Search Results (1,521)

Senotherapeutic agents targeting senescent cell (SnC) accumulation represent a promising frontier in aging research. These agents encompass senolytics that selectively eliminate accumulated SnCs and senomorphics that suppress the pathological persistence of the senescence-associated secretory phenotype (SASP). Concerns regarding off-target effects of synthetic senolytics have intensified interest in plant-derived alternatives that offer multitargeted mechanisms and favorable safety profiles. This scoping review was conducted following Joanna Briggs Institute guidelines and PRISMA-ScR, mapped preclinical evidence on plant-derived senotherapeutics published between 2015 and 2025 across PubMed, Scopus, Web of Science, Wiley Library and Google Scholar. Of 1355 identified articles, 111 studies met inclusion criteria. Most characterized compound classes included flavonoids, non-flavonoid polyphenols and stilbenes, terpenoids and alkaloids, and combination and complex plant extracts. Mechanistically, BCL-2/BCL-XL apoptosis, PI3K/AKT/mTOR and p53/p21/p16^INK4a modulation emerged as senolytic mechanisms, while NF-κB-mediated SASP suppression predominated among senomorphic agents. Ginkgetin-mediated cyclic GMP-AMP-synthase–stimulator of interferon genes (cGAS-STING) inhibition was identified as a mechanistically novel target within natural senotherapy. Metabolomics demonstrated dual utility in guiding compound discovery from complex plant matrices (e.g., phenolamides from Allium hookeri) and mechanistic validation by characterizing senescence-associated metabolic remodeling, including retinoic acid metabolism restoration, lipotoxic metabolites attenuation, tricarboxylic acid (TCA) cycle, and choline-betaine-TCA cascade regulation. However, challenges in pharmacokinetic optimization, methodological heterogeneity in senescence induction and biomarker panels persist. Plant-derived senotherapy characterized through metabolomics-guided pipelines provides a compelling foundation for their progression toward clinical validation and functional food applications as accessible interventions for healthy aging and age-related disease management. Full article

Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) are emerging as the technology of choice for next-generation power conversion systems, offering switching speeds, on-state resistance, and power density unattainable with silicon or even silicon carbide (SiC) devices. However, the fast switching transients that make GaN attractive also make it a demanding source of electromagnetic interference (EMI), so credible electromagnetic compatibility (EMC) analysis requires an accurate functional device model. This paper addresses the functional electrothermal modeling of a commercial 650 V GaN HEMT (GS66504B) as a prerequisite to EMC validation. The manufacturer-supplied Level 3 SPICE model is evaluated against experimental static (I-V) and dynamic (C-V) measurements. Significant discrepancies motivate an optimization methodology in which an initial manual procedure is superseded by a fully automated pipeline coupling LTspice with a Genetic Algorithm in MATLAB R2025b. A forward/reverse and dual-temperature-segment strategy reduces the mean absolute relative error to below 7% (forward I-V) and 13% (reverse I-V) over 25–100 °C, while a dedicated two-stage C–V optimization reduces the reverse-transfer capacitance error from 95.4% to 2.89%. The resulting compact, unified, and fully validated model underpins the ongoing EMC validation phase, where it will be combined with extracted parasitic and cable models in a DC-DC converter topology. Full article

Gleeble-3800 thermal simulation testing machine was adopted to investigate the evolution laws of microstructure and properties in the coarse-grained heat-affected zone (CGHAZ) of pipeline steels with different Cr mass fractions (0.2, 0.5, 0.8 wt.%) under welding heat inputs ranging from 8 kJ/cm to 20 kJ/cm. Combined with optical microscopy, scanning electron microscopy and electron backscatter diffraction, the coupled influencing mechanism of heat input and Cr content on the properties of CGHAZ was systematically analyzed. The results show that as the Cr content increases, the range of valid welding heat input for maintaining satisfactory CGHAZ impact toughness gradually narrows with increasing Cr mass fraction of the steel. Specifically, the 0.2Cr experimental steel maintains high toughness under a thermal input ranging from 8 to 20 kJ/cm, the 0.5Cr experimental steel exhibits relatively high toughness in the range 8 to 13 kJ/cm, while the 0.8Cr experimental steel shows high toughness only at 15 kJ/cm. The coupled effect of weld heat input and Cr content on CGHAZ properties originates from a combination of microstructural composition types, phase fractions, substructures, and grain sizes. Increasing the heat input and Cr content leads to a reduction in the bainite ferrite with superior toughness and an increase in the large-sized granular bainite with inferior toughness. Meanwhile, the effective grain size of the overall microstructure first decreases and then rises. Grain coarsening and an increased fraction of Martensitic/Austenitic (M/A) constituent are the key factors responsible for the deterioration of CGHAZ toughness in deep-sea oil and gas transportation pipeline steel. Full article

European energy markets have experienced significant instability as a result of consecutive global systemic shocks, particularly the COVID-19 pandemic and the geopolitical conflict in Ukraine. This paper analyses the development of crude oil and natural gas prices between 2019 and 2024 and discusses their implications for sustainable logistics, energy security and supply chain resilience in Europe. The study is based on secondary data from internationally recognized sources, including the International Energy Agency, OPEC, Eurostat, the European Council, the World Bank and the U.S. Energy Information Administration. An event-based comparative approach supported by descriptive price-change calculations was applied to distinguish between the pandemic-related demand shock and the geopolitical supply-side shock after 2022. The results show that crude oil prices declined from approximately 64 USD/barrel in 2019 to 41 USD/barrel in 2020, representing a decrease of about 3f5.9%, mainly in connection with reduced mobility, lower transport activity and industrial slowdown during the COVID-19 pandemic. In contrast, crude oil prices increased to approximately 100 USD/barrel in 2022, representing an increase of about 143.9% compared to 2020, coinciding with geopolitical uncertainty and supply-side pressures. The European natural gas market appeared particularly vulnerable to the 2022 crisis because of supplier dependence, pipeline infrastructure constraints and reduced Russian gas flows. EU natural gas demand declined by 55 billion m³, or 13%, in 2022, indicating the effect of high prices, energy savings and crisis adaptation. The findings suggest that crude oil shocks are mainly related to transport costs and freight rates, while natural gas shocks may influence energy-intensive production, warehousing, cold chains and broader supply chain stability. The study highlights the need for energy diversification, renewable and low-carbon energy development, energy efficiency and more resilient logistics strategies. Full article

Accurate tracking and localization of in-line inspection (ILI) tools are essential for mileage calibration, defect mapping, and blockage prevention in oil and gas pipelines. This review summarizes sensor-based approaches for external ILI-tool localization, emphasizing how sensing physics, deployment geometry, and signal interpretation determine practical performance. Extremely low-frequency (ELF) magnetic tracking is first examined through dipole modeling, sensor evolution, and weak-signal recovery under steel-pipe and soil shielding. Distributed fiber-optic sensing is then reviewed as a continuous-tracking alternative, with attention to fading mitigation, spatiotemporal denoising, and trajectory extraction from distributed acoustic sensing data. Acoustic arrays and hybrid schemes are discussed as complementary options for subsea or cable-free environments. Finally, the review assesses how data fusion and lightweight artificial intelligence (AI) can improve robustness while noting unresolved issues in field data availability, edge computing, and uncertainty quantification. The synthesis indicates that next-generation ILI tracking should combine heterogeneous sensing, physics-aware signal processing, and deployment-aware model design rather than rely on a single high-sensitivity sensor. Full article

Prediction of the time to failure of pipeline materials supports inspection scheduling within risk-based inspection (RBI) frameworks. We compare three survival models, namely the Cox proportional hazards model (Cox PH), the random survival forest (RSF), and XGBoost with the survival:cox objective, on 823 corrosion incidents from the PHMSA database (1986–2025) retained after an engineering-validity screen. The comparison turns on a point that is easy to miss at the level of implementation. For the survival:cox objective, the predict() method of XGBoost returns the hazard ratio exp($\hat{\mathsf{\eta }}$), whereas the Breslow estimator of the baseline cumulative hazard expects the linear predictor $\hat{\mathsf{\eta }}$ and applies the exponential internally. Chaining the two evaluates exp(exp($\hat{\mathsf{\eta }}$)). Both maps are strictly increasing, so the concordance index is identical under either scale, to the last representable digit in our computation, and the defect does not appear in discrimination-based validation, while the estimated survival function is destroyed. A simulation with a known survival function isolates the magnitude: substituting the hazard ratio for the linear predictor raises the mean absolute deviation of Ŝ(t | x) from 0.073 to 0.398 and the integrated Brier score (IBS) from 0.154 to 0.395, at an unchanged concordance index of 0.723. On the pipeline corpus, the erroneous scale inflates the IBS to 0.356 ± 0.043 over 20 random splits. On the correct scale, XGBoost-Cox attains C = 0.871 ± 0.017 and IBS = 0.0614 ± 0.0056 and exceeds both reference models on both axes simultaneously (Cox PH 0.853 ± 0.018 and 0.0717 ± 0.0067; RSF 0.807 ± 0.020 and 0.0751 ± 0.0030; two-sided Wilcoxon p = 1.9 × 10⁻⁶ throughout). It also attains the lowest expected calibration error at every horizon from 10 to 40 years. No post hoc calibration is required, and adding one degrades the result. Out-of-time validation preserves the discrimination, whereas the absolute probabilities require periodic re-estimation of the baseline hazard. The feature-importance hierarchies agree with the ISO 8044 classification of corrosion factors. Full article

Blending hydrogen into existing natural-gas pipeline networks provides a practicable route toward future low-carbon applications. A Venturi-type mixer is a classical high-efficiency static gas-mixing device, and clarifying the effects of its structural parameters is important for efficient transport and downstream combustion stability. In this study, numerical simulations were performed in ANSYS Fluent 2024 R1. The contraction angle, throat length, and diffuser angle were selected as representative structural variables. First, the independent effects of these variables on the mixing process were examined through single-factor simulations. Then, three key levels of the three structural parameters were selected to establish a Box–Behnken experimental matrix for response-surface modeling. Based on the numerical results, entropy weighting and a genetic algorithm were used for multi-objective optimization, and the final solution was verified using the TOPSIS method. The results show that the optimized Venturi-type mixing device with optimized parameters of a contraction angle of 20.7°, a throat length of 60 mm, and a diffuser angle of 5° can reduce flow energy loss while maintaining high mixing uniformity. The diffuser angle is the dominant geometric parameter affecting both energy loss and mixing behavior. Compared with the reference central-point structure design, the overall TOPSIS score of the optimized structure increased from 0.41 to 0.82; the pressure loss decreased from 258.94 Pa to 206 Pa, corresponding to a reduction of approximately 20%; and the final-section mixing uniformity decreased only slightly, from 97.85% to 97.43%. Full article

Energy price shocks have been the main drivers of inflation in Europe during the last decade. This paper examines cross-country differences in the transmission and magnitude of energy supply shocks in the Visegrád Group countries (Poland, Czechia, Hungary, and Slovakia) over the period 2015–2026. To identify energy-related disturbances and evaluate their macroeconomic effects, we estimate Bayesian Structural Vector Autoregressive (BSVAR) models with sign restrictions and complement the analysis with Local Projections. The results indicate substantial heterogeneity in inflation responses across countries. Oil supply shocks increase inflation by approximately 0.8 percentage points in Czechia and 0.7 percentage points in Hungary, compared with around 0.5 percentage points in Poland and Slovakia. Similar response patterns are observed for gas supply shocks, suggesting that stronger commodity price pass-through contributes to the larger inflationary effects in Czechia and Hungary. The analysis also reveals methodological challenges in identifying the effects of increased LNG imports. Both models indicate LNG-related shocks are highly correlated with pipeline gas supply shocks. In fact, its imports have mitigated energy shortages and reduced price pressures. Full article

Online detection of micro-leakage in complex high-pressure gas pipeline networks is difficult to achieve using conventional methods. A high-pressure pipeline micro-leakage detection method based on background-oriented schlieren measurement and feature matching was proposed in this study to address this issue. Through the BOS measurement, the density distribution of the leakage fields was reconstructed through cross-correlation calculation and Poisson equation solving, which was further compared with numerical simulation results under specific operating conditions. The morphological characteristics of the jet field at different leakage pressures were revealed by comparing the density fields from different experimental conditions. Subsequently, the displacement field data were compressed into one-dimensional feature representations for structure-oriented matching of leakage-field characteristics, with temporal smoothing and a dual-threshold hysteresis strategy incorporated to improve matching robustness. The results show that the error in the peak density remains below 10%, which indicates good consistency between the background-oriented schlieren measurements and the numerical simulations. Meanwhile, the one-dimensional feature curve accelerates computation while retaining the dominant characteristics of the leakage field. The proposed framework achieves an area under the ROC curve of 0.992 and an average precision of 0.998. At the selected threshold of 0.650, the overall evaluation metric reaches 0.972, reflecting a favorable balance between sensitivity and reliability. Furthermore, the temporal stabilization strategy improves alarm continuity and suppresses chattering during detection. Full article

Magnetic leakage detection is one of the most commonly used methods for pipeline inspection, which mainly uses magnetic sensors to detect the magnetic leakage field on the internal and external surfaces of the pipeline to determine whether there are defects in the pipeline. The defect quantification algorithm includes a forward model and an optimization algorithm, in which the estimation of target defects using optimization algorithms is one of the key aspects of defect inversion. Most of the existing optimization algorithms are based on particle swarm algorithms (PSOs) and genetic algorithms (GAs), which are prone to premature problems and have low convergence accuracy. To address the problems in the process of defect inversion, this paper proposes a new inversion algorithm, which obtains part of the prior knowledge from the application context of defect inversion, and adopts the decay oscillation function as the nonlinear convergence factor based on the whale optimization algorithm (WOA). In addition, referring to the concepts of “genetic” and “mutation” in the GA, a diversity variation strategy based on dynamic step size is designed. The algorithm designed has the advantages of fast operation and high search accuracy. At the end of the paper, two sets of experiments are designed to compare the improved WOA with other existing optimization algorithms. The results demonstrate that the algorithm is significantly superior to other algorithms, both in the ideal case of simulation experiments and in the practical application of defect inversion. Full article

In salt cavern compressed air energy storage (CAES) systems, slag particles entrained by high-pressure airflow can cause pipeline wear and flow instability, posing challenges to long-term operational safety. However, direct experimental studies are constrained by high-pressure, large-scale conditions and transient multiphase flow complexities. This study uses Fluent, a numerical simulation method based on gas–solid two-phase flow theory, to investigate the flow characteristics, particle dynamics, and erosion behavior in the above-ground pipeline of CAES system. Results reveal uneven gas velocity distribution, with the lowest flow (≤2.3 (m/s)) in the main pipeline favoring particle deposition, and complex vortex structures at branch connections. Particles accumulate on the outer wall of 90° elbows due to centrifugal effects, leading to localized erosion, with severe wear occurring at impact angles of 20–30°. Over a 30-year operational cycle, the predicted maximum wear depth is 0.38 mm, which remains below the existing protective cladding thickness of 0.5 mm. The findings not only provide a theoretical basis and design insights for optimizing wear protection strategies, but also hold positive implications for enhancing the economic sustainability and environmental benefits of large-scale energy storage systems. Full article

River-channel washout is one of the common geological hazards threatening the safety of long-distance oil and gas pipelines, particularly under storm-flood conditions, when pipeline sections crossing rivers and gullies are more susceptible to damage. Existing assessment methods for river-channel washout are effective for single river cross-sections or post-disaster field investigations; however, their application remains limited when dealing with long-distance pipeline systems characterized by numerous river- and gully-crossing sections and large spatial variability in upstream catchment conditions. To address this issue, this study investigates storm-flood discharge and scour-depth calculation methods suitable for river- and gully-crossing sections of long-distance oil and gas pipelines, and establishes a quantitative evaluation index system that considers river-channel washout susceptibility, pipeline vulnerability, and pipeline failure consequences. Based on investigation results of river-channel washout hazards along multiple pipeline systems, including the Zhongxian–Yichang section of the Zhongwu Pipeline, the Hubei–Hunan section of the Lanzhou–Zhengzhou–Changsha Pipeline, and the Phase I Jiangxi Natural Gas Pipeline Network, the hazard characteristics and influencing factors of river-channel washout affecting long-distance oil and gas pipelines are analyzed and summarized. The proposed method was applied to 19 river- and gully-crossing pipeline sections in the Phase I Jiangxi Natural Gas Pipeline Network under different rainfall intensities. The results show that, under light-to-moderate rainfall conditions, 15 sites were classified as relatively low risk and 4 sites as medium risk. Under both the 50-year and 100-year return-period rainstorm scenarios, 12 sites were classified as relatively low risk, 6 sites as medium risk, and 1 site as relatively high risk. The results also indicate that the risk probability of some sites increases with increasing rainfall intensity. Among them, Site No. 19 shows the highest risk probability, increasing from 0.0997 under light-to-moderate rainfall conditions to 0.1474 and 0.1488 under the 50-year and 100-year return-period rainstorm scenarios, respectively. The proposed method can provide a reference for meteorological risk assessment of river-channel washout hazards along long-distance oil and gas pipelines. Full article

This study provides a site-specific, unlevered financial screening of gas-to-power, gas-to-pipeline, and gas-to-compressed-natural-gas (CNG) options at three Nigerian flare sites: Amukpe (1.16 MMscf/d), Oziengbe South (5.15 MMscf/d), and Oben (10.88 MMscf/d). The cases are evaluated from the perspective of an independent third-party developer using discounted cash-flow analysis. The base case results show that gas-to-power produces NPVs of −US$7.68 million, −US$17.30 million, and US$18.28 million at Amukpe, Oziengbe South, and Oben, respectively. Gas-to-pipeline produces −US$0.72 million, US$2.75 million, and US$7.32 million, while gas-to-CNG produces −US$3.97 million, US$7.20 million, and US$21.62 million. These results are conditional on the stated technical, commercial, and infrastructure assumptions because the analysis is equity-financed and excludes debt sizing, debt-service coverage, lender covenants, and contractual due diligence. This study therefore establishes a transparent comparison of three monetisation pathways under Nigeria’s third-party flare-commercialisation framework. Thus, investment and/or policy decisions must be made only with a clear identification of the required site data. Full article

Demand for high-quality interactive and real-time rendering remains challenging, as it requires balancing image realism with computational resources. Static parameter tuning of traditional approaches cannot provide adaptive rendering according to the varying complexity of dynamic scenes. This limitation arises from two main deficiencies in existing rendering pipelines: reactive methods that only enhance images after rendering without optimizing the renderer itself, and proactive methods that still rely on manual parameter calibration for each scene. These shortcomings are solved by this paper with an innovative optimization method that is a combination of a genetic algorithm (GA) and artificial neural networks (ANNs). This method offers a closed-loop system that is not found in any other static pipeline. Specifically, in our approach, ANNs will be used to predict the renderer’s initial parameter values from scene descriptor data, such as the number of polygons, lighting, and materials. After predicting the parameters, GA will optimize them based on the fitness value, which is determined by maximizing one objective (perceptual quality, defined by the SSIM measure) and minimizing another (rendering time). Our approach can be easily implemented within standard pipeline frameworks (Autodesk Maya Arnold). Full article

Digital Twin (DT) technology has emerged as a transformative approach in pipeline engineering, enabling real-time monitoring, predictive analytics, and enhanced decision-making across the asset lifecycle. This review critically examines recent advancements in the application of digital twins for pipeline systems, with a particular focus on condition monitoring, leak detection, corrosion assessment, and predictive maintenance. The study synthesizes findings from a wide range of literature to identify key enabling technologies, including Internet of Things (IoT) sensors, data-driven modeling, computational fluid dynamics (CFD), and machine learning algorithms. Special attention is given to the integration of physics-based and data-driven models for improving the accuracy and reliability of digital twin frameworks. In addition, this paper proposes a unified reference architecture for pipeline digital twins, supported by a mathematical formulation of synchronization and a comparative synthesis of existing approaches. The review highlights how digital twins facilitate early fault detection and operational optimization by continuously synchronizing physical assets with their virtual counterparts. The review also emphasizes the importance of uncertainty-aware and reliability-informed digital twin frameworks for robust decision-making in safety-critical pipeline applications. Applications in subsea, oil and gas, and water distribution pipelines are explored, demonstrating the versatility of DT systems under different environmental and operational conditions. Despite significant progress, challenges remain in data integration, model validation, scalability, and cybersecurity. Furthermore, the lack of standardized architectures and interoperability frameworks limits widespread adoption. This paper concludes by outlining future research directions, including the development of hybrid modeling techniques, edge computing integration, and AI-driven autonomous decision systems. Overall, digital twin technology represents a paradigm shift in pipeline engineering, offering substantial potential to enhance safety, efficiency, and sustainability in complex infrastructure systems. Full article