About One-Dimensional Conservative Systems with Position Depending Mass

For a one-dimensional conservative system with position depending mass, one deduces consistently a constant of motion, a Lagrangian, and a Hamiltonian for the nonrelativistic case. With these functions, one shows the trajectories on the spaces (x,v) and (x,p) for a linear position depending mass. For the relativistic case, the Lagrangian and Hamiltonian cannot be given explicitly in general. However, we study the particular system with constant force and mass linear dependence on the position where the Lagrangian can be found explicitly, but the Hamiltonian remains implicit in the constant of motion.

Size Depending Separation of HAP:Eu Nanoparticles in Dispersed Sol

The panicle size has a strong impact on the interactions between nanoparticles and cells. However, the synthesis process of nanoparticles limits the range of achievable average panicle sizes. When biocompatible hydroxyapatite nanoparticles （HAP） are doped with the luminescent rare earth elemeat Europium （Eu）, the panicle size becomes larger compared to pure HAP. Hence, a panicle size reduction is necessary to achieve similar experimental conditions when stbstituting pure HAP with luminescent HAP ： Eu nanoparticles to investigate particlecell-interactions in cell culture experiments. While the sedimentation process of particles in liquids and gels has been well described in literature, the separation of particles in dispersed colloids has not been studied, yet. In this study, the size depending separation and particle size redaction of a homogeneous dispersed nanoparticle sol by gravity and centrifugation were investigated. As the results showed, shorter time of centrifugation at higher speed can reduce the average particle size compared to the decline of the panicle concentation in the upper sol layer most efficiently. This ceatrifugation method has some similarity to the overspeeding technique which is commonly used to lower the transient time to reach the equilibrium of sedimentation.

Reaction Subdiffusion with Random Waiting Time Depending on the Preceding Jump Length

To describe the energy-dependent characteristics of the reaction-subdiffusion process, we analyze the simple reaction A--→B under subdiffsion with waiting time depending on the preceding jump length, and derive the corresponding master equations in the Fourier Laplace space for the distribution of A and B particles in a continuous time random walk scheme. Moreover, the generalizations of the reaction-diffusion equation for the Gaussian jump length with the probability density function of waiting time being quadratically dependent on the preceding jump length are obtained by applying the derived master equations.

Analytical Approximation to the Dynamics of a Binary Stars System with Time Depending Mass Variation

We study the classical dynamics of binary stars when there is an interchange of mass between them. Assuming that one of the stars is more massive than others, the dynamics of the lighter one is analyzed as a function of its time depending mass variation. Within our approximations and models for mass transference, we obtain a general result which establishes that if the lightest star looses mass, its period increases. If the lightest star wins mass, its period decreases.

THE INTEGRAL AS A FUNCTlON OF THE UPPER LIMIT AND DEPENDING ON A PARAMETER TO SOLVE DRAWING THROUGH IDLING ROLLS

The relocity and sirain-rate .field which are different from Avilzur's have beenestablished in Caitesian coordinates. Using the integral as a function of the upper limitand integration depending on a parameler, an analylical upper-bound solution todrawing stress through idling rolls has been obtained in this paper.

One Dimensional Conservative System with Quadratic Dissipation and Position Depending Mass

Forl a 1-D conservative system with a position depending mass within a dissipative medium, its effect on the body is to exert a force depending on the squared of its velocity, a constant of motion, Lagrangian, generalized linear momentum, and Hamiltonian are obtained. We apply these new results to the harmonic oscillator and pendulum under the characteristics mentioned about, obtaining their constant of motion, Lagrangian and Hamiltonian for the case when the body is increasing its mass.

Depending on a Diaspora

Somalia＇s rebirth relies greatly on the return of its many refugees MOGADISHU is on the mend and on the rise.A booming construction industry is building high-rise blocks that compete for skyline dominance,shops are opening up at a much faster rate than two years earlier and airlines are launching routes to what has hitherto been considered the world＇s most dangerous city. Despite a rise in the number of ambushes,assassinations and suicide bombings in the city in August,a sign that Somali militant group Al-Shabaab has renewed its campaign to bring instability to the nation＇s capital,a sense of optimism still abounds as the country slowly makes its way back into the international arena.The United Kingdom has reopened its embassy after a 22-year closure.

Electrical and dielectric characterization of Au/ZnO/n-Si device depending frequency and voltage

Au/Zn O/n-type Si device is obtained using atomic layer deposition（ALD） for Zn O layer, and some main electrical parameters are investigated, such as surface/interface state（Nss）, barrier height（Φb）, series resistance（Rs）, donor concentration（Nd）, and dielectric characterization depending on frequency or voltage. These parameters are acquired by use of impedance spectroscopy measurements at frequencies ranging from 10 k Hz to 1 MHz and the direct current（DC） bias voltages in a range from-2 V to ＋2 V at room temperature are used. The main electrical parameters and dielectric parameters,such as dielectric constant（ε＂）, dielectric loss（ε＂）, loss tangent（tan δ）, the real and imaginary parts of electric modulus（M and M）, and alternating current（AC） electrical conductivity（σ） are affected by changing voltage and frequency. The characterizations show that some main electrical parameters usually decrease with increasing frequency because charge carriers at surface states have not enough time to fallow an external AC signal at high frequencies, and all dielectric parameters strongly depend on the voltage and frequency especially in the depletion and accumulation regions. Consequently, it can be concluded that interfacial polarization and interface charges can easily follow AC signal at low frequencies.

Zeno and the Wrong Understanding of Motion—A Philosophical-Mathematical Inquiry into the Concept of Finitude as a Peculiarity of Infinity

In contrast to the solutions of applied mathematics to Zeno’s paradoxes, I focus on the concept of motion and show that, by distinguishing two different forms of motion, Zeno’s apparent paradoxes are not paradoxical at all. Zeno’s paradoxes indirectly prove that distances are not composed of extensionless points and, in general, that a higher dimension cannot be completely composed of lower ones. Conversely, lower dimensions can be understood as special cases of higher dimensions. To illustrate this approach, I consider Cantor’s only apparent proof that the real numbers are uncountable. However, his widely accepted indirect proof has the disadvantage that it depends on whether there is another way to make the real numbers countable. Cantor rightly assumes that there can be no smallest number between 0 and 1, and therefore no beginning of counting. For this reason he arbitrarily lists the real numbers in order to show with his diagonal method that this list can never be complete. The situation is different if we start with the largest number between 0 and 1 (0.999…) and use the method of an inverted triangle, which can be understood as a special fractal form. Here we can construct a vertical and a horizontal stratification with which it is actually possible to construct all real numbers between 0 and 1 without exception. Each column is infinite, and each number in that column is the starting point of a new triangle, while each row is finite. Even in a simple sine curve, we experience finiteness with respect to the y-axis and infinity with respect to the x-axis. The first parts of this article show that Zeno’s assumptions contradict the concept of motion as such, so it is not surprising that this misconstruction leads to contradictions. In the last part, I discuss Cantor’s diagonal method and explain the method of an inverted triangle that is internally structured like a fractal by repeating this inverted triangle at each column. The consequence is that we encounter two very different methods of counting. Vertically it is continuous, horizontally it is discrete. While Frege, Tarski, Cantor, Gödel and the Vienna Circle tried to derive the higher dimension from the lower, a procedure that always leads to new contradictions and antinomies (Tarski, Russell), I take the opposite approach here, in which I derive the lower dimension from the higher. This perspective seems to fail because Tarski, Russell, Wittgenstein, and especially the Vienna Circle have shown that the completeness of the absolute itself is logically contradictory. For this reason, we agree with Hegel in assuming that we can never fully comprehend the Absolute, but only its particular manifestations—otherwise we would be putting ourselves in the place of the Absolute, or even God. Nevertheless, we can understand the Absolute in its particular expressions, as I will show with the modest example of the triangle proof of the combined horizontal and vertical countability of the real numbers, which I developed in rejection of Cantor’s diagonal proof. .

Review of Sc microalloying effects in Al-Cu alloys

Artificially controlling the solid-state precipitation in aluminum (Al) alloys is an efficient way to achieve well-performed properties,and the microalloying strategy is the most frequently adopted method for such a purpose.In this paper,recent advances in lengthscale-dependent scandium (Sc) microalloying effects in Al-Cu model alloys are reviewed.In coarse-grained Al-Cu alloys,the Sc-aided Cu/Sc/vacancies complexes that act as heterogeneous nuclei and Sc segregation at the θ′-Al_(2)Cu/matrix interface that reduces interfacial energy contribute significantly to θ′precipitation.By grain size refinement to the fine/ultrafine-grained scale,the strongly bonded Cu/Sc/vacancies complexes inhibit Cu and vacancy diffusing toward grain boundaries,promoting the desired intragranular θ′precipitation.At nanocrystalline scale,the applied high strain producing high-density vacancies results in the formation of a large quantity of (Cu Sc,vacancy)-rich atomic complexes with high thermal stability,outstandingly improving the strength/ductility synergy and preventing the intractable low-temperature precipitation.This review recommends the use of microalloying technology to modify the precipitation behaviors toward better combined mechanical properties and thermal stability in Al alloys.

Long-term prognosis and its associated predictive factors in patients with eosinophilic gastroenteritis

BACKGROUND Eosinophilic gastroenteritis(EGE)is a chronic recurrent disease with abnormal eosinophilic infiltration in the gastrointestinal tract.Glucocorticoids remain the most common treatment method.However,disease relapse and glucocorticoid dependence remain notable problems.To date,few studies have illuminated the prognosis of EGE and risk factors for disease relapse.AIM To describe the clinical characteristics of EGE and possible predictive factors for disease relapse based on long-term follow-up.METHODS This was a retrospective cohort study of 55 patients diagnosed with EGE admitted to one medical center between 2013 and 2022.Clinical records were collected and analyzed.Kaplan-Meier curves and log-rank tests were conducted to reveal the risk factors for long-term relapse-free survival(RFS).RESULTS EGE showed a median onset age of 38 years and a slight female predominance(56.4%).The main clinical symptoms were abdominal pain(89.1%),diarrhea(61.8%),nausea(52.7%),distension(49.1%)and vomiting(47.3%).Forty-three(78.2%)patients received glucocorticoid treatment,and compared with patients without glucocorticoid treatments,they were more likely to have elevated serum immunoglobin E(IgE)(86.8%vs 50.0%,P=0.022)and descending duodenal involvement(62.8%vs 27.3%,P=0.046)at diagnosis.With a median follow-up of 67 mo,all patients survived,and 56.4%had at least one relapse.Six variables at baseline might have been associated with the overall RFS rate,including age at diagnosis<40 years[hazard ratio(HR)2.0408,95%confidence interval(CI):1.0082–4.1312,P=0.044],body mass index(BMI)>24 kg/m^(2)(HR 0.3922,95%CI:0.1916-0.8027,P=0.014),disease duration from symptom onset to diagnosis>3.5 mo(HR 2.4725,95%CI:1.220-5.0110,P=0.011),vomiting(HR 3.1259,95%CI:1.5246-6.4093,P=0.001),total serum IgE>300 KU/L at diagnosis(HR 0.2773,95%CI:0.1204-0.6384,P=0.022)and glucocorticoid treatment(HR 6.1434,95%CI:2.8446-13.2676,P=0.003).CONCLUSION In patients with EGE,younger onset age,longer disease course,vomiting and glucocorticoid treatment were risk factors for disease relapse,whereas higher BMI and total IgE level at baseline were protective.

BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.

An Improved CREAM Model Based on DS Evidence Theory and DEMATEL

Cognitive Reliability and Error Analysis Method(CREAM)is widely used in human reliability analysis(HRA).It defines nine common performance conditions(CPCs),which represent the factors thatmay affect human reliability and are used to modify the cognitive failure probability(CFP).However,the levels of CPCs are usually determined by domain experts,whichmay be subjective and uncertain.What’smore,the classicCREAMassumes that the CPCs are independent,which is unrealistic.Ignoring the dependence among CPCs will result in repeated calculations of the influence of the CPCs on CFP and lead to unreasonable reliability evaluation.To address the issue of uncertain information modeling and processing,this paper introduces evidence theory to evaluate the CPC levels in specific scenarios.To address the issue of dependence modeling,the Decision-Making Trial and Evaluation Laboratory(DEMATEL)method is used to process the dependence among CPCs and calculate the relative weights of each CPC,thus modifying the multiplier of the CPCs.The detailed process of the proposed method is illustrated in this paper and the CFP estimated by the proposed method is more reasonable.

Temperature inversion enables superior stability for low-temperature Zn-ion batteries

It is challenging for aqueous Zn-ion batteries(ZIBs)to achieve comparable low-temperature(low-T)performance due to the easy-frozen electrolyte and severe Zn dendrites.Herein,an aqueous electrolyte with a low freezing point and high ionic conductivity is proposed.Combined with molecular dynamics simulation and multi-scale interface analysis(time of flight secondary ion mass spectrometry threedimensional mapping and in-situ electrochemical impedance spectroscopy method),the temperature independence of the V_(2)O_(5)cathode and Zn anode is observed to be opposite.Surprisingly,dominated by the solvent structure of the designed electrolyte at low temperatures,vanadium dissolution/shuttle is significantly inhibited,and the zinc dendrites caused by this electrochemical crosstalk are greatly relieved,thus showing an abnormal temperature inversion effect.Through the disclosure and improvement of the above phenomena,the designed Zn||V_(2)O_(5)full cell delivers superior low-T performance,maintaining almost 99%capacity retention after 9500 cycles(working more than 2500 h)at-20°C.This work proposes a kind of electrolyte suitable for low-T ZIBs and reveals the inverse temperature dependence of the Zn anode,which might offer a novel perspective for the investigation of low-T aqueous battery systems.

Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.

Predicting Rock Burst in Underground Engineering Leveraging a Novel Metaheuristic-Based LightGBM Model

Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human safety.These sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural damage.Therefore,the development of reliable prediction models for rock bursts is paramount to mitigating these hazards.This study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the LightGBMmodel.Two population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM model.Finally,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock bursts.The results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM model.The developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,respectively.The sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.

Aspect-Level Sentiment Analysis Based on Deep Learning

In recent years,deep learning methods have developed rapidly and found application in many fields,including natural language processing.In the field of aspect-level sentiment analysis,deep learning methods can also greatly improve the performance of models.However,previous studies did not take into account the relationship between user feature extraction and contextual terms.To address this issue,we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method.To be specific,we design user comment feature extraction(UCFE)to distill salient features from users’historical comments and transform them into representative user feature vectors.Then,the aspect-sentence graph convolutional neural network(ASGCN)is used to incorporate innovative techniques for calculating adjacency matrices;meanwhile,ASGCN emphasizes capturing nuanced semantics within relationships among aspect words and syntactic dependency types.Afterward,three embedding methods are devised to embed the user feature vector into the ASGCN model.The empirical validations verify the effectiveness of these models,consistently surpassing conventional benchmarks and reaffirming the indispensable role of deep learning in advancing sentiment analysis methodologies.

Nonuniform Dependence on the Initial Data for Solutions of Conservation Laws

In this paper,we study systems of conservation laws in one space dimension.We prove that for classical solutions in Sobolev spaces H^(s),with s>3/2,the data-to-solution map is not uniformly continuous.Our results apply to all nonlinear scalar conservation laws and to nonlinear hyperbolic systems of two equations.

Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel efficiency.To improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic data.In recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic data.However,most models ignore the semantic spatial similarity between long-distance areas when mining spatial dependency.They also ignore the impact of predicted time steps on the next unpredicted time step for making long-term predictions.Moreover,these models lack a comprehensive data embedding process to represent complex spatiotemporal dependency.This paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in cities.MSPSTT adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these issues.The model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic characteristics.The spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term prediction.Experiments on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.