Search Results (202)

The Threshold Pressure Gradient (TPG) phenomenon exerts a profound influence on fluid flow dynamics in heavy oil reservoirs. However, the discrepancies between the True Threshold Pressure Gradient (TTPG) and Pseudo-Threshold Pressure Gradient (PTPG) significantly impede accurate residual oil evaluation and rational field development planning. This study proposes a dual-exponential conversion model that effectively bridges the discrepancy between TTPG and PTPG, achieving an average deviation of 12.77–17.89% between calculated and measured TTPG values. Nonlinear seepage simulations demonstrate that TTPG induces distinct flow barrier effects, driving residual oil accumulation within low-permeability interlayers and the formation of well-defined “dead oil zones.” In contrast, the linear approximation inherent in PTPG overestimates flow initiation resistance, resulting in a 47% reduction in recovery efficiency and widespread residual oil enrichment. By developing a TTPG–PTPG conversion model and incorporating genuine nonlinear seepage characteristics into simulations, this study effectively mitigates the systematic errors arising from the linear PTPG assumption, thereby providing a scientific basis for accurately predicting residual oil distribution and enhancing oil recovery efficiency. Full article

Deployable thin-film antennas deliver large aperture gains and high stowage efficiency for spaceborne phased arrays but suffer wrinkling-induced planarity loss and radiation distortion. To bridge the lack of electromechanical coupling models for tensioned thin-film patch antennas, we present a unified framework combining structural deformation and electromagnetic simulation. We derive a coupling model capturing the increased bending stiffness of stepped-thickness membranes, formulate a wrinkling analysis algorithm to compute tension-induced displacements, and fit representative unit-cell deformations to a dual-domain displacement model. Parametric studies across stiffness ratios confirm the framework’s ability to predict shifts in pattern, gain, and impedance due to wrinkling. This tool supports the optimized design of wrinkle-resistant thin-film phased arrays for reliable, high-performance space communications. Full article

Multi-agent reinforcement learning has demonstrated excellent performance in complex decision-making tasks such as electronic games, power grid management, and autonomous driving. However, its vulnerability to adversarial attacks may impede its widespread application. Currently, research on adversarial attacks in reinforcement learning primarily focuses on single-agent scenarios, while studies in multi-agent settings are relatively limited, especially regarding how to achieve optimized attacks with fewer steps. This paper aims to bridge the gap by proposing a heuristic exploration-based attack method named the Search for Key steps and Key agents Attack (SKKA). Unlike previous studies that train a reinforcement learning model to explore attack strategies, our approach relies on a constructed predictive model and a T-value function to search for the optimal attack strategy. The predictive model predicts the environment and agent states after executing the current attack for a certain period, based on simulated environment feedback. The T-value function is then used to evaluate the effectiveness of the current attack. We select the strategy with the highest attack effectiveness from all possible attacks and execute it in the real environment. Experimental results demonstrate that our attack method ensures maximum attack effectiveness while greatly reducing the number of attack steps, thereby improving attack efficiency. In the StarCraft Multi-Agent Challenge (SMAC) scenario, by attacking 5–15% of the time steps, we can reduce the win rate from 99% to nearly 0%. By attacking approximately 20% of the agents and 24% of the time steps, we can reduce the win rate to around 3%. Full article

The eGaN HEMT power devices face serious crosstalk problems when applied to high-frequency bridge circuits, thereby limiting the switching performance of these devices. To address this issue, a gate driver is proposed in this paper that can suppress both positive and negative crosstalk of eGaN HEMT power devices, offering the advantages of simple control and easy integration. The basic idea is to suppress positive crosstalk by constructing a negative voltage capacitor, and to suppress negative crosstalk by reducing the impedance of the gate loop. To verify the capability of the proposed gate driver, double-pulse and synchronous Buck test platforms are constructed. The experimental results clearly demonstrate that the proposed gate driver reduces the positive and negative crosstalk spikes by 2.03 V and 1.54 V, respectively, ensuring that the positive and negative crosstalk spikes fall within a safe operating range. Additionally, the turn-off speed of the device is enhanced, leading to a reduction in switching loss. Full article

Water’s unique physicochemical properties arise from its dynamic hydrogen-bonding network, yet the precise molecular threshold at which these cooperative behaviors emerge remains a key question. This study employed nuclear magnetic resonance (NMR) spectroscopy and density functional theory (DFT) calculations to investigate the evolution of hydrogen bonding strength in small water clusters, ranging from dimers to pentamers. The observed exponential increase in NMR chemical shift up to the pentamer reflects growing hydrogen bond cooperativity, identifying the (H₂O)₅ cluster as a critical structural and energetic threshold. At this size, the network achieves sufficient connectivity to support key bulk-like phenomena such as proton transfer and dielectric relaxation. These conclusions were corroborated by complementary FT-IR and electrochemical impedance spectroscopy (EIS) measurements of bulk water. Our results position the water pentamer as the molecular onset of emergent solvent behavior, effectively bridging the divide between discrete clusters and the macroscopic properties of liquid water. Full article

Purpose: This study aims to provide a comprehensive and objective view to investigate whether the motives of strong financial managers to adopt an accounting conservatism strategy have significant effects on improving financial growth opportunities in the context of banks listed on the Saudi Stock Exchange, while knowing how this relationship is affected by litigation risks. Design/Methodology/Approach: Using data from Saudi financial databases, this study examines how litigation risk moderates the relationship between accounting conservatism and financial growth in Saudi listed banks. Basu’s (1997) model and accrual-based metrics measure conservatism, whereas assets, liabilities, and business age are used to measure financial growth. Litigation risk factors included previous lawsuits. Validity was ensured using fixed-effects regression and robustness tests. Findings: The study found that accounting conservatism has a mixed impact on financial growth, litigation risk moderates the relationship between conservatism and financial growth, and litigation risk has a positive impact on accounting conservatism. Practical Implications: Use a balanced strategy to maintain accounting conservatism, lower litigation risk while maintaining the accuracy of financial statements, take legal risk into account when evaluating the quality of financial reporting, increase transparency without impeding growth, create guidelines tailored to a particular bank, and fortify governance to reduce lawsuits while permitting long-term financial growth. Originality/Value: In order to bridge the gap between conservatism strategies and long-term financial stability in emerging economies, this study examines how managerial decisions in accounting conservatism affect the financial growth of Saudi banks, incorporating litigation risk as a moderating factor. It also contributes to financial policies, risk management, and regulations. Full article

This paper presents a comprehensive summary of recent advances in circuit topologies for piezoelectric energy harvesting, leading to self-powered systems (SPSs), covering the full-bridge rectifier (FBR) and half-bridge rectifier (HBR), AC-DC converters, and maximum power point tracking (MPPT) techniques. These approaches are analyzed with respect to their advantages, limitations, and overall impact on energy harvesting efficiency. Th work explores alternative methods that leverage phase shifting between voltage and current waveform components to enhance conversion performance. Additionally, it provides detailed insights into advanced design strategies, including adaptive power management algorithms, low-power control techniques, and complex impedance matching. The paper also addresses the fundamental principles and challenges of converting mechanical vibrations into electrical energy. Experimental results and performance metrics are reviewed, particularly in relation to hybrid approaches, load impedance, vibration frequency, and power conditioning requirements in energy harvesting systems. This review aims to provide researchers and engineers with a critical understanding of the current state of the art, key challenges, and emerging opportunities in piezoelectric energy harvesting. By examining recent developments, it offers valuable insights into optimizing interface circuit design for the development of efficient and self-sustaining piezoelectric energy harvesting systems. Full article

Surface erosion of the coaxial thermocouple probe initiates continuous bridging of thermoelectric materials on the insulation layer surface, forming new temperature measurement junctions. This inherent ability to measure continuous self-erosion ensures the operational reliability of the coaxial thermocouples in high-temperature ablative environments. However, the fabrication of a high-temperature electrical insulation layer and a high-adhesion insulating layer in the coaxial thermocouples remains a challenge. Inspired by calcium carbonate/oxalate crystals in jujube leaves that strengthen the leaves, a bioinspired structural ceramic (BSC) mimicking these needle-like crystals is designed. This BSC demonstrates excellent high-temperature insulation (with insulation impedance of 2.55 kΩ at 1210 °C) and adhesion strength (35.3 Newtons). The BSC is successfully used as the insulating layer in a K-type coaxial thermocouple. The generation rules for surface junctions are systematically studied, revealing that stable and reliable measurement junctions can be created when the sandpaper grit does not exceed 600#. Static test results show that the K-type coaxial thermocouple ranges from 200 °C to 1200 °C with an accuracy of 1.1%, a drift rate better than 0.0137%/h, and hysteresis better than 0.81%. Dynamic test results show that the response time is 1.08 ms. The K-type coaxial thermocouple can withstand a high-temperature flame impact for 300 s at 1200 °C, as well as over forty cycles of high-power laser thermal shock, while maintaining good response characteristics. Therefore, the K-type coaxial thermocouple designed in this study provides an ideal solution for long-term temperature monitoring of the thermal components of aerospace engines under extremely high-temperature, high-speed, and strong thermal shock conditions. Full article

The proliferation of next-generation renewable energy systems has driven widespread adoption of electronic devices and nonlinear loads, causing grid distortion that degrades waveform quality in grid-forming (GFM) converters. Additionally, unbalanced grid faults exacerbate overcurrent risks and transient stability challenges when employing conventional virtual impedance strategies. While existing studies have separately examined these challenges, few have comprehensively addressed non-ideal grid conditions. To bridge this gap, a novel control strategy is proposed that reshapes the output current waveforms and enhances transient stability in GFM converters under such conditions. First, a sliding mode controller with an improved composite reaching law to achieve rapid reference tracking while eliminating chattering is designed. Second, a multi-quasi-resonance controller incorporating phase compensation is introduced to suppress harmonic distortion in the converter output current. Third, an individual-phase fuzzy adaptive virtual impedance strategy dynamically reshapes the current amplitude during unbalanced faults and improves the system’s transient stability. Validated through PSCAD/EMTDC simulations and hardware-in-the-loop experiments, the proposed strategy demonstrates superior transient stability and fault ride-through capability compared to state-of-the-art methods, ensuring reliable GFM converter operation under severe harmonic and unbalanced grid conditions. Full article

The building sector holds significant potential to mitigate climate change by adopting the circular economy. However, its transition is impeded by fragmented stakeholder engagement arising from complex socio-organisational dynamics. To address this, this article adopted the system dynamics (SD) modelling tool, which enables structured visualisation of the system while exploring and assessing stakeholder dynamics. The three-stage methodological approach includes the following: problem identification, building on the author’s prior publication, which identified the variables and their direct relationships; system conceptualisation, where the causal loop diagram was developed, followed by the identification of feedback loops, construction of the stock-flow diagram, and ultimately the SD model to capture indirect relationships; and model optimisation, which calibrated the SD model based on real-world circular building scenarios. The results revealed the stakeholder dynamics through their associated concerns. The results indicated that leveraging stakeholder dynamics within the complex system could foster collective action from fragmentation to enable the effective circular economy transition. This article proposes general and phase-specific actions tailored to each stakeholder, offering a structured framework for coordinated decision-making. These actions help bridge the gap between theory and practice, facilitating the sector’s transition from its current linear model toward a more collaborative and circular approach to climate change mitigation. Full article

The increasing global population and intensifying resource limitations present a formidable challenge for sustainable crop production, especially in developing regions. This review explores the pivotal role of microbial ecosystem services in alleviating environmental stresses that impede agricultural productivity. Soil microbiota, particularly plant growth-promoting microbes (PGPMs), are integral to soil health and fertility and plant resilience against both abiotic (drought, salinity, temperature extremes, heavy metals) and biotic (pathogen) stresses. These microorganisms employ a variety of direct and indirect mechanisms, including the modulation of phytohormones, nutrient solubilization, the production of stress-alleviating enzymes, and the synthesis of antimicrobial compounds, to enhance plant growth and mitigate adverse environmental impacts. Advances in microbial biotechnology have expanded the toolkit for harnessing beneficial microbes, enabling the development of microbial inoculants and consortia tailored for specific stress conditions. This review highlights the multifaceted contributions of soil microbes, such as improving nutrient uptake, promoting root development, facilitating pollutant degradation, and supporting carbon sequestration, all of which underpin ecosystem resilience and sustainable agricultural practices. Furthermore, the synergistic interactions between plant roots and rhizospheric microbes are emphasized as key drivers of soil structure enhancement and long-term productivity. By synthesizing current research on the mechanisms of microbe-mediated stress tolerance, this review underscores the potential of microbial interventions to bridge the gap between food security and environmental conservation. The integration of microbial solutions into agroecosystems offers a promising, eco-friendly strategy to revitalize soils, boost crop yields, and ensure agricultural sustainability in the face of mounting environmental challenges. Full article

Ships and offshore equipment operating in marine environments often face issues such as seawater corrosion and biofouling, leading to significant economic losses. To address the corrosion problems of ships and offshore equipment, heavy-duty anticorrosive coatings are widely used for corrosion protection in marine environments due to their long-term effectiveness, cost-efficiency, and excellent applicability. In this study, silane coupling agent (KH-560) was employed to modify sodium silicate, and the modified sodium silicate was then incorporated as a reinforcing phase into polyurethane to ultimately prepare a modified sodium silicate/polyurethane coating. The feasibility of the modified sodium silicate/polyurethane coating was investigated by characterizing its conventional physicochemical properties, weather resistance, acid and alkali resistance, and salt spray corrosion resistance. Experimental results indicate that the silane coupling agent acts as a bridge between the organic and inorganic interfaces through the hydrolysis and condensation reactions of its bifunctional groups, forming an interfacial layer connected by hydrogen bonds and covalent bonds, thereby improving the compatibility between the organic resin and inorganic sodium silicate. Comprehensive performance analysis revealed that when the content of modified sodium silicate was 60 wt%, the coating hardness reached 4H. Additionally, electrochemical tests demonstrated that the coating exhibited higher impedance (9.62 × 10⁴ Ω/cm²) and lower corrosion current density (5.82 × 10⁻⁷ A/cm²). This study provides a theoretical and experimental basis for the development of high-performance anticorrosive coatings for marine applications. Full article

The timely identification of probable causes in aviation incidents is crucial for averting future tragedies and safeguarding passengers. Typically, investigators rely on flight data recorders; however, delays in data retrieval or damage to the devices can impede progress. In such instances, experts resort to supplementary sources like eyewitness testimonies and radar data to construct analytical narratives. Delays in this process have tangible consequences, as evidenced by the Boeing 737 MAX accidents involving Lion Air and Ethiopian Airlines, where the same design flaw resulted in catastrophic outcomes. To streamline investigations, scholars advocate for natural language processing (NLP) and topic modelling methodologies, which organize pertinent aviation terms for rapid analysis. However, existing techniques lack a direct mechanism for deducing probable causes. To bridge this gap, this study trains and evaluates the performance of a transformer-based model in predicting the likely causes of aviation incidents based on long-input raw text analysis narratives. Unlike traditional models that classify incidents into predefined categories such as human error, weather conditions, or maintenance issues, the trained model infers and generates the likely cause in a human-like narrative, providing a more interpretable and contextually rich explanation. By training the model on comprehensive aviation incident investigation reports like those from the National Transportation Safety Board (NTSB), the proposed approach exhibits promising performance across key evaluation metrics, including BERTScore with Precision: (M = 0.749, SD = 0.109), Recall: (M = 0.772, SD = 0.101), F1-score: (M = 0.758, SD = 0.097), Bilingual Evaluation Understudy (BLEU) with (M = 0.727, SD = 0.33), Latent Semantic Analysis (LSA similarity) with (M = 0.696, SD = 0.152), and Recall Oriented Understudy for Gisting Evaluation (ROUGE) with a precision, recall and F-measure scores of (M = 0.666, SD = 0.217), (M = 0.610, SD = 0.211), (M = 0.618, SD = 0.192) for rouge-1, (M = 0.488, SD = 0.264), (M = 0.448, SD = 0.257), M = 0.452, SD = 0.248) for rouge-2 and (M = 0.602, SD = 0.241), (M = 0.553, SD = 0.235), (M = 0.5560, SD = 0.220) for rouge-L, respectively. This demonstrates its potential to expedite investigations by promptly identifying probable causes from analysis narratives, thus bolstering aviation safety protocols. Full article

In a photovoltaic power enhancer system, when it is operated in current-control mode, significant nonuniform temperature distribution occurs in the converter due to thermal coupling effects, dissipative boundary conditions, and differences in device losses within the in-phase bridge. Accurate on-site estimation of the power device’s junction temperature is critical in the system design. To address this problem, a novel thermal behavior estimation model based on electro-thermal analysis is proposed in this paper, which can be used for asymmetric power MOSFETs in a photovoltaic power enhancer system. Thermal coupling effects and dissipative boundary conditions are, firstly, analyzed in a three-dimensional finite element model. A coupling impedance matrix is constructed through step power response extraction to describe the significant thermal coupling effects among devices. The complete heat sink is decoupled into several sub-parts representing different dissipative boundary conditions. A compact RC network model for estimating junction temperature is established based on the combination of the coupling impedance and the sub-heat-sink impedance. The proposed model is verified by finite element simulation and experimental measurement. Full article

Background: Artificial intelligence (AI) has demonstrated remarkable diagnostic accuracy in controlled clinical trials, sometimes rivaling or even surpassing experienced clinicians. However, AI’s real-world effectiveness is frequently diminished when applied to diverse clinical settings, owing to methodological shortcomings, limited multicenter studies, and insufficient real-world validations. Objective: This narrative review critically examines the discrepancy between AI’s robust performance in clinical trials and its inconsistent real-world implementation. Our goal is to synthesize methodological, ethical, and operational challenges impeding AI integration and propose a comprehensive framework to bridge this gap. Methods: We conducted a thematic synthesis of peer-reviewed studies from the PubMed, IEEE Xplore, and Scopus databases, targeting studies from 2014 to 2024. Included studies addressed diagnostic, therapeutic, or operational AI applications and related implementation challenges in healthcare. Non-peer-reviewed articles and studies without rigorous analysis were excluded. Results: Our synthesis identified key barriers to AI’s real-world deployment, including algorithmic bias from homogeneous datasets, workflow misalignment, increased clinician workload, and ethical concerns surrounding transparency, accountability, and data privacy. Additionally, scalability remains a challenge due to interoperability issues, insufficient methodological rigor, and inconsistent reporting standards. To address these challenges, we introduce the AI Healthcare Integration Framework (AI-HIF), a structured model incorporating theoretical and operational strategies for responsible AI implementation in healthcare. Conclusions: Translating AI from controlled environments to real-world clinical practice necessitates a multifaceted, interdisciplinary approach. Future research should prioritize large-scale pragmatic trials and observational studies to empirically validate the proposed AI Healthcare Integration Framework (AI-HIF) in diverse, real-world healthcare contexts. Full article