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Oil is an important strategic material and civil energy. Accurate prediction of oil consumption can provide basis for relevant departments to reasonably arrange crude oil production, oil import and export, and optimize the allocation of social resources. Therefore, a new grey model FENBGM(1,1) is proposed to predict oil consumption in China. Firstly, the grey effect of the traditional GM(1,1) model was transformed into a quadratic equation. Four different parameters were introduced to improve the accuracy of the model, and the new initial conditions were designed by optimizing the initial values by weighted buffer operator. Combined with the reprocessing of the original data, the scheme eliminates the random disturbance effect, improves the stability of the system sequence, and can effectively extract the potential pattern of future development. Secondly, the cumulative order of the new model was optimized by fractional cumulative generation operation. At the same time, the smoothness rate quasi-smoothness condition was introduced to verify the stability of the model, and the particle swarm optimization algorithm (PSO) was used to search the optimal parameters of the model to enhance the adaptability of the model. Based on the above improvements, the new combination prediction model overcomes the limitation of the traditional grey model and obtains more accurate and robust prediction results. Then, taking the petroleum consumption of China's manufacturing industry and transportation, storage and postal industry as an example, this paper verifies the validity of FENBGM(1,1) model, analyzes and forecasts China's crude oil consumption with several commonly used forecasting models, and uses FENBGM(1,1) model to forecast China's oil consumption in the next four years. The results show that FENBGM(1,1) model performs best in all cases. Finally, based on the prediction results of FENBGM(1,1) model, some reasonable suggestions are put forward for China's oil consumption planning.

The unmanned aerial vehicle (UAV) swarm is regarded as having a significant role in modern warfare. The demand for UAV swarms with the capability of attack-defense confrontation is urgent. The existing decision-making methods of UAV swarm confrontation, such as multi-agent reinforcement learning (MARL), suffer from an exponential increase in training time as the size of the swarm increases. Inspired by group hunting behavior in nature, this paper presents a new bio-inspired decision-making method for UAV swarms for attack-defense confrontation via MARL. Firstly, a UAV swarm decision-making framework for confrontation based on grouping mechanisms is established. Secondly, a bio-inspired action space is designed, and a dense reward is added to the reward function to accelerate the convergence speed of training. Finally, numerical experiments are conducted to evaluate the performance of our method. The experiment results show that the proposed method can be applied to a swarm of 12 UAVs, and when the maximum acceleration of the enemy UAV is within 2.5 times ours, the swarm can well intercept the enemy, and the success rate is above 91%.

Self-centering bracing systems, by which residual deformations of structures after earthquakes can be minimized, are considered effective solutions to achieve seismic resilience. In this paper, a parametric study on the seismic response of intermediate and high-rise steel-framed buildings with novel self-centering tension-only braces (SC-TOBs) is numerically conducted. Three key parameters, the stiffness degradation factor, the activation strain, and the initial axial stiffness of the SC-TOBs, are investigated to explore the design space for the SC-TOB frames (SC-TOBFs) because of their unique tunability compared with traditional bracing systems. Identical steel frames equipped with buckling restrained braces (BRBs) are also designed and examined for comparison purposes. The results indicate that increasing the stiffness degradation factor can improve the second stiffness of SC-TOBFs and successfully make the distribution of interstory drifts more uniform; an increase in the activation strain leads to a larger activation deformation of SC-TOBFs, but it has a very limited effect on the interstory drifts; increasing the initial axial stiffness appropriately is beneficial to reduce the interstory drifts of the low stories. The lateral behavior of SC-TOBFs is comparable to that of BRB frames when a lower activation strain and a higher initial axial stiffness are selected. Furthermore, when a higher stiffness degradation factor and a lower initial axial stiffness are selected simultaneously, the seismic action on SC-TOBFs can be effectively reduced, and a relatively uniform distribution over the building height can be obtained. The SC-TOBFs are considered to be a type of performance-tunable structure, and tuning can be achieved by varying a frame’s adjustable parameters.

This work uses supervised learning to optimize the package design with validated experimental results for piezoelectric micromachined ultrasonic transducers (PMUTs) to increase and alter the sound pressure level (SPL). Advancements as compared to the state-of-art include: (1) a neural network model to achieve a mean squared error of less than 0.65 dB2 post 100 epochs; (2) increased vibration amplitude by 17.9 dBV at the first-mode resonance frequency of 33.5 kHz; and (3) SPL enhancements below the 20 kHz frequency range such as the magnitude increases of more than 60 dBV at 5 kHz. As such, the package design shifts the emitting acoustic energy from the ultrasound to audio range in favor of various applications, including audio speakers.

This work presents a continuous volumetric indoor air temperature measurement scheme using PMUTs (piezoelectric micromachined ultrasonic transducers). Advancements as compared to state-of-art works include: (1) 'volumetric' temperature monitoring instead traditional 'pointwise' temperature information; (2) accurate temperature readings spanning an operational distance of 5 m as validated by a commercial thermometer for 3 days; and (3) capable of detecting global average temperature and local temperature events by installing networked/grid PMUT pairs. This innovative sensing technique could be applicable for the precision control of future building/living environments.

This paper reports a 9-meter-long ultrasonic 3D detector based on a packaged lithium niobate PMUTs (piezoelectric micromachined ultrasonic transducers). Compared with the state-of-the-art reports, three distinctive achievements have been demonstrated: (1) high uniformity and wide bandwidth PMUTs by optimized package designs for highly efficient ultrasonic energy transfer; (2) a long-range receiving beamforming detection scheme on a 4×4 PMUT array for up to 9 m detection rang - comparable to the longest reported range via PMUTs; and (3) 3D detection of multiple static/moving objects with the field of view exceeding 50°. As such, this device is valuable for various applications such as obstacle avoidance when both low power consumption and small form factor are desirable, including aerial drones.

The function of the smooth muscle cells lining the walls of mammalian systemic arteries and arterioles is to regulate the diameter of the vessels to control blood flow and blood pressure. Here, we describe an in silico model, which we call the 'Hernandez-Hernandez model', of electrical and Ca2+ signaling in arterial myocytes based on new experimental data indicating sex-specific differences in male and female arterial myocytes from murine resistance arteries. The model suggests the fundamental ionic mechanisms underlying membrane potential and intracellular Ca2+ signaling during the development of myogenic tone in arterial blood vessels. Although experimental data suggest that KV1.5 channel currents have similar amplitudes, kinetics, and voltage dependencies in male and female myocytes, simulations suggest that the KV1.5 current is the dominant current regulating membrane potential in male myocytes. In female cells, which have larger KV2.1 channel expression and longer time constants for activation than male myocytes, predictions from simulated female myocytes suggest that KV2.1 plays a primary role in the control of membrane potential. Over the physiological range of membrane potentials, the gating of a small number of voltage-gated K+ channels and L-type Ca2+ channels are predicted to drive sex-specific differences in intracellular Ca2+ and excitability. We also show that in an idealized computational model of a vessel, female arterial smooth muscle exhibits heightened sensitivity to commonly used Ca2+ channel blockers compared to male. In summary, we present a new model framework to investigate the potential sex-specific impact of antihypertensive drugs.

The self-centering tension-only brace (SC-TOB) is a new and innovative bracing system that provides both a flag-shaped recentering hysteresis and load mitigation to structures. This paper presents an extensive investigation of the nonlinear seismic response of multistory steel frames built with SC-TOBs to internal force, drift, and energy dissipation. Pushover analysis subjected to two lateral load distributions and nonlinear dynamic analysis under ground motion ensembles corresponding to four hazard levels were conducted. The SC-TOBs can be designed to serve as conventional tension-only braces (TOBs) only providing lateral stiffness during minor earthquakes, to function with energy dissipation as intensity increases, and to fully recenter a structure even after severe earthquakes. The findings show that with an increase in the earthquake intensity, both the force response and drift response of the SC-TOB frames (SC-TOBFs) increased; however, the force distribution and drift distribution shapes of the SC-TOBFs remained almost constant. The SC-TOBFs generally experienced more energy dissipation in the lower parts of the building, while the upper stories dissipated almost no energy under certain load conditions, suggesting that the bracings on those stories could be replaced by conventional TOBs for economy. It is demonstrated that the SC-TOBs have immense potential to effectively improve seismic resilience to structures such that rehabilitation costs and operational disruptions after earthquakes are minimized.

Accelerated bridge construction is desired in urban areas or after natural disasters, thus, it has drawn the attention of the bridge community and transportation agencies worldwide. It has lead to the advancement of accelerated construction techniques using prefabricated bridge elements. In this paper, an innovative self-balanced bridge structure consisting of assembly truss units, flexible cables, and struts is proposed. All components of this truss string structure are fabricated in the precast plant before being shipped to the construction site, therefore reducing the amount of labor and time on-site significantly. The mechanical performance of this structure is studied with three-dimensional finite-element method under the influence of key parameters such as the number of struts, the distance between struts, sag-span ratio, and initial pretension. Parametric analysis of 6 pedestrian bridge models with typical span shows that the optimized structure form reduces steel consumption by up to 59.4% compared to regular truss structures.

Cardiac function is tightly regulated by the autonomic nervous system (ANS). Activation of the sympathetic nervous system increases cardiac output by increasing heart rate and stroke volume, while parasympathetic nerve stimulation instantly slows heart rate. Importantly, imbalance in autonomic control of the heart has been implicated in the development of arrhythmias and heart failure. Understanding of the mechanisms and effects of autonomic stimulation is a major challenge because synapses in different regions of the heart result in multiple changes to heart function. For example, nerve synapses on the sinoatrial node (SAN) impact pacemaking, while synapses on contractile cells alter contraction and arrhythmia vulnerability. Here, we present a multiscale neurocardiac modelling and simulator tool that predicts the effect of efferent stimulation of the sympathetic and parasympathetic branches of the ANS on the cardiac SAN and ventricular myocardium. The model includes a layered representation of the ANS and reproduces firing properties measured experimentally. Model parameters are derived from experiments and atomistic simulations. The model is a first prototype of a digital twin that is applied to make predictions across all system scales, from subcellular signalling to pacemaker frequency to tissue level responses. We predict conditions under which autonomic imbalance induces proarrhythmia and can be modified to prevent or inhibit arrhythmia. In summary, the multiscale model constitutes a predictive digital twin framework to test and guide high-throughput prediction of novel neuromodulatory therapy. KEY POINTS: A multi-layered model representation of the autonomic nervous system that includes sympathetic and parasympathetic branches, each with sparse random intralayer connectivity, synaptic dynamics and conductance based integrate-and-fire neurons generates firing patterns in close agreement with experiment. A key feature of the neurocardiac computational model is the connection between the autonomic nervous system and both pacemaker and contractile cells, where modification to pacemaker frequency drives initiation of electrical signals in the contractile cells. We utilized atomic-scale molecular dynamics simulations to predict the association and dissociation rates of noradrenaline with the β-adrenergic receptor. Multiscale predictions demonstrate how autonomic imbalance may increase proclivity to arrhythmias or be used to terminate arrhythmias. The model serves as a first step towards a digital twin for predicting neuromodulation to prevent or reduce disease.

This work presents an air-coupled piezoelectric micromachined ultrasonic transducer (pMUT) with high transmitting acoustic pressure by using sputtered potassium sodium niobate (K,Na)NbO3 (KNN) thin film with a high piezoelectric coefficient (e31 ∼ 8-10 C/m2) and low dielectric constant (ϵr ∼ 260-300) for the first time. The fabricated KNN pMUT with a resonant frequency at 104.5 kHz has been tested to exhibit unprecedented results: (1) high sound pressure level (SPL) of 109 dB/V at a distance of 10 cm, which is 8 times higher than that of AlN-based pMUTs at a similar frequency; (2) low-voltage operation of only 4 volts peak-to-peak amplitude (Vp-p); and (3) good receiving sensitivity. As such, this work presents a new class of high-SPL and low-driving-voltage pMUTs for potential applications in various fields, including consumer electronics, such as but not limited to haptic feedback, loudspeaker, and AR/VR systems.

Previous research suggests that there should be a correlation between computational thinking and traditional cognitive and motor skills. At present, academia either considers cognitive skills a part of computational thinking, or investigates the correlation between computational thinking and cognitive skills. Current research on computational thinking focuses on the design of teaching models to improve computational thinking ability and on measures for evaluating computational thinking. The association of computational thinking with motor skills is rarely discussed. From the perspective of embodied cognition theory, an association must exist, but its mechanisms have not been studied. Therefore, this study explores the effect of the teaching model of creative problem solving (CPS) on computational thinking and its internal correlation with technical skills. It was found that CPS can not only improve the computational thinking ability of students majoring in engineering, but also promote their corresponding motor skills. However, the students' previous academic background had a certain influence on their learning outcomes. Students at universities of technology experienced a greater improvement in their computational thinking and motor skills than students at general universities. There is a certain correlation between computational thinking and motor skills, and the interaction between them should be properly considered in future teaching and curriculum design so as to improve students' learning effectiveness.

Drug isomers may differ in their proarrhythmia risk. An interesting example is the drug sotalol, an antiarrhythmic drug comprising d- and l- enantiomers that both block the hERG cardiac potassium channel and confer differing degrees of proarrhythmic risk. We developed a multi-scale in silico pipeline focusing on hERG channel - drug interactions and used it to probe and predict the mechanisms of pro-arrhythmia risks of the two enantiomers of sotalol. Molecular dynamics (MD) simulations predicted comparable hERG channel binding affinities for d- and l-sotalol, which were validated with electrophysiology experiments. MD derived thermodynamic and kinetic parameters were used to build multi-scale functional computational models of cardiac electrophysiology at the cell and tissue scales. Functional models were used to predict inactivated state binding affinities to recapitulate electrocardiogram (ECG) QT interval prolongation observed in clinical data. Our study demonstrates how modeling and simulation can be applied to predict drug effects from the atom to the rhythm for dl-sotalol and also increased proarrhythmia proclivity of d- vs. l-sotalol when accounting for stereospecific beta-adrenergic receptor blocking.

It is imperative to develop better approaches to predict how antiarrhythmic drugs with multiple interactions and targets may alter the overall electrical and/or mechanical function of the heart. Safety Pharmacology studies have provided new insights into the multi-target effects of many different classes of drugs and have been aided by the addition of robust new in vitro and in silico technology. The primary focus of Safety Pharmacology studies has been to determine the risk profile of drugs and drug candidates by assessing their effects on repolarization of the cardiac action potential. However, for decades experimental and clinical studies have described substantial and potentially detrimental effects of Na+ channel blockers in addition to their well-known conduction slowing effects. One such side effect, associated with administration of some Na+ channel blocking drugs is negative inotropy. This reduces the pumping function of the heart, thereby resulting in hypotension. Flecainide is a well-known example of a Na+ channel blocking drug, that exhibits strong rate-dependent block of INa and may cause negative cardiac inotropy. While the phenomenon of Na+ channel suppression and resulting negative inotropy is well described, the mechanism(s) underlying this effect are not. Here, we set out to use a modeling and simulation approach to reveal plausible mechanisms that could explain the negative inotropic effect of flecainide. We utilized the Grandi-Bers model [1] of the cardiac ventricular myocyte because of its robust descriptions of ion homeostasis in order to characterize and resolve the relative effects of QRS widening, flecainide off-target effects and changes in intracellular Ca2+ and Na+ homeostasis. The results of our investigations and predictions reconcile multiple data sets and illustrate how multiple mechanisms may play a contributing role in the flecainide induced negative cardiac inotropic effect.