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Objective Faults are among the most prevalent geological structures in oil and gas basins. Because of their significant connection to oil and gas resources, they have consistently attracted the attention of experts and scholars in the field, making them a hot research topic. Although previous researchers delved tirelessly into the correlation between faults, oil, and gas, new theoretical breakthroughs have been steadily emerging and have been used to promote advancements in oil and gas exploration. Nonetheless, there continues to be a dearth of thorough investigations into the underlying links between faults and the formation and distribution of oil and gas reservoirs, as well as methods for comprehensively and quantitatively defining the connections between faults and oil and gas. Methods The formation of a fault from inception encompasses multiple stages of development, including implicit and explicit stages, and differentiating the diverse growth stages of a fault, ranging from the initial embryonic stage to the terminal stage, poses a significant challenge. To address this issue, the Dongying Sag in the Bohai Bay Basin was selected as the focal point of this study. By employing physical and numerical simulation techniques, the researchers sought to replicate the entire life cycle evolution of the Chennan Fault, a basin-controlling boundary fault, from its embryonic stage to its terminal stage while elucidating the distinct characteristics of each stage. Building upon this foundation, the relative ages of the primary faults in the Dongying Depression and the various modes of fault activity at different stages were qualitatively and quantitatively determined, leading to the establishment of a reservoir-control model. Results The research findings indicate that normal faults tend to grow in six distinct stages: the embryonic stage (0 < RA (relative age) ≤ 1), characterized by microfractures or induced fracture zones; juvenile stage (1 < RA ≤ 2), with an intermittent connection of fault geometry; mature stage (2 < RA ≤ 3), marked by the connection of plate-like fault geometry and clear fault throw; declining stage (3 < RA ≤ 4), in which induced fracture zones form on both sides of the fault core, resulting in a shovel-like fault geometry; terminal stage (4 < RA ≤ 5), ramp-flat fault geometry, which has complex derived structures; and death stage (5 < RA ≤ 6), in which fault movement stops or undergoes inversion. The activity pattern of a fault is intricately linked to the duration and intensity of its age. Stable continuous, and high-intensity fault activity promotes the evolution of faults into their terminal stage. Research on reservoir control traps indicates that faults can create reservoirs at all stages of their development. However, as faults age, their ability to control reservoir formation strengthens. The types of traps influenced by faults transition from individual, isolated structures to a variety of arrangements. Moreover, the diversity of oil and gas reservoirs evolves from singular to multifaceted, and the size of these reservoirs expands from small to large. The embryonic and juvenile stage faults primarily influence closure; the mature stage faults predominantly impact sand and reservoir; and the declining stage and terminal stage faults primarily govern the overall distribution range of source rocks, as well as the migration, accumulation, and dissipation of oil and gas. Conclusion The reservoir control potential of the Chennan Fault was assessed by considering factors such as reservoir control advantages, the degree of oil and gas enrichment, and the scale of oil and gas accumulation. The reservoir control capacity of the Chennan Fault was classified as “strong.” Reevaluation of the fault’s reservoir control potential from the perspective of its developmental and evolutionary stages significantly enhances and elevates theoretical research on fault reservoir control and also advances exploration efforts in established mature areas. [Significance] Identifying the formation age and evolutionary patterns of extensional faults has immense theoretical and practical importance for comprehending alterations in the fault’s reservoir control capabilities. Moreover, it offers crucial guidance for oil and gas exploration, particularly for enhancing the reserves in existing exploration areas.

Our subjective sense of time is intertwined with a plethora of perceptual, cognitive and motor functions, and likewise, the brain is equipped to expertly filter, weight and combine these signals for seamless interactions with a dynamic world. Until relatively recently, the literature on time perception has excluded the influence of simultaneous motor activity, yet it has been found that motor circuits in the brain are at the core of most timing functions. Several studies have now identified that concurrent movements exert robust effects on perceptual timing estimates, but critically have not assessed how humans consciously judge the duration of their own movements. This creates a gap in our understanding of the mechanisms driving movement-related effects on sensory timing. We sought to address this gap by administering a sensorimotor timing task in which we explicitly compared the timing of isolated auditory tones and arm movements, or both simultaneously. We contextualized our findings within a Bayesian cue combination framework, in which separate sources of temporal information are weighted by their reliability and integrated into a unitary time estimate that is more precise than either unisensory estimate. Our results revealed differences in accuracy between auditory, movement and combined trials, and (crucially) that combined trials were the most accurately timed. Under the Bayesian framework, we found that participants' combined estimates were more precise than isolated estimates, yet were sub-optimal when compared with the model's prediction, on average. These findings elucidate previously unknown qualities of conscious motor timing and propose computational mechanisms that can describe how movements combine with perceptual signals to create unified, multimodal experiences of time.

Normal aging is associated with a decline in memory and motor learning ability. However, the exact form of these impairments (e.g., the short-term temporal stability and affected learning mechanisms) is largely unknown. Here, we used a sensorimotor adaptation task to examine changes in the temporal stability of two forms of learning (explicit and implicit) because of normal aging. Healthy young subjects (age range, 19-28 years; 20 individuals) and older human subjects (age range, 63-85 years; 19 individuals) made reaching movements in response to altered visual feedback. On each trial, subjects turned a rotation dial to select an explicit aiming direction. Once selected, the display was removed and subjects moved the cursor from the start position to the target. After initial training with the rotational feedback perturbation, subjects completed a series of probe trials at different delay periods to systematically assess the short-term retention of learning. For both groups, the explicit aiming showed no significant decrease over 1.5 min. However, this was not the case for implicit learning; the decay pattern was markedly different between groups. Older subjects showed a linear decrease of the implicit component of adaptation over time, while young subjects showed an exponential decay over the same period (time constant, 25.61 s). Although older subjects adapted at a similar rate, these results suggest natural aging selectively impacts the short-term (seconds to minutes) temporal stability of implicit motor learning mechanisms. This understanding may provide a means to dissociate natural aging memory impairments from deficits caused by brain disorders that progress with aging.

In IoT (internet of things), most data from the connected devices change with time and have sampling intervals, which are called time-series data. It is challenging to design a time series storage model that can write massive time-series data in a short time and can query and analyze the persistent time-series data for a long time. This paper constructs the RHTSDB (Redis-HBase Time Series Database) storage model based on Redis and HBase. RHTSDB uses the memory database Redis (Remote Dictionary Server) to cache massive time-series data, providing efficient data storage and query functions. HBase is used in RHTSDB for long-term storage of time-series data to realize their persistence. The paper designs a cold and hot separation mechanism for time-series data, where the infrequently accessed cold data are stored in HBase, and the frequently accessed and latest data are stored in Redis. Experiments verify that RHTSDB has apparent advantages over Apache IoTDB and HBase in data intake and query efficiency., Author(s): Ruizhe Ma, University of Massachusetts, Lowell, USA, Weiwei Zhou, Nanjing University of Aeronautics and Astronautics, China, Zongmin Ma, Nanjing University of Aeronautics and Astronautics, China INTRODUCTION With the development [...]

Interval timing is a fundamental component of action and is susceptible to motor-related temporal distortions. Previous studies have shown that concurrent movement biases temporal estimates, but have primarily considered self-modulated movement only. However, real-world encounters often include situations in which movement is restricted or perturbed by environmental factors. In the following experiments, we introduced viscous movement environments to externally modulate movement and investigated the resulting effects on temporal perception. In two separate tasks, participants timed auditory intervals while moving a robotic arm that randomly applied four levels of viscosity. Results demonstrated that higher viscosity led to shorter perceived durations. Using a drift-diffusion model and a Bayesian observer model, we confirmed these biasing effects arose from perceptual mechanisms, instead of biases in decision making. These findings suggest that environmental perturbations are an important factor in movement-related temporal distortions, and enhance the current understanding of the interactions of motor activity and cognitive processes.