Search Results (212)

Large hydrocarbon fields are being developed in northern Sakhalin, whereas southern Sakhalin contains prospective resources hosted in unconventional, low-permeability siliceous source rocks. Their development requires stimulation to create conductive fracture networks, whose long-term integrity is critical for production feasibility. This study investigates permeability changes in four artificially fractured specimens subjected to cyclic confining pressure; the specimens are treated as case studies rather than as a statistically representative formation-scale data set. Cylindrical cores, 30 mm in diameter and approximately 30 mm long, containing an induced axial fracture were hydraulically tested under biaxial cyclic confinement. The tests showed irreversible loss of fracture conductivity, with residual permeability after unloading amounting to 7.1–37.4% of the initial value. Direct application of laboratory data to field scale fracture longevity models is inappropriate because cylindrical specimens develop nonuniform circumferential stresses and heterogeneous closure. A procedure is proposed to transfer core scale measurements to a planar fracture subjected to uniform normal stress in the rock mass. Fracture aperture was quantified by X-ray computed tomography (CT) and incorporated into a cell-based contact–hydraulic model accounting for geometric and hydraulic aperture. A nonuniform closure function was used to reconstruct the closure field and correct the laboratory results. At maximum pressure, Kmass/Klab ranged from 0.034 to 0.93 for the three specimens reproduced with acceptable fit; sample 3–4 was retained only as a diagnostic case because of its high root-mean-square error (RMSE). These values are model-based single-fracture scenario estimates pending direct-normal-loading validation. Full article

Objective: Recent advances in dry electrode EEG have enabled rapid setup and recording in unconventional scenarios. However, past developments were primarily driven by brain–computer interfaces (BCI), leaving their comparability to wet electrodes in clinical and daily life applications an open question. Here, we developed a new dry EEG system and systematically benchmarked its performance against a commercial wet EEG system across various tasks. Methods: Participants (n = 19) underwent simultaneous recording using both devices. We first collected resting-state EEG under both eyes-closed and eyes-open conditions, followed by a steady-state visual evoked potential (SSVEP) task at different flicker frequencies and a motor imagery (MI) task. System performance was evaluated using power spectral density (PSD), signal to noise ratio (SNR), event-related spectral perturbation (ERSP), and single-trial classification accuracy. Results: The two systems performed similarly across different tasks. During the resting state, no statistically significant differences were observed between the two systems in the PSD of the five frequency bands (p > 0.05 in all cases). Similarly, SNR in the SSVEP task showed no significant differences at 8 Hz, 10 Hz, and 12 Hz after correction. For cognitive tasks, classification accuracies were comparable (SSVEP: dry 80.08% ± 7.1% vs. wet 81.10% ± 6.5%; MI: dry 72.46% ± 3.89% vs. wet 70.7% ± 2.37%). Conclusions: The developed dry EEG system can effectively record electrophysiological measurements commonly employed in research and clinical settings, with quality comparable to that of traditional wet EEG systems. Full article

Reservoir stimulation through fracturing is essential for the commercial development of unconventional oil and gas resources. Supercritical CO₂ (scCO₂) combines liquid-like density with gas-like viscosity and compressibility, enabling efficient conversion of stored energy into shock waves and jet impacts. This study introduces an innovative scCO₂ shock fracturing technique, in which a downhole pressure-control valve rapidly releases compressed scCO₂ to generate transient shock pressures that induce rock fracture initiation and propagation. A series of scCO₂ shock fracturing experiments were conducted on sandstone and shale to evaluate the influence of different impact loads on both macroscopic and microscopic damage. Rock damage evolution was characterized using computed tomography (CT), nuclear magnetic resonance (NMR), mercury intrusion porosimetry (MIP), and quantitative analysis of fracture surface morphology. The results showed that increasing shock pressure enhanced fracture surface roughness, shear slip, and particle spalling in sandstone, producing rough tensile–shear fracture surfaces with a potential self-supporting tendency. NMR results indicated that sandstone mainly exhibited a single-peak T₂ response, and scCO₂ shock loading primarily affected pores and pore-fracture spaces larger than 0.08 µm. In contrast, shale showed a broader and more heterogeneous pore-fracture response, with preferential enlargement and connection of large pore-fracture spaces. The NMR-MIP-calibrated equivalent pore-fracture diameter distribution showed that scCO₂ shock fracturing mainly promoted pore-fracture spaces larger than 0.2 μm in shale; at 40 MPa, the volume of this pore-fracture range increased by approximately 6.75 times. However, the characteristic equivalent pore-fracture diameter decreased at 45 MPa, which is attributed to severe specimen fragmentation, fragment displacement, scCO₂ escape, and energy dissipation. These findings suggest that scCO₂ shock fracturing is a promising stimulation approach for enhancing macroscopic fracturing and microscopic pore-fracture reconstruction in unconventional reservoirs. Full article

Airdrops with autonomous ram-air type parachutes are increasingly important in today’s unconventional warfare conjuncture and humanitarian aid operations. However, there are fundamental issues to be considered by operators or decision makers as to its utilization in the theatre. It is inevitable that new threats will arise with the increase in technology. Therefore, cyber defense elements for air supply should be secured for guided parachute systems that have the ability to glide through long distances. Some of these implied cyber-attacks could target sensitive information (identity, location, etc.) carried by guided parachutes, which are basically unmanned aerial vehicles, and deviate the system by taking over the routing control. The capture of flight information could lead to the disclosure of such covert operations, or at least lead to unexpected complications such as unauthorized airspace violations. Due to various adverse situations that may occur, ensuring the cybersecurity of the parachute payload system both in flight and on the ground has always been an important research topic. In this study, the concept of information replenishment with autonomous parachute systems is introduced to the literature and the cybersecurity of the system is detailed. Specifically, an efficient, lightweight, and pairing-free Elliptic Curve Cryptography (ECC)-based authentication scheme is proposed to secure the system. Considering the resource-constrained nature of autonomous parachute platforms, the proposed scheme is designed to ensure robust security with minimal computational and communication overheads. Furthermore, a security evaluation of the proposed scheme is performed. Although ECC-based authentication protocols have been widely investigated for UAV and IoT systems, this study is, to the best of our knowledge, the first to adapt a lightweight authentication framework to the cybersecurity requirements of autonomous ram-air parachute systems. Full article

In characterizing unconventional reservoirs, conventional Digital Rock Physics (DRP) has long been constrained by three fundamental bottlenecks: the trade-off between imaging resolution and field of view, challenges in reconstructing multiscale pore topology, and the prohibitive computational cost of direct numerical simulation (DNS) at the pore scale. The deep integration of artificial intelligence and rock physics has given rise to a new paradigm—Intelligent Digital Rock Physics (IDRP). This paper provides a systematic review of the evolutionary trajectory of IDRP, with a focus on how machine learning is reshaping the end-to-end workflow from imaging and segmentation to reconstruction and simulation. First, we survey image super-resolution and 3D pore structure generation techniques based on convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, elucidating their mechanisms for surpassing optical diffraction limits and incorporating macroscopic petrophysical constraints. Second, we outline algorithmic strategies for fusing multi-source heterogeneous data (e.g., Micro-CT and SEM) and representing dual-porosity or multi-continuum systems. Third, we critically examine the application of machine learning surrogates in single- and multiphase flow prediction, highlighting how physics-informed machine learning (PIML) and reinforcement learning (RL)—by embedding governing equations such as Navier–Stokes or Muskat–Leverett into loss functions—achieve both computational acceleration and physical consistency. We further identify key limitations of current IDRP approaches, including insufficient validation of generated topological realism, narrow generalization across lithologies, inadequate representation of dynamic wettability, and limited model interpretability. Finally, we propose a forward-looking roadmap centered on multimodal foundation models for rocks, coupled with neural operators and uncertainty quantification frameworks, emphasizing the critical pathways for translating IDRP into engineering digital twins for unconventional hydrocarbon development, coalbed methane production enhancement, Enhanced Geothermal Systems, and geological CO₂ storage. This review offers a comprehensive reference for researchers at the intersection of geophysics, rock mechanics, and artificial intelligence. Full article

Artificial intelligence is typically formulated as an information-processing system composed of artificial neurons, where computation is understood as recursive operations connecting inputs and outputs. However, real neural systems are materially embodied and continuously reconfigured by metabolic and physical processes, suggesting that computation cannot be reduced to fixed causal structures. In this paper, we propose a theoretical framework that captures the interplay between informational and material processes as the interaction between two computational schemes: a vertical scheme, representing fixed cause–effect relations, and a horizontal scheme, representing transformations between such relations. We show that the vertical scheme corresponds to Bayesian inference, which updates probability distributions over a fixed hypothesis space, and is consistent with the free-energy minimization principle. In contrast, the horizontal scheme is formalized as inverse Bayesian inference, which modifies the hypothesis space itself by updating likelihood structures based on experienced data. We further demonstrate that the interplay between these schemes can be expressed algebraically as a process of continuously gluing Boolean algebras. This construction yields a non-distributive orthomodular lattice, i.e., quantum logic, without invoking Hilbert space formalism. In this view, quantum logic emerges not as a static logical system but as a structural consequence of dynamically reconfiguring causal contexts. This framework provides a unified perspective in which inference is understood not only as optimization within a fixed model but also as a process that generates and transforms the model itself. It offers a formal basis for describing open-ended computation and suggests a connection to approaches such as unconventional computing and Natural Born Intelligence, where computational structures evolve through interaction with material processes. Unlike existing approaches, this framework derives quantum-logic-like structure from the continual reconfiguration of causal contexts rather than from Hilbert-space assumptions or optimization within a fixed hypothesis space. Full article

During the CO₂ fracturing of unconventional oil and gas resources, the structural and operational parameters significantly influence the fracturing effectiveness. To quantitatively reveal the influence mechanisms of key parameters on the CO₂ jet flow field through perforations, this study employed computational fluid dynamics (CFD) via Ansys Fluent to simulate and compare the effects of the nozzle contraction angle, injection rate, confining pressure, and fluid temperature. The results indicate that the contraction angles and injection rates have a more significant influence on the jet temperature, pressure, and velocity than the confining pressures and fluid temperatures. As the contraction angle increases, the average velocity of the jet core region increases by 5.0% (with the most significant growth at 35°), and the length of the potential core increases correspondingly. The flow through the perforations is characterized by an instantaneous drop of 2.5 °C in temperature and 2.7 MPa in pressure, then transitions to a regime of temperature recovery and dynamical pressure decay along the fracture. Increasing the fracturing displacement raises the maximum jet velocity to 104.7 m/s (an average increase of 15.5%), extends the potential core length, and amplifies the temperature and pressure drops across the perforation from 1.1 °C and 1.2 MPa to 4.2 °C and 4.8 MPa, respectively. Conversely, higher confining pressure reduces the average jet velocity by 4.3%, shortens the potential core, and diminishes the perforation temperature and pressure drops from 5 °C and 3 MPa to 2 °C and 2.5 MPa. In contrast, elevating the fluid temperature increases the jet velocity by an average of 6.3% but exerts minimal influence on the potential core length; the temperature drop at the perforation remains at approximately 2 °C, while the pressure drop rises from 2.2 MPa to 2.9 MPa. Collectively, both the confining pressure and fluid temperature significantly affect the density and velocity characteristics of the jet. An increase in confining pressure enhances the density of the CO₂ jet fluid, which may potentially improve the fracturing impact in actual engineering applications. Quantitatively, the influence of each parameter on the temperature, pressure, and velocity of the CO₂ jet is ranked from the most significant to the least as follows: nozzle contraction angle > fracturing injection displacement > formation confining pressure > fluid temperature. The findings of this research have direct implications for practical application, informing the optimization of the fracturing design to achieve greater efficiency and lower risk in CO₂ fracturing operations. Full article

Accurate identification of fire smoke is a key link in realizing early fire prevention and control. Traditional intelligent video and image processing technologies are significantly restricted by environmental factors, with weak anti-interference capabilities and limitations in distinguishing fire smoke, leading to a high false alarm rate of fires. To address this problem, this paper proposes an unconventional visual field smoke detection method based on image enhancement. The method innovatively improves the Retinex algorithm by integrating improved guided filtering, adaptive brightness correction, and CLAHE-WWGIF joint processing, which realizes targeted optimization for the unique interference factors of under-construction nuclear power plants such as water mist, low illumination, and equipment occlusion. First, an improved Retinex algorithm is used to process the image to improve the image brightness and contrast, retain edge details while avoiding halo artifacts, reduce the impact of noise, and optimize visual features. Then, the sample data set is integrated, and the YOLOv11 target detection algorithm is used to achieve accurate identification and positioning of smoke targets. Experimental data shows that the fire identification method achieves an accuracy rate of 93.6% and 92.3% for fire smoke identification in interference-prone scenarios such as dark nights and water mist, respectively, and the response time to fire smoke is only 1.8 s and 2.1 s. In practical on-site applications at nuclear power plant construction sites, the method is integrated into an “edge computing + distributed deployment” hardware system, which realizes real-time smoke detection in core areas such as nuclear islands and conventional islands with a false alarm rate of less than 5% and a detection delay of ≤300 ms, meeting the ultra-strict safety monitoring requirements of nuclear power projects. Experiments show that this method can be effectively applied to smoke detection scenarios under unconventional visual fields, accurately identify smoke, provide reliable technical support for fire smoke identification under unconventional visual fields, significantly reduce the false alarm rate of fire detection, and provide technical support for the safety of under-construction nuclear power plants. Full article

A productivity simulation for hydraulically fractured wells with complex fracture geometry involves a heavy computational burden and is therefore not suitable for engineering-scale fracture-optimization designs and production-analysis applications. This paper develops a productivity-prediction surrogate model based on a deep convolution–bidirectional gated recurrent unit temporal network (DC-BiGRU) framework where a deep convolutional neural network is used to extract features from fracture images, while a BiGRU model was designed to fully capture valuable information from the production sequence. Some additional inputs, e.g., cluster spacing and stage spacing, that account for different fracture-placement designs in horizontal wells were also considered. A large number of shale-gas production data samples at different times were generated using a fractured-horizontal-well productivity simulator under diverse hydraulic-fracture geometries and bottom-hole flowing pressures. The surrogate model had relative errors below 10% with an average error of about 6%. Compared to high-fidelity capacity prediction simulators, the computational efficiency of the deep learning surrogate models was improved by two to three orders of magnitude. The runtime of the high-fidelity numerical simulator was about 20 min, while the surrogate model, which was run on an NVIDIA Tesla P100 GPU (NVIDIA, Santa Clara, CA, USA), took less than 1 s, which is almost negligible. The proposed surrogate model resolved the low efficiency of the productivity simulation for complex-fracture hydraulic fracturing wells in unconventional reservoirs, enabling rapid dynamic forecasting of fractured-well productivity. Full article

Filamentous fungi are increasingly recognized as versatile biological platforms for the development of advanced (bio)sensing technologies, owing to their extensive secretory capacity, material-forming ability, and intrinsic bioelectrical activity. This review critically surveys recent progress in fungal-based sensing within a multiscale framework spanning molecular, material, computational, and ecological domains, with particular emphasis on developments reported over the past five years. Key advances involving secretome-derived biomolecules, mycogenic nanomaterials, mycelium-based living materials, and fungal electrophysiology are discussed alongside emerging approaches for environmental monitoring that integrate sensor networks, imaging platforms, and data-driven analytics. Collectively, these works demonstrate that fungal systems can enhance biosensor sensitivity, selectivity, and sustainability, while enabling unconventional paradigms of signal transduction, material-integrated sensing, and biologically mediated computation. At larger spatial and temporal scales, mycelial growth dynamics and electrical activity provide measurable responses to mechanical, chemical, and environmental perturbations, supporting early applications in wearable devices, structural materials, and ecosystem monitoring. Despite significant progress, challenges remain in reproducibility, long-term stability, mechanistic understanding, and scalable device integration. Overall, the evidence reviewed highlights filamentous fungi as biologically adaptive and ecologically embedded systems with substantial potential to support next-generation (bio)sensing technologies, while underscoring the need for integrative approaches that combine biological insight with materials science, electronics, and artificial intelligence. Full article

Evaluating innovation and optimising its role in the inventions is fundamental for applied research, that requires planning the use of available resources. Traditional assessment approaches often miss to capture how innovation stagnates between the ideation and prototyping phases (the Valley of Death), and to learn how innovation emerges from intermediate-steps contributed by individuals. This paper focuses on tracing innovation as an approach enabling mapping of pathways of intermediate-steps and opportunities for valorising unplanned outcomes. We adopt a qualitative case study to explore how innovation pathways can be conceptualised through technological readiness levels. The operational settings of an EU-funded project defined the boundaries of the study. A network analysis explored relationships among themes that emerged from respondents involved in the activities, following an inductive approach to derive themes from data. Findings indicate that intermediate innovation steps, including failures, are viewed as cumulative contributions to novelty. Their documentation is seen as an investment for unlocking latent value embedded in distributed knowledge. Within this scope, we outline a blockchain-based knowledge graph as a proof-of-concept for tracing cumulative contributions, identifying breakthroughs leading to technological maturity and supporting generation of hypothesis grounded on experimental trials. As a result, we suggest that paths recombining prior knowledge into novelty encode latent value that can be interpreted as a function of the network topology, and propose a conceptual framework for analysing value by means of information theory metrics applicable to innovation graphs. Full article

The economic importance of Carlin-type gold deposits is complicated by the concealed nature of stratiform gold-bearing zones and their occurrence at depths of several tens of meters or more below the present-day surface. This necessitates the use of a wide range of technologies and unconventional, including cost-effective and environmentally friendly, exploration methods to delineate potentially prospective areas. This study explores the possibilities of applying remote sensing methods to organize prospecting and exploration activities for targeting Carlin-type deposits in a more efficient and cost-effective way. The location of Carlin-type gold deposits within areas of orogenic and post-orogenic magmatism, mantle plumes, and linear crustal structures—as demonstrated by previous research in the Nevada and South China metallogenic provinces—may serve as a basis for developing a conceptual model of their distribution. To this end, we developed the GeoNEM (Geodynamic Numeric Environmental Modeling) software in Python, which enables the analysis of the formation of fold and fault structures, melt emplacement and contamination, as well as the duration and rate of geodynamic processes. GeoNEM is based on the computational geodynamics “marker-in-cell” (MIC) method, which treats geological media as extremely high-viscosity fluids. Locations of the brittle deformations of the crust, the formation of which was simulated numerically, can be detected through lineament analysis of remote sensing images. The spatial distribution of such structures—lineaments—serves as a predictive criterion for assessing the prospectivity of territories for Carlin-type gold deposits. It has been demonstrated that remote sensing provides a modern level of efficiency, cost-effectiveness, and comprehensiveness in approaching the exploration and assessment of new Carlin-type gold deposits. This is particularly important in the context of rational resource utilization and cost reduction. Full article

The global transition toward electrification has accelerated the need for high-performance, sustainable electric machine designs. Emerging manufacturing techniques, particularly additive manufacturing, have enabled the development of complex and unconventional machine topologies. Designing novel machine topologies often relies on data-driven methods and topology optimization, which can be computationally intensive. Semi-analytic modeling offers an effective middle ground by balancing computational efficiency with modeling accuracy—positioned between fully analytical formulations and resource-intensive numerical simulations. While its advantages are recognized, the current literature lacks a unified overview of semi-analytic approaches applied across coupled multiphysics domains, including electromagnetic, thermal, and structural analyses. This paper addresses that gap by presenting a comprehensive review of recent semi-analytic modeling techniques relevant to electric machine design. The goal is to establish a foundational reference for researchers aiming to incorporate these models into advanced topology optimization frameworks. Full article