Search Results (9,299)

This study develops a field-based techno-economic model and decision framework for a CO₂-enhanced oil recovery and storage project under joint market uncertainty. Historical drilling and completion expenditures calibrate investment cost functions, and three years of production data are fitted with segmented hyperbolic Arps curves to forecast 20-year oil output. Markov-chain models jointly generate internally consistent pathways for crude oil, ETA, and purchased CO₂ prices, which are embedded in a Monte Carlo valuation. The framework outputs probability distributions of NPV and deferral option value; under the mid scenario, their mean values are USD 18.1M and USD 2.0M, respectively. PRCC-based global sensitivity analysis identifies the dominant value drivers as oil price, CO₂ price, utilization factor, oil density, pipeline length, and injection volume. Techno-economic boundary maps in the joint oil and CO₂ price space then delineate feasible regions and break-even thresholds for key design parameters. Results indicate that CCUS-EOR viability cannot be inferred from oil price or any single cost factor alone, but requires coordinated consideration of subsurface constraints, engineering configuration, and multi-market dynamics, including the value of waiting in unfavorable regimes, contributing to low-carbon development and sustainable energy transition objectives. Full article

The moldic pore-vuggy reservoirs of the Ma5₄-Ma5₁ sub-member in the Majiagou Formation, central Ordos Basin, are key targets for deep natural gas exploration, yet the alteration mechanisms and controlling factors of burial-stage pressure-released water karstification remain unclear. Herein, an integrated methodology encompassing core observation, thin-section analysis, and geochemical testing was adopted to systematically clarify the development characteristics and multi-factor coupling control mechanisms of this karst process. Results show that burial-stage pressure-released water karst is dominated by overprinting on pre-existing syndepositional and supergene pore networks, forming complex reservoir spaces via synergistic selective dissolution. The development of preferential dissolution zones is jointly controlled by differential compaction of the weathering crust, permeability heterogeneity of the overlying strata and weathered crust, and diagenetic fluid properties. After the supergene diagenetic stage, differential tectonic deformation and burial compaction induced overpressure in pore fluids, which drove acidic pressure-released water to migrate along high-permeability pathways such as the “sandstone windows” overlying the Ordovician weathering crust. These fluids preferentially dissolved high-permeability moldic pore-vuggy dolomites in paleo-karst platforms and steep slope zones, whereas tight micritic dolomites served as effective barriers. The acidic environment sustained by organic acids and H₂S in pressure-released water promoted carbonate dissolution, and carbon-oxygen isotopes as well as pyrite δ³⁴S values verify that the fluids were derived from mudstone compaction. This study reveals that the distribution of high-quality reservoirs is jointly determined by the synergistic preservation of moldic pore-vuggy systems in paleo-karst platforms and steep slopes and directional alteration of pressure-released water along preferential pathways, providing crucial geological guidance for the evaluation of deep carbonate reservoirs. Full article

As unique forms of intangible cultural heritage of Inner Mongolia, traditional musical instruments from the region have undergone significant changes alongside socioeconomic development and evolving performance styles. The performance environment has transitioned from early outdoor and non-fixed venues to professional concert halls. Existing research has demonstrated a correlation between the acoustic quality of performance halls and their objective architectural acoustic parameters. However, no studies have been conducted in China on the acoustic parameters suitable for the performance environments of traditional Inner Mongolian musical instruments. This study determined the optimal acoustic environment for performances of traditional musical instruments, unique to Inner Mongolia, by employing computer simulations and subjective listening experiments in representative performance spaces. Participants were asked to select preferred audio samples of different reverberation times, generated by convolving the impulse responses of simulated spatial models with dry recordings of the instruments. Statistical analysis of the results revealed that the optimal reverberation times for traditional Inner Mongolian instruments are 1.2 s and 1.4 s in a theater space, and 0.9 s and 1.1 s in a rectangular space. Furthermore, under the influence of different factors, the four instruments exhibited distinct preferences for optimal reverberation values in the sampled spaces. Full article

Currently, machine learning (ML) methods provide a practical approach to model complex systems. Unlike purely analytical models, ML methods can describe the uncertainties (e.g., hysteresis, temperature effects) that are difficult to deal with, potentially yielding higher-precision dynamics by a learning plant given a high-volume dataset. However, employing learning plants that lack explicit mathematical representations in real-time control remains challenging, namely, the model can be conversely looked at as a mapping from input data to output, and it is difficult to represent the corresponding time relationships in real applications. Hence, an ML and operator-based nonlinear control design is proposed in this paper. In this new framework, the bounded input/output spaces of the learning plant are addressed rather than mathematical dynamic formulation, which is realized by robust right coprime factorization (RRCF). While the stabilized learning plant is explored by RRCF, the desired tracking performance is also considered by an operator-based nonlinear internal model control (IMC) design. Eventually, practical application on a soft robotic finger system is conducted, which indicates the better performance of using the controlled learning plant and the feasibility of the proposed framework. Full article

In line with the trend of “native intelligence”, artificial intelligence (AI) will be more deeply integrated into communication networks in the future. Quality of AI service (QoAIS) will become an important factor in measuring the performance of native AI wireless networks. Networks should reasonably allocate multi-dimensional resources to ensure QoAIS for users. Extended Reality (XR) is one of the important application scenarios for future 6G networks. To ensure both the accuracy and latency requirements of users for AI services are met, this paper proposes a resource allocation algorithm called Asynchronous Multi-Agent Deep Deterministic Policy Gradient with Independent State and Action (A-MADDPG-ISA). The proposed algorithm supports agents to use different dimensional state spaces and action spaces; therefore, it enables agents to address different strategy issues separately and makes the algorithm design more flexible. The actions of different agents are executed asynchronously, enabling actions outputted earlier to be transmitted as additional information to other agents. The simulation results show that the proposed algorithm has a 10.41% improvement compared to MADDPG (Multi-Agent Deep Deterministic Policy Gradient). Furthermore, to overcome the limitations of directly applying AI or manual rule-based schemes to real networks, this research establishes a digital twin network (DTN) system and designs pre-validation functionality. The DTN system contributes to better ensuring users’ QoAIS. Full article

At performance venues, a well-recognized factor-shaping sound quality is surface scattering. However, how scattering coefficients relate to auditory perception remains underexplored. This study mapped surface scattering coefficients to listening preferences under numerous conditions. Specifically, it used traditional Mongolian instruments in two simulated environments: a theater-type space and a rectangular performance space. Impulse responses were generated under four scattering coefficients (0.1, 0.3, 0.6, and 0.9) and convolved with dry recordings to produce experimental audio samples. Forty-eight participants of varying musical expertise completed paired-comparison listening tests to identify preferred coefficients. The results showed that a scattering coefficient of 0.6 consistently yielded the highest preference across spatial, surface, listener, and tempo variations. Side-wall scattering had a stronger perceptual impact than ceiling scattering, and listener expertise significantly influenced preference. Non-professionals favored lower scattering values, while instrumental specialists preferred moderate-to-high diffusion. This study provides empirical evidence and design guidance for optimizing acoustic diffusion in theaters and auditoriums. Full article

In this study, a three-dimensional nonlinear finite element (FE) model was developed using Abaqus/Explicit to simulate the effects of internal blasts. The numerical model was validated against two previously published numerical and experimental works, demonstrating strong agreement in deformation results. A parametric study was carried out to evaluate the influence of several key factors on the deformation of the receiver tunnel subjected to an explosion in the adjacent donor tunnel. The investigation considered critical variables such as lining material, tunnel inner diameter, cross-sectional shape, spacing between tunnels, and TNT charge weight. The results clearly indicate that expanded polystyrene (EPS) foam, across various densities, demonstrates superior capacity for absorbing blast waves compared to polyurethane and aluminum foams. Furthermore, it was found that lower-density EPS foam provides enhanced mitigation of deformation in tunnel linings. The findings also revealed that damage to the tunnel walls is more strongly correlated with the tunnel shape where the circular tunnel exhibited the best performance. It showed the lowest deformation and delayed peak response. In addition, tunnel deformation increases markedly with higher TNT charge weights. A blast of 1814 kg produced approximately five times the deformation compared to a 454 kg charge. Moreover, it is seen that increasing the spacing between donor and receiver tunnels from 1.5 D to 2.5 D led to a 38.7% reduction in maximum deformation. Full article

Passive building ventilation features, such as wind towers, can help meet rising cooling and ventilation demands in hot, arid regions. However, most prior studies rely on scaled models or isolate single design parameters, limiting holistic insight. This study conducts a full-scale, validated computational fluid dynamics (CFD) parametric analysis of wind tower geometry and its impact on ventilation efficiency in semi-enclosed spaces. Five geometric properties are investigated: tower shape, roof type, number of shafts, separator height, and number of louvres. Additionally, the sensitivity of the optimal configuration to wind speed, wind direction, and louvre orientation is assessed. Results from 88 CFD cases highlight strong interactions among design parameters and show that straight towers with curved roofs consistently perform best. Compared with a tower with six shafts, a flat internal roof, and downward-facing louvres, an optimized tower with four shafts, a convex internal roof, and upward-facing louvres increases airflow rate by a factor of 2.7 and occupied-zone air velocity by 45%, underscoring the importance of holistic geometric optimization. Full article

Maintaining adequate root-zone temperature in solar greenhouses during extreme cold is crucial for crop production. This study investigated the optimization of an auxiliary biomass heating system in a solar greenhouse. The heating performance was evaluated using an integrated methodology that combined orthogonal experimental design, Computational Fluid Dynamics (CFD) simulation, and Machine Learning (ML) surrogate modeling. First, a reliable CFD model, validated against experimental data (Index of Agreement, IA = 0.954), was used to generate high-fidelity temperature field data for nine layout schemes. Parameter sensitivity analysis revealed that the burning cave Diameter is the dominant factor (R = 6.01), followed by burial Depth (R = 2.00), with inter-pool Spacing having the least impact (R = 0.89). Subsequently, six ML algorithms were compared for use as a predictive surrogate model, with Lasso Regression demonstrating superior performance (R² = 0.934). Comprehensive optimization focused on maximizing the Suitable Area Ratio (R_s) in the critical 0.2 m depth root zone. The analysis conclusively identified the 2.5 m diameter group as optimal, achieving a maximum R_s of 90% and the lowest temperature standard deviation. The final recommended optimal design (2.5 m diameter, 0.7 m depth, 10 m spacing) significantly improves heating uniformity and efficiency. This integrated CFD-ML approach provides a scientific basis and a rapid assessment tool for the design and structural optimization of similar underground thermal systems in cold-climate agriculture. Full article

In this paper, a coupled negative bias temperature instability (NBTI)/hot carrier degradation (HCD) failure model is proposed on the 2-D voltage plane for aging simulation of SRAM circuits. According to the physical mechanism of failure, based on the reaction–diffusion and hot carrier energy-driven theory, revised degradation models of threshold voltage shift (∆Vth) for the NBTI and HCD are established, respectively, with explicit expressions for gate voltage (V_G)/drain voltage (V_D). An NBTI/HCD coupling model is built on the 2-D {V_G, V_D} voltage plane with a weighting factor in the form of V_G and V_D power law. The model also takes into account the AC effect and long-term saturation behavior. The predicted ∆Vth under various stress conditions shows an average relative error of 11.6% with experimental data across the entire bias space. SRAM circuit simulation shows that the read static noise margin (RSNM) and write static noise margin (WSNM) have a maximum absolute error of 4.2% and 3.1%, respectively. This research provides a valuable reference for the reliability simulation of nanoscale integrated circuits. Full article

Perceived safety strongly shapes how residents use and experience their neighborhoods, yet evidence on how spatial and social factors interact in rapidly urbanizing Asian cities remains limited. This study investigates the role of natural surveillance, spatial connectivity, and perceived incivilities in shaping residents’ perceived safety in Bayan Baru, Malaysia, with fear of crime examined as a key mediating factor. A face-to-face survey of 300 adults measured five constructs: natural surveillance, spatial connectivity, perceived incivilities, fear of crime, and perceived safety. Data were analyzed using PLS-SEM in SmartPLS 4.0, supported by bootstrapping and predictive relevance tests. Results showed that natural surveillance and spatial connectivity increased perceived safety both directly and indirectly by reducing fear, while perceived incivilities undermined perceived safety through heightened fear. Additional interdependencies indicated that spatial connectivity strengthened natural surveillance, which in turn reduced perceived incivilities and reinforced perceived safety, though connectivity alone did not directly reduce incivilities. Mediation analysis confirmed fear of crime as a central psychological bridge linking environmental cues to safety evaluations. These findings highlight how the interplay of visibility, connectivity, and disorder shape perceived safety in Malaysian neighbourhood settings. Interventions should combine design improvements, maintenance of public space, and community engagement to reduce fear and strengthen everyday confidence in neighborhood safety. Full article

In view of the complex flight scenarios existing in UAV path planning, it is necessary to model the UAV flight trajectory. When constructing the model, cost factors such as the minimum flight path of the UAV, obstacle avoidance, flight altitude, and trajectory smoothness are fully taken into account. To reduce the overall flight cost, a novel secretary bird optimization algorithm (NSBOA) is proposed in this paper, which effectively addresses the limitations of traditional algorithms in handling UAV path planning tasks. First of all, the Singer chaotic map is adopted to initialize the population instead of the conventional random initialization method. This improvement increases population diversity, enables the initial population to be more evenly distributed in the search space, and further accelerates the algorithm’s convergence speed in the subsequent optimization process. Second, an adaptive adjustment mechanism is integrated with the Levy flight mechanism to optimize the core logic of the algorithm, with a specific focus on improving the exploitation stage. By introducing appropriate perturbations near the current optimal solution, the algorithm is guided to jump out of local optimal traps, thereby enhancing its global optimization capability and avoiding premature convergence caused by insufficient population diversity. By comparing and analyzing NSBOA with SBOA, WOA, PSO, POA, NGO, and HHO algorithms in 12 common evaluation functions and CEC 2017 test functions, and applying NSBOA to the UAV path optimization problem, the simulation results show the effectiveness and superiority of the proposed scheme. Full article

Supercritical CO₂ (SC-CO₂) fracturing has emerged as a promising technology for the effective stimulation of unconventional tight reservoirs due to its low viscosity, high diffusivity, and environmental advantages. However, existing fracturability evaluation models often oversimplify key parameters and lack validation under realistic dual-well conditions. To address these gaps, we developed a multi-factor coupled evaluation model incorporating well spacing, stress anisotropy, and fluid viscosity and proposed a fracturability index (FI) to quantify the potential for complex fracture development. True triaxial SC-CO₂ fracturing experiments using both single- and dual-well setups were conducted, and 3D fracture networks were analyzed via CT imaging and U-Net segmentation. Results show strong agreement between FI and fracture complexity. Optimal fracturing conditions were identified, providing a practical framework for the design and optimization of SC-CO₂ fracturing in tight reservoirs. Full article

Permanent magnet synchronous motors (PMSMs) are widely preferred in modern applications due to their high efficiency, high torque-to-inertia ratio, high power factor, and rapid dynamic response. Achieving optimal PMSM performance requires precise control, which depends on accurate estimation of motor speed and rotor position. This information is traditionally obtained through sensors such as encoders; however, these devices increase system cost and introduce size and integration constraints, limiting their use in many PMSM-based applications. To overcome these limitations, sensorless control strategies have gained significant attention. Since PMSMs inherently exhibit nonlinear dynamic behavior, accurate modeling of these nonlinearities is essential for reliable sensorless operation. In this study, a Runge–Kutta Extended Kalman Filter (RKEKF) approach is developed and implemented to enhance estimation accuracy for both rotor position and speed. The developed method utilizes the applied stator voltages and measured phase currents to estimate the motor states. Experimental validation was conducted on the dSPACE DS1104 platform under various operating conditions, including forward and reverse rotation, acceleration, low- and high-speed operation, and loaded operation. Furthermore, the performance of the developed RKEKF under load was compared with the conventional Extended Kalman Filter (EKF), demonstrating its improved estimation capability. The real-time feasibility of the developed RKEKF was experimentally verified through execution-time measurements on the dSPACE DS1104 platform, where the conventional EKF and the RKEKF required 47 µs and 55 µs, respectively, confirming that the proposed approach remains suitable for real-time PMSM control while accommodating the additional computational effort associated with Runge–Kutta integration. Full article

The optimization of low-Reynolds-number airfoils for micro unmanned aerial vehicles (UAVs) is challenging due to strong geometric nonlinearities, tight endurance requirements, and the need to maintain performance across multiple operating conditions. Classical surrogate-assisted optimization (SAO) methods combined with genetic algorithms become increasingly expensive and less reliable when class–shape transformation (CST)-based geometries are coupled with several flight conditions. Although deep learning surrogates have higher expressive power, their use in this context is often limited by insufficient local feature extraction, weak adaptation to changes in operating conditions, and a lack of robustness analysis. In this study, we construct a task-specific convolutional neural network–long short-term memory (CNN–LSTM) surrogate that jointly predicts the power factor, lift, and drag coefficients at three representative operating conditions (cruise, forward flight, and maneuver) for the same CST-parameterized airfoil and integrate it into an Non-dominated Sorting Genetic Algorithm II (NSGA-II)-based three-objective optimization framework. The CNN encoder captures local geometric sensitivities, while the LSTM aggregates dependencies across operating conditions, forming a compact encoder–aggregator tailored to low-Re micro-UAV design. Trained on a computational fluid dynamics (CFD) dataset from a validated SD7032-based pipeline, the proposed surrogate achieves substantially lower prediction errors than several fully connected and single-condition baselines and maintains more favorable error distributions on CST-family parameter-range extrapolation samples (±40%, geometry-valid) under the same CFD setup, while being about three orders of magnitude faster than conventional CFD during inference. When embedded in NSGA-II under thickness and pitching-moment constraints, the surrogate enables efficient exploration of the design space and yields an optimized airfoil that simultaneously improves power factor, reduces drag, and increases lift compared with the baseline SD7032. This work therefore contributes a three-condition surrogate–optimizer workflow and physically interpretable low-Re micro-UAV design insights, rather than introducing a new generic learning or optimization algorithm. Full article