Search

The state-to-state (STS) inelastic energy transfer and O-atom exchange reaction between O and CO(v), as two fundamental processes in non-equilibrium air flow around spacecraft entering Mars' atmosphere, yield the same products and both make significant contributions to the O + CO(v) → O + CO(v′) collisions. The inelastic energy transfer competes with the O-atom exchange reaction. The detailed reaction mechanisms of these two elementary processes and their specific contributions to the CO relaxation process are still unclear. To address these concerns, we performed systematic investigations on the 3A′ and 3A″ potential energy surfaces (PESs) of CO2 using quasi-classical trajectory (QCT) calculations. Analysis of the collision mechanisms reveals that inelastic collisions have an apparent PES preference (i.e., they tend to occur on the 3A′ PES), while reactive collisions do not. Reactive rates decrease significantly when the total collision energy approaches dissociation energy, which differs from the inelastic process. Inelastic rates are generally lower than the reactive rates below ∼10 000 K, except for single quantum jumps, whereas the reverse is observed above ∼10 000 K. In addition, by combining QCT with convolutional neural networks, we have established neural network (NN)-STS1 (inelastic) and NN-STS2 (reactive) models to generate all possible STS cross sections. The NN-based models accurately reproduce the results calculated from QCT calculations. In this study, all calculations have been focused on analyzing collisions at the ground rotational level. [ABSTRACT FROM AUTHOR]

Designing polymers with high intrinsic thermal conductivity (TC) is critically important for the thermal management of organic electronics and photonics. However, this is a challenging task owing to the diversity of the chemical space and the barriers to advanced synthetic experiments/characterization techniques for polymers. In this Tutorial, the fundamentals and implementation of combining classical molecular dynamics simulation and machine learning (ML) for the development of polymers with high TC are comprehensively introduced. We begin by describing the core components of a universal ML framework, involving polymer data sets, property calculators, feature engineering, and informatics algorithms. Then, the process of constructing interpretable regression algorithms for TC prediction is introduced, aiming to extract the underlying relationships between microstructures and TCs for polymers. We also explore the design of sequence-ordered polymers with high TC using lightweight and mainstream active learning algorithms. Lastly, we conclude by addressing the current limitations and suggesting potential avenues for future research on this topic. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) have shown immense potential in fields such as text and image generation. Only very recently attempts to exploit GANs to statistical-mechanics models have been reported. Here we quantitatively test this approach by applying it to a prototypical stochastic process on a lattice. By suitably adding noise to the original data we succeed in bringing both the Generator and the Discriminator loss functions close to their ideal value. Importantly, the discreteness of the model is retained despite the noise. As typical for adversarial approaches, oscillations around the convergence limit persist also at large epochs. This undermines model selection and the quality of the generated trajectories. We demonstrate that a simple multi-model procedure where stochastic trajectories are advanced at each step upon randomly selecting a Generator leads to a remarkable increase in accuracy. This is illustrated by quantitative analysis of both the predicted equilibrium probability distribution and of the escape-time distribution. Based on the reported findings, we believe that GANs are a promising tool to tackle complex statistical dynamics by machine learning techniques. [ABSTRACT FROM AUTHOR]

Faced with the terrible spread of electronic attacks and intrusions, which tend to increase, securing computer systems has become an urgent necessity for enterprises. In this work, we are interested in a very specific security problem, which is the identity theft of business experts that causes major problems for the company, such as loss of money, fraud, information leakage, customer distrust of using the enterprise's services, etc. The implementation of an implicit authentication system based on the study of the expert behavior is a very effective way to fight this problem. The objective of this work is to propose a system dedicated to business experts, which will be able to detect impostors and false profiles during the authentication phase and during the negotiation phase in a continuous manner. The proposed system relies on machine learning algorithms, behavioral authentication, the keystroke dynamics of real experts and their behaviors during the negotiation phase, as well as their ways of interacting with the application, in order to prevent imposters to infiltrate the system by trying to login with the account of a real expert. So, we followed the following steps: Data acquisition, Criteria extraction, Model creation and Storage. We Have used three machine learning algorithms, which are: K Nearest Neighbor, Random Forest and Logistic Regression. [ABSTRACT FROM AUTHOR]

Process damping can provide improved machining productivity by increasing the stability limit at low spindle speeds. While the phenomenon is well known, experimental identification of process damping model parameters can limit pre-process parameter selection that leverages the potential increases in material removal rates. This paper proposes a physics-informed Bayesian method that can identify the cutting force and process damping model coefficients from a limited set of test cuts without requiring direct measurements of cutting force or vibration. The method uses time-domain simulation to incorporate process damping and provide a basis for test selection. New strategies for efficient sampling and dimensionality reduction are applied to lower computation time and minimize the effect of model error. The proposed method is demonstrated, and the identified cutting and damping force coefficients are compared to values obtained using machining tests and least-squares fitting. [ABSTRACT FROM AUTHOR]

This paper introduces a complete real-time algorithm, where the chatter is detected and eliminated by spindle speed manipulation via the chatter energy feedback calculated from the axis encoder measurement. The proposed method does not require profound knowledge of the machining dynamics; instead, the entire algorithm exploits the fact that milling vibrations consist of forced vibrations at spindle speed harmonics and chatter vibrations that are close to one of the natural modes, with sidebands which are spread at the multiples of spindle speed frequency above and below the chatter frequency. The developed algorithm is able to identify the amplitude, phase and frequency of all the harmonics constituting the periodic forced and chatter vibrations. The key challenge is to select dominant chatter frequencies for the calculation of a robust and accurate chatter energy ratio feedback; this is achieved by utilizing the frequency estimation variance of EKF as a novel chatter indicator. Based on the chatter energy ratio feedback, the controller overrides the spindle speed in order to suppress the chatter energy below a particular threshold value. The varying spindle speed challenge is handled by updating the state transition matrices of the Kalman filters and real-time calculation of the band-pass filter coefficients, based on the derived discrete time transfer functions. The developed algorithm is tested on a Deckel FP5cc CNC which is in-house retrofitted and has a PC-based controller for the real-time application of the proposed algorithm. It is shown that the real-time chatter frequency and amplitude estimates are compatible with off-line FFT analysis, and chatter can be successfully eliminated by energy feedback. [ABSTRACT FROM AUTHOR]

Annual urban forest expansion dynamics are crucial for assessing the benefits and potential issues associated with vegetation accumulation over time. LandTrendr (Landsat-Based Detection of Trends in Disturbance and Recovery) can efficiently detect the dynamics of interannual land cover change, but it has difficulty distinguishing urban forest expansion from urban surface rapid conversions, as changes are usually filtered by magnitude-of-change thresholds. To accurately detect annual urban forest expansion dynamics, we developed an improved method using random forest-supervised classification to filter urban forests. We further enhanced the performance of the improved method by incorporating trend features between segments. Additionally, we tested two threshold-based filtering baseline methods. These methods were tested with various spectral and parameter combinations in Beijing's Central District and the 1st Greenbelt from 1994 to 2022. The improved method with trend features achieved the highest average accuracy of 89.35%, representing a 25% improvement over baseline methods. Post-change trend features aided in accurate identification, while quantitative features rather than extremum features were more important in filtering. The improved method with trend features tested in Beijing's 2nd Greenbelt also showed an accuracy of 88.27%, confirming its stability. SWIR2 and a higher maximum segment number are efficient for filtering by providing the most detailed dynamics. Accurate annual expansion dynamic mapping offers insights into change rates and precise expansion years, providing a new perspective for urban forest research and management. [ABSTRACT FROM AUTHOR]

Kinematic analysis is the first task that needs to be solved for further real-life problems such as machine dynamics and durability, machine balancing, and optimization, chemistry, etc. Currently, there are many methods to analyze the kinematic problem, for instance: vector graphics method, vector-analytic method, or Dennavit-Hartenberg-Craig matrix method. Although each method has its advantages, they have mostly encountered difficulties in establishing equations associated with complicated spatial structures. This research proposed a novel method, entitled "'perpendicular projection method" to solve the problem of spatial structural kinematic analysis. This is an approximate solution method that combines the use of generalized coordinates and direction cosine matrices. With this method, the problem of kinematic analysis of spatial structures will be solved simply and easily by establishing connection equations as well as simulating the motion of spatial structures. This method was then applied in conducting the kinematic analysis of a spatial mechanism with 1 rotating joint (R), 1 ball joint (S), 1 translational joint (P), and one cylindrical joint (C). The calculated results, including the velocity, acceleration of generalized coordinates, angular velocity, angular acceleration of the links, velocity, and acceleration of the center of mass of the links, showed the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative condition leading to progressive muscle weakness, atrophy, and ultimately death. Traditional ALS clinical evaluations often depend on subjective metrics, making accurate disease detection and monitoring disease trajectory challenging. To address these limitations, we developed the nQiALS toolkit, a machine learning-powered system that leverages smartphone typing dynamics to detect and track motor impairment in people with ALS. The study included 63 ALS patients and 30 age- and sex-matched healthy controls. We introduce the three core components of this toolkit: the nQiALS-Detection, which differentiated ALS from healthy typing patterns with an AUC of 0.89; the nQiALS-Progression, which separated slow and fast progression at specific thresholds with AUCs ranging between 0.65 and 0.8; and the nQiALS-Fine Motor, which identified subtle progression in fine motor dysfunction, suggesting earlier prediction than the state-of-the-art assessment. Together, these tools represent an innovative approach to ALS assessment, offering a complementary, objective metric to traditional clinical methods and which may reshape our understanding and monitoring of ALS progression. [ABSTRACT FROM AUTHOR]

Mitochondrial shape and network formation have been primarily associated with the well-established processes of fission and fusion. However, recent research has unveiled an intricate and multifaceted landscape of mitochondrial morphology that extends far beyond the conventional fission–fusion paradigm. These less-explored dimensions harbor numerous unresolved mysteries. This review navigates through diverse processes influencing mitochondrial shape and network formation, highlighting the intriguing complexities and gaps in our understanding of mitochondrial architecture. The exploration encompasses various scales, from biophysical principles governing membrane dynamics to molecular machineries shaping mitochondria, presenting a roadmap for future research in this evolving field. Mitochondria come in many shapes and sizes. This Unsolved Mystery navigates the diverse processes that influence mitochondrial shape and network formation, highlighting the intriguing complexities and gaps in our understanding of mitochondrial architecture. [ABSTRACT FROM AUTHOR]

This study aimed at the current problems of low efficiency and high labour intensity of mango manual picking faced by tropical agricultural regions in China. The research results in the field of mango-picking machines at home and abroad are analysed, and it is proposed to explore the coupling mechanism of mango biological tissue-mechanical trait and establish mango biomechanical model. Based on the biomechanical properties of mangoes, the law of picking motion based on bio-agricultural-mechanical coupling should be revealed to create a coupling model of mango mechanised picking process. An anthropomorphic simulation of the mango-picking process is proposed to obtain a flexible bionic design technology for picking machines adapted to the biomechanical properties of mangoes. To improve the flexibility, stability, and dynamics of mango-picking machines, it is proposed to apply the bionic design technology to optimise the structure of mango-picking machines, thus forming a new method of bionic optimisation design of mango-picking machines. The proposed research provides new ideas for the optimal design of mango-picking equipment. [ABSTRACT FROM AUTHOR]

Molecular dynamics (MD) simulation has an intrinsic limitation when calculating thermal conductivity of metals. That is, only phonon (lattice) thermal conductivity can be derived from trajectory of atoms, which is obtained by solving the Newton’s equations of motion numerically. Therefore, significant contribution of electrons in metals remains unaccounted for. In this study, a Light Gradient Boosting Machine (LightGBM) regression model is employed to predict thermal conductivity of metals using heat flux calculated by MD simulation, electrical conductivity and others as input variables. The LightGBM model successfully predicts the complex non-linear Green-Kubo relation for thermal conductivity calculation even though the underlying physical mechanisms are not entirely clear. The model predicts various temperature dependences of thermal conductivity of metals accurately. Furthermore, the model trained with known compositions of Al-Cu alloys is proved to estimate the thermal conductivity of alloys with unknown compositions. The model also demonstrates a certain level of predictive ability for alloys with different compositions and temperatures. This study demonstrates the potential of a data-driven approach as an efficient method for uncovering complex relationships between incomplete data from MD simulations and true materials properties, especially in cases where the underlying physics is elusive. [ABSTRACT FROM AUTHOR]

This paper examines the effect of drivers in the development of eco‐innovation from a system dynamics perspective. While previous literature has made important contributions in identifying factors that influence the development of eco‐innovations, there remains limited understanding of how these drivers act and interact in promoting its development. Therefore, there is a need to develop a framework of relationships and drivers that encourage and support eco‐innovation in companies. This paper develops an integrated framework encompassing key internal, market and governmental factors and their complex interactions using principles of system dynamics and machine learning to address this gap. The research questions how these drivers interact in a dynamic and non‐linear manner to influence the development of eco‐innovation in companies and how can these interactions be effectively modelled and understood, considering the complexities of sustainable business practices and the limitations of traditional linear approaches. We empirically test these questions by using the Spanish Technological Innovation Panel database. The findings demonstrate that eco‐innovation is not solely driven by isolated factors; instead, it emerges from the complex interplay between internal capabilities, governmental policies and market dynamics. By emphasising the synergistic effects of these drivers, the research offers a nuanced understanding of their systemic interactions. Furthermore, our analysis highlights the varying efficiency levels of different drivers, underscoring the pivotal role of environmental corporate policies and the strategic allocation of financial resources. In contrast, cooperation, market forces and regulations exhibit lower efficiency in driving eco‐innovation processes. These insights not only advance theoretical knowledge but also provide valuable guidance for businesses and policymakers, offering a more holistic approach to fostering sustainable innovation. [ABSTRACT FROM AUTHOR]

The substitution of natural gas hydrates with CO2 offers a compelling dual advantage by enabling the extracting of CH4 while simultaneously sequestering CO2. This process, however, is intricately tied to the mechanical stability of CO2-CH4 heterohydrates. In this study, we report the mechanical properties and cage transformations in CO2-CH4 heterohydrates subjected to uniaxial straining via molecular dynamics (MD) simulations and machine learning (ML). Results indicate that guest molecule occupancy, the ratio of CO2 to CH4 and their spatial arrangements within heterohydrate structure greatly dictate the mechanical properties of CO2–CH4 heterohydrates including Young's modulus, tensile strength, and critical strain. Notable, the introduction of CO2 within clathrate cages, particularly within 512 small cages, weakens the stability of CO2–CH4 heterohydrates in terms of mechanical properties. Upon critical strains, unconventional clathrate cages form, contributing to loading stress oscillation before fracture of heterohydrates. Intriguingly, predominant cage transformations, such as 51262–4151063 or 425864 and 512–425861 cages, are identified, in which 4151062 appears as primary intermediate cage that is able to transform into 4151063, 425862, 425863, 512 and 51262 cages, unveiling the dynamic nature of heterohydrate structures under straining. Additionally, ML models developed using MD data well predict the mechanical properties of heterohydrates, and underscore the critical influence of the spatial arrangement of guest molecules on the mechanical properties. These newly-developed ML models serve as valuable tools for accurately predicting the mechanical properties of heterohydrates. This study provides fresh insights into the mechanical properties and cage transformations in heterohydrates in response to strain, holding significant implications for environmentally sustainable utilization of CO2–CH4 heterohydrates. [ABSTRACT FROM AUTHOR]

The "Virtual Rotor Kit (VRK) Project" is a lightweight and standalone application developed in response to the challenges posed by the COVID-19 outbreak in the field of rotating machinery dynamics. This alternative emulates a rotor test rig, enabling virtual balancing tests on a single-plane rotor and offering students a comprehensive learning experience that simulates on-site balancing. The Virtual Rotor Kit utilizes a three-dimensional model based on a well-known rotor test rig widely used in educational and research settings. To simulate the dynamics of the rotor test rig, the application employs PyChrono, a dedicated Python library that wraps Chrono, a validated open-source multi-physics simulation engine. A detailed usability assessment involving 41 senior mechanical engineering students yielded promising results, indicating the Virtual Rotor Kit's potential to enhance comprehension of vibration phenomena in unbalanced rotating machines. Despite revealing certain issues related to the graphical user interface and the application's execution time, three defined achievement indicators demonstrated positive outcomes for a virtual balancing laboratory experience using the Virtual Rotor Kit application. By focusing on the dynamic behavior of a rotor test rig instead of simulating a specific laboratory experience, the application offers instructors greater flexibility in developing learning experiences. Unlike other virtual testbeds, the Virtual Rotor Kit allows students to conduct both steady-state and transient experiments, offering a comprehensive understanding of rotating machinery dynamics. Additionally, the application is distributed under the BSD-3 license and can serve as a complement to on-site laboratory experiences. The results presented in this study inspire further research and development to enhance its efficacy. [ABSTRACT FROM AUTHOR]

Recent developments in machine learning have enabled accurate predictions of the dynamics of slow structural relaxation in glass-forming systems. However, existing machine learning models for these tasks are mostly designed such that they learn a single dynamic quantity and relate it to the structural features of glassy liquids. In this study, we propose a graph neural network model, "BOnd TArgeting Network," that learns relative motion between neighboring pairs of particles, in addition to the self-motion of particles. By relating the structural features to these two different dynamical variables, the model autonomously acquires the ability to discern how the self motion of particles undergoing slow relaxation is affected by different dynamical processes, strain fluctuations and particle rearrangements, and thus can predict with high precision how slow structural relaxation develops in space and time. [ABSTRACT FROM AUTHOR]

Quasi-liquid layers (QLLs) are present on the surface of ice and play a significant role in its distinctive chemical and physical properties. These layers exhibit considerable heterogeneity across different scales ranging from nanometers to millimeters. Although the formation of partially ice-like structures has been proposed, the molecular-level understanding of this heterogeneity remains unclear. Here, we examined the heterogeneity of molecular dynamics on QLLs based on molecular dynamics simulations and machine learning analysis of the simulation data. We demonstrated that the molecular dynamics of QLLs do not comprise a mixture of solid- and liquid water molecules. Rather, molecules having similar behaviors form dynamical domains that are associated with the dynamical heterogeneity of supercooled water. Nonetheless, molecules in the domains frequently switch their dynamical state. Furthermore, while there is no observable characteristic domain size, the long-range ordering strongly depends on the temperature and crystal face. Instead of a mixture of static solid- and liquid-like regions, our results indicate the presence of heterogeneous molecular dynamics in QLLs, which offers molecular-level insights into the surface properties of ice. Quasi-liquid layers on the surface of water ice significantly affect its distinctive chemical and physical properties, but a molecular-level understanding of these heterogeneous layers is missing. Here, the authors use molecular dynamics simulations and machine learning analyses to show that the quasi-liquid layers resemble supercooled water. [ABSTRACT FROM AUTHOR]

Over half of the world's population resides in urban areas. We anticipate that this pattern will become more evident, notably in South Africa. Therefore, research on urban spirals, both past and projected, is necessary for efficient urban land use planning and management. This study aims to assess the spatio-temporal urban sprawl dynamics from 2003 to 2033 in Mbombela, South Africa. We employed robust approaches such as machine learning, the cellular automata-Markov chain, and the Shannon entropy model to look at how urban sprawl changes over time using both the Landsat 4–5 Thematic Mapper and the 8 Operational Land Imagers. We conducted this study to bridge the gaps in existing research, which primarily focuses on past and current urban growth trends rather than future trends. The findings indicated that the coverage of built-up areas and vegetation has expanded by 1.98 km2 and 13.23 km2 between the years 2003 and 2023. On the other hand, the amount of land continues to decrease by -12.56 km2 and − 2.65 km2 annually, respectively. We anticipate an increase in the built-up area and vegetation to a total of 7.60 km2 and 0.57 km2, respectively, by the year 2033. We anticipate a total annual decline of -7.78 km2 and − 0.39 km2 in water bodies and open land coverage, respectively. This work has the potential to assist planners and policymakers in improving sustainable urban land-use planning. [ABSTRACT FROM AUTHOR]

This contribution presents the results of structural investigations on the cured and high temperatures of three series of geopolymers. The specimens were synthesized at 80 °C from coal fly ash and three activators of variable composition based on sodium hydroxide and sodium silicate solutions. Structural and microstructural analysis was performed, especially using in-situ measurements of XRD patterns and IR spectra as a function of temperature. The cured compounds' phase content and compressive strength changed slightly depending on the starting chemical composition. All analyzed materials experience a mass loss due to water removal at 300 °C, followed by increased porosity from disintegrating compounds above 300 °C. Higher alkali content improves strength (400–600 °C) possibly due to nepheline formation. The amorphous phase gradually softens during heating, influenced by alkali content. Structural analyses were supported by model calculations of a number of aluminosilicate structures. The cluster analysis using the k-means algorithm was used to divide the PCA space into regions that represent structural similarities, and analyzing specific point positions in the space allowed for several conclusions to be drawn about the studied materials, including that changes in chemical composition and thermal treatment can promote the transformation from sodalite to nepheline and that the glass network has elements that are nepheline-like and may promote its crystallization. [ABSTRACT FROM AUTHOR]

Condition-based maintenance of a machine tool spindle unit is known to reduce the total cost of operation in a manufacturing environment. However, standard vibration monitoring thresholds, which are based only on average vibration energy levels, do not account for the machine's dynamics, such as dynamic stiffness and damping ratios, which, ultimately, control the final quality of a machined workpiece. In this study, a new mechanical device is proposed for the estimation of spindle dynamics characteristics through measurement of the frequency response function (FRF) of the spindle. The device is simple, robust in a manufacturing environment, small, and requires no power source. The new device is shown to be comparable in performance to conventional impact hammer testing, with the advantage of being fully automated and capable of dynamic assessment on a rotating spindle. Such a device allows a machine tool operator to routinely monitor the stiffness and damping ratios of the spindle unit and make informed maintenance decisions based on meaningful changes in operating conditions that impact production. [ABSTRACT FROM AUTHOR]

Significant variations in the abundance of mitochondrial RNA processing proteins and their target RNAs across trypanosome life stages present an opportunity to explore the regulatory mechanisms that drive these changes. Utilizing omics approaches can uncover unconventional targets, aiding our understanding of the parasites' adaptation and enabling targeted interventions for differentiation. [ABSTRACT FROM AUTHOR]

Fly cutting is widely used in manufacturing of large-scale, high-precision optical components. However, the discontinuity of fly cutting machining leads to significant relative vibrations between the tool and the workpiece. The cutting process generates periodic waves along the cutting direction, which will deteriorate the wavefront characteristics of optical components. Based on the machining dynamics, this paper proposes a direct integration method to predict the waviness error of the machined surface. The cutting force model of fly cutting is established. The multi-mode characteristics of the spindle-tool system are measured by the experimental method. Then, the influence of uncertainties on the calculation results is analyzed by the variance-based sensitivity analysis method. Finally, the plane cutting experiment verifies that the direct integration method effectively predicts the waviness error and its variation trend, and the waviness prediction research is important for optimization of the machining parameters. [ABSTRACT FROM AUTHOR]

We provide a framework to analyze the convergence of discretized kinetic Langevin dynamics for M-Δ Lipschitz, m-convex potentials. Our approach gives convergence rates of O(m/M), with explicit step size restrictions, which are of the same order as the stability threshold for Gaussian targets and are valid for a large interval of the friction parameter. We apply this methodology to various integration schemes which are popular in the molecular dynamics and machine learning communities. Further, we introduce the property "&#947-limit convergent" to characterize underdamped Langevin schemes that converge to overdamped dynamics in the high-friction limit and which have step size restrictions that are independent of the friction parameter; we show that this property is not generic by exhibiting methods from both the class and its complement. Finally, we provide asymptotic bias estimates for the BAOAB scheme, which remain accurate in the high-friction limit by comparison to a modified stochastic dynamics which preserves the invariant measure. [ABSTRACT FROM AUTHOR]

Vibrating flip-flow screens are widely employed in the deep screening processes of coal washing, solid waste treatment, metallurgy, and other fields, playing a crucial role in enhancing product quality and production efficiency. The screen surface and material movement of vibrating flip-flow screens are highly complex, and there is currently insufficient understanding of their screening mechanism, limiting further optimization and application. In this paper, the Discrete Element Method (DEM), Finite Element Method (FEM), and Multi-Body Dynamics (MBD) were integrated to establish a numerical coupling model for vibrating flip-flow screens, considering material loads, screen surface deformation, and screen machine dynamics. The Response Surface Method was utilized to analyze the significant impact of relative amplitude, tension amount, amplitude of driving screen frame, vibration frequency, and screen surface inclination on screening efficiency and material velocity. The results indicate that the most significant factor influencing the screening of flip-flow screens is the screen surface inclination. Based on a BP neural network, a five-degree-of-freedom inclination surrogate model for flip-flow screens was established. The whale algorithm was employed for multi-objective optimization of the surrogate model, resulting in a screen surface inclination distribution that meets the requirements of different operating conditions. [ABSTRACT FROM AUTHOR]

The dynamics of a vibrating feeder at Raman parametric resonance are studied. Mathematical and dynamic models of a vibrating feeder with a parametric vibration exciter are given. Stationary solutions are obtained by the averaging method, taking dissipative forces, nonlinear components of restoring forces, and resistance forces as small parameters. The amplitude–frequency and phase–frequency characteristics of the feeder and the dependences of the generation frequencies are plotted. The trajectory of the center of mass of spherical rolling elements relative to the axis of rotation of the vibration exciter is obtained. [ABSTRACT FROM AUTHOR]

The scarcity of high-quality data presents a major challenge to the prediction of material properties using machine learning (ML) models. Obtaining material property data from experiments is economically cost-prohibitive, if not impossible. In this work, we address this challenge by generating an extensive material property dataset comprising thousands of data points pertaining to the elastic properties of Fe-C alloys. The data were generated using molecular dynamic (MD) calculations utilizing reference-free Modified embedded atom method (RF-MEAM) interatomic potential. This potential was developed by fitting atomic structure-dependent energies, forces, and stress tensors evaluated at ground state and finite temperatures using ab-initio. Various ML algorithms were subsequently trained and deployed to predict elastic properties. In addition to individual algorithms, super learner (SL), an ensemble ML technique, was incorporated to refine predictions further. The input parameters comprised the alloy's composition, crystal structure, interstitial sites, lattice parameters, and temperature. The target properties were the bulk modulus and shear modulus. Two distinct prediction approaches were undertaken: employing individual models for each property prediction and simultaneously predicting both properties using a single integrated model, enabling a comparative analysis. The efficiency of these models was assessed through rigorous evaluation using a range of accuracy metrics. This work showcases the synergistic power of MD simulations and ML techniques for accelerating the prediction of elastic properties in alloys. [ABSTRACT FROM AUTHOR]

At the Joint European Torus, the reference diagnostic to measure electron density is Thomson scattering. However, this diagnostic has a low sampling rate, which makes it impractical to study the temporal dynamics of fast processes, such as edge localized modes. In this work, we use machine learning to predict the density profile based on data from another diagnostic, namely reflectometry. By learning to transform reflectometry data into Thomson scattering profiles, the model is able to generate the density profile at a much higher sampling rate than Thomson scattering, and more accurately than reflectometry alone. This enables the study of pedestal dynamics, by analyzing the time evolution of the pedestal height, width, position and gradient. We also discuss the accuracy of the model when applied on experimental campaigns that are different from the one it was trained on. [ABSTRACT FROM AUTHOR]

A ventilated vehicle exiting water in a wave environment is a complex nonlinear process, and the mechanism by which the wave conditions influence this process remains poorly understood. This paper describes realistic simulations of a ventilated vehicle exiting a water body under various wave conditions. Comprehensive analysis is conducted for a range of distinct wave scenarios, and a machine learning-based method is developed for the rapid forecasting of vehicle-related parameters. A three-layer backpropagation neural network is constructed, and its prediction performance is verified. Subsequently, predictive and optimization procedures are employed to determine the optimal wave phase for the water exit of the vehicle. Different wave conditions are shown to significantly affect the evolution of the ventilated cavity as well as the kinematic and loading characteristics of the vehicle. The pitch angular velocity and angle at the moment when the head of the vehicle reaches the free surface exhibit a positive cosine trend under different wave conditions. No regularity of the pitch angular velocity at the moment when the tail reaches the free surface is evident. The neural network exhibits exceptional proficiency in predicting the motion parameters and load characteristics of the vehicle. The optimal point for the vehicle to exit the water is determined to be at a wave phase of 0.125π, while the most hazardous point occurs when the wave phase is 1.1875π. [ABSTRACT FROM AUTHOR]

Recurrent respiratory papillomatosis (RRP) is a chronic condition primarily affecting children, known as juvenile onset RRP (JORRP), caused by a viral infection. Antiviral medications have been used to reduce the need for frequent surgeries, slow the growth of papillomata, and prevent disease spread. Effective treatment of JORRP necessitates targeted drug delivery (TDD) to ensure that inhaled aerosolized drugs reach specific sites, such as the larynx and glottis, without harming healthy tissues. Using computational fluid particle dynamics (CFPD) and machine learning (ML), this study (1) investigated how drug properties and individual factors influence TDD efficiency for JORRP treatment and (2) developed personalized inhalation therapy using an ML-empowered smart inhaler control algorithm for precise medication release. This algorithm optimizes the inhaler nozzle position and diameter based on drug and patient-specific data, enhancing drug delivery to the larynx and glottis. CFPD simulations show that particle size significantly affects deposition fractions in the upper airway, emphasizing the importance of particle size selection. Additionally, optimal nozzle diameter and delivery efficiency depend on particle size, inhalation flow rate, and release time. The ML-based TDD strategy, employing a classification and regression tree model, outperforms conventional inhalation therapy by achieving a higher delivery efficiency to the larynx and glottis. This innovative concept of an ML-empowered smart inhaler represents a promising step toward personalized and precise pulmonary healthcare through inhalation therapy. It demonstrates the potential of AI-driven smart inhalers for improving the treatment outcomes of lung diseases that require TDD at designated lung sites. [ABSTRACT FROM AUTHOR]

Mobility robots may soon be among us, triggering a need for safety regulation. Robot safety regulation, however, remains underexplored, with only a few articles analyzing what regulatory approaches could be feasible. This article offers an account of the available regulatory strategies and attempts to theorize the effects of simulation-based safety regulation. The article first discusses the distinctive features of mobility robots as regulatory targets and argues that emergent behavior constitutes the key regulatory concern in designing robot safety regulation regimes. In contrast to many accounts, the article posits that emergent behavior dynamics do not arise from robot autonomy, learning capability, or code unexplainability. Instead, they emerge from the complexity of robot technological constitutions coupled with near-infinite environmental variability and non-linear performance dynamics of the machine learning components. Second, the article reviews rules-based and performance-based regulation and argues that both will fail adequately constrain emergent robot behaviors. The article claims that controlling mobility robots requires a simulation-based regulatory approach. Simulation-based regulation is a novelty with significant theoretical and practical implications. The article argues that the approach signifies a radical break in regulatory forms of knowledge and temporalities. Simulations enact virtual futures to create a new regulatory knowledge type. Practically, the novel safety knowledge type may destabilize the existing conceptual space of safety politics and liability allocation patterns. [ABSTRACT FROM AUTHOR]

Purpose: The paper's aim is to use the dynamic theory of multi-rigid body systems to establish a dynamic model of the ammunition belt for the gas-operated machine gun. Then, an experimental study is also carried out on the PKMS Kalashnikov 7.62 mm machine gun to verify the reliability of the proposed model. Method: A multi-body mechanical model for dynamic analysis of the ammunition belt is proposed to serve as a foundation for the theoretical calculation problem. In addition, a set of experimental procedures for the PKMS Kalashnikov 7.62 mm machine gun is carried out dynamic analysis problem. Results: The calculation results are compared with the experimental results, including the motion, displacement, and velocity of the ammunition belt and the interaction force between two link joints to confirm the suitability of the proposed model. From here, it can be asserted that our model is an effective one and should be encouraged in research into the dynamics of the automatic machine and the design of the gas-operated machine gun. [ABSTRACT FROM AUTHOR]

In 2015, a 45 MW vertical hydropower machine exhibited excessive vibration after refurbishment. Measurements revealed a substantial bearing clearance at the lower generator guide bearing. Consequently, the bearing was unable to generate sufficient opposing force to drive the rotor toward the bearing center, resulting in more pronounced overall system vibration. Addressing this challenge required a cost-effective and feasible solution for mitigating the vibration problem. To this end, a design modification was implemented wherein the lower generator guide bearing (originally a sleeve bearing) was modified to a four-lobe bearing by offsetting the two halves of the bearing twice in two axes. Numerical simulations and experimentations were conducted, and the dynamics of the machine before and after the design modification were investigated. Both the simulation and experimental results showed that the machine with the four-lobe bearing improved the system stability and reduced the vibration amplitudes. The numerical simulation result demonstrated that, due to the design modification, the first and second critical speeds were effectively eliminated for a speed range of up to three times the nominal speed. Furthermore, for nominal operation with unbalanced magnetic pull, the four-lobe bearing provided a stability advantage in terms of the modal parameters relative to the original sleeve bearing. [ABSTRACT FROM AUTHOR]

This paper undertakes an examination of elevator car dispatching methods in response to hall calls. Firstly, our study focuses on the establishment of a representation of an elevator group control system as a finite-state machine to understand the dynamics of elevator group control. Secondly, two primary heuristics are explored, with the first advocating directional continuity unless the highest or lowest floor has been reached, while the second permits direction change upon completing the final call, regardless of floor extremes. Identified inefficiencies in these heuristic solutions lead us to explore enhanced alternatives. Consequently, we delve into genetic algorithm (GA) and simulated annealing (SA) methodologies. Our focus initially centers on devising solution representations and determining fitness evaluations for both approaches. We employ a simulation-based optimization approach to identify the optimal parameter values for both simulated annealing and genetic algorithms. A subsequent comparative analysis is conducted to ascertain the most effective approach among these diverse solutions. A comparative analysis reveals that the GA-based approach significantly outperforms both existing heuristics and the SA-based method in minimizing average passenger waiting time at the cost of longer computational time. [ABSTRACT FROM AUTHOR]

Adjusting motility patterns according to environmental cues is important for bacterial survival. Myxococcus xanthus, a bacterium moving on surfaces by gliding and twitching mechanisms, modulates the reversal frequency of its front-back polarity in response to mechanical cues like substrate stiffness and cell-cell contact. In this study, we propose that M. xanthus's gliding machinery senses environmental mechanical cues during force generation and modulates cell reversal accordingly. To examine our hypothesis, we expand an existing mathematical model for periodic polarity reversal in M. xanthus, incorporating the experimental data on the intracellular dynamics of the gliding machinery and the interaction between the gliding machinery and a key polarity regulator. The model successfully reproduces the dependence of cell reversal frequency on substrate stiffness observed in M. xanthus gliding. We further propose reversal control networks between the gliding and twitching motility machineries to explain the opposite reversal responses observed in wild type M. xanthus cells that possess both motility mechanisms. These results provide testable predictions for future experimental investigations. In conclusion, our model suggests that the gliding machinery in M. xanthus can function as a mechanosensor, which transduces mechanical cues into a cell reversal signal. [ABSTRACT FROM AUTHOR]

The paper presents an off-line application that determines the maximum accuracy of the reference points for the given dynamics parameters of a CNC machine. These parameters are maximum speed, acceleration, and JERK. The JERK parameter determines the rate of change of acceleration. These parameters are defined for each working axis of the machine. The main achievement of the algorithm proposed in the article is the determination of the smallest error specified for each reference point resulting from the implemented G-code for the considered dynamic parameters of the CNC machine. The solutions to this problem in industry consider the improvement in the accuracy of hitting the reference points, but they do not provide information on whether the obtained solution is optimal for such parameters of the machine dynamics. The algorithm makes the accuracy dependent on the adopted dynamic parameters of the machine and the parameters of the PLC controller used in the CNC machine. [ABSTRACT FROM AUTHOR]

In the exploitation of machinery and equipment, one of the most important parameters is the dynamic state of the object. Observing residual processes is the most popular method of assessing the dynamic state. In particular, dynamic residual processes are a carrier of diagnostic information. Measuring the vibrations and noise of working mini-mining machines provides knowledge about the dynamics of the assessed machinery. Many years of knowledge and experience have allowed for the creation of diagnostic procedures. Within most machines used in the industry, the procedures considered standard allow for an effective diagnostic assessment and optimal management of mini-mining machine exploitation. The applicability of these standards is quite wide. However, there are machines with parameters that prevent the application of said standards. Mini-mining machines used in subterranean mining are one of these machines. In the case of diagnostic assessment of continuous miners, chain conveyors, as well as other machinery used in the technological chain, the usage of assessment brackets consistent with adopted standards is incorrect. In the diagnostic assessment of these types of mini-mining machines, an analysis of long-term trends is often used. The determination of acceptable vibration levels is obtained heuristically through observations of the damage up to the state of emergency. In the article, methods of mini mining machines state assessment using models of machine degradation, using Weibull distribution, are presented. The presented method has been used in real results of vibration measurement of underground machinery used in a coal mine. The analyses showed a large effectiveness of the presented method. [ABSTRACT FROM AUTHOR]

The advancement of plasma technology is intricately linked with the utilization of computational fluid dynamics (CFD) models, which play a pivotal role in the design and optimization of industrial-scale plasma reactors. This comprehensive compilation encapsulates the evolving landscape of plasma reactor design, encompassing fluid dynamics, chemical kinetics, heat transfer, and radiation energy. By employing diverse tools such as FLUENT, Python, MATLAB, and Abaqus, CFD techniques unravel the complexities of turbulence, multiphase flow, and species transport. The spectrum of plasma behavior equations, including ion and electron densities, electric fields, and recombination reactions, is presented in a holistic manner. The modeling of non-thermal plasma reactors, underpinned by precise mathematical formulations and computational strategies, is further empowered by the integration of machine learning algorithms for predictive modeling and optimization. From biomass gasification to intricate chemical reactions, this work underscores the versatile potential of plasma hybrid modeling in reshaping various industrial processes. Within the sphere of plasma catalysis, modeling and simulation methodologies have paved the way for transformative progress. Encompassing reactor configurations, kinetic pathways, hydrogen production, waste valorization, and beyond, this compilation offers a panoramic view of the multifaceted dimensions of plasma catalysis. Microkinetic modeling and catalyst design emerge as focal points for optimizing CO2 conversion, while the intricate interplay between plasma and catalysts illuminates insights into ammonia synthesis, methane reforming, and hydrocarbon conversion. Leveraging neural networks and advanced modeling techniques enables predictive prowess in the optimization of plasma-catalytic processes. The integration of plasma and catalysts for diverse applications, from waste valorization to syngas production and direct CO2/CH4 conversion, exemplifies the wide-reaching potential of plasma catalysis in sustainable practices. Ultimately, this anthology underscores the transformative influence of modeling and simulation in shaping the forefront of plasma-catalytic processes, fostering innovation and sustainable applications. [ABSTRACT FROM AUTHOR]

Accurate prediction of tool tip dynamics is vital for understanding machine tool behavior and chatter. Traditional methods involve several impact tests, finite element simulations, and the receptance coupling (RC) approach. However, substructure coupling necessitates multiple experiments and encounters difficulties due to complexities of capturing rotational dynamics. The intricate nature of RC inhibits its widespread industrial applicability in predicting tool tip dynamics. We introduce machine learning (ML)-based approach relying on a few experiments and computer vision to predict dynamics. Comparative analysis with direct experiments shows the ML-based method's potential to expedite dynamic identification with accuracy, chatter prediction, and machining process optimization. [ABSTRACT FROM AUTHOR]

Process integration and automation are becoming increasingly important for improving productivity. When two workpieces are parallelly machined on a single machine, vibration transmission by multiple machining points makes it difficult to select optimal cutting conditions, achieving both accuracy and efficiency. This study presents a method to introduce the optimal condition in simultaneous machining with different processes. The developed simulation includes machine dynamics and simultaneous machining process model. Vibration transmissibility between the processes is calculated and the cutting condition with less roundness is proposed. An experimental result shows the proposed method can determine cutting condition the higher accuracy in short time. [ABSTRACT FROM AUTHOR]

Obtaining the mechanical properties of boron nitride nanosheets (BNNSs) requires extensive computational atomistic simulations, so it is necessary to predict to reduce time costs. In this work, we obtained the ultimate tensile strength and Young's modulus of the BNNS material through molecular dynamics (MDs) simulations by taking into account factors, such as the BNNSs' chirality, layer number, ambient temperature, and strain rate. Subsequently, employing comprehensive training and optimization of the MDs data, we developed multiple ML models to estimate the ultimate tensile strength and Young's modulus. Among these models, the random forest model was chosen for its accurate prediction of the mechanical properties of the BNNSs, offering significant benefits for performance analysis and the engineering design of two-dimensional nanomaterials resembling BNNSs. Finally, based on the predicted results of the ML models, we propose a predictive model for the mechanical properties of the BNNSs, which serves as a valuable reference for future research endeavors. [ABSTRACT FROM AUTHOR]

Monolayer films have shown promise as a lubricating layer to reduce friction and wear of mechanical devices with separations on the nanoscale. These films have a vast design space with many tunable properties that can affect their tribological effectiveness. For example, terminal group chemistry, film composition, and backbone chemistry can all lead to films with significantly different tribological properties. This design space, however, is very difficult to explore without a combinatorial approach and an automatable, reproducible, and extensible workflow to screen for promising candidate films. Using the Molecular Simulation Design Framework (MoSDeF), a combinatorial screening study was performed to explore 9747 unique monolayer films (116 964 total simulations) and a machine learning (ML) model using a random forest regressor, an ensemble learning technique, to explore the role of terminal group chemistry and its effect on tribological effectiveness. The most promising films were found to contain small terminal groups such as cyano and ethylene. The ML model was subsequently applied to screen terminal group candidates identified from the ChEMBL small molecule library. Approximately 193 131 unique film candidates were screened with approximately a five order of magnitude speed-up in analysis compared to simulation alone. The ML model was thus able to be used as a predictive tool to greatly speed up the initial screening of promising candidate films for future simulation studies, suggesting that computational screening in combination with ML can greatly increase the throughput in combinatorial approaches to generate in silico data and then train ML models in a controlled, self-consistent fashion. [ABSTRACT FROM AUTHOR]

A methodology for analyzing the influence of gear backlash in the axis drive systems of a Cartesian CNC machine on positional accuracy is developed. The approach is based on solving the machine dynamical equations in the context of an angular dead-zone backlash model and an osculating circle approximation of smooth paths in a neighborhood each path turning point, which admit an essentially exact solution for a P controller. This methodology is the basis for schemes to minimize backlash degradation of positional accuracy through feedrate or path geometry modifications, rather than hardware or controller upgrades (that may be expensive or disruptive for CNC machines in continuous production use). As a preliminary demonstration of the methodology, results are presented from the use of smooth feedrate reductions about each path turning point as a means to suppress positional inaccuracies incurred by gear backlash in CNC machine axis drive systems.

A formal approach to rephrasing nonlinear filtering of stochastic differential equations is the Kushner setting in applied mathematics and dynamical systems. Thanks to the ability of the Carleman linearization, the 'nonlinear' stochastic differential equation can be equivalently expressed as a finite system of 'bilinear' stochastic differential equations with the augmented state under the finite closure. Interestingly, the novelty of this paper is to embed the Carleman linearization into a stochastic evolution of the Markov process. The nonlinear swing equation is the cornerstone and lays the foundation of the power systems dynamics. To illustrate the Carleman linearization of the Markov process, this paper embeds the Carleman linearization into a nonlinear swing stochastic differential equation. Furthermore, we achieve the nonlinear swing equation filtering in the Carleman setting. Filtering in the Carleman setting has simplified algorithmic procedures. The concerning augmented state accounts for the nonlinearity as well as stochasticity. We show that filtering the nonlinear stochastic swing equation in the Carleman framework is more refined as well as sharper in contrast to the benchmark nonlinear extended Kalman filter (EKF). The Carleman filtering framework reduces approximately three times more conditional mean absolute errors as compared to the benchmark EKF method for the application of machine swing dynamics. This paper suggests the usefulness of the Carleman embedding into the stochastic differential equation to filter the concerning nonlinear stochastic differential system. This paper will interest nonlinear stochastic dynamists exploring and unfolding linearization embedding techniques to their research. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been used in different ways in the fight against COVID-19 disease. ML models have been developed, e.g., for diagnostic or prognostic purposes and using various modalities of data (e.g., textual, visual, or structured). Due to the many specific aspects of this disease and its evolution over time, there is still not enough understanding of all relevant factors influencing the course of COVID-19 in particular patients. In all aspects of our work, there was a strong involvement of a medical expert following the human-in-the-loop principle. This is a very important but usually neglected part of the ML and knowledge extraction (KE) process. Our research shows that explainable artificial intelligence (XAI) may significantly support this part of ML and KE. Our research focused on using ML for knowledge extraction in two specific scenarios. In the first scenario, we aimed to discover whether adding information about the predominant COVID-19 variant impacts the performance of the ML models. In the second scenario, we focused on prognostic classification models concerning the need for an intensive care unit for a given patient in connection with different explainability AI (XAI) methods. We have used nine ML algorithms, namely XGBoost, CatBoost, LightGBM, logistic regression, Naive Bayes, random forest, SGD, SVM-linear, and SVM-RBF. We measured the performance of the resulting models using precision, accuracy, and AUC metrics. Subsequently, we focused on knowledge extraction from the best-performing models using two different approaches as follows: (a) features extracted automatically by forward stepwise selection (FSS); (b) attributes and their interactions discovered by model explainability methods. Both were compared with the attributes selected by the medical experts in advance based on the domain expertise. Our experiments showed that adding information about the COVID-19 variant did not influence the performance of the resulting ML models. It also turned out that medical experts were much more precise in the identification of significant attributes than FSS. Explainability methods identified almost the same attributes as a medical expert and interesting interactions among them, which the expert discussed from a medical point of view. The results of our research and their consequences are discussed. [ABSTRACT FROM AUTHOR]

As transistor integration accelerates and miniaturization progresses, improving the interfacial adhesion characteristics of complex metal interconnect has become a major issue in ensuring semiconductor device reliability. Therefore, it is becoming increasingly important to interpret the adhesive properties of metal interconnects at the atomic level, predict their adhesive strength and failure mode, and develop computational methods that can be universally applied regardless of interface properties. In this study, we propose a method for theoretically understanding adhesion characteristics through steering molecular dynamics simulations based on machine learning interatomic potentials. We utilized this method to investigate the adhesion characteristics of tungsten deposited on titanium nitride barrier metal (W/TiN) as a representative metal interconnect structure in devices. Pulling tests that pull two materials apart and sliding tests that pull them against each other in a shear direction were implemented to investigate the failure mode and adhesive strength depending on TiN facet orientation. We found that the W/TiN interface showed an adhesive failure where they separate from each other when tested with pulling force on Ti-rich (111) or (001) facets while cohesive failures occurred where W itself was destroyed on N-rich (111) facet. The adhesion strength was defined as the maximum force causing failure during the pulling test for consistent interpretation and the strengths of tungsten were predicted to be strongest when deposited onto N-rich (111) facet while weakest on Ti-rich (111) facet. [ABSTRACT FROM AUTHOR]