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Reservoir impoundment and water level fluctuations often trigger landslides and their secondary disasters, such as potential impulse waves, posing a serious threat to the safety of people along the reservoir and dam areas, causing economic losses and even catastrophic consequences. This study delves into a comprehensive field investigation and monitoring of the engineering geological conditions and deformation mechanisms of the Mogu rock landslide. The impoundment is identified as the primary factor inducing slope deformation, with the cumulative displacement of the sliding body showing no signs of convergence, indicating potential instability. By coupling the elasto-visco-plasticity model and the RNG turbulence model in FLOW-3D, an actual impulse wave disaster near the Lianghekou reservoir dam area is replicated to validate the reliability of the numerical method. Building upon this, a three-dimensional model is established to calculate potential impulse waves generated by the Mogu rock landslide, and the risk to the dam is evaluated. Under different water level conditions, the simulated run-ups of impulse waves do not surpass the dam elevation, demonstrating a satisfactory safety margin. Given the inherent danger of landslide-induced wave disasters, continuous attention is warranted, and preventive measures and suggestions are proposed to address these concerns. Additionally, the study explores the contributions of water level fluctuations to the primary tsunami amplitude, the maximum run-up on the opposite bank and the dam, and the attenuation rate of the tsunami amplitude along the river channel. The results provide significant reference values for the early warning and prevention of comparable reservoir landslides and potential landslide-induced waves worldwide. [ABSTRACT FROM AUTHOR]

The hydro-mechanical behaviour and the constitutive model of sandy mudstone were investigated in this article. Two groups of hydro-mechanical triaxial compression tests were conducted. The results indicated that the inelastic deformation, damage, permeability and failure of the sandy mudstone were affected by the pore pressure and confining pressure. The hydro-mechanical behaviour was characterised by the coupled elastoplastic damage model. This model was based on the effective stress principle and numerical return mapping algorithms. Sensitivity analysis was also performed to examine the effects of four key parameters on the modelling of elastoplastic and damage behaviour. This study can provide a foundation for physical understanding and mathematical modelling of sandy mudstone under coupled loads. [ABSTRACT FROM AUTHOR]

Large deformations of soft rocks often occur during the excavation and support of the Chuxiong section tunnel in the Yunnan Central Diversion Project. The physical characteristics and creep mechanical properties of the red-bed soft rock were analyzed by combining engineering experience, laboratory tests, and scanning electron microscopy. Furthermore, the creep parameters of the red-bed soft rock were obtained. The issues related to large deformations of deeply buried tunnels with soft and weak rock masses were investigated. Based on construction engineering practice and numerical simulations, characteristics of extrusion damage of deeply buried tunnels with soft and weak rock masses were analyzed. Countermeasures were implemented to solve the problems of large deformations, which can be used as references for the construction of similar soft rock tunnels in the future. [ABSTRACT FROM AUTHOR]

The way the brain work and its principle of work has long been a big scientific question that scientists have dreamed of solving. However, as is known to all, the brain works at different levels, and the operation at different levels is interactional and mutually coupled. Unfortunately, until now, we still do not know how the nervous system at different levels is interacting and coupling with each other. This review provides some preliminary discussions on how to address these scientific questions, for which we propose a novel theory of the brain called neural energy. Such a theoretical and research approach can couple neural information with neural energy to address the interactions of the nervous system at various levels. Therefore, this review systematically summarizes the neural energy theories and methods proposed by our research in the field of brain science, as well as the internal relationship between mechanics and neural energy theory. Focuses on how to construct a Wang–Zhang (W–Z) neuron model equivalent to Hodgkin–Huxley (H–H) model by using the idea of analytical dynamics. Then, based on this model, we proposed a large-scale neural model and a theoretical framework of global neural coding of the brain in the field of neuroscience. It includes information processing of multiple sensory and perceptual nervous systems such as visual perception, neural mechanism of coupling between default mode network and functional network of brain, memory switching and brain state switching, brain navigation, prediction of new working mechanism of neurons, and interpretation of experimental phenomena that are difficult to be explained by neuroscience. It is proved that the new W–Z neuron model and neural energy theory have unique functions and advantages in neural modeling, neural information processing and methodology. The idea of large-scale neuroscience research with neural energy as the core will provide a potentially powerful research method for promoting the fusion of experimental neuroscience and theoretical neuroscience in the future, and propose a widely accepted brain theory system between experimental neuroscience and theoretical neuroscience. It is of great scientific significance to abandon the shortcomings of reductive and holism research methods in the field of neuroscience, and effectively integrate their respective advantages in methodology. [ABSTRACT FROM AUTHOR]

The liver plays a key role in keeping the homeostasis of glucose and lipid metabolism. Insulin resistance of the liver induced by extra glucose and lipid ingestion contributes greatly to chronic metabolic disease, which is greatly threatening to human health. The small peptide, VLPVPQK, originating from casein hydrolysates of milk, shows various health-promoting functions. However, the effects of VLPVPQK on metabolic disorders of the liver are still not fully understood. Therefore, in the present study, the effects and regulatory mechanism of VLPVPQK on insulin-resistant HepG2 cells was further investigated. The results showed that VLPVPQK exerted strong scavenging capacities against various free radicals, including oxygen radicals, hydroxyl radicals, and cellular reactive oxygen species. In addition, supplementation of VLPVPQK (62.5, 125, and 250 μM) significantly reversed the high glucose and fat (30 mM glucose and 0.2 mM palmitic acid) induced decrement of glucose uptake in HepG2 cells without affecting cell viability. Furthermore, VLPVPQK intervention affected the transcriptomic profiling of the cells. The differentially expressed (DE) genes (FDR < 0.05, and absolute fold change (FC) > 1.5) between VLPVPQK and the model group were mostly enriched in the carbohydrate metabolism-related KEGG pathways. Interestingly, the expression of two core genes (HKDC1 and G6PC1) involved in the above pathways was dramatically elevated after VLPVPQK intervention, which played a key role in regulating glucose metabolism. Furthermore, supplementation of VLPVPQK reversed the high glucose and fat-induced depression of AKR1B10. Overall, VLPVPQK could alleviate the metabolic disorder of hepatocytes by elevating the glucose uptake and eliminating the ROS, while the HKDC1 and AKR1B10 genes might be the potential target genes and play important roles in the process. [ABSTRACT FROM AUTHOR]

A triaxial unloading mechanical test is conducted on sandstone considering different initial confining pressures and unloading rates under the effect of hydraulic coupling. The energy evolution of sandstone unloading failure is studied, and the influences of the initial confining pressure and unloading rate on the deformation characteristics and permeability evolution of sandstone are discussed. It is found that the initial confining pressure and unloading rate have a significant impact on the axial and radial strains of the sandstone, and the radial deformation is more sensitive to the unloading process. The energy dissipation of the sandstone sample increases with the increase of the confining pressure and is nonlinearly correlated to the unloading time. The permeability during the unloading deformation of the sandstone decreases with the increase in the initial confining pressure, indicating that the initial confining pressure of unloading has an inhibitory effect on the permeability of the sandstone. The permeability decreases with the increase in unloading rate, indicating that the faster the increase in the unloading rate, the greater the dissipated instantaneous elastic energy, which is accumulated and released during failure. The higher the degree of fracture in the sample, the larger the peak value of permeability. [ABSTRACT FROM AUTHOR]

In order to investigate the hydromechanical behavior of basalt in Baihetan hydropower station in China, a series of uniaxial and triaxial hydromechanical tests were carried out in the laboratory. Experimental results indicated that the deformation state of samples transformed from compaction to dilation. Characteristic stresses were determined by a moving point regression technique, including the crack closure stress σ cc , crack initiation stress σ ci , crack damage stress σ cd , and peak stress σ p , revealing a strong dependence on the confining and pore pressures. Failure modes of the basalt changed from tensile to tensile-shear failure as the confining pressure increased. Moreover, scanning electron microscope (SEM) tests demonstrated that the growth of microcracks caused the failure of the basalt. Based on the experimental results, an elastoplastic constitutive model was proposed to take into account the hardening and softening, as well as the hydro-mechanical behavior of basalt. The return mapping algorithms of the elastoplastic constitutive model are deduced for plastic corrections. Model parameters were determined by experimental results. Finally, the validity of the model was demonstrated by comparing the experimental results with the simulated curves. [ABSTRACT FROM AUTHOR]

Large-scale physical models of landslides can potentially accurately reflect the interactions between many internal and external factors and elucidate the process of slope deformation and failure. In order to reveal the mechanism of deformation of the reservoir bank accumulation layer, in this study, a large-scale physical test model with a similarity ratio of 1:200 was constructed based on the actual engineering geological section. Two reservoir water level cycle fluctuation conditions were simulated, and the reservoir water level drop rate was strictly controlled to be two times the rise rate. This study analyzed pore water pressure and deformation characteristics in the accumulation layer in relation to fluctuating reservoir water levels. The results showed that the rise in reservoir water level will make landslides more stable. The periodic sudden drop in water level seriously endangers the stability of landslides. The deformation and failure of landslides are more likely to occur in the weak interlayer area. The failure mode of the accumulation body in the test was traction failure. It is suggested that the front part of the accumulation body can be reinforced in practical engineering. To reveal the progression of instability and movement during accumulation layer large-scale landslides, a numerical model was constructed using the material point method. The accumulation layer sliding process could be divided into three stages: acceleration sliding, deceleration sliding and stabilization. After destabilization, the river channel may be altered by the landslide mass to form a landslide dam, potentially threatening the integrity of the dam via impulse waves generated during destabilization. The research results provide technical support for reservoir scheduling in major water conservancy and hydroelectric power station reservoirs as well as engineering risk assessment and prevention. [ABSTRACT FROM AUTHOR]

The chemical corrosion has a significant degradation effect on the mechanical properties of non-persistently jointed rocks. In order to improve their physical and mechanical properties, the modification test was carried out by using silicone immersion and resin filling in the pre-existing joint with different geometries after chemical corrosion. This modification influence on the mechanical properties is verified by triaxial compression tests. Experimental results indicate that peak strength, elastic modulus and deformation modulus of jointed rock improved significantly after modification. Finally, the mechanism of strength improvement induced by the modification was revealed by SEM and EDS analysis. [ABSTRACT FROM AUTHOR]

Many models have been developed for landslide displacement prediction, but owing to complex landslide-formation mechanisms and landslide-inducing factors, such models have different prediction accuracies. Thus, landslide displacement prediction remains a popular but difficult topic of research. In this paper, a landslide prediction model is proposed by combining extreme learning machine (ELM) and random search support vector regression (RS-SVR) sub-models. Particularly, the combined model decomposed accumulative landslide displacement into two terms, trend and periodic displacements, using a time series model, and simulated and predicted the two terms using the ELM and RS-SVR sub-models, respectively. The predicted trend and periodic terms are then summed to obtain the total displacement. The ELM and RS-SVR sub-models are applied to predict the deformation of Baishuihe landslide in the Three Gorges Reservoir Area (TGRA) as an example. The results showed that the model effectively improved the accuracy, stability, and scope of application of landslide displacement prediction, thus providing a new method for landslide displacement prediction. [ABSTRACT FROM AUTHOR]

Landslide-induced impulse waves in alpine valleys are a significant risk to large-scale dam and reservoir engineering projects in the surrounding area. In this study, a 1 : 200-scale physical model of landslide-induced impulse waves in a V-shaped river channel was established, and 18 groups of tests were conducted to evaluate the influence of different parameters, such as the volume and shape of the landslide body, water entry velocity, and water depth of the reservoir. Based on the test results, a dimensionless formula was established for the first wave height of impulse waves caused by a deep-water landslide in a V-shaped channel. An energy conversion law was determined for the impact of landslide-induced impulse waves on the reservoir bank. Finally, a distribution law was obtained for the initial maximum pressure caused by landslide-induced impulse waves along the water depth on the opposite bank. The theoretical predictions of the dimensionless formula showed good agreement with the experimental results, and the energy conversion rate of the landslide-induced impulse waves initially increased and then decreased with an increasing Froude number. The maximum dynamic water pressure showed a triangular distribution with increasing water depth below the surface of the still water body. The impact pressure of the impulse waves on the slope on the opposite bank increased with the water entry velocity. This study provides a scientific basis for the risk prevention and control of landslide-induced impulse waves in river channels feeding into reservoirs. [ABSTRACT FROM AUTHOR]

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How the brain encodes spatial information is an important topic. Experimental and theoretical progresses achieved in this area mainly focused on the neuronal response in the lower-dimensional space such as a linear track or a horizontal flat arena. How the real three-dimensional (3-D) space is represented in the brain is unknown. Grid cells in the medial entorhinal cortex and the place cells in the hippocampus are the principal spatial neurons, and the grid cells provide important inputs to the place cells. In order to simulate the place cell activity in higher dimension, we proposed a rotating-integration model to generate the place field on non-horizontal surfaces for crawling animal in 3-D space. By referring to the gravity signal as an anchor, preferred directions of the grid cell will be rotated with the animal's body plane during navigating on the surfaces. Then, multiple grid cell patterns with distributed orientations and wavelengths are integrated to form the firing field(s) of a place cell. The results can not only account for the known experimental recordings but also predict a segment planar encoding property of place cell on novel complex surfaces. It suggests that the spatial cognition for crawling animal is achieved by a mosaic of lower-dimensional codes rather than the full volumetric perception. This work can help us understand how the spatial information provided by the external physical world is represented and processed by the neuronal systems. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is one of the most serious neuropsychiatric disorders. Exploring the pathogenesis and dynamical coding patterns of MDD can provide new targets for clinical drug treatment and new ideas for the research of other neuropsychiatric and neurodegenerative diseases. We selected the medium spiny neuron (MSN) of nucleus accumbens (NAc) as the research objective. NAc is located in the dopaminergic pathway, regulating rewards, emotions and other behaviors. Abnormalities in these behaviors are considered as the main clinical symptoms of MDD. We simulated the different spike patterns of MSNs in MDD group and control group by dynamical Hodgkin–Huxley model. The simulated results can match the electrophysiological experiments, which occurred due to following reasons: (1) The external stimulus current of MDD group was amplified by the local neural microcircuit; (2) the selective permeability to sodium was abnormally decreased; and (3) the dopamine D2 receptor signaling pathway was abnormal in the MDD group. Furthermore, we proposed a dynamical energy model, and the energy results demonstrated that the energy cost in MDD group was lower, which led to persistent depression in patients with MDD. Simultaneously, the negative-to-total energy ratio of MSN in MDD group was higher than that in control group, and the delay time of the power peak and the potential peak in MDD group was shorter than that in the control group. The results showed that the abnormal firing patterns were the direct cause of abnormal behaviors of MDD and indicated that subthreshold activities of MDD group were more intense. [ABSTRACT FROM AUTHOR]

Rainfalls with heavy intensity and long duration are the main factors that trigger landslide. A large-scale test of physical model of landslide induced by gradient rainfall is conducted to investigate the mechanism of landslide caused by rainfalls with an indoor large-scale physical model test system. By monitoring the variation of pore water pressure, soil pressure, slope surface displacement, and groundwater level, the deformation and failure mechanism of deposit landslide under gradient rainfall are studied. The results show that the pore water pressure and soil pressure increase with the increase of the duration of rainfall, and the greater the rainfall intensity is, the greater the peak values of pore water pressure and soil pressure are. The intensity and duration of rainfall are the main influencing factors of landslide deformation. The decrease of effective stress and the rise of groundwater level are the root causes of landslide deformation. During a rainfall, excessive pore water pressure appears in the slope, which can be used as an early warning indicator of landslide deformation and failure. The deformation of slope body is nonlinear and shows a stepped pattern under gradient rainfall. During a rainfall, the toe of the slope is usually damaged first. Tension cracks appear in the rear edge and the middle part of the slope successively, showing the characteristics of traction failure. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study on the mechanical behavior and permeability evolution of basalt, drilled from the dam foundation of the Baihetan Hydropower Station in China. Two groups of triaxial unloading creep tests under hydro-mechanical coupling conditions were conducted. Based on the experimental results, creep deformation and failure characteristics of basalt were investigated. It was found that the relationship between the confining pressure and steady creep rate could be described satisfactorily by an exponential function. A tensile-shear failure mode was observed for basalt under the action of unloading confining pressure and hydraulic-mechanical coupling. The permeability evolution of basalt in the unloading creep process exhibited phased characteristics: permeability decreased in the initial loading phase and remained stable for a certain range of confining pressure in the steady creep phase. Exponential functions were also used to describe the evolution of permeability and confining pressure. The results of this study can provide a comprehensive understanding for the long-term stability assessment of basalt under coupled hydro-mechanical conditions. [ABSTRACT FROM AUTHOR]

Default mode network (DMN) is a functional brain network with a unique neural activity pattern that shows high activity in resting states but low activity in task states. This unique pattern has been proved to relate with higher cognitions such as learning, memory and decision-making. But neural mechanisms of interactions between the default network and the task-related network are still poorly understood. In this paper, a theoretical model of coupling the DMN and working memory network (WMN) is proposed. The WMN and DMN both consist of excitatory and inhibitory neurons connected by AMPA, NMDA, GABA synapses, and are coupled with each other only by excitatory synapses. This model is implemented to demonstrate dynamical processes in a working memory task containing encoding, maintenance and retrieval phases. Simulated results have shown that: (1) AMPA channels could produce significant synchronous oscillations in population neurons, which is beneficial to change oscillation patterns in the WMN and DMN. (2) Different NMDA conductance between the networks could generate multiple neural activity modes in the whole network, which may be an important mechanism to switch states of the networks between three different phases of working memory. (3) The number of sequentially memorized stimuli was related to the energy consumption determined by the network's internal parameters, and the DMN contributed to a more stable working memory process. (4) Finally, this model demonstrated that, in three phases of working memory, different memory phases corresponded to different functional connections between the DMN and WMN. Coupling strengths that measured these functional connections differed in terms of phase synchronization. Phase synchronization characteristics of the contained energy were consistent with the observations of negative and positive correlations between the WMN and DMN reported in referenced fMRI experiments. The results suggested that the coupled interaction between the WMN and DMN played important roles in working memory. [ABSTRACT FROM AUTHOR]

The information processing mechanisms of the visual nervous system remain to be unsolved scientific issues in neuroscience field, owing to a lack of unified and widely accepted theory for explanation. It has been well documented that approximately 80% of the rich and complicated perceptual information from the real world is transmitted to the visual cortex, and only a small fraction of visual information reaches the primary visual cortex (V1). This, nevertheless, does not affect our visual perception. Furthermore, how neurons in the secondary visual cortex (V2) encode such a small amount of visual information has yet to be addressed. To this end, the current paper established a visual network model for retina-lateral geniculate nucleus (LGN)-V1–V2 and quantitatively accounted for that response to the scarcity of visual information and encoding rules, based on the principle of neural mapping from V1 to V2. The results demonstrated that the visual information has a small degree of dynamic degradation when it is mapped from V1 to V2, during which there is a convolution calculation occurring. Therefore, visual information dynamic degradation mainly manifests itself along the pathway of the retina to V1, rather than V1 to V2. The slight changes in the visual information are attributable to the fact that the receptive fields (RFs) of V2 cannot further extract the image features. Meanwhile, despite the scarcity of visual information mapped from the retina, the RFs of V2 can still accurately respond to and encode "corner" information, due to the effects of synaptic plasticity, but the similar function does not exist in V1. This is a new discovery that has never been noticed before. To sum up, the coding of the "contour" feature (edge and corner) is achieved in the pathway of retina-LGN-V1–V2. [ABSTRACT FROM AUTHOR]

The ancient Zhenggang landslide (47.5 million m3) represents a potential threat to the construction and safe operation of the proposed Gushui Hydropower Project and to the people living downstream. The landslide was caused by continuous rainfall from October 20 to November 5, 2008, indicating that groundwater aggravated sliding and deformation, and it can be divided into two distinct zones: zone I and zone II. Investigations of the Zhenggang landslide deposits have been conducted for 10 years, but the evolution of the landslide deformation is yet to be revealed. Geological surveys and stability analysis have revealed that the Zhenggang landslide is currently relatively stable and is not sliding. The deformational data for the deep soil layers show that subslide zone I is a translational slide, while subslide zone II is an uplift slide, and zone II is slightly more stable than zone I. Obvious interior deformational failure occurred and was observed in the exploratory adits. The numerical results show that rainfall infiltration is the main factor to induce the revival of ancient landslide, and it is necessary to strengthen the landslide risk assessment and reliability of Zhenggang landslide under the condition of rainfall infiltration. Finally, in order to reduce landslide risks, the local residents living near the landslide mass should be relocated, and measures should be taken to increase slope stability. [ABSTRACT FROM AUTHOR]

The information processing mechanism of the visual nervous system is an unresolved scientific problem that has long puzzled neuroscientists. The amount of visual information is significantly degraded when it reaches the V1 after entering the retina; nevertheless, this does not affect our visual perception of the outside world. Currently, the mechanisms of visual information degradation from retina to V1 are still unclear. For this purpose, the current study used the experimental data summarized by Marcus E. Raichle to investigate the neural mechanisms underlying the degradation of the large amount of data from topological mapping from retina to V1, drawing on the photoreceptor model first. The obtained results showed that the image edge features of visual information were extracted by the convolution algorithm with respect to the function of synaptic plasticity when visual signals were hierarchically processed from low-level to high-level. The visual processing was characterized by the visual information degradation, and this compensatory mechanism embodied the principles of energy minimization and transmission efficiency maximization of brain activity, which matched the experimental data summarized by Marcus E. Raichle. Our results further the understanding of the information processing mechanism of the visual nervous system. [ABSTRACT FROM AUTHOR]

Spatial navigation relies on various types of neurons to form an internal representation in the brain of the external world. Among them, grid cells are generally believed to serve as an invariant metric system by their spatially periodic firing fields. But how this metrical coordinate system is organized in three-dimensional (3-D) real world remains a mystery, since most researches merely concerned the encoding scheme on the horizontal plane. We computationally explored the activity pattern of grid cell in the medial entorhinal cortex of crawling animal in 3-D space. By including the presumably referring signals of gravity and animal's body plane, grid cell firing fields on curved surfaces were produced based on the novel gravity-modulated oscillatory model. The results can account for the known experimental recordings and predict a mosaic-type grid code consisting of dynamically rotated planar arrangements. We further analyzed the path integration mechanism and derived the condition to ensure the invariant grid fields on any curved surface in 3-D space. It turns out that if the grid code is indeed not fully volumetric, it may become trajectory-dependent in 3-D space. And thus for crawling animals, 3-D grid fields could be degenerated and impaired, causing the path integration and distance measurement inaccurate. Besides, the volumetric firing fields were also discussed, although it is more suitable for flying or aquatic animals. This work can help us understand the intrinsically different spatial codes and navigating abilities among species with various locomotion modes and provide new insights of how the actual physical world is represented in the brain. [ABSTRACT FROM AUTHOR]

Spontaneous brain activities consume most of the brain's energy. So if we want to understand how the brain operates, we must take into account these spontaneous activities. Up and down transitions of membrane potentials are considered to be one of significant spontaneous activities. This kind of oscillation always shows bistable and bimodal distribution of membrane potentials. Our previous theoretical studies on up and down oscillations mainly looked at the ion channel dynamics. In this paper, we focus on energy feature of spontaneous up and down transitions based on a network model and its simulation. The simulated results indicate that the energy is a robust index and distinguishable of excitatory and inhibitory neurons. Meanwhile, one the whole, energy consumption of neurons shows bistable feature and bimodal distribution as well as the membrane potential, which turns out that the indicator of energy consumption encodes up and down states in this spontaneous activity. In detail, energy consumption mainly occurs during up states temporally, and mostly concentrates inside neurons rather than synapses spatially. The stimulation related energy is small, indicating that energy consumption is not driven by external stimulus, but internal spontaneous activity. This point of view is also consistent with brain imaging results. Through the observation and analysis of the findings, we prove the validity of the model again, and we can further explore the energy mechanism of more spontaneous activities. [ABSTRACT FROM AUTHOR]

Neural activity alters with the changes in cerebral blood flow (CBF) and blood oxygen saturation. Despite that these changes can be detected with functional magnetic resonance imaging (fMRI), the underlying physiological mechanism remains obscure. Upon activation of the specific brain region, CBF increases substantially, albeit with 6–8 s delay. Neuroscience has no scientific explanation for this experimental discovery yet. This study proposed a physiological mechanism for generating hemodynamic phenomena from the perspective of energy metabolism. The ratio of reduction (NADH) and oxidation states (NAD+) of nicotinamide adenine dinucleotide in cell was considered as the variable for CBF regulation. After the specific brain region was activated, brain glycogen was rapidly consumed as reserve energy, resulting in no significant change in the ratio of NADH and NAD+ concentrations. However, when the stored energy in the cell is exhausted, the dynamic equilibrium state of the transition between NADH and NAD + is changed, and the ratio of NADH and NAD+ concentrations is significantly increased, which regulates the blood flow to be greatly increased. Based on this physiological mechanism, this paper builds a large-scale visual nervous system network based on the Wang–Zhang neuron model, and quantitatively reproduced the hemodynamics observed in fMRI by computer numerical simulation. The results demonstrated that the negative energy mechanism, which was previously reported by our group using Wang–Zhang neuronal model, played a vital role in governing brain hemodynamics. Also, it precisely predicted the neural coupling mechanism between the energy metabolism and blood flow changes in the brain under stimulation. In nature, this mechanism is determined by imbalance and mismatch between the positive and negative energy during the spike of neuronal action potentials. A quantitative analysis was adopted to elucidate the physiological mechanism underlying this phenomenon, which would provide an insight into the principle of brain operation and the neural model of the overall brain function. [ABSTRACT FROM AUTHOR]

About 50-80% of total energy is consumed by signaling in neural networks. A neural network consumes much energy if there are many active neurons in the network. If there are few active neurons in a neural network, the network consumes very little energy. The ratio of active neurons to all neurons of a neural network, that is, the sparseness, affects the energy consumption of a neural network. Laughlin's studies show that the sparseness of an energy-efficient code depends on the balance between signaling and fixed costs. Laughlin did not give an exact ratio of signaling to fixed costs, nor did they give the ratio of active neurons to all neurons in most energy-efficient neural networks. In this paper, we calculated the ratio of signaling costs to fixed costs by the data from physiology experiments. The ratio of signaling costs to fixed costs is between 1.3 and 2.1. We calculated the ratio of active neurons to all neurons in most energy-efficient neural networks. The ratio of active neurons to all neurons in neural networks is between 0.3 and 0.4. Our results are consistent with the data from many relevant physiological experiments, indicating that the model used in this paper may meet neural coding under real conditions. The calculation results of this paper may be helpful to the study of neural coding. [ABSTRACT FROM AUTHOR]

The real-time assessment of mental workload (MWL) is critical for development of intelligent human–machine cooperative systems in various safety–critical applications. Although data-driven machine learning (ML) approach has shown promise in MWL recognition, there is still difficulty in acquiring a sufficient number of labeled data to train the ML models. This paper proposes a semi-supervised extreme learning machine (SS-ELM) algorithm for MWL pattern classification requiring only a small number of labeled data. The measured data analysis results show that the proposed SS-ELM paradigm can effectively improve the accuracy and efficiency of MWL classification and thus provide a competitive ML approach to utilizing a large number of unlabeled data which are available in many real-world applications. [ABSTRACT FROM AUTHOR]

The energy efficiency of neural signal transmission is thought to be an important constraint in the nervous system. It is generally measured as the energy consumed per unit of information. Most of the previous studies have demonstrated this efficiency by focusing on single action potentials. However, neural information is more likely to be encoded by a spike train rather than by a single spike. To date, how the energy efficiency is dependent on patterns of spike trains is still unclear. In this study, we examined the energy efficiency of various firing patterns simulated by the Chay neuron model, including relatively high-frequency activities with massive spikes, medium-frequency activities with a moderate number of spikes, and low-frequency activities with rare spikes. Our results indicate that medium-frequency patterns are more energy efficient than both the high-frequency and low-frequency patterns. The most efficient medium-frequency pattern is a sparse burst firing (SBF) pattern because it consumes minimal energy and transmits an amount of neural information comparable to that of high-frequency patterns which consume much more energy. SBF patterns minimize energy consumption not only by producing fewer spikes than high-frequency patterns, but more importantly, also by consuming available energy sources, namely the potential energy stored in ionic concentration gradients, in a balanced way. Furthermore, with fewer spikes, the irregular spike trains of SBF patterns with short bursts and single spikes maximize the neural information that they carry, leading to higher energy efficiency. Thus, the sensory system may give priority to limiting energy costs over maximizing information to achieve greater energy efficiency. [ABSTRACT FROM AUTHOR]

Visual attention is a selective process of visual information and improves perceptual performance by modulating activities of neurons in the visual system. It has been reported that attention increased firing rates of neurons, reduced their response variability and improved reliability of coding relevant stimuli. Recent neurophysiological studies demonstrated that attention also enhanced the synaptic efficacy between neurons mediated through NMDA and AMPA receptors. Majority of computational models of attention usually are based on firing rates, which cannot explain attentional modulations observed at the synaptic level. To understand mechanisms of attentional modulations at the synaptic level, we proposed a neural network consisting of three layers, corresponding to three different brain regions. Each layer has excitatory and inhibitory neurons. Each neuron was modeled by the Hodgkin–Huxley model. The connections between neurons were through excitatory AMPA and NMDA receptors, as well as inhibitory GABAA receptors. Since the binding process of neurotransmitters with receptors is stochastic in the synapse, it is hypothesized that attention could reduce the variation of the stochastic binding process and increase the fraction of bound receptors in the model. We investigated how attention modulated neurons' responses at the synaptic level on the basis of this hypothesis. Simulated results demonstrated that attention increased firing rates of neurons and reduced their response variability. The attention-induced effects were stronger in higher regions compared to those in lower regions, and stronger for inhibitory neurons than for excitatory neurons. In addition, AMPA receptor antagonist (CNQX) impaired attention-induced modulations on neurons' responses, while NMDA receptor antagonist (APV) did not. These results suggest that attention may modulate neuronal activity at the synaptic level. [ABSTRACT FROM AUTHOR]

The permeability of sandstone during hydrostatic compaction and triaxial deformation was measured using a rock triaxial servo-controlled system. The gas permeability was also measured using an integrated permeability and porosity test system to study the difference between gas and water permeability. The experimental results suggested that gas permeability is larger than water permeability by almost one order of magnitude. This phenomenon is due to the slippage effect. The modified permeability is much closer to the water permeability. An empirical exponential relationship can describe the stress-dependent permeability of sandstone, while a power law is suitable to describe the relationship between porosity and effective confining pressure. During triaxial deformation, permeability initially decreases and then begins to increase at an accelerating rate. The peak value of permeability is hysteretic to peak stress. The initial permeability, lowest permeability, peak permeability, and stable value of permeability all decrease with the increase of effective confining pressure. The volumetric strain has a great effect on permeability. The turning point where permeability starts to increase coincides well with the onset of dilatancy. [ABSTRACT FROM AUTHOR]

Rainfall, groundwater, and fluctuation in reservoir levels are considered among the main external factors affecting the stability of slopes. The impact of groundwater is particularly evident during the rainy season. In this paper, a case study on a geological investigation of the Donglingxing (DLX) landslide is presented to demonstrate a drainage control strategy. Due to the characteristics of the landslide and the limitations on the construction of stabilizing piles, a drainage tunnel system is herein used to reduce the risk of landslide. A comprehensive analysis is conducted on the monitoring data of rainfall, groundwater levels, reservoir water level, and drainage discharges from the tunnels. Based on field measurement data on groundwater level, water discharge, and deformation of the landslide before and after construction of the drainage tunnel, the effectiveness of the drainage tunnel system is evaluated. The results indicate that the drainage tunnel is effective in increasing the stability of landslide, and this work can serve as a reference case for other landslides. [ABSTRACT FROM AUTHOR]

This study surveys the interaction between working memory and long-term memory using the neural energy coding method based on a working memory model with Ca 2 + subsystem-induced bi-stability. We apply theta-burst stimulation (TBS) and high-frequency stimulation (HFS) to the bi-stable dynamical model to induce long-term memory. During studying the formation of long-term memory, we develop methods to measure the changes in energy input of these two stimuli and the corresponding energy consumption of the memory system. We investigate the minimum energy cost of these two stimuli to induce long-term memory and define an energy ratio to quantitatively describe the energy efficiency of the stimulus. We found that both stimuli induce the similar long-term effects by the dynamical model, but TBS is more energy efficient than HFS. The results provide a more comprehensive understanding in the transformation from working memory, by an energy coding approach, to long-term memory in response to the two types of long-term potentiation-induced protocols, which reflect the physiological mechanisms and neurodynamics of long-term memory generation, and also reveal the energy-efficient principle of the neural system. [ABSTRACT FROM AUTHOR]

As a type of pyroclastic rock, the breccia lava in the dam foundation of the Baihetan Hydropower Project is characterized by relatively low density, high natural moisture content and porosity, and lower ultrasonic velocity. When it is used as a bearing rock, its mechanical behavior will be critical for the safety and stability of the world's second largest hydropower station. Therefore, uniaxial and triaxial compression tests were performed to study the mechanical behavior of the breccia lava and scanning electron microscope (SEM) tests were carried out to reveal the microscopic failure modes of this rock. The experimental results indicated that all critical stresses, including the crack initiation stress (σci), crack damage stress (σcd), and peak stress (σp), exhibit strong dependence on the confining pressure. Experiential functions were used to describe the evolution of the elastic modulus and Poisson's ratio with confining pressure. Grain crushing and the growth and frictional sliding of microcracks were determined to cause the failure of the specimens. Based on the experimental results, a coupled elastoplastic damage model was proposed within a thermodynamic framework. In this model, two separate loading functions were employed to describe the damage and plasticity behavior of the breccia lava. A computational integration algorithm with high numerical accuracy and efficiency was developed to deal with the material under three different loading conditions: plasticity, damage, and coupled elastoplastic damage. The model was validated through comparison with the experimental data, and the good agreement between the two datasets confirms that the model can provide a good representation of mechanical behavior, particularly the post-peak behavior of the breccia lava. [ABSTRACT FROM AUTHOR]

The present study aimed to identify the mechanism of tactile sensation by analyzing the regularity of the firing pattern of Merkel cell–neurite complex (MCNC) under the stimulation of different compression depths. The fingertips were exposed to the contact pressure of a spherical object to sense external stimuli in this study. The distribution structure of slowly adapting type I (SAI) mechanoreceptors was considered for analyzing the neural coding of tactile stimuli, especially the firing pattern of SAI neural network for perceiving the external stimulation. The numerical simulation results showed that (1) when the skin was pressed by the same sphere and the depth of the pressing finger skin and position of the force application point remained unchanged, the firing rate of the neuron depended on the synergistic effect of the number of receptors connected with the neuron and the distance between the neuron and the force application point. (2) When the fingertip was pressed by the same sphere at a constant depth and the different contact position, the overall firing rate of the MCNC neural network increased with the number of SAI mechanoreceptors in the area where the force application point was located. [ABSTRACT FROM AUTHOR]

Neurobiological studies have shown that neurons in the primary visual cortex (V1) may employ sparse presentations to represent stimuli. We describe a network model for sparse coding which includes input layer, base functional layer and output layer. We simulated standard sparse coding and sparse coding based on fast independent component analysis (ICA). The duration of training bases, the convergence speed of objective function and the sparsity of coefficient matrix were compared, respectively. The results show that sparse coding based on fast ICA is more effective than standard sparse coding. [ABSTRACT FROM AUTHOR]

Brief bursts of high-frequency spikes are a common firing pattern of neurons. The cellular mechanisms of bursting and its biological significance remain a matter of debate. Focusing on the energy aspect, this paper proposes a neural energy calculation method based on the Chay model of bursting. The flow of ions across the membrane of the bursting neuron with or without current stimulation and its power which contributes to the change of the transmembrane electrical potential energy are analyzed here in detail. We find that during the depolarization of spikes in bursting this power becomes negative, which was also discovered in previous research with another energy model. We also find that the neuron's energy consumption during bursting is minimal. Especially in the spontaneous state without stimulation, the total energy consumption (2.152 × 10−7 J) during 30 s of bursting is very similar to the biological energy consumption (2.468 × 10−7 J) during the generation of a single action potential, as shown in Wang et al. (Neural Plast 2017, 2017a). Our results suggest that this property of low energy consumption could simply be the consequence of the biophysics of generating bursts, which is consistent with the principle of energy minimization. Our results also imply that neural energy plays a critical role in neural coding, which opens a new avenue for research of a central challenge facing neuroscience today. [ABSTRACT FROM AUTHOR]

Advances in neurobiology suggest that neuronal response of the primary visual cortex to natural stimuli may be attributed to sparse approximation of images, encoding stimuli to activate specific neurons although the underlying mechanisms are still unclear. The responses of retinal ganglion cells (RGCs) to natural and random checkerboard stimuli were simulated using fast independent component analysis. The neuronal response to stimuli was measured using kurtosis and Treves-Rolls sparseness, and the kurtosis, lifetime and population sparseness were analyzed. RGCs exhibited significant lifetime sparseness in response to natural stimuli and random checkerboard stimuli. About 65 and 72% of RGCs do not fire all the time in response to natural and random checkerboard stimuli, respectively. Both kurtosis of single neurons and lifetime response of single neurons values were larger in the case of natural than in random checkerboard stimuli. The population of RGCs fire much less in response to random checkerboard stimuli than natural stimuli. However, kurtosis of population sparseness and population response of the entire neurons were larger with natural than random checkerboard stimuli. RGCs fire more sparsely in response to natural stimuli. Individual neurons fire at a low rate, while the occasional “burst” of neuronal population transmits information efficiently. [ABSTRACT FROM AUTHOR]

The strength behavior and deformation modulus of columnar jointed rock mass have been investigated by conducting a series of uniaxial compressive tests, along with consideration of columnar joint effect. The tests have been performed on the artificial physical model samples, incorporating different angles β (which was the between the loading direction and axial direction of the columns) and heights of specimen. Columnar joint is a special geological structure with self-organized joint network which separates the rock into an assemblage of blocks. Due to the special joint network, the anisotropic characteristics of columnar joint rock mass is very remarkable. The physical model consisted of “intact rock” and “jointed filler”, replaced by cement mortar and white cement, respectively. The angle β were set to 0°, 15°, 30°, 45°, 60°, 75° and 90°, respectively, and the heights of specimen were set to five different values. Through the test, failure strength and failure modes of columnar joint rock mass with different angles β were observed. Based on the analysis of the test results, the uniaxial compression strength and average modulus of columnar joint rock mass showed strong anisotropy, showing a special “U” shape in terms of loading orientation. The size effect of columnar jointed rock mass was very typical, and the failure mode of columnar joint rock mass were analyzed. Furthermore, the relationship between failure strength and joint factor was studied. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to investigate the anisotropic characteristics of the special structure of a columnar jointed rock masses and provide reference to forecast the behavioral characteristics of real samples.Design/methodology/approach This study used FLAC3D numerical software to simulate the mechanical behavior of columnar jointed rock masses with different columns angles (ß) under different stress conditions. The peak strength, elastic modulus and Poisson’s ratio were obtained to investigate the strength, deformation characteristics and failure modes of the rock masses under conventional and true triaxial compression.Findings The results showed that the compressive strength of the specimens presents a U-shape under different joint inclinations. The strength of the specimens reaches a maximum value when ß = 90°, and the value for ß = 0° is slightly lower and reaches a minimum value when ß = 50°. The elastic modulus and Poisson’s ratio of the samples are obviously anisotropic, the anisotropic coefficient decreases with increasing confining pressure. When σ2 ≠ σ3, the peak strengths of the samples are related to the direction of the minor principal stress, and the failure modes of the samples are related to the confining pressure and joint inclination.Originality/value The present paper uses a numerical simulation method to examine the strength and deformation characteristics of a columnar jointed rock mass under conventional and true triaxial compression. The aim is to provide a reference to forecast the mechanical characteristics of test samples in the laboratory. [ABSTRACT FROM AUTHOR]

Based on the H-H equation, this study has proposed the calculation and analysis of energy expenditure for a single neuron which is activated at sup-threshold and subthreshold, as well as the criterion of the energy expenditure of neurons activated sup-threshold and subthreshold, which was the maximum power of a sodium ion pump. Results of the study showed that not only the electrophysiological activities of neurons were strictly restricted by the energy levels in the brain, but also the activities of neurons also had dual nature, meaning that subthreshold neurons were mainly with energy expenditure, while sup-threshold neurons were with both energy absorption and energy expenditure. These new findings were compared with the novel neuro-biophysical models that we have published last year, uncovering that the two models were essentially equivalent. [ABSTRACT FROM AUTHOR]

Place cells are important elements in the spatial representation system of the brain. A considerable amount of experimental data and classical models are achieved in this area. However, an important question has not been addressed, which is how the three dimensional space is represented by the place cells. This question is preliminarily surveyed by energy coding method in this research. Energy coding method argues that neural information can be expressed by neural energy and it is convenient to model and compute for neural systems due to the global and linearly addable properties of neural energy. Nevertheless, the models of functional neural networks based on energy coding method have not been established. In this work, we construct a place cell network model to represent three dimensional space on an energy level. Then we define the place field and place field center and test the locating performance in three dimensional space. The results imply that the model successfully simulates the basic properties of place cells. The individual place cell obtains unique spatial selectivity. The place fields in three dimensional space vary in size and energy consumption. Furthermore, the locating error is limited to a certain level and the simulated place field agrees to the experimental results. In conclusion, this is an effective model to represent three dimensional space by energy method. The research verifies the energy efficiency principle of the brain during the neural coding for three dimensional spatial information. It is the first step to complete the three dimensional spatial representing system of the brain, and helps us further understand how the energy efficiency principle directs the locating, navigating, and path planning function of the brain. [ABSTRACT FROM AUTHOR]

Cross-frequency phase-amplitude coupling (PAC) in neuronal oscillations network plays an important functional role in large scale neuronal communication and neuronal encoding. In the present study, a novel approach named permutation mutual information (PMI) was applied in measuring PAC. It is derived from the permutation entropy based on the mutual information theory, by which the mutual information of permutations of two time series can be evaluated. In order to verify the ability of PMI, a numerical test was performed by using both simulation data and experimental data. The performances of PMI were compared with that of two well-known methods, which were the mean vector length (MVL) and the modulation index (MI). It was found that the performance of PMI was similar to that of MI when measuring PAC intensity, but the coupling sensitivity of PMI was the highest among all these three approaches. Moreover, there was the lowest sensitivity in the MVL measurement, suggesting that MVL was a more conservative approach in detecting the existence of PAC. In addition, an ROC analysis showed that PMI performed better in measuring PAC compared to that of others. Furthermore, the experimental data, obtained from rats’ hippocampal CA3 regions, were analyzed by using the three approaches. The result was essentially in line with that of the simulation performances. In a word, the results suggest that PMI is a better choice for assessing PAC under the certain conditions. [ABSTRACT FROM AUTHOR]

Among the theories of neural information coding, the neural energy coding is more accessible to global coding features than traditional neural encoding. According to the shortcomings existing in the neuronal energy model, that is, the non-smooth nature of the energy curve, we proposed an improved neuronal energy model in this paper. The modified energy model is a good choice for establishment of the global model of brain function. And it is also the basis of energy calculation for functional cognitive neural networks in the future. [ABSTRACT FROM AUTHOR]

Sandstone is a typical sedimentary rock. Chemical corrosion can accelerate the development of damage and reduce the strength of sandstone. An elastoplastic damage model considering the coupled chemo-mechanical damage is proposed to describe short-term mechanical behaviour of sandstone after immersing in the chemical solution. In the first part, the coupled damage variable is defined based on the analysis of chemical damage mechanism. In the second part, based on the framework of irreversible thermodynamics, the general formulation of the phenomenological model is presented. The model considers the effect of initial chemical damage on the evolution of mechanical damage. It can also describe the coupling between plastic deformation and mechanical damage. Meanwhile, a non-associated rule is chosen in order to describe the compression-dilatation transition of volumetric deformation. Comparisons between numerical simulations and experimental data show that the model is able to describe the main mechanical behaviour of sandstone under chemical corrosion. [ABSTRACT FROM AUTHOR]

Single-unit studies in monkeys have demonstrated that neurons in the prefrontal cortex predict the reward type, reward amount or reward availability associated with a stimulus. To examine contributions of pyramidal cells and interneurons in reward processing, single-unit activity was extracellularly recorded in prefrontal cortices of four monkeys performing a reward prediction task. Based on their shapes of spike waveforms, prefrontal neurons were classified into broad-spike and narrow-spike units that represented putative pyramidal cells and interneurons, respectively. We mainly observed that narrow-spike neurons showed higher firing rates but less bursty discharges than did broad-spike neurons. Both narrow-spike and broad-spike cells selectively responded to the stimulus, reward and their interaction, and the proportions of each type of selective neurons were similar between the two cell classes. Moreover, the two types of cells displayed equal reliability of reward or stimulus discrimination. Furthermore, we found that broad-spike and narrow-spike cells showed distinct mechanisms for encoding reward or stimulus information. Broad-spike neurons raised their firing rate relative to the baseline rate to represent the preferred reward or stimulus information, whereas narrow-spike neurons inhibited their firing rate lower than the baseline rate to encode the non-preferred reward or stimulus information. Our results suggest that narrow-spike and broad-spike cells were equally involved in reward and stimulus processing in the prefrontal cortex. They utilized a binary strategy to complementarily represent reward or stimulus information, which was consistent with the task structure in which the monkeys were required to remember two reward conditions and two visual stimuli. [ABSTRACT FROM AUTHOR]

In this paper, the collective behaviors of a small-world neuronal network motivated by the anatomy of a mammalian cortex based on both Izhikevich model and Rulkov model are studied. The Izhikevich model can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. Rulkov model is in the form of difference equations that generate a sequence of membrane potential samples in discrete moments of time to improve computational efficiency. These two models are suitable for the construction of large scale neural networks. By varying some key parameters, such as the connection probability and the number of nearest neighbor of each node, the coupled neurons will exhibit types of temporal and spatial characteristics. It is demonstrated that the implementation of GPU can achieve more and more acceleration than CPU with the increasing of neuron number and iterations. These two small-world network models and GPU acceleration give us a new opportunity to reproduce the real biological network containing a large number of neurons. [ABSTRACT FROM AUTHOR]

Identifying the temporal variations in mental workload level (MWL) is crucial for enhancing the safety of human-machine system operations, especially when there is cognitive overload or inattention of human operator. This paper proposed a cost-sensitive majority weighted minority oversampling strategy to address the imbalanced MWL data classification problem. Both the inter-class and intra-class imbalance problems are considered. For the former, imbalance ratio is defined to determine the number of the synthetic samples in the minority class. The latter problem is addressed by assigning different weights to borderline samples in the minority class based on the distance and density meaures of the sample distribution. Furthermore, multi-label classifier is designed based on an ensemble of binary classifiers. The results of analyzing 21 imbalanced UCI multi-class datasets showed that the proposed approach can effectively cope with the imbalanced classification problem in terms of several performance metrics including geometric mean (G-mean) and average accuracy (ACC). Moreover, the proposed approach was applied to the analysis of the EEG data of eight experimental participants subject to fluctuating levels of mental workload. The comparative results showed that the proposed method provides a competing alternative to several existing imbalanced learning algorithms and significantly outperforms the basic/referential method that ignores the imbalance nature of the dataset. [ABSTRACT FROM AUTHOR]