Genesis of LCT Pegmatites during Early Paleozoic Orogeny of the North Qinling Orogenic Belt,China:Emplacement Conditions and Structural Control

The Guanpo pegmatite field in the North Qinling orogenic belt(NQB),China,hosts the most abundant LCT pegmatites.However,their emplacement conditions and structural control remain unexplored.In this contribution,we investigated it combining pegmatite orientation measurement with oxygen isotope geothermometry and fluid inclusion study.The orientations of type A1 pegmatites(P_(f)<σ_(2))are predominantly influenced by P-and T-fractures due to simple shearing in Shiziping dextral thrust shear zone during D_(2)deformation,whereas type A2 pegmatites(contemporaneous with D_(4))are governed by hydraulic fractures aligned with S_(0)and S_(0+1)stemming from fluid pressure(P_(f)<σ_(2)).Additionally,type B pegmatites(P_(f)≤σ_(2))exhibit orientations shaped by en echelon extensional fractures in local ductile shear zones(contemporaneous with D_(3)).The albite-quartz oxygen isotope geothermometry and microthermometric analysis of fluid inclusions in elbaites from the latest pegmatites(including types B and A2)suggest that the crystallization P-T for late magmatic and hydrothermal stages are 527.5-559.2℃,320℃,3.1-3.6 kbar and 2.0 kbar,respectively.Our observations along with previous studies suggest that the genesis of the LCT pegmatites was a long-term,multi-stage event during early Paleozoic orogeny(including the collision stage)of the NQB,and was facilitated by various local fractures.

Ambient-Condition Strategy for Production of Hollow Ga_(2)O_(3)@rGO Crystalline Nanostructures Toward Efficient Lithium Storage

Crystallineγ-Ga_(2)O_(3)@rGO core-shell nanostructures are synthesized in gram scale,which are accomplished by a facile sonochemical strategy under ambient condition.They are composed of uniformγ-Ga_(2)O_(3)nanospheres encapsulated by reduced graphene oxide(rGO)nanolayers,and their formation is mainly attributed to the existed opposite zeta potential between the Ga_(2)O_(3)and rGO.The as-constructed lithium-ion batteries(LIBs)based on as-fabricatedγ-Ga_(2)O_(3)@rGO nanostructures deliver an initial discharge capacity of 1000 mAh g^(-1)at 100 mA g^(-1)and reversible capacity of 600 mAh g^(-1)under 500 mA g^(-1)after 1000 cycles,respectively,which are remarkably higher than those of pristineγ-Ga_(2)O_(3)with a much reduced lifetime of 100 cycles and much lower capacity.Ex situ XRD and XPS analyses demonstrate that the reversible LIBs storage is dominant by a conversion reaction and alloying mechanism,where the discharged product of liquid metal Ga exhibits self-healing ability,thus preventing the destroy of electrodes.Additionally,the rGO shell could act robustly as conductive network of the electrode for significantly improved conductivity,endowing the efficient Li storage behaviors.This work might provide some insight on mass production of advanced electrode materials under mild condition for energy storage and conversion applications.

Necessary and Sufficient Conditions for Oscillations of the Generalized Liénard Systems

In this paper, we study the second-order nonlinear differential systems of Liénard-type x˙=1a(x)[ h(y)−F(x) ], y˙=−a(x)g(x). Necessary and sufficient conditions to ensure that all nontrivial solutions are oscillatory are established by using a new nonlinear integral inequality. Our results substantially extend and improve previous results known in the literature.

Autophagy and mitophagy as potential therapeutic targets in diabetic heart condition:Harnessing the power of nanotheranostics

Autophagy and mitophagy pose unresolved challenges in understanding the pathology of diabetic heart condition(DHC),which encompasses a complex range of cardiovascular issues linked to diabetes and associated cardiomyopathies.Despite significant progress in reducing mortality rates from cardiovascular diseases(CVDs),heart failure remains a major cause of increased morbidity among diabetic patients.These cellular processes are essential for maintaining cellular balance and removing damaged or dysfunctional components,and their involvement in the development of diabetic heart disease makes them attractive targets for diagnosis and treatment.While a variety of conventional diagnostic and therapeutic strategies are available,DHC continues to present a significant challenge.Point-of-care diagnostics,supported by nanobiosensing techniques,offer a promising alternative for these complex scenarios.Although conventional medications have been widely used in DHC patients,they raise several concerns regarding various physiological aspects.Modern medicine places great emphasis on the application of nanotechnology to target autophagy and mitophagy in DHC,offering a promising approach to deliver drugs beyond the limitations of traditional therapies.This article aims to explore the potential connections between autophagy,mitophagy and DHC,while also discussing the promise of nanotechnology-based theranostic interventions that specifically target these molecular pathways.

Fermions: Spin, Hidden Variables, Violation of Bell’s Inequality and Quantum Entanglement

Using real fields instead of complex ones, it was recently claimed, that all fermions are made of pairs of coupled fields (strings) with an internal tension related to mutual attraction forces, related to Planck’s constant. Quantum mechanics is described with real fields and real operators. Schrodinger and Dirac equations then are solved. The solution to Dirac equation gives four, real, 2-vectors solutions ψ1=(U1D1)ψ2=(U2D2)ψ3=(U3D3)ψ4=(U4D4)where (ψ1,ψ4) are coupled via linear combinations to yield spin-up and spin-down fermions. Likewise, (ψ2,ψ3) are coupled via linear combinations to represent spin-up and spin-down anti-fermions. For an incoming entangled pair of fermions, the combined solution is Ψin=c1ψ1+c4ψ4where c1and c4are some hidden variables. By applying a magnetic field in +Z and +x the theoretical results of a triple Stern-Gerlach experiment are predicted correctly. Then, by repeating Bell’s and Mermin Gedanken experiment with three magnetic filters σθ, at three different inclination angles θ, the violation of Bell’s inequality is proven. It is shown that all fermions are in a mixed state of spins and the ratio between spin-up to spin-down depends on the hidden variables.

Oscillation properties of eigenfunctions for Sturm-Liouville problems with interface conditions via Prufer transformation

A class of Sturm-Liouville problems with discontinuity is studied in this paper.The oscillation properties of eigenfunctions for Sturm-Liouville problems with interface conditions are obtained.The main method used in this paper is based on Prufer transformation,which is different from the classical ones.Moreover,we give two examples to verify our main results.

Role of outdoor trees on pedestrian wind and thermal conditions around a pre-education building for sustainable energy management

Finding sustainable energy resources is essential to face the increasing energy demand.Trees are an important part of ancient architecture but are becoming rare in urban areas.Trees can control and tune the pedestrian-level wind velocity and thermal condition.In this study,a numerical investigation is employed to assess the role of trees planted in the windward direction of the building complex on the thermal and pedestrian wind velocity conditions around/inside a pre-education building located in the center of the complex.Compared to the previous studies(which considered only outside buildings),this work considers the effects of trees on microclimate change both inside/outside buildings.Effects of different parameters including the leaf area density and number of trees,number of rows,far-field velocity magnitude,and thermal condition around the main building are assessed.The results show that the flow velocity in the spacing between the first-row buildings is reduced by 30%-40% when the one-row trees with 2 m height are planted 15 m farther than the buildings.Furthermore,two rows of trees are more effective in higher velocities and reduce the maximum velocity by about 50%.The investigation shows that trees also could reduce the temperature by about 1℃around the building.

Design and high-power testing of offline conditioning cavity for CiADS RFQ high-power coupler

To validate the design rationality of the power coupler for the RFQ cavity and minimize cavity contamination,we designed a low-loss offline conditioning cavity and conducted high-power testing.This offline cavity features two coupling ports and two tuners,operating at a frequency of 162.5 MHz with a tuning range of 3.2 MHz.Adjusting the installation angle of the coupling ring and the insertion depth of the tuner helps minimize cavity losses.We performed electromagnetic structural and multiphysics simulations,revealing a minimal theoretical power loss of 4.3%.However,when the cavity frequency varied by110 kHz,theoretical power losses increased to10%,necessitating constant tuner adjustments during conditioning.Multiphysics simulations indicated that increased cavity temperature did not affect frequency variation.Upon completion of the offline high-power conditioning platform,we measured the transmission performance,revealing a power loss of 6.3%,exceeding the theoretical calculation.Conditioning utilized efficient automatic range scanning and standing wave resonant methods.To fully condition the power coupler,a 15°phase difference between two standing wave points in the condition-ing system was necessary.Notably,the maximum continuous wave power surpassed 20 kW,exceeding the expected target.

Enhancing Erectile Function and Alleviating Andropause Symptoms: Clinical Efficacy of a Human Stem Cell Conditioned Medium Cream

Erectile dysfunction (ED) is increasingly prevalent in Japan, exceeding 30%, and increasing with age. Unhealthy lifestyle habits, obesity, insufficient exercise, and smoking have been implicated in its pathogenesis, along with endothelial dysfunction of the corpora cavernosa and impaired blood flow to the penis considered underlying factors. However, the current treatments are limited to Phosphodiesterase-5 (PDE5) inhibitors. ED is the primary symptom of andropathy. This study reports the clinical efficacy of human stem cell-conditioned medium cream for ED treatment. Ten men without underlying diseases suspected of andropause with ED (mean age 43.2 ± 4.4 y, Hb 15.2 ± 0.6 gm/dL, AST/ALT 30.2/37.9 ± 12.4/14.0, eGFR 82.7 ± 12.4 mL/min/1.73 m2) were targeted. The cream was applied twice daily to the genital and scrotal areas. The erectile hardness score (EHS), International Index of Erectile Function-5 (IIEF-5), and Aging Male Symptoms (AMS) scale were used to evaluate the participants before and 30 days after use, and the results were compared using paired t-tests. The post-use qualitative opinions were collected through interviews. Significant improvements were observed compared to baseline in the IIEF-5 (11.8 ± 4.6→17.2 ± 5.1, P < 0.001), and AMS (46.3 ± 6.7→37.6 ± 5.3, P < 0.001) scores post cream use. EHS did not show a statistically significant difference, but a trend towards improvement was observed. Qualitative feedback included increased morning erection, improved maintenance of erection during intercourse, and reduced post work fatigue. Human stem cell-conditioned medium contains endothelial growth factors that potentially contribute to the improvement of ED and andropause by enhancing corporal endothelial function. Future studies should include control groups to further investigate the efficacy of these treatments.

Multi-soliton solutions of coupled Lakshmanan-Porsezian-Daniel equations with variable coefficients under nonzero boundary conditions

This paper aims to investigate the multi-soliton solutions of the coupled Lakshmanan–Porsezian–Daniel equations with variable coefficients under nonzero boundary conditions.These equations are utilized to model the phenomenon of nonlinear waves propagating simultaneously in non-uniform optical fibers.By analyzing the Lax pair and the Riemann–Hilbert problem,we aim to provide a comprehensive understanding of the dynamics and interactions of solitons of this system.Furthermore,we study the impacts of group velocity dispersion or the fourth-order dispersion on soliton behaviors.Through appropriate parameter selections,we observe various nonlinear phenomena,including the disappearance of solitons after interaction and their transformation into breather-like solitons,as well as the propagation of breathers with variable periodicity and interactions between solitons with variable periodicities.

Effects of Initial and Boundary Conditions on Heavy Rainfall Simulation over the Yellow Sea and the Korean Peninsula:Comparison of ECMWF and NCEP Analysis Data Effects and Verification with Dropsonde Observation

This study evaluated the simulation performance of mesoscale convective system(MCS)-induced precipitation,focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula.The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP)analysis data,as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model.Particularly,temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data.Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis,which exhibited a cold and dry bias in the lower levels below 850 hPa.The model,in terms of the precipitation simulations,particularly for high-intensity precipitation over the Yellow Sea,demonstrated higher accuracy when applying ECMWF analysis data as the initial condition.This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features(i.e.,warm and humid lower-level atmosphere)over the Yellow Sea.In conclusion,this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data.Furthermore,it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.

Application of the Conditional Nonlinear Local Lyapunov Exponent to Second-Kind Predictability

In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent(NLLE)and signal-to-noise ratio methods using a coupled Lorenz model.The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings,therefore,it can quantify the predictability limit induced by the external forcings.On this basis,a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields.The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method.This similarity supports ENSO as the major predictable signal for weather and climate prediction.In addition,a ratio of predictability limit(RPL)calculated by the CNLLE method to that calculated by the NLLE method was proposed.The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit.For instance,ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months,as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean.Moreover,the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.

Intelligent pavement condition survey:Overview of current researches and practices

Automated pavement condition survey is of critical importance to road network management.There are three primary tasks involved in pavement condition surveys,namely data collection,data processing and condition evaluation.Artificial intelligence(AI)has achieved many breakthroughs in almost every aspect of modern technology over the past decade,and undoubtedly offers a more robust approach to automated pavement condition survey.This article aims to provide a comprehensive review on data collection systems,data processing algorithms and condition evaluation methods proposed between 2010 and 2023 for intelligent pavement condition survey.In particular,the data collection system includes AI-driven hardware devices and automated pavement data collection vehicles.The AI-driven hardware devices including right-of-way(ROW)cameras,ground penetrating radar(GPR)devices,light detection and ranging(LiDAR)devices,and advanced laser imaging systems,etc.These different hardware components can be selectively mounted on a vehicle to simultaneously collect multimedia information about the pavement.In addition,this article pays close attention to the application of artificial intelligence methods in detecting pavement distresses,measuring pavement roughness,identifying pavement rutting,analyzing skid resistance and evaluating structural strength of pavements.Based upon the analysis of a variety of the state-of-the-art artificial intelligence methodologies,remaining challenges and future needs with respect to intelligent pavement condition survey are discussed eventually.

Conditional Generative Adversarial Network Enabled Localized Stress Recovery of Periodic Composites

Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial.Repeating unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of composites.This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs.The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading direction.Subsequently,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized stresses.The stresses predicted by cGAN are validated against the remaining true data not used for training,showing good agreement.This work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite RUCs.It can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.

Impact of wetting-drying cycles and acidic conditions on the soil aggregate stability of yellow‒brown soil

Soil aggregate is the basic structural unit of soil,which is the foundation for supporting ecosystem functions,while its composition and stability is significantly affected by the external environment.This study was conducted to explore the effect of external environment(wetting-drying cycles and acidic conditions)on the soil aggregate distribution and stability and identify the key soil physicochemical factors that affect the soil aggregate stability.The yellow‒brown soil from the Three Gorges Reservoir area(TGRA)was used,and 8 wetting-drying conditions(0,1,2,3,4,5,10 and 15 cycles)were simulated under 4 acidic conditions(pH=3,4,5 and 7).The particle size distribution and soil aggregate stability were determined by wet sieving method,the contribution of environmental factors(acid condition,wetting-drying cycle and their combined action)to the soil aggregate stability was clarified and the key soil physicochemical factors that affect the soil aggregate stability under wetting-drying cycles and acidic conditions were determined by using the Pearson’s correlation analysis,Partial least squares path modeling(PLS‒PM)and multiple linear regression analysis.The results indicate that wetting-drying cycles and acidic conditions have significant effects on the stability of soil aggregates,the soil aggregate stability gradually decreases with increasing number of wetting-drying cycles and it obviously decreases with the increase of acidity.Moreover,the combination of wetting-drying cycles and acidic conditions aggravate the reduction in the soil aggregate stability.The wetting-drying cycles,acidic conditions and their combined effect imposes significant impact on the soil aggregate stability,and the wetting-drying cycles exert the greatest influence.The soil aggregate stability is significantly correlated with the pH,Ca^(2+),Mg^(2+),maximum disintegration index(MDI)and soil bulk density(SBD).The PLS‒PM and multiple linear regression analysis further reveal that the soil aggregate stability is primarily influenced by SBD,Ca^(2+),and MDI.These results offer a scientific basis for understanding the soil aggregate breakdown mechanism and are helpful for clarifying the coupled effect of wetting-drying cycles and acid rain on terrestrial ecosystems in the TGRA.

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

Classification research of TCM pulse conditions based on multi-label voice analysis

Objective To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine(TCM)pulse conditions through voice signals.Methods We used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning.Audio features were extracted from voice recordings in the TCM pulse condition dataset.The obtained features were combined with information from tongue and facial diagnoses.A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation,and the modeling methods were validated using publicly available datasets.Results The analysis showed that the proposed method achieved an accuracy of 92.59%on the public dataset.The accuracies of the three single-label pulse manifestation models in the test set were 94.27%,96.35%,and 95.39%.The absolute accuracy of the multi-label model was 92.74%.Conclusion Voice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.

Impact of Initial Soil Conditions on Soil Hydrothermal and Surface Energy Fluxes in the Permafrost Region of the Tibetan Plateau

Accurate initial soil conditions play a crucial role in simulating soil hydrothermal and surface energy fluxes in land surface process modeling.This study emphasized the influence of the initial soil temperature(ST)and soil moisture(SM)conditions on a land surface energy and water simulation in the permafrost region in the Tibetan Plateau(TP)using the Community Land Model version 5.0(CLM5.0).The results indicate that the default initial schemes for ST and SM in CLM5.0 were simplistic,and inaccurately represented the soil characteristics of permafrost in the TP which led to underestimating ST during the freezing period while overestimating ST and underestimating SLW during the thawing period at the XDT site.Applying the long-term spin-up method to obtain initial soil conditions has only led to limited improvement in simulating soil hydrothermal and surface energy fluxes.The modified initial soil schemes proposed in this study comprehensively incorporate the characteristics of permafrost,which coexists with soil liquid water(SLW),and soil ice(SI)when the ST is below freezing temperature,effectively enhancing the accuracy of the simulated soil hydrothermal and surface energy fluxes.Consequently,the modified initial soil schemes greatly improved upon the results achieved through the long-term spin-up method.Three modified initial soil schemes experiments resulted in a 64%,88%,and 77%reduction in the average mean bias error(MBE)of ST,and a 13%,21%,and 19%reduction in the average root-mean-square error(RMSE)of SLW compared to the default simulation results.Also,the average MBE of net radiation was reduced by 7%,22%,and 21%.

Effect of boundary conditions on shakedown analysis of heterogeneous materials

The determination of the ultimate load-bearing capacity of structures made of elastoplastic heterogeneous materials under varying loads is of great importance for engineering analysis and design. Therefore, it is necessary to accurately predict the shakedown domains of these materials. The static shakedown theorem, also known as Melan's theorem, is a fundamental method used to predict the shakedown domains of structures and materials. Within this method, a key aspect lies in the construction and application of an appropriate self-equilibrium stress field(SSF). In the structural shakedown analysis, the SSF is typically constructed by governing equations that satisfy no external force(NEF) boundary conditions. However, we discover that directly applying these governing equations is not suitable for the shakedown analysis of heterogeneous materials. Researchers must consider the requirements imposed by the Hill-Mandel condition for boundary conditions and the physical significance of representative volume elements(RVEs). This paper addresses this issue and demonstrates that the sizes of SSFs vary under different boundary conditions, such as uniform displacement boundary conditions(DBCs), uniform traction boundary conditions(TBCs), and periodic boundary conditions(PBCs). As a result, significant discrepancies arise in the predicted shakedown domain sizes of heterogeneous materials. Built on the demonstrated relationship between SSFs under different boundary conditions, this study explores the conservative relationships among different shakedown domains, and provides proof of the relationship between the elastic limit(EL) factors and the shakedown loading factors under the loading domain of two load vertices. By utilizing numerical examples, we highlight the conservatism present in certain results reported in the existing literature. Among the investigated boundary conditions, the obtained shakedown domain is the most conservative under TBCs.Conversely, utilizing PBCs to construct an SSF for the shakedown analysis leads to less conservative lower bounds, indicating that PBCs should be employed as the preferred boundary conditions for the shakedown analysis of heterogeneous materials.

Weak and Strong Convergence of Self Adaptive Inertial Subgradient Extragradient Algorithms for Solving Variational Inequality Problems

Many solutions of variational inequalities have been proposed,among which the subgradient extragradient method has obvious advantages.Two different algorithms are given for solving variational inequality problem in this paper.The problem we study is defined in a real Hilbert space and has L-Lipschitz and pseudomonotone condition.Two new algorithms adopt inertial technology and non-monotonic step size rule,and their convergence can still be proved when the value of L is not given in advance.Finally,some numerical results are designed to demonstrate the computational efficiency of our two new algorithms.