs and insufficient operational control. Theoretical-applied research addresses these barriers by improving the design and operation of small-scale biodigesters, elevating pH and Electrical Conductivity (EC) from passive indicators to first-order control variables. Based on the design of a compact biodigester previously validated in the Chillón Valley and replicated in Huaycán under a utility model patent process (INDECOPI, Exp. 001087-2025/DIN), a stoichiometric NaHCO${}_3$ strategy with joint pH–EC monitoring was formalized, defining operational windows (pH 6.92–6.97; EC 6200–6300 μS/cm and dose–response curves (0.3–0.4 kg/day for 3–4 day) to buffer VFA shocks and preserve methanogenic ionic strength. The system achieved stable productions of 370–462 L/day, surpassing the theoretical potential of 352.88 L/day calculated by Buswell’s equation. A multivariable predictive model (linear, quadratic, interaction terms pH × EC, temperature, and loading rate) was developed and validated with field data: R² = 0.78; MAPE = 2.7%; MAE = 11.2 L/day; RMSE = 13.8 L/day; r = 0.89; residuals normally distributed (Shapiro–Wilk p = 0.79). The proposed approach enables daily decision-making in low-instrumentation environments and provides a replicable and scalable pathway for the safe valorization of organic waste in rural areas. The design consolidates the shift from reactive to proactive and co-optimized pH–EC control, laying the foundation not only for standardized protocols and training in rural systems but also for improved environmental sustainability. Full article

The China Railway Express (China-Europe container railway freight transport) is pivotal to Eurasian freight, yet its transcontinental railway faces escalating cascading risks. We develop a coupled map lattice (CML) model representing the physical infrastructure layer and the operational traffic layer concurrently to quantify and mitigate cascading failures. Twenty critical stations are identified by integrating TOPSIS entropy weighting with grey relational analysis in dual-layer networks. The enhanced CML embeds node-degree, edge-betweenness, and freight-flow coupling coefficients, and introduces two adaptive cargo-redistribution rules—distance-based and load-based for real-time rerouting. Extensive simulations reveal that network resilience peaks when the coupling coefficient equals 0.4. Under targeted attacks, cascading failures propagate within three to four iterations and reduce network efficiency by more than 50%, indicating the vital function of higher importance nodes. Distance-based redistribution outperforms load-based redistribution after node failures, whereas the opposite occurs after edge failures. These findings attract our attention that redundant border corridors and intelligent monitoring should be deployed, while redistribution rules and multi-tier emergency response systems should be employed according to different scenarios. The proposed methodology provides a dual-layer analytical framework for addressing cascading risks of transcontinental networks, offering actionable guidance for intelligent transportation management of international intermodal freight networks. Full article

Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H₂–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H₂ and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article

Orthopedic implants have a critical role in modern medical practice, being useful in bone regeneration, joint arthroplasty, and healing fractures. The success of osseointegration depends on implant properties (composition, stability, geometry, biocompatibility) and host factors (local reactivity, comorbidities). Preclinical evaluation in animal models is essential before clinical application. In orthopedic implantology, the selection and real utility of a range of animals are important, with an emphasis placed on bone–implant interface, biomechanical function, and long-term integration. Smaller animals such as rabbits and rats have widespread use in early biocompatibility and osseointegration testing, but larger animals such as pigs, sheep, and canines have a larger physiological bone similarity and can, therefore, be utilized for bearing loads in testing. Considering the utility and disadvantages of certain species—including suitability for new biomaterials, coatings, and biomechanical function—this article discusses testing methodologies such as push-out/pull-out tests, histomorphometry, and micro-CT and their utility in testing the integration of implants and regeneration of bone. Conclusions confirm a multi-species model in use in preclinical testing for the development of implants and improvements in clinical success. Unlike previous reviews, this article emphasizes translational strategies, integrates ethical perspectives in model selection, and discusses the synergistic use of imaging modalities with biomechanical tests for comprehensive assessment. Full article

Background: Healthcare 5.0 represents a shift toward intelligent, human-centric care systems. Intensive care units generate vast amounts of data that require real-time decisions, but current decision support systems lack comprehensive frameworks for safe integration of artificial intelligence. Objective: We developed and validated the Learn–Predict–Monitor–Detect–Correct (LPMDC) framework as a methodology for systematic artificial intelligence integration across the critical care workflow. The framework improves predictive analytics, continuous patient monitoring, intelligent alerting, and therapeutic decision support while maintaining essential human clinical oversight. Methods: Framework development employed systematic theoretical modeling integrating Healthcare 5.0 principles, comprehensive literature synthesis covering 2020–2024, clinical workflow analysis across 15 international ICU sites, technology assessment of mature and emerging AI applications, and multi-round expert validation by 24 intensive care physicians and medical informaticists. Each LPMDC phase was designed with specific integration requirements, performance metrics, and safety protocols. Results: LPMDC implementation and aggregated evidence from prior studies demonstrated significant clinical improvements: 30% mortality reduction, 18% ICU length-of-stay decrease (7.5 to 6.1 days), 45% clinician cognitive load reduction, and 85% sepsis bundle compliance improvement. Machine learning algorithms achieved an 80% sensitivity for sepsis prediction three hours before clinical onset, with false-positive rates below 15%. Additional applications demonstrated effectiveness in predicting respiratory failure, preventing cardiovascular crises, and automating ventilator management. Digital twins technology enabled personalized treatment simulations, while the integration of the Internet of Medical Things provided comprehensive patient and environmental surveillance. Implementation challenges were systematically addressed through phased deployment strategies, staff training programs, and regulatory compliance frameworks. Conclusions: The Healthcare 5.0-enabled LPMDC framework provides the first comprehensive theoretical foundation for systematic AI integration in critical care while preserving human oversight and clinical safety. The cyclical five-phase architecture enables processing beyond traditional cognitive limits through continuous feedback loops and system optimization. Clinical validation demonstrates measurable improvements in patient outcomes, operational efficiency, and clinician satisfaction. Future developments incorporating quantum computing, federated learning, and explainable AI technologies offer additional advancement opportunities for next-generation critical care systems. Full article