Apples to oranges:environmentally derived,dynamic regulation of serotonin neuron subpopulations in adulthood?

Traumatic brain injury(TBI)is a public health problem with an undue economic burden that impacts nearly every age,ethnic,and gender group across the globe(Capizzi et al.,2020).TBIs are often sustained during a dynamic range of exposures to energetic environmental forces and as such outcomes are typically heterogeneous regarding severity and pathology(Capizzi et al.,2020).

A Rapid Adaptation Approach for Dynamic Air‑Writing Recognition Using Wearable Wristbands with Self‑Supervised Contrastive Learning

Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.

Efficient and Stable Perovskite Solar Cells and Modules Enabled by Tailoring Additive Distribution According to the Film Growth Dynamics

Gas quenching and vacuum quenching process are widely applied to accelerate solvent volatilization to induce nucleation of perovskites in blade-coating method.In this work,we found these two pre-crystallization processes lead to different order of crystallization dynamics within the perovskite thin film,resulting in the differences of additive distribution.We then tailor-designed an additive molecule named 1,3-bis(4-methoxyphenyl)thiourea to obtain films with fewer defects and holes at the buried interface,and prepared perovskite solar cells with a certified efficiency of 23.75%.Furthermore,this work also demonstrates an efficiency of 20.18%for the large-area perovskite solar module(PSM)with an aperture area of 60.84 cm^(2).The PSM possesses remarkable continuous operation stability for maximum power point tracking of T_(90)>1000 h in ambient air.

Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

Emerging structures and dynamic mechanisms ofγ-secretase for Alzheimer’s disease

γ-Secretase,called“the proteasome of the membrane,”is a membrane-embedded protease complex that cleaves 150+peptide substrates with central roles in biology and medicine,including amyloid precursor protein and the Notch family of cell-surface receptors.Mutations inγ-secretase and amyloid precursor protein lead to early-onset familial Alzheimer’s disease.γ-Secretase has thus served as a critical drug target for treating familial Alzheimer’s disease and the more common late-onset Alzheimer’s disease as well.However,critical gaps remain in understanding the mechanisms of processive proteolysis of substrates,the effects of familial Alzheimer’s disease mutations,and allosteric modulation of substrate cleavage byγ-secretase.In this review,we focus on recent studies of structural dynamic mechanisms ofγ-secretase.Different mechanisms,including the“Fit-Stay-Trim,”“Sliding-Unwinding,”and“Tilting-Unwinding,”have been proposed for substrate proteolysis of amyloid precursor protein byγ-secretase based on all-atom molecular dynamics simulations.While an incorrect registry of the Notch1 substrate was identified in the cryo-electron microscopy structure of Notch1-boundγ-secretase,molecular dynamics simulations on a resolved model of Notch1-boundγ-secretase that was reconstructed using the amyloid precursor protein-boundγ-secretase as a template successfully capturedγ-secretase activation for proper cleavages of both wildtype and mutant Notch,being consistent with biochemical experimental findings.The approach could be potentially applied to decipher the processing mechanisms of various substrates byγ-secretase.In addition,controversy over the effects of familial Alzheimer’s disease mutations,particularly the issue of whether they stabilize or destabilizeγ-secretase-substrate complexes,is discussed.Finally,an outlook is provided for future studies ofγ-secretase,including pathways of substrate binding and product release,effects of modulators on familial Alzheimer’s disease mutations of theγ-secretase-substrate complexes.Comprehensive understanding of the functional mechanisms ofγ-secretase will greatly facilitate the rational design of effective drug molecules for treating familial Alzheimer’s disease and perhaps Alzheimer’s disease in general.

Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.

Study on the coupling calculation method for the launch dynamics of a self-propelled artillery multibody system considering engraving process

The launch dynamics theory for multibody systems emerges as an innovative and efficacious approach for the study of launch dynamics,capable of addressing the challenges of complex modeling,diminished computational efficiency,and imprecise analyses of system dynamic responses found in the dynamics research of intricate multi-rigid-flexible body systems,such as self-propelled artillery.This advancement aims to enhance the firing accuracy and launch safety of self-propelled artillery.Recognizing the shortfall of overlooking the band engraving process in existing theories,this study introduces a novel coupling calculation methodology for the launch dynamics of a self-propelled artillery multibody system.This method leverages the ABAQUS subroutine interface VUAMP to compute the dynamic response of the projectile and barrel during the launch process of large-caliber self-propelled artillery.Additionally,it examines the changes in projectile resistance and band deformation in relation to projectile motion throughout the band engraving process.Comparative analysis of the computational outcomes with experimental data evidences that the proposed method offers a more precise depiction of the launch process of self-propelled artillery,thereby enhancing the accuracy of launch dynamics calculations for self-propelled artillery.

Community structure and species diversity dynamics of a subtropical evergreen broad-leaved forest in China:2005 to 2020

Here,we characterize the temporal and spatial dynamics of forest community structure and species diversity in a subtropical evergreen broad-leaved forest in China.We found that community structure in this forest changed over a 15-year period.Specifically,renewal and death of common species was large,with the renewal of individuals mainly concentrated within a few populations,especially those of Aidia canthioides and Cryptocarya concinna.The numbers of individual deaths for common species were concentrated in the small and mid-diameter level.The spatial distribution of community species diversity fluctuated in each monitoring period,showing a more dispersed diversity after the 15-year study period,and the coefficient of variation on quadrats increased.In 2010,the death and renewal of the community and the spatial variation of species diversity were different compared to other survey years.Extreme weather may have affected species regeneration and community stability in our subtropical monsoon evergreen broad-leaved forests.Our findings suggest that strengthening the monitoring and management of the forest community will help better understand the long-and short-term causes of dynamic fluctuations of community structure and species diversity,and reveal the factors that drive changes in community structure.

Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO

The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices.The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 models.Compared with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern Pacific.The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.

Time-domain dynamic constitutive model suitable for mucky soil site seismic response

Soil nonlinear behavior displays noticeable effects on the site seismic response.This study proposes a new functional expression of the skeleton curve to replace the hyperbolic skeleton curve.By integrating shear modulus and combining the dynamic skeleton curve and the damping degradation coefficient,the constitutive equation of the logarithmic dynamic skeleton can be obtained,which considers the damping effect in a soil dynamics problem.Based on the finite difference method and the multi-transmitting boundary condition,a 1D site seismic response analysis program called Soilresp1D has been developed herein and used to analyze the time-domain seismic response in three types of sites.At the same time,this study also provides numerical simulation results based on the hyperbolic constitutive model and the equivalent linear method.The results verify the rationality of the new soil dynamic constitutive model.It can analyze the mucky soil site nonlinear seismic response,reflecting the deformation characteristics and damping effect of the silty soil.The hysteresis loop area is more extensive,and the residual strain is evident.

The impact of demographic dynamics on food consumption and its environmental outcomes:Evidence from China

With increasing population and changing demographics,food consumption has experienced a significant transition in quantity and quality.However,a dearth of knowledge remains regarding its environmental impacts and how it responds to demographic dynamics,particularly in emerging economies like China.Using the two-stage Quadratic Almost Demand System(QUAIDS)model,this study empirically examines the impact of demographic dynamics on food consumption and its environmental outcomes based on the provincial data from 2000 to 2020 in China.Under various scenarios,according to changes in demographics,we extend our analysis to project the long-term trend of food consumption and its environmental impacts,including greenhouse gas(GHG)emissions,water footprint(WF),and land appropriation(LA).The results reveal that an increase in the proportion of senior people significantly decreases the consumption of grain and livestock meat and increases the consumption of poultry,egg,and aquatic products,particularly for urban residents.Moreover,an increase in the proportion of males in the population leads to higher consumption of poultry and aquatic products.Correspondingly,in the current scenario of an increased aging population and sex ratio,it is anticipated that GHG emissions,WF,and LA are likely to decrease by 1.37,2.52,and 3.56%,respectively.More importantly,in the scenario adhering to the standards of nutritional intake according to the Dietary Guidelines for Chinese Residents in 2022,GHG emissions,WF,and LA in urban areas would increase by 12.78,20.94,and 18.32%,respectively.Our findings suggest that changing demographics should be considered when designing policies to mitigate the diet-environment-health trilemma and achieve sustainable food consumption.

Dynamic simulation analysis of molten salt reactor-coupled air-steam combined cycle power generation system

A nonlinear dynamic simulation model based on coordinated control of speed and flow rate for the molten salt reactor and combined cycle systems is proposed here to ensure the coordination and stability between the molten salt reactor and power system.This model considers the impact of thermal properties of fluid variation on accuracy and has been validated with Simulink.This study reveals the capability of the control system to compensate for anomalous situations and maintain shaft stability in the event of perturbations occurring in high-temperature molten salt tank outlet parameters.Meanwhile,the control system’s impact on the system’s dynamic characteristics under molten salt disturbance is also analyzed.The results reveal that after the disturbance occurs,the controlled system benefits from the action of the control,and the overshoot and disturbance amplitude are positively correlated,while the system power and frequency eventually return to the initial values.This simulation model provides a basis for utilizing molten salt reactors for power generation and maintaining grid stability.

Dynamically triggered seismicity on a tectonic scale:A review

Earthquakes triggered by dynamic disturbances have been confirmed by numerous observations and experiments.In the past several decades,earthquake triggering has attracted increasing attention of scholars in relation to exploring the mechanism of earthquake triggering,earthquake prediction,and the desire to use the mechanism of earthquake triggering to reduce,prevent,or trigger earthquakes.Natural earthquakes and large‐scale explosions are the most common sources of dynamic disturbances that trigger earthquakes.In the past several decades,some models have been developed,including static,dynamic,quasi‐static,and other models.Some reviews have been published,but explosiontriggered seismicity was not included.In recent years,some new results on earthquake triggering have emerged.Therefore,this paper presents a new review to reflect the new results and include the content of explosion‐triggered earthquakes for the reference of scholars in this area.Instead of a complete review of the relevant literature,this paper primarily focuses on the main aspects of dynamic earthquake triggering on a tectonic scale and makes some suggestions on issues that need to be resolved in this area in the future.

Analysis of CH_(4) and H_(2) Adsorption on Heterogeneous Shale Surfaces Using aMolecular Dynamics Approach

Determining the adsorption of shale gas on complex surfaces remains a challenge in molecular simulation studies.Difficulties essentially stem from the need to create a realistic shale structure model in terms of mineral heterogeneityand multiplicity.Moreover,precise characterization of the competitive adsorption of hydrogen andmethane in shale generally requires the experimental determination of the related adsorptive capacity.In thisstudy,the adsorption of adsorbates,methane(CH_(4)),and hydrogen(H_(2))on heterogeneous shale surface modelsof Kaolinite,Orthoclase,Muscovite,Mica,C_(60),and Butane has been simulated in the frame of a moleculardynamic’s numerical technique.The results show that these behaviors are influenced by pressure and potentialenergy.On increasing the pressure from 500 to 2000 psi,the sorption effect for CH_(4)significantly increasesbut shows a decline at a certain stage(if compared to H_(2)).The research findings also indicate that raw shalehas a higher capacity to adsorb CH_(4)compared to hydrogen.However,in shale,this difference is negligible.

Mechanical behavior and failure mechanisms of rock bolts subjected to static-dynamic loads

This study explores the effects of dynamic and static loading on rock bolt performance a key factor in maintaining the structural safety of coal mine roadways susceptible to coal bursts.Employing a housemade load frame to simulate various failure scenarios,pretension-impact-pull tests on rock bolts were conducted to scrutinize their dynamic responses under varied static load conditions and their failure traits under combined loads.The experimental results denote that with increased impact energy,maximum and average impact loads on rock bolts escalate significantly under pretension,initiating plastic deformation beyond a certain threshold.Despite minor reductions in the yield load due to impactinduced damage,pretension aids in constraining post-impact deformation rate and fluctuation degree of rock bolts.Moreover,impact-induced plastic deformation causes internal microstructure dislocation,fortifying the stiffness of the rock bolt support system.The magnitude of this fortification is directly related to the plastic deformation induced by the impact.These findings provide crucial guidance for designing rock bolt support in coal mine roadway excavation,emphasizing the necessity to consider both static and dynamic loads for improved safety and efficiency.

Millisecond dynamics of colloidal suspension studied by X-ray photon correlation spectroscopy at the Shanghai Synchrotron Radiation Facility

X-ray photon correlation spectroscopy(XPCS)has emerged as a powerful tool for probing the nanoscale dynamics of soft condensed matter and strongly correlated materials owing to its high spatial resolution and penetration capabilities.This technique requires high brilliance and beam coherence,which are not directly available at modern synchrotron beamlines in China.To facilitate future XPCS experiments,we modified the optical setup of the newly commissioned BL10U1 USAXS beamline at the Shanghai Synchrotron Radiation Facility(SSRF).Subsequently,we performed XPCS measurements on silica suspensions in glycerol,which were opaque owing to their high concentrations.Images were collected using a high frame rate area detector.A comprehensive analysis was performed,yielding correlation functions and several key dynamic parameters.All the results were consistent with the theory of Brownian motion and demonstrated the feasibility of XPCS at SSRF.Finally,by carefully optimizing the setup and analyzing the algorithms,we achieved a time resolution of 2 ms,which enabled the characterization of millisecond dynamics in opaque systems.

Application of GNSS-PPP on Dynamic Deformation Monitoring of Offshore Platforms

The real-time dynamic deformation monitoring of offshore platforms under environmental excitation is crucial to their safe operation.Although Global Navigation Satellite System-Precise Point Positioning(GNSS-PPP)has been considered for this purpose,its monitoring accuracy is relatively low.Moreover,the influence of background noise on the dynamic monitoring accuracy of GNSS-PPP remains unclear.Hence,it is imperative to further validate the feasibility of GNSS-PPP for deformation monitoring of offshore platforms.To address these concerns,vibration table tests with different amplitudes and frequencies are conducted.The results demonstrate that GNSS-PPP can effectively monitor horizontal vibration displacement as low as±30 mm,which is consistent with GNSS-RTK.Furthermore,the spectral characteristic of background noise in GNSS-PPP is similar to that of GNSS-RTK(Real Time Kinematic).Building on this observation,an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)has been proposed to de-noise the data and enhance the dynamic monitoring accuracy of GNSS-PPP.Field monitoring application research is also undertaken,successfully extracting and analyzing the dynamic deformation of an offshore platform structure under environmental excitation using GNSS-PPP monitoring in conjunction with improved CEEMDAN de-noising.By comparing the de-noised dynamic deformation trajectories of the offshore platform during different periods,it is observed that the platform exhibits reversible alternating vibration responses under environmental excitation,with more pronounced displacement deformation in the direction of load action.The research results confirm the feasibility and potential of GNSS-PPP for dynamic deformation monitoring of offshore platforms.

Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

Mechanical properties and acoustic emission characteristics of soft rock with different water contents under dynamic disturbance

Uniaxial compression tests and cyclic loading acoustic emission tests were conducted on 20%,40%,60%,80%,dry and saturated muddy sandstone by using a creep impact loading system to investigate the mechanical properties and acoustic emission characteristics of soft rocks with different water contents under dynamic disturbance.The mechanical properties and acoustic emission characteristics of muddy sandstones at different water contents were analysed.Results of experimental studies show that water is a key factor in the mechanical properties of rocks,softening them,increasing their porosity,reducing their brittleness and increasing their plasticity.Under uniaxial compression,the macroscopic damage characteristics of the muddy sandstone change from mono-bevel shear damage and‘X’type conjugate bevel shear damage to a roadway bottom-drum type damage as the water content increases.Dynamic perturbation has a strengthening effect on the mechanical properties of samples with 60%and less water content,and a weakening effect on samples with 80%and more water content,but the weakening effect is not obvious.Macroscopic damage characteristics of dry samples remain unchanged,water samples from shear damage and tensile–shear composite damage gradually transformed into cleavage damage,until saturation transformation monoclinic shear damage.The evolution of acoustic emission energy and event number is mainly divided into four stages:loading stage(Ⅰ),dynamic loading stage(Ⅱ),yield failure stage(Ⅲ),and post-peak stage(Ⅳ),the acoustic emission characteristics of the stages were different for different water contents.The characteristic value of acoustic emission key point frequency gradually decreases,and the damage degree of the specimen increases,corresponding to low water content—high main frequency—low damage and high water content—low main frequency—high damage.