Effects of Biochar Inoculation with Bacillus megaterium on Rice Soil Phosphorus Fraction Transformation and Bacterial Community Dynamics

Rice husk biochar inoculated with Bacillus megaterium(BM)(referred to as BM-inoculated biochar, BMB) and uninoculated rice husk biochar(RHB) were added to soil at two rates(0.5%, as BMB1 and RHB1, respectively, and 1.0%, as BMB2 and RHB2, respectively) in an incubation experiment to comprehensively evaluate their effects on basic soil properties, phosphorus(P) fractions, bacterial community composition, and P-cycling genes.

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.

Stage IV malignant transformation of mature cystic teratoma palliatively treated with concurrent chemoradiotherapy:A case report

BACKGROUND Malignant transformation(MT)of mature cystic teratoma(MCT)has a poor prognosis,especially in advanced cases.Concurrent chemoradiotherapy(CCRT)has an inhibitory effect on MT.CASE SUMMARY Herein,we present a case in which CCRT had a reduction effect preoperatively.A 73-year-old woman with pyelonephritis was referred to our hospital.Computed tomography revealed right hydronephrosis and a 6-cm pelvic mass.Endoscopic ultrasound-guided fine-needle biopsy(EUS-FNB)revealed squamous cell carci-noma.The patient was diagnosed with MT of MCT.Due to her poor general con-dition and renal malfunction,we selected CCRT,expecting fewer adverse effects.After CCRT,her performance status improved,and the tumor size was reduced;surgery was performed.Five months postoperatively,the patient developed dis-semination and lymph node metastases.Palliative chemotherapy was ineffective.She died 18 months after treatment initiation.CONCLUSION EUS-FNB was useful in the diagnosis of MT of MCT;CCRT suppressed the disea-se and improved quality of life.

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.

Dynamics of phase transformation of Cu-Ni-Si alloy with super-high strength and high conductivity during aging

The precipitation behaviors of the Cu-Ni-Si alloys during aging were studied by analyzing the variations of electric conductivity.The Avrami-equation of phase transformation kinetics and the Avrami-equation of electric conductivity during aging were established for Cu-Ni-Si alloys,on the basis of linear relationship between the electric conductivity and the volume fraction of precipitates,and the calculation results coincide well with the experiment ones.The transformation kinetics curves were established to characterize the aging process.The characteristics of precipitates in the supersaturated solid solution alloy aged at 723 K were established,and the results show that the precipitates areβ-Ni3Si andδ-Ni2Si phases.

Post-Mesozoic Transformation of Tectonic Domain in Southeastern China and Its Geodynamic Mechanism

Since the Mesozoic and Cenozoic, a transformation from a Tethyan Himalayan tectonic domain into a circum Pacific tectonic domain from Indosinian to Yanshanian is indicated in this paper, resulting in conspicuous changes in geophysics, tectono magmatic distribution, lithofacies and paleo geography, tectonic system in southeastern China. Tectonic analysis shows that the tectonic framework resulted from the compounding, transforming and superimposing of the two tectonic domains. The geodynamic mechanism of the transformation is mainly shown as the transverse and longitudinal heterogeneity of lithosphere, and the exchange between the crust and the mantle.

The Analysis of Thermal-Induced Phase Transformation and Microstructural Evolution in Ni-Ti Based Shape Memory Alloys By Molecular Dynamics

Shape memory alloys has been widely applied on actuators and medical devices.The transformation temperature and microstructural evolution play two crucial factors and dominate the behavior of shape memory alloys.In order to understand the influence of the composition of the Ni-Ti alloys on the two factors,molecular dynamics was adopted to simulate the temperature-induced phase transformation.The results were post-processed by the martensite variant identification method.The method allows to reveal the detailed microstructural evolution of variants/phases in each case of the composition of Ni-Ti.Many features were found and having good agreement with those reported in the literature,such as the well-known Rank-2 herringbone structures;the X-interface;Ni-rich alloys have lower transformation temperature than Ti-rich alloys.In addition,some new features were also discovered.For example,the Ti-rich alloys enabled an easier martensitic transformation;the nucleated martensite pattern determined the microstructural evolution path,which also changed the atomic volume and temperature curves.The results generated in the current study are expected to provide the design guidelines for the applications of shape memory alloys.

THERMODYNAMICS OF MARTENSITIC TRANSFORMATION IN Fe-Mn-C AND Fe-Ni-C ALLOYS

A Central Atom Model is introduced and the LFG and Hsu models are modified in order to evaluate the driving force for the martensitic transformation in Fe-Mn-C and Fe-Ni-C al- loys.The results show that the relationship between the driving force and the yield strength of austenite at Ms temperature,σ_(0.2)~γ/M_s,fits Hsu's formula;ΔG~=2.1σ_(0.2)~γ/M_s+907 J/mol.The M_s temperatures of Fe-Mn-C and Fe-Ni-C alloys are also calculated.The calculated results are in good agreement with experimental values.

Thermodynamic Functions and Phase Transformation of Metal Nanocrystals

A model to calculate the thermodynamic functions of the pure metal nanocrystals has been developed, with the consideration of the effects of both the interfaces and the crystal in the nano-grain interior. As an example, the enthalpy, entropy and Gibbs free energy, as functions of the excess free volume at interfaces, temperature and grain size, are calculated for the Co nanocrystals. Furthermore, the characteristics of β-Co→α-Co phase transformation are studied, and the transformation temperatures at different levels of grain size, as well as the critical grain sizes at different temperatures, are predicted. The calculation results show that, the nano-grained β-Co （fcc） is thermodynamically stable at temperatures much lower than that for the conventional coarse-grained materials, and may also stably exist at room temperature when the grain size is reduced to be small enough. The present model is verified by comparisons between the experimental findings and the theoretical predictions.

Effects of tensile temperatures on phase transformations in zirconium by molecular dynamics simulations

The effects of tensile temperatures ranging from 100 K to 900 K on the phase transition of hexagonal close-packed(HCP)zirconium were investigated by molecular dynamics simulations,which were combined with experimental observation under high resolution transmission electron microscopy.The results show that externally applied loading first induced the HCP to body-centered cubic(BCC)phase transition in the Pitsch-Schrader(PS)orientation relationship(OR).Then,the face-centered cubic(FCC)structure transformed from the BCC phase in the Bain path.However,the HCP-to-BCC transition was incomplete at 100 K and 300 K,resulting in a prismatic-type OR between the FCC and original HCP phase.Additionally,at the temperature ranging from 100 K to 600 K,the inverse BCC-to-HCP transition occurred locally following other variants of the PS OR,resulting in a basal-type relation between the newly generated HCP and FCC phases.A higher tensile temperature promoted the amount of FCC phase transforming into the BCC phase when the strain exceeded 45%.Besides,the crystal stretched at lower temperatures exhibits relatively higher strength but by the compromise of plasticity.This study reveals the deformation mechanisms in HCP-Zr at different temperatures,which may provide a better understanding of the deformation mechanism of zirconium alloys under different application environments.

LORENTZ TIME-SPACE TRANSFORMATION IS A APPROXIMATE CALCULATION METHOD IN AERODYNAMICS FOR COMPRESSIBILITY

Between the transformations, witch can transform the compressible wave equation to the incompressible flow, a kind of relativity character can be found, which have the almost equal character as Lorenz time and space relation. This result leads to a new inference: incompressible wave equation with time and space structure of sonic special relativity is only different description of approximate compressible flow. This conclusion can be extended to Euler equation, and arise the interest of "compressible expression" of Maxwell equation. To study the rule of compressibility and thermodynamic character of metastructure field, a try is made by the using Kaman Tsian virtual gas method, this would give the relation, similar as mass and energy of special relativity theory. At first searching a transformation, witch can transform the compressible wave equation to the incompressible flow, but it is almost equal Lorenz time and space relation. So arrive to the conclusion: incompressible wave equation with approximate Lorentz transformation is only different description of compressible flow. This conclusion is expected be used to Maxwell equation, because its wave equation is also perfectly equal form. To search the rule of electromagnet and gravity field, by the using of Kaman Tsian virtual gas method, the relation of mass and energy of relativity theory is given.

THERMODYNAMICS OF BAINITIC TRANSFORMATION IN Cu-24 at.-% Al ALLOY

Two driving forces △G^((β_1)+α')and △G^(β_1→β_2+α)for the bainitic transformation have been calcu- lated for Cu-24 at.-% Al alloy.Results reveal △G^(β_1→α')>0 within the transformation tem- perature range 600—750 K.Therefore,the bainitic transformation cannot be initiated by the β_1→α' shearing mechanism.On the other hand,the driving force △G^(β_1→β_2+α)to produce 5% of the α becomes negative only when the composition of the α satisfies x_(Al)~α≤0.204 at 700 K or x_(Al)~α=0.209 at 750 K.So the bainitic transformation can only proceed by β_1→β_2+α diffusional reaction with different diffusion amount at different temperatures.

Dynamic expression of hepatic GP73 mRNA and protein and circulating GP73 during hepatocytes malignant transformation

Background:Hepatic Golgi protein-73(GP73)expression is related to hepatocellular carcinoma(HCC)progression.The aim of this study was to investigate the dynamic expression of GP73 mRNA and protein during hepatocytes malignant transformation.Methods:Human GP73 expressions in 88 HCC tissues and their self-control surrounding tissues were examined by immunohistochemistry,and survival time of HCC patients was evaluated by the Kaplan-Meier method.HCC model of Sprague-Dawley rats was made by diet containing 2-fluorenylacetamide.The rats were divided into the control,hepatocyte degeneration,precanceration,and HCC groups to observe GP73 protein and mRNA alterations during hepatocytes malignant transformation.Results:The GP73 expression was significantly higher in the cancerous tissues than that in the surrounding tissues,with shorter survival time,and the positive rates of GP73 protein in human HCC tissues were 53.3%at stage I,84.0%at stage II,84.6%at stage III,and 60.0%at stage IV,respectively.The positive rates of hepatic GP73 protein and mRNA in the rat models were none in the control group,66.7%and 44.4%in the hepatocytes degeneration group,88.9%and 77.8%in the hepatocytes precanceration group,and 100%in the HCC group,respectively.There was a positive correlation(r=0.91,P<0.01)between hepatic GP73 and serum GP73 during rat hepatocytes malignant transformation.Conclusions:Abnormal GP73 expression may be a sensitive and valuable biomarker in hepatocarcinogensis.

Numerical simulation of the dimensional transformation of atomization in a supersonic aerodynamic atomization dust-removing nozzle based on transonic speed compressible flow

To simulate the transonic atomization jet process in Laval nozzles,to test the law of droplet atomization and distribution,to find a method of supersonic atomization for dust-removing nozzles,and to improve nozzle efficiency,the finite element method has been used in this study based on the COMSOL computational fluid dynamics module.The study results showed that the process cannot be realized alone under the two-dimensional axisymmetric,three-dimensional and three-dimensional symmetric models,but it can be calculated with the transformation dimension method,which uses the parameter equations generated from the two-dimensional axisymmetric flow field data of the three-dimensional model.The visualization of this complex process,which is difficult to measure and analyze experimentally,was realized in this study.The physical process,macro phenomena and particle distribution of supersonic atomization are analyzed in combination with this simulation.The rationality of the simulation was verified by experiments.A new method for the study of the atomization process and the exploration of its mechanism in a compressible transonic speed flow field based on the Laval nozzle has been provided,and a numerical platform for the study of supersonic atomization dust removal has been established.

Mechanical properties of fine-grained dual phase low-carbon steels based on dynamic transformation

The fine grained dual phase （FG-DP） steel with ferrite grains of 2-4.5 μm and martensite islands smaller than 3 μm was obtained through the mechanism of deformation-enhanced ferrite transformation （DEFT）. Mechanical properties of the steel were tested at room temperature. The results indicated that with a similar volume fraction of martensite （about 20vol%）,FG-DP steel exhibited a superior combination of higher strength and more rapid strain hardening at low strains compared with the coarse-grained dual phase （CG-DP） steel obtained by critical annealing. The combination of higher strength,large elongation,and more rapid strain hardening of FG-DP steel can be attributed to the fine ferrite grain and finely dispersed martensite islands. In addition,the uniformly distributed martensite islands in FG-DP steel have smaller interspacing compared with that of CG-DP steel. So,at the initial plastic deformation stage,the plastic deformation of ferrite was restrained and more pronounced load was transferred from ferrite to martensite. The plastic deformation of martensite in FG-DP steel started earlier.